{"document_id":"3","document_content":"# A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24\/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user's text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse and understand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2201.02735v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"137492\", \"159867\", \"299491\"]}","task_split":"paper_retrieval"}
{"document_id":"71","document_content":"# Disjoint Contrastive Regression Learning for Multi-Sourced Annotations\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nLarge-scale datasets are important for the development of deep learning models. Such datasets usually require a heavy workload of annotations, which are extremely time-consuming and expensive. To accelerate the annotation procedure, multiple annotators may be employed to label different subsets of the data. However, the inconsistency and bias among different annotators are harmful to the model training, especially for qualitative and subjective tasks.To address this challenge, in this paper, we propose a novel contrastive regression framework to address the disjoint annotations problem, where each sample is labeled by only one annotator and multiple annotators work on disjoint subsets of the data. To take account of both the intra-annotator consistency and inter-annotator inconsistency, two strategies are employed.Firstly, a contrastive-based loss is applied to learn the relative ranking among different samples of the same annotator, with the assumption that the ranking of samples from the same annotator is unanimous. Secondly, we apply the gradient reversal layer to learn robust representations that are invariant to different annotators. Experiments on the facial expression prediction task, as well as the image quality assessment task, verify the effectiveness of our proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.15411v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"66897\", \"75558\", \"98936\", \"115038\", \"125960\", \"128941\", \"131664\", \"131670\", \"148580\", \"151719\", \"175601\", \"191399\", \"199491\", \"203998\", \"204327\", \"207872\", \"209178\", \"219188\", \"221692\", \"256776\", \"270545\", \"272839\"]}","task_split":"paper_retrieval"}
{"document_id":"90","document_content":"# Conditional Generative Data-free Knowledge Distillation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nKnowledge distillation has made remarkable achievements in model compression. However, most existing methods require the original training data, which is usually unavailable due to privacy and security issues. In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data. This method realizes efficient knowledge distillation based on conditional image generation. Specifically, we treat the preset labels as ground truth to train a conditional generator in a semi-supervised manner. The trained generator can produce specified classes of training images. For training the student network, we force it to extract the knowledge hidden in teacher feature maps, which provide crucial cues for the learning process. Moreover, an adversarial training framework for promoting distillation performance is constructed by designing several loss functions. This framework helps the student model to explore larger data space. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on different datasets. Compared with other data-free works, our work obtains state-of-the-art results on CIFAR100, Caltech101, and different versions of ImageNet datasets. The codes will be released.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.15358v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"47001\", \"48820\", \"56732\", \"60036\", \"75482\", \"81440\", \"109032\", \"131467\", \"144840\", \"148853\", \"150516\", \"151798\", \"155027\", \"157044\", \"163614\", \"163676\", \"165835\", \"170278\", \"170810\", \"173254\", \"181284\", \"185555\", \"190464\", \"191248\", \"192746\", \"195661\", \"199616\", \"216133\", \"231164\", \"232302\", \"252221\", \"252897\", \"255606\", \"260782\", \"262065\", \"275665\", \"278175\", \"286661\", \"296421\", \"302907\", \"304173\", \"309161\", \"311552\", \"313499\", \"316293\", \"320046\", \"322163\", \"331635\", \"344133\"]}","task_split":"paper_retrieval"}
{"document_id":"92","document_content":"# OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data\n## Categories\n- Computation and Language\n## Abstract\nSearch engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages. How to quickly, accurately and effectively obtain the information needed by users from massive Internet data has become one of the key issues urgently needed to be solved. We propose an intelligent question-answering system based on structured KB and unstructured data, called OpenQA, in which users can give query questions and the model can quickly give accurate answers back to users. We integrate KBQA structured question answering based on semantic parsing and deep representation learning, and two-stage unstructured question answering based on retrieval and neural machine reading comprehension into OpenQA, and return the final answer with the highest probability through the Transformer answer selection module in OpenQA. We carry out preliminary experiments on our constructed dataset, and the experimental results prove the effectiveness of the proposed intelligent question answering system. At the same time, the core technology of each module of OpenQA platform is still in the forefront of academic hot spots, and the theoretical essence and enrichment of OpenQA will be further explored based on these academic hot spots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.15356v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22719\", \"36194\", \"78772\", \"102310\", \"114755\", \"131921\", \"180895\", \"183076\", \"192346\", \"211664\", \"216563\", \"238610\", \"256171\", \"270020\", \"301767\", \"311684\", \"313440\"]}","task_split":"paper_retrieval"}
{"document_id":"121","document_content":"# An Intelligent Self-driving Truck System For Highway Transportation\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nRecently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https:\/\/github.com\/InceptioResearch\/IITS","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.15304v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"48997\", \"76456\", \"138452\", \"157035\", \"193066\", \"201953\", \"234762\", \"250822\", \"253953\", \"266485\", \"269382\"]}","task_split":"paper_retrieval"}
{"document_id":"222","document_content":"# Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph neural network (GNN) has shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However, many GNNs encounter problems in effectiveness and efficiency when they are designed with a deeper network structure or handle large-sized graphs. Several sampling algorithms have been proposed for improving and accelerating the training of GNNs, yet they ignore understanding the source of GNN performance gain. The measurement of information within graph data can help the sampling algorithms to keep high-value information while removing redundant information and even noise. In this paper, we propose a Metric-Guided (MeGuide) subgraph learning framework for GNNs. MeGuide employs two novel metrics: Feature Smoothness and Connection Failure Distance to guide the subgraph sampling and mini-batch based training. Feature Smoothness is designed for analyzing the feature of nodes in order to retain the most valuable information, while Connection Failure Distance can measure the structural information to control the size of subgraphs. We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.15015v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75522\", \"90518\", \"104578\", \"141774\", \"155900\", \"164732\", \"167195\", \"173673\", \"176611\", \"179704\", \"185584\", \"188300\", \"203857\", \"214311\", \"221587\", \"227067\", \"228663\", \"234153\", \"243791\", \"244510\", \"245486\", \"252105\", \"265828\", \"279575\", \"311030\", \"336185\", \"356343\"]}","task_split":"paper_retrieval"}
{"document_id":"262","document_content":"# RheFrameDetect: A Text Classification System for Automatic Detection of Rhetorical Frames in AI from Open Sources\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRhetorical Frames in AI can be thought of as expressions that describe AI development as a competition between two or more actors, such as governments or companies. Examples of such Frames include robotic arms race, AI rivalry, technological supremacy, cyberwarfare dominance and 5G race. Detection of Rhetorical Frames from open sources can help us track the attitudes of governments or companies towards AI, specifically whether attitudes are becoming more cooperative or competitive over time. Given the rapidly increasing volumes of open sources (online news media, twitter, blogs), it is difficult for subject matter experts to identify Rhetorical Frames in (near) real-time. Moreover, these sources are in general unstructured (noisy) and therefore, detecting Frames from these sources will require state-of-the-art text classification techniques. In this paper, we develop RheFrameDetect, a text classification system for (near) real-time capture of Rhetorical Frames from open sources. Given an input document, RheFrameDetect employs text classification techniques at multiple levels (document level and paragraph level) to identify all occurrences of Frames used in the discussion of AI. We performed extensive evaluation of the text classification techniques used in RheFrameDetect against human annotated Frames from multiple news sources. To further demonstrate the effectiveness of RheFrameDetect, we show multiple case studies depicting the Frames identified by RheFrameDetect compared against human annotated Frames.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.14933v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210090\", \"271934\"]}","task_split":"paper_retrieval"}
{"document_id":"267","document_content":"# Retrieving Black-box Optimal Images from External Databases\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nSuppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.14921v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\", \"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"47260\", \"121171\", \"190461\", \"197601\", \"200601\", \"227056\", \"230664\", \"241493\", \"291537\", \"295204\", \"314568\", \"317165\", \"342438\", \"357161\"]}","task_split":"paper_retrieval"}
{"document_id":"275","document_content":"# Motif Graph Neural Network\n## Categories\n- Machine Learning\n## Abstract\nGraphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \\emph{high-order} graph structures as opposed to \\emph{low-order} structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose Motif Graph Neural Network (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations w.r.t. each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN utilizes an injective function to combine the representations w.r.t. different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both node classification and graph classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.14900v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92075\", \"101267\", \"111014\", \"124721\", \"155642\", \"160051\", \"170157\", \"170705\", \"184286\", \"198364\", \"215435\", \"227067\", \"229324\", \"230330\", \"243382\", \"243791\", \"248459\", \"250373\", \"261085\", \"277250\", \"302934\", \"303167\", \"356982\", \"357242\"]}","task_split":"paper_retrieval"}
{"document_id":"385","document_content":"# On some Foundational Aspects of Human-Centered Artificial Intelligence\n## Categories\n- Artificial Intelligence\n## Abstract\nThe burgeoning of AI has prompted recommendations that AI techniques should be \"human-centered\". However, there is no clear definition of what is meant by Human Centered Artificial Intelligence, or for short, HCAI. This paper aims to improve this situation by addressing some foundational aspects of HCAI. To do so, we introduce the term HCAI agent to refer to any physical or software computational agent equipped with AI components and that interacts and\/or collaborates with humans. This article identifies five main conceptual components that participate in an HCAI agent: Observations, Requirements, Actions, Explanations and Models. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI. In this paper, we focus our analysis on scenarios consisting of a single agent operating in dynamic environments in presence of humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.14480v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117113\"]}","task_split":"paper_retrieval"}
{"document_id":"404","document_content":"# Deformable Graph Convolutional Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GNNs have two limitations. Since the graph convolution is performed in a small local neighborhood on the input graph, it is inherently incapable to capture long-range dependencies between distance nodes. In addition, when a node has neighbors that belong to different classes, i.e., heterophily, the aggregated messages from them often negatively affect representation learning. To address the two common problems of graph convolution, in this paper, we propose Deformable Graph Convolutional Networks (Deformable GCNs) that adaptively perform convolution in multiple latent spaces and capture short\/long-range dependencies between nodes. Separated from node representations (features), our framework simultaneously learns the node positional embeddings (coordinates) to determine the relations between nodes in an end-to-end fashion. Depending on node position, the convolution kernels are deformed by deformation vectors and apply different transformations to its neighbor nodes. Our extensive experiments demonstrate that Deformable GCNs flexibly handles the heterophily and achieve the best performance in node classification tasks on six heterophilic graph datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.14438v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"98076\", \"117308\", \"122212\", \"142571\", \"150166\", \"157816\", \"164464\", \"187381\", \"188300\", \"189860\", \"198382\", \"208418\", \"227067\", \"228663\", \"240758\", \"244510\", \"246291\", \"264326\", \"265739\", \"271196\", \"271216\", \"316095\"]}","task_split":"paper_retrieval"}
{"document_id":"615","document_content":"# LINDA: Unsupervised Learning to Interpolate in Natural Language Processing\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDespite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have thus relied on domain-specific heuristics and manually crafted resources, such as dictionaries, in order to apply mixup in NLP. In this paper, we instead propose an unsupervised learning approach to text interpolation for the purpose of data augmentation, to which we refer as \"Learning to INterpolate for Data Augmentation\" (LINDA), that does not require any heuristics nor manually crafted resources but learns to interpolate between any pair of natural language sentences over a natural language manifold. After empirically demonstrating the LINDA's interpolation capability, we show that LINDA indeed allows us to seamlessly apply mixup in NLP and leads to better generalization in text classification both in-domain and out-of-domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.13969v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"55926\"], \"outgoing_citations\": [\"41974\", \"62767\", \"96445\", \"99332\", \"128975\", \"131414\", \"138517\", \"171020\", \"185283\", \"188176\", \"188492\", \"200867\", \"205715\", \"220017\", \"228120\", \"302824\", \"306182\"]}","task_split":"paper_retrieval"}
{"document_id":"843","document_content":"# An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles\n## Categories\n- Computation and Language\n## Abstract\nMedia coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of gold-standard data sets and high context dependencies. In this research project, I aim to devise data sets and methods to identify media bias. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from psychology and linguistics. The first results indicate the effectiveness of an interdisciplinary research approach. My vision is to devise a system that helps news readers become aware of media coverage differences caused by bias. So far, my best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels, indicating that distant supervision has the potential to become a solution for the difficult task of bias detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.13352v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"3111\", \"3121\", \"3122\", \"3123\", \"48329\", \"48389\", \"92720\", \"126188\", \"168387\", \"181098\", \"210148\", \"244755\", \"272081\"]}","task_split":"paper_retrieval"}
{"document_id":"982","document_content":"# An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecently, deep reinforcement learning (RL) has proven its feasibility in solving combinatorial optimization problems (COPs). The learning-to-rank techniques have been studied in the field of information retrieval. While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs. In this paper, we present the learning-to-rank distillation-based COP framework, where a high-performance ranking policy obtained by RL for a COP can be distilled into a non-iterative, simple model, thereby achieving a low-latency COP solver. Specifically, we employ the approximated ranking distillation to render a score-based ranking model learnable via gradient descent. Furthermore, we use the efficient sequence sampling to improve the inference performance with a limited delay. With the framework, we demonstrate that a distilled model not only achieves comparable performance to its respective, high-performance RL, but also provides several times faster inferences. We evaluate the framework with several COPs such as priority-based task scheduling and multidimensional knapsack, demonstrating the benefits of the framework in terms of inference latency and performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2201.00695v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129043\", \"141071\", \"142267\", \"165441\", \"194309\", \"215520\", \"217191\", \"238077\", \"242432\", \"263863\", \"279191\", \"281175\", \"281269\", \"289589\", \"358914\"]}","task_split":"paper_retrieval"}
{"document_id":"1113","document_content":"# Towards more patient friendly clinical notes through language models and ontologies\n## Categories\n- Computation and Language\n## Abstract\nClinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians time. We present a novel approach to automated simplification of medical text based on word frequencies and language modelling, grounded on medical ontologies enriched with layman terms. We release a new dataset of pairs of publicly available medical sentences and a version of them simplified by clinicians. Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.12672v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"115939\", \"214312\", \"214570\", \"237283\"]}","task_split":"paper_retrieval"}
{"document_id":"1158","document_content":"# Curriculum Learning for Safe Mapless Navigation\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nThis work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigation, comparing over a standard end-to-end (E2E) training strategy. To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent. For a fair comparison, our evaluation considers an equal computational demand for every learning approach (i.e., the same number of interactions and difficulty of the environments) and confirms that our CL-based method that uses ToL outperforms the E2E methodology. In particular, we improve the average success rate and the safety of the trained policy, resulting in 10% fewer collisions in unseen testing scenarios. To further confirm these results, we employ a formal verification tool to quantify the number of correct behaviors of Reinforcement Learning policies over desired specifications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3477314.3507182\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70865\", \"73516\", \"98682\", \"127961\", \"133821\", \"190536\", \"194963\", \"206231\", \"217206\", \"218770\", \"233055\", \"233865\", \"237008\", \"272668\", \"277797\", \"283956\", \"291995\", \"302952\", \"317251\"]}","task_split":"paper_retrieval"}
{"document_id":"1230","document_content":"# Making sense of electrical vehicle discussions using sentiment analysis on closely related news and user comments\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe used a token-wise and document-wise sentiment analysis using both unsupervised and supervised models applied to both news and user reviews dataset. And our token-wise sentiment analysis found a statistically significant difference in sentiment between the two groups (both of which were very large N), our document-wise supervised sentiment analysis found no significant difference in sentiment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.12327v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"63964\", \"228021\"]}","task_split":"paper_retrieval"}
{"document_id":"1310","document_content":"# Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations derived from the reduced size of the skin lesion imaging datasets. ImageNet is often used as the pretraining dataset, but its transferring potential is hindered by the domain gap between the source dataset and the target dermatoscopic scenario. In this work, we introduce a novel pretraining approach that sequentially trains a series of Self-Supervised Learning pretext tasks and only requires the unlabeled skin lesion imaging data. We present a simple methodology to establish an ordering that defines a pretext task curriculum. For the multi-class skin lesion classification problem, and ISIC-2019 dataset, we provide experimental evidence showing that: i) a model pretrained by a curriculum of pretext tasks outperforms models pretrained by individual pretext tasks, and ii) a model pretrained by the optimal pretext task curriculum outperforms a model pretrained on ImageNet. We demonstrate that this performance gain is related to the fact that the curriculum of pretext tasks better focuses the attention of the final model on the skin lesion. Beyond performance improvement, this strategy allows for a large reduction in the training time with respect to ImageNet pretraining, which is especially advantageous for network architectures tailored for a specific problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.12086v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"41481\", \"49523\", \"58203\", \"117646\", \"117898\", \"123821\", \"152356\", \"162635\", \"172832\", \"188169\", \"221186\", \"224597\", \"237466\", \"295591\", \"295741\", \"312587\"]}","task_split":"paper_retrieval"}
{"document_id":"1340","document_content":"# Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nCurrent researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenarios when an utterance is semantically ambiguous, which cannot be achieved by the text-based SLU models. In this paper, we first introduce a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots. To this end, we further introduce a large-scale human-annotated Chinese dataset with over 5K utterances and their corresponding supporting profile information (Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition, we evaluate several state-of-the-art baseline models and explore a multi-level knowledge adapter to effectively incorporate profile information. Experimental results reveal that all existing text-based SLU models fail to work when the utterances are semantically ambiguous and our proposed framework can effectively fuse the supporting information for sentence-level intent detection and token-level slot filling. Finally, we summarize key challenges and provide new points for future directions, which hopes to facilitate the research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11953v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"45038\", \"65237\", \"93627\", \"95751\", \"95759\", \"99497\", \"129831\", \"135296\", \"168571\", \"178308\", \"183080\", \"201322\", \"204763\", \"231442\", \"234783\", \"236938\", \"244339\", \"244479\", \"260475\", \"268344\", \"307969\"]}","task_split":"paper_retrieval"}
{"document_id":"1447","document_content":"# A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone\n## Categories\n- Optimization and Control\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination -- a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.12545v3\", \"primary_category\": \"math.OC\", \"categories\": [\"cs.LG\", \"math.OC\", \"cs.AI\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Machine Learning\", \"Optimization and Control\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"13497\", \"67428\", \"90319\", \"96458\", \"113035\", \"138001\", \"142472\", \"154998\", \"235985\", \"238077\", \"242457\", \"269617\", \"279191\", \"311464\", \"322180\", \"336316\"]}","task_split":"paper_retrieval"}
{"document_id":"1502","document_content":"# Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHuman-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming and, hence, expensive. In this paper, we introduce a proof-of-concept system for annotating sentences \"laterally.\" The approach is based on the observation that sentences that are similar in meaning often have the same label in terms of a particular type system. We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents. Here, we present the interface of the system and empirically evaluate the approach. The experiments show that lateral annotation has the potential to make the annotation process quicker and more consistent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3233\/FAIA200860\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2939\", \"82242\", \"2949\"], \"outgoing_citations\": [\"3842\", \"156575\", \"237274\", \"267161\", \"313872\"]}","task_split":"paper_retrieval"}
{"document_id":"1509","document_content":"# Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computers and Society\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nAs AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11471v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CY\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computers and Society\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"24413\", \"31927\", \"38677\", \"45284\", \"52458\", \"64483\", \"73733\", \"75604\", \"77839\", \"87447\", \"94168\", \"100775\", \"105985\", \"115953\", \"127043\", \"141414\", \"143004\", \"144241\", \"144378\", \"145662\", \"145981\", \"147166\", \"148071\", \"148191\", \"150190\", \"167996\", \"171988\", \"175710\", \"177198\", \"181717\", \"197416\", \"199568\", \"199757\", \"202016\", \"206169\", \"206531\", \"208922\", \"209451\", \"211336\", \"212500\", \"213326\", \"215081\", \"215230\", \"220097\", \"220666\", \"227047\", \"239137\", \"241330\", \"243395\", \"243731\", \"244557\", \"250393\", \"271562\", \"272864\", \"290677\", \"291537\"]}","task_split":"paper_retrieval"}
{"document_id":"1531","document_content":"# Shape from Polarization for Complex Scenes in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. Existing shape from polarization (SfP) works mainly focus on estimating the normal of a single object rather than complex scenes in the wild. A key barrier to high-quality scene-level SfP is the lack of real-world SfP data in complex scenes. Hence, we contribute the first real-world scene-level SfP dataset with paired input polarization images and ground-truth normal maps. Then we propose a learning-based framework with a multi-head self-attention module and viewing encoding, which is designed to handle increasing polarization ambiguities caused by complex materials and non-orthographic projection in scene-level SfP. Our trained model can be generalized to far-field outdoor scenes as the relationship between polarized light and surface normals is not affected by distance. Experimental results demonstrate that our approach significantly outperforms existing SfP models on two datasets. Our dataset and source code will be publicly available at https:\/\/github.com\/ChenyangLEI\/sfp-wild","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11377v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51214\", \"61446\", \"70752\", \"80879\", \"95913\", \"104023\", \"109661\", \"111228\", \"111407\", \"120528\", \"134499\", \"181809\", \"187386\", \"193251\", \"193319\", \"193877\", \"194752\", \"208052\", \"225289\", \"229003\", \"243882\", \"269261\", \"274005\", \"277452\", \"284214\", \"287664\", \"321620\", \"321655\"]}","task_split":"paper_retrieval"}
{"document_id":"1854","document_content":"# Towards Trustworthy Cross-patient Model Development\n## Categories\n- Machine Learning\n- Software Engineering\n## Abstract\nMachine learning is used in medicine to support physicians in examination, diagnosis, and predicting outcomes. One of the most dynamic area is the usage of patient generated health data from intensive care units. The goal of this paper is to demonstrate how we advance cross-patient ML model development by combining the patient's demographics data with their physiological data. We used a population of patients undergoing Carotid Enderarterectomy (CEA), where we studied differences in model performance and explainability when trained for all patients and one patient at a time. The results show that patients' demographics has a large impact on the performance and explainability and thus trustworthiness. We conclude that we can increase trust in ML models in a cross-patient context, by careful selection of models and patients based on their demographics and the surgical procedure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.10441v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": [\"63004\"]}","task_split":"paper_retrieval"}
{"document_id":"2012","document_content":"# D-HAN: Dynamic News Recommendation with Hierarchical Attention Network\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n## Abstract\nNews recommendation is an effective information dissemination solution in modern society. While recent years have witnessed many promising news recommendation models, they mostly capture the user-news interactions on the document-level in a static manner. However, in real-world scenarios, the news can be quite complex and diverse, blindly squeezing all the contents into an embedding vector can be less effective in extracting information compatible with the personalized preference of the users. In addition, user preferences in the news recommendation scenario can be highly dynamic, and a tailored dynamic mechanism should be designed for better recommendation performance. In this paper, we propose a novel dynamic news recommender model. For better understanding the news content, we leverage the attention mechanism to represent the news from the sentence-, element- and document-levels, respectively. For capturing users' dynamic preferences, the continuous time information is seamlessly incorporated into the computing of the attention weights. More specifically, we design a hierarchical attention network, where the lower layer learns the importance of different sentences and elements, and the upper layer captures the correlations between the previously interacted and the target news. To comprehensively model the dynamic characters, we firstly enhance the traditional attention mechanism by incorporating both absolute and relative time information, and then we propose a dynamic negative sampling method to optimize the users' implicit feedback. We conduct extensive experiments based on three real-world datasets to demonstrate our model's effectiveness. Our source code and pre-trained representations are available at https:\/\/github.com\/lshowway\/D-HAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.10085v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133908\", \"142159\", \"190649\", \"217192\", \"221248\", \"244250\", \"250622\", \"258567\", \"263158\", \"263938\", \"302767\", \"323243\", \"348033\"]}","task_split":"paper_retrieval"}
{"document_id":"2048","document_content":"# Online Grounding of Symbolic Planning Domains in Unknown Environments\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nIf a robotic agent wants to exploit symbolic planning techniques to achieve some goal, it must be able to properly ground an abstract planning domain in the environment in which it operates. However, if the environment is initially unknown by the agent, the agent needs to explore it and discover the salient aspects of the environment needed to reach its goals. Namely, the agent has to discover: (i) the objects present in the environment, (ii) the properties of these objects and their relations, and finally (iii) how abstract actions can be successfully executed. The paper proposes a framework that aims to accomplish the aforementioned perspective for an agent that perceives the environment partially and subjectively, through real value sensors (e.g., GPS, and on-board camera) and can operate in the environment through low level actuators (e.g., move forward of 20 cm). We evaluate the proposed architecture in photo-realistic simulated environments, where the sensors are RGB-D on-board camera, GPS and compass, and low level actions include movements, grasping\/releasing objects, and manipulating objects. The agent is placed in an unknown environment and asked to find objects of a certain type, place an object on top of another, close or open an object of a certain type. We compare our approach with the state of the art methods on object goal navigation based on reinforcement learning, showing better performances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.10007v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"57326\", \"63032\", \"104772\", \"114824\", \"131180\", \"131814\", \"137930\", \"158547\", \"195772\", \"197475\", \"207499\", \"212102\", \"224223\", \"231955\", \"247418\", \"247749\", \"280681\", \"284470\"]}","task_split":"paper_retrieval"}
{"document_id":"2120","document_content":"# Face Deblurring Based on Separable Normalization and Adaptive Denormalization\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nFace deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient details. Considering that faces and backgrounds have different distribution information, in this study, we designed an effective face deblurring network based on separable normalization and adaptive denormalization (SNADNet). First, We fine-tuned the face parsing network to obtain an accurate face structure. Then, we divided the face parsing feature into face foreground and background. Moreover, we constructed a new feature adaptive denormalization to regularize fafcial structures as a condition of the auxiliary to generate more harmonious and undistorted face structure. In addition, we proposed a texture extractor and multi-patch discriminator to enhance the generated facial texture information. Experimental results on both CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring network restores face structure with more facial details and performs favorably against state-of-the-art methods in terms of structured similarity indexing method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance (FID) and L1, and qualitative comparisons.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09833v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71439\", \"96383\", \"112219\", \"134043\", \"172265\", \"173743\", \"190402\", \"191876\", \"203578\", \"209098\", \"226061\", \"230415\", \"239574\", \"242497\", \"243188\", \"257765\", \"263731\", \"278546\", \"280600\", \"287664\"]}","task_split":"paper_retrieval"}
{"document_id":"2186","document_content":"# Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe consider the challenge of policy simplification and verification in the context of policies learned through reinforcement learning (RL) in continuous environments. In well-behaved settings, RL algorithms have convergence guarantees in the limit. While these guarantees are valuable, they are insufficient for safety-critical applications. Furthermore, they are lost when applying advanced techniques such as deep-RL. To recover guarantees when applying advanced RL algorithms to more complex environments with (i) reachability, (ii) safety-constrained reachability, or (iii) discounted-reward objectives, we build upon the DeepMDP framework introduced by Gelada et al. to derive new bisimulation bounds between the unknown environment and a learned discrete latent model of it. Our bisimulation bounds enable the application of formal methods for Markov decision processes. Finally, we show how one can use a policy obtained via state-of-the-art RL to efficiently train a variational autoencoder that yields a discrete latent model with provably approximately correct bisimulation guarantees. Additionally, we obtain a distilled version of the policy for the latent model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09655v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70557\", \"87269\", \"123065\", \"125089\", \"141797\", \"142445\", \"159243\", \"160566\", \"176830\", \"178094\", \"182108\", \"242444\", \"249008\", \"251693\", \"257501\", \"281269\", \"281358\", \"303049\", \"304741\", \"356867\", \"357095\"]}","task_split":"paper_retrieval"}
{"document_id":"2191","document_content":"# Improving neural implicit surfaces geometry with patch warping\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nNeural implicit surfaces have become an important technique for multi-view 3D reconstruction but their accuracy remains limited. In this paper, we argue that this comes from the difficulty to learn and render high frequency textures with neural networks. We thus propose to add to the standard neural rendering optimization a direct photo-consistency term across the different views. Intuitively, we optimize the implicit geometry so that it warps views on each other in a consistent way. We demonstrate that two elements are key to the success of such an approach: (i) warping entire patches, using the predicted occupancy and normals of the 3D points along each ray, and measuring their similarity with a robust structural similarity (SSIM); (ii) handling visibility and occlusion in such a way that incorrect warps are not given too much importance while encouraging a reconstruction as complete as possible. We evaluate our approach, dubbed NeuralWarp, on the standard DTU and EPFL benchmarks and show it outperforms state of the art unsupervised implicit surfaces reconstructions by over 20% on both datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09648v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"28090\", \"38936\", \"40195\", \"40788\", \"52462\", \"54590\", \"56096\", \"56736\", \"59877\", \"66863\", \"82893\", \"95454\", \"105382\", \"135330\", \"135562\", \"135924\", \"137564\", \"140528\", \"150974\", \"154640\", \"154756\", \"169517\", \"172067\", \"183970\", \"197270\", \"202836\", \"206570\", \"236476\", \"259277\"]}","task_split":"paper_retrieval"}
{"document_id":"2220","document_content":"# Topic-Aware Encoding for Extractive Summarization\n## Categories\n- Computation and Language\n## Abstract\nDocument summarization provides an instrument for faster understanding the collection of text documents and has several real-life applications. With the growth of online text data, numerous summarization models have been proposed recently. The Sequence-to-Sequence (Seq2Seq) based neural summarization model is the most widely used in the summarization field due to its high performance. This is because semantic information and structure information in the text is adequately considered when encoding. However, the existing extractive summarization models pay little attention to and use the central topic information to assist the generation of summaries, which leads to models not ensuring the generated summary under the primary topic. A lengthy document can span several topics, and a single summary cannot do justice to all the topics. Therefore, the key to generating a high-quality summary is determining the central topic and building a summary based on it, especially for a long document. We propose a topic-aware encoding for document summarization to deal with this issue. This model effectively combines syntactic-level and topic-level information to build a comprehensive sentence representation. Specifically, a neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information for capturing the critical content in the original document. The experimental results on three public datasets show that our model outperforms the state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09572v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"168613\", \"181713\", \"197339\", \"230212\", \"231869\", \"232632\", \"241070\", \"259563\", \"264795\", \"268902\", \"280552\", \"280557\", \"284092\", \"311423\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"2280","document_content":"# Search Strategy Formulation for Systematic Reviews: issues, challenges and opportunities\n## Categories\n- Information Retrieval\n## Abstract\nSystematic literature reviews play a vital role in identifying the best available evidence for health and social care policy. The resources required to produce systematic reviews can be significant, and a key to the success of any review is the search strategy used to identify relevant literature. However, the methods used to construct search strategies can be complex, time consuming, resource intensive and error prone. In this review, we examine the state of the art in resolving complex structured information needs, focusing primarily on the healthcare context. We analyse the literature to identify key challenges and issues and explore appropriate solutions and workarounds. From this analysis we propose a way forward to facilitate trust and transparency and to aid explainability, reproducibility and replicability through a set of key design principles for tools to support the development of search strategies in systematic literature reviews.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09424v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"39549\", \"214486\", \"254281\", \"299973\"]}","task_split":"paper_retrieval"}
{"document_id":"2329","document_content":"# WebGPT: Browser-assisted question-answering with human feedback\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09332v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15924\", \"24984\", \"36644\", \"2084\", \"26365\"], \"outgoing_citations\": [\"16723\", \"24984\", \"26365\", \"55799\", \"63848\", \"71196\", \"77468\", \"102649\", \"107494\", \"123394\", \"127538\", \"131921\", \"143089\", \"169889\", \"175089\", \"205085\", \"209453\", \"213652\", \"266850\"]}","task_split":"paper_retrieval"}
{"document_id":"2340","document_content":"# Optimal discharge of patients from intensive care via a data-driven policy learning framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Optimization and Control\n## Abstract\nClinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers, including through better management of intensive care units. In particular, it is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay (and associated hospitalization costs) and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions given a patient's electronic health records. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose value is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.09315v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"math.OC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Optimization and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"23098\", \"170695\", \"265649\", \"290019\", \"350523\"]}","task_split":"paper_retrieval"}
{"document_id":"2531","document_content":"# Ditch the Gold Standard: Re-evaluating Conversational Question Answering\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nConversational question answering aims to provide natural-language answers to users in information-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human-machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human machine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and gold-history evaluation in terms of model ranking. We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we analyze the impact of various modeling strategies and discuss future directions towards building better conversational question answering systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08812v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"96631\"], \"outgoing_citations\": [\"19499\", \"40367\", \"45703\", \"95304\", \"98907\", \"123404\", \"125555\", \"127214\", \"127625\", \"170238\", \"173570\", \"173884\", \"174596\", \"183590\", \"186455\", \"189217\", \"197860\", \"219975\", \"220723\", \"220724\", \"235555\", \"238321\", \"243759\", \"257858\", \"266155\", \"276382\", \"279518\", \"295863\"]}","task_split":"paper_retrieval"}
{"document_id":"2568","document_content":"# Unsupervised Reinforcement Learning in Multiple Environments\n## Categories\n- Machine Learning\n## Abstract\nSeveral recent works have been dedicated to unsupervised reinforcement learning in a single environment, in which a policy is first pre-trained with unsupervised interactions, and then fine-tuned towards the optimal policy for several downstream supervised tasks defined over the same environment. Along this line, we address the problem of unsupervised reinforcement learning in a class of multiple environments, in which the policy is pre-trained with interactions from the whole class, and then fine-tuned for several tasks in any environment of the class. Notably, the problem is inherently multi-objective as we can trade off the pre-training objective between environments in many ways. In this work, we foster an exploration strategy that is sensitive to the most adverse cases within the class. Hence, we cast the exploration problem as the maximization of the mean of a critical percentile of the state visitation entropy induced by the exploration strategy over the class of environments. Then, we present a policy gradient algorithm, $\\alpha$MEPOL, to optimize the introduced objective through mediated interactions with the class. Finally, we empirically demonstrate the ability of the algorithm in learning to explore challenging classes of continuous environments and we show that reinforcement learning greatly benefits from the pre-trained exploration strategy w.r.t. learning from scratch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08746v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"7252\", \"12959\", \"26551\", \"64566\", \"67153\", \"67624\", \"68436\", \"70473\", \"76745\", \"113199\", \"118123\", \"119548\", \"143630\", \"151100\", \"161071\", \"176698\", \"181066\", \"181532\", \"197061\", \"203991\", \"206945\", \"212299\", \"217595\", \"222199\", \"223225\", \"228299\", \"241496\", \"241900\", \"266419\", \"272422\", \"279790\", \"280457\", \"283289\", \"291025\", \"293995\", \"317251\", \"317861\", \"328415\", \"331072\"]}","task_split":"paper_retrieval"}
{"document_id":"2611","document_content":"# Taming Repetition in Dialogue Generation\n## Categories\n- Computation and Language\n## Abstract\nThe wave of pre-training language models has been continuously improving the quality of the machine-generated conversations, however, some of the generated responses still suffer from excessive repetition, sometimes repeating words from utterance, sometimes repeating words within self-generated responses, or both. Inappropriate repetition of words can significantly degrade the quality of the generated texts. Penalized sampling is one popular solution, reducing the sampling probability of existing words during inference, however, it is highly vulnerable to the inappropriate setting of the static weight. Setting it too high can yield strange and unrealistic sentences while setting it too low makes the task of suppressing repetition trivial. To remedy the shortcomings of the above methods, we design a context-aware classifier to explicitly decide when to allow repetition and when to employ penalized sampling. Such a classifier can be easily integrated with existing decoding methods, reducing repetitions where appropriate while preserving the diversity of the text. Experimental results demonstrate that our method can generate higher quality and more authentic dialogues.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08657v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"35166\", \"145384\", \"158493\", \"165308\", \"167338\", \"172088\", \"189504\", \"192158\", \"192811\", \"197924\", \"201867\", \"219618\", \"260314\", \"268902\", \"277028\", \"291102\", \"292831\", \"299936\", \"302815\", \"305200\", \"310903\", \"311470\", \"352129\"]}","task_split":"paper_retrieval"}
{"document_id":"2612","document_content":"# DREAM: Improving Situational QA by First Elaborating the Situation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWhen people answer questions about a specific situation, e.g., \"I cheated on my mid-term exam last week. Was that wrong?\", cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models (LMs) answer such questions, we conjecture that they may answer more accurately if they are also provided with additional details about the question situation, elaborating the \"scene\". To test this conjecture, we train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about, and then provide those elaborations as additional context to a question-answering (QA) model. We find that DREAM is able to create better scene elaborations (more accurate, useful, and consistent) than a representative state-of-the-art, zero-shot model (Macaw). We also find that using the scene elaborations as additional context improves the answer accuracy of a downstream QA system, including beyond that obtainable by simply further finetuning the QA system on DREAM's training data. These results suggest that adding focused elaborations about a situation can improve a system's reasoning about it, and may serve as an effective way of injecting new scenario based knowledge into QA models. Finally, our approach is dataset-neutral; we observe improved QA performance across different models, with even bigger gains on models with fewer parameters. We make our dataset and model publicly available at https:\/\/github.com\/allenai\/dream.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08656v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"2627\", \"3595\", \"6203\", \"14159\", \"15941\", \"25460\", \"51371\", \"62937\", \"72160\", \"74993\", \"77463\", \"77587\", \"89808\", \"94815\", \"96781\", \"107630\", \"120172\", \"127481\", \"127513\", \"131681\", \"143050\", \"153825\", \"158621\", \"171572\", \"182261\", \"189515\", \"212117\", \"231813\", \"238321\", \"296279\"]}","task_split":"paper_retrieval"}
{"document_id":"2636","document_content":"# Knowledge-Augmented Language Models for Cause-Effect Relation Classification\n## Categories\n- Computation and Language\n## Abstract\nPrevious studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the augmentation of pretrained language models with commonsense knowledge in the cause-effect relation classification and commonsense causal reasoning tasks. After automatically verbalizing ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, and GLUCOSE, a dataset of implicit commonsense causal knowledge, we continually pretrain BERT and RoBERTa with the verbalized data. Then we evaluate the resulting models on cause-effect pair classification and answering commonsense causal reasoning questions. Our results show that continually pretrained language models augmented with commonsense knowledge outperform our baselines on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, and the Temporal and Causal Reasoning (TCR) dataset, without additional improvement in model architecture or using quality-enhanced data for fine-tuning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08615v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"33114\", \"34255\", \"37617\", \"91739\", \"94815\", \"100285\", \"120762\", \"129503\", \"147108\", \"158595\", \"169182\", \"181204\", \"182349\", \"185514\", \"189515\", \"221212\"]}","task_split":"paper_retrieval"}
{"document_id":"2651","document_content":"# Idiomatic Expression Paraphrasing without Strong Supervision\n## Categories\n- Computation and Language\n## Abstract\nIdiomatic expressions (IEs) play an essential role in natural language. In this paper, we study the task of idiomatic sentence paraphrasing (ISP), which aims to paraphrase a sentence with an IE by replacing the IE with its literal paraphrase. The lack of large-scale corpora with idiomatic-literal parallel sentences is a primary challenge for this task, for which we consider two separate solutions. First, we propose an unsupervised approach to ISP, which leverages an IE's contextual information and definition and does not require a parallel sentence training set. Second, we propose a weakly supervised approach using back-translation to jointly perform paraphrasing and generation of sentences with IEs to enlarge the small-scale parallel sentence training dataset. Other significant derivatives of the study include a model that replaces a literal phrase in a sentence with an IE to generate an idiomatic expression and a large scale parallel dataset with idiomatic\/literal sentence pairs. The effectiveness of the proposed solutions compared to competitive baselines is seen in the relative gains of over 5.16 points in BLEU, over 8.75 points in METEOR, and over 19.57 points in SARI when the generated sentences are empirically validated on a parallel dataset using automatic and manual evaluations. We demonstrate the practical utility of ISP as a preprocessing step in En-De machine translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08592v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"15122\", \"56276\", \"73415\", \"94898\", \"96377\", \"119895\", \"129921\", \"139107\", \"146003\", \"167338\", \"170273\", \"193731\", \"219618\", \"235238\", \"235345\", \"242324\", \"255894\", \"262088\", \"268902\", \"279224\", \"307068\", \"307969\", \"309148\"]}","task_split":"paper_retrieval"}
{"document_id":"2693","document_content":"# Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis\n## Categories\n- Computation and Language\n## Abstract\nWe present the first parsing results on the Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.9 million word treebank that is an important resource for research in syntactic change. We describe key features of PPCEME that make it challenging for parsing, including a larger and more varied set of function tags than in the Penn Treebank. We present results for this corpus using a modified version of the Berkeley Neural Parser and the approach to function tag recovery of Gabbard et al (2006). Despite its simplicity, this approach works surprisingly well, suggesting it is possible to recover the original structure with sufficient accuracy to support linguistic applications (e.g., searching for syntactic structures of interest). However, for a subset of function tags (e.g., the tag indicating direct speech), additional work is needed, and we discuss some further limits of this approach. The resulting parser will be used to parse Early English Books Online, a 1.1 billion word corpus whose utility for the study of syntactic change will be greatly increased with the addition of accurate parse trees.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08532v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96350\", \"140362\", \"157137\", \"170021\", \"192402\", \"193725\", \"204342\", \"233407\", \"266758\", \"296816\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"2746","document_content":"# Design Challenges for a Multi-Perspective Search Engine\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nMany users turn to document retrieval systems (e.g. search engines) to seek answers to controversial questions. Answering such user queries usually require identifying responses within web documents, and aggregating the responses based on their different perspectives.   Classical document retrieval systems fall short at delivering a set of direct and diverse responses to the users. Naturally, identifying such responses within a document is a natural language understanding task. In this paper, we examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. Following the design challenges and principles, we demonstrate and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs for controversial queries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08357v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51468\", \"70389\", \"101670\", \"127111\", \"127538\", \"154465\", \"181273\", \"181804\", \"238986\", \"259723\", \"263265\", \"266850\", \"268671\", \"270020\"]}","task_split":"paper_retrieval"}
{"document_id":"2763","document_content":"# Is \"My Favorite New Movie\" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases\n## Categories\n- Computation and Language\n## Abstract\nRecursive noun phrases (NPs) have interesting semantic properties. For example, \"my favorite new movie\" is not necessarily my favorite movie, whereas \"my new favorite movie\" is. This is common sense to humans, yet it is unknown whether language models have such knowledge. We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs. When evaluated on RNPC, state-of-the-art Transformer models only perform around chance. Still, we show that such knowledge is learnable with appropriate data. We further probe the models for relevant linguistic features that can be learned from our tasks, including modifier semantic category and modifier scope. Finally, models trained on RNPC achieve strong zero-shot performance on an extrinsic Harm Detection evaluation task, showing the usefulness of the understanding of recursive NPs in downstream applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08326v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16884\", \"154632\", \"158683\", \"168097\", \"168951\", \"171988\", \"181098\", \"186130\", \"192195\", \"197416\", \"238196\", \"253818\", \"259175\", \"268604\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"2793","document_content":"# One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots\n## Categories\n- Human-Computer Interaction\n- Computation and Language\n## Abstract\nCustomer service chatbots are conversational systems designed to provide information to customers about products\/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that a chatbot is trained to recognize, there is a set of them that is universal to any customer service chatbot. Universal intents may include salutation, switch the conversation to a human agent, farewells, among others. A system to recognize those universal intents will be very helpful to optimize the training process of specific customer service chatbots. We propose the development of a universal intent recognition system, which is trained to recognize a selected group of 11 intents that are common in 28 different chatbots. The proposed system is trained considering state-of-the-art word-embedding models such as word2vec and BERT, and deep classifiers based on convolutional and recurrent neural networks. The proposed model is able to discriminate between those universal intents with a balanced accuracy up to 80.4\\%. In addition, the proposed system is equally accurate to recognize intents expressed both in short and long text requests. At the same time, misclassification errors often occurs between intents with very similar semantic fields such as farewells and positive comments. The proposed system will be very helpful to optimize the training process of a customer service chatbot because some of the intents will be already available and detected by our system. At the same time, the proposed approach will be a suitable base model to train more specific chatbots by applying transfer learning strategies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.08261v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"21909\", \"40001\", \"88668\", \"195286\", \"197154\", \"223084\", \"235922\"]}","task_split":"paper_retrieval"}
{"document_id":"2844","document_content":"# Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort\n## Categories\n- Digital Libraries\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nLarge amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations, thus strongly reducing annotation cost and effort. For this purpose, we combine an active learning (AL) approach with a pre-trained language model to semi-automatically identify annotation categories in the given text documents. To highlight our research direction's potential, we evaluate the approach on the task of identifying frames in news articles. Our preliminary results show that employing AL strongly reduces the number of annotations for correct classification of even these complex and subtle frames. On the framing dataset, the AL approach needs only 16.3\\% of the annotations to reach the same performance as a model trained on the full dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11914v1\", \"primary_category\": \"cs.DL\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.DL\"], \"primary_category_human_readable\": \"Digital Libraries\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Digital Libraries\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"2947","document_content":"# Tracing Text Provenance via Context-Aware Lexical Substitution\n## Categories\n- Cryptography and Security\n- Computation and Language\n## Abstract\nText content created by humans or language models is often stolen or misused by adversaries. Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news. There have been some attempts to achieve this, mainly based on watermarking techniques. Specifically, traditional text watermarking methods embed watermarks by slightly altering text format like line spacing and font, which, however, are fragile to cross-media transmissions like OCR. Considering this, natural language watermarking methods represent watermarks by replacing words in original sentences with synonyms from handcrafted lexical resources (e.g., WordNet), but they do not consider the substitution's impact on the overall sentence's meaning. Recently, a transformer-based network was proposed to embed watermarks by modifying the unobtrusive words (e.g., function words), which also impair the sentence's logical and semantic coherence. Besides, one well-trained network fails on other different types of text content. To address the limitations mentioned above, we propose a natural language watermarking scheme based on context-aware lexical substitution (LS). Specifically, we employ BERT to suggest LS candidates by inferring the semantic relatedness between the candidates and the original sentence. Based on this, a selection strategy in terms of synchronicity and substitutability is further designed to test whether a word is exactly suitable for carrying the watermark signal. Extensive experiments demonstrate that, under both objective and subjective metrics, our watermarking scheme can well preserve the semantic integrity of original sentences and has a better transferability than existing methods. Besides, the proposed LS approach outperforms the state-of-the-art approach on the Stanford Word Substitution Benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.07873v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CL\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computation and Language\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"43878\", \"81800\", \"91982\", \"101963\", \"140130\", \"176154\", \"191143\", \"199361\", \"223323\", \"259884\", \"284357\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"3036","document_content":"# Towards Interactive Language Modeling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nInteraction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction plays little to no role in artificial language modeling -- which also targets the acquisition of human language, yet by artificial models. Moreover, an interactive approach to language modeling has the potential to make language models substantially more versatile and to considerably impact downstream applications. Motivated by these considerations, we pioneer the space of interactive language modeling. As a first contribution we present a road map in which we detail the steps that need to be taken towards interactive language modeling. We then lead by example and take the first steps on this road map, showing the initial feasibility of our approach. As such, this work aims to be the start of a larger research agenda on interactive language modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11911v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"15851\", \"37830\", \"96571\", \"132309\", \"147773\", \"153289\", \"170653\", \"192616\", \"192811\", \"195137\", \"216554\", \"241070\", \"249109\", \"251911\", \"262325\"]}","task_split":"paper_retrieval"}
{"document_id":"3080","document_content":"# CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Multimedia\n## Abstract\nLocalizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instance (i.e., sub-texts). In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue. CORE first leverages a vanilla relation block to model the relations among all text proposals (sub-texts of multiple text instances) and further enhances relational reasoning via instance-level sub-text discrimination in a contrastive manner. Such way naturally learns instance-aware representations of text proposals and thus facilitates scene text detection. We integrate the CORE module into a two-stage text detector of Mask R-CNN and devise our text detector CORE-Text. Extensive experiments on four benchmarks demonstrate the superiority of CORE-Text. Code is available: \\url{https:\/\/github.com\/jylins\/CORE-Text}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.07513v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Multimedia\"], \"incoming_citations\": [\"3078\"], \"outgoing_citations\": [\"97564\", \"108123\", \"119397\", \"133970\", \"136466\", \"155242\", \"170212\", \"189021\", \"193382\", \"217310\", \"225195\", \"248133\", \"248734\", \"252159\", \"271112\", \"284871\"]}","task_split":"paper_retrieval"}
{"document_id":"3219","document_content":"# Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nLegal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how have they been used in past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and, hence, expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer-based models pre-trained on large language corpora to detect which of the sentences are useful. In light of models' predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.07165v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"2949\", \"28030\", \"94572\", \"158750\", \"181098\", \"182360\", \"190743\", \"193627\"]}","task_split":"paper_retrieval"}
{"document_id":"3261","document_content":"# Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent years, people from all over the world are suffering from one of the most severe diseases in history, known as Coronavirus disease 2019, COVID-19 for short. When the virus reaches the lungs, it has a higher probability to cause lung pneumonia and sepsis. X-ray image is a powerful tool in identifying the typical features of the infection for COVID-19 patients. The radiologists and pathologists observe that ground-glass opacity appears in the chest X-ray for infected patient \\cite{cozzi2021ground}, and it could be used as one of the criteria during the diagnosis process. In the past few years, deep learning has proven to be one of the most powerful methods in the field of image classification. Due to significant differences in Chest X-Ray between normal and infected people \\cite{rousan2020chest}, deep models could be used to identify the presence of the disease given a patient's Chest X-Ray. Many deep models are complex, and it evolves with lots of input parameters. Designers sometimes struggle with the tuning process for deep models, especially when they build up the model from scratch. Genetic Algorithm, inspired by the biological evolution process, plays a key role in solving such complex problems. In this paper, I proposed a genetic-based approach to optimize the Convolutional Neural Network(CNN) for the Chest X-Ray classification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.07087v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.LG\", \"eess.IV\", \"cs.NE\", \"cs.CV\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Neural and Evolutionary Computing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"202576\", \"341335\"]}","task_split":"paper_retrieval"}
{"document_id":"3333","document_content":"# A cognitively driven weighted-entropy model for embedding semantic categories in hyperbolic geometry\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, an unsupervised and cognitively driven weighted-entropy method for embedding semantic categories in hyperbolic geometry is proposed. The model is driven by two fields of research in cognitive linguistics: the first is the statistical learning theory of language acquisition and the proposal of using high-dimensional networks to represent semantic knowledge in cognition, and the second is the domain-specific approach to semantic communication. Weighted conditional entropy of word co-occurrence is proposed as the embedding metric, and the two weighting parameters are collocation diversity and conditional probability ranking in the corresponding statistical distribution. The Boltzmann distribution is then used on the weighted-entropy metric and embedded into a hyperbolic Poincare disk model. Testing has been in particular performed in the domains of basic color and kinship words, which belong to the classes that domain-specificity focused research in cognitive semantics has most intensively investigated. Results show that this new approach can successfully model and map the semantic relationships of popularity and similarity for most of the basic color and kinship words in English and have potential to be generalized to other semantic domains and different languages. Generally, this paper contributes to both computational cognitive semantics and the research on network and geometry-driven language embedding in computational linguistics and NLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06876v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"9935\", \"184568\", \"186776\", \"228417\", \"236182\", \"265739\"]}","task_split":"paper_retrieval"}
{"document_id":"3371","document_content":"# The King is Naked: on the Notion of Robustness for Natural Language Processing\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThere is growing evidence that the classical notion of adversarial robustness originally introduced for images has been adopted as a de facto standard by a large part of the NLP research community. We show that this notion is problematic in the context of NLP as it considers a narrow spectrum of linguistic phenomena. In this paper, we argue for semantic robustness, which is better aligned with the human concept of linguistic fidelity. We characterize semantic robustness in terms of biases that it is expected to induce in a model. We study semantic robustness of a range of vanilla and robustly trained architectures using a template-based generative test bed. We complement the analysis with empirical evidence that, despite being harder to implement, semantic robustness can improve performance %gives guarantees for on complex linguistic phenomena where models robust in the classical sense fail.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.07605v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"124477\"], \"outgoing_citations\": [\"29312\", \"32779\", \"49046\", \"50908\", \"51130\", \"56222\", \"74993\", \"81845\", \"96709\", \"97442\", \"105077\", \"105435\", \"115891\", \"126188\", \"127746\", \"128071\", \"129087\", \"129858\", \"131414\", \"139587\", \"139638\", \"146147\", \"157244\", \"157497\", \"168809\", \"168988\", \"180177\", \"180873\", \"202296\", \"212366\", \"228366\", \"229886\", \"234716\", \"240174\", \"245196\", \"246146\", \"247127\", \"251240\", \"251412\", \"255035\", \"258100\", \"260450\", \"267955\", \"282181\", \"307068\", \"350314\"]}","task_split":"paper_retrieval"}
{"document_id":"3402","document_content":"# Learning Semantic-Aligned Feature Representation for Text-based Person Search\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Multimedia\n## Abstract\nText-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve the feature alignment across modalities. In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features. First, we introduce two Transformer-based backbones to encode robust feature representations of the images and texts. Second, we design a semantic-aligned feature aggregation network to adaptively select and aggregate features with the same semantics into part-aware features, which is achieved by a multi-head attention module constrained by a cross-modality part alignment loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K datasets show that our method achieves state-of-the-art performances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06714v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"33140\", \"125002\", \"179422\", \"216895\", \"222262\", \"250405\", \"259197\", \"273696\", \"320761\"]}","task_split":"paper_retrieval"}
{"document_id":"3438","document_content":"# Detecting Emotion Carriers by Combining Acoustic and Lexical Representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Sound\n- Audio and Speech Processing\n## Abstract\nPersonal narratives (PN) - spoken or written - are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks are usually defined at the utterance or document level. However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator (\"loss of father\", \"made me choose\"). Once extracted, such EC can provide a richer representation of the user state to improve natural language understanding and dialogue modeling. In previous work, it has been shown that EC can be identified using lexical features. However, spoken narratives should provide a richer description of the context and the users' emotional state. In this paper, we leverage word-based acoustic and textual embeddings as well as early and late fusion techniques for the detection of ECs in spoken narratives. For the acoustic word-level representations, we use Residual Neural Networks (ResNet) pretrained on separate speech emotion corpora and fine-tuned to detect EC. Experiments with different fusion and system combination strategies show that late fusion leads to significant improvements for this task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06603v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105550\", \"139687\", \"187048\", \"223222\", \"227994\"]}","task_split":"paper_retrieval"}
{"document_id":"3461","document_content":"# MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nText-based image captioning (TextCap) requires simultaneous comprehension of visual content and reading the text of images to generate a natural language description. Although a task can teach machines to understand the complex human environment further given that text is omnipresent in our daily surroundings, it poses additional challenges in normal captioning. A text-based image intuitively contains abundant and complex multimodal relational content, that is, image details can be described diversely from multiview rather than a single caption. Certainly, we can introduce additional paired training data to show the diversity of images' descriptions, this process is labor-intensive and time-consuming for TextCap pair annotations with extra texts. Based on the insight mentioned above, we investigate how to generate diverse captions that focus on different image parts using an unpaired training paradigm. We propose the Multimodal relAtional Graph adversarIal inferenCe (MAGIC) framework for diverse and unpaired TextCap. This framework can adaptively construct multiple multimodal relational graphs of images and model complex relationships among graphs to represent descriptive diversity. Moreover, a cascaded generative adversarial network is developed from modeled graphs to infer the unpaired caption generation in image-sentence feature alignment and linguistic coherence levels. We validate the effectiveness of MAGIC in generating diverse captions from different relational information items of an image. Experimental results show that MAGIC can generate very promising outcomes without using any image-caption training pairs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06558v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"5837\", \"5843\", \"33300\", \"40810\", \"53992\", \"108136\", \"135255\", \"137828\", \"155831\", \"162242\", \"173039\", \"182657\", \"185622\", \"189847\", \"193328\", \"193738\", \"217310\", \"238941\", \"251816\", \"262064\", \"264326\", \"271527\", \"278954\", \"287504\", \"302621\", \"305200\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"3480","document_content":"# Automated Evidence Collection for Fake News Detection\n## Categories\n- Computation and Language\n## Abstract\nFake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society, especially when dealing with an epidemic like COVID-19. The task of Fake News Detection aims to tackle the effects of such misinformation by classifying news items as fake or real. In this paper, we propose a novel approach that improves over the current automatic fake news detection approaches by automatically gathering evidence for each claim. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. We use a pre-trained summarizer on these evidence sets and then use the extracted summary as supporting evidence to aid the classification task. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach. The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared Task. We also release the augmented dataset, our code and models for any further research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06507v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"72864\", \"72890\", \"75448\", \"75513\", \"76033\", \"76628\", \"79024\", \"84522\", \"88648\", \"94401\", \"117359\", \"124916\", \"203557\", \"208471\", \"210466\", \"219367\", \"238986\", \"259202\", \"267552\", \"284357\", \"291019\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"3492","document_content":"# ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nRecently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representations. However, it is difficult to model such interactions as image and text representations are trained separately on the data of their respective modality and are not aligned in the same space. As text representations take the most important role in MNER, in this paper, we propose {\\bf I}mage-{\\bf t}ext {\\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized. ITA first aligns the image into regional object tags, image-level captions and optical characters as visual contexts, concatenates them with the input texts as a new cross-modal input, and then feeds it into a pretrained textual embedding model. This makes it easier for the attention module of a pretrained textual embedding model to model the interaction between the two modalities since they are both represented in the textual space. ITA further aligns the output distributions predicted from the cross-modal input and textual input views so that the MNER model can be more practical in dealing with text-only inputs and robust to noises from images. In our experiments, we show that ITA models can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets, even without image information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06482v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"50900\", \"71366\", \"77306\", \"87107\", \"89406\", \"97100\", \"115124\", \"125061\", \"131401\", \"164927\", \"170926\", \"172370\", \"172774\", \"211953\", \"239242\", \"241309\", \"278954\", \"297147\", \"297150\", \"308333\", \"311824\", \"313440\"]}","task_split":"paper_retrieval"}
{"document_id":"3565","document_content":"# Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGiven the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty. We propose leveraging a unified text-to-text Transformer for LJP, where the dependencies among sub-tasks can be naturally established within the auto-regressive decoder. Compared with previous works, it has three advantages: (1) it fits in the pretraining pattern of masked language models, and thereby can benefit from the semantic prompts of each sub-task rather than treating them as atomic labels, (2) it utilizes a single unified architecture, enabling full parameter sharing across all sub-tasks, and (3) it can incorporate both classification and generative sub-tasks. We show that this unified transformer, albeit pretrained on general-domain text, outperforms pretrained models tailored specifically for the legal domain. Through an extensive set of experiments, we find that the best order to capture dependencies is different from human intuitions, and the most reasonable logical order for humans can be sub-optimal for the model. We further include two more auxiliary tasks: court view generation and article content prediction, showing they can not only improve the prediction accuracy, but also provide interpretable explanations for model outputs even when an error is made. With the best configuration, our model outperforms both previous SOTA and a single-tasked version of the unified transformer by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.06370v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"32774\", \"54317\", \"61481\", \"92112\", \"100541\", \"127463\", \"129023\", \"131921\", \"132828\", \"166518\", \"169521\", \"177592\", \"182363\", \"187002\", \"214739\", \"217447\", \"225839\", \"235913\", \"241110\", \"259303\", \"259931\"]}","task_split":"paper_retrieval"}
{"document_id":"3699","document_content":"# Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nMany historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-ofdomain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open- StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations easily. However, the cartographic styles between OSM map tiles and historical maps are significantly different. This paper proposes a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We use a style transfer model to convert contemporary map images into historical style and place text labels upon them. We show that the state-of-the-art text detection models (e.g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3486635.3491070\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"5561\", \"135263\", \"178095\", \"225795\", \"257286\", \"262570\", \"269205\", \"294023\", \"299461\"]}","task_split":"paper_retrieval"}
{"document_id":"3852","document_content":"# On Causally Disentangled Representations\n## Categories\n- Machine Learning\n## Abstract\nRepresentation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. Initially consisting of unsupervised models with independence assumptions, more recently, weak supervision and correlated features have been explored, but without a causal view of the generative process. In contrast, we work under the regime of a causal generative process where generative factors are either independent or can be potentially confounded by a set of observed or unobserved confounders. We present an analysis of disentangled representations through the notion of disentangled causal process. We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. We show that our metrics capture the desiderata of disentangled causal process. Finally, we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.05746v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"90921\", \"113870\", \"116297\", \"118768\", \"123374\", \"130407\", \"138086\", \"143581\", \"160471\", \"175837\", \"181359\", \"181394\", \"181909\", \"182154\", \"182775\", \"184690\", \"190384\", \"199971\", \"208041\", \"212169\", \"223886\", \"241933\", \"242142\", \"242239\", \"251597\", \"256720\", \"290677\", \"335083\"]}","task_split":"paper_retrieval"}
{"document_id":"4039","document_content":"# Dynamic hardware system for cascade SVM classification of melanoma\n## Categories\n- Hardware Architecture\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nMelanoma is the most dangerous form of skin cancer, which is responsible for the majority of skin cancer-related deaths. Early diagnosis of melanoma can significantly reduce mortality rates and treatment costs. Therefore, skin cancer specialists are using image-based diagnostic tools for detecting melanoma earlier. We aim to develop a handheld device featured with low cost and high performance to enhance early detection of melanoma at the primary healthcare. But, developing this device is very challenging due to the complicated computations required by the embedded diagnosis system. Thus, we aim to exploit the recent hardware technology in reconfigurable computing to achieve a high-performance embedded system at low cost. Support vector machine (SVM) is a common classifier that shows high accuracy for classifying melanoma within the diagnosis system and is considered as the most compute-intensive task in the system. In this paper, we propose a dynamic hardware system for implementing a cascade SVM classifier on FPGA for early melanoma detection. A multi-core architecture is proposed to implement a two-stage cascade classifier using two classifiers with accuracies of 98% and 73%. The hardware implementation results were optimized by using the dynamic partial reconfiguration technology, where very low resource utilization of 1% slices and power consumption of 1.5 W were achieved. Consequently, the implemented dynamic hardware system meets vital embedded system constraints of high performance and low cost, resource utilization, and power consumption, while achieving efficient classification with high accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s00521-018-3656-1\", \"primary_category\": \"cs.AR\", \"categories\": [\"cs.CV\", \"cs.AR\", \"eess.IV\"], \"primary_category_human_readable\": \"Hardware Architecture\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Hardware Architecture\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"27467\"]}","task_split":"paper_retrieval"}
{"document_id":"4040","document_content":"# PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records\n## Categories\n- Machine Learning\n## Abstract\nFederated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays. Federated learning method exploits not only the data but the computational power of all devices in the network to achieve more efficient model training. Nevertheless, while most traditional federated learning methods work well for homogeneous data and tasks, adapting the method to a different heterogeneous data and task distribution is challenging. This limitation has constrained the applications of federated learning in real-world contexts, especially in healthcare settings. Inspired by the fundamental idea of meta-learning, in this study we propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. In addition, owing to the advantage of transfer learning for model generalization, we further improve our algorithm by introducing partial parameter sharing. We name this method partial meta-federated learning (PMFL). Finally, we apply the algorithms to two medical datasets. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.05321v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54410\", \"67820\", \"131702\", \"141462\", \"156643\", \"159558\", \"167551\", \"171823\", \"194178\", \"195581\", \"199085\", \"202208\", \"215015\", \"229563\", \"239655\", \"241304\", \"246939\", \"248216\", \"251819\", \"259336\", \"282453\", \"283045\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"4211","document_content":"# Multimodal Fake News Detection\n## Categories\n- Computation and Language\n## Abstract\nOver the last years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have in different segments of our society. Thus, the development of tools for automatic detection of fake news plays and important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories such as Manipulated content, Satire or False connection strongly benefit from the use of images. Using images also improves the results of the other categories, but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model with an accuracy of 78%. Therefore, exploiting both text and image data significantly improves the performance of fake news detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04831v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"56085\", \"141447\", \"145167\", \"146683\", \"177045\", \"196472\", \"249420\", \"259202\", \"265384\", \"268465\"]}","task_split":"paper_retrieval"}
{"document_id":"4327","document_content":"# Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach\n## Categories\n- Machine Learning\n- Optimization and Control\n- Statistics Theory\n- Statistics Theory\n## Abstract\nA main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process. However, available methods in finding optimal DTRs often rely on assumptions that are violated in real-world applications (e.g., medical decision-making or public policy), especially when (a) the existence of unobserved confounders cannot be ignored, and (b) the unobserved confounders are time-varying (e.g., affected by previous actions). When such assumptions are violated, one often faces ambiguity regarding the underlying causal model. This ambiguity is inevitable, since the dynamics of unobserved confounders and their causal impact on the observed part of the data cannot be understood from the observed data. Motivated by a case study of finding superior treatment regimes for patients who underwent transplantation in our partner hospital and faced a medical condition known as New Onset Diabetes After Transplantation (NODAT), we extend DTRs to a new class termed Ambiguous Dynamic Treatment Regimes (ADTRs), in which the causal impact of treatment regimes is evaluated based on a \"cloud\" of causal models. We then connect ADTRs to Ambiguous Partially Observable Mark Decision Processes (APOMDPs) and develop Reinforcement Learning methods, which enable using the observed data to efficiently learn an optimal treatment regime. We establish theoretical results for these learning methods, including (weak) consistency and asymptotic normality. We further evaluate the performance of these learning methods both in our case study and in simulation experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04571v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.OC\", \"math.ST\", \"stat.ML\", \"stat.TH\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Optimization and Control\", \"Statistics Theory\", \"Machine Learning\", \"Statistics Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"65803\", \"109385\", \"137194\", \"142892\", \"167909\", \"170607\", \"274345\", \"288237\", \"295281\", \"303539\", \"348583\", \"356455\"]}","task_split":"paper_retrieval"}
{"document_id":"4390","document_content":"# Transformer based trajectory prediction\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nTo plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task which recently gained significant attention of the research community. In this work, we present a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has shown its effectiveness in conditions of domain change. While being easy-to-implement, the proposed approach achieves competitive performance and ranks 1$^{st}$ on the 2021 Shifts Vehicle Motion Prediction Competition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04350v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"25301\", \"35204\", \"101580\", \"109433\", \"126156\", \"128967\", \"147880\", \"162130\", \"221071\", \"237350\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"4392","document_content":"# Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04344v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22231\", \"61182\", \"92832\", \"150134\", \"154601\", \"154705\", \"181930\", \"182026\", \"182279\", \"185805\", \"189542\", \"211937\", \"216563\", \"219377\", \"233062\", \"270020\", \"309886\"]}","task_split":"paper_retrieval"}
{"document_id":"4468","document_content":"# Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThe extraction of relevant information carried out by named entities in handwriting documents is still a challenging task. Unlike traditional information extraction approaches that usually face text transcription and named entity recognition as separate subsequent tasks, we propose in this paper an end-to-end transformer-based approach to jointly perform these two tasks. The proposed approach operates at the paragraph level, which brings two main benefits. First, it allows the model to avoid unrecoverable early errors due to line segmentation. Second, it allows the model to exploit larger bi-dimensional context information to identify the semantic categories, reaching a higher final prediction accuracy. We also explore different training scenarios to show their effect on the performance and we demonstrate that a two-stage learning strategy can make the model reach a higher final prediction accuracy. As far as we know, this work presents the first approach that adopts the transformer networks for named entity recognition in handwritten documents. We achieve the new state-of-the-art performance in the ICDAR 2017 Information Extraction competition using the Esposalles database, for the complete task, even though the proposed technique does not use any dictionaries, language modeling, or post-processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04189v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"46110\"], \"outgoing_citations\": [\"80427\", \"82145\", \"93552\", \"98454\", \"118987\", \"122798\", \"150132\", \"157123\", \"162477\", \"194746\", \"238732\", \"284605\", \"294753\"]}","task_split":"paper_retrieval"}
{"document_id":"4614","document_content":"# Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time\n## Categories\n- Machine Learning\n## Abstract\nChoosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observational data sets constitute rich sources of information to learn from, they also contain biases as treatments are rarely assigned randomly in practice. To provide accurate and unbiased forecasts, we introduce the Disentangled Counterfactual Recurrent Network (DCRN), a novel sequence-to-sequence architecture that estimates treatment outcomes over time by learning representations of patient histories that are disentangled into three separate latent factors: a treatment factor, influencing only treatment selection; an outcome factor, influencing only the outcome; and a confounding factor, influencing both. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding, as our architecture allows for practitioners to infer which patient features influence which part in a patient's trajectory, contrasting other approaches in this domain. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.03811v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"119172\", \"143064\", \"147244\", \"269527\", \"270120\", \"286370\", \"290547\", \"292736\"]}","task_split":"paper_retrieval"}
{"document_id":"4621","document_content":"# Adapting Procedural Content Generation to Player Personas Through Evolution\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nAutomatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, \"Grave Rave\", we demonstrate that this approach successfully adapts to four rule-based persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.04406v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"38564\", \"48859\", \"51175\", \"55260\", \"64439\", \"203756\", \"233501\", \"241619\"]}","task_split":"paper_retrieval"}
{"document_id":"4719","document_content":"# Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Human-Computer Interaction\n- Information Retrieval\n- Machine Learning\n## Abstract\nThe usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field.   Question answering involves but not limited to the steps like mapping of user question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks.   In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.03572v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"2107\", \"2105\"], \"outgoing_citations\": [\"74933\", \"114545\", \"123404\", \"126346\", \"131681\", \"131921\", \"135112\", \"136840\", \"137436\", \"158855\", \"167880\", \"168542\", \"171110\", \"174555\", \"179017\", \"186455\", \"200155\", \"202124\", \"217522\", \"219048\", \"220351\", \"220383\", \"220723\", \"230556\", \"232274\", \"238962\", \"247060\", \"249504\", \"250398\", \"252059\", \"256171\", \"258382\", \"261389\", \"266768\", \"266850\", \"267694\", \"268671\", \"268809\", \"270020\", \"279213\", \"279343\", \"280615\", \"281162\", \"283345\", \"288062\", \"294898\", \"296198\", \"303827\", \"304481\", \"311423\", \"313143\", \"313435\", \"313440\", \"316803\"]}","task_split":"paper_retrieval"}
{"document_id":"4879","document_content":"# Quantifying Adaptability in Pre-trained Language Models with 500 Tasks\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWhen a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model? In NLP, systematic features of LM generalization to individual examples are well characterized, but systematic aspects of LM adaptability to new tasks are not nearly as well understood. We present a large-scale empirical study of the features and limits of LM adaptability using a new benchmark, TaskBench500, built from 500 procedurally generated sequence modeling tasks. These tasks combine core aspects of language processing, including lexical semantics, sequence processing, memorization, logical reasoning, and world knowledge. Using TaskBench500, we evaluate three facets of adaptability, finding that: (1) adaptation procedures differ dramatically in their ability to memorize small datasets; (2) within a subset of task types, adaptation procedures exhibit compositional adaptability to complex tasks; and (3) failure to match training label distributions is explained by mismatches in the intrinsic difficulty of predicting individual labels. Our experiments show that adaptability to new tasks, like generalization to new examples, can be systematically described and understood, and we conclude with a discussion of additional aspects of adaptability that could be studied using the new benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.03204v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16299\", \"48369\", \"55224\", \"77463\", \"95004\", \"131446\", \"135112\", \"137359\", \"166494\", \"168951\", \"188123\", \"192616\", \"200362\", \"220137\", \"221540\", \"229524\", \"251911\", \"260450\", \"280713\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"4995","document_content":"# Distance and Hop-wise Structures Encoding Enhanced Graph Attention Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nNumerous works have proven that existing neighbor-averaging Graph Neural Networks cannot efficiently catch structure features, and many works show that injecting structure, distance, position or spatial features can significantly improve performance of GNNs, however, injecting overall structure and distance into GNNs is an intuitive but remaining untouched idea. In this work, we shed light on the direction. We first extracting hop-wise structure information and compute distance distributional information, gathering with node's intrinsic features, embedding them into same vector space and then adding them up. The derived embedding vectors are then fed into GATs(like GAT, AGDN) and then Correct and Smooth, experiments show that the DHSEGATs achieve competitive result. The code is available at https:\/\/github.com\/hzg0601\/DHSEGATs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.02868v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"43338\", \"49891\", \"54881\", \"73530\", \"77861\", \"91086\", \"91654\", \"103085\", \"115700\", \"115834\", \"117690\", \"118201\", \"129410\", \"138857\", \"141927\", \"143081\", \"151458\", \"176579\", \"181255\", \"184400\", \"185055\", \"186164\", \"210730\", \"214311\", \"243382\", \"244510\"]}","task_split":"paper_retrieval"}
{"document_id":"5114","document_content":"# Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. In this review, we focus on the interpretability of the DL models in healthcare. We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this field. Besides the methods' details, we also include a discussion of advantages and disadvantages of these methods and which scenarios each of them is suitable for, so that interested readers can know how to compare and choose among them for use. Moreover, we discuss how these methods, originally developed for solving general-domain problems, have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies. Overall, we hope this survey can help researchers and practitioners in both artificial intelligence (AI) and clinical fields understand what methods we have for enhancing the interpretability of their DL models and choose the optimal one accordingly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.02625v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1103\"], \"outgoing_citations\": [\"91124\", \"95868\", \"99323\", \"117505\", \"122497\", \"125214\", \"127556\", \"132402\", \"137068\", \"140921\", \"141672\", \"146722\", \"153542\", \"164211\", \"165974\", \"166967\", \"167737\", \"168672\", \"170627\", \"171131\", \"171212\", \"172108\", \"174139\", \"174630\", \"174985\", \"179187\", \"181468\", \"182261\", \"184049\", \"184390\", \"185112\", \"186566\", \"188812\", \"190836\", \"193512\", \"193619\", \"193725\", \"197416\", \"198892\", \"201425\", \"203156\", \"208291\", \"208546\", \"212053\", \"213326\", \"216097\", \"216591\", \"218156\", \"223886\", \"225381\", \"229519\", \"229538\", \"230708\", \"235580\", \"238348\", \"239043\", \"241329\", \"242037\", \"242280\", \"242562\", \"244917\", \"246146\", \"247042\", \"250247\", \"250829\", \"258657\", \"260424\", \"262092\", \"263454\", \"266513\", \"267761\", \"268485\", \"269338\", \"270005\", \"271562\", \"272864\", \"273542\", \"273959\", \"277288\", \"279840\", \"286253\", \"290227\", \"290677\", \"293152\", \"302645\", \"303380\", \"319949\", \"320493\"]}","task_split":"paper_retrieval"}
{"document_id":"5164","document_content":"# Snapshot HDR Video Construction Using Coded Mask\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper study the reconstruction of High Dynamic Range (HDR) video from snapshot-coded LDR video. Constructing an HDR video requires restoring the HDR values for each frame and maintaining the consistency between successive frames. HDR image acquisition from single image capture, also known as snapshot HDR imaging, can be achieved in several ways. For example, the reconfigurable snapshot HDR camera is realized by introducing an optical element into the optical stack of the camera; by placing a coded mask at a small standoff distance in front of the sensor. High-quality HDR image can be recovered from the captured coded image using deep learning methods. This study utilizes 3D-CNNs to perform a joint demosaicking, denoising, and HDR video reconstruction from coded LDR video. We enforce more temporally consistent HDR video reconstruction by introducing a temporal loss function that considers the short-term and long-term consistency. The obtained results are promising and could lead to affordable HDR video capture using conventional cameras.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.02522v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227145\", \"228199\", \"248813\", \"250193\", \"320532\"]}","task_split":"paper_retrieval"}
{"document_id":"5474","document_content":"# Semantic Segmentation of Legal Documents via Rhetorical Roles\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nLegal documents are unstructured, use legal jargon, and have considerable length, making them difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be segmented into coherent information units. This paper proposes a new corpus of legal documents annotated (with the help of legal experts) with a set of 13 semantically coherent units labels (referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue, precedent, ruling, and ratio. We perform a thorough analysis of the corpus and the annotations. For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles. Using the created corpus, we experiment extensively with various deep learning-based baseline models for the task. Further, we develop a multitask learning (MTL) based deep model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. The proposed model shows superior performance over the existing models. We also experiment with model performance in the case of domain transfer and model distillation techniques to see the model performance in limited data conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01836v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46768\", \"96350\", \"97839\", \"101301\", \"156575\", \"156893\", \"169521\", \"182360\", \"187002\", \"241110\"]}","task_split":"paper_retrieval"}
{"document_id":"5495","document_content":"# Controversy Detection: a Text and Graph Neural Network Based Approach\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n- Social and Information Networks\n## Abstract\nControversial content refers to any content that attracts both positive and negative feedback. Its automatic identification, especially on social media, is a challenging task as it should be done on a large number of continuously evolving posts, covering a large variety of topics. Most of the existing approaches rely on the graph structure of a topic-discussion and\/or the content of messages. This paper proposes a controversy detection approach based on both graph structure of a discussion and text features. Our proposed approach relies on Graph Neural Network (gnn) to encode the graph representation (including its texts) in an embedding vector before performing a graph classification task. The latter will classify the post as controversial or not. Two controversy detection strategies are proposed. The first one is based on a hierarchical graph representation learning. Graph user nodes are embedded hierarchically and iteratively to compute the whole graph embedding vector. The second one is based on the attention mechanism, which allows each user node to give more or less importance to its neighbors when computing node embeddings. We conduct experiments to evaluate our approach using different real-world datasets. Conducted experiments show the positive impact of combining textual features and structural information in terms of performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.11445v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"103972\", \"124756\", \"147188\", \"170925\", \"177593\", \"186386\", \"190417\", \"227067\", \"312774\"]}","task_split":"paper_retrieval"}
{"document_id":"5518","document_content":"# Probing Linguistic Information For Logical Inference In Pre-trained Language Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nProgress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of linguistic properties encoded in their contextualized representations. However, it is unclear whether they encode semantic knowledge that is crucial to symbolic inference methods. We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations. Our probing datasets cover a list of linguistic phenomena required by major symbolic inference systems. We find that (i) pre-trained language models do encode several types of linguistic information for inference, but there are also some types of information that are weakly encoded, (ii) language models can effectively learn missing linguistic information through fine-tuning. Overall, our findings provide insights into which aspects of linguistic information for logical inference do language models and their pre-training procedures capture. Moreover, we have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01753v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46486\", \"55247\", \"90366\", \"120762\", \"127991\", \"132635\", \"138515\", \"139638\", \"153748\", \"160911\", \"162693\", \"168097\", \"168951\", \"180652\", \"186130\", \"216455\", \"233045\", \"234307\", \"234628\", \"234840\", \"268604\", \"288921\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"5536","document_content":"# Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this paper, we propose a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited to using the structural information in the feature space. Additionally, the single step of GCs only uses features on the one-hop neighboring nodes from the target node. In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step. In the first method, we define three structural features in the feature space: feature angle, feature distance, and relational embedding. The second method aggregates the node-wise features of multi-hop neighbors in a GC. Both methods can be simultaneously used. We also propose graph neural networks (GNNs) integrating the proposed GC for classifying nodes in 3D point clouds and citation networks. In experiments, the proposed GNNs exhibited a higher classification accuracy than existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01714v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"137812\", \"139204\", \"152466\", \"161144\", \"172026\", \"189436\", \"190189\", \"192310\", \"205863\", \"208245\", \"209673\", \"227067\", \"228849\", \"229324\", \"238852\", \"247126\", \"255737\", \"265828\", \"269273\", \"296091\", \"305550\", \"328054\", \"336185\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"5568","document_content":"# The Influence of Data Pre-processing and Post-processing on Long Document Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nLong document summarization is an important and hard task in the field of natural language processing. A good performance of the long document summarization reveals the model has a decent understanding of the human language. Currently, most researches focus on how to modify the attention mechanism of the transformer to achieve a higher ROUGE score. The study of data pre-processing and post-processing are relatively few. In this paper, we use two pre-processing methods and a post-processing method and analyze the effect of these methods on various long document summarization models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01660v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"58194\", \"109314\", \"168185\", \"235463\"]}","task_split":"paper_retrieval"}
{"document_id":"5596","document_content":"# Neurosymbolic Systems of Perception & Cognition: The Role of Attention\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nA cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information stack. The more varied the environment of a learning agent is, the more general and flexible must be these mechanisms to handle a wider variety of relevant patterns, tasks, and goal structures. While many researchers agree that information at different levels of abstraction likely differs in its makeup and structure and processing mechanisms, agreement on the particulars of such differences is not generally shared in the research community. A binary processing architecture (often referred to as System-1 and System-2) has been proposed as a model of cognitive processing for low- and high-level information, respectively. We posit that cognition is not binary in this way and that knowledge at any level of abstraction involves what we refer to as neurosymbolic information, meaning that data at both high and low levels must contain both symbolic and subsymbolic information. Further, we argue that the main differentiating factor between the processing of high and low levels of data abstraction can be largely attributed to the nature of the involved attention mechanisms. We describe the key arguments behind this view and review relevant evidence from the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01603v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"37352\", \"69973\", \"83792\", \"241392\", \"242322\", \"245441\", \"251911\", \"336091\"]}","task_split":"paper_retrieval"}
{"document_id":"5669","document_content":"# Training Efficiency and Robustness in Deep Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep Learning has revolutionized machine learning and artificial intelligence, achieving superhuman performance in several standard benchmarks. It is well-known that deep learning models are inefficient to train; they learn by processing millions of training data multiple times and require powerful computational resources to process large batches of data in parallel at the same time rather than sequentially. Deep learning models also have unexpected failure modes; they can be fooled into misbehaviour, producing unexpectedly incorrect predictions.   In this thesis, we study approaches to improve the training efficiency and robustness of deep learning models. In the context of learning visual-semantic embeddings, we find that prioritizing learning on more informative training data increases convergence speed and improves generalization performance on test data. We formalize a simple trick called hard negative mining as a modification to the learning objective function with no computational overhead. Next, we seek improvements to optimization speed in general-purpose optimization methods in deep learning. We show that a redundancy-aware modification to the sampling of training data improves the training speed and develops an efficient method for detecting the diversity of training signal, namely, gradient clustering. Finally, we study adversarial robustness in deep learning and approaches to achieve maximal adversarial robustness without training with additional data. For linear models, we prove guaranteed maximal robustness achieved only by appropriate choice of the optimizer, regularization, or architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01423v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"5674","document_content":"# FedRAD: Federated Robust Adaptive Distillation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning framework by typically aggregating model updates is vulnerable to model poisoning attacks from adversarial clients. Since the shared information between the global server and participants are only limited to model parameters, it is challenging to detect bad model updates. Moreover, real-world datasets are usually heterogeneous and not independent and identically distributed (Non-IID) among participants, which makes the design of such robust FL pipeline more difficult. In this work, we propose a novel robust aggregation method, Federated Robust Adaptive Distillation (FedRAD), to detect adversaries and robustly aggregate local models based on properties of the median statistic, and then performing an adapted version of ensemble Knowledge Distillation. We run extensive experiments to evaluate the proposed method against recently published works. The results show that FedRAD outperforms all other aggregators in the presence of adversaries, as well as in heterogeneous data distributions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01405v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CR\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"100058\", \"102417\", \"119091\", \"167369\", \"184968\", \"185555\", \"206571\", \"229563\", \"240048\", \"282399\"]}","task_split":"paper_retrieval"}
{"document_id":"5729","document_content":"# Computing Class Hierarchies from Classifiers\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA class or taxonomic hierarchy is often manually constructed, and part of our knowledge about the world. In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days. The information that we need from a classifier is its confusion matrix which contains, for each pair of base classes, the number of errors the classifier makes by mistaking one for another. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models trained on the CIFAR-10 dataset, a neural network model for predicting the native language of a non-native English speaker, a neural network model for detecting the language of a written text, and a classifier for identifying music genre. In the literature, such class hierarchies have been used to provide interpretability to the neural networks. We also discuss some other potential uses of the acquired hierarchies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01187v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133774\", \"209723\", \"249204\"]}","task_split":"paper_retrieval"}
{"document_id":"5751","document_content":"# Bio-inspired Polarization Event Camera\n## Categories\n- Computer Vision and Pattern Recognition\n- Instrumentation and Detectors\n- Optics\n## Abstract\nThe stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired sensors lack the high dynamic range (HDR) and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to \\~12 ms and dynamic range to \\~ 72 dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with sub-millisecond latencies over a million-fold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45 degrees. Polarization information is reconstructed using both low cost and latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image HDR polarization scenes which vary at high speeds and to observe dynamical properties of single collagen fibers in bovine tendon under rapid cyclical loads","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01933v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"physics.ins-det\", \"physics.optics\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Instrumentation and Detectors\", \"Optics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"118857\", \"180645\", \"211978\"]}","task_split":"paper_retrieval"}
{"document_id":"5789","document_content":"# Syntax Customized Video Captioning by Imitating Exemplar Sentences\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nEnhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research. In this paper, we explore this problem from a novel perspective of customizing video captions by imitating exemplar sentence syntaxes. Specifically, given a video and any syntax-valid exemplar sentence, we introduce a new task of Syntax Customized Video Captioning (SCVC) aiming to generate one caption which not only semantically describes the video contents but also syntactically imitates the given exemplar sentence. To tackle the SCVC task, we propose a novel video captioning model, where a hierarchical sentence syntax encoder is firstly designed to extract the syntactic structure of the exemplar sentence, then a syntax conditioned caption decoder is devised to generate the syntactically structured caption expressing video semantics. As there is no available syntax customized groundtruth video captions, we tackle such a challenge by proposing a new training strategy, which leverages the traditional pairwise video captioning data and our collected exemplar sentences to accomplish the model learning. Extensive experiments, in terms of semantic, syntactic, fluency, and diversity evaluations, clearly demonstrate our model capability to generate syntax-varied and semantics-coherent video captions that well imitate different exemplar sentences with enriched diversities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.01062v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"89936\", \"127765\", \"139263\", \"162693\", \"170020\", \"193328\", \"206567\", \"208555\", \"229734\", \"231420\", \"231647\", \"237201\", \"243816\", \"250035\", \"259121\", \"267523\", \"271234\", \"279337\", \"313189\", \"313435\", \"316803\", \"320332\", \"321621\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"6054","document_content":"# Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nReinforcement learning (RL) has been used in a range of simulated real-world tasks, e.g., sensor coordination, traffic light control, and on-demand mobility services. However, real world deployments are rare, as RL struggles with dynamic nature of real world environments, requiring time for learning a task and adapting to changes in the environment. Transfer Learning (TL) can help lower these adaptation times. In particular, there is a significant potential of applying TL in multi-agent RL systems, where multiple agents can share knowledge with each other, as well as with new agents that join the system. To obtain the most from inter-agent transfer, transfer roles (i.e., determining which agents act as sources and which as targets), as well as relevant transfer content parameters (e.g., transfer size) should be selected dynamically in each particular situation. As a first step towards fully dynamic transfers, in this paper we investigate the impact of TL transfer parameters with fixed source and target roles. Specifically, we label every agent-environment interaction with agent's epistemic confidence, and we filter the shared examples using varying threshold levels and sample sizes. We investigate impact of these parameters in two scenarios, a standard predator-prey RL benchmark and a simulation of a ride-sharing system with 200 vehicle agents and 10,000 ride-requests.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.00424v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46902\", \"157662\", \"189797\", \"195771\", \"212299\", \"222270\", \"256169\", \"259960\"]}","task_split":"paper_retrieval"}
{"document_id":"6219","document_content":"# A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nGraph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we extend the framework for evaluating graph embeddings that was recently introduced by the authors. Now, the framework assigns two scores, local and global, to each embedding that measure the quality of an evaluated embedding for tasks that require good representation of local and, respectively, global properties of the network. The best embedding, if needed, can be selected in an unsupervised way, or the framework can identify a few embeddings that are worth further investigation. The framework is flexible, scalable, and can deal with undirected\/directed, weighted\/unweighted graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.00075v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"3289\"], \"outgoing_citations\": [\"39225\", \"67557\", \"68940\", \"101231\", \"147192\", \"183737\", \"213446\", \"277109\", \"289672\", \"318433\"]}","task_split":"paper_retrieval"}
{"document_id":"6253","document_content":"# Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches\n## Categories\n- Computation and Language\n## Abstract\nThe Biocreative VII Track-2 challenge consists of named entity recognition, entity-linking (or entity-normalization), and topic indexing tasks -- with entities and topics limited to chemicals for this challenge. Named entity recognition is a well-established problem and we achieve our best performance with BERT-based BioMegatron models. We extend our BERT-based approach to the entity linking task. After the second stage of pretraining BioBERT with a metric-learning loss strategy called self-alignment pretraining (SAP), we link entities based on the cosine similarity between their SAP-BioBERT word embeddings. Despite the success of our named entity recognition experiments, we find the chemical indexing task generally more challenging.   In addition to conventional NER methods, we attempt both named entity recognition and entity linking with a novel text-to-text or \"prompt\" based method that uses generative language models such as T5 and GPT. We achieve encouraging results with this new approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.15622v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"6309","document_content":"# KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers\n## Categories\n- Computation and Language\n## Abstract\nThe inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarization and Named Entity Recognition, to name a few. In this work, in addition to using these dominant context-aware representations, we propose a Knowledge Aware Representation Learning (KARL) Network for Named Entity Recognition (NER). We discuss the challenges of using existing methods in incorporating world knowledge for NER and show how our proposed methods could be leveraged to overcome those challenges. KARL is based on a Transformer Encoder that utilizes large knowledge bases represented as fact triplets, converts them to a graph context, and extracts essential entity information residing inside to generate contextualized triplet representation for feature augmentation. Experimental results show that the augmentation done using KARL can considerably boost the performance of our NER system and achieve significantly better results than existing approaches in the literature on three publicly available NER datasets, namely CoNLL 2003, CoNLL++, and OntoNotes v5. We also observe better generalization and application to a real-world setting from KARL on unseen entities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.15436v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"50900\", \"90408\", \"95236\", \"97100\", \"121505\", \"125061\", \"133626\", \"157092\", \"157587\", \"157726\", \"157803\", \"157837\", \"168832\", \"203452\", \"212253\", \"212472\", \"232494\", \"232980\", \"235594\", \"239884\", \"261049\", \"288979\", \"297147\", \"297150\", \"302500\", \"311970\", \"312608\"]}","task_split":"paper_retrieval"}
{"document_id":"6617","document_content":"# FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nOne underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have different computing capacities and communication capabilities. To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model. Our solution enables the training of heterogeneous models with varying computational complexities and aggregates them into a single global model. Furthermore, FedHM significantly reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that FedHM is superior in the performance and robustness of models of different sizes, compared with state-of-the-art heterogeneous FL methods under various FL settings. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.14655v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"10866\", \"29804\", \"35424\", \"36515\", \"51993\", \"53607\", \"59584\", \"60944\", \"63956\", \"64892\", \"94744\", \"96045\", \"96999\", \"106348\", \"109147\", \"111927\", \"112023\", \"119091\", \"139315\", \"142093\", \"142405\", \"148382\", \"161758\", \"162752\", \"166970\", \"170131\", \"195428\", \"205171\", \"224282\", \"240876\", \"320045\"]}","task_split":"paper_retrieval"}
{"document_id":"6683","document_content":"# What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations\n## Categories\n- Information Retrieval\n## Abstract\nPersonalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.14467v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"67252\", \"151879\", \"222706\", \"318310\", \"327702\"]}","task_split":"paper_retrieval"}
{"document_id":"7093","document_content":"# Learning Long-Term Reward Redistribution via Randomized Return Decomposition\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMany practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.13485v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"17457\"], \"outgoing_citations\": [\"12253\", \"29189\", \"40273\", \"43409\", \"46380\", \"67140\", \"83222\", \"90812\", \"91719\", \"96184\", \"97832\", \"106122\", \"108091\", \"121892\", \"140438\", \"142380\", \"151618\", \"152614\", \"164517\", \"175157\", \"176493\", \"183337\", \"186457\", \"198481\", \"205913\", \"206160\", \"207506\", \"213629\", \"227313\", \"230498\", \"230669\", \"235228\", \"237193\", \"238493\", \"248911\", \"251924\", \"254725\", \"261983\", \"263576\", \"263957\", \"289913\", \"290679\", \"293922\", \"354620\"]}","task_split":"paper_retrieval"}
{"document_id":"7132","document_content":"# Non-IID data and Continual Learning processes in Federated Learning: A long road ahead\n## Categories\n- Machine Learning\n## Abstract\nFederated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone to suffer the consequences of data statistical heterogeneity, both across the different entities and over time, which may lead to a lack of convergence. To avoid such issues, different methods have been proposed in the past few years. However, data may be heterogeneous in lots of different ways, and current proposals do not always determine the kind of heterogeneity they are considering. In this work, we formally classify data statistical heterogeneity and review the most remarkable learning strategies that are able to face it. At the same time, we introduce approaches from other machine learning frameworks, such as Continual Learning, that also deal with data heterogeneity and could be easily adapted to the Federated Learning settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.13394v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"24804\", \"46867\", \"50431\", \"71707\", \"118376\", \"129194\", \"138175\", \"138557\", \"140332\", \"141462\", \"143220\", \"145846\", \"148671\", \"157855\", \"158597\", \"159224\", \"162752\", \"164869\", \"175096\", \"177601\", \"178420\", \"196107\", \"200704\", \"201776\", \"206720\", \"206770\", \"207239\", \"207623\", \"219188\", \"221692\", \"231347\", \"231847\", \"235592\", \"241392\", \"244665\", \"245957\", \"249428\", \"250354\", \"259175\", \"260314\", \"263051\", \"264709\", \"273777\", \"279725\", \"289331\", \"292142\", \"293876\", \"303516\", \"304441\", \"307558\", \"317764\", \"336181\", \"356201\", \"357986\"]}","task_split":"paper_retrieval"}
{"document_id":"7166","document_content":"# Data Fusion Challenges Privacy: What Can Privacy Regulation Do?\n## Categories\n- Computers and Society\n- Artificial Intelligence\n- Cryptography and Security\n## Abstract\nThis paper focuses on some shortcomings in current privacy and data protection regulations' ability to adequately address the ramifications of AI-driven data processing practices, in particular where data sets are combined and processed by AI systems. We raise attention to two regulatory anomalies related to two fundamental assumptions underlying traditional privacy and data protection approaches: (1) Only Personally Identifiable Information (PII) and Personal Data (PD) require privacy protection: Privacy and data protection regulations are only triggered with respect to PII\/PD, but not anonymous data. This is not only problematic because determining whether data falls in the former or latter category is no longer straightforward, but also because privacy risks associated with data processing may exist whether or not an individual can be identified. (2) Given sufficient information provided in a transparent and understandable manner, individuals are able to adequately assess the privacy implications of their actions and protect their privacy interests: However, relying on human privacy expectations fails to address important privacy threats, because those expectations are at odds with the actual privacy implications of data processing practices, as most people lack the necessary technical literacy to understand the sophisticated technologies at play, and to correctly assess their privacy implications. To tackle these anomalies we recommend regulatory reform in two directions: (1) Abolishing the distinction between personal and anonymized data for the purposes of triggering the application of privacy and data protection regulations and (2) developing methods to prioritize regulatory intervention based on the level of privacy risk posed by individual data processing actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.13304v4\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.AI\", \"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123495\"]}","task_split":"paper_retrieval"}
{"document_id":"7226","document_content":"# Homogeneous Low-Resolution Face Recognition Method based Correlation Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images makes it difficult for high-resolution face recognition algorithms to extract effective feature information, Algorithms applied to high-resolution face recognition are difficult to migrate directly to low-resolution situations. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low-resolution surveillance cameras. This paper study on the Correlation Features-based Face Recognition (CoFFaR) method which using for homogeneous low-resolution surveillance videos, the theory, experimental details, and experimental results are elaborated in detail. The experimental results validate the effectiveness of the correlation features method that improves the accuracy of homogeneous face recognition in surveillance security scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.13175v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"209118\", \"230097\", \"234204\"]}","task_split":"paper_retrieval"}
{"document_id":"7249","document_content":"# Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nA core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills to long-horizon tasks, robots should be able to learn and later refine their skills in a structured manner through trajectories rather than making instantaneous decisions individually at each time step. To this end, we propose the Soft Actor-Critic Gaussian Mixture Model (SAC-GMM), a novel hybrid approach that learns robot skills through a dynamical system and adapts the learned skills in their own trajectory distribution space through interactions with the environment. Our approach combines classical robotics techniques of learning from demonstration with the deep reinforcement learning framework and exploits their complementary nature. We show that our method utilizes sensors solely available during the execution of preliminarily learned skills to extract relevant features that lead to faster skill refinement. Extensive evaluations in both simulation and real-world environments demonstrate the effectiveness of our method in refining robot skills by leveraging physical interactions, high-dimensional sensory data, and sparse task completion rewards. Videos, code, and pre-trained models are available at http:\/\/sac-gmm.cs.uni-freiburg.de.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.13129v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"38123\", \"43763\", \"51982\", \"80332\", \"82654\", \"91461\", \"108319\", \"118561\", \"122189\", \"122744\", \"157471\", \"160619\", \"161213\", \"174114\", \"180884\", \"184637\", \"190237\", \"193562\", \"195687\", \"205497\", \"254184\", \"255939\", \"268248\", \"290679\", \"292009\", \"295435\"]}","task_split":"paper_retrieval"}
{"document_id":"7296","document_content":"# Meaningful human control: actionable properties for AI system development\n## Categories\n- Computers and Society\n- Artificial Intelligence\n## Abstract\nHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human's ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically-minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s43681-022-00167-3\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.AI\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"131526\", \"142217\", \"147483\", \"179224\", \"188350\", \"209432\", \"247344\", \"290744\"]}","task_split":"paper_retrieval"}
{"document_id":"7319","document_content":"# Observing Interventions: A logic for thinking about experiments\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nThis paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. In a first step we extend the well-known causal models with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk not only about the knowledge of an agent about the values of variables and how interventions affect them, but also about knowledge update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the no learning principle for interventions. Therefore, in a second step, we implement a more complex notion of knowledge that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logical systems, we provide a sound and complete axiomatization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.12978v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"90226\", \"147944\", \"177610\", \"204104\", \"284928\", \"326077\"]}","task_split":"paper_retrieval"}
{"document_id":"7370","document_content":"# Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nBuilding upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life reinforcement learning control, here we propose a non-exhaustive set of nine real world challenges for reinforcement learning control in grid-interactive buildings. We argue that research in this area should be expressed in this framework in addition to providing a standardized environment for repeatability. Advanced controllers such as model predictive control and reinforcement learning (RL) control have both advantages and disadvantages that prevent them from being implemented in real world problems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we can investigate the performance of the controllers under a variety of situations and generate a fair comparison. As a demonstration, we implement the offline learning challenge in CityLearn and study the impact of different levels of domain knowledge and complexity of RL algorithms. We show that the sequence of operations utilized in a rule based controller (RBC) used for offline training affects the performance of the RL agents when evaluated on a set of four energy flexibility metrics. Longer offline learning from an optimized RBC leads to improved performance in the long run. RL agents that learn from a simplified RBC risk poorer performance as the offline learning period increases. We also observe no impact on performance from information sharing amongst agents. We call for a more interdisciplinary effort of the research community to address the real world challenges, and unlock the potential of grid-interactive building","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.egyai.2022.100202\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"72267\", \"79572\", \"87681\", \"99188\", \"188486\", \"241037\", \"291103\", \"343094\"]}","task_split":"paper_retrieval"}
{"document_id":"7413","document_content":"# ReAct: Out-of-distribution Detection With Rectified Activations\n## Categories\n- Machine Learning\n## Abstract\nOut-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks. One of the primary challenges is that models often produce highly confident predictions on OOD data, which undermines the driving principle in OOD detection that the model should only be confident about in-distribution samples. In this work, we propose ReAct--a simple and effective technique for reducing model overconfidence on OOD data. Our method is motivated by novel analysis on internal activations of neural networks, which displays highly distinctive signature patterns for OOD distributions. Our method can generalize effectively to different network architectures and different OOD detection scores. We empirically demonstrate that ReAct achieves competitive detection performance on a comprehensive suite of benchmark datasets, and give theoretical explication for our method's efficacy. On the ImageNet benchmark, ReAct reduces the false positive rate (FPR95) by 25.05% compared to the previous best method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.12797v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"4032\", \"5924\", \"8654\", \"13459\", \"14585\", \"19767\", \"20364\"], \"outgoing_citations\": [\"14585\", \"19767\", \"20364\", \"36411\", \"51639\", \"80696\", \"91496\", \"95818\", \"115896\", \"117565\", \"118495\", \"138272\", \"140045\", \"164362\", \"165029\", \"182065\", \"182179\", \"193267\", \"194052\", \"202268\", \"206037\", \"225094\", \"228878\", \"238521\", \"242547\", \"260655\", \"264232\", \"283125\", \"290238\", \"291677\", \"297625\", \"302634\", \"302985\", \"311413\", \"313914\", \"320829\", \"335083\", \"337853\"]}","task_split":"paper_retrieval"}
{"document_id":"7479","document_content":"# GreedyNASv2: Greedier Search with a Greedy Path Filter\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTraining a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge (e.g., $13^{21}$). In order to enhance the supernet's evaluation ability, one greedy strategy is to sample good paths, and let the supernet lean towards the good ones and ease its evaluation burden as a result. However, in practice the search can be still quite inefficient since the identification of good paths is not accurate enough and sampled paths still scatter around the whole search space. In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently. Concretely, based on the fact that good paths are much less than the weak ones in the space, we argue that the label of \"weak paths\" will be more confident and reliable than that of \"good paths\" in multi-path sampling. In this way, we thus cast the training of path filter in the positive and unlabeled (PU) learning paradigm, and also encourage a \\textit{path embedding} as better path\/operation representation to enhance the identification capacity of the learned filter. By dint of this embedding, we can further shrink the search space by aggregating similar operations with similar embeddings, and the search can be more efficient and accurate. Extensive experiments validate the effectiveness of the proposed method GreedyNASv2. For example, our obtained GreedyNASv2-L achieves $81.1\\%$ Top-1 accuracy on ImageNet dataset, significantly outperforming the ResNet-50 strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.12609v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39480\", \"92871\"], \"outgoing_citations\": [\"19663\", \"42775\", \"48015\", \"61495\", \"70339\", \"73114\", \"85497\", \"94708\", \"128458\", \"134141\", \"134839\", \"135041\", \"135572\", \"174955\", \"177741\", \"180342\", \"182913\", \"192998\", \"193623\", \"195000\", \"272618\", \"281175\"]}","task_split":"paper_retrieval"}
{"document_id":"7551","document_content":"# The Evolving Path of \"the Right to Be Left Alone\" - When Privacy Meets Technology\n## Categories\n- Cryptography and Security\n- Computers and Society\n## Abstract\nThis paper deals with the hot, evergreen topic of the relationship between privacy and technology. We give extensive motivation for why the privacy debate is still alive for private citizens and institutions, and we investigate the privacy concept. This paper proposes a novel vision of the privacy ecosystem, introducing privacy dimensions, the related users' expectations, the privacy violations, and the changing factors. We provide a critical assessment of the Privacy by Design paradigm, strategies, tactics, patterns, and Privacy-Enhancing Technologies, highlighting the current open issues. We believe that promising approaches to tackle the privacy challenges move in two directions: (i) identification of effective privacy metrics; and (ii) adoption of formal tools to design privacy-compliant applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.12434v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"87178\", \"319126\", \"324845\", \"338290\"]}","task_split":"paper_retrieval"}
{"document_id":"7620","document_content":"# Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThis paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language's dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3469877.3490571\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"134664\", \"135263\", \"146003\", \"152219\", \"162242\", \"163092\", \"178095\", \"183268\", \"192389\", \"192483\", \"202124\", \"210634\", \"220129\", \"225336\", \"268829\", \"294023\", \"296685\", \"296828\", \"309239\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"7737","document_content":"# A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks\n## Categories\n- Computation and Language\n## Abstract\nFake news and misinformation are a matter of concern for people around the globe. Users of the internet and social media sites encounter content with false information much frequently. Fake news detection is one of the most analyzed and prominent areas of research. These detection techniques apply popular machine learning and deep learning algorithms. Previous work in this domain covers fake news detection vastly among text circulating online. Platforms that have extensively been observed and analyzed include news websites and Twitter. Facebook, Reddit, WhatsApp, YouTube, and other social applications are gradually gaining attention in this emerging field. Researchers are analyzing online data based on multiple modalities composed of text, image, video, speech, and other contributing factors. The combination of various modalities has resulted in efficient fake news detection. At present, there is an abundance of surveys consolidating textual fake news detection algorithms. This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text. We provide a comprehensive literature survey of eighty articles presenting state-of-the-art detection techniques, thereby identifying research gaps and building a pathway for researchers to further advance this domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2112.00803v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"113687\", \"119855\", \"126830\", \"135336\", \"137403\", \"141447\", \"148895\", \"157154\", \"169458\", \"172047\", \"180878\", \"188099\", \"194943\", \"196780\", \"202576\", \"205223\", \"211841\", \"212878\", \"220660\", \"220822\", \"226387\", \"229508\", \"232516\", \"237252\", \"247623\", \"249053\", \"261957\", \"265282\", \"280322\", \"303593\", \"315200\", \"325438\"]}","task_split":"paper_retrieval"}
{"document_id":"7740","document_content":"# Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration\n## Categories\n- Artificial Intelligence\n- 68T05, 68T27, 68T40, 68T10\n## Abstract\nWe update the method of describing and assessing the process of the study of an abstract environment by a system, proposed earlier. We do not model any biological cognition mechanisms and consider the system as an agent equipped with an information processor (or a group of such agents), which makes a move in the environment, consumes information supplied by the environment, and gives out the next move (hence, the process is considered as a game). The system moves in an unknown environment and should recognize new objects located in it. In this case, the system should build comprehensive images of visible things and memorize them if necessary (and it should also choose the current goal set). The main problems here are object recognition, and the informational reward rating in the game. Thus, the main novelty of the paper is a new method of evaluating the amount of visual information about the object as the reward. In such a system, we suggest using a minimally pre-trained neural network to be responsible for the recognition: at first, we train the network only for Biederman geons (geometrical primitives). The geons are generated programmatically and we demonstrate that such a trained network recognizes geons in real objects quite well. We also offer to generate, procedurally, new objects from geon schemes (geon combinations in images) obtained from the environment and to store them in a database. In this case, we do not obtain new information about an object (i.e., our reward is maximal, thus the game and the object cognition process stop) when we stop getting new schemes of this kind. These schemes are generated from geons connected with the object. In the case of a possibly known item, the informational reward is maximal when we have no more detection uncertainty for any of the objects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.11965v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"68T05, 68T27, 68T40, 68T10\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"68T05, 68T27, 68T40, 68T10\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204661\", \"211312\", \"251649\", \"337468\", \"337478\", \"361344\"]}","task_split":"paper_retrieval"}
{"document_id":"7769","document_content":"# Variational Learning for Unsupervised Knowledge Grounded Dialogs\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG and REALM, marginalize the document probabilities over the documents retrieved from the index to define the log likelihood loss function which is optimized end-to-end.   In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, that has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO. To the best of our knowledge we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.24963\/ijcai.2022\/597\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16245\", \"103430\", \"120816\", \"123394\", \"123404\", \"127214\", \"127772\", \"131921\", \"141639\", \"143089\", \"199077\", \"213052\", \"216019\", \"220724\", \"251580\", \"266850\", \"272822\", \"280882\", \"281269\", \"281358\", \"302948\", \"309886\"]}","task_split":"paper_retrieval"}
{"document_id":"7914","document_content":"# Learnable Structural Semantic Readout for Graph Classification\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWith the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. Particularly, it limits the discrimination power by enforcing the same weight parameters of the classifier for all the node representations; in practice, each of them contributes to target classes differently depending on its structural semantic. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.11523v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92075\", \"110697\", \"111508\", \"117754\", \"118201\", \"142571\", \"150166\", \"173673\", \"178263\", \"181255\", \"184135\", \"188398\", \"190159\", \"216376\", \"227067\", \"302934\", \"303167\"]}","task_split":"paper_retrieval"}
{"document_id":"8303","document_content":"# Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOver the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination. Despite the impressive performance achieved by position-patch and deep learning-based methods, most of these techniques are still unable to recover identity-specific features of faces. The former group of algorithms often produces blurry and oversmoothed outputs particularly in the presence of higher levels of degradation, whereas the latter generates faces which sometimes by no means resemble the individuals in the input images. In this paper, a novel face super-resolution approach will be introduced, in which the hallucinated face is forced to lie in a subspace spanned by the available training faces. Therefore, in contrast to the majority of existing face hallucination techniques and thanks to this face subspace prior, the reconstruction is performed in favor of recovering person-specific facial features, rather than merely increasing image quantitative scores. Furthermore, inspired by recent advances in the area of 3D face reconstruction, an efficient 3D dictionary alignment scheme is also presented, through which the algorithm becomes capable of dealing with low-resolution faces taken in uncontrolled conditions. In extensive experiments carried out on several well-known face datasets, the proposed algorithm shows remarkable performance by generating detailed and close to ground truth results which outperform the state-of-the-art face hallucination algorithms by significant margins both in quantitative and qualitative evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10634v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"28002\", \"33893\", \"60633\", \"83295\", \"125816\", \"134370\", \"137881\", \"205408\", \"219346\", \"219505\", \"222871\", \"223755\", \"238254\", \"239727\", \"260981\", \"296120\", \"301276\", \"317711\"]}","task_split":"paper_retrieval"}
{"document_id":"8335","document_content":"# Generating meta-learning tasks to evolve parametric loss for classification learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe field of meta-learning has seen a dramatic rise in interest in recent years. In existing meta-learning approaches, learning tasks for training meta-models are usually collected from public datasets, which brings the difficulty of obtaining a sufficient number of meta-learning tasks with a large amount of training data. In this paper, we propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data. The loss is represented by a deep neural network, called meta-loss network (MLN). To train the MLN, we construct a large number of classification learning tasks through randomly generating training data, validation data, and corresponding ground-truth linear classifier. Our approach has two advantages. First, sufficient meta-learning tasks with large number of training data can be obtained easily. Second, the ground-truth classifier is given, so that the difference between the learned classifier and the ground-truth model can be measured to reflect the performance of MLN more precisely than validation accuracy. Based on this difference, we apply the evolutionary strategy algorithm to find out the optimal MLN. The resultant MLN not only leads to satisfactory learning effects on generated linear classifier learning tasks for testing, but also behaves very well on generated nonlinear classifier learning tasks and various public classification tasks. Our MLN stably surpass cross-entropy (CE) and mean square error (MSE) in testing accuracy and generalization ability. These results illustrate the possibility of achieving satisfactory meta-learning effects using generated learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10583v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"134915\", \"138141\", \"181039\", \"184166\", \"212579\", \"242281\", \"244112\", \"244888\", \"290426\"]}","task_split":"paper_retrieval"}
{"document_id":"8420","document_content":"# Fast Discovery of Nested Dependencies on JSON Data\n## Categories\n- Databases\n- H.3.4\n## Abstract\nFunctional and inclusion dependencies are the most widely used classes of data dependencies in data profiling due to their ability to identify relationships in data such as primary and foreign keys. These relationships are equally important when dealing with nested data formats such as JSON. However, the definition of functional and inclusion dependencies makes use of a flat, unnested relational model which misses many useful types of dependencies on data which involve nested data models.   In this work, we identify types of dependencies which are not captured by traditional functional and inclusion dependencies but which nevertheless capture meaningful relationships among nested data. We also demonstrate how algorithms for mining these traditional dependencies can be adapted to also mine nested dependencies. The first strategy simply flattens the input data and feeds into unmodified existing algorithms. We present a second strategy which instead adapts the algorithm to efficiently process JSON data as input which in some cases leads to a reduction in runtime by multiple orders of magnitude on real-world datasets. We further show how these algorithms can be adapted to produce useful results in the presence of a percentage of incomplete or invalid data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10398v1\", \"primary_category\": \"cs.DB\", \"categories\": [\"H.3.4\", \"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"H.3.4\", \"Databases\"], \"incoming_citations\": [], \"outgoing_citations\": [\"296169\", \"298945\"]}","task_split":"paper_retrieval"}
{"document_id":"8484","document_content":"# Xp-GAN: Unsupervised Multi-object Controllable Video Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nVideo Generation is a relatively new and yet popular subject in machine learning due to its vast variety of potential applications and its numerous challenges. Current methods in Video Generation provide the user with little or no control over the exact specification of how the objects in the generate video are to be moved and located at each frame, that is, the user can't explicitly control how each object in the video should move. In this paper we propose a novel method that allows the user to move any number of objects of a single initial frame just by drawing bounding boxes over those objects and then moving those boxes in the desired path. Our model utilizes two Autoencoders to fully decompose the motion and content information in a video and achieves results comparable to well-known baseline and state of the art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10233v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"72808\", \"123086\", \"150504\", \"151695\", \"162798\", \"170211\", \"176017\", \"195578\", \"196733\", \"220636\", \"221244\", \"254361\", \"258148\", \"261089\", \"279997\", \"285093\"]}","task_split":"paper_retrieval"}
{"document_id":"8515","document_content":"# Positional Encoder Graph Neural Networks for Geographic Data\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nGraph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). Here, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. On spatial interpolation and regression tasks, we show the effectiveness of our approach, improving performance over different state-of-the-art GNN approaches. We observe that our approach not only vastly improves over the GNN baselines, but can match Gaussian processes, the most commonly utilized method for spatial interpolation problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10144v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"19979\", \"117704\", \"129000\", \"141321\", \"142057\", \"164842\", \"181947\", \"184954\", \"211148\", \"216113\", \"266003\", \"280296\"]}","task_split":"paper_retrieval"}
{"document_id":"8529","document_content":"# Graph Neural Networks with Feature and Structure Aware Random Walk\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph Neural Networks (GNNs) have received increasing attention for representation learning in various machine learning tasks. However, most existing GNNs applying neighborhood aggregation usually perform poorly on the graph with heterophily where adjacent nodes belong to different classes. In this paper, we show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models. Furthermore, due to the limitation of heterophily, it is highly beneficial for the nodes to aggregate messages from similar nodes beyond local neighborhood.These motivate us to develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes. We first generalize the graph Laplacian to digraph based on the proposed Feature-Aware PageRank algorithm, which simultaneously considers the graph directionality and long-distance feature similarity between nodes. Then digraph Laplacian defines a graph propagation matrix that leads to a model called {\\em DiglacianGCN}. Based on this, we further leverage the node proximity measured by commute times between nodes, in order to preserve the nodes' long-distance correlation on the topology level. Extensive experiments on ten datasets with different levels of homophily demonstrate the effectiveness of our method over existing solutions in the task of node classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.10102v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"53447\", \"58522\", \"77179\", \"86002\", \"98076\", \"114193\", \"117308\", \"118719\", \"128281\", \"129410\", \"142571\", \"159315\", \"186164\", \"188300\", \"197997\", \"198382\", \"210119\", \"214311\", \"228663\", \"240758\", \"244510\", \"261412\"]}","task_split":"paper_retrieval"}
{"document_id":"8780","document_content":"# Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor's images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.09463v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"277386\"]}","task_split":"paper_retrieval"}
{"document_id":"8946","document_content":"# Using Convolutional Neural Networks to Detect Compression Algorithms\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMachine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional Neural Networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.09034v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"10980\", \"26921\", \"156094\", \"223790\", \"232924\", \"238110\"]}","task_split":"paper_retrieval"}
{"document_id":"9109","document_content":"# Document AI: Benchmarks, Models and Applications\n## Categories\n- Computation and Language\n## Abstract\nDocument AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.08609v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"15859\", \"27498\", \"39564\", \"40383\", \"40910\", \"43849\", \"44251\", \"47534\", \"47716\", \"49990\", \"50149\", \"55130\", \"55400\", \"62258\", \"68388\", \"73149\", \"73803\", \"73913\", \"77979\", \"94101\", \"96466\", \"101629\", \"107516\", \"114913\", \"121700\", \"122763\", \"123526\", \"127548\", \"128774\", \"130903\", \"132918\", \"141395\", \"149004\", \"152003\", \"154514\", \"165784\", \"167422\", \"171596\", \"174612\", \"184301\", \"193543\", \"216765\", \"234009\", \"242784\", \"244011\", \"249781\", \"258857\", \"264315\", \"269105\", \"269649\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"9113","document_content":"# Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills\n## Categories\n- Machine Learning\n## Abstract\nA promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target environment. It is, however, often too expensive to obtain enough feedback of good quality. To mitigate the issue, we aim to rely on a group of multiple experts (and non-experts) with different skill levels to generate enough feedback. Such feedback can therefore be inconsistent and infrequent. In this paper, we build upon prior work -- Advise, a Bayesian approach attempting to maximise the information gained from human feedback -- extending the algorithm to accept feedback from this larger group of humans, the trainers, while also estimating each trainer's reliability. We show how aggregating feedback from multiple trainers improves the total feedback's accuracy and make the collection process easier in two ways. Firstly, this approach addresses the case of some of the trainers being adversarial. Secondly, having access to the information about each trainer reliability provides a second layer of robustness and offers valuable information for people managing the whole system to improve the overall trust in the system. It offers an actionable tool for improving the feedback collection process or modifying the reward function design if needed. We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.08596v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"9153","document_content":"# Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nSegmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICAC3N53548.2021.9725739\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"34583\", \"196151\", \"213857\", \"216827\", \"230522\", \"237466\", \"241584\", \"257015\", \"271426\", \"271680\", \"272621\", \"300951\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"9237","document_content":"# HADFL: Heterogeneity-aware Decentralized Federated Learning Framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFederated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high communication pressure and model generalization challenge. Existing optimizations on FL either fail to speedup training on heterogeneous devices or suffer from poor communication efficiency. In this paper, we propose HADFL, a framework that supports decentralized asynchronous training on heterogeneous devices. The devices train model locally with heterogeneity-aware local steps using local data. In each aggregation cycle, they are selected based on probability to perform model synchronization and aggregation. Compared with the traditional FL system, HADFL can relieve the central server's communication pressure, efficiently utilize heterogeneous computing power, and can achieve a maximum speedup of 3.15x than decentralized-FedAvg and 4.68x than Pytorch distributed training scheme, respectively, with almost no loss of convergence accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.08274v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96999\", \"163571\", \"170831\", \"178962\", \"195752\", \"200347\", \"212497\", \"220686\", \"234581\", \"242007\"]}","task_split":"paper_retrieval"}
{"document_id":"9282","document_content":"# Attention-based Multi-hypothesis Fusion for Speech Summarization\n## Categories\n- Audio and Speech Processing\n- Computation and Language\n## Abstract\nSpeech summarization, which generates a text summary from speech, can be achieved by combining automatic speech recognition (ASR) and text summarization (TS). With this cascade approach, we can exploit state-of-the-art models and large training datasets for both subtasks, i.e., Transformer for ASR and Bidirectional Encoder Representations from Transformers (BERT) for TS. However, ASR errors directly affect the quality of the output summary in the cascade approach. We propose a cascade speech summarization model that is robust to ASR errors and that exploits multiple hypotheses generated by ASR to attenuate the effect of ASR errors on the summary. We investigate several schemes to combine ASR hypotheses. First, we propose using the sum of sub-word embedding vectors weighted by their posterior values provided by an ASR system as an input to a BERT-based TS system. Then, we introduce a more general scheme that uses an attention-based fusion module added to a pre-trained BERT module to align and combine several ASR hypotheses. Finally, we perform speech summarization experiments on the How2 dataset and a newly assembled TED-based dataset that we will release with this paper. These experiments show that retraining the BERT-based TS system with these schemes can improve summarization performance and that the attention-based fusion module is particularly effective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.08201v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.CL\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Computation and Language\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"52178\", \"84964\", \"121667\", \"129786\", \"150522\", \"166518\", \"166975\", \"170657\", \"210397\", \"211986\", \"263103\", \"268344\", \"268902\", \"269888\", \"276376\"]}","task_split":"paper_retrieval"}
{"document_id":"9385","document_content":"# Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection\n## Categories\n- Computation and Language\n## Abstract\nIn this work, we propose a method for incorporating question-answering (QA) signals into a summarization model. Our method identifies salient noun phrases (NPs) in the input document by automatically generating wh-questions that are answered by the NPs and automatically determining whether those questions are answered in the gold summaries. This QA-based signal is incorporated into a two-stage summarization model which first marks salient NPs in the input document using a classification model, then conditionally generates a summary. Our experiments demonstrate that the models trained using QA-based supervision generate higher-quality summaries than baseline methods of identifying salient spans on benchmark summarization datasets. Further, we show that the content of the generated summaries can be controlled based on which NPs are marked in the input document. Finally, we propose a method of augmenting the training data so the gold summaries are more consistent with the marked input spans used during training and show how this results in models which learn to better exclude unmarked document content.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.07935v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"20965\", \"25222\", \"47214\", \"55781\", \"61182\", \"68562\", \"81985\", \"93920\", \"97363\", \"126346\", \"127411\", \"132176\", \"135296\", \"136011\", \"159095\", \"168744\", \"183065\", \"189542\", \"192342\", \"218638\", \"219377\", \"220230\", \"230212\", \"230464\", \"250505\", \"250690\", \"280557\", \"298016\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"9644","document_content":"# Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nAutomatically understanding and recognising human affective states using images and computer vision can improve human-computer and human-robot interaction. However, privacy has become an issue of great concern, as the identities of people used to train affective models can be exposed in the process. For instance, malicious individuals could exploit images from users and assume their identities. In addition, affect recognition using images can lead to discriminatory and algorithmic bias, as certain information such as race, gender, and age could be assumed based on facial features. Possible solutions to protect the privacy of users and avoid misuse of their identities are to: (1) extract anonymised facial features, namely action units (AU) from a database of images, discard the images and use AUs for processing and training, and (2) federated learning (FL) i.e. process raw images in users' local machines (local processing) and send the locally trained models to the main processing machine for aggregation (central processing). In this paper, we propose a two-level deep learning architecture for affect recognition that uses AUs in level 1 and FL in level 2 to protect users' identities. The architecture consists of recurrent neural networks to capture the temporal relationships amongst the features and predict valence and arousal affective states. In our experiments, we evaluate the performance of our privacy-preserving architecture using different variations of recurrent neural networks on RECOLA, a comprehensive multimodal affective database. Our results show state-of-the-art performance of $0.426$ for valence and $0.401$ for arousal using the Concordance Correlation Coefficient evaluation metric, demonstrating the feasibility of developing models for affect recognition that are both accurate and ensure privacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.07344v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71073\", \"177251\", \"230415\", \"267284\", \"267821\", \"293179\", \"297724\", \"317841\"]}","task_split":"paper_retrieval"}
{"document_id":"9702","document_content":"# Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn natural language processing, most models try to learn semantic representations merely from texts. The learned representations encode the distributional semantics but fail to connect to any knowledge about the physical world. In contrast, humans learn language by grounding concepts in perception and action and the brain encodes grounded semantics for cognition. Inspired by this notion and recent work in vision-language learning, we design a two-stream model for grounding language learning in vision. The model includes a VGG-based visual stream and a Bert-based language stream. The two streams merge into a joint representational space. Through cross-modal contrastive learning, the model first learns to align visual and language representations with the MS COCO dataset. The model further learns to retrieve visual objects with language queries through a cross-modal attention module and to infer the visual relations between the retrieved objects through a bilinear operator with the Visual Genome dataset. After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space. This semantic space manifests principal dimensions explainable with human intuition and neurobiological knowledge. Word embeddings in this semantic space are predictive of human-defined norms of semantic features and are segregated into perceptually distinctive clusters. Furthermore, the visually grounded language model also enables compositional language understanding based on visual knowledge and multimodal image search with queries based on images, texts, or their combinations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.07180v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116433\", \"243782\", \"251721\", \"264902\"]}","task_split":"paper_retrieval"}
{"document_id":"9714","document_content":"# Robust Deep Reinforcement Learning for Extractive Legal Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nAutomatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent advances of deep models trained end-to-end with differentiable losses can well-summarize natural text, yet when applied to legal domain, they show limited results. In this paper, we propose to use reinforcement learning to train current deep summarization models to improve their performance on the legal domain. To this end, we adopt proximal policy optimization methods and introduce novel reward functions that encourage the generation of candidate summaries satisfying both lexical and semantic criteria. We apply our method to training different summarization backbones and observe a consistent and significant performance gain across 3 public legal datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-92310-5_69\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"102649\", \"130347\", \"140642\", \"163917\", \"166082\", \"169338\", \"170384\", \"170657\", \"183012\", \"193848\", \"235041\", \"235232\", \"241070\", \"258551\"]}","task_split":"paper_retrieval"}
{"document_id":"9819","document_content":"# Contrastive Feature Loss for Image Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nTraining supervised image synthesis models requires a critic to compare two images: the ground truth to the result. Yet, this basic functionality remains an open problem. A popular line of approaches uses the L1 (mean absolute error) loss, either in the pixel or the feature space of pretrained deep networks. However, we observe that these losses tend to produce overly blurry and grey images, and other techniques such as GANs need to be employed to fight these artifacts. In this work, we introduce an information theory based approach to measuring similarity between two images. We argue that a good reconstruction should have high mutual information with the ground truth. This view enables learning a lightweight critic to \"calibrate\" a feature space in a contrastive manner, such that reconstructions of corresponding spatial patches are brought together, while other patches are repulsed. We show that our formulation immediately boosts the perceptual realism of output images when used as a drop-in replacement for the L1 loss, with or without an additional GAN loss.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.06934v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"738\"], \"outgoing_citations\": [\"110055\", \"114820\", \"117811\", \"123820\", \"128649\", \"130790\", \"136971\", \"151327\", \"152790\", \"180880\", \"188943\", \"192020\", \"194775\", \"219134\", \"235796\", \"239914\", \"248731\", \"250250\", \"255272\", \"259890\", \"262675\", \"287020\", \"296773\", \"311530\", \"328587\"]}","task_split":"paper_retrieval"}
{"document_id":"9901","document_content":"# Neural Motion Planning for Autonomous Parking\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nThis paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. A non-uniform expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.06739v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"142891\", \"170388\", \"255779\"]}","task_split":"paper_retrieval"}
{"document_id":"10111","document_content":"# An End-to-End Authentication Mechanism for Wireless Body Area Networks\n## Categories\n- Cryptography and Security\n## Abstract\nWireless Body Area Network (WBAN) ensures high-quality healthcare services by endowing distant and continual monitoring of patients' health conditions. The security and privacy of the sensitive health-related data transmitted through the WBAN should be preserved to maximize its benefits. In this regard, user authentication is one of the primary mechanisms to protect health data that verifies the identities of entities involved in the communication process. Since WBAN carries crucial health data, every entity engaged in the data transfer process must be authenticated. In literature, an end-to-end user authentication mechanism covering each communicating party is absent. Besides, most of the existing user authentication mechanisms are designed assuming that the patient's mobile phone is trusted. In reality, a patient's mobile phone can be stolen or comprised by malware and thus behaves maliciously. Our work addresses these drawbacks and proposes an end-to-end user authentication and session key agreement scheme between sensor nodes and medical experts in a scenario where the patient's mobile phone is semi-trusted. We present a formal security analysis using BAN logic. Besides, we also provide an informal security analysis of the proposed scheme. Both studies indicate that our method is robust against well-known security attacks. In addition, our scheme achieves comparable computation and communication costs concerning the related existing works. The simulation shows that our method preserves satisfactory network performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.06158v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"10264","document_content":"# A Two-Stage Approach towards Generalization in Knowledge Base Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMost existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05825v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2943\"], \"outgoing_citations\": [\"25047\", \"132413\", \"157181\", \"162246\", \"171110\", \"189600\", \"211723\", \"219306\", \"256171\"]}","task_split":"paper_retrieval"}
{"document_id":"10373","document_content":"# 3D modelling of survey scene from images enhanced with a multi-exposure fusion\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn current practice, scene survey is carried out by workers using total stations. The method has high accuracy, but it incurs high costs if continuous monitoring is needed. Techniques based on photogrammetry, with the relatively cheaper digital cameras, have gained wide applications in many fields. Besides point measurement, photogrammetry can also create a three-dimensional (3D) model of the scene. Accurate 3D model reconstruction depends on high quality images. Degraded images will result in large errors in the reconstructed 3D model. In this paper, we propose a method that can be used to improve the visibility of the images, and eventually reduce the errors of the 3D scene model. The idea is inspired by image dehazing. Each original image is first transformed into multiple exposure images by means of gamma-correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local binary patterns. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature and image saturation. Performance evaluation has been performed on benchmark image dehazing datasets. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that the method works on different types of degradation that exist in both outdoor and indoor images. When fed into the photogrammetry software, the enhanced images can reconstruct 3D scene models with sub-millimeter mean errors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05541v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"235658\", \"287993\"]}","task_split":"paper_retrieval"}
{"document_id":"10455","document_content":"# The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning\n## Categories\n- Machine Learning\n## Abstract\nThe Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics\/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05326v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"6891\"], \"outgoing_citations\": [\"10866\", \"12816\", \"79711\", \"83494\", \"91185\", \"91544\", \"95211\", \"97012\", \"102417\", \"117147\", \"118319\", \"118376\", \"123384\", \"125758\", \"131702\", \"132145\", \"132901\", \"133876\", \"134225\", \"135986\", \"138557\", \"139315\", \"140332\", \"142093\", \"142802\", \"143220\", \"148245\", \"148382\", \"150470\", \"153202\", \"154097\", \"154897\", \"158597\", \"160301\", \"160722\", \"161557\", \"161758\", \"161865\", \"162209\", \"162803\", \"162883\", \"163734\", \"164627\", \"164869\", \"166037\", \"166134\", \"173721\", \"174590\", \"175032\", \"178024\", \"178600\", \"179698\", \"179932\", \"181474\", \"182752\", \"183881\", \"183930\", \"184652\", \"185299\", \"186247\", \"186386\", \"186781\", \"191555\", \"193748\", \"195775\", \"199913\", \"200704\", \"200761\", \"200782\", \"202646\", \"206873\", \"207491\", \"207951\", \"209118\", \"210868\", \"215015\", \"217320\", \"223336\", \"223847\", \"224464\", \"224906\", \"225779\", \"226146\", \"227419\", \"228339\", \"228540\", \"228896\", \"229563\", \"230640\", \"238966\", \"239350\", \"239655\", \"240229\", \"241304\", \"242351\", \"244112\", \"245421\", \"247399\", \"248291\", \"248359\", \"248982\", \"250393\", \"254260\", \"258657\", \"259762\", \"264804\", \"265073\", \"265459\", \"265645\", \"268271\", \"271783\", \"283045\", \"286531\", \"290952\", \"309330\", \"320503\", \"324038\", \"334496\", \"335904\", \"340484\", \"357176\", \"359369\", \"360780\"]}","task_split":"paper_retrieval"}
{"document_id":"10531","document_content":"# Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n## Abstract\nBoth politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, however, the canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system. This paper aims to determine which of these techniques is best suited for this purpose and how each technique might best be used towards this end. Training datasets of the same size and nearly identical neural architectures (a BERT transformer as a word embedder with a single feed-forward layer thereafter) are used for each approach, which are then tested on sentiment- and stance-specific datasets to establish a baseline of how well each method can be used to do the other tasks. Four different datasets relating to COVID-19 disinformation are used to test the ability of each technique to detect disinformation on a topic that did not appear in the training data set. Quantitative and qualitative results from these tests are then used to provide insight into how best to employ these techniques in practice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05139v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"66512\", \"77170\", \"84522\", \"87591\", \"88648\", \"92208\", \"93756\", \"94997\", \"99189\", \"105795\", \"106772\", \"108176\", \"111820\", \"122156\", \"125904\", \"127851\", \"139519\", \"140730\", \"148729\", \"150783\", \"154039\", \"159882\", \"163765\", \"169404\", \"181665\", \"181804\", \"192378\", \"194089\", \"194215\", \"202453\", \"207715\", \"211841\", \"227425\", \"228156\", \"231786\", \"259358\", \"267552\", \"268073\", \"282785\", \"290208\", \"293170\"]}","task_split":"paper_retrieval"}
{"document_id":"10667","document_content":"# Inferential SIR-GN: Scalable Graph Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nGraph representation learning methods generate numerical vector representations for the nodes in a network, thereby enabling their use in standard machine learning models. These methods aim to preserve relational information, such that nodes that are similar in the graph are found close to one another in the representation space. Similarity can be based largely on one of two notions: connectivity or structural role. In tasks where node structural role is important, connectivity based methods show poor performance. Recent work has begun to focus on scalability of learning methods to massive graphs of millions to billions of nodes and edges. Many unsupervised node representation learning algorithms are incapable of scaling to large graphs, and are unable to generate node representations for unseen nodes. In this work, we propose Inferential SIR-GN, a model which is pre-trained on random graphs, then computes node representations rapidly, including for very large networks. We demonstrate that the model is able to capture node's structural role information, and show excellent performance at node and graph classification tasks, on unseen networks. Additionally, we observe the scalability of Inferential SIR-GN is comparable to the fastest current approaches for massive graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.04826v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"186056\", \"242416\", \"252195\", \"253807\", \"269202\", \"289672\"]}","task_split":"paper_retrieval"}
{"document_id":"10705","document_content":"# Interactive Inverse Reinforcement Learning for Cooperative Games\n## Categories\n- Machine Learning\n- Computer Science and Game Theory\n## Abstract\nWe study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible and so that the joint policy is as close to optimal as possible? We analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.04698v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.GT\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Science and Game Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"89945\", \"142761\", \"163195\", \"183993\", \"201891\", \"226773\", \"231418\", \"290744\"]}","task_split":"paper_retrieval"}
{"document_id":"10711","document_content":"# Stain-free Detection of Embryo Polarization using Deep Learning\n## Categories\n- Quantitative Methods\n- Biological Physics\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nPolarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05315v1\", \"primary_category\": \"q-bio.QM\", \"categories\": [\"q-bio.QM\", \"physics.bio-ph\", \"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Quantitative Methods\", \"categories_human_readable\": [\"Quantitative Methods\", \"Biological Physics\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122147\", \"133775\"]}","task_split":"paper_retrieval"}
{"document_id":"10799","document_content":"# Adversarial sampling of unknown and high-dimensional conditional distributions\n## Categories\n- Machine Learning\n- Atmospheric and Oceanic Physics\n- Computational Physics\n- Fluid Dynamics\n## Abstract\nMany engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Two approaches, stochastic estimation, and an external neural network are compared here for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jcp.2021.110853\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"physics.ao-ph\", \"physics.comp-ph\", \"stat.ML\", \"physics.flu-dyn\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Atmospheric and Oceanic Physics\", \"Computational Physics\", \"Machine Learning\", \"Fluid Dynamics\"], \"incoming_citations\": [\"479\", \"502\"], \"outgoing_citations\": [\"88998\", \"115997\", \"138634\", \"165537\", \"182534\", \"184320\", \"190533\", \"201577\", \"211395\", \"214142\", \"229730\", \"241031\", \"242751\", \"248730\", \"256620\", \"276542\", \"281002\", \"282643\", \"284986\", \"333361\"]}","task_split":"paper_retrieval"}
{"document_id":"10934","document_content":"# DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering\n## Categories\n- Machine Learning\n## Abstract\nMachine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federated Learning allows various parties to train a machine learning algorithm on their shared data jointly. This method of collective privacy learning results in the expense of important communication during training. Most large-scale machine-learning applications require decentralized learning based on data sets generated on various devices and places. Such datasets represent an essential obstacle to decentralized learning, as their diverse contexts contribute to significant differences in the delivery of data across devices and locations. Researchers have proposed several ways to achieve data privacy in Federated Learning systems. However, there are still challenges with homogeneous local data. This research approach is to select nodes (users) to share their data in Federated Learning for independent data-based equilibrium to improve accuracy, reduce training time, and increase convergence. Therefore, this research presents a combined Deep-QReinforcement Learning Ensemble based on Spectral Clustering called DQRE-SCnet to choose a subset of devices in each communication round. Based on the results, it has been displayed that it is possible to decrease the number of communication rounds needed in Federated Learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jksuci.2021.08.019\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105490\", \"114692\", \"164048\", \"182721\", \"196107\", \"200347\", \"229563\", \"252956\"]}","task_split":"paper_retrieval"}
{"document_id":"11038","document_content":"# Robust Deep Reinforcement Learning for Quadcopter Control\n## Categories\n- Robotics\n- Systems and Control\n- Artificial Intelligence\n- Systems and Control\n- Optimization and Control\n- Machine Learning\n## Abstract\nDeep reinforcement learning (RL) has made it possible to solve complex robotics problems using neural networks as function approximators. However, the policies trained on stationary environments suffer in terms of generalization when transferred from one environment to another. In this work, we use Robust Markov Decision Processes (RMDP) to train the drone control policy, which combines ideas from Robust Control and RL. It opts for pessimistic optimization to handle potential gaps between policy transfer from one environment to another. The trained control policy is tested on the task of quadcopter positional control. RL agents were trained in a MuJoCo simulator. During testing, different environment parameters (unseen during the training) were used to validate the robustness of the trained policy for transfer from one environment to another. The robust policy outperformed the standard agents in these environments, suggesting that the added robustness increases generality and can adapt to non-stationary environments.   Codes: https:\/\/github.com\/adipandas\/gym_multirotor","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03915v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"eess.SY\", \"cs.AI\", \"cs.SY\", \"math.OC\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Systems and Control\", \"Artificial Intelligence\", \"Systems and Control\", \"Optimization and Control\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71397\", \"98682\", \"111886\", \"115585\", \"128663\", \"166672\", \"185463\", \"195548\", \"201518\", \"235938\", \"261056\"]}","task_split":"paper_retrieval"}
{"document_id":"11155","document_content":"# Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes\n## Categories\n- Signal Processing\n- Systems and Control\n- Machine Learning\n- Systems and Control\n## Abstract\nWhen signals are measured through physical sensors, they are perturbed by noise. To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency components in the incoming signal, regardless if they come from noise or the actual signal. Therefore, low-pass filters must be carefully tuned in order to avoid significant deterioration of the signal. This tuning requires prior knowledge about the signal, which is often not available in applications such as reinforcement learning or learning-based control. In order to overcome this limitation, we propose an adaptive low-pass filter based on Gaussian process regression. By considering a constant window of previous observations, updates and predictions fast enough for real-world filtering applications can be realized. Moreover, the online optimization of hyperparameters leads to an adaptation of the low-pass behavior, such that no prior tuning is necessary. We show that the estimation error of the proposed method is uniformly bounded, and demonstrate the flexibility and efficiency of the approach in several simulations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03617v1\", \"primary_category\": \"eess.SP\", \"categories\": [\"cs.SY\", \"cs.LG\", \"eess.SY\", \"eess.SP\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Systems and Control\", \"Machine Learning\", \"Systems and Control\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"85657\", \"182624\", \"198668\"]}","task_split":"paper_retrieval"}
{"document_id":"11203","document_content":"# SocialVec: Social Entity Embeddings\n## Categories\n- Social and Information Networks\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper introduces SocialVec, a general framework for eliciting social world knowledge from social networks, and applies this framework to Twitter. SocialVec learns low-dimensional embeddings of popular accounts, which represent entities of general interest, based on their co-occurrences patterns within the accounts followed by individual users, thus modeling entity similarity in socio-demographic terms. Similar to word embeddings, which facilitate tasks that involve text processing, we expect social entity embeddings to benefit tasks of social flavor. We have learned social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network that includes more than 1.3 million users and the accounts that they follow, and evaluate the resulting embeddings on two different tasks. The first task involves the automatic inference of personal traits of users from their social media profiles. In another study, we exploit SocialVec embeddings for gauging the political bias of news sources in Twitter. In both cases, we prove SocialVec embeddings to be advantageous compared with existing entity embedding schemes. We will make the SocialVec entity embeddings publicly available to support further exploration of social world knowledge as reflected in Twitter.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03514v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"94568\", \"126025\", \"193259\", \"205857\", \"230664\", \"286594\", \"288921\", \"296605\", \"300498\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"11296","document_content":"# Dataset of Fake News Detection and Fact Verification: A Survey\n## Categories\n- Machine Learning\n- Computation and Language\n- Computers and Society\n## Abstract\nThe rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. Finally, we highlight the challenges in the fake news dataset construction and some research opportunities that address these challenges. Our survey facilitates fake news research by helping researchers find suitable datasets without reinventing the wheel, and thereby, improves fake news studies in depth.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03299v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"27087\", \"43106\", \"45555\", \"51100\", \"70658\", \"82006\", \"88313\", \"88412\", \"88648\", \"88767\", \"93011\", \"93422\", \"93549\", \"94401\", \"96086\", \"105378\", \"108176\", \"109331\", \"111820\", \"111821\", \"117359\", \"119855\", \"120266\", \"123410\", \"123765\", \"125421\", \"125450\", \"125576\", \"126830\", \"127851\", \"128094\", \"131485\", \"145400\", \"149300\", \"157612\", \"159123\", \"163253\", \"163349\", \"165627\", \"168163\", \"168571\", \"169458\", \"171777\", \"198258\", \"202453\", \"207715\", \"211841\", \"215771\", \"216563\", \"217496\", \"219169\", \"221930\", \"227367\", \"227425\", \"228156\", \"228585\", \"234529\", \"234708\", \"235717\", \"236127\", \"238072\", \"238986\", \"242149\", \"246833\", \"246910\", \"257863\", \"259202\", \"261988\", \"267336\", \"267552\", \"268073\", \"268466\", \"270399\", \"271015\", \"273723\", \"275247\", \"275670\", \"276001\", \"282854\", \"297117\", \"302653\", \"313061\", \"315108\", \"326044\", \"350344\"]}","task_split":"paper_retrieval"}
{"document_id":"11298","document_content":"# A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction\n## Categories\n- Computation and Language\n## Abstract\nGrammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences. Recently, neural machine translation systems have become popular approaches for this task. However, these methods lack the use of syntactic knowledge which plays an important role in the correction of grammatical errors. In this work, we propose a syntax-guided GEC model (SG-GEC) which adopts the graph attention mechanism to utilize the syntactic knowledge of dependency trees. Considering the dependency trees of the grammatically incorrect source sentences might provide incorrect syntactic knowledge, we propose a dependency tree correction task to deal with it. Combining with data augmentation method, our model achieves strong performances without using any large pre-trained models. We evaluate our model on public benchmarks of GEC task and it achieves competitive results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03294v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122918\", \"127352\", \"162976\", \"169184\", \"171817\", \"177974\", \"191276\", \"192811\", \"196990\", \"212261\", \"235400\", \"244130\", \"281121\", \"290778\"]}","task_split":"paper_retrieval"}
{"document_id":"11324","document_content":"# Dynamic Data Augmentation with Gating Networks for Time Series Recognition\n## Categories\n- Machine Learning\n## Abstract\nData augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a mutually beneficial gating network and a feature consistency loss. The gating network is able to control how much of each data augmentation is used for the representation within the network. The feature consistency loss gives a constraint that augmented features from the same input should be in similar. In experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03253v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"64480\", \"68928\", \"89553\", \"108606\", \"156056\", \"164174\", \"164509\", \"186466\", \"188176\", \"218156\", \"240129\", \"264752\", \"268784\", \"280043\"]}","task_split":"paper_retrieval"}
{"document_id":"11336","document_content":"# Improving RNA Secondary Structure Design using Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRising costs in recent years of developing new drugs and treatments have led to extensive research in optimization techniques in biomolecular design. Currently, the most widely used approach in biomolecular design is directed evolution, which is a greedy hill-climbing algorithm that simulates biological evolution. In this paper, we propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. In addition to experimenting with the vanilla implementations of each reinforcement learning algorithm from standard libraries, we analyze variants of each algorithm in which we modify the algorithm's reward function and tune the model's hyperparameters. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches and its ability to search the possible space of RNA sequences. We find that our DQN algorithm performs by far the best in this setting, contrasting with, in which PPO performs the best among all tested algorithms. Our results should be of interest to those in the biomolecular design community and should serve as a baseline for future experiments involving machine learning in molecule design.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.04504v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"213629\", \"229927\", \"247249\", \"249746\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"11364","document_content":"# Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nReinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and chaining lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low-level skills as action abstractions. Hierarchies can further improve on this by abstracting the space states as well. We posit that a suitable state abstraction should depend on the capabilities of the available lower-level policies. We propose Value Function Spaces: a simple approach that produces such a representation by using the value functions corresponding to each lower-level skill. These value functions capture the affordances of the scene, thus forming a representation that compactly abstracts task relevant information and robustly ignores distractors. Empirical evaluations for maze-solving and robotic manipulation tasks demonstrate that our approach improves long-horizon performance and enables better zero-shot generalization than alternative model-free and model-based methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.03189v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"39730\", \"55486\", \"75117\", \"75606\", \"86509\", \"90962\", \"94561\", \"102601\", \"117599\", \"132207\", \"152912\", \"155106\", \"161476\", \"163705\", \"165350\", \"177830\", \"178094\", \"179594\", \"181081\", \"200947\", \"207100\", \"207323\", \"208351\", \"209469\", \"210517\", \"211425\", \"212772\", \"219180\", \"220103\", \"222446\", \"223212\", \"224840\", \"229890\", \"238577\", \"240323\", \"242444\", \"248841\", \"253268\", \"253291\", \"253974\", \"260136\", \"260583\", \"261493\", \"261983\", \"269251\", \"270667\", \"271975\", \"277472\", \"280296\", \"283391\", \"284567\", \"293876\", \"294142\", \"307147\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"11487","document_content":"# Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning\n## Categories\n- Machine Learning\n## Abstract\nWithin hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.02848v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"11582","document_content":"# On Semantic Cognition, Inductive Generalization, and Language Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMy doctoral research focuses on understanding semantic knowledge in neural network models trained solely to predict natural language (referred to as language models, or LMs), by drawing on insights from the study of concepts and categories grounded in cognitive science. I propose a framework inspired by 'inductive reasoning,' a phenomenon that sheds light on how humans utilize background knowledge to make inductive leaps and generalize from new pieces of information about concepts and their properties. Drawing from experiments that study inductive reasoning, I propose to analyze semantic inductive generalization in LMs using phenomena observed in human-induction literature, investigate inductive behavior on tasks such as implicit reasoning and emergent feature recognition, and analyze and relate induction dynamics to the learned conceptual representation space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.02603v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51177\", \"119381\", \"168951\", \"171429\", \"181063\", \"192036\"]}","task_split":"paper_retrieval"}
{"document_id":"11646","document_content":"# Panoptic 3D Scene Reconstruction From a Single RGB Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nUnderstanding 3D scenes from a single image is fundamental to a wide variety of tasks, such as for robotics, motion planning, or augmented reality. Existing works in 3D perception from a single RGB image tend to focus on geometric reconstruction only, or geometric reconstruction with semantic segmentation or instance segmentation. Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction - from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations. We thus propose a new approach for holistic 3D scene understanding from a single RGB image which learns to lift and propagate 2D features from an input image to a 3D volumetric scene representation. We demonstrate that this holistic view of joint scene reconstruction, semantic, and instance segmentation is beneficial over treating the tasks independently, thus outperforming alternative approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.02444v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5910\", \"5930\", \"57154\", \"7348\"], \"outgoing_citations\": [\"83905\", \"86210\", \"99522\", \"109716\", \"128633\", \"133231\", \"133978\", \"134066\", \"136856\", \"139681\", \"150881\", \"153535\", \"171092\", \"179804\", \"182734\", \"188772\", \"196637\", \"198525\", \"205649\", \"206570\", \"206724\", \"212265\", \"222082\", \"236720\", \"237468\", \"238016\", \"242117\", \"246082\", \"246328\", \"249539\", \"255575\", \"279420\", \"295402\"]}","task_split":"paper_retrieval"}
{"document_id":"11837","document_content":"# Automated, real-time hospital ICU emergency signaling: A field-level implementation\n## Categories\n- Computers and Society\n- Machine Learning\n## Abstract\nContemporary patient surveillance systems have streamlined central surveillance into the electronic health record interface. They are able to process the sheer volume of patient data by adopting machine learning approaches. However, these systems are not suitable for implementation in many hospitals, mostly in developing countries, with limited human, financial, and technological resources. Through conducting thorough research on intensive care facilities, we designed a novel central patient monitoring system and in this paper, we describe the working prototype of our system. The proposed prototype comprises of inexpensive peripherals and simplistic user interface. Our central patient monitoring system implements Kernel-based On-line Anomaly Detection (KOAD) algorithm for emergency event signaling. By evaluating continuous patient data, we show that the system is able to detect critical events in real-time reliably and has low false alarm rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.01999v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.LG\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"11873","document_content":"# Deep learning for identification and face, gender, expression recognition under constraints\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nBiometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully connected layers, FC6 and FC7 respectively, in the structure of the VGG19 network provide robust features with each of these two layers containing 4096 features. The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile. Our experimental results indicate that we obtain high accuracy for all the tasks. The best recorded accuracy values are up to 99.95% for identifying persons, 99.9% for gender recognition, 99.9% for age recognition and 80.9% for facial expression (eye smile) recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.01930v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133736\", \"139829\", \"206088\", \"208928\", \"212094\", \"213951\", \"234576\", \"255859\", \"275209\", \"295574\", \"297167\", \"303467\", \"306004\", \"312846\", \"317768\"]}","task_split":"paper_retrieval"}
{"document_id":"12191","document_content":"# Interpretable and Explainable Machine Learning for Materials Science and Chemistry\n## Categories\n- Materials Science\n- Machine Learning\n## Abstract\nWhile the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need of uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1021\/accountsmr.1c00244\", \"primary_category\": \"cond-mat.mtrl-sci\", \"categories\": [\"cond-mat.mtrl-sci\", \"cs.LG\"], \"primary_category_human_readable\": \"Materials Science\", \"categories_human_readable\": [\"Materials Science\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"32386\", \"209271\", \"250247\", \"263098\", \"290677\"]}","task_split":"paper_retrieval"}
{"document_id":"12199","document_content":"# With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn egocentric videos, actions occur in quick succession. We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order to improve recognition performance. To incorporate the temporal context, we propose a transformer-based multimodal model that ingests video and audio as input modalities, with an explicit language model providing action sequence context to enhance the predictions. We test our approach on EPIC-KITCHENS and EGTEA datasets reporting state-of-the-art performance. Our ablations showcase the advantage of utilising temporal context as well as incorporating audio input modality and language model to rescore predictions. Code and models at: https:\/\/github.com\/ekazakos\/MTCN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.01024v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"4582\"], \"outgoing_citations\": [\"38350\", \"43012\", \"43276\", \"53978\", \"54820\", \"55636\", \"59838\", \"65062\", \"65208\", \"70426\", \"74195\", \"82059\", \"88384\", \"89560\", \"105283\", \"110469\", \"110610\", \"116568\", \"121785\", \"136352\", \"145455\", \"145835\", \"151919\", \"152275\", \"166787\", \"170617\", \"172394\", \"180920\", \"183677\", \"185249\", \"191371\", \"192531\", \"203727\", \"206227\", \"206525\", \"206940\", \"207542\", \"209299\", \"222771\", \"227837\", \"236377\", \"239884\", \"248813\", \"249567\", \"266053\", \"287286\", \"302126\", \"307547\", \"308468\", \"311470\", \"320202\"]}","task_split":"paper_retrieval"}
{"document_id":"12360","document_content":"# VSEC: Transformer-based Model for Vietnamese Spelling Correction\n## Categories\n- Computation and Language\n## Abstract\nSpelling error correction is one of topics which have a long history in natural language processing. Although previous studies have achieved remarkable results, challenges still exist. In the Vietnamese language, a state-of-the-art method for the task infers a syllable's context from its adjacent syllables. The method's accuracy can be unsatisfactory, however, because the model may lose the context if two (or more) spelling mistakes stand near each other. In this paper, we propose a novel method to correct Vietnamese spelling errors. We tackle the problems of mistyped errors and misspelled errors by using a deep learning model. The embedding layer, in particular, is powered by the byte pair encoding technique. The sequence to sequence model based on the Transformer architecture makes our approach different from the previous works on the same problem. In the experiment, we train the model with a large synthetic dataset, which is randomly introduced spelling errors. We test the performance of the proposed method using a realistic dataset. This dataset contains 11,202 human-made misspellings in 9,341 different Vietnamese sentences. The experimental results show that our method achieves encouraging performance with 86.8% errors detected and 81.5% errors corrected, which improves the state-of-the-art approach 5.6% and 2.2%, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00640v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"202018\", \"265903\"]}","task_split":"paper_retrieval"}
{"document_id":"12392","document_content":"# Data Breaches in Healthcare Security Systems\n## Categories\n- Cryptography and Security\n## Abstract\nProviding security to Health Information is considered to be the topmost priority when compared to any other field. After the digitalization of the patient's records in the medical field, the healthcare\/medical field has become a victim of several internal and external cyberattacks. Data breaches in the healthcare industry have been increasing rapidly. Despite having security standards such as HIPAA (Health Insurance Portability and Accountability Act), data breaches still happen on a daily basis. All various types of data breaches have the same harmful impact on healthcare data, especially on patients' privacy. The main objective of this paper is to analyze why healthcare data breaches occur and what is the impact of these breaches. The paper also presents the possible improvements that can be made in the current standards, such as HIPAA, to increase security in the healthcare field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00582v3\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46598\", \"358636\"]}","task_split":"paper_retrieval"}
{"document_id":"12497","document_content":"# Causal Discovery in Linear Structural Causal Models with Deterministic Relations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Theory\n- Information Theory\n## Abstract\nLinear structural causal models (SCMs) -- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources -- are pervasive in causal inference and casual discovery. However, for the task of causal discovery, existing work almost exclusively focus on the submodel where each observed variable is associated with a distinct source with non-zero variance. This results in the restriction that no observed variable can deterministically depend on other observed variables or latent confounders. In this paper, we extend the results on structure learning by focusing on a subclass of linear SCMs which do not have this property, i.e., models in which observed variables can be causally affected by any subset of the sources, and are allowed to be a deterministic function of other observed variables or latent confounders. This allows for a more realistic modeling of influence or information propagation in systems. We focus on the task of causal discovery form observational data generated from a member of this subclass. We derive a set of necessary and sufficient conditions for unique identifiability of the causal structure. To the best of our knowledge, this is the first work that gives identifiability results for causal discovery under both latent confounding and deterministic relationships. Further, we propose an algorithm for recovering the underlying causal structure when the aforementioned conditions are satisfied. We validate our theoretical results both on synthetic and real datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00341v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"172217\", \"313385\", \"360719\"]}","task_split":"paper_retrieval"}
{"document_id":"12522","document_content":"# EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation\n## Categories\n- Computation and Language\n## Abstract\nWe introduce EventNarrative, a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. Given the recent advances in event-driven Information Extraction (IE), and that prior research on graph-to-text only focused on entity-driven KGs, this paper focuses on event-centric data. However, our data generation system can still be adapted to other other types of KG data. Existing large-scale datasets in the graph-to-text area are non-parallel, meaning there is a large disconnect between the KGs and text. The datasets that have a paired KG and text, are small scale and manually generated or generated without a rich ontology, making the corresponding graphs sparse. Furthermore, these datasets contain many unlinked entities between their KG and text pairs. EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text, 6 times larger than the current largest parallel dataset. It makes use of a rich ontology, all of the KGs entities are linked to the text, and our manual annotations confirm a high data quality. Our aim is two-fold: help break new ground in event-centric research where data is lacking, and to give researchers a well-defined, large-scale dataset in order to better evaluate existing and future knowledge graph-to-text models. We also evaluate two types of baseline on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00276v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"134\"], \"outgoing_citations\": [\"34349\", \"56522\", \"62199\", \"111610\", \"120247\", \"128398\", \"129168\", \"139463\", \"152920\", \"158668\", \"167167\", \"185370\", \"192332\", \"201341\", \"219022\", \"219946\", \"220723\", \"227492\", \"235814\", \"244479\", \"283971\", \"295920\", \"298016\", \"307223\", \"311423\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"12548","document_content":"# Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review\n## Categories\n- Signal Processing\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nObjectives-Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients (age 65 years and above) functional ability, physical health, and cognitive wellbeing. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods-We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results-We identified 35 eligible studies and classified in three groups-psychological disorder (n=22), eye diseases (n=6), and others (n=7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion- More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1093\/jamiaopen\/ooaa034\", \"primary_category\": \"eess.SP\", \"categories\": [\"cs.AI\", \"cs.LG\", \"eess.SP\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"149834\"]}","task_split":"paper_retrieval"}
{"document_id":"12551","document_content":"# Unpaired Learning for High Dynamic Range Image Tone Mapping\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nHigh dynamic range (HDR) photography is becoming increasingly popular and available by DSLR and mobile-phone cameras. While deep neural networks (DNN) have greatly impacted other domains of image manipulation, their use for HDR tone-mapping is limited due to the lack of a definite notion of ground-truth solution, which is needed for producing training data.   In this paper we describe a new tone-mapping approach guided by the distinct goal of producing low dynamic range (LDR) renditions that best reproduce the visual characteristics of native LDR images. This goal enables the use of an unpaired adversarial training based on unrelated sets of HDR and LDR images, both of which are widely available and easy to acquire.   In order to achieve an effective training under this minimal requirements, we introduce the following new steps and components: (i) a range-normalizing pre-process which estimates and applies a different level of curve-based compression, (ii) a loss that preserves the input content while allowing the network to achieve its goal, and (iii) the use of a more concise discriminator network, designed to promote the reproduction of low-level attributes native LDR possess.   Evaluation of the resulting network demonstrates its ability to produce photo-realistic artifact-free tone-mapped images, and state-of-the-art performance on different image fidelity indices and visual distances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00219v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"146514\", \"172113\", \"180400\", \"239574\", \"248731\", \"259863\", \"261627\"]}","task_split":"paper_retrieval"}
{"document_id":"12587","document_content":"# Pseudo-Labeling for Massively Multilingual Speech Recognition\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nSemi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised learning on a target language, generate pseudo-labels for that language, and train a final model using pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better performance for many languages that also transfers well to LibriSpeech.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.00161v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"17765\"], \"outgoing_citations\": [\"27578\", \"41603\", \"44362\", \"77367\", \"92204\", \"92302\", \"92353\", \"92775\", \"113832\", \"116282\", \"124040\", \"124174\", \"126512\", \"151274\", \"155439\", \"165878\", \"167320\", \"189849\", \"192389\", \"209096\", \"209901\", \"215470\", \"239884\", \"250653\", \"251358\", \"308468\", \"310601\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"12778","document_content":"# BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nThe task of text-video retrieval aims to understand the correspondence between language and vision, has gained increasing attention in recent years. Previous studies either adopt off-the-shelf 2D\/3D-CNN and then use average\/max pooling to directly capture spatial features with aggregated temporal information as global video embeddings, or introduce graph-based models and expert knowledge to learn local spatial-temporal relations. However, the existing methods have two limitations: 1) The global video representations learn video temporal information in a simple average\/max pooling manner and do not fully explore the temporal information between every two frames. 2) The graph-based local video representations are handcrafted, it depends heavily on expert knowledge and empirical feedback, which may not be able to effectively mine the higher-level fine-grained visual relations. These limitations result in their inability to distinguish videos with the same visual components but with different relations. To solve this problem, we propose a novel cross-modal retrieval framework, Bi-Branch Complementary Network (BiC-Net), which modifies transformer architecture to effectively bridge text-video modalities in a complementary manner via combining local spatial-temporal relation and global temporal information. Specifically, local video representations are encoded using multiple transformer blocks and additional residual blocks to learn spatio-temporal relation features, calling the module a Spatio-Temporal Residual transformer (SRT). Meanwhile, Global video representations are encoded using a multi-layer transformer block to learn global temporal features. Finally, we align the spatio-temporal relation and global temporal features with the text feature on two embedding spaces for cross-modal text-video retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15609v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"33374\", \"40597\", \"54595\", \"55118\", \"58876\", \"60135\", \"69945\", \"70426\", \"84992\", \"89825\", \"96230\", \"101354\", \"110610\", \"111415\", \"114022\", \"118223\", \"118354\", \"139259\", \"151327\", \"172394\", \"173627\", \"181897\", \"189395\", \"217562\", \"221984\", \"229179\", \"236468\", \"256883\", \"266053\", \"270325\", \"313435\"]}","task_split":"paper_retrieval"}
{"document_id":"12817","document_content":"# Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress. The ongoing surge to solve math word problems (MWPs) and hence achieve better mathematical reasoning ability would continue to be a key line of research in the coming time. We inspect non-neural and neural methods to solve math word problems narrated in a natural language. We also highlight the ability of these methods to be generalizable, mathematically reasonable, interpretable, and explainable. Neural approaches dominate the current state of the art, and we survey them highlighting three strategies to MWP solving: (1) direct answer generation, (2) expression tree generation for inferring answers, and (3) template retrieval for answer computation. Moreover, we discuss technological approaches, review the evolution of intuitive design choices to solve MWPs, and examine them for mathematical reasoning ability. We finally identify several gaps that warrant the need for external knowledge and knowledge-infused learning, among several other opportunities in solving MWPs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2111.05364v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"31848\", \"35236\", \"37796\", \"38612\", \"45526\", \"57181\", \"63475\", \"64908\", \"78252\", \"79536\", \"93618\", \"94434\", \"96310\", \"96833\", \"98821\", \"128041\", \"143992\", \"153185\", \"157885\", \"161625\", \"183403\", \"188123\", \"192616\", \"231479\", \"233912\", \"238788\", \"246516\", \"266702\", \"278863\"]}","task_split":"paper_retrieval"}
{"document_id":"12824","document_content":"# Topological Relational Learning on Graphs\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To address these challenges, we propose a novel topological neural framework of topological relational inference (TRI) which allows for integrating higher-order graph information to GNNs and for systematically learning a local graph structure. The key idea is to rewire the original graph by using the persistent homology of the small neighborhoods of nodes and then to incorporate the extracted topological summaries as the side information into the local algorithm. As a result, the new framework enables us to harness both the conventional information on the graph structure and information on the graph higher order topological properties. We derive theoretical stability guarantees for the new local topological representation and discuss their implications on the graph algebraic connectivity. The experimental results on node classification tasks demonstrate that the new TRI-GNN outperforms all 14 state-of-the-art baselines on 6 out 7 graphs and exhibit higher robustness to perturbations, yielding up to 10\\% better performance under noisy scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15529v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62653\", \"76233\", \"92250\", \"102283\", \"109065\", \"111211\", \"118292\", \"120210\", \"123835\", \"139815\", \"141927\", \"160051\", \"168172\", \"174492\", \"182665\", \"184856\", \"186164\", \"187085\", \"188300\", \"188700\", \"189673\", \"195676\", \"196521\", \"203910\", \"204706\", \"206470\", \"214311\", \"218016\", \"221587\", \"229027\", \"240985\", \"242699\", \"243382\", \"253573\", \"279653\", \"308268\"]}","task_split":"paper_retrieval"}
{"document_id":"12884","document_content":"# Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems\n## Categories\n- Cryptography and Security\n## Abstract\nCyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while overprotecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15417v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22125\", \"22128\", \"22133\", \"65058\", \"207072\"]}","task_split":"paper_retrieval"}
{"document_id":"12897","document_content":"# Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nDespite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the difficulty of exploration in purely continuous action spaces. Addressing this problem is an active area of research with the majority of focus on improving RL methods via better optimization or more efficient exploration. An alternate but important component to consider improving is the interface of the RL algorithm with the robot. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. These parameterized primitives are expressive, simple to implement, enable efficient exploration and can be transferred across robots, tasks and environments. We perform a thorough empirical study across challenging tasks in three distinct domains with image input and a sparse terminal reward. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance irrespective of the underlying RL algorithm, significantly outperforming prior methods which learn skills from offline expert data. Code and videos at https:\/\/mihdalal.github.io\/raps\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15360v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"357157\", \"4134\", \"18176\", \"19003\"], \"outgoing_citations\": [\"18176\", \"59809\", \"60348\", \"82654\", \"84266\", \"85855\", \"86598\", \"87027\", \"88081\", \"91231\", \"92106\", \"96527\", \"98568\", \"115366\", \"125475\", \"127845\", \"131003\", \"152912\", \"159675\", \"160036\", \"164125\", \"164957\", \"177830\", \"179727\", \"180894\", \"195457\", \"196387\", \"196593\", \"213321\", \"214765\", \"223225\", \"226635\", \"231175\", \"241900\", \"252334\", \"272464\", \"284567\", \"303459\", \"307066\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"12954","document_content":"# Towards Model Agnostic Federated Learning Using Knowledge Distillation\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n- Optimization and Control\n- 68W40, 68W15, 90C25, 90C06\n- G.1.6; F.2.1; E.4\n## Abstract\nIs it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning? Such an API would necessarily need to be model-agnostic i.e. make no assumption about the model architecture being used by the agents, and also cannot rely on having representative public data at hand. Knowledge distillation (KD) is the obvious tool of choice to design such protocols. However, surprisingly, we show that most natural KD-based federated learning protocols have poor performance.   To investigate this, we propose a new theoretical framework, Federated Kernel ridge regression, which can capture both model heterogeneity as well as data heterogeneity. Our analysis shows that the degradation is largely due to a fundamental limitation of knowledge distillation under data heterogeneity. We further validate our framework by analyzing and designing new protocols based on KD. Their performance on real world experiments using neural networks, though still unsatisfactory, closely matches our theoretical predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15210v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"math.OC\", \"stat.ML\", \"68W40, 68W15, 90C25, 90C06\", \"G.1.6; F.2.1; E.4\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Optimization and Control\", \"Machine Learning\", \"68W40, 68W15, 90C25, 90C06\", \"G.1.6; F.2.1; E.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"6786\", \"13893\", \"26780\", \"32634\", \"35424\", \"46940\", \"79809\", \"89048\", \"96223\", \"97129\", \"107082\", \"119091\", \"119873\", \"123758\", \"134225\", \"139315\", \"140332\", \"142405\", \"143263\", \"160301\", \"162023\", \"235592\", \"236228\", \"264787\", \"265677\", \"280506\"]}","task_split":"paper_retrieval"}
{"document_id":"12973","document_content":"# Extracting Expert's Goals by What-if Interpretable Modeling\n## Categories\n- Machine Learning\n## Abstract\nAlthough reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed. In this work, we focus on recovering clinicians' rewards in treating patients. We incorporate the what-if reasoning to explain the clinician's treatments based on their potential future outcomes. We use generalized additive models (GAMs) - a class of accurate, interpretable models - to recover the reward. In both simulation and a real-world hospital dataset, we show our model outperforms baselines. Finally, our model's explanations match several clinical guidelines when treating patients while we found the commonly-used linear model often contradicts them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15165v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"13074","document_content":"# Hierarchical User Intent Graph Network forMultimedia Recommendation\n## Categories\n- Information Retrieval\n- Multimedia\n## Abstract\nIn this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance the recommendation performance. Towards this end, we develop a novel framework, Hierarchical User Intent Graph Network, which exhibits user intents in a hierarchical graph structure, from the fine-grained to coarse-grained intents. In particular, we get the multi-level user intents by recursively performing two operations: 1) intra-level aggregation, which distills the signal pertinent to user intents from co-interacted item graphs; and 2) inter-level aggregation, which constitutes the supernode in higher levels to model coarser-grained user intents via gathering the nodes' representations in the lower ones. Then, we refine the user and item representations as a distribution over the discovered intents, instead of simple pre-existing features. To demonstrate the effectiveness of our model, we conducted extensive experiments on three public datasets. Our model achieves significant improvements over the state-of-the-art methods, including MMGCN and DisenGCN. Furthermore, by visualizing the item representations, we provide the semantics of user intents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14925v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"36039\", \"109072\", \"158823\", \"163363\", \"170062\", \"185557\", \"185616\", \"188507\", \"189630\", \"198364\", \"227067\", \"281431\", \"283649\", \"295221\", \"305418\"]}","task_split":"paper_retrieval"}
{"document_id":"13124","document_content":"# Tractability from overparametrization: The example of the negative perceptron\n## Categories\n- Machine Learning\n- Probability\n- Statistics Theory\n- Statistics Theory\n## Abstract\nIn the negative perceptron problem we are given $n$ data points $({\\boldsymbol x}_i,y_i)$, where ${\\boldsymbol x}_i$ is a $d$-dimensional vector and $y_i\\in\\{+1,-1\\}$ is a binary label. The data are not linearly separable and hence we content ourselves to find a linear classifier with the largest possible \\emph{negative} margin. In other words, we want to find a unit norm vector ${\\boldsymbol \\theta}$ that maximizes $\\min_{i\\le n}y_i\\langle {\\boldsymbol \\theta},{\\boldsymbol x}_i\\rangle$. This is a non-convex optimization problem (it is equivalent to finding a maximum norm vector in a polytope), and we study its typical properties under two random models for the data.   We consider the proportional asymptotics in which $n,d\\to \\infty$ with $n\/d\\to\\delta$, and prove upper and lower bounds on the maximum margin $\\kappa_{\\text{s}}(\\delta)$ or -- equivalently -- on its inverse function $\\delta_{\\text{s}}(\\kappa)$. In other words, $\\delta_{\\text{s}}(\\kappa)$ is the overparametrization threshold: for $n\/d\\le \\delta_{\\text{s}}(\\kappa)-\\varepsilon$ a classifier achieving vanishing training error exists with high probability, while for $n\/d\\ge \\delta_{\\text{s}}(\\kappa)+\\varepsilon$ it does not. Our bounds on $\\delta_{\\text{s}}(\\kappa)$ match to the leading order as $\\kappa\\to -\\infty$. We then analyze a linear programming algorithm to find a solution, and characterize the corresponding threshold $\\delta_{\\text{lin}}(\\kappa)$. We observe a gap between the interpolation threshold $\\delta_{\\text{s}}(\\kappa)$ and the linear programming threshold $\\delta_{\\text{lin}}(\\kappa)$, raising the question of the behavior of other algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.15824v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.PR\", \"math.ST\", \"stat.TH\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Probability\", \"Statistics Theory\", \"Statistics Theory\"], \"incoming_citations\": [\"90396\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"13197","document_content":"# Play to Grade: Testing Coding Games as Classifying Markov Decision Process\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- Machine Learning\n## Abstract\nContemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, there are no contemporary autonomous methods for providing feedback. Notably, interactive programs are impossible to grade by traditional unit tests. In this paper we formalize the challenge of providing feedback to interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP should be categorized as correct or broken. We demonstrate that by designing a cooperative objective between an agent and an autoregressive model, we can use the agent to sample differential trajectories from the input MDP that allows a classifier to determine membership: Play to Grade. Our method enables an automatic feedback system for interactive code assignments. We release a dataset of 711,274 anonymized student submissions to a single assignment with hand-coded bug labels to support future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14615v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"60722\", \"139759\", \"152542\", \"184911\", \"194175\", \"197403\", \"200920\", \"201994\", \"206800\", \"207267\", \"219144\", \"241900\", \"257984\", \"261038\", \"266419\", \"296256\", \"299534\", \"305802\", \"310890\", \"312437\", \"356588\"]}","task_split":"paper_retrieval"}
{"document_id":"13216","document_content":"# V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Science and Game Theory\n- Multiagent Systems\n## Abstract\nA major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents, where the size of the joint action space scales exponentially with the number of agents. This remains to be a bottleneck for designing efficient MARL algorithms even in a basic scenario with finitely many states and actions. This paper resolves this challenge for the model of episodic Markov games. We design a new class of fully decentralized algorithms -- V-learning, which provably learns Nash equilibria (in the two-player zero-sum setting), correlated equilibria and coarse correlated equilibria (in the multiplayer general-sum setting) in a number of samples that only scales with $\\max_{i\\in[m]} A_i$, where $A_i$ is the number of actions for the $i^{\\rm th}$ player. This is in sharp contrast to the size of the joint action space which is $\\prod_{i=1}^m A_i$. V-learning (in its basic form) is a new class of single-agent RL algorithms that convert any adversarial bandit algorithm with suitable regret guarantees into a RL algorithm. Similar to the classical Q-learning algorithm, it performs incremental updates to the value functions. Different from Q-learning, it only maintains the estimates of V-values instead of Q-values. This key difference allows V-learning to achieve the claimed guarantees in the MARL setting by simply letting all agents run V-learning independently.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14555v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.GT\", \"cs.MA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Science and Game Theory\", \"Multiagent Systems\", \"Machine Learning\"], \"incoming_citations\": [\"14875\", \"17173\", \"17934\", \"774\", \"2955\", \"7817\", \"25807\", \"29225\", \"42359\"], \"outgoing_citations\": [\"17186\", \"17934\", \"44240\", \"44607\", \"70657\", \"72233\", \"75971\", \"90678\", \"96808\", \"112031\", \"117042\", \"118109\", \"139382\", \"141799\", \"143087\", \"166489\", \"169677\", \"176464\", \"183013\", \"186457\", \"201891\", \"215222\", \"225125\", \"237193\", \"241037\", \"248557\", \"263576\", \"270826\", \"271358\", \"281635\", \"282884\", \"286910\", \"311433\", \"334172\"]}","task_split":"paper_retrieval"}
{"document_id":"13297","document_content":"# Transfer learning with causal counterfactual reasoning in Decision Transformers\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe ability to adapt to changes in environmental contingencies is an important challenge in reinforcement learning. Indeed, transferring previously acquired knowledge to environments with unseen structural properties can greatly enhance the flexibility and efficiency by which novel optimal policies may be constructed. In this work, we study the problem of transfer learning under changes in the environment dynamics. In this study, we apply causal reasoning in the offline reinforcement learning setting to transfer a learned policy to new environments. Specifically, we use the Decision Transformer (DT) architecture to distill a new policy on the new environment. The DT is trained on data collected by performing policy rollouts on factual and counterfactual simulations from the source environment. We show that this mechanism can bootstrap a successful policy on the target environment while retaining most of the reward.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14355v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"44986\", \"45297\", \"127099\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"13315","document_content":"# Node-wise Localization of Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local contexts, making their distributions vary across the graph. Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes. To utilize node locality without overfitting, we propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. Globally, all nodes on the graph depend on an underlying global GNN to encode the general patterns across the graph; locally, each node is localized into a unique model as a function of the global model and its local context. Finally, we conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.24963\/ijcai.2021\/210\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [\"34086\", \"49630\"], \"outgoing_citations\": [\"114193\", \"142571\", \"162941\", \"166857\", \"178533\", \"210119\", \"228663\", \"244510\", \"255134\", \"255202\", \"295676\"]}","task_split":"paper_retrieval"}
{"document_id":"13398","document_content":"# Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nKnowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14170v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"45140\", \"49630\", \"53346\", \"65004\", \"80138\", \"91601\", \"93121\", \"100979\", \"117926\", \"119358\", \"127546\", \"127923\", \"148680\", \"154289\", \"156074\", \"157481\", \"157487\", \"158668\", \"168182\", \"168575\", \"168796\", \"194274\", \"195502\", \"197408\", \"211596\", \"211788\", \"217754\", \"220131\", \"228663\", \"260644\", \"261577\", \"261989\", \"263488\", \"264533\", \"272929\", \"289972\", \"320035\"]}","task_split":"paper_retrieval"}
{"document_id":"13410","document_content":"# Diversity Matters When Learning From Ensembles\n## Categories\n- Machine Learning\n## Abstract\nDeep ensembles excel in large-scale image classification tasks both in terms of prediction accuracy and calibration. Despite being simple to train, the computation and memory cost of deep ensembles limits their practicability. While some recent works propose to distill an ensemble model into a single model to reduce such costs, there is still a performance gap between the ensemble and distilled models. We propose a simple approach for reducing this gap, i.e., making the distilled performance close to the full ensemble. Our key assumption is that a distilled model should absorb as much function diversity inside the ensemble as possible. We first empirically show that the typical distillation procedure does not effectively transfer such diversity, especially for complex models that achieve near-zero training error. To fix this, we propose a perturbation strategy for distillation that reveals diversity by seeking inputs for which ensemble member outputs disagree. We empirically show that a model distilled with such perturbed samples indeed exhibits enhanced diversity, leading to improved performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14149v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"18291\"], \"outgoing_citations\": [\"94530\", \"136707\", \"141103\", \"141948\", \"142075\", \"147272\", \"152551\", \"163574\", \"182179\", \"188296\", \"240363\", \"263687\", \"314929\"]}","task_split":"paper_retrieval"}
{"document_id":"13426","document_content":"# Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nBehavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal confusion problem where a policy relies on the noticeable effect of expert actions due to the strong correlation but not the cause we desire. This paper presents Object-aware REgularizatiOn (OREO), a simple technique that regularizes an imitation policy in an object-aware manner. Our main idea is to encourage a policy to uniformly attend to all semantic objects, in order to prevent the policy from exploiting nuisance variables strongly correlated with expert actions. To this end, we introduce a two-stage approach: (a) we extract semantic objects from images by utilizing discrete codes from a vector-quantized variational autoencoder, and (b) we randomly drop the units that share the same discrete code together, i.e., masking out semantic objects. Our experiments demonstrate that OREO significantly improves the performance of behavioral cloning, outperforming various other regularization and causality-based methods on a variety of Atari environments and a self-driving CARLA environment. We also show that our method even outperforms inverse reinforcement learning methods trained with a considerable amount of environment interaction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14118v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"61341\", \"90726\", \"127845\", \"128392\", \"132207\", \"154589\", \"155989\", \"165238\", \"176738\", \"183941\", \"184304\", \"189835\", \"196770\", \"200942\", \"205913\", \"206814\", \"208041\", \"212301\", \"225289\", \"230067\", \"230068\", \"234517\", \"248417\", \"253953\", \"257968\", \"265829\", \"271937\", \"272501\", \"278175\", \"290679\", \"291800\", \"293864\", \"317817\", \"327996\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"13441","document_content":"# Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWord Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.14091v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"69065\", \"98728\", \"126763\", \"167753\", \"170992\", \"171633\", \"179267\", \"188123\", \"220098\", \"231258\"]}","task_split":"paper_retrieval"}
{"document_id":"13561","document_content":"# DPCOVID: Privacy-Preserving Federated Covid-19 Detection\n## Categories\n- Cryptography and Security\n- Machine Learning\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nCoronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. The number of COVID-19 cases has been rapidly increasing, and there is no sign of stopping. It leads to a severe shortage of test kits and accurate detection models. A recent study demonstrated that the chest X-ray radiography outperformed laboratory testing in COVID-19 detection. Therefore, using chest X-ray radiography analysis can help to screen suspected COVID-19 cases at an early stage. Moreover, the patient data is sensitive, and it must be protected to avoid revealing through model updates and reconstruction from the malicious attacker. In this paper, we present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images. First, a Federated Learning system is constructed from chest X-ray images. The main idea is to build a decentralized model across multiple hospitals without sharing data among hospitals. Second, we first show that the accuracy of Federated Learning for COVID-19 identification reduces significantly for Non-IID data. We then propose a strategy to improve model's accuracy on Non-IID COVID-19 data by increasing the total number of clients, parallelism (client fraction), and computation per client. Finally, we apply a Differential Privacy Stochastic Gradient Descent (DP-SGD) to enhance the preserving of patient data privacy for our Federated Learning model. A strategy is also proposed to keep the robustness of Federated Learning to ensure the security and accuracy of the model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.13760v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"eess.IV\", \"cs.CR\", \"cs.CV\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Cryptography and Security\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"84636\", \"98443\", \"112927\", \"114133\", \"134340\", \"134930\", \"135550\", \"179698\", \"207491\", \"232524\", \"329116\"]}","task_split":"paper_retrieval"}
{"document_id":"13801","document_content":"# Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Image and Video Processing\n## Abstract\nTo characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases. In this work, we propose a learning procedure to obtain a desired target output from a physical system. We use Variational Auto-Encoders (VAE) to provide a generative model of the system function and use this model to obtain the required input of the system that produces the target output. We showcase the applicability of our method for two datasets in optical physics and neuroscience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.13228v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"eess.IV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215653\", \"227734\", \"248906\", \"266587\"]}","task_split":"paper_retrieval"}
{"document_id":"13883","document_content":"# Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSimultaneous reconstruction of geometry and reflectance properties in uncontrolled environments remains a challenging problem. In this paper, we propose an efficient method to reconstruct the scene's 3D geometry and reflectance from multi-view photography using conventional hand-held cameras. Our method automatically builds a virtual scene in a differentiable rendering system that roughly matches the real world's scene parameters, optimized by minimizing photometric objectives alternatingly and stochastically. With the optimal scene parameters evaluated, photo-realistic novel views for various viewing angles and distances can then be generated by our approach. We present the results of captured scenes with complex geometry and various reflection types. Our method also shows superior performance compared to state-of-the-art alternatives in novel view synthesis visually and quantitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.12975v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"60069\", \"60902\", \"82893\", \"83084\", \"110269\", \"117600\", \"124705\", \"147179\", \"150515\", \"151894\", \"160894\", \"180284\", \"182539\", \"187386\", \"187991\", \"188170\", \"188623\", \"191545\", \"192520\", \"192689\", \"207490\", \"210367\", \"232672\", \"235794\", \"286320\"]}","task_split":"paper_retrieval"}
{"document_id":"13933","document_content":"# Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nGiven a gallery of uncaptioned video sequences, this paper considers the task of retrieving videos based on their relevance to an unseen text query. To compensate for the lack of annotations, we rely instead on a related video gallery composed of video-caption pairs, termed the source gallery, albeit with a domain gap between its videos and those in the target gallery. We thus introduce the problem of Unsupervised Domain Adaptation for Cross-modal Video Retrieval, along with a new benchmark on fine-grained actions. We propose a novel iterative domain alignment method by means of pseudo-labelling target videos and cross-domain (i.e. source-target) ranking. Our approach adapts the embedding space to the target gallery, consistently outperforming source-only as well as marginal and conditional alignment methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.12812v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"1081\"], \"outgoing_citations\": [\"45139\", \"54595\", \"55847\", \"59465\", \"62318\", \"69945\", \"93326\", \"101354\", \"110610\", \"138320\", \"139259\", \"145455\", \"148380\", \"151327\", \"155791\", \"170617\", \"172394\", \"173627\", \"173678\", \"173856\", \"181897\", \"189030\", \"190980\", \"191383\", \"193956\", \"196009\", \"203998\", \"204141\", \"208749\", \"209240\", \"234087\", \"236468\", \"237579\", \"249988\", \"265083\", \"265386\", \"272917\", \"288929\", \"303011\", \"303155\", \"306520\"]}","task_split":"paper_retrieval"}
{"document_id":"13952","document_content":"# A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments\n## Categories\n- Sound\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nIn this work we apply deep reinforcement learning to the problems of navigating a three-dimensional environment and inferring the locations of human speaker audio sources within, in the case where the only available information is the raw sound from the environment, as a simulated human listener placed in the environment would hear it. For this purpose we create two virtual environments using the Unity game engine, one presenting an audio-based navigation problem and one presenting an audio source localization problem. We also create an autonomous agent based on PPO online reinforcement learning algorithm and attempt to train it to solve these environments. Our experiments show that our agent achieves adequate performance and generalization ability in both environments, measured by quantitative metrics, even when a limited amount of training data are available or the environment parameters shift in ways not encountered during training. We also show that a degree of agent knowledge transfer is possible between the environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.12778v3\", \"primary_category\": \"cs.SD\", \"categories\": [\"eess.AS\", \"cs.LG\", \"cs.SD\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Machine Learning\", \"Sound\"], \"incoming_citations\": [], \"outgoing_citations\": [\"21298\", \"37438\", \"50519\", \"98682\", \"156378\", \"186927\", \"218770\", \"222993\", \"247650\", \"248736\", \"293071\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"14068","document_content":"# Understanding the World Through Action\n## Categories\n- Machine Learning\n## Abstract\nThe recent history of machine learning research has taught us that machine learning methods can be most effective when they are provided with very large, high-capacity models, and trained on very large and diverse datasets. This has spurred the community to search for ways to remove any bottlenecks to scale. Often the foremost among such bottlenecks is the need for human effort, including the effort of curating and labeling datasets. As a result, considerable attention in recent years has been devoted to utilizing unlabeled data, which can be collected in vast quantities. However, some of the most widely used methods for training on such unlabeled data themselves require human-designed objective functions that must correlate in some meaningful way to downstream tasks. I will argue that a general, principled, and powerful framework for utilizing unlabeled data can be derived from reinforcement learning, using general purpose unsupervised or self-supervised reinforcement learning objectives in concert with offline reinforcement learning methods that can leverage large datasets. I will discuss how such a procedure is more closely aligned with potential downstream tasks, and how it could build on existing techniques that have been developed in recent years.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.12543v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"343094\"], \"outgoing_citations\": [\"54546\", \"55721\", \"56552\", \"77736\", \"86305\", \"90884\", \"118157\", \"120201\", \"127099\", \"177830\", \"182796\", \"195849\", \"208351\", \"226635\", \"240918\", \"241900\", \"279790\", \"291025\", \"295435\", \"339382\"]}","task_split":"paper_retrieval"}
{"document_id":"14126","document_content":"# Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGoal oriented dialogue systems have become a prominent customer-care interaction channel for most businesses. However, not all interactions are smooth, and customer intent misunderstanding is a major cause of dialogue failure. We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. For that, we define a multi-task training regime that utilizes successive user-utterance generation to improve the intent prediction. Our approach achieves the reported improvement due to two complementary factors: First, it uses a large amount of unlabeled dialogue data for an auxiliary generation task. Second, it uses the generated user utterance as an additional signal for the intent prediction model. Lastly, we present a novel look-ahead approach that uses user utterance generation to improve intent prediction in inference time. Specifically, we generate counterfactual successive user utterances for conversations with ambiguous predicted intents, and disambiguate the prediction by reassessing the concatenated sequence of available and generated utterances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.12412v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"91687\", \"96643\", \"150131\", \"150360\", \"157123\", \"167138\", \"173940\", \"197154\", \"207522\", \"216019\", \"216953\", \"219937\", \"231668\", \"247467\", \"251348\", \"258160\", \"277773\", \"285317\", \"289260\", \"294144\"]}","task_split":"paper_retrieval"}
{"document_id":"14352","document_content":"# Graph Filtration Kernels\n## Categories\n- Machine Learning\n## Abstract\nThe majority of popular graph kernels is based on the concept of Haussler's $\\mathcal{R}$-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Using meaningful orders on the set of edges, which allow to construct a sequence of nested graphs, we can consider a graph at multiple granularities. For one thing, this provides access to features on different levels of resolution. Furthermore, rather than to simply compare frequencies of features in graphs, it allows for their comparison in terms of when and for how long they exist in the sequences. In this work, we propose a family of graph kernels that incorporate these existence intervals of features. While our approach can be applied to arbitrary graph features, we particularly highlight Weisfeiler-Lehman vertex labels, leading to efficient kernels. We show that using Weisfeiler-Lehman labels over certain filtrations strictly increases the expressive power over the ordinary Weisfeiler-Lehman procedure in terms of deciding graph isomorphism. In fact, this result directly yields more powerful graph kernels based on such features and has implications to graph neural networks due to their close relationship to the Weisfeiler-Lehman method. We empirically validate the expressive power of our graph kernels and show significant improvements over state-of-the-art graph kernels in terms of predictive performance on various real-world benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11862v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42645\", \"49956\", \"88420\", \"103085\", \"111508\", \"118201\", \"175310\", \"192622\", \"193372\", \"196221\", \"200627\", \"215435\", \"229066\", \"291213\", \"319946\"]}","task_split":"paper_retrieval"}
{"document_id":"14600","document_content":"# Video and Text Matching with Conditioned Embeddings\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nWe present a method for matching a text sentence from a given corpus to a given video clip and vice versa. Traditionally video and text matching is done by learning a shared embedding space and the encoding of one modality is independent of the other. In this work, we encode the dataset data in a way that takes into account the query's relevant information. The power of the method is demonstrated to arise from pooling the interaction data between words and frames. Since the encoding of the video clip depends on the sentence compared to it, the representation needs to be recomputed for each potential match. To this end, we propose an efficient shallow neural network. Its training employs a hierarchical triplet loss that is extendable to paragraph\/video matching. The method is simple, provides explainability, and achieves state-of-the-art results for both sentence-clip and video-text by a sizable margin across five different datasets: ActivityNet, DiDeMo, YouCook2, MSR-VTT, and LSMDC. We also show that our conditioned representation can be transferred to video-guided machine translation, where we improved the current results on VATEX. Source code is available at https:\/\/github.com\/AmeenAli\/VideoMatch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11298v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"4186\"], \"outgoing_citations\": [\"54696\", \"69945\", \"87013\", \"96230\", \"110610\", \"127728\", \"139259\", \"142128\", \"164525\", \"169293\", \"173627\", \"181897\", \"190945\", \"190949\", \"191861\", \"191966\", \"206567\", \"207818\", \"214015\", \"217562\", \"221984\", \"231300\", \"235053\", \"235648\", \"250712\", \"259318\", \"266053\", \"266120\", \"266992\", \"267223\", \"267523\", \"270325\", \"291430\", \"303224\", \"303593\", \"303837\", \"304481\", \"311849\", \"313435\", \"319169\", \"320332\"]}","task_split":"paper_retrieval"}
{"document_id":"14641","document_content":"# Topic-Guided Abstractive Multi-Document Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nA critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that \"summarizes\" texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge metrics and human evaluation, meanwhile learns high-quality topics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11207v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2919\"], \"outgoing_citations\": [\"94662\", \"128886\", \"130347\", \"181632\", \"182486\", \"183462\", \"186043\", \"216554\", \"218378\", \"219618\", \"220230\", \"220695\", \"235463\", \"243803\", \"264795\", \"268902\", \"280557\"]}","task_split":"paper_retrieval"}
{"document_id":"14642","document_content":"# Robustness through Data Augmentation Loss Consistency\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution shifts or adversarial attacks. Synthetic data augmentation followed by empirical risk minimization (DA-ERM) is a simple and widely used solution to improve robustness in ERM. In addition, consistency regularization can be applied to further improve the robustness of the model by forcing the representation of the original sample and the augmented one to be similar. However, existing consistency regularization methods are not applicable to covariant data augmentation, where the label in the augmented sample is dependent on the augmentation function. For example, dialog state covaries with named entity when we augment data with a new named entity. In this paper, we propose data augmented loss invariant regularization (DAIR), a simple form of consistency regularization that is applied directly at the loss level rather than intermediate features, making it widely applicable to both invariant and covariant data augmentation regardless of network architecture, problem setup, and task. We apply DAIR to real-world learning problems involving covariant data augmentation: robust neural task-oriented dialog state tracking and robust visual question answering. We also apply DAIR to tasks involving invariant data augmentation: robust regression, robust classification against adversarial attacks, and robust ImageNet classification under distribution shift. Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation. Our code of all experiments is available at: https:\/\/github.com\/optimization-for-data-driven-science\/DAIR.git","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11205v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"2765\", \"43651\", \"44101\", \"45029\", \"46102\", \"46501\", \"64533\", \"93910\", \"94939\", \"112804\", \"114487\", \"114597\", \"115360\", \"117897\", \"117916\", \"127463\", \"137052\", \"139100\", \"149454\", \"150930\", \"155339\", \"156893\", \"167138\", \"167539\", \"177371\", \"185347\", \"198870\", \"200398\", \"201710\", \"207239\", \"216019\", \"216292\", \"223226\", \"229919\", \"230755\", \"238702\", \"253681\", \"266155\", \"268999\", \"269203\", \"278894\", \"283114\", \"288929\", \"307383\", \"320317\"]}","task_split":"paper_retrieval"}
{"document_id":"14679","document_content":"# Applying Second-Order Quantifier Elimination in Inspecting G\u00f6del's Ontological Proof\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n## Abstract\nIn recent years, G\\\"odel's ontological proof and variations of it were formalized and analyzed with automated tools in various ways. We supplement these analyses with a modeling in an automated environment based on first-order logic extended by predicate quantification. Formula macros are used to structure complex formulas and tasks. The analysis is presented as a generated type-set document where informal explanations are interspersed with pretty-printed formulas and outputs of reasoners for first-order theorem proving and second-order quantifier elimination. Previously unnoticed or obscured aspects and details of G\\\"odel's proof become apparent. Practical application possibilities of second-order quantifier elimination are shown and the encountered elimination tasks may serve as benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11108v1\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\", \"cs.AI\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"140429\", \"147268\", \"188183\"]}","task_split":"paper_retrieval"}
{"document_id":"14770","document_content":"# A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nThis paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \\leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10887v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"118386\", \"169911\", \"183192\", \"239029\", \"258393\", \"271665\", \"289623\"]}","task_split":"paper_retrieval"}
{"document_id":"14774","document_content":"# CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level\n## Categories\n- Computation and Language\n## Abstract\nAutomatic text summarization aims to produce a brief but crucial summary for the input documents. Both extractive and abstractive methods have witnessed great success in English datasets in recent years. However, there has been a minimal exploration of text summarization in Chinese, limited by the lack of large-scale datasets. In this paper, we present a large-scale Chinese news summarization dataset CNewSum, which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which can encourage document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set contains adequacy and deducibility annotations for the summaries. The adequacy level measures the degree of summary information covered by the document, and the deducibility indicates the reasoning ability the model needs to generate the summary. These annotations can help researchers analyze and target their model performance bottleneck. We examine recent methods on CNewSum and release our dataset to provide a solid testbed for automatic Chinese summarization research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10874v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"165983\", \"170657\", \"175306\", \"206114\", \"225556\", \"233710\", \"266671\", \"268902\", \"280557\", \"290789\", \"310904\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"14806","document_content":"# Hierarchical Skills for Efficient Exploration\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. In previous work on continuous control, the sensitivity of methods to this trade-off has not been addressed explicitly, as locomotion provides a suitable prior for navigation tasks, which have been of foremost interest. In this work, we analyze this trade-off for low-level policy pre-training with a new benchmark suite of diverse, sparse-reward tasks for bipedal robots. We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner. For utilization on downstream tasks, we present a three-layered hierarchical learning algorithm to automatically trade off between general and specific skills as required by the respective task. In our experiments, we show that our approach performs this trade-off effectively and achieves better results than current state-of-the-art methods for end- to-end hierarchical reinforcement learning and unsupervised skill discovery. Code and videos are available at https:\/\/facebookresearch.github.io\/hsd3 .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10809v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75117\", \"91231\", \"92106\", \"93350\", \"103818\", \"115366\", \"143204\", \"149771\", \"156199\", \"165350\", \"177830\", \"180894\", \"184557\", \"184951\", \"206160\", \"208234\", \"208932\", \"213321\", \"215702\", \"223225\", \"231175\", \"240506\", \"241900\", \"249187\", \"261780\", \"261805\", \"269251\", \"272464\", \"272530\", \"279790\", \"280457\", \"284567\", \"293995\", \"307066\", \"310974\"]}","task_split":"paper_retrieval"}
{"document_id":"14861","document_content":"# SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nExisting work in language grounding typically study single environments. How do we build unified models that apply across multiple environments? We propose the multi-environment Symbolic Interactive Language Grounding benchmark (SILG), which unifies a collection of diverse grounded language learning environments under a common interface. SILG consists of grid-world environments that require generalization to new dynamics, entities, and partially observed worlds (RTFM, Messenger, NetHack), as well as symbolic counterparts of visual worlds that require interpreting rich natural language with respect to complex scenes (ALFWorld, Touchdown). Together, these environments provide diverse grounding challenges in richness of observation space, action space, language specification, and plan complexity. In addition, we propose the first shared model architecture for RL on these environments, and evaluate recent advances such as egocentric local convolution, recurrent state-tracking, entity-centric attention, and pretrained LM using SILG. Our shared architecture achieves comparable performance to environment-specific architectures. Moreover, we find that many recent modelling advances do not result in significant gains on environments other than the one they were designed for. This highlights the need for a multi-environment benchmark. Finally, the best models significantly underperform humans on SILG, which suggests ample room for future work. We hope SILG enables the community to quickly identify new methodologies for language grounding that generalize to a diverse set of environments and their associated challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10661v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"20726\", \"74771\", \"93950\", \"95814\", \"98224\", \"102494\", \"116357\", \"124122\", \"124850\", \"137359\", \"147685\", \"152871\", \"152918\", \"160530\", \"161143\", \"162764\", \"163895\", \"181620\", \"188123\", \"196598\", \"208042\", \"208773\", \"209452\", \"240617\", \"243235\", \"248742\", \"249906\", \"250567\", \"255134\", \"259693\", \"261378\", \"263270\", \"263650\", \"267779\", \"281106\", \"282900\", \"307569\", \"310723\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"14984","document_content":"# Deep Learning for HDR Imaging: State-of-the-Art and Future Trends\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nHigh dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number\/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10394v3\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"7584\", \"20637\", \"25803\", \"45270\", \"46943\", \"47942\", \"60199\", \"61885\", \"61960\", \"65538\", \"71818\", \"79005\", \"89969\", \"97687\", \"100061\", \"111652\", \"114390\", \"115189\", \"115507\", \"120528\", \"124705\", \"124998\", \"130207\", \"131467\", \"133418\", \"135873\", \"136384\", \"139680\", \"148389\", \"149650\", \"150468\", \"152307\", \"153050\", \"155374\", \"156796\", \"160894\", \"166875\", \"167645\", \"172113\", \"173375\", \"180260\", \"180645\", \"181211\", \"183868\", \"187033\", \"187984\", \"189044\", \"189323\", \"190036\", \"191920\", \"192746\", \"197135\", \"205608\", \"207031\", \"209333\", \"213719\", \"214493\", \"214530\", \"225426\", \"226061\", \"230997\", \"234573\", \"235063\", \"235857\", \"239299\", \"239855\", \"241612\", \"244729\", \"246985\", \"247268\", \"247346\", \"249439\", \"252829\", \"258612\", \"261627\", \"264787\", \"270221\", \"280057\", \"280600\", \"298331\", \"313878\"]}","task_split":"paper_retrieval"}
{"document_id":"15055","document_content":"# A Simple Approach to Continual Learning by Transferring Skill Parameters\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nIn order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. A great promise of continual learning is to endow robots with this ability, by using their accumulated knowledge and experience from prior skills. We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies. We show that storing skill policies, careful pre-training, and appropriately choosing when to transfer those skill policies is sufficient to build a continual learner in the context of robotic manipulation. We analyze which conditions are needed to transfer skills in the challenging Meta-World simulation benchmark. Using this analysis, we introduce a pair-wise metric relating skills that allows us to predict the effectiveness of skill transfer between tasks, and use it to reduce the problem of continual learning to curriculum selection. Given an appropriate curriculum, we show how to continually acquire robotic manipulation skills without forgetting, and using far fewer samples than needed to train them from scratch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10255v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"38123\", \"59809\", \"60348\", \"63319\", \"70370\", \"75992\", \"78091\", \"78473\", \"82278\", \"84405\", \"85855\", \"88081\", \"89599\", \"90484\", \"90880\", \"90890\", \"90966\", \"94561\", \"108757\", \"112186\", \"118157\", \"129794\", \"134149\", \"137441\", \"156199\", \"156707\", \"158215\", \"159949\", \"160036\", \"160598\", \"164703\", \"164957\", \"175872\", \"176551\", \"180928\", \"184951\", \"194100\", \"195713\", \"195849\", \"198915\", \"204289\", \"204750\", \"205477\", \"207983\", \"215102\", \"216404\", \"219180\", \"224142\", \"224840\", \"226635\", \"233165\", \"234025\", \"234517\", \"237229\", \"237605\", \"240506\", \"243236\", \"244755\", \"253045\", \"254599\", \"255359\", \"255939\", \"263636\", \"271030\", \"272585\", \"277564\", \"280681\", \"282684\", \"282696\", \"283391\", \"284565\", \"289719\", \"290378\", \"296980\", \"306156\", \"306266\", \"315055\", \"327091\", \"339440\"]}","task_split":"paper_retrieval"}
{"document_id":"15072","document_content":"# Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings\n## Categories\n- Computation and Language\n## Abstract\nDrug prescriptions are essential information that must be encoded in electronic medical records. However, much of this information is hidden within free-text reports. This is why the medication extraction task has emerged. To date, most of the research effort has focused on small amount of data and has only recently considered deep learning methods. In this paper, we present an independent and comprehensive evaluation of state-of-the-art neural architectures on the I2B2 medical prescription extraction task both in the supervised and semi-supervised settings. The study shows the very competitive performance of simple DNN models on the task as well as the high interest of pre-trained models. Adapting the latter models on the I2B2 dataset enables to push medication extraction performances above the state-of-the-art. Finally, the study also confirms that semi-supervised techniques are promising to leverage large amounts of unlabeled data in particular in low resource setting when labeled data is too costly to acquire.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.10213v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122929\", \"129792\", \"141886\", \"144494\", \"164349\", \"180997\", \"191942\", \"197910\", \"202548\", \"266955\"]}","task_split":"paper_retrieval"}
{"document_id":"15151","document_content":"# Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters\n## Categories\n- Artificial Intelligence\n## Abstract\nDealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.artint.2022.103789\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96527\", \"199714\", \"210155\", \"212811\", \"216263\", \"223487\", \"268072\", \"304250\", \"334724\"]}","task_split":"paper_retrieval"}
{"document_id":"15203","document_content":"# Learning a self-supervised tone mapping operator via feature contrast masking loss\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nHigh Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.09866v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92970\", \"124998\", \"172113\", \"180010\", \"223474\", \"235857\", \"249439\", \"252829\", \"259863\", \"270008\"]}","task_split":"paper_retrieval"}
{"document_id":"15263","document_content":"# A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Multimedia\n## Abstract\nA creative image-and-text generative AI system mimics humans' extraordinary abilities to provide users with diverse and comprehensive caption suggestions, as well as rich image creations. In this work, we demonstrate such an AI creation system to produce both diverse captions and rich images. When users imagine an image and associate it with multiple captions, our system paints a rich image to reflect all captions faithfully. Likewise, when users upload an image, our system depicts it with multiple diverse captions. We propose a unified multi-modal framework to achieve this goal. Specifically, our framework jointly models image-and-text representations with a Transformer network, which supports rich image creation by accepting multiple captions as input. We consider the relations among input captions to encourage diversity in training and adopt a non-autoregressive decoding strategy to enable real-time inference. Based on these, our system supports both diverse captions and rich images generations. Our code is available online.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3474085.3478561\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"738\"], \"outgoing_citations\": [\"98913\", \"111062\", \"131401\", \"172088\", \"189696\", \"192697\", \"193328\", \"270154\", \"280004\", \"320761\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"15317","document_content":"# Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words\/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.09646v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"77556\", \"78000\", \"78852\", \"100492\", \"125626\", \"127531\", \"127964\", \"128053\", \"128585\", \"132420\", \"164666\", \"168771\", \"172088\", \"181097\", \"182737\", \"184361\", \"192811\", \"213698\", \"219439\", \"220017\", \"220951\", \"228604\", \"234469\", \"235345\", \"241611\", \"252083\", \"264427\", \"283451\", \"290992\"]}","task_split":"paper_retrieval"}
{"document_id":"15325","document_content":"# A Systematic Review on the Detection of Fake News Articles\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIt has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction\/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11240v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105795\", \"186896\", \"198258\", \"202453\", \"211841\", \"236127\", \"246910\", \"252668\"]}","task_split":"paper_retrieval"}
{"document_id":"15553","document_content":"# Learning to Learn a Cold-start Sequential Recommender\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user's cold-start recommendation problem. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user\/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users' behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely-used datasets show the remarkable performance of metaCSR in dealing with user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.09083v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122689\", \"146038\", \"152848\", \"173662\", \"183029\", \"185557\", \"188930\", \"211118\", \"230899\", \"244250\", \"250111\", \"259762\", \"263861\", \"302767\", \"311119\", \"330720\", \"358914\"]}","task_split":"paper_retrieval"}
{"document_id":"15576","document_content":"# Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nImproving the resilience of a network is a fundamental problem in network science, which protects the underlying system from natural disasters and malicious attacks. This is traditionally achieved via successive degree-preserving edge rewiring operations, with the major limitation of being transductive. Inductively solving graph-related tasks with sequential actions is accomplished by adopting graph neural networks (GNNs) coupled with reinforcement learning under the scenario with rich graph features. However, such frameworks cannot be directly applied to resilience tasks where only pure topological structure is available. In this case, GNNs can barely learn useful information, resulting in prohibitive difficulty in making actions for successively rewiring edges under a reinforcement learning context. In this paper, we study in depth the reasons why typical GNNs cause such failure. Based on this investigation, we propose ResiNet, the first end-to-end trainable inductive framework to discover resilient network topologies while balancing network utility. To this end, we reformulate resilience optimization as an MDP equipped with edge rewiring action space, and propose a pure topology-oriented variant of GNN called filtration enhanced graph neural network (FireGNN), which can learn from graphs without rich features. Extensive experiments demonstrate that ResiNet achieves a near-optimal resilience gain on various graphs while balancing the utility, and outperforms existing approaches by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.09035v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"17350\", \"39957\", \"43737\", \"73530\", \"76589\", \"79509\", \"92075\", \"101873\", \"103085\", \"113589\", \"116197\", \"117926\", \"118851\", \"119164\", \"119843\", \"123835\", \"143387\", \"150220\", \"175310\", \"182690\", \"183816\", \"184368\", \"213046\", \"215435\", \"216376\", \"228663\", \"228992\", \"229027\", \"238077\", \"242457\", \"269617\", \"279191\", \"311464\", \"315332\"]}","task_split":"paper_retrieval"}
{"document_id":"15654","document_content":"# Network Augmentation for Tiny Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe introduce Network Augmentation (NetAug), a new training method for improving the performance of tiny neural networks. Existing regularization techniques (e.g., data augmentation, dropout) have shown much success on large neural networks by adding noise to overcome over-fitting. However, we found these techniques hurt the performance of tiny neural networks. We argue that training tiny models are different from large models: rather than augmenting the data, we should augment the model, since tiny models tend to suffer from under-fitting rather than over-fitting due to limited capacity. To alleviate this issue, NetAug augments the network (reverse dropout) instead of inserting noise into the dataset or the network. It puts the tiny model into larger models and encourages it to work as a sub-model of larger models to get extra supervision, in addition to functioning as an independent model. At test time, only the tiny model is used for inference, incurring zero inference overhead. We demonstrate the effectiveness of NetAug on image classification and object detection. NetAug consistently improves the performance of tiny models, achieving up to 2.2% accuracy improvement on ImageNet. On object detection, achieving the same level of performance, NetAug requires 41% fewer MACs on Pascal VOC and 38% fewer MACs on COCO than the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08890v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"12900\", \"43336\", \"54192\", \"58866\", \"75679\", \"92443\", \"110742\", \"114752\", \"118718\", \"134141\", \"139780\", \"146703\", \"163574\", \"164174\", \"164933\", \"170131\", \"185872\", \"189752\", \"192998\", \"193414\", \"199359\", \"206678\", \"207710\", \"212301\", \"214530\", \"222834\", \"227817\", \"230755\", \"230919\", \"232302\", \"235093\", \"258001\", \"258534\", \"258631\", \"260782\", \"263636\", \"278175\", \"278830\", \"295571\", \"296421\", \"311552\", \"320046\", \"321736\"]}","task_split":"paper_retrieval"}
{"document_id":"15658","document_content":"# Schr\u00f6dinger's Tree -- On Syntax and Neural Language Models\n## Categories\n- Computation and Language\n## Abstract\nIn the last half-decade, the field of natural language processing (NLP) has undergone two major transitions: the switch to neural networks as the primary modeling paradigm and the homogenization of the training regime (pre-train, then fine-tune). Amidst this process, language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities and proving to be an indispensable means of knowledge transfer downstream. Due to the otherwise opaque, black-box nature of such models, researchers have employed aspects of linguistic theory in order to characterize their behavior. Questions central to syntax -- the study of the hierarchical structure of language -- have factored heavily into such work, shedding invaluable insights about models' inherent biases and their ability to make human-like generalizations. In this paper, we attempt to take stock of this growing body of literature. In doing so, we observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form, as well as the conclusions they draw from their findings. To remedy this, we urge researchers make careful considerations when investigating coding properties, selecting representations, and evaluating via downstream tasks. Furthermore, we outline the implications of the different types of research questions exhibited in studies on syntax, as well as the inherent pitfalls of aggregate metrics. Ultimately, we hope that our discussion adds nuance to the prospect of studying language models and paves the way for a less monolithic perspective on syntax in this context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08887v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"15705","document_content":"# Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks\n## Categories\n- Robotics\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nWe address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle this fundamental trade-off by decoupling our overall intractable problem into tractable sub-problems that we solve stage-wise. The first stage solves only for trucks, by computing paths that make them more likely to be useful transit options for drones. The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage. We design a comprehensive algorithmic framework that frames each stage as a multi-agent path-finding problem and implement two distinct methods for solving them. We evaluate our approach on extensive simulations with up to $100$ agents on the real-world Manhattan road network containing nearly $4500$ vertices and $10000$ edges. Our framework saves on more than $50\\%$ of vehicle distance traveled compared to independently solving for trucks and drones, and computes solutions for all settings within $5$ minutes on commodity hardware.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08802v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"47802\", \"55181\", \"142403\", \"164890\", \"175164\", \"179942\", \"200199\", \"264979\", \"268271\", \"309684\", \"314810\"]}","task_split":"paper_retrieval"}
{"document_id":"15822","document_content":"# Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments\n## Categories\n- Robotics\n- Systems and Control\n- Artificial Intelligence\n- Machine Learning\n- Systems and Control\n## Abstract\nAutonomous driving is a complex task, which has been tackled since the first self-driving car ALVINN in 1989, with a supervised learning approach, or behavioral cloning (BC). In BC, a neural network is trained with state-action pairs that constitute the training set made by an expert, i.e., a human driver. However, this type of imitation learning does not take into account the temporal dependencies that might exist between actions taken in different moments of a navigation trajectory. These type of tasks are better handled by reinforcement learning (RL) algorithms, which need to define a reward function. On the other hand, more recent approaches to imitation learning, such as Generative Adversarial Imitation Learning (GAIL), can train policies without explicitly requiring to define a reward function, allowing an agent to learn by trial and error directly on a training set of expert trajectories. In this work, we propose two variations of GAIL for autonomous navigation of a vehicle in the realistic CARLA simulation environment for urban scenarios. Both of them use the same network architecture, which process high dimensional image input from three frontal cameras, and other nine continuous inputs representing the velocity, the next point from the sparse trajectory and a high-level driving command. We show that both of them are capable of imitating the expert trajectory from start to end after training ends, but the GAIL loss function that is augmented with BC outperforms the former in terms of convergence time and training stability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/SSCI50451.2021.9660156\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.SY\", \"cs.AI\", \"cs.LG\", \"cs.RO\", \"eess.SY\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Systems and Control\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"15850","document_content":"# Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDistilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time. The student models are typically compact transformers with fewer parameters, while expensive operations such as self-attention persist. Therefore, the improved inference speed may still be unsatisfactory for real-time or high-volume use cases. In this paper, we aim to further push the limit of inference speed by distilling teacher models into bigger, sparser student models -- bigger in that they scale up to billions of parameters; sparser in that most of the model parameters are n-gram embeddings. Our experiments on six single-sentence text classification tasks show that these student models retain 97% of the RoBERTa-Large teacher performance on average, and meanwhile achieve up to 600x speed-up on both GPUs and CPUs at inference time. Further investigation reveals that our pipeline is also helpful for sentence-pair classification tasks, and in domain generalization settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08536v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"25356\", \"56222\", \"69514\", \"76037\", \"77661\", \"79182\", \"100684\", \"120206\", \"126161\", \"126834\", \"127490\", \"131817\", \"132687\", \"139854\", \"140175\", \"141303\", \"165441\", \"166278\", \"168025\", \"169179\", \"170447\", \"192811\", \"193295\", \"195564\", \"281119\", \"281768\", \"287941\", \"307068\"]}","task_split":"paper_retrieval"}
{"document_id":"15958","document_content":"# Clean or Annotate: How to Spend a Limited Data Collection Budget\n## Categories\n- Computation and Language\n## Abstract\nCrowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling. There are two common strategies to manage the impact of such noise. The first involves aggregating redundant annotations, but comes at the expense of labeling substantially fewer examples. Secondly, prior works have also considered using the entire annotation budget to label as many examples as possible and subsequently apply denoising algorithms to implicitly clean the dataset. We find a middle ground and propose an approach which reserves a fraction of annotations to explicitly clean up highly probable error samples to optimize the annotation process. In particular, we allocate a large portion of the labeling budget to form an initial dataset used to train a model. This model is then used to identify specific examples that appear most likely to be incorrect, which we spend the remaining budget to relabel. Experiments across three model variations and four natural language processing tasks show our approach outperforms or matches both label aggregation and advanced denoising methods designed to handle noisy labels when allocated the same finite annotation budget.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08355v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"24571\", \"24719\", \"77747\", \"99110\", \"120762\", \"141691\", \"145212\", \"157125\", \"168232\", \"169349\", \"181743\", \"182163\", \"189025\", \"189515\", \"194712\", \"199491\", \"203033\", \"212078\", \"224833\", \"231347\", \"235096\", \"237224\", \"246497\", \"247500\", \"249957\", \"256776\", \"259604\", \"263559\", \"268604\", \"270545\", \"279213\", \"288921\", \"298544\", \"307756\", \"320027\"]}","task_split":"paper_retrieval"}
{"document_id":"15998","document_content":"# Coherence boosting: When your pretrained language model is not paying enough attention\n## Categories\n- Computation and Language\n## Abstract\nLong-range semantic coherence remains a challenge in automatic language generation and understanding. We demonstrate that large language models have insufficiently learned the effect of distant words on next-token prediction. We present coherence boosting, an inference procedure that increases a LM's focus on a long context. We show the benefits of coherence boosting with pretrained models by distributional analyses of generated ordinary text and dialog responses. It is also found that coherence boosting with state-of-the-art models for various zero-shot NLP tasks yields performance gains with no additional training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08294v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16022\", \"22489\", \"24491\", \"27172\", \"30577\", \"36889\", \"41478\", \"45930\", \"55442\", \"55454\", \"68331\", \"84923\", \"101855\", \"124253\", \"127513\", \"130347\", \"131817\", \"137072\", \"146195\", \"150522\", \"154152\", \"158493\", \"168951\", \"182546\", \"184848\", \"185629\", \"189504\", \"210121\", \"211788\", \"217640\", \"218698\", \"220230\", \"232274\", \"232355\", \"238962\", \"243154\", \"268902\", \"290041\", \"305200\", \"307068\"]}","task_split":"paper_retrieval"}
{"document_id":"16039","document_content":"# Collaborating with Humans without Human Data\n## Categories\n- Machine Learning\n- Human-Computer Interaction\n- Multiagent Systems\n## Abstract\nCollaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning techniques, such as self-play (SP) or population play (PP), produce agents that overfit to their training partners and do not generalize well to humans. Alternatively, researchers can collect human data, train a human model using behavioral cloning, and then use that model to train \"human-aware\" agents (\"behavioral cloning play\", or BCP). While such an approach can improve the generalization of agents to new human co-players, it involves the onerous and expensive step of collecting large amounts of human data first. Here, we study the problem of how to train agents that collaborate well with human partners without using human data. We argue that the crux of the problem is to produce a diverse set of training partners. Drawing inspiration from successful multi-agent approaches in competitive domains, we find that a surprisingly simple approach is highly effective. We train our agent partner as the best response to a population of self-play agents and their past checkpoints taken throughout training, a method we call Fictitious Co-Play (FCP). Our experiments focus on a two-player collaborative cooking simulator that has recently been proposed as a challenge problem for coordination with humans. We find that FCP agents score significantly higher than SP, PP, and BCP when paired with novel agent and human partners. Furthermore, humans also report a strong subjective preference to partnering with FCP agents over all baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08176v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.HC\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\", \"Multiagent Systems\"], \"incoming_citations\": [\"8471\", \"1428\", \"4891\", \"11237\"], \"outgoing_citations\": [\"17989\", \"40705\", \"57844\", \"63104\", \"75515\", \"80268\", \"81402\", \"84187\", \"87787\", \"90372\", \"121735\", \"128500\", \"138270\", \"143799\", \"144255\", \"145405\", \"147483\", \"161370\", \"161959\", \"164729\", \"187390\", \"200580\", \"203923\", \"209926\", \"211574\", \"226697\", \"237964\", \"241900\", \"243235\", \"249196\", \"251601\", \"253045\", \"263957\", \"271030\", \"297186\", \"311815\", \"315405\"]}","task_split":"paper_retrieval"}
{"document_id":"16053","document_content":"# milIE: Modular & Iterative Multilingual Open Information Extraction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nOpen Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction. Based on this hypothesis, we propose a neural OpenIE system, milIE, that operates in an iterative fashion. Due to the iterative nature, the system is also modular -- it is possible to seamlessly integrate rule based extraction systems with a neural end-to-end system, thereby allowing rule based systems to supply extraction slots which milIE can leverage for extracting the remaining slots. We confirm our hypothesis empirically: milIE outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. Additionally, we are the first to provide an OpenIE test dataset for Arabic and Galician.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08144v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23258\", \"23554\"], \"outgoing_citations\": [\"23258\", \"77963\", \"96094\", \"100125\", \"124608\", \"188564\", \"200951\", \"244439\", \"268570\", \"288699\", \"292674\"]}","task_split":"paper_retrieval"}
{"document_id":"16088","document_content":"# Dual-Arm Adversarial Robot Learning\n## Categories\n- Robotics\n- Systems and Control\n- Artificial Intelligence\n- Systems and Control\n- I.2.6; I.2.9\n- Machine Learning\n## Abstract\nRobot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be still limited. This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time. Unless we develop scalable solutions to these problems, learning complex tasks involving hand-eye coordination and rich contacts will remain an untouched vision that is only feasible in controlled lab environments. We propose dual-arm settings as platforms for robot learning. Such settings enable safe data collection for acquiring manipulation skills as well as training perception modules in a robot-supervised manner. They also ease the processes of resetting the environment. Furthermore, adversarial learning could potentially boost the generalization capability of robot learning methods by maximizing the exploration based on game-theoretic objectives while ensuring safety based on collaborative task spaces. In this paper, we will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.08066v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.SY\", \"cs.AI\", \"eess.SY\", \"I.2.6; I.2.9\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Systems and Control\", \"Artificial Intelligence\", \"Systems and Control\", \"I.2.6; I.2.9\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"18008\", \"20583\", \"87242\", \"87588\", \"90194\", \"143661\", \"149380\", \"167770\", \"204750\", \"213161\", \"235092\", \"272030\", \"315055\", \"318666\"]}","task_split":"paper_retrieval"}
{"document_id":"16143","document_content":"# Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community\n## Categories\n- Computation and Language\n- Computers and Society\n## Abstract\nDrawing causal conclusions from observational real-world data is a very much desired but challenging task. In this paper we present mixed-method analyses to investigate causal influences of publication trends and behavior on the adoption, persistence, and retirement of certain research foci -- methodologies, materials, and tasks that are of interest to the computational linguistics (CL) community. Our key findings highlight evidence of the transition to rapidly emerging methodologies in the research community (e.g., adoption of bidirectional LSTMs influencing the retirement of LSTMs), the persistent engagement with trending tasks and techniques (e.g., deep learning, embeddings, generative, and language models), the effect of scientist location from outside the US, e.g., China on propensity of researching languages beyond English, and the potential impact of funding for large-scale research programs. We anticipate this work to provide useful insights about publication trends and behavior and raise the awareness about the potential for causal inference in the computational linguistics and a broader scientific community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07938v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"24742\", \"91573\", \"99412\", \"127545\", \"133385\", \"146636\", \"162159\", \"168609\", \"183649\", \"215808\", \"219946\", \"232806\", \"235507\", \"242204\", \"280626\", \"304780\", \"320501\", \"335762\", \"352356\"]}","task_split":"paper_retrieval"}
{"document_id":"16180","document_content":"# Graph Neural Networks with Learnable Structural and Positional Representations\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional information of nodes, which decreases the representation power of GNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers. Possible graph PE are Laplacian eigenvectors. In this work, we propose to decouple structural and positional representations to make easy for the network to learn these two essential properties. We introduce a novel generic architecture which we call LSPE (Learnable Structural and Positional Encodings). We investigate several sparse and fully-connected (Transformer-like) GNNs, and observe a performance increase for molecular datasets, from 1.79% up to 64.14% when considering learnable PE for both GNN classes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07875v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"117616\", \"138999\"], \"outgoing_citations\": [\"37770\", \"43122\", \"43338\", \"43981\", \"59455\", \"67685\", \"68391\", \"79849\", \"81711\", \"96209\", \"103085\", \"111508\", \"113581\", \"115600\", \"118201\", \"118851\", \"120001\", \"127525\", \"131562\", \"137772\", \"138999\", \"145997\", \"163945\", \"167136\", \"177249\", \"181255\", \"183719\", \"184286\", \"184749\", \"191735\", \"196221\", \"199511\", \"204871\", \"210039\", \"215435\", \"242430\", \"249839\", \"272626\", \"279575\", \"291892\"]}","task_split":"paper_retrieval"}
{"document_id":"16183","document_content":"# A Dual-Perception Graph Neural Network with Multi-hop Graph Generator\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs excessively rely on topological structures and aggregate multi-hop neighborhood information by simply stacking network layers, which may introduce superfluous noise information, limit the expressive power of GNNs and lead to the over-smoothing problem ultimately. In light of this, we propose a novel Dual-Perception Graph Neural Network (DPGNN) to address these issues. In DPGNN, we utilize node features to construct a feature graph, and perform node representations learning based on the original topology graph and the constructed feature graph simultaneously, which conduce to capture the structural neighborhood information and the feature-related information. Furthermore, we design a Multi-Hop Graph Generator (MHGG), which applies a node-to-hop attention mechanism to aggregate node-specific multi-hop neighborhood information adaptively. Finally, we apply self-ensembling to form a consistent prediction for unlabeled node representations. Experimental results on five datasets with different topological structures demonstrate that our proposed DPGNN outperforms all the latest state-of-the-art models on all datasets, which proves the superiority and versatility of our model. The source code of our model is available at https:\/\/github.com.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07869v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"111211\", \"114134\", \"114193\", \"142571\", \"150834\", \"157816\", \"164939\", \"168172\", \"174492\", \"182282\", \"184400\", \"187534\", \"188300\", \"193345\", \"194773\", \"208815\", \"214311\", \"244510\", \"284622\"]}","task_split":"paper_retrieval"}
{"document_id":"16191","document_content":"# Modeling Endorsement for Multi-Document Abstractive Summarization\n## Categories\n- Computation and Language\n## Abstract\nA crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information is frequently reiterated in a set of documents related to a particular topic, resulting in an endorsement effect that increases information salience. In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents. Strongly endorsed text segments are used to enrich a neural encoder-decoder model to consolidate them into an abstractive summary. The method has a great potential to learn from fewer examples to identify salient content, which alleviates the need for costly retraining when the set of documents is dynamically adjusted. Through extensive experiments on benchmark multi-document summarization datasets, we demonstrate the effectiveness of our proposed method over strong published baselines. Finally, we shed light on future research directions and discuss broader challenges of this task using a case study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07844v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"45223\", \"46445\", \"50181\", \"58194\", \"97507\", \"123878\", \"127889\", \"139638\", \"150522\", \"159889\", \"168085\", \"168405\", \"169401\", \"170449\", \"181306\", \"181468\", \"182486\", \"183200\", \"183462\", \"186043\", \"187236\", \"220230\", \"220955\", \"230212\", \"268902\", \"311754\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"16224","document_content":"# A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification\n## Categories\n- Computation and Language\n## Abstract\nWe present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e.g., M-BERT). It leverages the multilingual nature of Wikidata: entities in multiple languages representing the same concept are defined with a unique identifier. This enables entities described in multiple languages to be represented using shared embeddings. A model trained on entity features in a resource-rich language can thus be directly applied to other languages. Our experimental results on cross-lingual topic classification (using the MLDoc and TED-CLDC datasets) and entity typing (using the SHINRA2020-ML dataset) show that the proposed model consistently outperforms state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07792v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"37401\", \"97100\", \"108957\", \"127839\", \"131012\", \"156620\", \"157255\", \"158102\", \"166130\", \"166664\", \"167513\", \"167753\", \"168892\", \"169335\", \"185889\", \"202124\", \"204715\", \"205857\", \"230727\", \"289972\", \"330941\"]}","task_split":"paper_retrieval"}
{"document_id":"16284","document_content":"# Distribution-Free Federated Learning with Conformal Predictions\n## Categories\n- Machine Learning\n- Image and Video Processing\n## Abstract\nFederated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privacy. However, additional challenges such as poor calibration and lack of interpretability may also hamper widespread deployment of federated models into clinical practice, leading to user distrust or misuse of ML tools in high-stakes clinical decision-making. In this paper, we propose to address these challenges by incorporating an adaptive conformal framework into federated learning to ensure distribution-free prediction sets that provide coverage guarantees. Importantly, these uncertainty estimates can be obtained without requiring any additional modifications to the model. Empirical results on the MedMNIST medical imaging benchmark demonstrate our federated method provides tighter coverage over local conformal predictions on 6 different medical imaging datasets for 2D and 3D multi-class classification tasks. Furthermore, we correlate class entropy with prediction set size to assess task uncertainty.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07661v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"35175\"], \"outgoing_citations\": [\"24725\", \"35175\", \"43448\", \"76493\", \"86847\", \"90709\", \"97795\", \"129795\", \"133775\", \"136201\", \"156643\", \"164015\", \"183930\", \"208787\", \"228498\", \"229623\", \"283125\", \"311316\", \"354570\"]}","task_split":"paper_retrieval"}
{"document_id":"16302","document_content":"# Compressibility of Distributed Document Representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nContemporary natural language processing (NLP) revolves around learning from latent document representations, generated either implicitly by neural language models or explicitly by methods such as doc2vec or similar. One of the key properties of the obtained representations is their dimension. Whilst the commonly adopted dimensions of 256 and 768 offer sufficient performance on many tasks, it is many times unclear whether the default dimension is the most suitable choice for the subsequent downstream learning tasks. Furthermore, representation dimensions are seldom subject to hyperparameter tuning due to computational constraints. The purpose of this paper is to demonstrate that a surprisingly simple and efficient recursive compression procedure can be sufficient to both significantly compress the initial representation, but also potentially improve its performance when considering the task of text classification. Having smaller and less noisy representations is the desired property during deployment, as orders of magnitude smaller models can significantly reduce the computational overload and with it the deployment costs. We propose CoRe, a straightforward, representation learner-agnostic framework suitable for representation compression. The CoRe's performance is showcased and studied on a collection of 17 real-life corpora from biomedical, news, social media, and literary domains. We explored CoRe's behavior when considering contextual and non-contextual document representations, different compression levels, and 9 different compression algorithms. Current results based on more than 100,000 compression experiments indicate that recursive Singular Value Decomposition offers a very good trade-off between the compression efficiency and performance, making CoRe useful in many existing, representation-dependent NLP pipelines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07595v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"16331","document_content":"# Toward Degradation-Robust Voice Conversion\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Sound\n## Abstract\nAny-to-any voice conversion technologies convert the vocal timbre of an utterance to any speaker even unseen during training. Although there have been several state-of-the-art any-to-any voice conversion models, they were all based on clean utterances to convert successfully. However, in real-world scenarios, it is difficult to collect clean utterances of a speaker, and they are usually degraded by noises or reverberations. It thus becomes highly desired to understand how these degradations affect voice conversion and build a degradation-robust model. We report in this paper the first comprehensive study on the degradation robustness of any-to-any voice conversion. We show that the performance of state-of-the-art models nowadays was severely hampered given degraded utterances. To this end, we then propose speech enhancement concatenation and denoising training to improve the robustness. In addition to common degradations, we also consider adversarial noises, which alter the model output significantly yet are human-imperceptible. It was shown that both concatenations with off-the-shelf speech enhancement models and denoising training on voice conversion models could improve the robustness, while each of them had pros and cons.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07537v3\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.LG\", \"cs.SD\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Machine Learning\", \"Sound\"], \"incoming_citations\": [], \"outgoing_citations\": [\"41620\", \"57558\", \"57825\", \"85008\", \"91019\", \"116691\", \"124359\", \"124632\", \"147546\", \"178013\", \"186295\", \"191165\", \"192116\", \"209981\", \"217178\", \"229074\", \"236348\"]}","task_split":"paper_retrieval"}
{"document_id":"16341","document_content":"# SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nCurrent open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend to react to feedback with defensive or oblivious responses. This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future. This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures. We collect a dataset of 10k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07518v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"25311\", \"29312\", \"36866\", \"45169\", \"116873\", \"128396\", \"130525\", \"145384\", \"145923\", \"158493\", \"167648\", \"168228\", \"171356\", \"202748\", \"212046\", \"266155\"]}","task_split":"paper_retrieval"}
{"document_id":"16406","document_content":"# Transferring Semantic Knowledge Into Language Encoders\n## Categories\n- Computation and Language\n## Abstract\nWe introduce semantic form mid-tuning, an approach for transferring semantic knowledge from semantic meaning representations into transformer-based language encoders. In mid-tuning, we learn to align the text of general sentences -- not tied to any particular inference task -- and structured semantic representations of those sentences. Our approach does not require gold annotated semantic representations. Instead, it makes use of automatically generated semantic representations, such as from off-the-shelf PropBank and FrameNet semantic parsers. We show that this alignment can be learned implicitly via classification or directly via triplet loss. Our method yields language encoders that demonstrate improved predictive performance across inference, reading comprehension, textual similarity, and other semantic tasks drawn from the GLUE, SuperGLUE, and SentEval benchmarks. We evaluate our approach on three popular baseline models, where our experimental results and analysis concludes that current pre-trained language models can further benefit from structured semantic frames with the proposed mid-tuning method, as they inject additional task-agnostic knowledge to the encoder, improving the generated embeddings as well as the linguistic properties of the given model, as evident from improvements on a popular sentence embedding toolkit and a variety of probing tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07382v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128282\", \"141983\", \"146003\", \"168549\", \"169008\", \"188123\", \"211737\", \"233400\", \"234307\", \"238963\", \"267161\", \"307756\", \"310737\", \"320009\"]}","task_split":"paper_retrieval"}
{"document_id":"16494","document_content":"# Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nWe propose a light-weight, self-supervised adaptation for a visual navigation agent to generalize to unseen environment. Given an embodied agent trained in a noiseless environment, our objective is to transfer the agent to a noisy environment where actuation and odometry sensor noise is present. Our method encourages the agent to maximize the consistency between the global maps generated at different time steps in a round-trip trajectory. The proposed task is completely self-supervised, not requiring any supervision from ground-truth pose data or explicit noise model. In addition, optimization of the task objective is extremely light-weight, as training terminates within a few minutes on a commodity GPU. Our experiments show that the proposed task helps the agent to successfully transfer to new, noisy environments. The transferred agent exhibits improved localization and mapping accuracy, further leading to enhanced performance in downstream visual navigation tasks. Moreover, we demonstrate test-time adaptation with our self-supervised task to show its potential applicability in real-world deployment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.07184v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"43678\", \"49226\", \"75621\", \"79811\", \"82126\", \"87242\", \"90871\", \"104828\", \"108736\", \"112798\", \"113327\", \"114824\", \"116020\", \"123031\", \"127001\", \"131814\", \"151367\", \"158547\", \"179954\", \"185747\", \"192738\", \"196393\", \"196898\", \"207401\", \"209090\", \"214419\", \"219658\", \"238288\", \"240323\", \"247900\", \"248742\", \"255575\", \"262593\", \"274102\", \"290110\", \"318081\"]}","task_split":"paper_retrieval"}
{"document_id":"16626","document_content":"# OPEn: An Open-ended Physics Environment for Learning Without a Task\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHumans have mental models that allow them to plan, experiment, and reason in the physical world. How should an intelligent agent go about learning such models? In this paper, we will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused for downstream physics reasoning tasks. To this end, we build a benchmark Open-ended Physics ENvironment (OPEn) and also design several tasks to test learning representations in this environment explicitly. This setting reflects the conditions in which real agents (i.e. rolling robots) find themselves, where they may be placed in a new kind of environment and must adapt without any teacher to tell them how this environment works. This setting is challenging because it requires solving an exploration problem in addition to a model building and representation learning problem. We test several existing RL-based exploration methods on this benchmark and find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results. However, all models still fall short in sample efficiency when transferring to the downstream tasks. We expect that OPEn will encourage the development of novel rolling robot agents that can build reusable mental models of the world that facilitate many tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06912v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"57662\", \"59458\", \"60623\", \"113084\", \"125475\", \"127845\", \"128392\", \"132207\", \"134771\", \"139653\", \"152918\", \"160036\", \"162372\", \"163549\", \"164814\", \"166489\", \"167238\", \"171413\", \"171629\", \"196784\", \"196921\", \"203756\", \"212299\", \"238328\", \"241436\", \"259045\", \"266419\", \"269458\", \"279883\", \"289719\", \"308711\", \"327091\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"16658","document_content":"# Compositional Generalization in Dependency Parsing\n## Categories\n- Computation and Language\n## Abstract\nCompositionality -- the ability to combine familiar units like words into novel phrases and sentences -- has been the focus of intense interest in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behavior of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser's lower performance on the most challenging splits.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06843v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"30857\", \"5985\"], \"outgoing_citations\": [\"55840\", \"80391\", \"94026\", \"95004\", \"100923\", \"111343\", \"129611\", \"136604\", \"137359\", \"150226\", \"151527\", \"225119\", \"251911\", \"274271\"]}","task_split":"paper_retrieval"}
{"document_id":"16684","document_content":"# Leveraging Automated Unit Tests for Unsupervised Code Translation\n## Categories\n- Software Engineering\n- Computation and Language\n- Machine Learning\n## Abstract\nWith little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently involves training on noisy inputs. Unfortunately, source code is highly sensitive to small changes; a single token can result in compilation failures or erroneous programs, unlike natural languages where small inaccuracies may not change the meaning of a sentence. To address this issue, we propose to leverage an automated unit-testing system to filter out invalid translations, thereby creating a fully tested parallel corpus. We found that fine-tuning an unsupervised model with this filtered data set significantly reduces the noise in the translations so-generated, comfortably outperforming the state-of-the-art for all language pairs studied. In particular, for Java $\\to$ Python and Python $\\to$ C++ we outperform the best previous methods by more than 16% and 24% respectively, reducing the error rate by more than 35%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06773v2\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SE\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": [\"29470\", \"36889\", \"48280\", \"48521\", \"63847\", \"69291\", \"70595\", \"92302\", \"92986\", \"100035\", \"120826\", \"123956\", \"128391\", \"134074\", \"141361\", \"150065\", \"158180\", \"166679\", \"181212\", \"204849\", \"205222\", \"229845\", \"234807\", \"242644\", \"249244\", \"249946\", \"251240\", \"251741\", \"261784\", \"279759\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"16718","document_content":"# Diverse Audio Captioning via Adversarial Training\n## Categories\n- Audio and Speech Processing\n- Sound\n## Abstract\nAudio captioning aims at generating natural language descriptions for audio clips automatically. Existing audio captioning models have shown promising improvement in recent years. However, these models are mostly trained via maximum likelihood estimation (MLE),which tends to make captions generic, simple and deterministic. As different people may describe an audio clip from different aspects using distinct words and grammars, we argue that an audio captioning system should have the ability to generate diverse captions for a fixed audio clip and across similar audio clips. To address this problem, we propose an adversarial training framework for audio captioning based on a conditional generative adversarial network (C-GAN), which aims at improving the naturalness and diversity of generated captions. Unlike processing data of continuous values in a classical GAN, a sentence is composed of discrete tokens and the discrete sampling process is non-differentiable. To address this issue, policy gradient, a reinforcement learning technique, is used to back-propagate the reward to the generator. The results show that our proposed model can generate more diverse captions, as compared to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06691v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16991\", \"31202\", \"34188\", \"34266\", \"67563\", \"80730\", \"114995\", \"150055\", \"160636\", \"236907\", \"262465\", \"270154\", \"271234\", \"278954\", \"284481\", \"287504\", \"311470\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"16742","document_content":"# ADOP: Approximate Differentiable One-Pixel Point Rendering\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nIn this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel. The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance. Because our pipeline includes photometric parameters, e.g.~exposure and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, e.g. with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points. https:\/\/github.com\/darglein\/ADOP","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06635v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"645\", \"1544\", \"2174\", \"3324\", \"4111\", \"5633\", \"6623\", \"6693\", \"7025\", \"32828\", \"10256\"], \"outgoing_citations\": [\"7025\", \"25537\", \"26593\", \"55819\", \"56324\", \"82893\", \"87011\", \"94164\", \"100051\", \"107634\", \"111573\", \"112560\", \"117017\", \"118041\", \"126297\", \"129363\", \"130893\", \"132347\", \"150515\", \"150974\", \"151076\", \"151894\", \"153557\", \"154293\", \"158372\", \"173162\", \"181617\", \"187991\", \"188170\", \"188623\", \"191545\", \"191951\", \"192520\", \"202836\", \"206570\", \"206932\", \"207490\", \"208268\", \"208590\", \"213394\", \"227699\", \"227856\", \"228631\", \"230666\", \"235354\", \"249836\", \"263147\", \"268404\", \"282271\", \"287801\", \"288753\", \"292758\", \"298480\", \"313371\", \"328077\"]}","task_split":"paper_retrieval"}
{"document_id":"16773","document_content":"# A Review of 3D Face Reconstruction From a Single Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\n3D face reconstruction is a challenging problem but also an important task in the field of computer vision and graphics. Recently, many researchers put attention to the problem and a large number of articles have been published. Single image reconstruction is one of the branches of 3D face reconstruction, which has a lot of applications in our life. This paper is a review of the recent literature on 3D face reconstruction from a single image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.09299v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"58273\", \"112015\", \"134076\", \"137181\", \"155623\", \"161915\", \"164821\", \"191463\", \"191844\", \"194156\", \"194402\", \"195806\", \"198793\", \"219961\", \"223229\", \"227856\", \"237222\", \"238254\", \"247495\", \"248320\", \"250290\", \"254971\", \"257846\", \"260916\", \"267940\", \"268703\", \"270134\", \"270235\", \"270795\", \"275879\", \"277948\", \"280394\", \"284716\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"16789","document_content":"# NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment\n## Categories\n- Artificial Intelligence\n## Abstract\nHuman intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. Understanding the complexity of the challenge, we split it into sub-tasks to make it feasible for participants.   This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU\/G) and Reinforcement Learning (RL). Therefore, the suggested challenge can bring two communities together to approach one of the important challenges in AI. Another important aspect of the challenge is the dedication to perform a human-in-the-loop evaluation as a final evaluation for the agents developed by contestants.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06536v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"3136\"], \"outgoing_citations\": [\"24007\", \"89875\", \"98892\", \"126793\", \"127524\", \"128396\", \"145384\", \"156297\", \"157661\", \"158493\", \"162157\", \"169742\", \"175055\", \"175344\", \"182870\", \"189416\", \"189948\", \"196049\", \"200725\", \"216019\", \"230008\", \"232341\", \"247002\", \"253974\", \"255580\", \"261081\", \"307266\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"16859","document_content":"# MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome. Current digital assistants take spoken input and try to determine the user intent from conversational or device context. So, a dataset, which includes visual input (i.e. images or videos for the corresponding questions targeted for multimodal assistant use cases, is not readily available. The research in visual question answering (VQA) and visual question generation (VQG) is a great step forward. However, they do not capture questions that a visually-abled person would ask multimodal assistants. Moreover, many times questions do not seek information from external knowledge. In this paper, we provide a new dataset, MMIU (MultiModal Intent Understanding), that contains questions and corresponding intents provided by human annotators while looking at images. We, then, use this dataset for intent classification task in multimodal digital assistant. We also experiment with various approaches for combining vision and language features including the use of multimodal transformer for classification of image-question pairs into 14 intents. We provide the benchmark results and discuss the role of visual and text features for the intent classification task on our dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.06416v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"106227\", \"170603\", \"170926\", \"183201\", \"236938\", \"269122\", \"296279\"]}","task_split":"paper_retrieval"}
{"document_id":"17043","document_content":"# Early Melanoma Diagnosis with Sequential Dermoscopic Images\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TMI.2021.3120091\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150465\", \"152363\", \"162635\", \"167310\", \"195957\", \"196375\", \"211753\", \"223827\", \"247535\", \"248813\", \"261577\", \"271595\", \"278825\", \"282613\", \"287286\", \"294046\", \"294503\", \"305615\", \"315200\", \"328601\"]}","task_split":"paper_retrieval"}
{"document_id":"17046","document_content":"# Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models\n## Categories\n- Audio and Speech Processing\n- Artificial Intelligence\n## Abstract\nA deep neural network (DNN)-based speech enhancement (SE) aiming to maximize the performance of an automatic speech recognition (ASR) system is proposed in this paper. In order to optimize the DNN-based SE model in terms of the character error rate (CER), which is one of the metric to evaluate the ASR system and generally non-differentiable, our method uses two DNNs: one for speech processing and one for mimicking the output CERs derived through an acoustic model (AM). Then both of DNNs are alternately optimized in the training phase. Even if the AM is a black-box, e.g., like one provided by a third-party, the proposed method enables the DNN-based SE model to be optimized in terms of the CER since the DNN mimicking the AM is differentiable. Consequently, it becomes feasible to build CER-centric SE model that has no negative effect, e.g., additional calculation cost and changing network architecture, on the inference phase since our method is merely a training scheme for the existing DNN-based methods. Experimental results show that our method improved CER by 8.8% relative derived through a black-box AM although certain noise levels are kept.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05968v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.AI\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"57558\", \"71253\", \"90478\", \"117784\", \"124902\", \"142340\", \"145197\", \"150817\", \"186643\", \"189849\", \"210647\", \"217178\", \"233558\", \"237567\", \"241941\", \"304370\"]}","task_split":"paper_retrieval"}
{"document_id":"17070","document_content":"# Investigation on Data Adaptation Techniques for Neural Named Entity Recognition\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nData processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2021.acl-srw.1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138517\", \"146051\", \"184635\", \"196117\", \"205715\", \"216925\", \"220611\", \"223071\", \"225791\", \"231898\", \"234577\", \"241616\", \"259758\", \"297147\", \"302500\", \"302824\", \"308333\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"17212","document_content":"# CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search\n## Categories\n- Machine Learning\n- Methodology\n- Applications\n## Abstract\nPersonalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this paper, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-specified clinically meaningful mean outcome, such as the average treatment effect. We derive two equivalent theoretical forms of the optimal SSR based on the contrast function that describes the treatment-covariates interaction in the outcome. We further propose a ConstrAined PolIcy Tree seArch aLgorithm (CAPITAL) to find the optimal SSR within the interpretable decision tree class. The proposed method is flexible to handle multiple constraints that penalize the inclusion of patients with negative treatment effects, and to address time to event data using the restricted mean survival time as the clinically interesting mean outcome. Extensive simulations, comparison studies, and real data applications are conducted to demonstrate the validity and utility of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05636v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ME\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Methodology\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214660\", \"274345\"]}","task_split":"paper_retrieval"}
{"document_id":"17242","document_content":"# Spatial Data Mining of Public Transport Incidents reported in Social Media\n## Categories\n- Social and Information Networks\n- Computation and Language\n- Machine Learning\n## Abstract\nPublic transport agencies use social media as an essential tool for communicating mobility incidents to passengers. However, while the short term, day-to-day information about transport phenomena is usually posted in social media with low latency, its availability is short term as the content is rarely made an aggregated form. Social media communication of transport phenomena usually lacks GIS annotations as most social media platforms do not allow attaching non-POI GPS coordinates to posts. As a result, the analysis of transport phenomena information is minimal. We collected three years of social media posts of a polish public transport company with user comments. Through exploration, we infer a six-class transport information typology. We successfully build an information type classifier for social media posts, detect stop names in posts, and relate them to GPS coordinates, obtaining a spatial understanding of long-term aggregated phenomena. We show that our approach enables citizen science and use it to analyze the impact of three years of infrastructure incidents on passenger mobility, and the sentiment and reaction scale towards each of the events. All these results are achieved for Polish, an under-resourced language when it comes to spatial language understanding, especially in social media contexts. To improve the situation, we released two of our annotated data sets: social media posts with incident type labels and matched stop names and social media comments with the annotated sentiment. We also opensource the experimental codebase.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05573v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"288921\"]}","task_split":"paper_retrieval"}
{"document_id":"17388","document_content":"# Learning a subspace of policies for online adaptation in Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) on which a policy will run. It can be caused by different internal factors (e.g., calibration issues, system attrition, defective modules) or also by external changes (e.g., weather conditions). There is a need to develop RL methods that generalize well to variations of the training conditions. In this article, we consider the simplest yet hard to tackle generalization setting where the test environment is unknown at train time, forcing the agent to adapt to the system's new dynamics. This online adaptation process can be computationally expensive (e.g., fine-tuning) and cannot rely on meta-RL techniques since there is just a single train environment. To do so, we propose an approach where we learn a subspace of policies within the parameter space. This subspace contains an infinite number of policies that are trained to solve the training environment while having different parameter values. As a consequence, two policies in that subspace process information differently and exhibit different behaviors when facing variations of the train environment. Our experiments carried out over a large variety of benchmarks compare our approach with baselines, including diversity-based methods. In comparison, our approach is simple to tune, does not need any extra component (e.g., discriminator) and learns policies able to gather a high reward on unseen environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05169v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16157\", \"39671\", \"66880\", \"67958\", \"74331\", \"90890\", \"95506\", \"107437\", \"107746\", \"126647\", \"180731\", \"182100\", \"186101\", \"212511\", \"240665\", \"241900\", \"243235\", \"248225\", \"253045\", \"258580\", \"261316\", \"280870\", \"311595\"]}","task_split":"paper_retrieval"}
{"document_id":"17477","document_content":"# Pre-trained Language Models in Biomedical Domain: A Systematic Survey\n## Categories\n- Computation and Language\n## Abstract\nPre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It expects a survey that not only systematically reviews recent advances of biomedical PLMs and their applications but also standardizes terminology and benchmarks. In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks. Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed. At last, we illustrate various limitations and future trends, which we hope can provide inspiration for the future research of the research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05006v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"677\", \"26335\"], \"outgoing_citations\": [\"2951\", \"4987\", \"8625\", \"26335\", \"29241\", \"29312\", \"30272\", \"32774\", \"35280\", \"36993\", \"40395\", \"41478\", \"41905\", \"41963\", \"42313\", \"42396\", \"46749\", \"47624\", \"47670\", \"49873\", \"53003\", \"54540\", \"54796\", \"55085\", \"55453\", \"55715\", \"55815\", \"56081\", \"56297\", \"56539\", \"59708\", \"64046\", \"64576\", \"65129\", \"68923\", \"69906\", \"72781\", \"75106\", \"77597\", \"78795\", \"79013\", \"83697\", \"87828\", \"88816\", \"89948\", \"92814\", \"94756\", \"94785\", \"94884\", \"95299\", \"95827\", \"96019\", \"97258\", \"98293\", \"98352\", \"98421\", \"99717\", \"100684\", \"102549\", \"102563\", \"104050\", \"104221\", \"108689\", \"109222\", \"109314\", \"112516\", \"114998\", \"115509\", \"115797\", \"115939\", \"118072\", \"118229\", \"118442\", \"120172\", \"121364\", \"122414\", \"123534\", \"124916\", \"125047\", \"125241\", \"125694\", \"126216\", \"127141\", \"127526\", \"127753\", \"127922\", \"128087\", \"128167\", \"129372\", \"129503\", \"129596\", \"131380\", \"131817\", \"132243\", \"132412\", \"132507\", \"133524\", \"136153\", \"136564\", \"137227\", \"137937\", \"139404\", \"141361\", \"145761\", \"149374\", \"149451\", \"150121\", \"152565\", \"157249\", \"157706\", \"158405\", \"158493\", \"164664\", \"164927\", \"165441\", \"165848\", \"166218\", \"167028\", \"168008\", \"168231\", \"168951\", \"169330\", \"169511\", \"170852\", \"170926\", \"171936\", \"171975\", \"172370\", \"174985\", \"179679\", \"179955\", \"179992\", \"180492\", \"180997\", \"182316\", \"183050\", \"186386\", \"191145\", \"191942\", \"192402\", \"193725\", \"196679\", \"197910\", \"197921\", \"201023\", \"202208\", \"209220\", \"213077\", \"213418\", \"219328\", \"220013\", \"220797\", \"220951\", \"228459\", \"232098\", \"235463\", \"237682\", \"249726\", \"259870\", \"260469\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"17520","document_content":"# SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records\n## Categories\n- Machine Learning\n## Abstract\nContrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical risk prediction problems based on longitudinal electronic health records (EHR). We propose a general supervised contrastive loss $\\mathcal{L}_{\\text{Contrastive Cross Entropy} } + \\lambda \\mathcal{L}_{\\text{Supervised Contrastive Regularizer}}$ for learning both binary classification (e.g. in-hospital mortality prediction) and multi-label classification (e.g. phenotyping) in a unified framework. Our supervised contrastive loss practices the key idea of contrastive learning, namely, pulling similar samples closer and pushing dissimilar ones apart from each other, simultaneously by its two components: $\\mathcal{L}_{\\text{Contrastive Cross Entropy} }$ tries to contrast samples with learned anchors which represent positive and negative clusters, and $\\mathcal{L}_{\\text{Supervised Contrastive Regularizer}}$ tries to contrast samples with each other according to their supervised labels. We propose two versions of the above supervised contrastive loss and our experiments on real-world EHR data demonstrate that our proposed loss functions show benefits in improving the performance of strong baselines and even state-of-the-art models on benchmarking tasks for clinical risk predictions. Our loss functions work well with extremely imbalanced data which are common for clinical risk prediction problems. Our loss functions can be easily used to replace (binary or multi-label) cross-entropy loss adopted in existing clinical predictive models. The Pytorch code is released at \\url{https:\/\/github.com\/calvin-zcx\/SCEHR}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04943v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"97258\"], \"outgoing_citations\": [\"76032\", \"89486\", \"89969\", \"92377\", \"93090\", \"95191\", \"97063\", \"97123\", \"97258\", \"112823\", \"118617\", \"145697\", \"153944\", \"153949\", \"174985\", \"244340\", \"250829\", \"279891\", \"286253\"]}","task_split":"paper_retrieval"}
{"document_id":"17665","document_content":"# An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design\n## Categories\n- Quantitative Methods\n- J.3; I.2\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAs a promising tool to navigate in the vast chemical space, artificial intelligence (AI) is leveraged for drug design. From the year 2017 to 2021, the number of applications of several recent AI models (i.e. graph neural network (GNN), recurrent neural network (RNN), variation autoencoder (VAE), generative adversarial network (GAN), flow and reinforcement learning (RL)) in drug design increases significantly. Many relevant literature reviews exist. However, none of them provides an in-depth summary of many applications of the recent AI models in drug design. To complement the existing literature, this survey includes the theoretical development of the previously mentioned AI models and detailed summaries of 42 recent applications of AI in drug design. Concretely, 13 of them leverage GNN for molecular property prediction and 29 of them use RL and\/or deep generative models for molecule generation and optimization. In most cases, the focus of the summary is the models, their variants, and modifications for specific tasks in drug design. Moreover, 60 additional applications of AI in molecule generation and optimization are briefly summarized in a table. Finally, this survey provides a holistic discussion of the abundant applications so that the tasks, potential solutions, and challenges in AI-based drug design become evident.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.05478v1\", \"primary_category\": \"q-bio.QM\", \"categories\": [\"J.3; I.2\", \"q-bio.QM\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Quantitative Methods\", \"categories_human_readable\": [\"J.3; I.2\", \"Quantitative Methods\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"13043\"], \"outgoing_citations\": [\"36300\", \"43280\", \"43922\", \"43991\", \"64403\", \"64915\", \"70629\", \"71676\", \"72111\", \"72345\", \"76245\", \"79842\", \"81711\", \"84737\", \"85092\", \"87018\", \"89239\", \"90146\", \"91129\", \"93248\", \"93602\", \"97884\", \"100519\", \"102870\", \"104877\", \"104937\", \"112333\", \"114250\", \"114873\", \"115175\", \"116959\", \"117694\", \"117868\", \"118224\", \"120045\", \"120756\", \"123896\", \"127805\", \"127864\", \"128144\", \"130963\", \"133414\", \"134044\", \"134115\", \"134116\", \"134526\", \"137073\", \"139460\", \"141562\", \"141817\", \"142825\", \"143060\", \"143408\", \"143417\", \"144656\", \"145591\", \"152549\", \"154628\", \"156624\", \"164233\", \"164955\", \"170321\", \"173067\", \"181855\", \"182109\", \"182121\", \"183366\", \"183816\", \"184120\", \"184729\", \"189851\", \"192615\", \"193039\", \"194233\", \"195609\", \"200016\", \"205026\", \"207472\", \"208130\", \"209026\", \"213629\", \"218718\", \"218768\", \"225328\", \"225410\", \"227935\", \"228992\", \"229927\", \"230880\", \"233672\", \"239220\", \"239581\", \"241029\", \"242430\", \"242739\", \"243529\", \"248911\", \"256259\", \"258199\", \"264984\", \"266025\", \"268060\", \"268784\", \"279653\", \"280783\", \"283078\", \"290092\", \"291677\", \"296356\", \"302948\", \"305591\", \"305707\", \"312051\", \"312474\", \"322496\", \"331356\", \"341730\", \"352129\"]}","task_split":"paper_retrieval"}
{"document_id":"17667","document_content":"# Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nA major challenge in research involving artificial intelligence (AI) is the development of algorithms that can find solutions to problems that can generalize to different environments and tasks. Unlike AI, humans are adept at finding solutions that can transfer. We hypothesize this is because their solutions are informed by causal models. We propose to use human-guided causal knowledge to help robots find solutions that can generalize to a new environment. We develop and test the feasibility of a language interface that na\\\"ive participants can use to communicate these causal models to a planner. We find preliminary evidence that participants are able to use our interface and generate causal models that achieve near-generalization. We outline an experiment aimed at testing far-generalization using our interface and describe our longer terms goals for these causal models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04664v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"130231\", \"143420\", \"154335\", \"163450\", \"240495\", \"276133\"]}","task_split":"paper_retrieval"}
{"document_id":"17669","document_content":"# Cognitively Inspired Learning of Incremental Drifting Concepts\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHumans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences. In contrast, machine learning models perform poorly in a continual learning setting, where input data distribution changes over time. Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts and expand its learned knowledge to new domains incrementally in a continual learning setting. We rely on the Parallel Distributed Processing theory to encode abstract concepts in an embedding space in terms of a multimodal distribution. This embedding space is modeled by internal data representations in a hidden network layer. We also leverage the Complementary Learning Systems theory to equip the model with a memory mechanism to overcome catastrophic forgetting through implementing pseudo-rehearsal. Our model can generate pseudo-data points for experience replay and accumulate new experiences to past learned experiences without causing cross-task interference.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04662v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"98477\", \"181710\", \"194365\", \"195562\", \"213196\", \"216051\", \"248027\", \"253621\", \"262842\", \"265568\", \"269555\", \"270623\", \"279725\", \"282655\", \"303049\", \"310225\"]}","task_split":"paper_retrieval"}
{"document_id":"17727","document_content":"# Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms to allow safe and efficient operation in a dense crowd. Reinforcement learning (RL) approaches have shown superior ability in solving sequential decision making problems, and recent work has explored its potential to learn navigation polices in a socially compliant manner. However, the expert demonstration data used in existing methods is usually expensive and difficult to obtain. In this work, we consider the task of training an RL agent without employing the demonstration data, to achieve efficient and collision-free navigation in a crowded environment. To address the sparse reward navigation problem, we propose to incorporate the hindsight experience replay (HER) and curriculum learning (CL) techniques with RL to efficiently learn the optimal navigation policy in the dense crowd. The effectiveness of our method is validated in a simulated crowd-robot coexisting environment. The results demonstrate that our method can effectively learn human-aware navigation without requiring additional demonstration data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04564v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"66291\", \"216750\", \"233165\", \"233432\", \"253933\", \"261983\", \"270525\"]}","task_split":"paper_retrieval"}
{"document_id":"17820","document_content":"# Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content\n## Categories\n- Human-Computer Interaction\n- Computation and Language\n## Abstract\nNatural language descriptions sometimes accompany visualizations to better communicate and contextualize their insights, and to improve their accessibility for readers with disabilities. However, it is difficult to evaluate the usefulness of these descriptions, and how effectively they improve access to meaningful information, because we have little understanding of the semantic content they convey, and how different readers receive this content. In response, we introduce a conceptual model for the semantic content conveyed by natural language descriptions of visualizations. Developed through a grounded theory analysis of 2,147 sentences, our model spans four levels of semantic content: enumerating visualization construction properties (e.g., marks and encodings); reporting statistical concepts and relations (e.g., extrema and correlations); identifying perceptual and cognitive phenomena (e.g., complex trends and patterns); and elucidating domain-specific insights (e.g., social and political context). To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful. Together, our model and findings suggest that access to meaningful information is strongly reader-specific, and that research in automatic visualization captioning should orient toward descriptions that more richly communicate overall trends and statistics, sensitive to reader preferences. Our work further opens a space of research on natural language as a data interface coequal with visualization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TVCG.2021.3114770\/\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.CL\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"19549\", \"47101\", \"74417\", \"74514\", \"93360\", \"100553\", \"103368\", \"103370\", \"104267\", \"167372\", \"182062\", \"204665\", \"248870\", \"252885\", \"320761\", \"321682\", \"337713\"]}","task_split":"paper_retrieval"}
{"document_id":"17932","document_content":"# Medical Dead-ends and Learning to Identify High-risk States and Treatments\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMachine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning. In data-constrained offline settings, both approaches may fail as they assume fully optimal behavior or rely on exploring alternatives that may not exist. We introduce an inherently different approach that identifies possible \"dead-ends\" of a state space. We focus on the condition of patients in the intensive care unit, where a \"medical dead-end\" indicates that a patient will expire, regardless of all potential future treatment sequences. We postulate \"treatment security\" as avoiding treatments with probability proportional to their chance of leading to dead-ends, present a formal proof, and frame discovery as an RL problem. We then train three independent deep neural models for automated state construction, dead-end discovery and confirmation. Our empirical results discover that dead-ends exist in real clinical data among septic patients, and further reveal gaps between secure treatments and those that were administered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04186v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"117210\"], \"outgoing_citations\": [\"63851\", \"85341\", \"109865\", \"115825\", \"127099\", \"154254\", \"170695\", \"176738\", \"178278\", \"182535\", \"182796\", \"185773\", \"202934\", \"206875\", \"243235\", \"247075\", \"251082\", \"255154\", \"265649\", \"290744\"]}","task_split":"paper_retrieval"}
{"document_id":"17953","document_content":"# Text analysis and deep learning: A network approach\n## Categories\n- Computation and Language\n- Social and Information Networks\n- I.2.7; I.5.4; J.4\n## Abstract\nMuch information available to applied researchers is contained within written language or spoken text. Deep language models such as BERT have achieved unprecedented success in many applications of computational linguistics. However, much less is known about how these models can be used to analyze existing text. We propose a novel method that combines transformer models with network analysis to form a self-referential representation of language use within a corpus of interest. Our approach produces linguistic relations strongly consistent with the underlying model as well as mathematically well-defined operations on them, while reducing the amount of discretionary choices of representation and distance measures. It represents, to the best of our knowledge, the first unsupervised method to extract semantic networks directly from deep language models. We illustrate our approach in a semantic analysis of the term \"founder\". Using the entire corpus of Harvard Business Review from 1980 to 2020, we find that ties in our network track the semantics of discourse over time, and across contexts, identifying and relating clusters of semantic and syntactic relations. Finally, we discuss how this method can also complement and inform analyses of the behavior of deep learning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04151v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\", \"I.2.7; I.5.4; J.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\", \"I.2.7; I.5.4; J.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"57457\", \"60013\", \"63444\", \"105435\", \"109314\", \"110305\", \"127594\", \"127813\", \"128202\", \"139638\", \"145533\", \"169179\", \"174108\", \"182003\", \"182116\", \"183703\", \"184943\", \"186250\", \"190642\", \"201259\", \"209985\", \"213677\", \"218237\"]}","task_split":"paper_retrieval"}
{"document_id":"17990","document_content":"# Towards Math-Aware Automated Classification and Similarity Search of Scientific Publications: Methods of Mathematical Content Representations\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- H.3; H.4; I.2; I.7; I.1\n- Computation and Language\n- 97E40 (Primary) 00Axx, 68T50, 97-XX (Secondary)\n## Abstract\nIn this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Latent Semantic Indexing (LSI). The methods are evaluated on a subset of arXiv.org papers with the Mathematics Subject Classification (MSC) as a reference classification and using the standard precision\/recall\/F1-measure metrics. The results give insight into how different math representations may influence the performance of the classification and similarity search tasks in STEM repositories. Non-surprisingly, machine learning methods are able to grab distributional semantics from textual tokens. A proper selection of weighted tokens representing math may improve the quality of the results slightly. A structured math representation that imitates successful text-processing techniques with math is shown to yield better results than flat TeX tokens.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.04040v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\", \"H.3; H.4; I.2; I.7; I.1\", \"cs.CL\", \"97E40 (Primary) 00Axx, 68T50, 97-XX (Secondary)\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\", \"H.3; H.4; I.2; I.7; I.1\", \"Computation and Language\", \"97E40 (Primary) 00Axx, 68T50, 97-XX (Secondary)\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"18135","document_content":"# Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels\n## Categories\n- Machine Learning\n- Signal Processing\n## Abstract\nGraph neural networks (GNNs) have achieved superior performance on node classification tasks in the last few years. Commonly, this is framed in a transductive semi-supervised learning setup wherein the entire graph, including the target nodes to be labeled, is available for training. Driven in part by scalability, recent works have focused on the inductive case where only the labeled portion of a graph is available for training. In this context, our current work considers a challenging inductive setting where a set of labeled graphs are available for training while the unlabeled target graph is completely separate, i.e., there are no connections between labeled and unlabeled nodes. Under the implicit assumption that the testing and training graphs come from similar distributions, our goal is to develop a labeling function that generalizes to unobserved connectivity structures. To that end, we employ a graph neural tangent kernel (GNTK) that corresponds to infinitely wide GNNs to find correspondences between nodes in different graphs based on both the topology and the node features. We augment the capabilities of the GNTK with residual connections and empirically illustrate its performance gains on standard benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.03763v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.SP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"50499\", \"67984\", \"73530\", \"142226\", \"168996\", \"183447\", \"184400\", \"186769\", \"188780\", \"199104\", \"206470\", \"243791\", \"284644\", \"292186\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"18144","document_content":"# From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness\n## Categories\n- Machine Learning\n## Abstract\nMessage Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations (messages) from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, how-ever their expressiveness is upper-bounded by the 1st-order Weisfeiler-Lehman isomorphism test (1-WL). In response, prior works propose highly expressive models at the cost of scalability and sometimes generalization performance. Our work stands between these two regimes: we introduce a general framework to uplift any MPNN to be more expressive, with limited scalability overhead and greatly improved practical performance. We achieve this by extending local aggregation in MPNNs from star patterns to general subgraph patterns (e.g.,k-egonets):in our framework, each node representation is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only (i.e. a star). We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN. We call our proposed method GNN-AK(GNN As Kernel), as the framework resembles a convolutional neural network by replacing the kernel with GNNs. Theoretically, we show that our framework is strictly more powerful than 1&2-WL, and is not less powerful than 3-WL. We also design subgraph sampling strategies which greatly reduce memory footprint and improve speed while maintaining performance. Our method sets new state-of-the-art performance by large margins for several well-known graph ML tasks; specifically, 0.08 MAE on ZINC,74.79% and 86.887% accuracy on CIFAR10 and PATTERN respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.03753v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"16311\", \"2056\", \"7776\"], \"outgoing_citations\": [\"13814\", \"18515\", \"19609\", \"33840\", \"39957\", \"42645\", \"43338\", \"43757\", \"65206\", \"65828\", \"73530\", \"82471\", \"96209\", \"97047\", \"103085\", \"111508\", \"115600\", \"115834\", \"116959\", \"118201\", \"118766\", \"125230\", \"127525\", \"131562\", \"138999\", \"143081\", \"143456\", \"151458\", \"176236\", \"177249\", \"183719\", \"184286\", \"186624\", \"188300\", \"192622\", \"195609\", \"196221\", \"196255\", \"198364\", \"198382\", \"204871\", \"210730\", \"213646\", \"215435\", \"243382\", \"279575\", \"292407\", \"305707\"]}","task_split":"paper_retrieval"}
{"document_id":"18310","document_content":"# A Logic-Based Framework for Natural Language Inference in Dutch\n## Categories\n- Computation and Language\n- F.4.1; I.2.7\n## Abstract\nWe present a framework for deriving inference relations between Dutch sentence pairs. The proposed framework relies on logic-based reasoning to produce inspectable proofs leading up to inference labels; its judgements are therefore transparent and formally verifiable. At its core, the system is powered by two ${\\lambda}$-calculi, used as syntactic and semantic theories, respectively. Sentences are first converted to syntactic proofs and terms of the linear ${\\lambda}$-calculus using a choice of two parsers: an Alpino-based pipeline, and Neural Proof Nets. The syntactic terms are then converted to semantic terms of the simply typed ${\\lambda}$-calculus, via a set of hand designed type- and term-level transformations. Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using the lexical relations found in the Open Dutch WordNet. We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset, and achieve promising results, remaining only within a $1.1-3.2{\\%}$ performance margin to strong neural baselines. To the best of our knowledge, the reasoning pipeline is the first logic-based system for Dutch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.03323v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"F.4.1; I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"F.4.1; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"73361\", \"90366\", \"146697\", \"166494\", \"180652\", \"183339\", \"188708\", \"200362\", \"232482\", \"233045\", \"233416\", \"234664\", \"234840\", \"239831\", \"241060\", \"257324\", \"274095\"]}","task_split":"paper_retrieval"}
{"document_id":"18351","document_content":"# Multimodal Colored Point Cloud to Image Alignment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.03249v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"76184\", \"110136\", \"189509\", \"190689\", \"229003\", \"238018\", \"257363\", \"271648\", \"288753\", \"304729\", \"321655\", \"328587\"]}","task_split":"paper_retrieval"}
{"document_id":"18515","document_content":"# Equivariant Subgraph Aggregation Networks\n## Categories\n- Machine Learning\n## Abstract\nMessage-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Subgraph Aggregation Networks (ESAN) to address this issue. Our main observation is that while two graphs may not be distinguishable by an MPNN, they often contain distinguishable subgraphs. Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture. We develop novel variants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graph isomorphism, and prove lower bounds on the expressiveness of ESAN in terms of these new WL variants. We further prove that our approach increases the expressive power of both MPNNs and more expressive architectures. Moreover, we provide theoretical results that describe how design choices such as the subgraph selection policy and equivariant neural architecture affect our architecture's expressive power. To deal with the increased computational cost, we propose a subgraph sampling scheme, which can be viewed as a stochastic version of our framework. A comprehensive set of experiments on real and synthetic datasets demonstrates that our framework improves the expressive power and overall performance of popular GNN architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.02910v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"51256\", \"18144\", \"56646\", \"2056\", \"7776\", \"138999\"], \"outgoing_citations\": [\"19609\", \"33840\", \"37535\", \"39957\", \"43757\", \"56646\", \"65206\", \"65828\", \"73530\", \"82471\", \"95026\", \"96209\", \"97047\", \"110102\", \"111508\", \"111646\", \"115600\", \"115834\", \"116959\", \"118201\", \"118788\", \"127525\", \"131562\", \"137772\", \"138999\", \"141179\", \"143081\", \"143456\", \"151458\", \"163945\", \"174492\", \"176611\", \"177249\", \"183719\", \"184286\", \"185584\", \"186624\", \"192622\", \"196221\", \"196255\", \"198364\", \"204871\", \"210730\", \"213875\", \"215435\", \"228663\", \"236736\", \"242430\", \"242645\", \"245763\", \"250088\", \"279653\", \"280499\", \"283078\", \"297682\", \"303863\", \"323312\", \"336185\"]}","task_split":"paper_retrieval"}
{"document_id":"18648","document_content":"# Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nThe task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a \\emph{large} improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. Code with our metrics and adaptive margin formulation will be made public.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.02623v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70049\", \"75658\", \"76974\", \"81968\", \"96188\", \"101699\", \"111384\", \"111530\", \"112250\", \"127826\", \"131401\", \"133742\", \"136066\", \"137906\", \"152680\", \"155791\", \"165046\", \"167256\", \"168374\", \"238205\", \"248171\", \"260926\", \"265587\", \"277419\", \"281407\", \"287504\", \"312643\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"18735","document_content":"# SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFederated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data privacy. An essential but rarely studied challenge in FL is label deficiency at the edge. This problem is even more pronounced in FL compared to centralized training due to the fact that FL users are often reluctant to label their private data. Furthermore, due to the heterogeneous nature of the data at edge devices, it is crucial to develop personalized models. In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges. First, under the SSFL framework, we demonstrate that the standard FedAvg algorithm is compatible with recent breakthroughs in centralized self-supervised learning such as SimSiam networks. Moreover, to deal with data heterogeneity at the edge devices in this framework, we have innovated a series of algorithms that broaden existing supervised personalization algorithms into the setting of self-supervised learning. We further propose a novel personalized federated self-supervised learning algorithm, Per-SSFL, which balances personalization and consensus by carefully regulating the distance between the local and global representations of data. To provide a comprehensive comparative analysis of all proposed algorithms, we also develop a distributed training system and related evaluation protocol for SSFL. Our findings show that the gap of evaluation accuracy between supervised learning and unsupervised learning in FL is both small and reasonable. The performance comparison indicates the representation regularization-based personalization method is able to outperform other variants.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.02470v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"20321\", \"8081\"], \"outgoing_citations\": [\"35424\", \"59388\", \"72911\", \"89680\", \"93444\", \"104012\", \"106063\", \"109503\", \"109725\", \"112023\", \"117000\", \"118376\", \"139315\", \"141462\", \"166970\", \"187534\", \"233153\", \"268999\", \"283114\", \"290407\", \"297625\"]}","task_split":"paper_retrieval"}
{"document_id":"19090","document_content":"# HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThe prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.01660v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74251\", \"114390\", \"124998\", \"130065\", \"133418\", \"149650\", \"189323\", \"205608\", \"235857\", \"239855\", \"241584\", \"244729\", \"249439\", \"252829\"]}","task_split":"paper_retrieval"}
{"document_id":"19152","document_content":"# Distributed Learning Approaches for Automated Chest X-Ray Diagnosis\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep Learning has established in the latest years as a successful approach to address a great variety of tasks. Healthcare is one of the most promising field of application for Deep Learning approaches since it would allow to help clinicians to analyze patient data and perform diagnoses. However, despite the vast amount of data collected every year in hospitals and other clinical institutes, privacy regulations on sensitive data - such as those related to health - pose a serious challenge to the application of these methods. In this work, we focus on strategies to cope with privacy issues when a consortium of healthcare institutions needs to train machine learning models for identifying a particular disease, comparing the performances of two recent distributed learning approaches - Federated Learning and Split Learning - on the task of Automated Chest X-Ray Diagnosis. In particular, in our analysis we investigated the impact of different data distributions in client data and the possible policies on the frequency of data exchange between the institutions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.01474v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"149407\", \"202211\", \"207602\", \"214805\"]}","task_split":"paper_retrieval"}
{"document_id":"19379","document_content":"# Graph Pointer Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invariant local aggregation of neighbors to learn a representation for each node. However, they fail to generalize to heterophilic graphs, where most neighboring nodes have different labels or features, and the relevant nodes are distant. Few recent studies attempt to address this problem by combining multiple hops of hidden representations of central nodes (i.e., multi-hop-based approaches) or sorting the neighboring nodes based on attention scores (i.e., ranking-based approaches). As a result, these approaches have some apparent limitations. On the one hand, multi-hop-based approaches do not explicitly distinguish relevant nodes from a large number of multi-hop neighborhoods, leading to a severe over-smoothing problem. On the other hand, ranking-based models do not joint-optimize node ranking with end tasks and result in sub-optimal solutions. In this work, we present Graph Pointer Neural Networks (GPNN) to tackle the challenges mentioned above. We leverage a pointer network to select the most relevant nodes from a large amount of multi-hop neighborhoods, which constructs an ordered sequence according to the relationship with the central node. 1D convolution is then applied to extract high-level features from the node sequence. The pointer-network-based ranker in GPNN is joint-optimized with other parts in an end-to-end manner. Extensive experiments are conducted on six public node classification datasets with heterophilic graphs. The results show that GPNN significantly improves the classification performance of state-of-the-art methods. In addition, analyses also reveal the privilege of the proposed GPNN in filtering out irrelevant neighbors and reducing over-smoothing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.00973v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"34086\"], \"outgoing_citations\": [\"43338\", \"43981\", \"46662\", \"76825\", \"114415\", \"117308\", \"122212\", \"126868\", \"142571\", \"159315\", \"164464\", \"168172\", \"176579\", \"188300\", \"198382\", \"214311\", \"221587\", \"243791\", \"244510\", \"269202\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"19396","document_content":"# SDR: Efficient Neural Re-ranking using Succinct Document Representation\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nBERT based ranking models have achieved superior performance on various information retrieval tasks. However, the large number of parameters and complex self-attention operation come at a significant latency overhead. To remedy this, recent works propose late-interaction architectures, which allow pre-computation of intermediate document representations, thus reducing the runtime latency. Nonetheless, having solved the immediate latency issue, these methods now introduce storage costs and network fetching latency, which limits their adoption in real-life production systems.   In this work, we propose the Succinct Document Representation (SDR) scheme that computes highly compressed intermediate document representations, mitigating the storage\/network issue. Our approach first reduces the dimension of token representations by encoding them using a novel autoencoder architecture that uses the document's textual content in both the encoding and decoding phases. After this token encoding step, we further reduce the size of entire document representations using a modern quantization technique.   Extensive evaluations on passage re-reranking on the MSMARCO dataset show that compared to existing approaches using compressed document representations, our method is highly efficient, achieving 4x-11.6x better compression rates for the same ranking quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.02065v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"5648\"], \"outgoing_citations\": [\"28637\", \"48917\", \"93824\", \"94572\", \"96122\", \"96287\", \"100372\", \"114755\", \"127516\", \"128166\", \"128510\", \"128701\", \"131921\", \"139525\", \"142174\", \"144160\", \"165441\", \"168891\", \"169911\", \"203064\", \"205390\", \"242325\", \"272822\", \"279343\", \"279785\", \"281424\", \"283126\", \"317797\"]}","task_split":"paper_retrieval"}
{"document_id":"19404","document_content":"# Subtractive mountain clustering algorithm applied to a chatbot to assist elderly people in medication intake\n## Categories\n- Computation and Language\n## Abstract\nErrors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm, included in unsupervised learned models, is the chosen solution since the processing of natural languages is a necessary step in view to construct a chatbot able to answer questions that older people may pose upon themselves concerning a particular drug. In this work, the subtractive mountain clustering algorithm has been adapted to the problem of natural languages processing. This algorithm version allows for the association of a set of words into clusters. After finding the centre of every cluster -- the most relevant word, all the others are aggregated according to a defined metric adapted to the language processing realm. All the relevant stored information is processed, as well as the questions, by the algorithm. The correct processing of the text enables the chatbot to produce answers that relate to the posed queries. To validate the method, we use the package insert of a drug as the available information and formulate associated questions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.00933v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"19530","document_content":"# Asking questions on handwritten document collections\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work addresses the problem of Question Answering (QA) on handwritten document collections. Unlike typical QA and Visual Question Answering (VQA) formulations where the answer is a short text, we aim to locate a document snippet where the answer lies. The proposed approach works without recognizing the text in the documents. We argue that the recognition-free approach is suitable for handwritten documents and historical collections where robust text recognition is often difficult. At the same time, for human users, document image snippets containing answers act as a valid alternative to textual answers. The proposed approach uses an off-the-shelf deep embedding network which can project both textual words and word images into a common sub-space. This embedding bridges the textual and visual domains and helps us retrieve document snippets that potentially answer a question. We evaluate results of the proposed approach on two new datasets: (i) HW-SQuAD: a synthetic, handwritten document image counterpart of SQuAD1.0 dataset and (ii) BenthamQA: a smaller set of QA pairs defined on documents from the popular Bentham manuscripts collection. We also present a thorough analysis of the proposed recognition-free approach compared to a recognition-based approach which uses text recognized from the images using an OCR. Datasets presented in this work are available to download at docvqa.org","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10032-021-00383-3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"104973\", \"114913\", \"122798\", \"140469\", \"149004\", \"183268\", \"184301\", \"189847\", \"241203\", \"244278\", \"248620\", \"252885\", \"270020\", \"277538\", \"278894\", \"279213\", \"279343\", \"284528\", \"285811\", \"286589\", \"295459\", \"302662\", \"309239\", \"311423\", \"312506\", \"313143\", \"315108\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"19536","document_content":"# Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences\n## Categories\n- Computation and Language\n## Abstract\nSentence embedding methods offer a powerful approach for working with short textual constructs or sequences of words. By representing sentences as dense numerical vectors, many natural language processing (NLP) applications have improved their performance. However, relatively little is understood about the latent structure of sentence embeddings. Specifically, research has not addressed whether the length and structure of sentences impact the sentence embedding space and topology. This paper reports research on a set of comprehensive clustering and network analyses targeting sentence and sub-sentence embedding spaces. Results show that one method generates the most clusterable embeddings. In general, the embeddings of span sub-sentences have better clustering properties than the original sentences. The results have implications for future sentence embedding models and applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.00697v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"168219\", \"196212\", \"204715\", \"215953\", \"220400\", \"227792\", \"239686\", \"260223\", \"267161\", \"281792\", \"286594\", \"288921\"]}","task_split":"paper_retrieval"}
{"document_id":"19609","document_content":"# Reconstruction for Powerful Graph Representations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Discrete Mathematics\n## Abstract\nGraph neural networks (GNNs) have limited expressive power, failing to represent many graph classes correctly. While more expressive graph representation learning (GRL) alternatives can distinguish some of these classes, they are significantly harder to implement, may not scale well, and have not been shown to outperform well-tuned GNNs in real-world tasks. Thus, devising simple, scalable, and expressive GRL architectures that also achieve real-world improvements remains an open challenge. In this work, we show the extent to which graph reconstruction -- reconstructing a graph from its subgraphs -- can mitigate the theoretical and practical problems currently faced by GRL architectures. First, we leverage graph reconstruction to build two new classes of expressive graph representations. Secondly, we show how graph reconstruction boosts the expressive power of any GNN architecture while being a (provably) powerful inductive bias for invariances to vertex removals. Empirically, we show how reconstruction can boost GNN's expressive power -- while maintaining its invariance to permutations of the vertices -- by solving seven graph property tasks not solvable by the original GNN. Further, we demonstrate how it boosts state-of-the-art GNN's performance across nine real-world benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.00577v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.DM\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Discrete Mathematics\"], \"incoming_citations\": [\"18144\", \"18515\", \"2056\", \"7776\", \"82471\"], \"outgoing_citations\": [\"49956\", \"64367\", \"65206\", \"70398\", \"73530\", \"80611\", \"89502\", \"97047\", \"103085\", \"110102\", \"111508\", \"115834\", \"118201\", \"126374\", \"127525\", \"127744\", \"131562\", \"132809\", \"134585\", \"138212\", \"138999\", \"140399\", \"142226\", \"143081\", \"143456\", \"151458\", \"155744\", \"159292\", \"162683\", \"174492\", \"179516\", \"181255\", \"182074\", \"183719\", \"184194\", \"184286\", \"186624\", \"188300\", \"196221\", \"201448\", \"201584\", \"211417\", \"211646\", \"215435\", \"227067\", \"228663\", \"269273\", \"272626\", \"279575\", \"292407\", \"301645\", \"302934\", \"305707\", \"315215\", \"356982\"]}","task_split":"paper_retrieval"}
{"document_id":"19911","document_content":"# Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Robotics\n## Abstract\nIn the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.15207v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"18840\", \"81574\", \"93950\", \"109092\", \"125969\", \"132720\", \"132774\", \"147685\", \"158547\", \"176445\", \"177674\", \"183049\", \"183823\", \"191570\", \"192738\", \"203398\", \"208042\", \"208773\", \"224223\", \"228916\", \"249906\", \"255575\"]}","task_split":"paper_retrieval"}
{"document_id":"19914","document_content":"# Multilingual AMR Parsing with Noisy Knowledge Distillation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher. We constrain our exploration in a strict multilingual setting: there is but one model to parse all different languages including English. We identify that noisy input and precise output are the key to successful distillation. Together with extensive pre-training, we obtain an AMR parser whose performances surpass all previously published results on four different foreign languages, including German, Spanish, Italian, and Chinese, by large margins (up to 18.8 \\textsc{Smatch} points on Chinese and on average 11.3 \\textsc{Smatch} points). Our parser also achieves comparable performance on English to the latest state-of-the-art English-only parser.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.15196v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2988\"], \"outgoing_citations\": [\"43661\", \"47485\", \"71668\", \"96708\", \"100560\", \"108342\", \"131627\", \"133328\", \"135112\", \"146003\", \"157641\", \"167691\", \"168410\", \"172483\", \"185364\", \"197295\", \"198349\", \"204715\", \"215017\", \"220570\", \"227985\", \"265199\", \"267881\", \"268859\", \"273872\", \"274601\", \"278175\", \"284043\", \"295215\", \"320046\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"19993","document_content":"# Deep Neural Compression Via Concurrent Pruning and Self-Distillation\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nPruning aims to reduce the number of parameters while maintaining performance close to the original network. This work proposes a novel \\emph{self-distillation} based pruning strategy, whereby the representational similarity between the pruned and unpruned versions of the same network is maximized. Unlike previous approaches that treat distillation and pruning separately, we use distillation to inform the pruning criteria, without requiring a separate student network as in knowledge distillation. We show that the proposed {\\em cross-correlation objective for self-distilled pruning} implicitly encourages sparse solutions, naturally complementing magnitude-based pruning criteria. Experiments on the GLUE and XGLUE benchmarks show that self-distilled pruning increases mono- and cross-lingual language model performance. Self-distilled pruned models also outperform smaller Transformers with an equal number of parameters and are competitive against (6 times) larger distilled networks. We also observe that self-distillation (1) maximizes class separability, (2) increases the signal-to-noise ratio, and (3) converges faster after pruning steps, providing further insights into why self-distilled pruning improves generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.15014v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"65198\", \"69742\", \"79809\", \"94092\", \"133328\", \"142405\", \"142775\", \"170447\", \"181718\", \"186258\", \"190971\", \"191248\", \"232302\", \"243648\", \"244665\", \"254077\", \"258001\", \"262063\", \"265846\", \"275877\", \"300845\", \"311586\", \"315745\", \"355149\"]}","task_split":"paper_retrieval"}
{"document_id":"20025","document_content":"# Crowdsourcing through Cognitive Opportunistic Networks\n## Categories\n- Networking and Internet Architecture\n- Artificial Intelligence\n- Human-Computer Interaction\n- I.6.1\n## Abstract\nUntile recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However the emergence of opportunistic networking (ON) has opened up crowdsourcing to the spatial domain. In this paper we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features to the ON that allow users' mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics, i.e. mental models that describe how the human brain handle decision-making amongst complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in the cyber-world of their users and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants, using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2733379\", \"primary_category\": \"cs.NI\", \"categories\": [\"cs.NI\", \"cs.AI\", \"cs.HC\", \"I.6.1\"], \"primary_category_human_readable\": \"Networking and Internet Architecture\", \"categories_human_readable\": [\"Networking and Internet Architecture\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"I.6.1\"], \"incoming_citations\": [\"105418\", \"20016\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"20268","document_content":"# Multilingual Fact Linking\n## Categories\n- Computation and Language\n## Abstract\nKnowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representation of the fact) in the KG are often present only in a few languages. This makes it challenging to link KG facts to sentences in languages other than the limited set of languages. To address this problem, we introduce the task of Multilingual Fact Linking (MFL) where the goal is to link fact expressed in a sentence to corresponding fact in the KG, even when the fact label in the KG is not available in the language of the sentence. To facilitate research in this area, we present a new evaluation dataset, IndicLink. This dataset contains 11,293 linked WikiData facts and 6,429 sentences spanning English and six Indian languages. We propose a Retrieval+Generation model, ReFCoG, that can scale to millions of KG facts by combining Dual Encoder based retrieval with a Seq2Seq based generation model which is constrained to output only valid KG facts. ReFCoG outperforms standard Retrieval+Re-ranking models by 10.7 pts in Precision@1. In spite of this gain, the model achieves an overall score of 52.1, showing ample scope for improvement in the task.ReFCoG code and IndicLink data are available at https:\/\/github.com\/SaiKeshav\/mfl","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.14364v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2756\"], \"outgoing_citations\": [\"68336\", \"72781\", \"88910\", \"89351\", \"93238\", \"114308\", \"153825\", \"192811\"]}","task_split":"paper_retrieval"}
{"document_id":"20683","document_content":"# WarpedGANSpace: Finding non-linear RBF paths in GAN latent space\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors. In doing so, it addresses some of the limitations of the state-of-the-art works, namely, a) that they discover directions that are independent of the latent code, i.e., paths that are linear, and b) that their evaluation relies either on visual inspection or on laborious human labeling. More specifically, we propose to learn non-linear warpings on the latent space, each one parametrized by a set of RBF-based latent space warping functions, and where each warping gives rise to a family of non-linear paths via the gradient of the function. Building on the work of Voynov and Babenko, that discovers linear paths, we optimize the trainable parameters of the set of RBFs, so as that images that are generated by codes along different paths, are easily distinguishable by a discriminator network. This leads to easily distinguishable image transformations, such as pose and facial expressions in facial images. We show that linear paths can be derived as a special case of our method, and show experimentally that non-linear paths in the latent space lead to steeper, more disentangled and interpretable changes in the image space than in state-of-the art methods, both qualitatively and quantitatively. We make the code and the pretrained models publicly available at: https:\/\/github.com\/chi0tzp\/WarpedGANSpace.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.13357v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"298698\", \"7821\"], \"outgoing_citations\": [\"81560\", \"132835\", \"143164\", \"145299\", \"147421\", \"149810\", \"155122\", \"171905\", \"174537\", \"175835\", \"179226\", \"184464\", \"207443\", \"208621\", \"237427\", \"290620\"]}","task_split":"paper_retrieval"}
{"document_id":"20822","document_content":"# Text-based Person Search in Full Images via Semantic-Driven Proposal Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nFinding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12965v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"64203\", \"76369\", \"79284\", \"130118\", \"133768\", \"134019\", \"139248\", \"147460\", \"166087\", \"166809\", \"168957\", \"169858\", \"169977\", \"171857\", \"179422\", \"187888\", \"192501\", \"192662\", \"203331\", \"216895\", \"222262\", \"223678\", \"223855\", \"235021\", \"237045\", \"237094\", \"249058\", \"250405\", \"259197\", \"260620\", \"273696\", \"287800\", \"294953\", \"295099\", \"296989\"]}","task_split":"paper_retrieval"}
{"document_id":"20893","document_content":"# Distributionally Robust Multi-Output Regression Ranking\n## Categories\n- Machine Learning\n## Abstract\nDespite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introduce a new listwise LTR model called Distributionally Robust Multi-output Regression Ranking (DRMRR). Different from existing methods, the scoring function of DRMRR was designed as a multivariate mapping from a feature vector to a vector of deviation scores, which captures local context information and cross-document interactions. DRMRR uses a Distributionally Robust Optimization (DRO) framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution defined by a Wasserstein ball. We show that this is equivalent to a regularized regression problem with a matrix norm regularizer. Our experiments were conducted on two real-world applications, medical document retrieval, and drug response prediction, showing that DRMRR notably outperforms state-of-the-art LTR models. We also conducted a comprehensive analysis to assess the resilience of DRMRR against various types of noise: Gaussian noise, adversarial perturbations, and label poisoning. We show that DRMRR is not only able to achieve significantly better performance than other baselines, but it can maintain a relatively stable performance as more noise is added to the data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12803v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"119596\", \"146300\", \"152898\", \"154912\", \"252103\", \"252257\", \"258100\", \"262848\", \"281243\"]}","task_split":"paper_retrieval"}
{"document_id":"20933","document_content":"# Research on facial expression recognition based on Multimodal data fusion and neural network\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nFacial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes CNN, LNN and HNN three sub neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, benefiting by the complementarity of multimodal data, the algorithm has a great improvement in accuracy, robustness and detection speed compared with the traditional facial expression recognition algorithm. Especially in the case of partial occlusion, illumination and head posture transformation, the algorithm also shows a high confidence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12724v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"239905\", \"278399\", \"343074\"]}","task_split":"paper_retrieval"}
{"document_id":"20974","document_content":"# Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)\n## Categories\n- Social and Information Networks\n- E.0; D.4.3; F.2.2; H.2; H.1; K.4; K.6; J.4\n- Computers and Society\n- 91-XX, 68-XX\n- Databases\n- Machine Learning\n## Abstract\nThe volume of discussions concerning brands within social media provides digital marketers with great opportunities for tracking and analyzing the feelings and views of consumers toward brands, products, influencers, services, and ad campaigns in CGC. The present study aims to assess and compare the performance of firms and celebrities (i.e., influencers that with the experience of being in an ad campaign of those companies) with the automated sentiment analysis that was employed for CGC at social media while exploring the feeling of the consumers toward them to observe which influencer (of two for each company) had a closer effect with the corresponding corporation on consumer minds. For this purpose, several consumer tweets from the pages of brands and influencers were utilized to make a comparison of machine learning and lexicon-based approaches to the sentiment analysis through the Naive algorithm (lexicon-based) and Naive Bayes algorithm (machine learning method) and obtain the desired results to assess the campaigns. The findings suggested that the approaches were dissimilar in terms of accuracy; the machine learning method yielded higher accuracy. Finally, the results showed which influencer was more appropriate according to their existence in previous campaigns and helped choose the right influencer in the future for our company and have a better, more appropriate, and more efficient ad campaign subsequently. It is required to conduct further studies on the accuracy improvement of the sentiment classification. This approach should be employed for other social media CGC types. The results revealed decision-making for which sentiment analysis methods are the best approaches for the analysis of social media. It was also found that companies should be aware of their consumers' sentiments and choose the right person every time they think of a campaign.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12630v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"E.0; D.4.3; F.2.2; H.2; H.1; K.4; K.6; J.4\", \"cs.CY\", \"91-XX, 68-XX\", \"cs.SI\", \"cs.DB\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"E.0; D.4.3; F.2.2; H.2; H.1; K.4; K.6; J.4\", \"Computers and Society\", \"91-XX, 68-XX\", \"Social and Information Networks\", \"Databases\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"49162\"]}","task_split":"paper_retrieval"}
{"document_id":"21027","document_content":"# Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nReinforcement learning (RL) requires skillful definition and remarkable computational efforts to solve optimization and control problems, which could impair its prospect. Introducing human guidance into reinforcement learning is a promising way to improve learning performance. In this paper, a comprehensive human guidance-based reinforcement learning framework is established. A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm. To relieve the heavy workload on human participants, a behavior model is established based on an incremental online learning method to mimic human actions. We design two challenging autonomous driving tasks for evaluating the proposed algorithm. Experiments are conducted to access the training and testing performance and learning mechanism of the proposed algorithm. Comparative results against the state-of-the-art methods suggest the advantages of our algorithm in terms of learning efficiency, performance, and robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2022.3177685\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"113681\", \"118298\", \"168853\", \"209926\", \"210015\", \"253974\", \"254596\", \"254597\", \"260018\", \"261038\", \"267428\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"21067","document_content":"# Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nA large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When ambiguities are detected, the agent should engage in a clarifying dialog to resolve the ambiguities before committing to actions. However, asking clarifying questions for all the ambiguity occurrences could lead to asking too many questions, essentially hampering the user experience. To trigger clarifying questions only when necessary for the user satisfaction, we propose a neural self-attentive model that leverages the hypotheses with ambiguities and contextual signals. We conduct extensive experiments on five common ambiguity types using real data from a large-scale commercial conversational agent and demonstrate significant improvement over a set of baseline approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12451v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117853\", \"119644\", \"119906\", \"122197\", \"124982\", \"129610\", \"137928\", \"176028\", \"202748\", \"205501\", \"215835\", \"232341\", \"234683\", \"234684\", \"251669\"]}","task_split":"paper_retrieval"}
{"document_id":"21113","document_content":"# Local Learning at the Network Edge for Efficient & Secure Real-Time Predictive Analytics\n## Categories\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe ability to perform computation on devices, such as smartphones, cars, or other nodes present at the Internet of Things leads to constraints regarding bandwidth, storage, and energy, as most of these devices are mobile and operate on batteries. Using their computational power to perform locally machine learning and analytics tasks can enable accurate and real-time predictions at the network edge. A trained machine learning model requires high accuracy towards the prediction outcome, as wrong decisions can lead to negative consequences on the efficient conclusion of applications. Most of the data sensed in these devices are contextual and personal requiring privacy-preserving without their distribution over the network. When working with these privacy-preserving data, not only the protection is important but, also, the model needs the ability to adapt to regular occurring concept drifts and data distribution changes to guarantee a high accuracy of the prediction outcome. We address the importance of personalization and generalization in edge devices to adapt to data distribution updates over continuously evolving environments. The methodology we propose relies on the principles of Federated Learning and Optimal Stopping Theory extended with a personalization component. The privacy-efficient and quality-awareness of personalization and generalization is the overarching aim of this work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.future.2022.03.030\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.DC\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107209\", \"134225\", \"135986\", \"141462\", \"143220\", \"148382\", \"160301\", \"164627\", \"196107\", \"201741\", \"235592\", \"265645\", \"282501\", \"283045\"]}","task_split":"paper_retrieval"}
{"document_id":"21228","document_content":"# GERNERMED -- An Open German Medical NER Model\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nThe current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation. Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER). In this work, we present GERNERMED, the first open, neural NLP model for NER tasks dedicated to detect medical entity types in German text data. Here, we avoid the conflicting goals of protection of sensitive patient data from training data extraction and the publication of the statistical model weights by training our model on a custom dataset that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model. The sample code and the statistical model is available at: https:\/\/github.com\/frankkramer-lab\/GERNERMED","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.12104v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112425\", \"123341\", \"123534\", \"180997\", \"191942\", \"192811\", \"193725\", \"289319\"]}","task_split":"paper_retrieval"}
{"document_id":"21316","document_content":"# Sinkhorn Distributionally Robust Optimization\n## Categories\n- Optimization and Control\n- Machine Learning\n- Machine Learning\n## Abstract\nWe study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We provide convex programming dual reformulation for a general nominal distribution. Compared with Wasserstein DRO, it is computationally tractable for a larger class of loss functions, and its worst-case distribution is more reasonable. We propose an efficient first-order algorithm with bisection search to solve the dual reformulation. We demonstrate that our proposed algorithm finds $\\delta$-optimal solution of the new DRO formulation with computation cost $\\tilde{O}(\\delta^{-3})$ and memory cost $\\tilde{O}(\\delta^{-2})$, and the computation cost further improves to $\\tilde{O}(\\delta^{-2})$ when the loss function is smooth. Finally, we provide various numerical examples using both synthetic and real data to demonstrate its competitive performance and light computational speed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.11926v2\", \"primary_category\": \"math.OC\", \"categories\": [\"cs.LG\", \"stat.ML\", \"math.OC\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Optimization and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"31475\", \"51852\", \"53262\", \"81605\", \"88305\", \"92102\", \"104418\", \"111094\", \"113284\", \"119005\", \"119209\", \"138307\", \"159340\", \"170522\", \"172059\", \"173730\", \"184403\", \"197904\", \"204901\", \"229949\", \"238716\", \"246852\", \"247278\", \"252257\", \"254851\", \"265318\", \"265523\", \"290264\", \"291720\", \"333305\"]}","task_split":"paper_retrieval"}
{"document_id":"21382","document_content":"# Dense Contrastive Visual-Linguistic Pretraining\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nInspired by the success of BERT, several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. In particular, LXMERT and UNITER adopt visual region feature regression and label classification as pretext tasks. However, they tend to suffer from the problems of noisy labels and sparse semantic annotations, based on the visual features having been pretrained on a crowdsourced dataset with limited and inconsistent semantic labeling. To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations. Two data augmentation strategies (Mask Perturbation and Intra-\/Inter-Adversarial Perturbation) are developed to improve the quality of negative samples used in contrastive learning. Overall, DCVLP allows cross-modality dense region contrastive learning in a self-supervised setting independent of any object annotations. We compare our method against prior visual-linguistic pretraining frameworks to validate the superiority of dense contrastive learning on multimodal representation learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.11778v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"66904\"], \"outgoing_citations\": [\"86506\", \"91359\", \"107967\", \"113500\", \"119349\", \"119547\", \"131401\", \"148700\", \"164923\", \"170926\", \"172370\", \"179017\", \"180880\", \"188495\", \"193246\", \"197653\", \"201585\", \"202353\", \"206164\", \"208526\", \"211953\", \"238205\", \"278894\", \"281860\", \"287377\"]}","task_split":"paper_retrieval"}
{"document_id":"21570","document_content":"# A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification\n## Categories\n- Machine Learning\n## Abstract\nPool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2021.3135514\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96713\", \"99291\", \"121465\", \"145662\", \"146969\", \"150875\", \"179973\", \"186614\", \"201108\", \"201544\", \"207081\", \"210925\", \"239285\", \"241443\", \"248246\", \"258992\", \"259604\", \"260407\", \"269103\", \"272509\", \"282742\", \"335184\", \"350478\"]}","task_split":"paper_retrieval"}
{"document_id":"21681","document_content":"# Social-Media Activity Forecasting with Exogenous Information Signals\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nDue to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling social media activity has numerous practical implications such as supporting efforts to analyze strategic information operations, designing intervention techniques to mitigate disinformation, or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from two different platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.11024v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"118809\", \"145280\", \"169138\", \"261876\", \"288644\", \"301967\", \"358535\"]}","task_split":"paper_retrieval"}
{"document_id":"21729","document_content":"# Investigating Entropy for Extractive Document Summarization\n## Categories\n- Information Retrieval\n## Abstract\nAutomatic text summarization aims to cut down readers time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document summary generated by an algorithm, and selecting sentences that capture the essence of a document is the primary goal of extractive document summarization. In this paper, we employ Shannon entropy to capture informativeness of sentences. We employ Non-negative Matrix Factorization (NMF) to reveal probability distributions for computing entropy of terms, topics, and sentences in latent space. We present an information theoretic interpretation of the computed entropy, which is the bedrock of the proposed E-Summ algorithm, an unsupervised method for extractive document summarization. The algorithm systematically applies information theoretic principle for selecting informative sentences from important topics in the document. The proposed algorithm is generic and fast, and hence amenable to use for summarization of documents in real time. Furthermore, it is domain-, collection-independent and agnostic to the language of the document. Benefiting from strictly positive NMF factor matrices, E-Summ algorithm is transparent and explainable too. We use standard ROUGE toolkit for performance evaluation of the proposed method on four well known public data-sets. We also perform quantitative assessment of E-Summ summary quality by computing its semantic similarity w.r.t the original document. Our investigation reveals that though using NMF and information theoretic approach for document summarization promises efficient, explainable, and language independent text summarization, it needs to be bolstered to match the performance of deep neural methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.eswa.2021.115820\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78031\", \"88308\", \"93920\", \"93973\", \"95485\", \"109884\", \"127832\", \"130347\", \"141539\", \"168708\", \"169077\", \"174829\", \"181817\", \"189542\", \"213836\", \"225556\", \"231366\", \"233710\", \"241070\", \"244102\", \"259306\", \"280552\", \"280557\", \"296029\", \"311423\", \"360209\"]}","task_split":"paper_retrieval"}
{"document_id":"21737","document_content":"# Recursively Summarizing Books with Human Feedback\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nA major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction novels. Our method combines learning from human feedback with recursive task decomposition: we use models trained on smaller parts of the task to assist humans in giving feedback on the broader task. We collect a large volume of demonstrations and comparisons from human labelers, and fine-tune GPT-3 using behavioral cloning and reward modeling to do summarization recursively. At inference time, the model first summarizes small sections of the book and then recursively summarizes these summaries to produce a summary of the entire book. Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves. Our resulting model generates sensible summaries of entire books, even matching the quality of human-written summaries in a few cases ($\\sim5\\%$ of books). We achieve state-of-the-art results on the recent BookSum dataset for book-length summarization. A zero-shot question-answering model using these summaries achieves state-of-the-art results on the challenging NarrativeQA benchmark for answering questions about books and movie scripts. We release datasets of samples from our model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.10862v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15609\", \"15876\", \"15993\", \"16045\", \"25863\", \"5892\", \"11431\"], \"outgoing_citations\": [\"48976\", \"50614\", \"78200\", \"81870\", \"87747\", \"87999\", \"99754\", \"102649\", \"110925\", \"117830\", \"126362\", \"127040\", \"127513\", \"132176\", \"140689\", \"142670\", \"166082\", \"166268\", \"167137\", \"168185\", \"168744\", \"168908\", \"170657\", \"178278\", \"181713\", \"182261\", \"183462\", \"186043\", \"202748\", \"209453\", \"209926\", \"211946\", \"213652\", \"232274\", \"233342\", \"235393\", \"235463\", \"243803\", \"247060\", \"263905\", \"263957\", \"265642\", \"287823\", \"289913\", \"345105\"]}","task_split":"paper_retrieval"}
{"document_id":"21784","document_content":"# Natural Typing Recognition via Surface Electromyography\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nBy using a computer keyboard as a finger recording device, we construct the largest existing dataset for gesture recognition via surface electromyography (sEMG), and use deep learning to achieve over 90% character-level accuracy on reconstructing typed text entirely from measured muscle potentials. We prioritize the temporal structure of the EMG signal instead of the spatial structure of the electrode layout, using network architectures inspired by those used for real-time spoken language transcription. Our architecture recognizes the rapid movements of natural computer typing, which occur at irregular intervals and often overlap in time. The extensive size of our dataset also allows us to study gesture recognition after synthetically downgrading the spatial or temporal resolution, showing the system capabilities necessary for real-time gesture recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.10743v2\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.LG\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"21875","document_content":"# Cram\u00e9r-Rao bound-informed training of neural networks for quantitative MRI\n## Categories\n- Machine Learning\n- Image and Video Processing\n- Medical Physics\n- 92B20(Primary) 68T07, 92C55(Secondary)\n## Abstract\nNeural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-linear least square fitting are their superior speed and their immunity to the non-convexity of many fitting problems. We find, however, that in heterogeneous parameter spaces, i.e. in spaces in which the variance of the estimated parameters varies considerably, good performance is hard to achieve and requires arduous tweaking of the loss function, hyper parameters, and the distribution of the training data in parameter space. Here, we address these issues with a theoretically well-founded loss function: the Cram\\'er-Rao bound (CRB) provides a theoretical lower bound for the variance of an unbiased estimator and we propose to normalize the squared error with respective CRB. With this normalization, we balance the contributions of hard-to-estimate and not-so-hard-to-estimate parameters and areas in parameter space, and avoid a dominance of the former in the overall training loss. Further, the CRB-based loss function equals one for a maximally-efficient unbiased estimator, which we consider the ideal estimator. Hence, the proposed CRB-based loss function provides an absolute evaluation metric. We compare a network trained with the CRB-based loss with a network trained with the commonly used means squared error loss and demonstrate the advantages of the former in numerical, phantom, and in vivo experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1002\/mrm.29206\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\", \"physics.med-ph\", \"92B20(Primary) 68T07, 92C55(Secondary)\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Medical Physics\", \"92B20(Primary) 68T07, 92C55(Secondary)\"], \"incoming_citations\": [], \"outgoing_citations\": [\"199789\", \"205352\", \"219034\", \"224355\", \"238110\", \"251026\", \"253292\", \"262366\", \"275361\", \"336820\"]}","task_split":"paper_retrieval"}
{"document_id":"21923","document_content":"# A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Computation\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nWhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.10462v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"stat.CO\", \"cs.SI\", \"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Computation\", \"Social and Information Networks\", \"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22474\", \"121214\", \"148895\", \"166027\", \"179643\", \"188013\", \"217672\", \"217825\", \"218263\", \"234529\", \"236768\", \"244759\", \"261566\", \"262597\", \"275441\", \"278007\", \"287459\", \"299049\", \"305657\"]}","task_split":"paper_retrieval"}
{"document_id":"21959","document_content":"# Towards a Real-Time Facial Analysis System\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nFacial analysis is an active research area in computer vision, with many practical applications. Most of the existing studies focus on addressing one specific task and maximizing its performance. For a complete facial analysis system, one needs to solve these tasks efficiently to ensure a smooth experience. In this work, we present a system-level design of a real-time facial analysis system. With a collection of deep neural networks for object detection, classification, and regression, the system recognizes age, gender, facial expression, and facial similarity for each person that appears in the camera view. We investigate the parallelization and interplay of individual tasks. Results on common off-the-shelf architecture show that the system's accuracy is comparable to the state-of-the-art methods, and the recognition speed satisfies real-time requirements. Moreover, we propose a multitask network for jointly predicting the first three attributes, i.e., age, gender, and facial expression. Source code and trained models are available at https:\/\/github.com\/mahehu\/TUT-live-age-estimator.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.10393v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"194848\", \"297167\"]}","task_split":"paper_retrieval"}
{"document_id":"22157","document_content":"# Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nDespite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as system-produced abstractions used by the AI system in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. Symbols, like emotions, may well not be sine qua non for intelligence per se, but they will be crucial for AI systems to interact with us humans -- as we can neither turn off our emotions nor get by without our symbols. In particular, in many human-designed domains, humans would be interested in providing explicit (symbolic) knowledge and advice -- and expect machine explanations in kind. This alone requires AI systems to to maintain a symbolic interface for interaction with humans. In this blue sky paper, we argue this point of view, and discuss research directions that need to be pursued to allow for this type of human-AI interaction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09904v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"137688\", \"144265\", \"115937\"], \"outgoing_citations\": [\"8851\", \"17353\", \"115937\", \"144265\", \"167240\", \"186204\", \"194964\", \"199919\", \"224361\", \"251785\", \"263957\", \"275194\", \"279628\"]}","task_split":"paper_retrieval"}
{"document_id":"22227","document_content":"# Reconstructing Cosmic Polarization Rotation with ResUNet-CMB\n## Categories\n- Cosmology and Nongalactic Astrophysics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nCosmic polarization rotation, which may result from parity-violating new physics or the presence of primordial magnetic fields, converts $E$-mode polarization of the cosmic microwave background (CMB) into $B$-mode polarization. Anisotropic cosmic polarization rotation leads to statistical anisotropy in CMB polarization and can be reconstructed with quadratic estimator techniques similar to those designed for gravitational lensing of the CMB. At the sensitivity of upcoming CMB surveys, lensing-induced $B$-mode polarization will act as a limiting factor in the search for anisotropic cosmic polarization rotation, meaning that an analysis which incorporates some form of delensing will be required to improve constraints on the effect with future surveys. In this paper we extend the ResUNet-CMB convolutional neural network to reconstruct anisotropic cosmic polarization rotation in the presence of gravitational lensing and patchy reionization, and we show that the network simultaneously reconstructs all three effects with variance that is lower than that from the standard quadratic estimator nearly matching the performance of an iterative reconstruction method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1088\/1475-7516\/2022\/01\/030\", \"primary_category\": \"astro-ph.CO\", \"categories\": [\"astro-ph.CO\", \"stat.ML\", \"cs.CV\"], \"primary_category_human_readable\": \"Cosmology and Nongalactic Astrophysics\", \"categories_human_readable\": [\"Cosmology and Nongalactic Astrophysics\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"77046\", \"155437\", \"215654\", \"248978\", \"272870\", \"276386\", \"313465\", \"320858\"]}","task_split":"paper_retrieval"}
{"document_id":"22231","document_content":"# A Plug-and-Play Method for Controlled Text Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nLarge pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09707v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"4392\", \"48100\"], \"outgoing_citations\": [\"56832\", \"60000\", \"96296\", \"97214\", \"100711\", \"132799\", \"152729\", \"157244\", \"166082\", \"167338\", \"172088\", \"189504\", \"210121\", \"210155\", \"219833\", \"231949\", \"232274\", \"235175\", \"268166\", \"283971\", \"284481\", \"295863\", \"301178\", \"305200\", \"343448\"]}","task_split":"paper_retrieval"}
{"document_id":"22247","document_content":"# Acoustic Echo Cancellation using Residual U-Nets\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nThis paper presents an acoustic echo canceler based on a U-Net convolutional neural network for single-talk and double-talk scenarios. U-Net networks have previously been used in the audio processing area for source separation problems because of their ability to reproduce the finest details of audio signals, but to our knowledge, this is the first time they have been used for acoustic echo cancellation (AEC). The U-Net hyperparameters have been optimized to obtain the best AEC performance, but using a reduced number of parameters to meet a latency restriction of 40 ms. The training and testing of our model have been carried out within the framework of the 'ICASSP 2021 AEC Challenge' organized by Microsoft. We have trained the optimized U-Net model with a synthetic dataset only (S-U-Net) and with a synthetic dataset and the single-talk set of a real dataset (SR-U-Net), both datasets were released for the challenge. The S-U-Net model presented better results for double-talk scenarios, thus their inferred near-end signals from the blind testset were submitted to the challenge. Our canceler ranked 12th among 17 teams, and 5th among 10 academia teams, obtaining an overall mean opinion score of 3.57.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09686v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123402\", \"124628\", \"124876\", \"145197\", \"248978\"]}","task_split":"paper_retrieval"}
{"document_id":"22250","document_content":"# Actionable Approaches to Promote Ethical AI in Libraries\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- Digital Libraries\n## Abstract\nThe widespread use of artificial intelligence (AI) in many domains has revealed numerous ethical issues from data and design to deployment. In response, countless broad principles and guidelines for ethical AI have been published, and following those, specific approaches have been proposed for how to encourage ethical outcomes of AI. Meanwhile, library and information services too are seeing an increase in the use of AI-powered and machine learning-powered information systems, but no practical guidance currently exists for libraries to plan for, evaluate, or audit the ethics of intended or deployed AI. We therefore report on several promising approaches for promoting ethical AI that can be adapted from other contexts to AI-powered information services and in different stages of the software lifecycle.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09672v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"cs.DL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"Digital Libraries\"], \"incoming_citations\": [], \"outgoing_citations\": [\"48414\", \"148613\", \"186232\", \"188350\", \"197069\"]}","task_split":"paper_retrieval"}
{"document_id":"22298","document_content":"# Scaling TensorFlow to 300 million predictions per second\n## Categories\n- Machine Learning\n- Performance\n## Abstract\nWe present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we successfully tackled them; notably, implementing the models in TF and serving them efficiently with low latency using various optimization techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3460231.3474605\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.PF\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Performance\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271665\", \"283094\"]}","task_split":"paper_retrieval"}
{"document_id":"22323","document_content":"# A Survey of Text Games for Reinforcement Learning informed by Natural Language\n## Categories\n- Artificial Intelligence\n- I.2.0; I.2.1; I.2.7\n## Abstract\nReinforcement Learning has shown success in a number of complex virtual environments. However, many challenges still exist towards solving problems with natural language as a core component. Interactive Fiction Games (or Text Games) are one such problem type that offer a set of partially observable environments where natural language is required as part of the reinforcement learning solutions.   Therefore, this survey's aim is to assist in the development of new Text Game problem settings and solutions for Reinforcement Learning informed by natural language. Specifically, this survey summarises: 1) the challenges introduced in Text Game Reinforcement Learning problems, 2) the generation tools for evaluating Text Games and the subsequent environments generated and, 3) the agent architectures currently applied are compared to provide a systematic review of benchmark methodologies and opportunities for future researchers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09478v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"I.2.0; I.2.1; I.2.7\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.0; I.2.1; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"2495\", \"13824\", \"92228\", \"95805\", \"95814\", \"96195\", \"96449\", \"96524\", \"97289\", \"98703\", \"119028\", \"127451\", \"130056\", \"132686\", \"140959\", \"145784\", \"145805\", \"153843\", \"160575\", \"167303\", \"168729\", \"169742\", \"181620\", \"188486\", \"196049\", \"199295\", \"207273\", \"207533\", \"214421\", \"218923\", \"226321\", \"226327\", \"266342\", \"271868\", \"303349\", \"310370\"]}","task_split":"paper_retrieval"}
{"document_id":"22373","document_content":"# Augmenting the User-Item Graph with Textual Similarity Models\n## Categories\n- Computation and Language\n- I.2.7\n## Abstract\nThis paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that are added to the user-item graph. This increases the density of the graph without needing further labeled data. The data augmentation is evaluated on a variety of recommendation algorithms, using Euclidean, hyperbolic, and complex spaces, and over three categories of Amazon product reviews with differing characteristics. Results show that the data augmentation technique provides significant improvements to all types of models, with the most pronounced gains for knowledge graph-based recommenders, particularly in cold-start settings, leading to state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.09358v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7\"], \"incoming_citations\": [\"13685\"], \"outgoing_citations\": [\"43481\", \"96581\", \"123052\", \"127594\", \"139564\", \"141147\", \"159292\", \"166006\", \"169826\", \"171916\", \"181089\", \"182188\", \"182384\", \"184958\", \"185616\", \"188507\", \"189325\", \"197408\", \"197928\", \"198683\", \"200395\", \"201962\", \"214193\", \"219044\", \"219756\", \"227492\", \"228417\", \"228536\", \"229607\", \"230673\", \"232728\", \"236182\", \"238651\", \"244250\", \"260297\", \"260407\", \"261037\", \"261756\", \"263636\", \"265739\", \"269445\", \"276004\", \"279653\", \"281747\", \"289543\", \"289972\", \"310387\", \"318944\", \"320035\", \"353564\"]}","task_split":"paper_retrieval"}
{"document_id":"22549","document_content":"# Augmenting semantic lexicons using word embeddings and transfer learning\n## Categories\n- Computation and Language\n- Machine Learning\n- Social and Information Networks\n- Physics and Society\n## Abstract\nSentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/frai.2021.783778\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SI\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Social and Information Networks\", \"Physics and Society\"], \"incoming_citations\": [\"19607\"], \"outgoing_citations\": [\"39225\", \"40191\", \"45249\", \"54580\", \"107643\", \"109741\", \"112844\", \"134574\", \"203452\", \"220951\", \"225384\", \"250376\", \"260541\", \"261258\", \"267692\", \"272246\", \"274627\", \"284357\", \"288921\", \"290799\", \"301967\", \"328319\"]}","task_split":"paper_retrieval"}
{"document_id":"22598","document_content":"# A Studious Approach to Semi-Supervised Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe problem of learning from few labeled examples while using large amounts of unlabeled data has been approached by various semi-supervised methods. Although these methods can achieve superior performance, the models are often not deployable due to the large number of parameters. This paper is an ablation study of distillation in a semi-supervised setting, which not just reduces the number of parameters of the model but can achieve this while improving the performance over the baseline supervised model and making it better at generalizing. After the supervised pretraining, the network is used as a teacher model, and a student network is trained over the soft labels that the teacher model generates over the entire unlabeled data. We find that the fewer the labels, the more this approach benefits from a smaller student network. This brings forward the potential of distillation as an effective solution to enhance performance in semi-supervised computer vision tasks while maintaining deployability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.08924v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"262063\", \"281598\", \"303141\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"22686","document_content":"# Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nHigh Dynamic Range (HDR) images are the ones that contain a greater range of luminosity as compared to the standard images. HDR images have a higher detail and clarity of structure, objects, and color, which the standard images lack. HDR images are useful in capturing scenes that pose high brightness, darker areas, and shadows, etc. An HDR image comprises multiple narrow-range-exposure images combined into one high-quality image. As these HDR images cannot be displayed on standard display devices, the real challenge comes while converting these HDR images to Low dynamic range (LDR) images. The conversion of HDR image to LDR image is performed using Tone-mapped operators (TMOs). This conversion results in the loss of much valuable information in structure, color, naturalness, and exposures. The loss of information in the LDR image may not directly be visible to the human eye. To calculate how good an LDR image is after conversion, various metrics have been proposed previously. Some are not noise resilient, some work on separate color channels (Red, Green, and Blue one by one), and some lack capacity to identify the structure. To deal with this problem, we propose a metric in this paper called the Tone Mapping Quality Index (TMQI-3), which evaluates the quality of the LDR image based on its objective score. TMQI-3 is noise resilient, takes account of structure and naturalness, and works on all three color channels combined into one luminosity component. This eliminates the need to use multiple metrics at the same time. We compute results for several HDR and LDR images from the literature and show that our quality index metric performs better than the baseline models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.08774v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"15340\"], \"outgoing_citations\": [\"24221\", \"28940\", \"30884\", \"144502\", \"149310\", \"149389\", \"149394\", \"149418\", \"239855\", \"268551\"]}","task_split":"paper_retrieval"}
{"document_id":"22774","document_content":"# Slot Filling for Biomedical Information Extraction\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nInformation Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text. The task typically consists of a series of sub-tasks such as Named Entity Recognition and Relation Extraction. Sourcing entity and relation type specific training data is a major bottleneck in domains with limited resources such as biomedicine. In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings. We follow the recently proposed paradigm of coupling a Tranformer-based bi-encoder, Dense Passage Retrieval, with a Transformer-based reading comprehension model to extract relations from biomedical text. We assemble a biomedical slot filling dataset for both retrieval and reading comprehension and conduct a series of experiments demonstrating that our approach outperforms a number of simpler baselines. We also evaluate our approach end-to-end for standard as well as zero-shot settings. Our work provides a fresh perspective on how to solve biomedical IE tasks, in the absence of relevant training data. Our code, models and datasets are available at https:\/\/github.com\/ypapanik\/biomedical-slot-filling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.08564v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"26559\", \"77502\", \"102310\", \"114545\", \"123394\", \"131921\", \"143089\", \"183076\", \"216563\", \"240504\", \"263874\", \"270020\", \"272822\"]}","task_split":"paper_retrieval"}
{"document_id":"22826","document_content":"# A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis\n## Categories\n- Computation and Language\n## Abstract\nChatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia. In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information. Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared encoder. Extensive experiments on two public datasets demonstrate the effectiveness of our model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.08412v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"80638\", \"96378\", \"105764\", \"137057\", \"139785\", \"146218\", \"155877\", \"166518\", \"171071\", \"175662\", \"192175\", \"192223\", \"205147\", \"211975\", \"245399\", \"245627\", \"249229\", \"256124\", \"268841\", \"297133\"]}","task_split":"paper_retrieval"}
{"document_id":"22888","document_content":"# Formalisation of Action with Durations in Answer Set Programming\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n## Abstract\nIn this paper, I will discuss the work I am currently doing as a Ph.D. student at the University of Potsdam, under the tutoring of T. Schaub. I'm currently looking into action description in ASP. More precisely, my goal is to explore how to represent actions with durations in ASP, in different contexts. Right now, I'm focused on Multi-Agent Path Finding (MAPF), looking at how to represent speeds for different agents and contexts.   Before tackling duration, I wanted to explore and compare different representations of action taking in ASP. For this, I started comparing different simple encodings tackling the MAPF problem. Even in simple code, choices and assumptions have been made in their creations. The objective of my work is to present the consequences of those design decisions in terms of performance and knowledge representation. As far as I know, there is no current research on this topic.   Besides that, I'm also exploring different ways to represent duration and to solve related problems. I planed to compare them the same way I described before. I also want this to help me find innovative and effective ways to solve problems with duration.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.345.47\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\", \"cs.AI\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179942\", \"285598\", \"347102\"]}","task_split":"paper_retrieval"}
{"document_id":"22996","document_content":"# Context-NER : Contextual Phrase Generation at Scale\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n## Abstract\nNLP research has been focused on NER extraction and how to efficiently extract them from a sentence. However, generating relevant context of entities from a sentence has remained under-explored. In this work we introduce the task Context-NER in which relevant context of an entity has to be generated. The extracted context may not be found exactly as a substring in the sentence. We also introduce the EDGAR10-Q dataset for the same, which is a corpus of 1,500 publicly traded companies. It is a manually created complex corpus and one of the largest in terms of number of sentences and entities (1 M and 2.8 M). We introduce a baseline approach that leverages phrase generation algorithms and uses the pre-trained BERT model to get 33% ROUGE-L score. We also do a one shot evaluation with GPT-3 and get 39% score, signifying the hardness and future scope of this task. We hope that addition of this dataset and our study will pave the way for further research in this domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.08079v3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"23097\", \"25863\", \"55171\", \"186405\", \"227323\", \"263874\", \"288699\", \"300593\"]}","task_split":"paper_retrieval"}
{"document_id":"23004","document_content":"# Urdu text in natural scene images: a new dataset and preliminary text detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nText detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English\/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.7717\/peerj-cs.717\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150793\", \"256316\", \"268281\"]}","task_split":"paper_retrieval"}
{"document_id":"23169","document_content":"# A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMedical image segmentation has significantly benefitted thanks to deep learning architectures. Furthermore, semi-supervised learning (SSL) has recently been a growing trend for improving a model's overall performance by leveraging abundant unlabeled data. Moreover, learning multiple tasks within the same model further improves model generalizability. To generate smoother and accurate segmentation masks from 3D cardiac MR images, we present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justifies the effectiveness of our model for the segmentation of the left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR) images. With the incorporation of uncertainty estimates to detect failures in the segmentation masks generated by CNNs, our study further showcases the potential of our model to flag low-quality segmentation from a given model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.07702v3\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"64491\", \"101536\", \"110564\", \"135902\", \"175869\", \"203929\", \"210920\", \"217911\", \"231489\", \"241281\", \"258357\"]}","task_split":"paper_retrieval"}
{"document_id":"23232","document_content":"# RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs\n## Categories\n- Machine Learning\n## Abstract\nIn recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs. The majority of GNN architectures are designed based on developing new convolutional and\/or pooling layers that better extract the hidden and deeper representations of the graphs to be used for different prediction tasks. The inputs to these layers are mainly the three default descriptors of a graph, node features $(X)$, adjacency matrix $(A)$, and edge features $(W)$ (if available). To provide a more enriched input to the network, we propose a random walk data processing of the graphs based on three selected lengths. Namely, (regular) walks of length 1 and 2, and a fractional walk of length $\\gamma \\in (0,1)$, in order to capture the different local and global dynamics on the graphs. We also calculate the stationary distribution of each random walk, which is then used as a scaling factor for the initial node features ($X$). This way, for each graph, the network receives multiple adjacency matrices along with their individual weighting for the node features. We test our method on various molecular datasets by passing the processed node features to the network in order to perform several classification and regression tasks. Interestingly, our method, not using edge features which are heavily exploited in molecular graph learning, let a shallow network outperform well known deep GNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.07555v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"33376\", \"43338\", \"96209\", \"101873\", \"118201\", \"127525\", \"213629\", \"215435\", \"239220\", \"265828\", \"272626\", \"279653\", \"302934\", \"303863\", \"305707\"]}","task_split":"paper_retrieval"}
{"document_id":"23233","document_content":"# A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nAlthough melanoma occurs more rarely than several other skin cancers, patients' long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that allows pathology labs to sort and prioritize melanoma cases in their workflow could improve turnaround time by prioritizing challenging cases and routing them directly to the appropriate subspecialist. We present a pathology deep learning system (PDLS) that performs hierarchical classification of digitized whole slide image (WSI) specimens into six classes defined by their morphological characteristics, including classification of \"Melanocytic Suspect\" specimens likely representing melanoma or severe dysplastic nevi. We trained the system on 7,685 images from a single lab (the reference lab), including the the largest set of triple-concordant melanocytic specimens compiled to date, and tested the system on 5,099 images from two distinct validation labs. We achieved Area Underneath the ROC Curve (AUC) values of 0.93 classifying Melanocytic Suspect specimens on the reference lab, 0.95 on the first validation lab, and 0.82 on the second validation lab. We demonstrate that the PDLS is capable of automatically sorting and triaging skin specimens with high sensitivity to Melanocytic Suspect cases and that a pathologist would only need between 30% and 60% of the caseload to address all melanoma specimens.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.07554v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"130003\", \"164988\", \"198577\", \"242311\", \"273633\"]}","task_split":"paper_retrieval"}
{"document_id":"23300","document_content":"# Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks\n## Categories\n- Robotics\n## Abstract\nThe ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for autonomous cars has gained a lot of attention in recent years. It is, however, still a hard task to achieve human-level performance. Interdependencies between vehicle behaviors and the multimodal nature of future intentions in a dynamic and complex driving environment render trajectory prediction a challenging problem. In this work, we propose a new, data-driven approach for predicting the motion of vehicles in a road environment. The model allows for inferring future intentions from the past interaction among vehicles in highway driving scenarios. Using our neighborhood-based data representation, the proposed system jointly exploits correlations in the spatial and temporal domain using convolutional neural networks. Our system considers multiple possible maneuver intentions and their corresponding motion and predicts the trajectory for five seconds into the future. We implemented our approach and evaluated it on two highway datasets taken in different countries and are able to achieve a competitive prediction performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.07365v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74563\", \"78890\", \"106162\", \"134965\", \"136612\", \"149568\", \"158269\", \"179692\", \"182082\", \"187960\", \"189614\", \"191356\", \"196618\", \"196757\", \"206814\", \"214529\", \"217456\", \"219340\", \"223299\", \"229266\", \"232095\", \"232096\", \"233978\", \"236083\", \"237350\", \"241801\", \"265546\", \"268197\", \"275566\"]}","task_split":"paper_retrieval"}
{"document_id":"23461","document_content":"# Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Data Structures and Algorithms\n- Human-Computer Interaction\n## Abstract\nFluid human-agent communication is essential for the future of human-in-the-loop reinforcement learning. An agent must respond appropriately to feedback from its human trainer even before they have significant experience working together. Therefore, it is important that learning agents respond well to various feedback schemes human trainers are likely to provide. This work analyzes the COnvergent Actor-Critic by Humans (COACH) algorithm under three different types of feedback-policy feedback, reward feedback, and advantage feedback. For these three feedback types, we find that COACH can behave sub-optimally. We propose a variant of COACH, episodic COACH (E-COACH), which we prove converges for all three types. We compare our COACH variant with two other reinforcement-learning algorithms: Q-learning and TAMER.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.07054v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.DS\", \"cs.HC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Data Structures and Algorithms\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"173489\", \"179906\", \"263252\", \"272543\", \"275728\"]}","task_split":"paper_retrieval"}
{"document_id":"23543","document_content":"# Focus on Impact: Indoor Exploration with Intrinsic Motivation\n## Categories\n- Robotics\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nExploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LRA.2022.3145971\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"49968\", \"54330\", \"80273\", \"84889\", \"101182\", \"104828\", \"112104\", \"131180\", \"131814\", \"139653\", \"148172\", \"151367\", \"158547\", \"179954\", \"181532\", \"192738\", \"196393\", \"200648\", \"212299\", \"219658\", \"255575\", \"266419\", \"272422\", \"290238\", \"291025\", \"291677\"]}","task_split":"paper_retrieval"}
{"document_id":"23701","document_content":"# Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks\n## Categories\n- Computation and Language\n## Abstract\nLoading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects. However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap. After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap. To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all. Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks. Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06524v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15935\"], \"outgoing_citations\": [\"129503\", \"131150\", \"167138\", \"168621\", \"169192\", \"222981\", \"266411\", \"270018\", \"290588\", \"294494\"]}","task_split":"paper_retrieval"}
{"document_id":"23735","document_content":"# Dodging Attack Using Carefully Crafted Natural Makeup\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nDeep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e.g., airports). Previous studies have demonstrated the use of adversarial machine learning (AML) attacks to successfully evade identification by such systems, both in the digital and physical domains. Attacks in the physical domain, however, require significant manipulation to the human participant's face, which can raise suspicion by human observers (e.g. airport security officers). In this study, we present a novel black-box AML attack which carefully crafts natural makeup, which, when applied on a human participant, prevents the participant from being identified by facial recognition models. We evaluated our proposed attack against the ArcFace face recognition model, with 20 participants in a real-world setup that includes two cameras, different shooting angles, and different lighting conditions. The evaluation results show that in the digital domain, the face recognition system was unable to identify all of the participants, while in the physical domain, the face recognition system was able to identify the participants in only 1.22% of the frames (compared to 47.57% without makeup and 33.73% with random natural makeup), which is below a reasonable threshold of a realistic operational environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06467v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"8241\"], \"outgoing_citations\": [\"51088\", \"152154\", \"170530\", \"189943\", \"191481\", \"208127\", \"243986\", \"244877\", \"246146\", \"246211\", \"267940\", \"278526\", \"287611\", \"306222\", \"321122\", \"328183\", \"332936\"]}","task_split":"paper_retrieval"}
{"document_id":"23761","document_content":"# Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nScanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing often include image preprocessing, optical character recognition (OCR), and text mining. However, there is limited work that evaluates the choice of image preprocessing methods, the selection of NLP models, and the role of document layout. The impact of each element remains unknown. We evaluated this method on a use case of two key indicators for sleep apnea, Apnea hypopnea index (AHI) and oxygen saturation (SaO2) values, from scanned sleep study reports. Our data that included 955 manually annotated reports was secondarily utilized from a previous study in the University of Texas Medical Branch. We performed image preprocessing: gray-scaling followed by 1 iteration of dilating and erode, and 20% contrast increasing. The OCR was implemented with the Tesseract OCR engine. A total of seven Bag-of-Words models (Logistic Regression, Ridge Regression, Lasso Regression, Support Vector Machine, k-Nearest Neighbor, Na\\\"ive Bayes, and Random Forest) and three deep learning-based models (BiLSTM, BERT, and Clinical BERT) were evaluated. We also evaluated the combinations of image preprocessing methods (gray-scaling, dilate & erode, increased contrast by 20%, increased contrast by 60%), and two deep learning architectures (with and without structured input that provides document layout information). Our proposed method using Clinical BERT reached an AUROC of 0.9743 and document accuracy of 94.76% for AHI, and an AUROC of 0.9523, and document accuracy of 91.61% for SaO2. We demonstrated the proper use of image preprocessing and document layout could be beneficial to scanned document processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2110.11864v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62258\", \"183610\", \"191942\", \"283908\", \"321994\"]}","task_split":"paper_retrieval"}
{"document_id":"23847","document_content":"# SituatedQA: Incorporating Extra-Linguistic Contexts into QA\n## Categories\n- Computation and Language\n## Abstract\nAnswers to the same question may change depending on the extra-linguistic contexts (when and where the question was asked). To study this challenge, we introduce SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. To construct SituatedQA, we first identify such questions in existing QA datasets. We find that a significant proportion of information seeking questions have context-dependent answers (e.g., roughly 16.5% of NQ-Open). For such context-dependent questions, we then crowdsource alternative contexts and their corresponding answers. Our study shows that existing models struggle with producing answers that are frequently updated or from uncommon locations. We further quantify how existing models, which are trained on data collected in the past, fail to generalize to answering questions asked in the present, even when provided with an updated evidence corpus (a roughly 15 point drop in accuracy). Our analysis suggests that open-retrieval QA benchmarks should incorporate extra-linguistic context to stay relevant globally and in the future. Our data, code, and datasheet are available at https:\/\/situatedqa.github.io\/ .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06157v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"9612\", \"16640\", \"18368\", \"19499\", \"25862\", \"29727\"], \"outgoing_citations\": [\"29727\", \"38858\", \"45225\", \"56463\", \"62920\", \"77366\", \"77493\", \"77747\", \"91699\", \"94815\", \"123394\", \"126138\", \"126188\", \"129610\", \"131012\", \"131921\", \"137436\", \"143080\", \"143089\", \"157386\", \"168951\", \"183076\", \"196980\", \"200258\", \"220724\", \"221212\", \"238321\", \"267692\", \"270020\", \"279343\", \"281121\", \"290041\", \"357878\"]}","task_split":"paper_retrieval"}
{"document_id":"23863","document_content":"# Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nVisual question answering (VQA) is challenging not only because the model has to handle multi-modal information, but also because it is just so hard to collect sufficient training examples -- there are too many questions one can ask about an image. As a result, a VQA model trained solely on human-annotated examples could easily over-fit specific question styles or image contents that are being asked, leaving the model largely ignorant about the sheer diversity of questions. Existing methods address this issue primarily by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. In this paper, we take a drastically different approach. We found that many of the \"unknowns\" to the learned VQA model are indeed \"known\" in the dataset implicitly. For instance, questions asking about the same object in different images are likely paraphrases; the number of detected or annotated objects in an image already provides the answer to the \"how many\" question, even if the question has not been annotated for that image. Building upon these insights, we present a simple data augmentation pipeline SimpleAug to turn this \"known\" knowledge into training examples for VQA. We show that these augmented examples can notably improve the learned VQA models' performance, not only on the VQA-CP dataset with language prior shifts but also on the VQA v2 dataset without such shifts. Our method further opens up the door to leverage weakly-labeled or unlabeled images in a principled way to enhance VQA models. Our code and data are publicly available at https:\/\/github.com\/heendung\/simpleAUG.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06122v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68653\", \"75177\", \"79890\", \"80253\", \"84465\", \"92644\", \"99962\", \"111053\", \"120414\", \"124191\", \"131572\", \"136840\", \"141296\", \"146474\", \"150930\", \"167539\", \"167930\", \"170926\", \"174060\", \"179219\", \"179886\", \"180742\", \"182982\", \"184872\", \"192123\", \"193573\", \"197653\", \"198870\", \"199453\", \"207196\", \"214988\", \"228579\", \"228580\", \"237073\", \"248620\", \"255298\", \"258511\", \"258888\", \"266825\", \"268173\", \"278894\", \"289739\", \"303603\", \"320761\"]}","task_split":"paper_retrieval"}
{"document_id":"23877","document_content":"# The Grammar-Learning Trajectories of Neural Language Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult speaker. To apply a similar approach to analyze neural language models (NLM), it is first necessary to establish that different models are similar enough in the generalizations they make. In this paper, we show that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena. Taking inspiration from psycholinguistics, we argue that studying this inductive bias is an opportunity to study the linguistic representation implicit in NLMs.   Leveraging these findings, we compare the relative performance on different phenomena at varying learning stages with simpler reference models. Results suggest that NLMs exhibit consistent \"developmental\" stages. Moreover, we find the learning trajectory to be approximately one-dimensional: given an NLM with a certain overall performance, it is possible to predict what linguistic generalizations it has already acquired. Initial analysis of these stages presents phenomena clusters (notably morphological ones), whose performance progresses in unison, suggesting a potential link between the generalizations behind them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06096v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"50088\"], \"outgoing_citations\": [\"30505\", \"48640\", \"55647\", \"56330\", \"71888\", \"78009\", \"92536\", \"95299\", \"99110\", \"127392\", \"157547\", \"166757\", \"170447\", \"176600\", \"182936\", \"184451\", \"185112\", \"186130\", \"191787\", \"200362\", \"203452\", \"206200\", \"211702\", \"212036\", \"224837\", \"233722\", \"241030\", \"256948\", \"272741\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"23881","document_content":"# Towards Better Model Understanding with Path-Sufficient Explanations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFeature based local attribution methods are amongst the most prevalent in explainable artificial intelligence (XAI) literature. Going beyond standard correlation, recently, methods have been proposed that highlight what should be minimally sufficient to justify the classification of an input (viz. pertinent positives). While minimal sufficiency is an attractive property, the resulting explanations are often too sparse for a human to understand and evaluate the local behavior of the model, thus making it difficult to judge its overall quality. To overcome these limitations, we propose a novel method called Path-Sufficient Explanations Method (PSEM) that outputs a sequence of sufficient explanations for a given input of strictly decreasing size (or value) -- from original input to a minimally sufficient explanation -- which can be thought to trace the local boundary of the model in a smooth manner, thus providing better intuition about the local model behavior for the specific input. We validate these claims, both qualitatively and quantitatively, with experiments that show the benefit of PSEM across all three modalities (image, tabular and text). A user study depicts the strength of the method in communicating the local behavior, where (many) users are able to correctly determine the prediction made by a model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.06181v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148797\", \"157897\", \"161028\", \"166967\", \"206308\", \"224036\", \"228089\", \"230290\", \"234801\", \"241376\", \"251597\", \"262066\", \"290515\", \"290620\", \"321467\"]}","task_split":"paper_retrieval"}
{"document_id":"23929","document_content":"# Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language\n## Categories\n- Computers and Society\n- Computation and Language\n- A.1; J.4; G.2; K.4; I.2.7\n- Social and Information Networks\n## Abstract\nWith the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information overload problem. The aim of this survey is threefold: (1) we present the results of a network-based analysis of the existing multidisciplinary literature to support the search for relevant trends and central publications; (2) we describe the main results and necessary background to attack the problem under a computational perspective; (3) we review selected contributions using network science as a unifying framework and computational linguistics as the tool to make sense of the shared content. Despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that those interested in the area of fake news should be aware of crucial aspects of both disciplines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.cosrev.2022.100531\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CL\", \"cs.CY\", \"A.1; J.4; G.2; K.4; I.2.7\", \"cs.SI\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"A.1; J.4; G.2; K.4; I.2.7\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"14416\", \"21518\", \"27370\", \"73741\", \"82729\", \"108987\", \"116646\", \"119276\", \"132259\", \"134813\", \"137435\", \"155160\", \"161659\", \"163253\", \"163342\", \"163578\", \"169458\", \"170074\", \"187019\", \"194215\", \"196472\", \"199511\", \"210948\", \"211841\", \"215771\", \"217496\", \"220159\", \"227367\", \"230998\", \"234054\", \"241490\", \"242451\", \"245901\", \"246910\", \"249184\", \"249420\", \"253563\", \"254383\", \"257863\", \"259202\", \"260376\", \"267552\", \"270399\", \"271331\", \"271945\", \"276396\", \"282703\", \"289269\", \"290362\", \"294912\", \"299889\", \"305024\", \"310865\", \"316949\", \"321966\", \"326392\", \"326852\", \"329586\", \"338988\", \"344660\", \"354099\", \"354752\", \"362636\"]}","task_split":"paper_retrieval"}
{"document_id":"23992","document_content":"# End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs\n## Categories\n- Computation and Language\n## Abstract\nRecently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leaving the important and non-trivial task of entity resolution (ER) outside the scope of E2E learning. In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime. Our approach is fully differentiable, thanks to its reliance on a recent method for building differentiable KGs (Cohen et al., 2020). We evaluate our E2E trained model on two public datasets and show that it comes close to baseline models that use hand-annotated entities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05817v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"90377\", \"96431\", \"132413\", \"142243\", \"175775\", \"189600\", \"198373\", \"216454\", \"226991\", \"234460\", \"234554\", \"269308\", \"290747\", \"311684\"]}","task_split":"paper_retrieval"}
{"document_id":"24034","document_content":"# Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFew-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. It is therefore tricky to decide which learning strategies to use under different task conditions. Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs. The goal of our work is to search for good parameter adaptation policies that are applied to different stages in the network for few-shot classification. We present a search space that covers many popular few-shot learning algorithms in the literature and develop a differentiable searching and decoding algorithm based on meta-learning that supports gradient-based optimization. We demonstrate the effectiveness of our searching-based method on multiple benchmark datasets. Extensive experiments show that our approach significantly outperforms baselines and demonstrates performance advantages over many state-of-the-art methods. Code and models will be made publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05749v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"28790\", \"27013\"], \"outgoing_citations\": [\"30498\", \"31367\", \"57756\", \"77005\", \"78147\", \"106472\", \"112029\", \"117757\", \"125518\", \"133923\", \"134322\", \"134763\", \"134828\", \"134943\", \"135245\", \"135957\", \"136747\", \"142572\", \"150432\", \"153371\", \"153462\", \"153967\", \"154359\", \"161314\", \"165713\", \"168361\", \"184299\", \"190020\", \"191555\", \"191732\", \"193515\", \"196265\", \"206595\", \"207031\", \"213370\", \"224464\", \"228896\", \"230792\", \"244112\", \"244888\", \"262225\"]}","task_split":"paper_retrieval"}
{"document_id":"24055","document_content":"# MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations\n## Categories\n- Computation and Language\n## Abstract\nEntity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05716v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"56380\", \"64775\", \"94753\", \"97100\", \"97185\", \"102310\", \"128701\", \"147649\", \"157181\", \"165378\", \"180267\", \"182677\", \"200632\", \"233941\", \"240614\", \"267123\", \"268728\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"24117","document_content":"# RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models\n## Categories\n- Computation and Language\n## Abstract\nTo audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of attack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05620v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2767\", \"15840\"], \"outgoing_citations\": [\"127252\", \"129858\", \"130889\", \"132035\", \"132214\", \"136604\", \"147611\", \"151084\", \"156640\", \"157181\", \"174139\", \"185283\", \"220285\", \"235345\", \"260450\", \"297147\"]}","task_split":"paper_retrieval"}
{"document_id":"24144","document_content":"# CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nGraph neural networks exhibit remarkable performance in graph data analysis. However, the robustness of GNN models remains a challenge. As a result, they are not reliable enough to be deployed in critical applications. Recent studies demonstrate that GNNs could be easily fooled with adversarial perturbations, especially structural perturbations. Such vulnerability is attributed to the excessive dependence on the structure information to make predictions. To achieve better robustness, it is desirable to build the prediction of GNNs with more comprehensive features. Graph data, in most cases, has two views of information, namely structure information and feature information. In this paper, we propose CoG, a simple yet effective co-training framework to combine these two views for the purpose of robustness. CoG trains sub-models from the feature view and the structure view independently and allows them to distill knowledge from each other by adding their most confident unlabeled data into the training set. The orthogonality of these two views diversifies the sub-models, thus enhancing the robustness of their ensemble. We evaluate our framework on three popular datasets, and results show that CoG significantly improves the robustness of graph models against adversarial attacks without sacrificing their performance on clean data. We also show that CoG still achieves good robustness when both node features and graph structures are perturbed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05558v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"86002\", \"87085\", \"95175\", \"97592\", \"97696\", \"118653\", \"123835\", \"125407\", \"181525\", \"196521\", \"197997\", \"201251\", \"201616\", \"219215\", \"229027\", \"231277\", \"269129\", \"305550\"]}","task_split":"paper_retrieval"}
{"document_id":"24186","document_content":"# Stylistic Retrieval-based Dialogue System with Unparallel Training Data\n## Categories\n- Computation and Language\n## Abstract\nThe ability of a dialog system to express consistent language style during conversations has a direct, positive impact on its usability and on user satisfaction. Although previous studies have demonstrated that style transfer is feasible with a large amount of parallel data, it is often impossible to collect such data for different styles. In this paper, instead of manually constructing conversation data with a certain style, we propose a flexible framework that adapts a generic retrieval-based dialogue system to mimic the language style of a specified persona without any parallel data. Our approach is based on automatic generation of stylized data by learning the usage of jargon, and then rewriting the generic conversations to a stylized one by incorporating the jargon. In experiments we implemented dialogue systems with five distinct language styles, and the result shows our framework significantly outperforms baselines in terms of the average score of responses' relevance and style degree, and content diversity. A\/B testing on a commercial chatbot shows that users are more satisfied with our system. This study demonstrates the feasibility of building stylistic dialogue systems by simple data augmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05477v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42895\", \"91686\", \"92267\", \"94898\", \"98388\", \"113240\", \"130818\", \"132977\", \"132983\", \"164697\", \"170273\", \"170743\", \"182442\", \"184854\", \"210493\", \"221478\", \"226959\", \"230014\", \"232164\", \"232783\", \"235238\", \"244479\", \"245831\", \"250035\", \"252860\", \"259515\", \"265309\", \"269757\", \"272531\", \"276603\", \"278671\", \"296250\", \"305200\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"24258","document_content":"# Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nWe present Border-SegGCN, a novel architecture to improve semantic segmentation by refining the border outline using graph convolutional networks (GCN). The semantic segmentation network such as Unet or DeepLabV3+ is used as a base network to have pre-segmented output. This output is converted into a graphical structure and fed into the GCN to improve the border pixel prediction of the pre-segmented output. We explored and studied the factors such as border thickness, number of edges for a node, and the number of features to be fed into the GCN by performing experiments. We demonstrate the effectiveness of the Border-SegGCN on the CamVid and Carla dataset, achieving a test set performance of 81.96% without any post-processing on CamVid dataset. It is higher than the reported state of the art mIoU achieved on CamVid dataset by 0.404%","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05353v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148556\", \"152466\", \"153457\", \"188889\", \"190993\", \"191735\", \"191933\", \"197748\", \"207281\", \"208133\", \"234596\", \"234611\", \"240758\", \"248038\", \"258592\", \"268287\", \"269273\", \"278217\", \"279732\", \"294327\", \"295765\", \"305707\", \"311030\", \"315007\", \"315865\", \"316332\", \"320915\", \"320997\", \"321655\"]}","task_split":"paper_retrieval"}
{"document_id":"24295","document_content":"# Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms\n## Categories\n- Distributed, Parallel, and Cluster Computing\n- Artificial Intelligence\n- Hardware Architecture\n- Machine Learning\n## Abstract\nMachine learning algorithms have enabled computers to predict things by learning from previous data. The data storage and processing power are increasing rapidly, thus increasing machine learning and Artificial intelligence applications. Much of the work is done to improve the accuracy of the models built in the past, with little research done to determine the computational costs of machine learning acquisitions. In this paper, I will proceed with this later research work and will make a performance comparison of multi-threaded machine learning clustering algorithms. I will be working on Linear Regression, Random Forest, and K-Nearest Neighbors to determine the performance characteristics of the algorithms as well as the computation costs to the obtained results. I will be benchmarking system hardware performance by running these multi-threaded algorithms to train and test the models on a dataset to note the differences in performance matrices of the algorithms. In the end, I will state the best performing algorithms concerning the performance efficiency of these algorithms on my system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05276v1\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.AI\", \"cs.DC\", \"cs.AR\", \"cs.LG\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Distributed, Parallel, and Cluster Computing\", \"Hardware Architecture\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"164681\"]}","task_split":"paper_retrieval"}
{"document_id":"24322","document_content":"# Empirical Analysis of Training Strategies of Transformer-based Japanese Chit-chat Systems\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn recent years, several high-performance conversational systems have been proposed based on the Transformer encoder-decoder model. Although previous studies analyzed the effects of the model parameters and the decoding method on subjective dialogue evaluations with overall metrics, they did not analyze how the differences of fine-tuning datasets affect on user's detailed impression. In addition, the Transformer-based approach has only been verified for English, not for such languages with large inter-language distances as Japanese. In this study, we develop large-scale Transformer-based Japanese dialogue models and Japanese chit-chat datasets to examine the effectiveness of the Transformer-based approach for building chit-chat dialogue systems. We evaluated and analyzed the impressions of human dialogues in different fine-tuning datasets, model parameters, and the use of additional information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05217v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128396\", \"145384\", \"146003\", \"182466\", \"189504\", \"211664\", \"212046\", \"220951\", \"234895\", \"244479\", \"316293\"]}","task_split":"paper_retrieval"}
{"document_id":"24424","document_content":"# BiSECT: Learning to Split and Rephrase Sentences with Bitexts\n## Categories\n- Computation and Language\n## Abstract\nAn important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this `split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05006v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"40203\", \"92547\", \"203211\", \"233420\", \"263927\"]}","task_split":"paper_retrieval"}
{"document_id":"24425","document_content":"# Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. The biggest challenge of distantly-supervised NER is that the distant supervision may induce incomplete and noisy labels, rendering the straightforward application of supervised learning ineffective. In this paper, we propose (1) a noise-robust learning scheme comprised of a new loss function and a noisy label removal step, for training NER models on distantly-labeled data, and (2) a self-training method that uses contextualized augmentations created by pre-trained language models to improve the generalization ability of the NER model. On three benchmark datasets, our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05003v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68851\", \"92217\", \"94280\", \"115664\", \"127943\", \"128975\", \"163402\", \"182622\", \"187755\", \"188492\", \"204499\", \"218407\", \"219485\", \"219866\", \"231347\", \"232596\", \"246497\", \"261734\", \"283114\", \"291899\", \"297147\", \"297150\", \"302500\", \"302824\", \"302955\", \"308333\"]}","task_split":"paper_retrieval"}
{"document_id":"24511","document_content":"# Asking It All: Generating Contextualized Questions for any Semantic Role\n## Categories\n- Computation and Language\n## Abstract\nAsking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.04832v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96236\", \"128049\", \"128398\", \"130408\", \"157429\", \"176028\", \"191177\", \"191907\", \"232123\", \"238321\", \"241628\", \"263874\", \"267694\"]}","task_split":"paper_retrieval"}
{"document_id":"24576","document_content":"# Heterogeneous Graph Neural Networks for Keyphrase Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyphrases. To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references. Our model first retrieves some document-keyphrases pairs similar to the source document from a pre-defined index as references. Then a heterogeneous graph is constructed to capture relationships of different granularities between the source document and its references. To guide the decoding process, a hierarchical attention and copy mechanism is introduced, which directly copies appropriate words from both the source document and its references based on their relevance and significance. The experimental results on multiple KG benchmarks show that the proposed model achieves significant improvements against other baseline models, especially with regard to the absent keyphrase prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.04703v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"3368\", \"5652\"], \"outgoing_citations\": [\"47740\", \"55153\", \"80993\", \"99320\", \"128886\", \"130503\", \"131025\", \"181558\", \"185805\", \"191880\", \"214542\", \"220295\", \"220696\", \"220788\", \"268251\", \"268902\", \"286181\", \"296198\", \"303300\"]}","task_split":"paper_retrieval"}
{"document_id":"24590","document_content":"# Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering. While pretraining steps proceed, we apply curriculum learning by incrementing the augmentation degree for each difficulty step. After data augmentation is finished, contrastive learning is applied on projected embeddings of original and augmented examples. When finetuned on GLUE benchmark, our model outperforms baseline models, especially for sentence-level tasks. Additionally, this improvement is capable with only 70% of computational memory compared to the baseline model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.05941v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"63347\", \"68851\", \"77555\", \"77689\", \"89486\", \"93612\", \"95455\", \"97123\", \"97943\", \"117898\", \"119643\", \"120760\", \"128975\", \"191787\", \"197273\"]}","task_split":"paper_retrieval"}
{"document_id":"24719","document_content":"# Learning with Different Amounts of Annotation: From Zero to Many Labels\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nTraining NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufficient to learn the spectrum of language interpretation. We explore new annotation distribution schemes, assigning multiple labels per example for a small subset of training examples. Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply first train with a single label data and then fine tune with multi label examples. Extending a MixUp data augmentation framework, we propose a learning algorithm that can learn from training examples with different amount of annotation (with zero, one, or multiple labels). This algorithm efficiently combines signals from uneven training data and brings additional gains in low annotation budget and cross domain settings. Together, our method achieves consistent gains in two tasks, suggesting distributing labels unevenly among training examples can be beneficial for many NLP tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.04408v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15958\", \"14057\"], \"outgoing_citations\": [\"45238\", \"52753\", \"55234\", \"57176\", \"69615\", \"92747\", \"95930\", \"95954\", \"99110\", \"128975\", \"129610\", \"136310\", \"168232\", \"168247\", \"171616\", \"188123\", \"188492\", \"224786\", \"259094\", \"281380\", \"302824\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"24818","document_content":"# Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nDespite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging. Here, we study the performance of a deep active inference (dAIF) agent on OpenAI's car racing benchmark, where there is no access to the car's state. The agent learns to encode the world's state from high-dimensional input through unsupervised representation learning. State inference and control are learned end-to-end by optimizing the expected free energy. Results show that our model achieves comparable performance to deep Q-learning. However, vanilla dAIF does not reach state-of-the-art performance compared to other world model approaches. Hence, we discuss the current model implementation's limitations and potential architectures to overcome them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.04155v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"5459\"], \"outgoing_citations\": [\"44791\", \"50602\", \"64647\", \"97277\", \"101783\", \"120483\", \"139508\", \"149319\", \"154548\", \"177020\", \"181460\", \"188651\", \"190127\"]}","task_split":"paper_retrieval"}
{"document_id":"24853","document_content":"# User Tampering in Reinforcement Learning Recommender Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper provides novel formal methods and empirical demonstrations of a particular safety concern in reinforcement learning (RL)-based recommendation algorithms. We call this safety concern `user tampering' -- a phenomenon whereby an RL-based recommender system might manipulate a media user's opinions via its recommendations as part of a policy to increase long-term user engagement. We then apply techniques from causal modelling to analyse the leading approaches in the literature for implementing scalable RL-based recommenders, and we observe that the current approaches permit user tampering. Additionally, we review the existing mitigation strategies for reward tampering problems and show that they do not transfer well to the user tampering phenomenon found in the recommendation context. Furthermore, we provide a simulation study of a media RL-based recommendation problem constrained to the recommendation of political content. We show that a Q-learning algorithm consistently learns to exploit its opportunities to polarise simulated users with its early recommendations in order to have more consistent success with later recommendations catering to that polarisation. This latter contribution calls for urgency in designing safer RL-based recommenders; the former suggests that creating such safe recommenders will require a fundamental shift in design away from the approaches we have seen in the recent literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.04083v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"25638\"], \"outgoing_citations\": [\"33848\", \"71885\", \"75234\", \"79430\", \"99754\", \"128389\", \"140249\", \"167869\", \"171948\", \"197218\", \"212016\", \"212600\", \"233018\", \"241732\", \"351390\"]}","task_split":"paper_retrieval"}
{"document_id":"24922","document_content":"# A Formal Description of Sorani Kurdish Morphology\n## Categories\n- Computation and Language\n## Abstract\nSorani Kurdish, also known as Central Kurdish, has a complex morphology, particularly due to the patterns in which morphemes appear. Although several aspects of Kurdish morphology have been studied, such as pronominal endoclitics and Izafa constructions, Sorani Kurdish morphology has received trivial attention in computational linguistics. Moreover, some morphemes, such as the emphasis endoclitic =\\^i\\c{s}, and derivational morphemes have not been previously studied. To tackle the complex morphology of Sorani, we provide a thorough description of Sorani Kurdish morphological and morphophonological constructions in a formal way such that they can be used as finite-state transducers for morphological analysis and synthesis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03942v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23778\"], \"outgoing_citations\": [\"123659\", \"208700\", \"216256\"]}","task_split":"paper_retrieval"}
{"document_id":"24928","document_content":"# Transformers in the loop: Polarity in neural models of language\n## Categories\n- Computation and Language\n## Abstract\nRepresentation of linguistic phenomena in computational language models is typically assessed against the predictions of existing linguistic theories of these phenomena. Using the notion of polarity as a case study, we show that this is not always the most adequate set-up. We probe polarity via so-called 'negative polarity items' (in particular, English 'any') in two pre-trained Transformer-based models (BERT and GPT-2). We show that - at least for polarity - metrics derived from language models are more consistent with data from psycholinguistic experiments than linguistic theory predictions. Establishing this allows us to more adequately evaluate the performance of language models and also to use language models to discover new insights into natural language grammar beyond existing linguistic theories. This work contributes to establishing closer ties between psycholinguistic experiments and experiments with language models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03926v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23796\"], \"outgoing_citations\": [\"41709\", \"44454\", \"46654\", \"73143\", \"91501\", \"126370\", \"127202\", \"127991\", \"129558\", \"149018\", \"168415\", \"173615\", \"180652\", \"185112\", \"188708\", \"195989\", \"219586\", \"219670\", \"220137\", \"237283\", \"281225\"]}","task_split":"paper_retrieval"}
{"document_id":"24963","document_content":"# Local Augmentation for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. The key idea for GNNs is to obtain informative representation through aggregating information from local neighborhoods. However, it remains an open question whether the neighborhood information is adequately aggregated for learning representations of nodes with few neighbors. To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features. Local augmentation is a general framework that can be applied to any GNN model in a plug-and-play manner. It samples feature vectors associated with each node from the learned conditional distribution as additional input for the backbone model at each training iteration. Extensive experiments and analyses show that local augmentation consistently yields performance improvement when applied to various GNN architectures across a diverse set of benchmarks. For example, experiments show that plugging in local augmentation to GCN and GAT improves by an average of 3.4\\% and 1.6\\% in terms of test accuracy on Cora, Citeseer, and Pubmed. Besides, our experimental results on large graphs (OGB) show that our model consistently improves performance over backbones. Code is available at https:\/\/github.com\/SongtaoLiu0823\/LAGNN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03856v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"52113\"], \"outgoing_citations\": [\"13814\", \"27710\", \"41783\", \"42239\", \"78261\", \"92075\", \"114193\", \"119293\", \"119843\", \"127525\", \"129410\", \"134526\", \"141321\", \"142571\", \"148423\", \"159538\", \"163169\", \"163745\", \"164464\", \"168172\", \"168870\", \"173673\", \"174492\", \"181686\", \"185584\", \"186789\", \"187534\", \"188300\", \"194123\", \"195794\", \"196193\", \"197676\", \"197726\", \"198305\", \"203857\", \"210119\", \"214311\", \"216376\", \"228663\", \"230880\", \"242739\", \"244510\", \"250707\", \"253780\", \"267601\", \"272626\", \"279575\", \"290407\", \"292407\", \"295676\", \"297052\", \"303863\", \"305707\", \"316095\", \"336185\"]}","task_split":"paper_retrieval"}
{"document_id":"24977","document_content":"# Desiderata for Representation Learning: A Causal Perspective\n## Categories\n- Machine Learning\n- Methodology\n## Abstract\nRepresentation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious, efficient, or disentangled. It can be challenging, however, to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data. In this paper, we take a causal perspective on representation learning, formalizing non-spuriousness and efficiency (in supervised representation learning) and disentanglement (in unsupervised representation learning) using counterfactual quantities and observable consequences of causal assertions. This yields computable metrics that can be used to assess the degree to which representations satisfy the desiderata of interest and learn non-spurious and disentangled representations from single observational datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03795v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"stat.ME\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Methodology\", \"Machine Learning\"], \"incoming_citations\": [\"6554\"], \"outgoing_citations\": [\"46027\", \"53092\", \"60282\", \"61475\", \"68818\", \"79725\", \"84901\", \"87968\", \"88624\", \"93956\", \"94275\", \"96402\", \"137448\", \"143581\", \"146281\", \"149182\", \"159447\", \"160471\", \"176649\", \"177371\", \"183649\", \"187863\", \"195912\", \"199352\", \"207627\", \"208041\", \"212169\", \"218829\", \"230776\", \"231181\", \"231706\", \"241933\", \"248417\", \"251597\", \"264549\", \"265526\", \"265877\", \"266865\", \"272687\", \"301465\", \"320760\", \"350165\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"24983","document_content":"# A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data\n## Categories\n- Machine Learning\n## Abstract\nMachine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a result, researchers started to focus on reducing data annotation and labeling costs. Transfer learning was designed and widely used as an efficient approach that can reasonably reduce the negative impact of limited data, which in turn, reduces the data preparation cost. Even transferring previous knowledge from a source domain reduces the amount of data needed in a target domain. However, large amounts of annotated data are still demanded to build robust models and improve the prediction accuracy of the model. Therefore, researchers started to pay more attention on auto annotation and labeling. In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03784v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"104641\", \"112405\", \"119989\", \"156893\", \"185589\", \"200464\", \"209140\", \"230854\", \"236368\", \"255918\", \"285677\", \"296421\"]}","task_split":"paper_retrieval"}
{"document_id":"24991","document_content":"# FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models\n## Categories\n- Machine Learning\n## Abstract\nFederated learning enables multiple distributed devices to collaboratively learn a shared prediction model without centralizing their on-device data. Most of the current algorithms require comparable individual efforts for local training with the same structure and size of on-device models, which, however, impedes participation from resource-constrained devices. Given the widespread yet heterogeneous devices nowadays, in this paper, we propose an innovative federated learning framework with heterogeneous on-device models through Zero-shot Knowledge Transfer, named by FedZKT. Specifically, FedZKT allows devices to independently determine the on-device models upon their local resources. To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator. Moreover, this compute-intensive distillation task is assigned to the server to allow the participation of resource-constrained devices, where a generator is adversarially learned with the ensemble of collected on-device models. The distilled central knowledge is then sent back in the form of the corresponding on-device model parameters, which can be easily absorbed on the device side. Extensive experimental studies demonstrate the effectiveness and robustness of FedZKT towards on-device knowledge agnostic, on-device model heterogeneity, and other challenging federated learning scenarios, such as heterogeneous on-device data and straggler effects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03775v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62355\", \"71546\", \"71707\", \"73140\", \"83890\", \"89048\", \"96999\", \"101146\", \"106063\", \"109147\", \"110742\", \"112023\", \"119091\", \"126180\", \"149696\", \"149786\", \"162752\", \"162883\", \"163574\", \"170131\", \"177601\", \"184968\", \"197068\", \"199085\", \"205910\", \"207559\", \"207602\", \"208297\", \"229563\", \"234581\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"25034","document_content":"# Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach\n## Categories\n- Computation and Language\n## Abstract\nIn the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences. In this paper, we propose to follow a completely different approach and present a multi-task DA approach in which we generate new sentence pairs with transformations, such as reversing the order of the target sentence, which produce unfluent target sentences. During training, these augmented sentences are used as auxiliary tasks in a multi-task framework with the aim of providing new contexts where the target prefix is not informative enough to predict the next word. This strengthens the encoder and forces the decoder to pay more attention to the source representations of the encoder. Experiments carried out on six low-resource translation tasks show consistent improvements over the baseline and over DA methods aiming at extending the support of the empirical data distribution. The systems trained with our approach rely more on the source tokens, are more robust against domain shift and suffer less hallucinations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03645v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51130\", \"56205\", \"86257\", \"92542\", \"92589\", \"97943\", \"126380\", \"157473\", \"180654\", \"184635\", \"192811\", \"219455\", \"220017\", \"220611\", \"221557\", \"229845\", \"234469\", \"240486\", \"251209\", \"251816\", \"251974\", \"267601\", \"272068\", \"285557\", \"290777\", \"290778\", \"302948\", \"334984\"]}","task_split":"paper_retrieval"}
{"document_id":"25056","document_content":"# Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes\n## Categories\n- Computer Vision and Pattern Recognition\n- 68T40, 68T07\n## Abstract\nThis paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes with more than eight-bit precision, available in their RAW-format image. We are interested in making full use of such high-precision information to match extremely low-light scene images that conventional methods cannot handle. For extreme low-light scenes, even if some of their brightness information exists in the RAW format images' low bits, the standard raw image processing on cameras fails to utilize them properly. As was recently shown by Chen et al., CNNs can learn to produce images with a natural appearance from such RAW-format images. To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark). Using it, we experimentally evaluated combinations of eight image-enhancing methods and eleven image matching methods consisting of classical\/neural local descriptors and classical\/neural initial point-matching methods. The results show the advantage of using the RAW-format images and the strengths and weaknesses of the above component methods. They also imply there is room for further research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03585v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68T40, 68T07\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68T40, 68T07\"], \"incoming_citations\": [], \"outgoing_citations\": [\"134516\", \"136739\", \"138773\", \"150230\", \"153269\", \"154106\", \"156417\", \"170230\", \"170436\", \"180750\", \"182932\", \"186946\", \"187122\", \"191270\", \"192851\", \"193922\", \"194322\", \"201300\", \"230706\", \"233177\", \"246925\", \"250285\", \"259959\", \"268476\", \"269123\", \"277635\", \"279779\", \"295615\", \"299903\", \"314520\", \"321680\"]}","task_split":"paper_retrieval"}
{"document_id":"25153","document_content":"# Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nSpoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03381v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"4248\", \"12915\", \"29764\", \"49492\", \"53231\", \"10837\", \"15362\"], \"outgoing_citations\": [\"29764\", \"49492\", \"70421\", \"77689\", \"79419\", \"92518\", \"92519\", \"93464\", \"96502\", \"99018\", \"100162\", \"102044\", \"105882\", \"114658\", \"121076\", \"123080\", \"140934\", \"156537\", \"159854\", \"171604\", \"174612\", \"180880\", \"185155\", \"190231\", \"190961\", \"191898\", \"191994\", \"208631\", \"215164\", \"216686\", \"220828\", \"221750\", \"222054\", \"233553\", \"237064\", \"241146\"]}","task_split":"paper_retrieval"}
{"document_id":"25182","document_content":"# Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths\n## Categories\n- Image and Video Processing\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nMalignant melanoma is a common skin cancer that is mostly curable before metastasis -when growths spawn in organs away from the original site. Melanoma is the most dangerous type of skin cancer if left untreated due to the high risk of metastasis. This paper presents Melatect, a machine learning (ML) model embedded in an iOS app that identifies potential malignant melanoma. Melatect accurately classifies lesions as malignant or benign over 96.6% of the time with no apparent bias or overfitting. Using the Melatect app, users have the ability to take pictures of skin lesions (moles) and subsequently receive a mole classification. The Melatect app provides a convenient way to get free advice on lesions and track these lesions over time. A recursive computer image analysis algorithm and modified MLOps pipeline was developed to create a model that performs at a higher accuracy than existing models. Our training dataset included 18,400 images of benign and malignant lesions, including 18,000 from the International Skin Imaging Collaboration (ISIC) archive, as well as 400 images gathered from local dermatologists; these images were augmented using DeepAugment, an AutoML tool, to 54,054 images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03310v3\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"299339\"]}","task_split":"paper_retrieval"}
{"document_id":"25186","document_content":"# Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models\n## Categories\n- Computation and Language\n## Abstract\nAll AI models are susceptible to learning biases in data that they are trained on. For generative dialogue models, being trained on real human conversations containing unbalanced gender and race\/ethnicity references can lead to models that display learned biases, which we define here broadly as any measurable differences in the distributions of words or semantic content of conversations based on demographic groups. We measure the strength of such biases by producing artificial conversations between two copies of a dialogue model, conditioning one conversational partner to state a name commonly associated with a certain gender and\/or race\/ethnicity. We find that larger capacity models tend to exhibit more gender bias and greater stereotyping of occupations by gender. We show that several methods of tuning these dialogue models, specifically name scrambling, controlled generation, and unlikelihood training, are effective in reducing bias in conversation, including on a downstream conversational task. Name scrambling is also effective in lowering differences in token usage across conversations where partners have names associated with different genders or races\/ethnicities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03300v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"10913\"], \"outgoing_citations\": [\"40456\", \"45880\", \"55201\", \"55676\", \"56479\", \"73804\", \"91536\", \"94347\", \"94771\", \"96145\", \"97502\", \"98259\", \"98669\", \"98838\", \"99088\", \"120608\", \"122414\", \"127565\", \"128396\", \"130073\", \"130525\", \"145923\", \"157151\", \"157164\", \"158087\", \"158493\", \"161329\", \"165441\", \"168228\", \"169036\", \"172088\", \"179568\", \"180274\", \"182936\", \"184078\", \"189902\", \"191190\", \"191947\", \"192069\", \"193779\", \"195777\", \"200725\", \"201401\", \"211890\", \"218898\", \"221363\", \"222795\", \"234307\", \"235095\", \"244479\", \"259870\", \"285959\", \"287941\", \"291102\"]}","task_split":"paper_retrieval"}
{"document_id":"25266","document_content":"# Sequential Diagnosis Prediction with Transformer and Ontological Representation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission\/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient's visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository (https:\/\/github.com\/Xueping\/SETOR).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03069v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"10426\", \"34436\"], \"outgoing_citations\": [\"27798\", \"98857\", \"118587\", \"166443\", \"166725\", \"185889\", \"192577\", \"213345\", \"229111\", \"263454\", \"264024\", \"271934\", \"279891\", \"286253\", \"298095\", \"350985\"]}","task_split":"paper_retrieval"}
{"document_id":"25377","document_content":"# Symbolic Computation in Software Science: My Personal View\n## Categories\n- Symbolic Computation\n- Artificial Intelligence\n- Software Engineering\n## Abstract\nIn this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.342.1\", \"primary_category\": \"cs.SC\", \"categories\": [\"cs.SC\", \"cs.AI\", \"cs.SE\"], \"primary_category_human_readable\": \"Symbolic Computation\", \"categories_human_readable\": [\"Symbolic Computation\", \"Artificial Intelligence\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"25638","document_content":"# Recommendation Fairness: From Static to Dynamic\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDriven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.03150v3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"24853\", \"39628\", \"49876\", \"55041\", \"63108\", \"70092\", \"76185\", \"79939\", \"89833\", \"92434\", \"104835\", \"105434\", \"119741\", \"121346\", \"122174\", \"124035\", \"125479\", \"127099\", \"139406\", \"140249\", \"151253\", \"156141\", \"158740\", \"166048\", \"173695\", \"178346\", \"188289\", \"190461\", \"201714\", \"202876\", \"206855\", \"207046\", \"219697\", \"220267\", \"220343\", \"239240\", \"241493\", \"241732\", \"253974\", \"258416\", \"262500\", \"265057\", \"265540\", \"284408\"]}","task_split":"paper_retrieval"}
{"document_id":"25753","document_content":"# Estimating Categorical Counterfactuals via Deep Twin Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCounterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference -- an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01904v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"9997\", \"17181\", \"41137\", \"61475\", \"61858\", \"91789\", \"112143\", \"118373\", \"119435\", \"147244\", \"174945\", \"182335\", \"186313\", \"215882\", \"256720\", \"263409\", \"265530\", \"269298\", \"271038\", \"290547\", \"292736\", \"312361\", \"350165\"]}","task_split":"paper_retrieval"}
{"document_id":"25767","document_content":"# Moving Object Detection for Event-based Vision using k-means Clustering\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Image and Video Processing\n## Abstract\nMoving object detection is important in computer vision. Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye. These cameras have multiple advantages over conventional frame-based cameras, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc. In spite of these advantages, event-based cameras are noise-sensitive and have low resolution. Moreover, the task of moving object detection in these cameras is difficult, as event-based sensors lack useful visual features like texture and color. In this paper, we investigate the application of the k-means clustering technique in detecting moving objects in event-based data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/UPCON52273.2021.9667636\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"80223\", \"146673\", \"162832\", \"171200\", \"180645\", \"190036\", \"239208\"]}","task_split":"paper_retrieval"}
{"document_id":"25823","document_content":"# Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWith AlphaGo defeats top human players, reinforcement learning(RL) algorithms have gradually become the code-base of building stronger artificial intelligence(AI). The RL algorithm design firstly needs to adapt to the specific environment, so the designed environment guides the rapid and profound development of RL algorithms. However, the existing environments, which can be divided into real world games and customized toy environments, have obvious shortcomings. For real world games, it is designed for human entertainment, and too much difficult for most of RL researchers. For customized toy environments, there is no widely accepted unified evaluation standard for all RL algorithms. Therefore, we introduce the first virtual user-friendly environment framework for RL. In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research. Then all the mainstream state-of-the-art(SOTA) RL algorithms can be conveniently evaluated and compared. Therefore, our contributions mainly includes the following aspects: 1.single configured environment for all classification of SOTA RL algorithms; 2.combined environment of more than one classification RL algorithms; 3.the evaluation standard for all kinds of RL algorithms. With all these efforts, a possibility for breeding an AI with capability of general competency in a variety of tasks is provided, and maybe it will open up a new chapter for AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01768v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"33080\", \"61166\", \"115175\", \"128664\", \"134247\", \"142272\", \"151271\", \"160036\", \"162456\", \"163467\", \"167551\", \"181532\", \"185834\", \"186550\", \"209076\", \"210609\", \"212299\", \"218124\", \"225762\", \"228540\", \"239655\", \"240471\", \"240495\", \"243235\", \"248423\", \"252249\", \"252334\", \"260583\", \"260770\", \"261316\", \"261577\", \"262381\", \"266419\", \"269251\", \"272422\", \"275087\", \"280202\", \"280457\", \"297268\", \"302967\", \"312389\"]}","task_split":"paper_retrieval"}
{"document_id":"25825","document_content":"# Effective user intent mining with unsupervised word representation models and topic modelling\n## Categories\n- Artificial Intelligence\n## Abstract\nUnderstanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increase in text conversation between customers and agents. In this paper, we propose an approach to data mining the conversation intents behind the textual data. Using the customer service data set, we train unsupervised text representation models, and then develop an intent mapping model which would rank the predefined intents base on cosine similarity between sentences and intents. Topic-modeling techniques are used to define intents and domain experts are also involved to interpret topic modelling results. With this approach, we can get a good understanding of the user intentions behind the unlabelled customer service textual data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01765v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"25862","document_content":"# CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMost benchmark datasets targeting commonsense reasoning focus on everyday scenarios: physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al., 2019], social knowledge like bumping into someone is awkward [Sap et al., 2019], and other generic situations. However, there is a rich space of commonsense inferences anchored to knowledge about specific entities: for example, deciding the truthfulness of a claim \"Harry Potter can teach classes on how to fly on a broomstick.\" Can models learn to combine entity knowledge with commonsense reasoning in this fashion? We introduce CREAK, a testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities (Harry Potter is a wizard and is skilled at riding a broomstick) with commonsense inferences (if you're good at a skill you can teach others how to do it). Our dataset consists of 13k human-authored English claims about entities that are either true or false, in addition to a small contrast set. Crowdworkers can easily come up with these statements and human performance on the dataset is high (high 90s); we argue that models should be able to blend entity knowledge and commonsense reasoning to do well here. In our experiments, we focus on the closed-book setting and observe that a baseline model finetuned on existing fact verification benchmark struggles on CREAK. Training a model on CREAK improves accuracy by a substantial margin, but still falls short of human performance. Our benchmark provides a unique probe into natural language understanding models, testing both its ability to retrieve facts (e.g., who teaches at the University of Chicago?) and unstated commonsense knowledge (e.g., butlers do not yell at guests).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01653v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"23847\", \"37451\", \"45583\", \"55256\", \"57064\", \"58176\", \"61046\", \"76684\", \"91626\", \"99110\", \"102310\", \"127526\", \"127760\", \"129610\", \"131012\", \"131921\", \"132773\", \"143050\", \"158701\", \"164659\", \"168951\", \"170800\", \"175089\", \"184848\", \"185629\", \"189515\", \"211788\", \"221212\", \"233416\", \"236979\", \"238986\", \"239831\", \"263947\", \"267552\"]}","task_split":"paper_retrieval"}
{"document_id":"25871","document_content":"# On the Interplay between Self-Driving Cars and Public Transportation\n## Categories\n- Systems and Control\n- Physics and Society\n## Abstract\nCities worldwide struggle with overloaded transportation systems and their externalities, such as traffic congestion and emissions. The emerging autonomous transportation technology has a potential to alleviate these issues. Yet, the decisions of profit-maximizing operators running large autonomous fleets could have a negative impact on other stakeholders, e.g., by disproportionately cannibalizing public transport, and therefore could make the transportation system even less efficient and sustainable. A careful analysis of these tradeoffs requires modeling the main modes of transportation, including public transport, within a unified framework. In this paper, we propose such a framework, which allows us to study the interplay among mobility service providers, public transport authorities, and customers. Our framework combines a graph-theoretic network model for the transportation system with a game-theoretic market model in which mobility service providers are profit-maximizers, while customers select individually-optimal transportation options. We apply our framework to data for the city of Berlin, Germany, and present sensitivity analyses to study parameters that mobility service providers or municipalities can influence to steer the system. We show that depending on market conditions and policy restrictions, autonomous ride-hailing systems may complement or cannibalize a public transportation system, serving between 7% and 80% of all customers. We discuss the main factors behind differences in these outcomes as well as strategic design options available to policymakers. Among others, we show that the monopolistic and the competitive cases yield similar modal shares, but differ in the profit outcome of each mobility service provider.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01627v1\", \"primary_category\": \"eess.SY\", \"categories\": [\"physics.soc-ph\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Physics and Society\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"105203\", \"38968\", \"54444\"], \"outgoing_citations\": [\"38968\", \"54444\", \"138033\", \"166694\", \"297230\", \"330984\"]}","task_split":"paper_retrieval"}
{"document_id":"25916","document_content":"# Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMotion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges. In this paper, we study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map that indicates the earliest time when each location can be occupied by either seen and unseen vehicles. The ability to predict unseen vehicles is critical for safety in autonomous driving. To tackle this challenging task, we propose a safety-aware deep learning model with three new loss functions to predict the earliest occupancy map. Experiments on the large-scale autonomous driving nuScenes dataset show that our proposed model significantly outperforms the state-of-the-art baselines on the safety-aware motion prediction task. To the best of our knowledge, our approach is the first one that can predict the existence of unseen vehicles in most cases. Project page at {\\url{https:\/\/github.com\/xrenaa\/Safety-Aware-Motion-Prediction}}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01510v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4576\", \"17143\", \"18162\"], \"outgoing_citations\": [\"51104\", \"64437\", \"65247\", \"78899\", \"106160\", \"109433\", \"114017\", \"116072\", \"126156\", \"134965\", \"147880\", \"151320\", \"158269\", \"161193\", \"162130\", \"163318\", \"166501\", \"177460\", \"179336\", \"179692\", \"183489\", \"185667\", \"191356\", \"206814\", \"212279\", \"214313\", \"217456\", \"221071\", \"229266\", \"237350\", \"257088\", \"265546\", \"267431\", \"268891\", \"311836\"]}","task_split":"paper_retrieval"}
{"document_id":"25942","document_content":"# An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe Linked Open Data practice has led to a significant growth of structured data on the Web in the last decade. Such structured data describe real-world entities in a machine-readable way, and have created an unprecedented opportunity for research in the field of Natural Language Processing. However, there is a lack of studies on how such data can be used, for what kind of tasks, and to what extent they can be useful for these tasks. This work focuses on the e-commerce domain to explore methods of utilising such structured data to create language resources that may be used for product classification and linking. We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords. Our evaluation on an extensive set of benchmarks shows word embeddings to be the most reliable and consistent method to improve the accuracy on both tasks (with up to 6.9 percentage points in macro-average F1 on some datasets). The other two methods however, are not as useful. Our analysis shows that this could be due to a number of reasons, including the biased domain representation in the structured data and lack of vocabulary coverage. We share our datasets and discuss how our lessons learned could be taken forward to inform future research in this direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01411v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"28587\", \"102041\", \"109416\", \"159867\", \"191942\", \"193725\", \"206015\", \"230296\", \"264668\", \"290029\", \"329586\"]}","task_split":"paper_retrieval"}
{"document_id":"25947","document_content":"# CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe propose CX-ToM, short for counterfactual explanations with theory-of mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose explanation as an iterative communication process, i.e. dialog, between the machine and human user. More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user. To do this, we use Theory of Mind (ToM) which helps us in explicitly modeling human's intention, machine's mind as inferred by the human as well as human's mind as inferred by the machine. Moreover, most state-of-the-art XAI frameworks provide attention (or heat map) based explanations. In our work, we show that these attention based explanations are not sufficient for increasing human trust in the underlying CNN model. In CX-ToM, we instead use counterfactual explanations called fault-lines which we define as follows: given an input image I for which a CNN classification model M predicts class c_pred, a fault-line identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class c_alt. We argue that, due to the iterative, conceptual and counterfactual nature of CX-ToM explanations, our framework is practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, demonstrating that our CX-ToM significantly outperforms the state-of-the-art explainable AI models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01401v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"19284\", \"23820\", \"92753\"], \"outgoing_citations\": [\"23820\", \"127093\", \"130231\", \"166713\", \"177711\", \"181468\", \"190384\", \"191140\", \"195249\", \"197416\", \"203593\", \"205589\", \"206349\", \"226723\", \"226768\", \"227272\", \"234045\", \"240780\", \"241354\", \"241376\", \"241551\", \"242046\", \"243505\", \"243651\", \"251709\", \"254253\", \"263098\", \"263813\", \"263942\", \"269165\", \"269197\", \"269338\", \"272864\", \"281257\", \"281860\", \"289430\", \"290515\", \"290677\", \"293068\", \"295742\", \"301767\", \"311683\", \"316549\"]}","task_split":"paper_retrieval"}
{"document_id":"26015","document_content":"# A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection\n## Categories\n- Computation and Language\n## Abstract\nThe success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn. By treating utterances in isolation, most SLU systems often overlook the semantic context in which a dialog act is expected. The act dependency between turns is non-trivial and yet critical to the identification of the correct semantic representations. Previous works with limited context awareness have exposed the inadequacy of dealing with complexity in multiproned user intents, which are subject to spontaneous change during turn transitions. In this work, we propose to enhance SLU in multi-turn dialogs, employing a context-aware hierarchical BERT fusion Network (CaBERT-SLU) to not only discern context information within a dialog but also jointly identify multiple dialog acts and slots in each utterance. Experimental results show that our approach reaches new state-of-the-art (SOTA) performances in two complicated multi-turn dialogue datasets with considerable improvements compared with previous methods, which only consider single utterances for multiple intents and slot filling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01267v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74469\", \"99373\", \"121509\", \"129831\", \"166043\", \"167138\", \"168563\", \"178308\", \"219441\", \"222981\", \"271934\", \"291916\", \"294494\"]}","task_split":"paper_retrieval"}
{"document_id":"26094","document_content":"# Optimal subgroup selection\n## Categories\n- Statistics Theory\n- Machine Learning\n- 62-XX, 62G08, 62Gxx, 62C20\n- Methodology\n- Machine Learning\n## Abstract\nIn clinical trials and other applications, we often see regions of the feature space that appear to exhibit interesting behaviour, but it is unclear whether these observed phenomena are reflected at the population level. Focusing on a regression setting, we consider the subgroup selection challenge of identifying a region of the feature space on which the regression function exceeds a pre-determined threshold. We formulate the problem as one of constrained optimisation, where we seek a low-complexity, data-dependent selection set on which, with a guaranteed probability, the regression function is uniformly at least as large as the threshold; subject to this constraint, we would like the region to contain as much mass under the marginal feature distribution as possible. This leads to a natural notion of regret, and our main contribution is to determine the minimax optimal rate for this regret in both the sample size and the Type I error probability. The rate involves a delicate interplay between parameters that control the smoothness of the regression function, as well as exponents that quantify the extent to which the optimal selection set at the population level can be approximated by families of well-behaved subsets. Finally, we expand the scope of our previous results by illustrating how they may be generalised to a treatment and control setting, where interest lies in the heterogeneous treatment effect.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.01077v1\", \"primary_category\": \"math.ST\", \"categories\": [\"math.ST\", \"stat.ML\", \"62-XX, 62G08, 62Gxx, 62C20\", \"stat.ME\", \"cs.LG\", \"stat.TH\"], \"primary_category_human_readable\": \"Statistics Theory\", \"categories_human_readable\": [\"Statistics Theory\", \"Machine Learning\", \"62-XX, 62G08, 62Gxx, 62C20\", \"Methodology\", \"Machine Learning\", \"Statistics Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"208811\", \"273619\"]}","task_split":"paper_retrieval"}
{"document_id":"26178","document_content":"# Dialog-based Automation of Decision Making in Processes\n## Categories\n- Software Engineering\n## Abstract\nThe use of chatbots has spread, generating great interest in the industry for the possibility of automating tasks within the execution of their processes. The implementation of chatbots, however simple, is a complex endeavor that involves many low-level details, which makes it a time-consuming and error-prone task. In this paper we aim at facilitating the development of decision-support chatbots that guide users or help knowledge workers to make decisions based on interactions between different process participants, aiming at decreasing the workload of human workers, for example, in healthcare to identify the first symptoms of a disease. Our work concerns a methodology to systematically build decision-support chatbots, semi-automatically, from existing DMN models. Chatbots are designed to leverage natural language understanding platforms, such as Dialogflow or LUIS. We implemented Dialogflow chatbot prototypes based on our methodology and performed a pilot test that revealed insights into the usability and appeal of the chatbots developed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00822v1\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"26314","document_content":"# Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions\n## Categories\n- Computation and Language\n## Abstract\nSocial media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00475v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"27150\"], \"outgoing_citations\": [\"26569\", \"27150\", \"66512\", \"121866\", \"127479\", \"127649\", \"128892\", \"136110\", \"163339\", \"172517\", \"183673\", \"199945\", \"209985\", \"211869\", \"215771\", \"219084\", \"228662\", \"234708\", \"237393\", \"248126\", \"250392\", \"254797\", \"260242\", \"271009\", \"271773\", \"273041\", \"290208\", \"290798\", \"290799\", \"291574\", \"293170\", \"302653\", \"315386\", \"333045\", \"359114\"]}","task_split":"paper_retrieval"}
{"document_id":"26327","document_content":"# Planning from video game descriptions\n## Categories\n- Artificial Intelligence\n## Abstract\nThis project proposes a methodology for the automatic generation of action models from video game dynamics descriptions, as well as its integration with a planning agent for the execution and monitoring of the plans. Planners use these action models to get the deliberative behaviour for an agent in many different video games and, combined with a reactive module, solve deterministic and no-deterministic levels. Experimental results validate the methodology and prove that the effort put by a knowledge engineer can be greatly reduced in the definition of such complex domains. Furthermore, benchmarks of the domains has been produced that can be of interest to the international planning community to evaluate planners in international planning competitions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00449v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78924\", \"229002\", \"335180\"]}","task_split":"paper_retrieval"}
{"document_id":"26460","document_content":"# An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThe availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual translation corpora, alleviating the need for SS supervised corpora. Our method is motivated by the following two findings: neural machine translation model usually tends to generate more high-frequency tokens and the difference of text complexity levels exists between the source and target language of a translation corpus. By taking the pair of the source sentences of translation corpus and the translations of their references in a bridge language, we can construct large-scale pseudo parallel SS data. Then, we keep these sentence pairs with a higher complexity difference as SS sentence pairs. The building SS corpora with an unsupervised approach can satisfy the expectations that the aligned sentences preserve the same meanings and have difference in text complexity levels. Experimental results show that SS methods trained by our corpora achieve the state-of-the-art results and significantly outperform the results on English benchmark WikiLarge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00165v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95566\", \"118053\", \"123457\", \"127613\", \"146003\", \"158546\", \"163045\", \"176008\", \"176154\", \"179990\", \"190719\", \"192811\", \"197727\", \"203211\", \"212887\", \"213824\", \"214312\", \"219175\", \"229674\", \"234469\", \"250360\", \"264427\", \"270048\", \"302824\", \"308756\"]}","task_split":"paper_retrieval"}
{"document_id":"26479","document_content":"# Cognitive science as a source of forward and inverse models of human decisions for robotics and control\n## Categories\n- Artificial Intelligence\n- Robotics\n- Systems and Control\n- Systems and Control\n## Abstract\nThose designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others' decision-making. We highlight relevant recent developments, including approaches that synthesize blackbox and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00127v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"11133\"], \"outgoing_citations\": [\"39686\", \"109401\", \"118955\", \"142761\", \"161546\", \"162995\", \"185117\", \"190582\", \"199302\", \"202556\", \"220029\", \"223562\", \"227635\", \"241354\", \"251082\", \"253613\", \"260731\", \"268910\", \"275728\", \"279119\", \"286877\", \"290744\", \"301305\", \"346769\"]}","task_split":"paper_retrieval"}
{"document_id":"26492","document_content":"# Position-based Hash Embeddings For Scaling Graph Neural Networks\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nGraph Neural Networks (GNNs) bring the power of deep representation learning to graph and relational data and achieve state-of-the-art performance in many applications. GNNs compute node representations by taking into account the topology of the node's ego-network and the features of the ego-network's nodes. When the nodes do not have high-quality features, GNNs learn an embedding layer to compute node embeddings and use them as input features. However, the size of the embedding layer is linear to the product of the number of nodes in the graph and the dimensionality of the embedding and does not scale to big data and graphs with hundreds of millions of nodes. To reduce the memory associated with this embedding layer, hashing-based approaches, commonly used in applications like NLP and recommender systems, can potentially be used. However, a direct application of these ideas fails to exploit the fact that in many real-world graphs, nodes that are topologically close will tend to be related to each other (homophily) and as such their representations will be similar.   In this work, we present approaches that take advantage of the nodes' position in the graph to dramatically reduce the memory required, with minimal if any degradation in the quality of the resulting GNN model. Our approaches decompose a node's embedding into two components: a position-specific component and a node-specific component. The position-specific component models homophily and the node-specific component models the node-to-node variation. Extensive experiments using different datasets and GNN models show that our methods are able to reduce the memory requirements by 88% to 97% while achieving, in nearly all cases, better classification accuracy than other competing approaches, including the full embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00101v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"98392\", \"109141\", \"127525\", \"167272\", \"168593\", \"168870\", \"228849\", \"234587\", \"256202\", \"263892\"]}","task_split":"paper_retrieval"}
{"document_id":"26495","document_content":"# Bio-inspired robot perception coupled with robot-modeled human perception\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nMy overarching research goal is to provide robots with perceptional abilities that allow interactions with humans in a human-like manner. To develop these perceptional abilities, I believe that it is useful to study the principles of the human visual system. I use these principles to develop new computer vision algorithms and validate their effectiveness in intelligent robotic systems. I am enthusiastic about this approach as it offers the dual benefit of uncovering principles inherent in the human visual system, as well as applying these principles to its artificial counterpart. Fig. 1 contains a depiction of my research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00097v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"63782\", \"65943\", \"92419\", \"123529\", \"180645\", \"190036\", \"249423\", \"290115\", \"296886\"]}","task_split":"paper_retrieval"}
{"document_id":"26504","document_content":"# Effectiveness of Deep Networks in NLP using BiDAF as an example architecture\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nQuestion Answering with NLP has progressed through the evolution of advanced model architectures like BERT and BiDAF and earlier word, character, and context-based embeddings. As BERT has leapfrogged the accuracy of models, an element of the next frontier can be the introduction of deep networks and an effective way to train them. In this context, I explored the effectiveness of deep networks focussing on the model encoder layer of BiDAF. BiDAF with its heterogeneous layers provides the opportunity not only to explore the effectiveness of deep networks but also to evaluate whether the refinements made in lower layers are additive to the refinements made in the upper layers of the model architecture. I believe the next greatest model in NLP will in fact fold in a solid language modeling like BERT with a composite architecture which will bring in refinements in addition to generic language modeling and will have a more extensive layered architecture. I experimented with the Bypass network, Residual Highway network, and DenseNet architectures. In addition, I evaluated the effectiveness of ensembling the last few layers of the network. I also studied the difference character embeddings make in adding them to the word embeddings, and whether the effects are additive with deep networks. My studies indicate that deep networks are in fact effective in giving a boost. Also, the refinements in the lower layers like embeddings are passed on additively to the gains made through deep networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.00074v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227615\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"26523","document_content":"# Sense representations for Portuguese: experiments with sense embeddings and deep neural language models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produce a single vector representation for a given word ignoring the fact that ambiguous words can assume different meanings. In this paper, we explore unsupervised sense representations which, different from traditional word embeddings, are able to induce different senses of a word by analyzing its contextual semantics in a text. The unsupervised sense representations investigated in this paper are: sense embeddings and deep neural language models. We present the first experiments carried out for generating sense embeddings for Portuguese. Our experiments show that the sense embedding model (Sense2vec) outperformed traditional word embeddings in syntactic and semantic analogies task, proving that the language resource generated here can improve the performance of NLP tasks in Portuguese. We also evaluated the performance of pre-trained deep neural language models (ELMo and BERT) in two transfer learning approaches: feature based and fine-tuning, in the semantic textual similarity task. Our experiments indicate that the fine tuned Multilingual and Portuguese BERT language models were able to achieve better accuracy than the ELMo model and baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10579-020-09525-1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"195157\", \"202124\", \"232527\", \"258128\", \"258826\", \"259706\", \"302936\", \"310117\", \"310738\", \"313947\", \"320132\", \"336720\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"26605","document_content":"# Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review\n## Categories\n- Social and Information Networks\n- Computation and Language\n- Physics and Society\n- History and Overview\n## Abstract\nMath anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20% of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance, and (iv) poses a concrete threat to students' well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances with network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic\/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but - more crucially - they can unveil the specific layout of interacting factors, e.g. key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students' perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.13800v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CL\", \"physics.soc-ph\", \"math.HO\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computation and Language\", \"Physics and Society\", \"History and Overview\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"10123\", \"103682\", \"110104\", \"111151\", \"126072\", \"240555\", \"265292\", \"289588\", \"320907\"]}","task_split":"paper_retrieval"}
{"document_id":"26622","document_content":"# Disentanglement Analysis with Partial Information Decomposition\n## Categories\n- Machine Learning\n## Abstract\nWe propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics designed to compare how one variable explains each generative factor. Current metrics, however, may fail to detect entanglement that involves more than two variables, e.g., representations that duplicate and rotate generative factors in high dimensional spaces. In this work, we establish a framework to analyze information sharing in a multivariate representation with Partial Information Decomposition and propose a new disentanglement metric. This framework enables us to understand disentanglement in terms of uniqueness, redundancy, and synergy. We develop an experimental protocol to assess how increasingly entangled representations are evaluated with each metric and confirm that the proposed metric correctly responds to entanglement. Through experiments on variational autoencoders, we find that models with similar disentanglement scores have a variety of characteristics in entanglement, for each of which a distinct strategy may be required to obtain a disentangled representation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.13753v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"40030\", \"80015\", \"140360\", \"160312\", \"164756\", \"170071\", \"207094\", \"235194\", \"237146\", \"241933\", \"241998\", \"242239\", \"244096\", \"272531\", \"321984\", \"337976\"]}","task_split":"paper_retrieval"}
{"document_id":"26639","document_content":"# TNNT: The Named Entity Recognition Toolkit\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Software Engineering\n## Abstract\nExtraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art Natural Language Processing (NLP) tools and NER models. TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enhanced data analysis to support the KGCP, and also, to aid further NLP tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3460210.3493550\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.SE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": [\"136604\", \"238321\", \"323243\"]}","task_split":"paper_retrieval"}
{"document_id":"26661","document_content":"# Heterogeneous Graph Neural Network with Multi-view Representation Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph. However, on the one hand, most of existing heterogeneous graph embedding methods either insufficiently model the local structure under specific semantic, or neglect the heterogeneity when aggregating information from it. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain versatile node embeddings. To address the problem, we propose a Heterogeneous Graph Neural Network with Multi-View Representation Learning (named MV-HetGNN) for heterogeneous graph embedding by introducing the idea of multi-view representation learning. The proposed model consists of node feature transformation, view-specific ego graph encoding and auto multi-view fusion to thoroughly learn complex structural and semantic information for generating comprehensive node representations. Extensive experiments on three real-world heterogeneous graph datasets show that the proposed MV-HetGNN model consistently outperforms all the state-of-the-art GNN baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.13650v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"69437\", \"73530\", \"83866\", \"87445\", \"103085\", \"104515\", \"113967\", \"114134\", \"122728\", \"123863\", \"138857\", \"144037\", \"150433\", \"157487\", \"157816\", \"187708\", \"194773\", \"198364\", \"240758\", \"248960\", \"271527\", \"320035\", \"336185\"]}","task_split":"paper_retrieval"}
{"document_id":"26753","document_content":"# Exploring Multi-Tasking Learning in Document Attribute Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this work, we adhere to explore a Multi-Tasking learning (MTL) based network to perform document attribute classification such as the font type, font size, font emphasis and scanning resolution classification of a document image. To accomplish these tasks, we operate on either segmented word level or on uniformed size patches randomly cropped out of the document. Furthermore, a hybrid convolution neural network (CNN) architecture \"MTL+MI\", which is based on the combination of MTL and Multi-Instance (MI) of patch and word is used to accomplish joint learning for the classification of the same document attributes. The contribution of this paper are three fold: firstly, based on segmented word images and patches, we present a MTL based network for the classification of a full document image. Secondly, we propose a MTL and MI (using segmented words and patches) based combined CNN architecture (\"MTL+MI\") for the classification of same document attributes. Thirdly, based on the multi-tasking classifications of the words and\/or patches, we propose an intelligent voting system which is based on the posterior probabilities of each words and\/or patches to perform the classification of document's attributes of complete document image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.13382v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"258765\", \"311668\"]}","task_split":"paper_retrieval"}
{"document_id":"26808","document_content":"# AEDA: An Easier Data Augmentation Technique for Text Classification\n## Categories\n- Computation and Language\n## Abstract\nThis paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code is available for further study and reproduction of the results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.13230v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"82584\", \"89415\", \"96445\", \"133106\", \"138517\", \"144930\", \"159333\", \"165441\", \"189595\", \"200867\", \"205715\", \"231898\", \"267601\", \"272068\", \"292448\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"26924","document_content":"# RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nRetrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB). However, retrofitting only works on concepts that are present in that KB. RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a one-to-one mapping between concepts and their retrofitted counterparts. It applies that mapping (post-specializes) to handle concepts that do not appear in the original KB in a manner similar to how some natural language systems handle out-of-vocabulary entries. We test our system on three word-similarity benchmarks and a downstream sentence simplification task and achieve the state of the art (CARD-660). Altogether, our results demonstrate our system's effectiveness for out-of-knowledge and rare word generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2021.findings-acl.183\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167753\", \"171165\", \"181704\", \"214333\", \"218235\", \"220041\", \"232765\", \"232774\", \"253087\", \"264791\", \"271419\", \"287281\", \"295130\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"27102","document_content":"# Goal-driven text descriptions for images\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nA big part of achieving Artificial General Intelligence(AGI) is to build a machine that can see and listen like humans. Much work has focused on designing models for image classification, video classification, object detection, pose estimation, speech recognition, etc., and has achieved significant progress in recent years thanks to deep learning. However, understanding the world is not enough. An AI agent also needs to know how to talk, especially how to communicate with a human. While perception (vision, for example) is more common across animal species, the use of complicated language is unique to humans and is one of the most important aspects of intelligence.   In this thesis, we focus on generating textual output given visual input. In Chapter 3, we focus on generating the referring expression, a text description for an object in the image so that a receiver can infer which object is being described. We use a comprehension machine to directly guide the generated referring expressions to be more discriminative. In Chapter 4, we introduce a method that encourages discriminability in image caption generation. We show that more discriminative captioning models generate more descriptive captions. In Chapter 5, we study how training objectives and sampling methods affect the models' ability to generate diverse captions. We find that a popular captioning training strategy will be detrimental to the diversity of generated captions. In Chapter 6, we propose a model that can control the length of generated captions. By changing the desired length, one can influence the style and descriptiveness of the captions. Finally, in Chapter 7, we rank\/generate informative image tags according to their information utility. The proposed method better matches what humans think are the most important tags for the images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.12575v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"27250","document_content":"# Translation Error Detection as Rationale Extraction\n## Categories\n- Computation and Language\n## Abstract\nRecent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sentences. Predicting translation errors, i.e. detecting specifically which words are incorrect, is a more challenging task, especially with limited amounts of training data. We hypothesize that, not unlike humans, successful QE models rely on translation errors to predict overall sentence quality. By exploring a set of feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level QE models and show that explanations (i.e. rationales) extracted from these models can indeed be used to detect translation errors. We therefore (i) introduce a novel semi-supervised method for word-level QE and (ii) propose to use the QE task as a new benchmark for evaluating the plausibility of feature attribution, i.e. how interpretable model explanations are to humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.12197v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22977\", \"1461\", \"17826\", \"17833\"], \"outgoing_citations\": [\"52753\", \"59754\", \"60415\", \"61008\", \"70866\", \"89858\", \"95086\", \"95513\", \"98602\", \"123670\", \"127779\", \"127844\", \"132402\", \"148797\", \"157352\", \"159144\", \"168856\", \"171988\", \"197416\", \"200263\", \"253639\", \"290515\", \"290677\", \"316314\"]}","task_split":"paper_retrieval"}
{"document_id":"27319","document_content":"# Towards Self-Explainable Graph Neural Network\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for graphs, GNNs lack explainability, which largely limits their adoption in scenarios that demand the transparency of models. Though many efforts are taken to improve the explainability of deep learning, they mainly focus on i.i.d data, which cannot be directly applied to explain the predictions of GNNs because GNNs utilize both node features and graph topology to make predictions. There are only very few work on the explainability of GNNs and they focus on post-hoc explanations. Since post-hoc explanations are not directly obtained from the GNNs, they can be biased and misrepresent the true explanations. Therefore, in this paper, we study a novel problem of self-explainable GNNs which can simultaneously give predictions and explanations. We propose a new framework which can find $K$-nearest labeled nodes for each unlabeled node to give explainable node classification, where nearest labeled nodes are found by interpretable similarity module in terms of both node similarity and local structure similarity. Extensive experiments on real-world and synthetic datasets demonstrate the effectiveness of the proposed framework for explainable node classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.12055v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"16062\", \"46303\"], \"outgoing_citations\": [\"43609\", \"62816\", \"62817\", \"88103\", \"92075\", \"102593\", \"114193\", \"117866\", \"117926\", \"121171\", \"121235\", \"123835\", \"139505\", \"146722\", \"170916\", \"173673\", \"185584\", \"186848\", \"195767\", \"197103\", \"197997\", \"210401\", \"212425\", \"216376\", \"227415\", \"231277\", \"238492\", \"239137\", \"243791\", \"244510\", \"265828\", \"269202\", \"271562\", \"292407\"]}","task_split":"paper_retrieval"}
{"document_id":"27393","document_content":"# Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition\n## Categories\n- Computation and Language\n## Abstract\nDespite impressive results of language models for named entity recognition (NER), their generalization to varied textual genres, a growing entity type set, and new entities remains a challenge. Collecting thousands of annotations in each new case for training or fine-tuning is expensive and time-consuming. In contrast, humans can easily identify named entities given some simple instructions. Inspired by this, we challenge the reliance on large datasets and study pre-trained language models for NER in a meta-learning setup. First, we test named entity typing (NET) in a zero-shot transfer scenario. Then, we perform NER by giving few examples at inference. We propose a method to select seen and rare \/ unseen names when having access only to the pre-trained model and report results on these groups. The results show: auto-regressive language models as meta-learners can perform NET and NER fairly well especially for regular or seen names; name irregularity when often present for a certain entity type can become an effective exploitable cue; names with words foreign to the model have the most negative impact on results; the model seems to rely more on name than context cues in few-shot NER.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.11857v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"49295\", \"68331\", \"93812\", \"127943\", \"129041\", \"130889\", \"132673\", \"133395\", \"146051\", \"147611\", \"153825\", \"159862\", \"159867\", \"168951\", \"177232\", \"181544\", \"181546\", \"218407\", \"241199\"]}","task_split":"paper_retrieval"}
{"document_id":"27413","document_content":"# Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe massive scale and growth of textual biomedical data have made its indexing and classification increasingly important. However, existing research on this topic mainly utilized convolutional and recurrent neural networks, which generally achieve inferior performance than the novel transformers. On the other hand, systems that apply transformers only focus on the target documents, overlooking the rich semantic information that label descriptions contain. To address this gap, we develop a transformer-based biomedical text classifier that considers label information. The system achieves this with a label attention module incorporated into the fine-tuning process of pretrained language models (PTMs). Our results on two public medical datasets show that the proposed fine-tuning scheme outperforms the vanilla PTMs and state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.11809v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175365\", \"185112\", \"186386\", \"195157\", \"210236\", \"242037\", \"244755\", \"286460\"]}","task_split":"paper_retrieval"}
{"document_id":"27450","document_content":"# SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling\n## Categories\n- Artificial Intelligence\n## Abstract\nUtterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often multiple intents within an utterance in real-life scenarios. In this paper, we propose a multi-intent NLU framework, called SLIM, to jointly learn multi-intent detection and slot filling based on BERT. To fully exploit the existing annotation data and capture the interactions between slots and intents, SLIM introduces an explicit slot-intent classifier to learn the many-to-one mapping between slots and intents. Empirical results on three public multi-intent datasets demonstrate (1) the superior performance of SLIM compared to the current state-of-the-art for NLU with multiple intents and (2) the benefits obtained from the slot-intent classifier.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.11711v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"24608\"], \"outgoing_citations\": [\"166518\", \"166702\", \"168563\", \"177825\", \"178308\", \"197154\", \"204763\", \"205014\", \"230543\", \"285317\", \"289624\"]}","task_split":"paper_retrieval"}
{"document_id":"27467","document_content":"# SVM Classifier on Chip for Melanoma Detection\n## Categories\n- Image and Video Processing\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nSupport Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM- based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip\/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/EMBC.2017.8036814\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.DC\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"20071\", \"4039\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"27533","document_content":"# Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i)landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.11510v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1691\", \"77439\"], \"outgoing_citations\": [\"103142\", \"111682\", \"112547\", \"124068\", \"133775\", \"145129\", \"162729\", \"178341\", \"179901\", \"207983\", \"222899\", \"223690\", \"226090\", \"227255\", \"227604\", \"230389\", \"235347\", \"249029\", \"255624\", \"267832\", \"269097\", \"269098\", \"285755\", \"289287\", \"289979\", \"291817\", \"298629\", \"299830\", \"306266\", \"326774\", \"327379\", \"330358\"]}","task_split":"paper_retrieval"}
{"document_id":"27697","document_content":"# TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https:\/\/github.com\/nnzhan\/TraverseNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2109.02474v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"21187\"], \"outgoing_citations\": [\"18532\", \"36313\", \"41201\", \"65820\", \"66586\", \"66588\", \"80375\", \"82393\", \"123274\", \"157011\", \"159315\", \"168870\", \"177342\", \"183174\", \"194771\", \"203910\", \"220160\", \"227067\", \"238422\", \"255946\", \"261876\", \"265828\", \"277410\", \"279575\"]}","task_split":"paper_retrieval"}
{"document_id":"27730","document_content":"# Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering\n## Categories\n- Computation and Language\n## Abstract\nDiscovering the logical sequence of events is one of the cornerstones in Natural Language Understanding. One approach to learn the sequence of events is to study the order of sentences in a coherent text. Sentence ordering can be applied in various tasks such as retrieval-based Question Answering, document summarization, storytelling, text generation, and dialogue systems. Furthermore, we can learn to model text coherence by learning how to order a set of shuffled sentences. Previous research has relied on RNN, LSTM, and BiLSTM architecture for learning text language models. However, these networks have performed poorly due to the lack of attention mechanisms. We propose an algorithm for sentence ordering in a corpus of short stories. Our proposed method uses a language model based on Universal Transformers (UT) that captures sentences' dependencies by employing an attention mechanism. Our method improves the previous state-of-the-art in terms of Perfect Match Ratio (PMR) score in the ROCStories dataset, a corpus of nearly 100K short human-made stories. The proposed model includes three components: Sentence Encoder, Language Model, and Sentence Arrangement with Brute Force Search. The first component generates sentence embeddings using SBERT-WK pre-trained model fine-tuned on the ROCStories data. Then a Universal Transformer network generates a sentence-level language model. For decoding, the network generates a candidate sentence as the following sentence of the current sentence. We use cosine similarity as a scoring function to assign scores to the candidate embedding and the embeddings of other sentences in the shuffled set. Then a Brute Force Search is employed to maximize the sum of similarities between pairs of consecutive sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.10986v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"27338\", \"141983\", \"148943\", \"225119\", \"232600\", \"280415\", \"280892\", \"289706\", \"291019\", \"291100\", \"294830\", \"298750\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"27798","document_content":"# Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health\n## Categories\n- Distributed, Parallel, and Cluster Computing\n- Cryptography and Security\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nPrivacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.10761v1\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.CR\", \"cs.LG\", \"cs.DC\", \"cs.AI\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Cryptography and Security\", \"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [\"52335\", \"25266\", \"83411\"], \"outgoing_citations\": [\"27802\", \"113040\", \"119091\", \"120533\", \"129708\", \"133977\", \"134225\", \"136201\", \"139315\", \"140332\", \"144599\", \"148031\", \"148382\", \"153202\", \"153401\", \"154095\", \"156643\", \"160401\", \"162752\", \"163734\", \"179698\", \"198714\", \"202443\", \"208297\", \"211857\", \"214805\", \"226146\", \"264024\", \"269208\"]}","task_split":"paper_retrieval"}
{"document_id":"27802","document_content":"# Federated Learning for Open Banking\n## Categories\n- Distributed, Parallel, and Cluster Computing\n- Machine Learning\n## Abstract\nOpen banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users' privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.10749v1\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"16829\", \"19284\", \"13693\", \"52335\", \"72013\", \"17947\", \"27798\", \"41102\", \"51081\", \"70616\", \"83411\", \"88830\"], \"outgoing_citations\": [\"118376\", \"120533\", \"128968\", \"129194\", \"134225\", \"140332\", \"142093\", \"142802\", \"143220\", \"148382\", \"153202\", \"157830\", \"162752\", \"197068\", \"200704\", \"208297\", \"229563\"]}","task_split":"paper_retrieval"}
{"document_id":"27835","document_content":"# Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- J.4; I.2.7\n## Abstract\nMoral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.10643v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"J.4; I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"J.4; I.2.7\"], \"incoming_citations\": [\"10490\"], \"outgoing_citations\": [\"187019\", \"190071\", \"236905\", \"282937\"]}","task_split":"paper_retrieval"}
{"document_id":"27929","document_content":"# Continuous Treatment Recommendation with Deep Survival Dose Response Function\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Econometrics\n## Abstract\nWe propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (DeepSDRF). That is, we consider the problem of learning the conditional average dose response (CADR) function solely from historical data in which observed factors (confounders) affect both observed treatment and time-to-event outcomes. The estimated treatment effect from DeepSDRF enables us to develop recommender algorithms with the correction for selection bias. We compared two recommender approaches based on random search and reinforcement learning and found similar performance in terms of patient outcome. We tested the DeepSDRF and the corresponding recommender on extensive simulation studies and the eICU Research Institute (eRI) database. To the best of our knowledge, this is the first time that causal models are used to address the continuous treatment effect with observational data in a medical context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.10453v4\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"econ.EM\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Econometrics\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"229839\", \"291026\"]}","task_split":"paper_retrieval"}
{"document_id":"28085","document_content":"# TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nContrastive learning has been widely used to train transformer-based vision-language models for video-text alignment and multi-modal representation learning. This paper presents a new algorithm called Token-Aware Cascade contrastive learning (TACo) that improves contrastive learning using two novel techniques. The first is the token-aware contrastive loss which is computed by taking into account the syntactic classes of words. This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words. Second, a cascade sampling method is applied to generate a small set of hard negative examples for efficient loss estimation for multi-modal fusion layers. To validate the effectiveness of TACo, in our experiments we finetune pretrained models for a set of downstream tasks including text-video retrieval (YouCook2, MSR-VTT and ActivityNet), video action step localization (CrossTask), video action segmentation (COIN). The results show that our models attain consistent improvements across different experimental settings over previous methods, setting new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.09980v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"10897\", \"7454\", \"10348\"], \"outgoing_citations\": [\"87013\", \"110610\", \"127728\", \"129809\", \"131401\", \"139259\", \"142128\", \"151327\", \"164927\", \"165195\", \"170926\", \"172394\", \"173627\", \"180920\", \"181897\", \"192531\", \"194508\", \"196110\", \"214015\", \"219048\", \"221984\", \"231686\", \"236918\", \"238205\", \"247535\", \"248813\", \"249241\", \"254889\", \"260926\", \"267523\", \"268871\", \"270325\", \"282943\", \"283914\", \"301767\", \"310322\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"28119","document_content":"# Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSarcasm is the use of words usually used to either mock or annoy someone, or for humorous purposes. Sarcasm is largely used in social networks and microblogging websites, where people mock or censure in a way that makes it difficult even for humans to tell if what is said is what is meant. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilized different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the contextual information in the sarcastic expression. Moreover, some datasets used in several studies have an unbalanced dataset which impacting the model result. In this paper, we propose a contextual model for sarcasm identification in twitter using RoBERTa, and augmenting the dataset by applying Global Vector representation (GloVe) for the construction of word embedding and context learning to generate more data and balancing the dataset. The effectiveness of this technique is tested with various datasets and data augmentation settings. In particular, we achieve performance gain by 3.2% in the iSarcasm dataset when using data augmentation to increase 20% of data labeled as sarcastic, resulting F-score of 40.4% compared to 37.2% without data augmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5391\/IJFIS.2022.22.4.401\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54915\", \"121778\", \"123381\", \"157465\", \"234646\", \"260786\"]}","task_split":"paper_retrieval"}
{"document_id":"28126","document_content":"# Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling\n## Categories\n- Computation and Language\n## Abstract\nWell-annotated datasets, as shown in recent top studies, are becoming more important for researchers than ever before in supervised machine learning (ML). However, the dataset annotation process and its related human labor costs remain overlooked. In this work, we analyze the relationship between the annotation granularity and ML performance in sequence labeling, using clinical records from nursing shift-change handover. We first study a model derived from textual language features alone, without additional information based on nursing knowledge. We find that this sequence tagger performs well in most categories under this granularity. Then, we further include the additional manual annotations by a nurse, and find the sequence tagging performance remaining nearly the same. Finally, we give a guideline and reference to the community arguing it is not necessary and even not recommended to annotate in detailed granularity because of a low Return on Investment. Therefore we recommend emphasizing other features, like textual knowledge, for researchers and practitioners as a cost-effective source for increasing the sequence labeling performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.09913v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"297147\", \"308333\"]}","task_split":"paper_retrieval"}
{"document_id":"28310","document_content":"# Learning Causal Models of Autonomous Agents using Interventions\n## Categories\n- Artificial Intelligence\n## Abstract\nOne of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems. We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system in stationary, fully observable, and deterministic settings. We also introduce dynamic causal decision networks (DCDNs) that capture the causal structure of STRIPS-like domains. A comparative analysis of different classes of queries is also presented in terms of the computational requirements needed to answer them and the efforts required to evaluate their responses to learn the correct model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.09586v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"142095\", \"267678\", \"282435\", \"313520\", \"350497\"]}","task_split":"paper_retrieval"}
{"document_id":"28498","document_content":"# Group-based Distinctive Image Captioning with Memory Attention\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nDescribing images using natural language is widely known as image captioning, which has made consistent progress due to the development of computer vision and natural language generation techniques. Though conventional captioning models achieve high accuracy based on popular metrics, i.e., BLEU, CIDEr, and SPICE, the ability of captions to distinguish the target image from other similar images is under-explored. To generate distinctive captions, a few pioneers employ contrastive learning or re-weighted the ground-truth captions, which focuses on one single input image. However, the relationships between objects in a similar image group (e.g., items or properties within the same album or fine-grained events) are neglected. In this paper, we improve the distinctiveness of image captions using a Group-based Distinctive Captioning Model (GdisCap), which compares each image with other images in one similar group and highlights the uniqueness of each image. In particular, we propose a group-based memory attention (GMA) module, which stores object features that are unique among the image group (i.e., with low similarity to objects in other images). These unique object features are highlighted when generating captions, resulting in more distinctive captions. Furthermore, the distinctive words in the ground-truth captions are selected to supervise the language decoder and GMA. Finally, we propose a new evaluation metric, distinctive word rate (DisWordRate) to measure the distinctiveness of captions. Quantitative results indicate that the proposed method significantly improves the distinctiveness of several baseline models, and achieves the state-of-the-art performance on both accuracy and distinctiveness. Results of a user study agree with the quantitative evaluation and demonstrate the rationality of the new metric DisWordRate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.09151v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112250\", \"132390\", \"133970\", \"135934\", \"139825\", \"170603\", \"171096\", \"171902\", \"174277\", \"180863\", \"191371\", \"193328\", \"212441\", \"230900\", \"238125\", \"239242\", \"249394\", \"249972\", \"253934\", \"260926\", \"269122\", \"270154\", \"271234\", \"276436\", \"278954\", \"280246\", \"296681\", \"320003\", \"320761\", \"321682\"]}","task_split":"paper_retrieval"}
{"document_id":"28533","document_content":"# PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nReal-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the second phase, these predictions are aggregated according to a set of 'strategic parameters' to meet a diverse set of business goals, such as longer user engagement, higher revenue potential, or more community\/network interactions. In addition to building accurate predictive models, it is also crucial to optimize this set of 'strategic parameters' so that primary goals are optimized while secondary guardrails are not hurt. In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter. The new probabilistic regime is to learn the best distribution over strategic parameter choices and sample one strategic parameter from the distribution when each user visits the platform. To pursue the optimal probabilistic solution, we formulate the problem into a stochastic compositional optimization problem, in which the unbiased stochastic gradient is unavailable. Our approach is applied in a popular social network platform with hundreds of millions of daily users and achieves +0.22% lift of user engagement in a recommendation task and +1.7% lift in revenue in an advertising optimization scenario comparing to using the best deterministic parameter strategy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.09076v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"104792\", \"120071\", \"152012\", \"183170\", \"200417\", \"201048\", \"213358\", \"234738\", \"242002\", \"260407\", \"263144\", \"359369\"]}","task_split":"paper_retrieval"}
{"document_id":"28598","document_content":"# Plug and Play, Model-Based Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nSample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce Plug and Play Markov Decision Processes, an object-based representation that allows zero-shot integration of new objects from known object classes. This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene. Transition dynamics from an object class can be pre-learnt and thus would be ready to use in a new environment. Each active object is also endowed with its reward function. Since there is no central reward function, addition or removal of objects can be handled efficiently by only updating the reward functions of objects involved. A new transfer learning mechanism is also proposed to adapt reward function in such cases. Experiments show that our representation can achieve sample-efficiency in a variety of set-ups.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.08960v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"146966\", \"171629\", \"176764\", \"185153\", \"263811\", \"279026\", \"284470\", \"311926\"]}","task_split":"paper_retrieval"}
{"document_id":"28699","document_content":"# How to cheat with metrics in single-image HDR reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Image and Video Processing\n## Abstract\nSingle-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improvement over existing methods by reporting higher image quality scores. This paper, however, highlights that such improvements in objective metrics do not necessarily translate to visually superior images. The first problem is the use of disparate evaluation conditions in terms of data and metric parameters, calling for a standardized protocol to make it possible to compare between papers. The second problem, which forms the main focus of this paper, is the inherent difficulty in evaluating SI-HDR reconstructions since certain aspects of the reconstruction problem dominate objective differences, thereby introducing a bias. Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics. Surprisingly, we found that methods that do not even reconstruct HDR information can compete with state-of-the-art deep learning methods. We show how such results are not representative of the perceived quality and that SI-HDR reconstruction needs better evaluation protocols.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.08713v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"45270\", \"60199\", \"100209\", \"124998\", \"133418\", \"149650\", \"167645\", \"189044\", \"239855\", \"249439\", \"252829\", \"270221\"]}","task_split":"paper_retrieval"}
{"document_id":"28754","document_content":"# Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nAnswering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique for doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.08597v9\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [\"28745\"], \"outgoing_citations\": [\"28745\", \"76113\", \"78029\", \"91739\", \"96431\", \"99089\", \"121381\", \"129128\", \"136604\", \"145708\", \"162246\", \"162857\", \"169683\", \"171110\", \"173415\", \"189600\", \"205857\", \"219306\", \"245346\", \"264636\", \"283927\", \"294419\", \"297226\"]}","task_split":"paper_retrieval"}
{"document_id":"28992","document_content":"# Joint Multiple Intent Detection and Slot Filling via Self-distillation\n## Categories\n- Computation and Language\n## Abstract\nIntent detection and slot filling are two main tasks in natural language understanding (NLU) for identifying users' needs from their utterances. These two tasks are highly related and often trained jointly. However, most previous works assume that each utterance only corresponds to one intent, ignoring the fact that a user utterance in many cases could include multiple intents. In this paper, we propose a novel Self-Distillation Joint NLU model (SDJN) for multi-intent NLU. First, we formulate multiple intent detection as a weakly supervised problem and approach with multiple instance learning (MIL). Then, we design an auxiliary loop via self-distillation with three orderly arranged decoders: Initial Slot Decoder, MIL Intent Decoder, and Final Slot Decoder. The output of each decoder will serve as auxiliary information for the next decoder. With the auxiliary knowledge provided by the MIL Intent Decoder, we set Final Slot Decoder as the teacher model that imparts knowledge back to Initial Slot Decoder to complete the loop. The auxiliary loop enables intents and slots to guide mutually in-depth and further boost the overall NLU performance. Experimental results on two public multi-intent datasets indicate that our model achieves strong performance compared to others.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.08042v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"98109\", \"129831\", \"132243\", \"168563\", \"178308\", \"185872\", \"204763\", \"230543\", \"285317\", \"320046\"]}","task_split":"paper_retrieval"}
{"document_id":"29022","document_content":"# Structured Outdoor Architecture Reconstruction by Exploration and Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents an explore-and-classify framework for structured architectural reconstruction from an aerial image. Starting from a potentially imperfect building reconstruction by an existing algorithm, our approach 1) explores the space of building models by modifying the reconstruction via heuristic actions; 2) learns to classify the correctness of building models while generating classification labels based on the ground-truth, and 3) repeat. At test time, we iterate exploration and classification, seeking for a result with the best classification score. We evaluate the approach using initial reconstructions by two baselines and two state-of-the-art reconstruction algorithms. Qualitative and quantitative evaluations demonstrate that our approach consistently improves the reconstruction quality from every initial reconstruction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.07990v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6433\"], \"outgoing_citations\": [\"134632\", \"151736\", \"152865\", \"171173\", \"181716\", \"186289\", \"187182\", \"187223\", \"197543\", \"235286\", \"237693\", \"268580\"]}","task_split":"paper_retrieval"}
{"document_id":"29040","document_content":"# SynFace: Face Recognition with Synthetic Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the recent success of deep neural networks, remarkable progress has been achieved on face recognition. However, collecting large-scale real-world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. Meanwhile, existing face recognition datasets are usually collected from web images, lacking detailed annotations on attributes (e.g., pose and expression), so the influences of different attributes on face recognition have been poorly investigated. In this paper, we address the above-mentioned issues in face recognition using synthetic face images, i.e., SynFace. Specifically, we first explore the performance gap between recent state-of-the-art face recognition models trained with synthetic and real face images. We then analyze the underlying causes behind the performance gap, e.g., the poor intra-class variations and the domain gap between synthetic and real face images. Inspired by this, we devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the above performance gap, demonstrating the great potentials of synthetic data for face recognition. Furthermore, with the controllable face synthesis model, we can easily manage different factors of synthetic face generation, including pose, expression, illumination, the number of identities, and samples per identity. Therefore, we also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.07960v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"65923\"], \"outgoing_citations\": [\"28374\", \"63068\", \"127532\", \"129244\", \"152850\", \"171379\", \"184017\", \"191218\", \"191293\", \"194402\", \"195230\", \"195961\", \"206226\", \"212096\", \"213905\", \"217374\", \"218795\", \"222344\", \"222824\", \"228120\", \"237272\", \"240261\", \"243986\", \"250005\", \"267940\", \"268405\", \"272918\", \"275665\", \"278526\", \"284716\", \"287611\", \"299885\", \"302157\", \"320009\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"29056","document_content":"# Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. We present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. Comprehensive experiments on two large datasets show that our method outperforms all baseline models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459637.3482269\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"28429\", \"34843\", \"74350\", \"98161\", \"123799\", \"128922\", \"130818\", \"131723\", \"156438\", \"158493\", \"171566\", \"181458\", \"192175\", \"218963\", \"226959\", \"244479\", \"245252\", \"245831\", \"263265\", \"278671\", \"290747\", \"291363\", \"296250\", \"305200\", \"309411\", \"310377\", \"310903\", \"316165\", \"316308\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"29085","document_content":"# Aggregated Customer Engagement Model\n## Categories\n- Machine Learning\n## Abstract\nE-commerce websites use machine learned ranking models to serve shopping results to customers. Typically, the websites log the customer search events, which include the query entered and the resulting engagement with the shopping results, such as clicks and purchases. Each customer search event serves as input training data for the models, and the individual customer engagement serves as a signal for customer preference. So a purchased shopping result, for example, is perceived to be more important than one that is not. However, new or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products. In this paper, we propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data. This aggregated customer engagement gives the models a complete picture of the relative importance of shopping results. Training models on this aggregated data leads to less reliance on behavioral features. This helps mitigate the cold start problem and boosted relevant new products to top search results. In this paper, we present the offline and online analysis and results comparing the individual and aggregated customer engagement models trained on e-commerce data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.07872v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"196874\", \"233684\"]}","task_split":"paper_retrieval"}
{"document_id":"29220","document_content":"# G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs. The conventional feature imitation paradigm introduces imitation masks to focus on informative foreground areas while excluding the background noises. However, we find that those methods fail to fully utilize the semantic information in all feature pyramid levels, which leads to inefficiency for knowledge distillation between FPN-based detectors. To this end, we propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels to provide the optimal guidance to the student. To push the envelop even further, we introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Finally, we propose a generalized detection KD pipeline, which is capable of distilling both homogeneous and heterogeneous detector pairs. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction; (2) for both homogeneous and heterogenous student-teacher pairs and (3) on multiple detection benchmarks. With a powerful X101-FasterRCNN-Instaboost detector as the teacher, R50-FasterRCNN reaches 44.0% AP, R50-RetinaNet reaches 43.3% AP and R50-FCOS reaches 43.1% AP on COCO dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.07482v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"21493\"], \"outgoing_citations\": [\"47001\", \"110454\", \"111757\", \"116618\", \"152638\", \"153874\", \"154837\", \"160122\", \"164933\", \"170823\", \"180880\", \"181769\", \"188940\", \"190110\", \"190406\", \"202067\", \"278175\", \"320046\"]}","task_split":"paper_retrieval"}
{"document_id":"29406","document_content":"# A Single Example Can Improve Zero-Shot Data Generation\n## Categories\n- Computation and Language\n## Abstract\nSub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and flexible datasets for experiments and evaluation. As collecting such datasets is time- and labor-consuming, we propose to use text generation methods to gather datasets. The generator should be trained to generate utterances that belong to the given intent. We explore two approaches to generating task-oriented utterances. In the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training. In the one-shot approach, the model is presented with a single utterance from a test intent. We perform a thorough automatic, and human evaluation of the dataset generated utilizing two proposed approaches. Our results reveal that the attributes of the generated data are close to original test sets, collected via crowd-sourcing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06991v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"52037\", \"132645\", \"152729\", \"167138\", \"168102\", \"169329\", \"175306\", \"176321\", \"180695\", \"189504\", \"189612\", \"200867\", \"217640\", \"230543\", \"232274\", \"232285\", \"235345\", \"241628\", \"251778\", \"263874\", \"266671\", \"270048\", \"278954\", \"280557\", \"291102\", \"296681\", \"302824\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"29494","document_content":"# Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n- H.0; J.4; K.4; I.2\n## Abstract\nOrganizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Article quality ratings on English language Wikipedia have been widely used by both Wikipedia community members and academic researchers for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use labels on discrete ordinal scales when assessing quality, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are \"evenly spaced\" from one another. This assumption runs counter to intuitions about the relative degrees of effort needed to raise Wikipedia encyclopedia articles to different quality levels. Furthermore, models from prior work are fit to datasets that oversample high-quality articles. This limits their accuracy for representative samples of articles or revisions. I describe a technique extending the Wikimedia Foundations' ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the \"evenly spaced\" assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3479986.3479991\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\", \"H.0; J.4; K.4; I.2\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\", \"H.0; J.4; K.4; I.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"66464\", \"120981\", \"167351\", \"181313\", \"192572\", \"199022\", \"207653\", \"248862\", \"284408\"]}","task_split":"paper_retrieval"}
{"document_id":"29497","document_content":"# Human Pose and Shape Estimation from Single Polarization Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper focuses on a new problem of estimating human pose and shape from single polarization images. Polarization camera is known to be able to capture the polarization of reflected lights that preserves rich geometric cues of an object surface. Inspired by the recent applications in surface normal reconstruction from polarization images, in this paper, we attempt to estimate human pose and shape from single polarization images by leveraging the polarization-induced geometric cues. A dedicated two-stage pipeline is proposed: given a single polarization image, stage one (Polar2Normal) focuses on the fine detailed human body surface normal estimation; stage two (Polar2Shape) then reconstructs clothed human shape from the polarization image and the estimated surface normal. To empirically validate our approach, a dedicated dataset (PHSPD) is constructed, consisting of over 500K frames with accurate pose and parametric shape annotations. Empirical evaluations on this real-world dataset as well as a synthetic dataset, SURREAL, demonstrate the effectiveness of our approach. It suggests polarization camera as a promising alternative to the more conventional RGB camera for human pose and shape estimation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06834v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"31115\", \"75087\", \"111226\", \"113497\", \"120777\", \"133673\", \"151576\", \"163616\", \"164082\", \"164514\", \"186553\", \"189945\", \"190958\", \"191962\", \"193877\", \"195025\", \"197500\", \"203160\", \"207276\", \"221041\", \"230920\", \"232517\", \"232518\", \"235715\", \"237681\", \"242121\", \"247152\", \"252266\", \"253057\", \"266960\", \"267150\", \"269400\", \"276521\", \"276764\", \"276981\", \"279219\", \"283716\", \"286847\", \"287636\", \"302266\", \"307539\", \"328588\"]}","task_split":"paper_retrieval"}
{"document_id":"29580","document_content":"# Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models\n## Categories\n- Computation and Language\n## Abstract\nConsistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding model. Although recent pretrained language models (PLMs) deliver outstanding performance in various downstream tasks, they should exhibit consistent behaviour provided the models truly understand language. In this paper, we propose a simple framework named consistency analysis on language understanding models (CALUM)} to evaluate the model's lower-bound consistency ability. Through experiments, we confirmed that current PLMs are prone to generate inconsistent predictions even for semantically identical inputs. We also observed that multi-task training with paraphrase identification tasks is of benefit to improve consistency, increasing the consistency by 13% on average.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06665v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15754\"], \"outgoing_citations\": [\"48369\", \"50969\", \"55224\", \"69362\", \"71074\", \"72160\", \"77463\", \"77501\", \"100386\", \"117723\", \"126188\", \"127526\", \"128071\", \"129807\", \"129858\", \"132526\", \"133106\", \"136153\", \"157151\", \"157386\", \"159316\", \"162933\", \"172088\", \"173615\", \"174139\", \"175763\", \"183590\", \"200362\", \"200764\", \"229524\", \"259358\"]}","task_split":"paper_retrieval"}
{"document_id":"29604","document_content":"# A Survey on GAN Acceleration Using Memory Compression Technique\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nSince its invention, Generative adversarial networks (GANs) have shown outstanding results in many applications. Generative Adversarial Networks are powerful yet, resource-hungry deep-learning models. Their main difference from ordinary deep learning models is the nature of their output. For example, GAN output can be a whole image versus other models detecting objects or classifying images. Thus, the architecture and numeric precision of the network affect the quality and speed of the solution. Hence, accelerating GANs is pivotal. Accelerating GANs can be classified into three main tracks: (1) Memory compression, (2) Computation optimization, and (3) Data-flow optimization. Because data transfer is the main source of energy usage, memory compression leads to the most savings. Thus, in this paper, we survey memory compression techniques for CNN-Based GANs. Additionally, the paper summarizes opportunities and challenges in GANs acceleration and suggests open research problems to be further investigated.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06626v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"65089\", \"104130\", \"112203\", \"118626\", \"119873\", \"131467\", \"135921\", \"138049\", \"154338\", \"170131\", \"174531\", \"195165\", \"197901\", \"200696\", \"201814\", \"240885\", \"243420\", \"247330\", \"249421\", \"251226\", \"252610\", \"258001\", \"274086\", \"314300\", \"329613\"]}","task_split":"paper_retrieval"}
{"document_id":"29645","document_content":"# Neuron Campaign for Initialization Guided by Information Bottleneck Theory\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nInitialization plays a critical role in the training of deep neural networks (DNN). Existing initialization strategies mainly focus on stabilizing the training process to mitigate gradient vanish\/explosion problems. However, these initialization methods are lacking in consideration about how to enhance generalization ability. The Information Bottleneck (IB) theory is a well-known understanding framework to provide an explanation about the generalization of DNN. Guided by the insights provided by IB theory, we design two criteria for better initializing DNN. And we further design a neuron campaign initialization algorithm to efficiently select a good initialization for a neural network on a given dataset. The experiments on MNIST dataset show that our method can lead to a better generalization performance with faster convergence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459637.3482153\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"44389\", \"83951\", \"91654\", \"189746\", \"198488\", \"215836\", \"220828\", \"272564\", \"278987\", \"302916\", \"336223\"]}","task_split":"paper_retrieval"}
{"document_id":"29765","document_content":"# Generalized Optimal Linear Orders\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe sequential structure of language, and the order of words in a sentence specifically, plays a central role in human language processing. Consequently, in designing computational models of language, the de facto approach is to present sentences to machines with the words ordered in the same order as in the original human-authored sentence. The very essence of this work is to question the implicit assumption that this is desirable and inject theoretical soundness into the consideration of word order in natural language processing. In this thesis, we begin by uniting the disparate treatments of word order in cognitive science, psycholinguistics, computational linguistics, and natural language processing under a flexible algorithmic framework. We proceed to use this heterogeneous theoretical foundation as the basis for exploring new word orders with an undercurrent of psycholinguistic optimality. In particular, we focus on notions of dependency length minimization given the difficulties in human and computational language processing in handling long-distance dependencies. We then discuss algorithms for finding optimal word orders efficiently in spite of the combinatorial space of possibilities. We conclude by addressing the implications of these word orders on human language and their downstream impacts when integrated in computational models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.7298\/5x0j-me63\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"71218\", \"5169\", \"5169\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"29819","document_content":"# Zero-shot Task Transfer for Invoice Extraction via Class-aware QA Ensemble\n## Categories\n- Information Retrieval\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nWe present VESPA, an intentionally simple yet novel zero-shot system for layout, locale, and domain agnostic document extraction. In spite of the availability of large corpora of documents, the lack of labeled and validated datasets makes it a challenge to discriminatively train document extraction models for enterprises. We show that this problem can be addressed by simply transferring the information extraction (IE) task to a natural language Question-Answering (QA) task without engineering task-specific architectures. We demonstrate the effectiveness of our system by evaluating on a closed corpus of real-world retail and tax invoices with multiple complex layouts, domains, and geographies. The empirical evaluation shows that our system outperforms 4 prominent commercial invoice solutions that use discriminatively trained models with architectures specifically crafted for invoice extraction. We extracted 6 fields with zero upfront human annotation or training with an Avg. F1 of 87.50.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06069v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"82024\", \"118673\", \"131921\", \"170151\", \"174612\", \"181988\", \"184848\", \"196980\", \"197154\", \"236156\", \"249126\", \"257785\", \"263874\", \"268809\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"30002","document_content":"# Reinforcement Learning Approach to Active Learning for Image Classification\n## Categories\n- Machine Learning\n## Abstract\nMachine Learning requires large amounts of labeled data to fit a model. Many datasets are already publicly available, nevertheless forcing application possibilities of machine learning to the domains of those public datasets. The ever-growing penetration of machine learning algorithms in new application areas requires solutions for the need for data in those new domains. This thesis works on active learning as one possible solution to reduce the amount of data that needs to be processed by hand, by processing only those datapoints that specifically benefit the training of a strong model for the task. A newly proposed framework for framing the active learning workflow as a reinforcement learning problem is adapted for image classification and a series of three experiments is conducted. Each experiment is evaluated and potential issues with the approach are outlined. Each following experiment then proposes improvements to the framework and evaluates their impact. After the last experiment, a final conclusion is drawn, unfortunately rejecting this work's hypothesis and outlining that the proposed framework at the moment is not capable of improving active learning for image classification with a trained reinforcement learning agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.05595v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55706\", \"259094\", \"271988\", \"276281\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"30089","document_content":"# Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n- 91G45\n- I.2.6; J.4; K.5.2\n## Abstract\nWe review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.06217v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\", \"91G45\", \"I.2.6; J.4; K.5.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\", \"91G45\", \"I.2.6; J.4; K.5.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68890\", \"88624\", \"89028\", \"95348\", \"98021\", \"103922\", \"105985\", \"115953\", \"142948\", \"147069\", \"147408\", \"148613\", \"151595\", \"180541\", \"182313\", \"183085\", \"197069\", \"218898\", \"251515\", \"255918\", \"289430\"]}","task_split":"paper_retrieval"}
{"document_id":"30101","document_content":"# An Approach to Partial Observability in Games: Learning to Both Act and Observe\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Computer Science and Game Theory\n## Abstract\nReinforcement learning (RL) is successful at learning to play games where the entire environment is visible. However, RL approaches are challenged in complex games like Starcraft II and in real-world environments where the entire environment is not visible. In these more complex games with more limited visual information, agents must choose where to look and how to optimally use their limited visual information in order to succeed at the game. We verify that with a relatively simple model the agent can learn where to look in scenarios with a limited visual bandwidth. We develop a method for masking part of the environment in Atari games to force the RL agent to learn both where to look and how to play the game in order to study where the RL agent learns to look. In addition, we develop a neural network architecture and method for allowing the agent to choose where to look and what action to take in the Pong game. Further, we analyze the strategies the agent learns to better understand how the RL agent learns to play the game.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.05701v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.GT\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Computer Science and Game Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"67640\", \"77056\", \"117599\", \"134247\", \"136185\", \"137441\", \"149805\", \"152090\", \"172691\", \"182190\", \"249438\", \"257128\", \"258472\", \"277757\", \"327963\"]}","task_split":"paper_retrieval"}
{"document_id":"30132","document_content":"# Instance-weighted Central Similarity for Multi-label Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep hashing has been widely applied to large-scale image retrieval by encoding high-dimensional data points into binary codes for efficient retrieval. Compared with pairwise\/triplet similarity based hash learning, central similarity based hashing can more efficiently capture the global data distribution. For multi-label image retrieval, however, previous methods only use multiple hash centers with equal weights to generate one centroid as the learning target, which ignores the relationship between the weights of hash centers and the proportion of instance regions in the image. To address the above issue, we propose a two-step alternative optimization approach, Instance-weighted Central Similarity (ICS), to automatically learn the center weight corresponding to a hash code. Firstly, we apply the maximum entropy regularizer to prevent one hash center from dominating the loss function, and compute the center weights via projection gradient descent. Secondly, we update neural network parameters by standard back-propagation with fixed center weights. More importantly, the learned center weights can well reflect the proportion of foreground instances in the image. Our method achieves the state-of-the-art performance on the image retrieval benchmarks, and especially improves the mAP by 1.6%-6.4% on the MS COCO dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.05274v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138613\", \"160718\", \"173455\", \"186821\", \"188672\", \"201169\", \"274846\", \"278179\", \"291400\", \"296798\", \"303512\", \"314568\", \"316473\", \"318535\", \"340633\"]}","task_split":"paper_retrieval"}
{"document_id":"30335","document_content":"# The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data\n## Categories\n- Machine Learning\n## Abstract\nErrors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics the nature of scientific data sets, where the noises arise as realizations of some random processes whose underlying distributions are known. The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.04742v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"87412\", \"117680\", \"129594\", \"210086\", \"218156\", \"219918\", \"237982\"]}","task_split":"paper_retrieval"}
{"document_id":"30599","document_content":"# IntenT5: Search Result Diversification using Causal Language Models\n## Categories\n- Information Retrieval\n## Abstract\nSearch result diversification is a beneficial approach to overcome under-specified queries, such as those that are ambiguous or multi-faceted. Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents. However, relying on user interaction data is problematic because one first needs a massive user base to build a sufficient log; public query logs are insufficient on their own. Given the recent success of causal language models (such as the Text-To-Text Transformer (T5) model) at text generation tasks, we explore the capacity of these models to generate potential query intents. We find that to encourage diversity in the generated queries, it is beneficial to adapt the model by including a new Distributional Causal Language Modeling (DCLM) objective during fine-tuning and a representation replacement during inference. Across six standard evaluation benchmarks, we find that our method (which we call IntenT5) improves search result diversity and attains (and sometimes exceeds) the diversity obtained when using query suggestions based on a proprietary query log. Our analysis shows that our approach is most effective for multi-faceted queries and is able to generalize effectively to queries that were unseen in training data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.04026v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75461\", \"94572\", \"94794\", \"96296\", \"114755\", \"119959\", \"128701\", \"136774\", \"144160\", \"185176\", \"190051\", \"190502\", \"203064\", \"279343\", \"283076\", \"298750\"]}","task_split":"paper_retrieval"}
{"document_id":"30616","document_content":"# Knowledge accumulating: The general pattern of learning\n## Categories\n- Artificial Intelligence\n## Abstract\nArtificial Intelligence has been developed for decades with the achievement of great progress. Recently, deep learning shows its ability to solve many real world problems, e.g. image classification and detection, natural language processing, playing GO. Theoretically speaking, an artificial neural network can fit any function and reinforcement learning can learn from any delayed reward. But in solving real world tasks, we still need to spend a lot of effort to adjust algorithms to fit task unique features. This paper proposes that the reason of this phenomenon is the sparse feedback feature of the nature, and a single algorithm, no matter how we improve it, can only solve dense feedback tasks or specific sparse feedback tasks. This paper first analyses how sparse feedback affects algorithm perfomance, and then proposes a pattern that explains how to accumulate knowledge to solve sparse feedback problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.03988v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"36709\", \"40011\", \"103886\", \"181133\", \"242806\", \"286335\"]}","task_split":"paper_retrieval"}
{"document_id":"30636","document_content":"# Model architecture can transform catastrophic forgetting into positive transfer\n## Categories\n- Machine Learning\n- Soft Condensed Matter\n- Adaptation and Self-Organizing Systems\n## Abstract\nThe work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks. In their case, learning the second task rapidly deteriorated the acquired knowledge about the previous one. We hypothesize that this could be a symptom of a fundamental problem: addition is an algorithmic task that should not be learned through pattern recognition. Therefore, other model architectures better suited for this task would avoid catastrophic forgetting. We use a neural network with a different architecture that can be trained to recover the correct algorithm for the addition of binary numbers. This neural network includes conditional clauses that are naturally treated within the back-propagation algorithm. We test it in the setting proposed by McCloskey and Cohen and training on random additions one by one. The neural network not only does not suffer from catastrophic forgetting but it improves its predictive power on unseen pairs of numbers as training progresses. We also show that this is a robust effect, also present when averaging many simulations. This work emphasizes the importance that neural network architecture has for the emergence of catastrophic forgetting and introduces a neural network that is able to learn an algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.03940v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cond-mat.soft\", \"nlin.AO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Soft Condensed Matter\", \"Adaptation and Self-Organizing Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"36460\", \"70976\", \"95925\", \"98711\", \"119224\", \"161521\", \"173202\", \"183437\", \"212647\", \"262842\", \"265568\", \"270623\", \"271679\", \"290378\", \"295435\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"30826","document_content":"# Language Model Evaluation in Open-ended Text Generation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAlthough current state-of-the-art language models have achieved impressive results in numerous natural language processing tasks, still they could not solve the problem of producing repetitive, dull and sometimes inconsistent text in open-ended text generation. Studies often attribute this problem to the maximum likelihood training objective, and propose alternative approaches by using stochastic decoding methods or altering the training objective. However, there is still a lack of consistent evaluation metrics to directly compare the efficacy of these solutions. In this work, we study different evaluation metrics that have been proposed to evaluate quality, diversity and consistency of machine-generated text. From there, we propose a practical pipeline to evaluate language models in open-ended generation task, and research on how to improve the model's performance in all dimensions by leveraging different auxiliary training objectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.03578v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"100503\", \"128766\", \"129680\", \"132799\", \"133508\", \"142153\", \"150522\", \"157151\", \"165308\", \"166082\", \"167338\", \"168951\", \"170862\", \"172001\", \"172088\", \"189504\", \"192158\", \"197416\", \"211205\", \"211890\", \"218973\", \"219226\", \"234728\", \"243154\", \"244479\", \"244755\", \"255035\", \"263848\", \"268219\", \"274817\", \"276365\", \"283971\", \"284481\", \"303250\", \"307223\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"30979","document_content":"# A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nDeep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signals by an expert. We argue that it is crucial to automate the reward learning process so that new skills can be taught to robots by their users. To address such automation, we consider task success classifiers using visual observations to estimate the rewards in terms of task success. In this work, we study the performance of multiple state-of-the-art deep reinforcement learning algorithms under different types of reward: Dense, Sparse, Visual Dense, and Visual Sparse rewards. Our experiments in various simulation tasks (Pendulum, Reacher, Pusher, and Fetch Reach) show that while DRL agents can learn successful behaviours using visual rewards when the goal targets are distinguishable, their performance may decrease if the task goal is not clearly visible. Our results also show that visual dense rewards are more successful than visual sparse rewards and that there is no single best algorithm for all tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.03222v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"3907\"], \"outgoing_citations\": [\"160120\", \"180884\", \"190237\", \"215643\", \"215931\", \"224840\", \"230042\", \"233924\", \"234342\", \"265464\", \"277472\", \"277564\", \"280296\", \"286689\", \"288804\", \"296980\", \"297317\"]}","task_split":"paper_retrieval"}
{"document_id":"31108","document_content":"# Is it Fake? News Disinformation Detection on South African News Websites\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n## Abstract\nDisinformation through fake news is an ongoing problem in our society and has become easily spread through social media. The most cost and time effective way to filter these large amounts of data is to use a combination of human and technical interventions to identify it. From a technical perspective, Natural Language Processing (NLP) is widely used in detecting fake news. Social media companies use NLP techniques to identify the fake news and warn their users, but fake news may still slip through undetected. It is especially a problem in more localised contexts (outside the United States of America). How do we adjust fake news detection systems to work better for local contexts such as in South Africa. In this work we investigate fake news detection on South African websites. We curate a dataset of South African fake news and then train detection models. We contrast this with using widely available fake news datasets (from mostly USA website). We also explore making the datasets more diverse by combining them and observe the differences in behaviour in writing between nations' fake news using interpretable machine learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.02941v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177196\", \"222721\", \"257863\", \"259202\", \"267552\", \"272864\"]}","task_split":"paper_retrieval"}
{"document_id":"31118","document_content":"# DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective single-stage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.02927v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73146\", \"9732\", \"18123\"], \"outgoing_citations\": [\"109645\", \"133188\", \"145738\", \"147155\", \"180184\", \"180750\", \"186126\", \"187122\", \"206658\", \"207283\", \"235575\", \"237248\", \"237488\", \"243986\", \"246925\", \"250094\", \"251473\", \"259302\", \"263387\", \"269599\", \"277635\", \"280378\", \"281953\", \"294983\", \"295615\", \"303067\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"31177","document_content":"# Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nThe rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed\/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multi-agent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.04087v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"47327\", \"69405\", \"80685\", \"88549\", \"105512\", \"117567\", \"124274\", \"130977\", \"132937\", \"141606\", \"141964\", \"144508\", \"153069\", \"153334\", \"156055\", \"168126\", \"170695\", \"172492\", \"175157\", \"180897\", \"181620\", \"186457\", \"188430\", \"202172\", \"202934\", \"213852\", \"214440\", \"222518\", \"230082\", \"233639\", \"237193\", \"239502\", \"242890\", \"244805\", \"246820\", \"246987\", \"249233\", \"250811\", \"258199\", \"265649\", \"269360\", \"270475\", \"271199\", \"292127\", \"293995\", \"360631\", \"361624\"]}","task_split":"paper_retrieval"}
{"document_id":"31178","document_content":"# Neural Twins Talk & Alternative Calculations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nInspired by how the human brain employs a higher number of neural pathways when describing a highly focused subject, we show that deep attentive models used for the main vision-language task of image captioning, could be extended to achieve better performance. Image captioning bridges a gap between computer vision and natural language processing. Automated image captioning is used as a tool to eliminate the need for human agent for creating descriptive captions for unseen images.Automated image captioning is challenging and yet interesting. One reason is that AI based systems capable of generating sentences that describe an input image could be used in a wide variety of tasks beyond generating captions for unseen images found on web or uploaded to social media. For example, in biology and medical sciences, these systems could provide researchers and physicians with a brief linguistic description of relevant images, potentially expediting their work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1142\/S1793351X21500045\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215181\", \"237655\", \"278604\", \"287504\", \"302621\", \"303209\", \"303224\", \"306073\", \"310136\", \"311464\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"31456","document_content":"# Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents\n## Categories\n- Robotics\n- Human-Computer Interaction\n## Abstract\nThe article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s12369-022-00868-z\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54112\", \"117860\", \"128396\", \"145384\", \"158493\", \"171356\", \"205147\", \"211664\", \"221363\", \"238119\", \"245831\", \"274567\", \"334905\"]}","task_split":"paper_retrieval"}
{"document_id":"31550","document_content":"# Using Interaction Data to Predict Engagement with Interactive Media\n## Categories\n- Human-Computer Interaction\n- Information Retrieval\n- Machine Learning\n## Abstract\nMedia is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is important in at least two ways: (1) a post-hoc understanding of how engaged audiences are with the content will help production teams learn from experience and improve future productions; (2), this type of media has potential for real-time measures of engagement to be used to enhance the user experience by adapting content on-the-fly. Engagement is typically measured by asking samples of users to self-report, which is time consuming and expensive. In some domains, however, interaction data have been used to infer engagement. Fortuitously, the nature of interactive media facilitates a much richer set of interaction data than traditional media; our research aims to understand if these data can be used to infer audience engagement. In this paper, we report a study using data captured from audience interactions with an interactive TV show to model and predict engagement. We find that temporal metrics, including overall time spent on the experience and the interval between events, are predictive of engagement. The results demonstrate that interaction data can be used to infer users' engagement during and after an experience, and the proposed techniques are relevant to better understand audience preference and responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3474085.3475631\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.IR\", \"cs.LG\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"285485\"]}","task_split":"paper_retrieval"}
{"document_id":"31567","document_content":"# Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWhile machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clustering-based FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.01903v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"490\", \"26435\"], \"outgoing_citations\": [\"129194\", \"237938\"]}","task_split":"paper_retrieval"}
{"document_id":"31773","document_content":"# Accelerating the Learning of TAMER with Counterfactual Explanations\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and interactively. Human-in-the-loop Reinforcement Learning (HRL) combines human feedback and Reinforcement Learning (RL) techniques. State-of-the-art interactive learning techniques suffer from slow learning speed, thus leading to a frustrating experience for the human. We approach this problem by extending the HRL framework TAMER for evaluative feedback with the possibility to enhance human feedback with two different types of counterfactual explanations (action and state based). We experimentally show that our extensions improve the speed of learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.01358v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"26797\", \"80071\", \"188486\", \"209989\", \"212704\", \"251863\", \"254581\", \"263098\", \"263957\", \"265505\"]}","task_split":"paper_retrieval"}
{"document_id":"32302","document_content":"# Anomaly Detection with Neural Parsers That Never Reject\n## Categories\n- Machine Learning\n- Formal Languages and Automata Theory\n- 68T07 (Primary) 68Q42 (Secondary)\n- I.2.6; F.4.2\n## Abstract\nReinforcement learning has recently shown promise as a technique for training an artificial neural network to parse sentences in some unknown format, through a body of work known as RL-GRIT. A key aspect of the RL-GRIT approach is that rather than explicitly inferring a grammar that describes the format, the neural network learns to perform various parsing actions (such as merging two tokens) over a corpus of sentences, with the goal of maximizing the estimated frequency of the resulting parse structures. This can allow the learning process to more easily explore different action choices, since a given choice may change the optimality of the parse (as expressed by the total reward), but will not result in the failure to parse a sentence. However, this also presents a limitation: because the trained neural network can successfully parse any sentence, it cannot be directly used to identify sentences that deviate from the format of the training sentences, i.e., that are anomalous. In this paper, we address this limitation by presenting procedures for extracting production rules from the neural network, and for using these rules to determine whether a given sentence is nominal or anomalous. When a sentence is anomalous, an attempt is made to identify the location of the anomaly. We empirically demonstrate that our approach is capable of grammatical inference and anomaly detection for both non-regular formats and those containing regions of high randomness\/entropy. While a format with high randomness typically requires large sets of production rules, we propose a two pass grammatical inference method to generate parsimonious rule sets for such formats. By further improving parser learning, and leveraging the presented rule extraction and anomaly detection algorithms, one might begin to understand common errors, either benign or malicious, in practical formats.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.00103v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.FL\", \"68T07 (Primary) 68Q42 (Secondary)\", \"I.2.6; F.4.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Formal Languages and Automata Theory\", \"68T07 (Primary) 68Q42 (Secondary)\", \"I.2.6; F.4.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"48980\", \"74431\", \"166853\", \"296256\"]}","task_split":"paper_retrieval"}
{"document_id":"32438","document_content":"# The Minimum Edit Arborescence Problem and Its Use in Compressing Graph Collections [Extended Version]\n## Categories\n- Computer Vision and Pattern Recognition\n- Data Structures and Algorithms\n## Abstract\nThe inference of minimum spanning arborescences within a set of objects is a general problem which translates into numerous application-specific unsupervised learning tasks. We introduce a unified and generic structure called edit arborescence that relies on edit paths between data in a collection, as well as the Min Edit Arborescence Problem, which asks for an edit arborescence that minimizes the sum of costs of its inner edit paths. Through the use of suitable cost functions, this generic framework allows to model a variety of problems. In particular, we show that by introducing encoding size preserving edit costs, it can be used as an efficient method for compressing collections of labeled graphs. Experiments on various graph datasets, with comparisons to standard compression tools, show the potential of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.14525v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.DS\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Data Structures and Algorithms\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112512\", \"160800\", \"173487\", \"229182\", \"273344\"]}","task_split":"paper_retrieval"}
{"document_id":"32621","document_content":"# Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nWith the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with \"micro-influencers\", which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be good measure of compatibility, we provide an effective visual method for interpreting our models' decisions, which can also be used to inform brands' media strategies. We compare with the current state-of-the-art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. The techniques for ranking and interpretation presented in this work can be generalised to arbitrary multimedia ranking tasks that have datasets with a similar structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISM52913.2021.00030\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"87000\", \"131081\", \"260314\", \"262955\", \"294694\", \"304189\", \"310667\", \"328394\"]}","task_split":"paper_retrieval"}
{"document_id":"32627","document_content":"# U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nDuring the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. However, when dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g. body weight or known co-morbidities) on the immediate course of disease is by and large unknown. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients is often determined only by acute indicators such as vital signs (e.g. breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic graph-based approach combining both imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality. Additionally, the network segments chest CT images as an auxiliary task and extracts image features and radiomics for feature fusion with the available metadata. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention allow for increased understanding of the patient relationships within the population graph and provide insight into the network's decision-making process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.00860v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"64738\", \"81472\", \"110720\", \"121555\", \"126302\", \"126461\", \"131079\", \"132371\", \"134674\", \"150932\", \"171755\", \"182380\", \"187277\", \"229197\", \"229319\", \"268084\", \"271967\", \"272506\"]}","task_split":"paper_retrieval"}
{"document_id":"32676","document_content":"# Non-Markovian Reinforcement Learning using Fractional Dynamics\n## Categories\n- Machine Learning\n## Abstract\nReinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment. In any given state, the agent takes some action, and the environment determines the probability distribution over the next state as well as gives the agent some reward. Most RL algorithms typically assume that the environment satisfies Markov assumptions (i.e. the probability distribution over the next state depends only on the current state). In this paper, we propose a model-based RL technique for a system that has non-Markovian dynamics. Such environments are common in many real-world applications such as in human physiology, biological systems, material science, and population dynamics. Model-based RL (MBRL) techniques typically try to simultaneously learn a model of the environment from the data, as well as try to identify an optimal policy for the learned model. We propose a technique where the non-Markovianity of the system is modeled through a fractional dynamical system. We show that we can quantify the difference in the performance of an MBRL algorithm that uses bounded horizon model predictive control from the optimal policy. Finally, we demonstrate our proposed framework on a pharmacokinetic model of human blood glucose dynamics and show that our fractional models can capture distant correlations on real-world datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.13790v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"2690\"], \"outgoing_citations\": [\"81434\", \"152597\", \"211870\", \"229890\", \"237510\", \"239410\", \"259045\", \"264941\", \"279429\", \"361126\"]}","task_split":"paper_retrieval"}
{"document_id":"32695","document_content":"# Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nIn recent years, with the advent of deep-learning, face recognition has achieved exceptional success. However, many of these deep face recognition models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose-invariant deep representations that are useful for profile face recognition. In this paper, we hypothesize that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. We look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. We leverage a coupled conditional generative adversarial network (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub-network tends to find a projection that maximizes the pair-wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of our approach compared with the state-of-the-art is demonstrated using the CFP, CMU Multi-PIE, IJB-A, and IJB-C datasets. Additionally, we have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal face recognition. We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, we have also evaluated our cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.13742v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"77972\", \"129059\", \"133319\", \"147325\", \"230095\", \"234787\", \"235160\", \"240261\", \"252448\", \"252688\", \"254268\", \"259118\", \"261657\", \"263835\", \"267940\", \"268379\", \"268405\", \"268970\", \"273937\", \"274313\", \"275999\", \"277822\", \"280996\", \"282032\", \"290620\", \"292385\", \"300606\", \"308384\", \"310922\", \"321122\", \"328183\"]}","task_split":"paper_retrieval"}
{"document_id":"32720","document_content":"# Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nNetwork telescopes or \"Darknets\" provide a unique window into Internet-wide malicious activities associated with malware propagation, denial of service attacks, scanning performed for network reconnaissance, and others. Analyses of the resulting data can provide actionable insights to security analysts that can be used to prevent or mitigate cyber-threats. Large Darknets, however, observe millions of nefarious events on a daily basis which makes the transformation of the captured information into meaningful insights challenging. We present a novel framework for characterizing Darknet behavior and its temporal evolution aiming to address this challenge. The proposed framework: (i) Extracts a high dimensional representation of Darknet events composed of features distilled from Darknet data and other external sources; (ii) Learns, in an unsupervised fashion, an information-preserving low-dimensional representation of these events (using deep representation learning) that is amenable to clustering; (iv) Performs clustering of the scanner data in the resulting representation space and provides interpretable insights using optimal decision trees; and (v) Utilizes the clustering outcomes as \"signatures\" that can be used to detect structural changes in the Darknet activities. We evaluate the proposed system on a large operational Network Telescope and demonstrate its ability to detect real-world, high-impact cybersecurity incidents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.00079v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"282580\"]}","task_split":"paper_retrieval"}
{"document_id":"33001","document_content":"# A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nAt the present time, sequential item recommendation models are compared by calculating metrics on a small item subset (target set) to speed up computation. The target set contains the relevant item and a set of negative items that are sampled from the full item set. Two well-known strategies to sample negative items are uniform random sampling and sampling by popularity to better approximate the item frequency distribution in the dataset. Most recently published papers on sequential item recommendation rely on sampling by popularity to compare the evaluated models. However, recent work has already shown that an evaluation with uniform random sampling may not be consistent with the full ranking, that is, the model ranking obtained by evaluating a metric using the full item set as target set, which raises the question whether the ranking obtained by sampling by popularity is equal to the full ranking. In this work, we re-evaluate current state-of-the-art sequential recommender models from the point of view, whether these sampling strategies have an impact on the final ranking of the models. We therefore train four recently proposed sequential recommendation models on five widely known datasets. For each dataset and model, we employ three evaluation strategies. First, we compute the full model ranking. Then we evaluate all models on a target set sampled by the two different sampling strategies, uniform random sampling and sampling by popularity with the commonly used target set size of 100, compute the model ranking for each strategy and compare them with each other. Additionally, we vary the size of the sampled target set. Overall, we find that both sampling strategies can produce inconsistent rankings compared with the full ranking of the models. Furthermore, both sampling by popularity and uniform random sampling do not consistently produce the same ranking ...","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3460231.3475943\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"12921\"], \"outgoing_citations\": [\"40830\", \"152700\", \"190649\", \"217192\", \"220924\", \"242002\", \"250622\", \"263938\", \"289543\", \"302767\", \"350452\"]}","task_split":"paper_retrieval"}
{"document_id":"33034","document_content":"# Emotion Stimulus Detection in German News Headlines\n## Categories\n- Computation and Language\n## Abstract\nEmotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e.g., in the sentence \"I am happy that I passed my exam\" the phrase \"passed my exam\" corresponds to the stimulus.). Previous work mainly focused on Mandarin and English, with no resources or models for German. We fill this research gap by developing a corpus of 2006 German news headlines annotated with emotions and 811 instances with annotations of stimulus phrases. Given that such corpus creation efforts are time-consuming and expensive, we additionally work on an approach for projecting the existing English GoodNewsEveryone (GNE) corpus to a machine-translated German version. We compare the performance of a conditional random field (CRF) model (trained monolingually on German and cross-lingually via projection) with a multilingual XLM-RoBERTa (XLM-R) model. Our results show that training with the German corpus achieves higher F1 scores than projection. Experiments with XLM-R outperform their respective CRF counterparts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12920v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21994\"], \"outgoing_citations\": [\"66948\", \"94122\", \"133941\", \"136366\", \"152396\", \"182669\", \"182683\", \"183275\", \"193187\", \"253593\", \"258292\", \"258761\"]}","task_split":"paper_retrieval"}
{"document_id":"33080","document_content":"# Open-Ended Learning Leads to Generally Capable Agents\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nIn this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. We show that through constructing an open-ended learning process, which dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation, simple tool use, option switching, and cooperation. Finally, we demonstrate that the general capabilities of this agent could unlock larger scale transfer of behaviour through cheap finetuning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12808v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"8658\", \"24178\", \"40573\", \"47401\", \"79803\", \"3378\", \"7674\", \"13224\", \"13228\", \"13824\", \"17752\", \"25823\", \"28906\", \"71164\"], \"outgoing_citations\": [\"17989\", \"35645\", \"38947\", \"41376\", \"75738\", \"81816\", \"82943\", \"95739\", \"116987\", \"119169\", \"130071\", \"133342\", \"136046\", \"137546\", \"145664\", \"148433\", \"151271\", \"152041\", \"161370\", \"161468\", \"164729\", \"166489\", \"184374\", \"195849\", \"196801\", \"200000\", \"201876\", \"214238\", \"218124\", \"224840\", \"226544\", \"229324\", \"231847\", \"232302\", \"239419\", \"249196\", \"251601\", \"261316\", \"261983\", \"262325\", \"264787\", \"266182\", \"269253\", \"271375\", \"274315\", \"280296\", \"283223\", \"297186\", \"297667\", \"303049\", \"357163\"]}","task_split":"paper_retrieval"}
{"document_id":"33101","document_content":"# ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nWe present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. We then study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations can improve (ensembles of) state-of-the-art convolutional neural networks via MTL. We hope that our dataset can be used in further research into multiple annotations and\/or MTL. All data and models are available on Github: https:\/\/github.com\/raumannsr\/ENHANCE.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12734v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123887\", \"128430\", \"227221\", \"237466\", \"245484\", \"263636\", \"270545\", \"277547\"]}","task_split":"paper_retrieval"}
{"document_id":"33145","document_content":"# Continual Learning with Neuron Activation Importance\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nContinual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. In this paper, we propose a neuron activation importance-based regularization method for stable continual learning regardless of the order of tasks. We conduct comprehensive experiments on existing benchmark data sets to evaluate not just the stability and plasticity of our method with improved classification accuracy also the robustness of the performance along the changes of task order.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12657v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"183710\", \"184114\", \"188169\", \"194365\", \"197667\", \"212647\", \"228062\", \"249234\", \"252086\", \"259336\", \"271679\", \"289331\", \"332151\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"33207","document_content":"# Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning\n## Categories\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expertise at the world's oldest board games and many classic video games, but they require vast quantities of experience to learn successfully -- none of today's algorithms account for the human ability to learn so many different tasks, so quickly. Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories -- rich, abstract, causal models of physical objects, intentional agents, and their interactions -- to explore and model an environment, and plan effectively to achieve task goals. We instantiate the approach in a video game playing agent called EMPA (the Exploring, Modeling, and Planning Agent), which performs Bayesian inference to learn probabilistic generative models expressed as programs for a game-engine simulator, and runs internal simulations over these models to support efficient object-based, relational exploration and heuristic planning. EMPA closely matches human learning efficiency on a suite of 90 challenging Atari-style video games, learning new games in just minutes of game play and generalizing robustly to new game situations and new levels. The model also captures fine-grained structure in people's exploration trajectories and learning dynamics. Its design and behavior suggest a way forward for building more general human-like AI systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12544v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"23386\", \"32844\", \"71387\"], \"outgoing_citations\": [\"96527\", \"134247\", \"155499\", \"174944\", \"185153\", \"196921\", \"201860\", \"212896\", \"225886\", \"229169\", \"229324\", \"241436\", \"260770\", \"263811\", \"264532\", \"266419\", \"269458\", \"271783\", \"279009\", \"279883\", \"281161\", \"284470\", \"284713\", \"295435\", \"302659\", \"308711\", \"310171\"]}","task_split":"paper_retrieval"}
{"document_id":"33326","document_content":"# A General Theory for Client Sampling in Federated Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nWhile client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains under-investigated. In this work, we provide a general theoretical framework to quantify the impact of a client sampling scheme and of the clients heterogeneity on the federated optimization. First, we provide a unified theoretical ground for previously reported sampling schemes experimental results on the relationship between FL convergence and the variance of the aggregation weights. Second, we prove for the first time that the quality of FL convergence is also impacted by the resulting covariance between aggregation weights. Our theory is general, and is here applied to Multinomial Distribution (MD) and Uniform sampling, two default unbiased client sampling schemes of FL, and demonstrated through a series of experiments in non-iid and unbalanced scenarios. Our results suggest that MD sampling should be used as default sampling scheme, due to the resilience to the changes in data ratio during the learning process, while Uniform sampling is superior only in the special case when clients have the same amount of data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12211v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"10866\", \"91185\", \"97012\", \"139315\", \"148245\", \"158749\", \"163880\", \"166970\", \"177601\", \"178679\", \"184652\", \"185106\", \"207462\", \"220594\", \"234581\", \"235592\", \"298782\"]}","task_split":"paper_retrieval"}
{"document_id":"33363","document_content":"# Towards the Unseen: Iterative Text Recognition by Distilling from Errors\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual text recognition is undoubtedly one of the most extensively researched topics in computer vision. Great progress have been made to date, with the latest models starting to focus on the more practical \"in-the-wild\" setting. However, a salient problem still hinders practical deployment -- prior arts mostly struggle with recognising unseen (or rarely seen) character sequences. In this paper, we put forward a novel framework to specifically tackle this \"unseen\" problem. Our framework is iterative in nature, in that it utilises predicted knowledge of character sequences from a previous iteration, to augment the main network in improving the next prediction. Key to our success is a unique cross-modal variational autoencoder to act as a feedback module, which is trained with the presence of textual error distribution data. This module importantly translate a discrete predicted character space, to a continuous affine transformation parameter space used to condition the visual feature map at next iteration. Experiments on common datasets have shown competitive performance over state-of-the-arts under the conventional setting. Most importantly, under the new disjoint setup where train-test labels are mutually exclusive, ours offers the best performance thus showcasing the capability of generalising onto unseen words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12081v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"191\", \"8735\", \"21983\", \"33360\", \"33358\", \"48901\"], \"outgoing_citations\": [\"111991\", \"123550\", \"126244\", \"126484\", \"135021\", \"136831\", \"150014\", \"172884\", \"180931\", \"183268\", \"192483\", \"194775\", \"195652\", \"196265\", \"198379\", \"205988\", \"209377\", \"211597\", \"211851\", \"225336\", \"237267\", \"239727\", \"250738\", \"255134\", \"256711\", \"270040\", \"294023\", \"295499\", \"296685\", \"296828\", \"299248\", \"302948\", \"309106\", \"309239\", \"316102\", \"320148\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"33374","document_content":"# HANet: Hierarchical Alignment Networks for Video-Text Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideo-text retrieval is an important yet challenging task in vision-language understanding, which aims to learn a joint embedding space where related video and text instances are close to each other. Most current works simply measure the video-text similarity based on video-level and text-level embeddings. However, the neglect of more fine-grained or local information causes the problem of insufficient representation. Some works exploit the local details by disentangling sentences, but overlook the corresponding videos, causing the asymmetry of video-text representation. To address the above limitations, we propose a Hierarchical Alignment Network (HANet) to align different level representations for video-text matching. Specifically, we first decompose video and text into three semantic levels, namely event (video and text), action (motion and verb), and entity (appearance and noun). Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text. Different level alignments capture fine-to-coarse correlations between video and text, as well as take the advantage of the complementary information among three semantic levels. Besides, our HANet is also richly interpretable by explicitly learning key semantic concepts. Extensive experiments on two public datasets, namely MSR-VTT and VATEX, show the proposed HANet outperforms other state-of-the-art methods, which demonstrates the effectiveness of hierarchical representation and alignment. Our code is publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.12059v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7454\", \"12778\"], \"outgoing_citations\": [\"60135\", \"62318\", \"69945\", \"76974\", \"84992\", \"89825\", \"95983\", \"96230\", \"101354\", \"110610\", \"113174\", \"114022\", \"118354\", \"131888\", \"139259\", \"151327\", \"154571\", \"172394\", \"173627\", \"176772\", \"181437\", \"191177\", \"191861\", \"214015\", \"217562\", \"223200\", \"236468\", \"238205\", \"256883\", \"260926\", \"282943\"]}","task_split":"paper_retrieval"}
{"document_id":"33399","document_content":"# A Shallow Ritz Method for Elliptic Problems with Singular Sources\n## Categories\n- Numerical Analysis\n- Machine Learning\n## Abstract\nIn this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jcp.2022.111547\", \"primary_category\": \"math.NA\", \"categories\": [\"cs.NA\", \"cs.LG\", \"math.NA\"], \"primary_category_human_readable\": \"Numerical Analysis\", \"categories_human_readable\": [\"Numerical Analysis\", \"Machine Learning\", \"Numerical Analysis\"], \"incoming_citations\": [\"43167\"], \"outgoing_citations\": [\"36034\", \"43167\", \"131271\", \"157866\", \"162132\", \"185358\", \"204091\", \"251913\", \"254429\", \"256582\", \"261713\"]}","task_split":"paper_retrieval"}
{"document_id":"33586","document_content":"# Automatic tempered posterior distributions for Bayesian inversion problems\n## Categories\n- Computation\n- Artificial Intelligence\n## Abstract\nWe propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/math9070784\", \"primary_category\": \"stat.CO\", \"categories\": [\"cs.AI\", \"stat.CO\"], \"primary_category_human_readable\": \"Computation\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation\"], \"incoming_citations\": [\"51330\"], \"outgoing_citations\": [\"124539\", \"312654\"]}","task_split":"paper_retrieval"}
{"document_id":"33769","document_content":"# Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss\n## Categories\n- Machine Learning\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nWe define disentanglement as how far class-different data points from each other are, relative to the distances among class-similar data points. When maximizing disentanglement during representation learning, we obtain a transformed feature representation where the class memberships of the data points are preserved. If the class memberships of the data points are preserved, we would have a feature representation space in which a nearest neighbour classifier or a clustering algorithm would perform well. We take advantage of this method to learn better natural language representation, and employ it on text classification and text clustering tasks. Through disentanglement, we obtain text representations with better-defined clusters and improve text classification performance. Our approach had a test classification accuracy of as high as 90.11% and test clustering accuracy of 88% on the AG News dataset, outperforming our baseline models -- without any other training tricks or regularization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.14597v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120860\", \"200093\", \"201070\", \"246513\", \"267161\", \"302803\"]}","task_split":"paper_retrieval"}
{"document_id":"34008","document_content":"# Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLanguage models (LMs) pretrained on a large text corpus and fine-tuned on a downstream text corpus and fine-tuned on a downstream task becomes a de facto training strategy for several natural language processing (NLP) tasks. Recently, an adaptive pretraining method retraining the pretrained language model with task-relevant data has shown significant performance improvements. However, current adaptive pretraining methods suffer from underfitting on the task distribution owing to a relatively small amount of data to re-pretrain the LM. To completely use the concept of adaptive pretraining, we propose a back-translated task-adaptive pretraining (BT-TAPT) method that increases the amount of task-specific data for LM re-pretraining by augmenting the task data using back-translation to generalize the LM to the target task domain. The experimental results show that the proposed BT-TAPT yields improved classification accuracy on both low- and high-resource data and better robustness to noise than the conventional adaptive pretraining method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.10474v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"126188\", \"129503\", \"138517\", \"176008\", \"186386\", \"187452\", \"188492\", \"189504\", \"193725\", \"200867\", \"220017\", \"228894\", \"234577\", \"244755\", \"292448\", \"297242\", \"298945\", \"302824\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"34019","document_content":"# Improve Learning from Crowds via Generative Augmentation\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nCrowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3447548.3467409\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.HC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"12314\", \"36175\"], \"outgoing_citations\": [\"78501\", \"78644\", \"141469\", \"171064\", \"183330\", \"190175\", \"199491\", \"242361\", \"249614\", \"250707\", \"256776\", \"264326\", \"265057\", \"270545\", \"290620\", \"291089\", \"302935\", \"333908\"]}","task_split":"paper_retrieval"}
{"document_id":"34289","document_content":"# Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives\n## Categories\n- Machine Learning\n- Computation and Language\n- Social and Information Networks\n- 68T05, 68T07\n- I.2; I.5\n## Abstract\nDuring the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine learning algorithms and NLP techniques. In the network-based process, we focus on social properties, network characteristics, and users. On the other hand, we classify misinformation using the content of the tweets directly in the content-based process, which contains text classification models (paragraph-level and sentence-level) and similarity models. The evaluation results on the network-based process show the best results for the artificial neural network model with an F1 score of 88.68%. In the content-based process, our novel similarity models, which obtained an F1 score of 90.26%, show an improvement in the misinformation classification results compared to the network-based models. In addition, in the text classification models, the best result was achieved using the stacking ensemble-learning model by obtaining an F1 score of 95.18%. Furthermore, we test our content-based models on the Constraint@AAAI2021 dataset, and by getting an F1 score of 94.38%, we improve the baseline results. Finally, we develop a fact-checking website called Checkovid that uses each process to detect misinformative and informative claims in the domain of COVID-19 from different perspectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.09768v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.SI\", \"68T05, 68T07\", \"I.2; I.5\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Social and Information Networks\", \"68T05, 68T07\", \"I.2; I.5\"], \"incoming_citations\": [], \"outgoing_citations\": [\"56773\", \"66278\", \"68638\", \"76033\", \"76125\", \"76199\", \"77467\", \"83834\", \"88313\", \"88648\", \"93422\", \"93549\", \"94401\", \"108176\", \"117359\", \"119855\", \"123410\", \"125576\", \"128075\", \"225253\"]}","task_split":"paper_retrieval"}
{"document_id":"34316","document_content":"# Towards Privacy-preserving Explanations in Medical Image Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning method was the best at preserving the disease-related semantic features while guaranteeing a high level of privacy among the compared state-of-the-art methods. Nevertheless, we emphasize the need to improve privacy-preserving methods for medical imaging, as we identified relevant drawbacks in all existing privacy-preserving approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.09652v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152608\", \"183000\", \"219512\", \"219527\", \"221505\", \"225328\", \"226576\", \"234474\", \"238486\", \"239137\", \"253401\", \"301835\"]}","task_split":"paper_retrieval"}
{"document_id":"34317","document_content":"# Learning to Share Autonomy Across Repeated Interaction\n## Categories\n- Robotics\n## Abstract\nWheelchair-mounted robotic arms (and other assistive robots) should help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot's motion: as the robot becomes confident it understands what the human wants, it increasingly intervenes to automate the task. But how does the robot know what tasks the human may want to perform in the first place? Today's shared autonomy approaches often rely on prior knowledge: for example, the robot must know the set of possible human goals a priori. In the long-term, however, this prior knowledge will inevitably break down -- sooner or later the human will reach for a goal that the robot did not expect. In this paper we propose a learning approach to shared autonomy that takes advantage of repeated interactions. Learning to assist humans would be impossible if they performed completely different tasks at every interaction: but our insight is that users living with physical disabilities repeat important tasks on a daily basis (e.g., opening the fridge, making coffee, and having dinner). We introduce an algorithm that exploits these repeated interactions to recognize the human's task, replicate similar demonstrations, and return control when unsure. As the human repeatedly works with this robot, our approach continually learns to assist tasks that were never specified beforehand: these tasks include both discrete goals (e.g., reaching a cup) and continuous skills (e.g., opening a drawer). Across simulations and an in-person user study, we demonstrate that robots leveraging our approach match existing shared autonomy methods for known goals, and outperform imitation learning baselines on new tasks. See videos here: https:\/\/youtu.be\/Plh4t3wQeIA","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.09650v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"22668\"], \"outgoing_citations\": [\"57844\", \"87273\", \"92106\", \"119110\", \"126543\", \"131830\", \"162391\", \"182269\", \"191821\", \"196387\", \"215235\", \"222967\", \"243197\", \"255571\", \"264845\", \"275319\", \"292179\"]}","task_split":"paper_retrieval"}
{"document_id":"34793","document_content":"# Pre-trained Language Models as Prior Knowledge for Playing Text-based Games\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Multiagent Systems\n- Robotics\n## Abstract\nRecently, text world games have been proposed to enable artificial agents to understand and reason about real-world scenarios. These text-based games are challenging for artificial agents, as it requires an understanding of and interaction using natural language in a partially observable environment. Agents observe the environment via textual descriptions designed to be challenging enough for even human players. Past approaches have not paid enough attention to the language understanding capability of the proposed agents. Typically, these approaches train from scratch, an agent that learns both textual representations and the gameplay online during training using a temporal loss function. Given the sample-inefficiency of RL approaches, it is inefficient to learn rich enough textual representations to be able to understand and reason using the textual observation in such a complicated game environment setting. In this paper, we improve the semantic understanding of the agent by proposing a simple RL with LM framework where we use transformer-based language models with Deep RL models. We perform a detailed study of our framework to demonstrate how our model outperforms all existing agents on the popular game, Zork1, to achieve a score of 44.7, which is 1.6 higher than the state-of-the-art model. Overall, our proposed approach outperforms 4 games out of the 14 text-based games, while performing comparable to the state-of-the-art models on the remaining games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.08408v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.MA\", \"cs.RO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Robotics\"], \"incoming_citations\": [\"15887\"], \"outgoing_citations\": [\"60832\", \"61113\", \"92228\", \"96195\", \"96449\", \"119028\", \"145805\", \"167303\", \"168951\", \"226321\", \"266419\", \"303349\"]}","task_split":"paper_retrieval"}
{"document_id":"34816","document_content":"# An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nFederated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew. We show that the performance degrades with the increasing degrees of data heterogeneity. We present several mitigation strategies to overcome performance drops from data heterogeneity, including weighted average for data quantity skew, weighted loss and batch normalization averaging for label distribution skew. The proposed optimizations to federated learning methods improve their capability of handling heterogeneity across institutions, which provides valuable guidance for the deployment of federated learning in real clinical applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.08371v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39697\"], \"outgoing_citations\": [\"164048\", \"166970\", \"207602\", \"248216\", \"310087\"]}","task_split":"paper_retrieval"}
{"document_id":"34986","document_content":"# POS tagging, lemmatization and dependency parsing of West Frisian\n## Categories\n- Computation and Language\n- Machine Learning\n- 68U15\n- J.5\n## Abstract\nWe present a lemmatizer\/POS-tagger\/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependency version 2. POS tags were assigned to words by using a Dutch POS tagger that was applied to a literal word-by-word translation, or to sentences of a Dutch parallel text. Best results were obtained when using literal translations that were created by using the Frisian translation program Oersetter. Morphologic and syntactic annotations were generated on the basis of a literal Dutch translation as well. The performance of the lemmatizer\/tagger\/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. A significant improvement was found for `lemma'. The Frisian lemmatizer\/PoS tagger\/dependency parser is released as a web app and as a web service.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.07974v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"stat.ML\", \"68U15\", \"J.5\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"68U15\", \"J.5\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51225\", \"128134\"]}","task_split":"paper_retrieval"}
{"document_id":"34988","document_content":"# How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?\n## Categories\n- Computation and Language\n## Abstract\nAs the spread of false information on the internet has increased dramatically in recent years, more and more attention is being paid to automated fake news detection. Some fake news detection methods are already quite successful. Nevertheless, there are still many vulnerabilities in the detection algorithms. The reason for this is that fake news publishers can structure and formulate their texts in such a way that a detection algorithm does not expose this text as fake news. This paper shows that it is possible to automatically attack state-of-the-art models that have been trained to detect Fake News, making these vulnerable. For this purpose, corresponding models were first trained based on a dataset. Then, using Text-Attack, an attempt was made to manipulate the trained models in such a way that previously correctly identified fake news was classified as true news. The results show that it is possible to automatically bypass Fake News detection mechanisms, leading to implications concerning existing policy initiatives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.07970v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"13501\"], \"outgoing_citations\": [\"73741\", \"123836\", \"126188\", \"128071\", \"133106\", \"142020\", \"159555\", \"166807\", \"174139\", \"184236\", \"203890\", \"206156\", \"228894\", \"234801\", \"245196\", \"281571\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"35042","document_content":"# Modeling User Behaviour in Research Paper Recommendation System\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nUser intention which often changes dynamically is considered to be an important factor for modeling users in the design of recommendation systems. Recent studies are starting to focus on predicting user intention (what users want) beyond user preference (what users like). In this work, a user intention model is proposed based on deep sequential topic analysis. The model predicts a user's intention in terms of the topic of interest. The Hybrid Topic Model (HTM) comprising Latent Dirichlet Allocation (LDA) and Word2Vec is proposed to derive the topic of interest of users and the history of preferences. HTM finds the true topics of papers estimating word-topic distribution which includes syntactic and semantic correlations among words. Next, to model user intention, a Long Short Term Memory (LSTM) based sequential deep learning model is proposed. This model takes into account temporal context, namely the time difference between clicks of two consecutive papers seen by a user. Extensive experiments with the real-world research paper dataset indicate that the proposed approach significantly outperforms the state-of-the-art methods. Further, the proposed approach introduces a new road map to model a user activity suitable for the design of a research paper recommendation system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.07831v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"35228","document_content":"# Level generation and style enhancement -- deep learning for game development overview\n## Categories\n- Computer Vision and Pattern Recognition\n- I.2.10; I.4.3; J.5\n## Abstract\nWe present practical approaches of using deep learning to create and enhance level maps and textures for video games -- desktop, mobile, and web. We aim to present new possibilities for game developers and level artists. The task of designing levels and filling them with details is challenging. It is both time-consuming and takes effort to make levels rich, complex, and with a feeling of being natural. Fortunately, recent progress in deep learning provides new tools to accompany level designers and visual artists. Moreover, they offer a way to generate infinite worlds for game replayability and adjust educational games to players' needs. We present seven approaches to create level maps, each using statistical methods, machine learning, or deep learning. In particular, we include:   - Generative Adversarial Networks for creating new images from existing examples (e.g. ProGAN).   - Super-resolution techniques for upscaling images while preserving crisp detail (e.g. ESRGAN).   - Neural style transfer for changing visual themes.   - Image translation - turning semantic maps into images (e.g. GauGAN).   - Semantic segmentation for turning images into semantic masks (e.g. U-Net).   - Unsupervised semantic segmentation for extracting semantic features (e.g. Tile2Vec).   - Texture synthesis - creating large patterns based on a smaller sample (e.g. InGAN).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.07397v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"I.2.10; I.4.3; J.5\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"I.2.10; I.4.3; J.5\"], \"incoming_citations\": [], \"outgoing_citations\": [\"50480\", \"55756\", \"95477\", \"119380\", \"131990\", \"137881\", \"152649\", \"153614\", \"175104\", \"194775\", \"202457\", \"219077\", \"224597\", \"230919\", \"232395\", \"232875\", \"266719\", \"270879\", \"274886\", \"283070\", \"293617\", \"297233\", \"307557\"]}","task_split":"paper_retrieval"}
{"document_id":"35322","document_content":"# Semantic Image Cropping\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic image cropping techniques are commonly used to enhance the aesthetic quality of an image; they do it by detecting the most beautiful or the most salient parts of the image and removing the unwanted content to have a smaller image that is more visually pleasing. In this thesis, I introduce an additional dimension to the problem of cropping, semantics. I argue that image cropping can also enhance the image's relevancy for a given entity by using the semantic information contained in the image. I call this problem, Semantic Image Cropping. To support my argument, I provide a new dataset containing 100 images with at least two different entities per image and four ground truth croppings collected using Amazon Mechanical Turk. I use this dataset to show that state-of-the-art cropping algorithms that only take into account aesthetics do not perform well in the problem of semantic image cropping. Additionally, I provide a new deep learning system that takes not just aesthetics but also semantics into account to generate image croppings, and I evaluate its performance using my new semantic cropping dataset, showing that using the semantic information of an image can help to produce better croppings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.07153v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"276752\", \"284450\"]}","task_split":"paper_retrieval"}
{"document_id":"35443","document_content":"# YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSoft biometrics inference in surveillance scenarios is a topic of interest for various applications, particularly in security-related areas. However, soft biometric analysis is not extensively reported in wild conditions. In particular, previous works on gender recognition report their results in face datasets, with relatively good image quality and frontal poses. Given the uncertainty of the availability of the facial region in wild conditions, we consider that these methods are not adequate for surveillance settings. To overcome these limitations, we: 1) present frontal and wild face versions of three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a model that effectively and dynamically complements facial and body information, which makes it suitable for gender recognition in wild conditions. The frontal and wild face datasets derive from widely used Pedestrian Attribute Recognition (PAR) sets (PETA, PA-100K, and RAP), using a pose-based approach to filter the frontal samples and facial regions. This approach retrieves the facial region of images with varying image\/subject conditions, where the state-of-the-art face detectors often fail. YY-Net combines facial and body information through a learnable fusion matrix and a channel-attention sub-network, focusing on the most influential body parts according to the specific image\/subject features. We compare it with five PAR methods, consistently obtaining state-of-the-art results on gender recognition, and reducing the prediction errors by up to 24% in frontal samples. The announced PAR datasets versions and YY-Net serve as the basis for wild soft biometrics classification and are available in https:\/\/github.com\/Tiago-Roxo.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.06847v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"49991\", \"87188\", \"123157\", \"123309\", \"162427\", \"174777\", \"180591\", \"207713\", \"220104\", \"225092\", \"243408\", \"248676\", \"252688\", \"253802\", \"254631\", \"254923\", \"257722\", \"264659\", \"270944\", \"279059\", \"296064\", \"297167\"]}","task_split":"paper_retrieval"}
{"document_id":"35672","document_content":"# Fairness-aware Summarization for Justified Decision-Making\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computers and Society\n## Abstract\nIn consequential domains such as recidivism prediction, facility inspection, and benefit assignment, it's important for individuals to know the decision-relevant information for the model's prediction. In addition, predictions should be fair both in terms of the outcome and the justification of the outcome. In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender. In this work, we focus on the problem of (un)fairness in the justification of the text-based neural models. We tie the explanatory power of the model to fairness in the outcome and propose a fairness-aware summarization mechanism to detect and counteract the bias in such models. Given a potentially biased natural language explanation for a decision, we use a multi-task neural model and an attribution mechanism based on integrated gradients to extract high-utility and low-bias justifications in form of a summary. The extracted summary is then used for training a model to make decisions for individuals. Results on several real world datasets suggest that our method drastically limits the demographic leakage in the input (fairness in justification) while moderately enhancing the fairness in the outcome. Our model is also effective in detecting and counteracting several types of data poisoning attacks that synthesize race-coded reasoning or irrelevant justifications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.06243v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computers and Society\"], \"incoming_citations\": [\"20171\"], \"outgoing_citations\": [\"20171\", \"91809\", \"118758\", \"141507\", \"156306\", \"159169\", \"162748\", \"167168\", \"170449\", \"171039\", \"183012\", \"193767\", \"193848\", \"201071\", \"206169\", \"213584\", \"226244\", \"226309\", \"227865\", \"230185\", \"237274\", \"239806\", \"241075\", \"241812\", \"242408\", \"244542\", \"245190\", \"262372\", \"262379\", \"264298\", \"268902\", \"280552\", \"281848\", \"284408\", \"287941\", \"299785\", \"303062\", \"305094\", \"307223\", \"309330\", \"320503\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"35761","document_content":"# Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction\n## Categories\n- Social and Information Networks\n- Artificial Intelligence\n## Abstract\nIn recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentivized is restricted. In this light, we intend to utilize social influence existing among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world scenarios, the topological structure of the network is usually unknown, which makes identifying influential users difficult. To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network. Meanwhile, we design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability. We evaluate the performance of the proposed approaches by conducting experiments on both synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05992v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177034\", \"199908\", \"231075\", \"313526\", \"331590\", \"339920\", \"359951\"]}","task_split":"paper_retrieval"}
{"document_id":"35896","document_content":"# Detecting Ideal Instagram Influencer Using Social Network Analysis\n## Categories\n- Social and Information Networks\n- Artificial Intelligence\n## Abstract\nSocial Media is a key aspect of modern society where people share their thoughts, views, feelings and sentiments. Over the last few years, the inflation in popularity of social media has resulted in a monumental increase in data. Users use this medium to express their thoughts, feelings, and opinions on a wide variety of subjects, including politics and celebrities. Social Media has thus evolved into a lucrative platform for companies to expand their scope and improve their prospects. The paper focuses on social network analysis (SNA) for a real-world online marketing strategy. The study contributes by comparing various centrality measures to identify the most central nodes in the network and uses a linear threshold model to understand the spreading behaviour of individual users. In conclusion, the paper correlates different centrality measures and spreading behaviour to identify the most influential user in the network","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05731v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"35897","document_content":"# Generalization of graph network inferences in higher-order probabilistic graphical models\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nProbabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we construct iterative message-passing algorithms using Graph Neural Networks defined on factor graphs to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method gains advantage over Belief Propagation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05729v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112378\", \"152437\", \"201866\", \"215435\", \"238297\", \"303167\", \"316291\", \"346695\", \"350478\"]}","task_split":"paper_retrieval"}
{"document_id":"35919","document_content":"# Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThis paper discusses the approach used by the Accenture Team for CLEF2021 CheckThat! Lab, Task 1, to identify whether a claim made in social media would be interesting to a wide audience and should be fact-checked. Twitter training and test data were provided in English, Arabic, Spanish, Turkish, and Bulgarian. Claims were to be classified (check-worthy\/not check-worthy) and ranked in priority order for the fact-checker. Our method used deep neural network transformer models with contextually sensitive lexical augmentation applied on the supplied training datasets to create additional training samples. This augmentation approach improved the performance for all languages. Overall, our architecture and data augmentation pipeline produced the best submitted system for Arabic, and performance scales according to the quantity of provided training data for English, Spanish, Turkish, and Bulgarian. This paper investigates the deep neural network architectures for each language as well as the provided data to examine why the approach worked so effectively for Arabic, and discusses additional data augmentation measures that should could be useful to this problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05684v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"21630\"], \"outgoing_citations\": [\"21630\", \"94933\", \"102258\", \"109640\", \"134870\", \"170447\", \"200867\"]}","task_split":"paper_retrieval"}
{"document_id":"35923","document_content":"# Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n- Optimization and Control\n## Abstract\nGenerative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural networks, posing a non-transparent optimization problem which is non-convex and non-concave over the generator and discriminator, respectively. Such networks are often heuristically optimized with gradient descent-ascent (GDA), but it is unclear whether the optimization problem contains any saddle points, or whether heuristic methods can find them in practice. In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games. Using this convex duality interpretation, we further demonstrate the impact of different activation functions of the discriminator. Our observations are verified with numerical results demonstrating the power of the convex interpretation, with applications in progressive training of convex architectures corresponding to linear generators and quadratic-activation discriminators for CelebA image generation. The code for our experiments is available at https:\/\/github.com\/ardasahiner\/ProCoGAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05680v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [\"16816\", \"43181\", \"65934\", \"15363\", \"16822\", \"17268\", \"78523\", \"140088\"], \"outgoing_citations\": [\"15363\", \"16822\", \"17268\", \"53568\", \"56647\", \"65934\", \"76597\", \"78523\", \"81616\", \"115940\", \"120045\", \"140088\", \"140358\", \"140681\", \"212705\", \"245177\", \"265604\", \"272918\", \"281002\", \"291301\", \"320016\"]}","task_split":"paper_retrieval"}
{"document_id":"36072","document_content":"# Prb-GAN: A Probabilistic Framework for GAN Modelling\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nGenerative adversarial networks (GANs) are very popular to generate realistic images, but they often suffer from the training instability issues and the phenomenon of mode loss. In order to attain greater diversity in GAN synthesized data, it is critical to solving the problem of mode loss. Our work explores probabilistic approaches to GAN modelling that could allow us to tackle these issues. We present Prb-GANs, a new variation that uses dropout to create a distribution over the network parameters with the posterior learnt using variational inference. We describe theoretically and validate experimentally using simple and complex datasets the benefits of such an approach. We look into further improvements using the concept of uncertainty measures. Through a set of further modifications to the loss functions for each network of the GAN, we are able to get results that show the improvement of GAN performance. Our methods are extremely simple and require very little modification to existing GAN architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05241v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152544\", \"155103\", \"172222\", \"172261\", \"174586\", \"177483\", \"181346\", \"181820\", \"188010\", \"190946\", \"192684\", \"195268\", \"210416\", \"212075\", \"222900\", \"236298\", \"237269\", \"245825\", \"249069\", \"266003\", \"269271\", \"272910\", \"275665\", \"278560\", \"281144\", \"291301\"]}","task_split":"paper_retrieval"}
{"document_id":"36084","document_content":"# A Simple Reward-free Approach to Constrained Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn constrained reinforcement learning (RL), a learning agent seeks to not only optimize the overall reward but also satisfy the additional safety, diversity, or budget constraints. Consequently, existing constrained RL solutions require several new algorithmic ingredients that are notably different from standard RL. On the other hand, reward-free RL is independently developed in the unconstrained literature, which learns the transition dynamics without using the reward information, and thus naturally capable of addressing RL with multiple objectives under the common dynamics. This paper bridges reward-free RL and constrained RL. Particularly, we propose a simple meta-algorithm such that given any reward-free RL oracle, the approachability and constrained RL problems can be directly solved with negligible overheads in sample complexity. Utilizing the existing reward-free RL solvers, our framework provides sharp sample complexity results for constrained RL in the tabular MDP setting, matching the best existing results up to a factor of horizon dependence; our framework directly extends to a setting of tabular two-player Markov games, and gives a new result for constrained RL with linear function approximation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05216v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71258\", \"84633\", \"96808\", \"105452\", \"117376\", \"118123\", \"120087\", \"139188\", \"139606\", \"143630\", \"176464\", \"179562\", \"184398\", \"194350\", \"199458\", \"230215\", \"271358\"]}","task_split":"paper_retrieval"}
{"document_id":"36127","document_content":"# LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution\n## Categories\n- Machine Learning\n## Abstract\nLexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such models do not take into account structured knowledge that exists in external lexical databases.   We introduce LexSubCon, an end-to-end lexical substitution framework based on contextual embedding models that can identify highly accurate substitute candidates. This is achieved by combining contextual information with knowledge from structured lexical resources. Our approach involves: (i) introducing a novel mix-up embedding strategy in the creation of the input embedding of the target word through linearly interpolating the pair of the target input embedding and the average embedding of its probable synonyms; (ii) considering the similarity of the sentence-definition embeddings of the target word and its proposed candidates; and, (iii) calculating the effect of each substitution in the semantics of the sentence through a fine-tuned sentence similarity model. Our experiments show that LexSubCon outperforms previous state-of-the-art methods on LS07 and CoInCo benchmark datasets that are widely used for lexical substitution tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05132v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122165\", \"168247\", \"170992\", \"171633\", \"179267\", \"200867\", \"244755\", \"264814\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"36175","document_content":"# Learning from Crowds with Sparse and Imbalanced Annotations\n## Categories\n- Machine Learning\n## Abstract\nTraditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases. Recently, crowdsourcing has established itself as an efficient labeling solution through resorting to non-expert crowds. To reduce the labeling error effects, one common practice is to distribute each instance to multiple workers, whereas each worker only annotates a subset of data, resulting in the {\\it sparse annotation} phenomenon. In this paper, we note that when meeting with class-imbalance, i.e., when the ground truth labels are {\\it class-imbalanced}, the sparse annotations are prone to be skewly distributed, which thus can severely bias the learning algorithm. To combat this issue, we propose one self-training based approach named {\\it Self-Crowd} by progressively adding confident pseudo-annotations and rebalancing the annotation distribution. Specifically, we propose one distribution aware confidence measure to select confident pseudo-annotations, which adopts the resampling strategy to oversample the minority annotations and undersample the majority annotations. On one real-world crowdsourcing image classification task, we show that the proposed method yields more balanced annotations throughout training than the distribution agnostic methods and substantially improves the learning performance at different annotation sparsity levels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05039v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"34019\", \"199491\", \"253266\", \"270545\"]}","task_split":"paper_retrieval"}
{"document_id":"36182","document_content":"# Similarity Guided Deep Face Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nFace image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly. In order to conduct fast and accurate retrieval, a compact hash code-based methods have been proposed, and recently, deep face image hashing methods with supervised classification training have shown outstanding performance. However, classification-based scheme has a disadvantage in that it cannot reveal complex similarities between face images into the hash code learning. In this paper, we attempt to improve the face image retrieval quality by proposing a Similarity Guided Hashing (SGH) method, which gently considers self and pairwise-similarity simultaneously. SGH employs various data augmentations designed to explore elaborate similarities between face images, solving both intra and inter identity-wise difficulties. Extensive experimental results on the protocols with existing benchmarks and an additionally proposed large scale higher resolution face image dataset demonstrate that our SGH delivers state-of-the-art retrieval performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.05025v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"132207\", \"139775\", \"140055\", \"173455\", \"187091\", \"198711\", \"213155\", \"252688\", \"264939\", \"274846\", \"291400\", \"316473\", \"329670\"]}","task_split":"paper_retrieval"}
{"document_id":"36260","document_content":"# From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview\n## Categories\n- Artificial Intelligence\n- I.2.4\n## Abstract\nIn this paper we discuss the relationships between conditional and preferential logics and neural network models, based on a multi-preferential semantics. We propose a concept-wise multipreference semantics, recently introduced for defeasible description logics to take into account preferences with respect to different concepts, as a tool for providing a semantic interpretation to neural network models. This approach has been explored both for unsupervised neural network models (Self-Organising Maps) and for supervised ones (Multilayer Perceptrons), and we expect that the same approach might be extended to other neural network models. It allows for logical properties of the network to be checked (by model checking) over an interpretation capturing the input-output behavior of the network. For Multilayer Perceptrons, the deep network itself can be regarded as a conditional knowledge base, in which synaptic connections correspond to weighted conditionals. The paper describes the general approach, through the cases of Self-Organising Maps and Multilayer Perceptrons, and discusses some open issues and perspectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.04870v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"I.2.4\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"45757\", \"63619\", \"78499\", \"103287\", \"120373\", \"139300\", \"186204\", \"187728\", \"220492\", \"230273\"]}","task_split":"paper_retrieval"}
{"document_id":"36268","document_content":"# Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls\n## Categories\n- Econometrics\n- Artificial Intelligence\n## Abstract\nThis article is an introduction to machine learning for financial forecasting, planning and analysis (FP\\&A). Machine learning appears well suited to support FP\\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP\\&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.04851v1\", \"primary_category\": \"econ.EM\", \"categories\": [\"econ.EM\", \"cs.AI\"], \"primary_category_human_readable\": \"Econometrics\", \"categories_human_readable\": [\"Econometrics\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"143344\", \"188490\", \"279895\", \"316341\"]}","task_split":"paper_retrieval"}
{"document_id":"36325","document_content":"# Systematic human learning and generalization from a brief tutorial with explanatory feedback\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Symbolic Computation\n## Abstract\nNeural networks have long been used to model human intelligence, capturing elements of behavior and cognition, and their neural basis. Recent advancements in deep learning have enabled neural network models to reach and even surpass human levels of intelligence in many respects, yet unlike humans, their ability to learn new tasks quickly remains a challenge. People can reason not only in familiar domains, but can also rapidly learn to reason through novel problems and situations, raising the question of how well modern neural network models capture human intelligence and in which ways they diverge. In this work, we explore this gap by investigating human adults' ability to learn an abstract reasoning task based on Sudoku from a brief instructional tutorial with explanatory feedback for incorrect responses using a narrow range of training examples. We find that participants who master the task do so within a small number of trials and generalize well to puzzles outside of the training range. We also find that most of those who master the task can describe a valid solution strategy, and such participants perform better on transfer puzzles than those whose strategy descriptions are vague or incomplete. Interestingly, fewer than half of our human participants were successful in acquiring a valid solution strategy, and this ability is associated with high school mathematics education. We consider the challenges these findings pose for building computational models that capture all aspects of our findings and point toward a possible role for learning to engage in explanation-based reasoning to support rapid learning and generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.06994v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Symbolic Computation\"], \"incoming_citations\": [], \"outgoing_citations\": [\"4339\", \"4639\", \"11792\", \"13400\", \"26023\", \"29312\", \"64908\", \"131702\", \"170653\", \"192616\", \"207428\", \"207866\", \"208082\", \"229324\", \"249694\", \"251911\", \"252519\", \"280202\", \"295435\", \"350497\", \"361902\"]}","task_split":"paper_retrieval"}
{"document_id":"36476","document_content":"# Graph-based Deep Generative Modelling for Document Layout Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nOne of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.04357v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"37203\", \"169267\", \"183355\", \"202311\", \"241029\", \"264315\", \"303167\", \"305707\", \"316961\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"36587","document_content":"# A Systematic Survey of Text Worlds as Embodied Natural Language Environments\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nText Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions. These environments offer an alternative to higher-fidelity 3D environments due to their low barrier to entry, providing the ability to study semantics, compositional inference, and other high-level tasks with rich high-level action spaces while controlling for perceptual input. This systematic survey outlines recent developments in tooling, environments, and agent modeling for Text Worlds, while examining recent trends in knowledge graphs, common sense reasoning, transfer learning of Text World performance to higher-fidelity environments, as well as near-term development targets that, once achieved, make Text Worlds an attractive general research paradigm for natural language processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.04132v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15847\"], \"outgoing_citations\": [\"25174\", \"41323\", \"41333\", \"43665\", \"53305\", \"55246\", \"55605\", \"60832\", \"63559\", \"73556\", \"82665\", \"87353\", \"91154\", \"92228\", \"95814\", \"96195\", \"96449\", \"97289\", \"97781\", \"98703\", \"111265\", \"116357\", \"119028\", \"127451\", \"127678\", \"129809\", \"132686\", \"137584\", \"140959\", \"143589\", \"145329\", \"145784\", \"145805\", \"152871\", \"153843\", \"155157\", \"159864\", \"160575\", \"166985\", \"167303\", \"167524\", \"168729\", \"169742\", \"171942\", \"173914\", \"175344\", \"181063\", \"184995\", \"187527\", \"193995\", \"196049\", \"199295\", \"200920\", \"207273\", \"210613\", \"212078\", \"213745\", \"216563\", \"218923\", \"219829\", \"222345\", \"226321\", \"226327\", \"231667\", \"244457\", \"245731\", \"247418\", \"256219\", \"266342\", \"271868\", \"290615\", \"303349\", \"310370\"]}","task_split":"paper_retrieval"}
{"document_id":"36651","document_content":"# Privacy Concerns in Chatbot Interactions: When to Trust and When to Worry\n## Categories\n- Computers and Society\n- Artificial Intelligence\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nThrough advances in their conversational abilities, chatbots have started to request and process an increasing variety of sensitive personal information. The accurate disclosure of sensitive information is essential where it is used to provide advice and support to users in the healthcare and finance sectors. In this study, we explore users' concerns regarding factors associated with the use of sensitive data by chatbot providers. We surveyed a representative sample of 491 British citizens. Our results show that the user concerns focus on deleting personal information and concerns about their data's inappropriate use. We also identified that individuals were concerned about losing control over their data after a conversation with conversational agents. We found no effect from a user's gender or education but did find an effect from the user's age, with those over 45 being more concerned than those under 45. We also considered the factors that engender trust in a chatbot. Our respondents' primary focus was on the chatbot's technical elements, with factors such as the response quality being identified as the most critical factor. We again found no effect from the user's gender or education level; however, when we considered some social factors (e.g. avatars or perceived 'friendliness'), we found those under 45 years old rated these as more important than those over 45. The paper concludes with a discussion of these results within the context of designing inclusive, digital systems that support a wide range of users.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-78642-7_53\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.HC\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Human-Computer Interaction\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122903\", \"186548\"]}","task_split":"paper_retrieval"}
{"document_id":"36730","document_content":"# Susceptibility to Image Resolution in Face Recognition and Trainings Strategies\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nFace recognition approaches often rely on equal image resolution for verifying faces on two images. However, in practical applications, those image resolutions are usually not in the same range due to different image capture mechanisms or sources. In this work, we first analyze the impact of image resolutions on face verification performance with a state-of-the-art face recognition model. For images synthetically reduced to $5\\,\\times\\,5$ px resolution, the verification performance drops from $99.23\\%$ increasingly down to almost $55\\%$. Especially for cross-resolution image pairs (one high- and one low-resolution image), the verification accuracy decreases even further. We investigate this behavior more in-depth by looking at the feature distances for every 2-image test pair. To tackle this problem, we propose the following two methods: 1) Train a state-of-the-art face-recognition model straightforwardly with $50\\%$ low-resolution images directly within each batch. 2) Train a siamese-network structure and add a cosine distance feature loss between high- and low-resolution features. Both methods show an improvement for cross-resolution scenarios and can increase the accuracy at very low resolution to approximately $70\\%$. However, a disadvantage is that a specific model needs to be trained for every resolution pair. Thus, we extend the aforementioned methods by training them with multiple image resolutions at once. The performances for particular testing image resolutions are slightly worse, but the advantage is that this model can be applied to arbitrary resolution images and achieves an overall better performance ($97.72\\%$ compared to $96.86\\%$). Due to the lack of a benchmark for arbitrary resolution images for the cross-resolution and equal-resolution task, we propose an evaluation protocol for five well-known datasets, focusing on high, mid, and low-resolution images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4230\/LITES.8.1.1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123715\", \"152527\", \"166256\", \"172947\", \"174767\", \"180372\", \"193164\", \"208805\", \"209118\", \"211273\", \"223755\", \"230095\", \"241237\", \"243986\", \"257586\", \"263345\", \"267940\", \"287611\"]}","task_split":"paper_retrieval"}
{"document_id":"36782","document_content":"# Deep Learning Based Image Retrieval in the JPEG Compressed Domain\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nContent-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image representations i.e. local and global image features have been studied in the literature. Extracting these features from pixel images and comparing them with images from the database is very time-consuming. Therefore, in recent years, there has been some effort to accomplish image analysis directly in the compressed domain with lesser computations. Furthermore, most of the images in our daily transactions are stored in the JPEG compressed format. Therefore, it would be ideal if we could retrieve features directly from the partially decoded or compressed data and use them for retrieval. Here, we propose a unified model for image retrieval which takes DCT coefficients as input and efficiently extracts global and local features directly in the JPEG compressed domain for accurate image retrieval. The experimental findings indicate that our proposed model performed similarly to the current DELG model which takes RGB features as an input with reference to mean average precision while having a faster training and retrieval speed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.03648v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"85899\", \"133188\", \"139611\", \"147155\", \"174291\", \"174787\", \"180184\", \"181568\", \"204367\", \"237248\", \"250094\", \"251473\", \"277635\", \"281953\", \"295615\"]}","task_split":"paper_retrieval"}
{"document_id":"36870","document_content":"# Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text\n## Categories\n- Computation and Language\n## Abstract\nThis work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate's reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text. Code available: https:\/\/github.com\/tingofurro\/keep_it_simple","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.03444v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8747\"], \"outgoing_citations\": [\"50181\", \"93583\", \"95485\", \"98965\", \"132176\", \"154650\", \"157706\", \"158683\", \"159316\", \"163045\", \"166959\", \"176154\", \"179990\", \"213824\", \"216792\", \"227488\", \"237494\", \"270048\", \"278954\", \"279213\"]}","task_split":"paper_retrieval"}
{"document_id":"36971","document_content":"# On Training Instance Selection for Few-Shot Neural Text Generation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLarge-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. We hope that this work will call for more attention on this largely unexplored area.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.03176v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8099\"], \"outgoing_citations\": [\"71089\", \"71091\", \"71092\", \"77463\", \"79038\", \"93421\", \"95674\", \"100541\", \"123752\", \"150522\", \"158621\", \"189612\", \"201075\", \"226758\", \"230190\", \"262648\", \"267694\", \"271881\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"37164","document_content":"# AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOne practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called \\textit{AdaRL}, that adapts reliably and efficiently to changes across domains with a few samples from the target domain, even in partially observable environments. Specifically, we leverage a parsimonious graphical representation that characterizes structural relationships over variables in the RL system. Such graphical representations provide a compact way to encode what and where the changes across domains are, and furthermore inform us with a minimal set of changes that one has to consider for the purpose of policy adaptation. We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided. We illustrate the efficacy of AdaRL through a series of experiments that vary factors in the observation, transition, and reward functions for Cartpole and Atari games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.02729v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"78473\", \"109082\"], \"outgoing_citations\": [\"26797\", \"68242\", \"75606\", \"100245\", \"112116\", \"117599\", \"132207\", \"133560\", \"137062\", \"143390\", \"152160\", \"154589\", \"164077\", \"192827\", \"194494\", \"210195\", \"214993\", \"218124\", \"228299\", \"229795\", \"230267\", \"237229\", \"243235\", \"251497\", \"261316\", \"274445\", \"280870\", \"311595\", \"317251\", \"331072\", \"337980\", \"341343\", \"356201\", \"361846\"]}","task_split":"paper_retrieval"}
{"document_id":"37203","document_content":"# DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.02638v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"10179\", \"36476\"], \"outgoing_citations\": [\"138393\", \"169267\", \"171596\", \"202311\", \"262306\"]}","task_split":"paper_retrieval"}
{"document_id":"37381","document_content":"# Polarized skylight orientation determination artificial neural network\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nThis paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1364\/AO.453177\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"130382\", \"226516\"]}","task_split":"paper_retrieval"}
{"document_id":"37392","document_content":"# LightFuse: Lightweight CNN based Dual-exposure Fusion\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nDeep convolutional neural networks (DCNNs) have aided high dynamic range (HDR) imaging recently and have received a lot of attention. The quality of DCNN-generated HDR images has overperformed the traditional counterparts. However, DCNNs are prone to be computationally intensive and power-hungry, and hence cannot be implemented on various embedded computing platforms with limited power and hardware resources. Embedded systems have a huge market, and utilizing DCNNs' powerful functionality into them will further reduce human intervention. To address the challenge, we propose LightFuse, a lightweight CNN-based algorithm for extreme dual-exposure image fusion, which achieves better functionality than a conventional DCNN and can be deployed in embedded systems. Two sub-networks are utilized: a GlobalNet (G) and a DetailNet (D). The goal of G is to learn the global illumination information on the spatial dimension, whereas D aims to enhance local details on the channel dimension. Both G and D are based solely on depthwise convolution (D_Conv) and pointwise convolution (P_Conv) to reduce required parameters and computations. Experimental results show that this proposed technique could generate HDR images in extremely exposed regions with sufficient details to be legible. Our model outperforms other state-of-the-art approaches in peak signal-to-noise ratio (PSNR) score by 0.9 to 8.7 while achieving 16.7 to 306.2 times parameter reduction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.02299v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"114390\", \"218414\", \"234314\", \"246985\", \"249439\", \"276130\", \"301197\"]}","task_split":"paper_retrieval"}
{"document_id":"37526","document_content":"# The MineRL BASALT Competition on Learning from Human Feedback\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are now being incorporated into commercial products. With this comes an additional challenge: how can we build AI systems that solve tasks where there is not a crisp, well-defined specification? While multiple solutions have been proposed, in this competition we focus on one in particular: learning from human feedback. Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.   The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. These tasks are defined by a paragraph of natural language: for example, \"create a waterfall and take a scenic picture of it\", with additional clarifying details. Participants must train a separate agent for each task, using any method they want. Agents are then evaluated by humans who have read the task description. To help participants get started, we provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline that leverages these demonstrations.   Our hope is that this competition will improve our ability to build AI systems that do what their designers intend them to do, even when the intent cannot be easily formalized. Besides allowing AI to solve more tasks, this can also enable more effective regulation of AI systems, as well as making progress on the value alignment problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.01969v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"4773\", \"19631\", \"34439\"], \"outgoing_citations\": [\"102649\", \"104830\", \"142761\", \"175344\", \"189416\", \"199330\", \"201870\", \"218636\", \"221473\", \"237653\", \"251082\", \"251924\", \"254581\", \"260483\", \"263957\", \"290679\"]}","task_split":"paper_retrieval"}
{"document_id":"37656","document_content":"# Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nGiven the abundance and ease of access of personal data today, individual privacy has become of paramount importance, particularly in the healthcare domain. In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy. We develop a scheduling algorithm in conjunction with a student-teacher algorithm that is deployed in a federated manner. This allows a central model to learn from batches of data at each federal node. The teacher acts between data centres to update the main task (student) algorithm using the data that is stored in the various data centres. We show that the scheduler, trained using meta-gradients, can effectively organise training and as a result train a machine learning model on a diverse dataset without needing explicit access to the patient data. We achieve state-of-the-art performance and show how our method overcomes some of the problems faced in the federated learning such as node poisoning. We further show how the scheduler can be used as a mechanism for transfer learning, allowing different teachers to work together in training a student for state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.01707v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22133\", \"111604\", \"114868\", \"118319\", \"139426\", \"142093\", \"150871\", \"151488\", \"154095\", \"163732\", \"200347\", \"209785\", \"226146\", \"229877\", \"249360\", \"254251\", \"257949\", \"258393\", \"265072\", \"269253\", \"271665\"]}","task_split":"paper_retrieval"}
{"document_id":"37733","document_content":"# DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG). A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of 21%, and 3% respectively, which shows the effectiveness of the approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.10648v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"29321\"], \"outgoing_citations\": [\"10589\", \"29321\", \"37995\", \"73181\", \"83480\", \"94071\", \"105378\", \"123410\", \"129247\", \"138930\", \"145400\", \"149300\", \"156620\", \"201040\", \"205022\", \"230998\", \"234054\", \"238072\", \"246910\", \"267552\", \"289972\"]}","task_split":"paper_retrieval"}
{"document_id":"37760","document_content":"# Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nModeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.06393v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51252\", \"110073\", \"111573\", \"113780\", \"118558\", \"140700\", \"196894\", \"229861\", \"230273\", \"230419\", \"237102\", \"251792\", \"270915\", \"281269\", \"281358\", \"295736\", \"297855\", \"311008\", \"312474\", \"328519\", \"333252\", \"334299\", \"348633\"]}","task_split":"paper_retrieval"}
{"document_id":"37770","document_content":"# On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n- 68T01, 68T07, 68T30\n- I.2.6; I.2.4\n## Abstract\nGraph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features, i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks, i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insights, thus leading to a practical guideline on the choices between different artificial node features for GNNs on non-attributed graphs. The code is available at https:\/\/github.com\/zjzijielu\/gnn-positional-structural-node-features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.01495v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\", \"68T01, 68T07, 68T30\", \"I.2.6; I.2.4\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\", \"68T01, 68T07, 68T30\", \"I.2.6; I.2.4\"], \"incoming_citations\": [\"7683\", \"16180\", \"22079\", \"102194\", \"19282\", \"36145\"], \"outgoing_citations\": [\"36154\", \"51077\", \"73530\", \"86320\", \"89373\", \"91086\", \"91654\", \"92363\", \"96691\", \"97047\", \"103085\", \"104688\", \"111508\", \"113226\", \"115393\", \"116470\", \"117308\", \"126374\", \"133777\", \"143456\", \"150166\", \"158126\", \"163945\", \"181255\", \"186789\", \"229272\", \"230359\", \"240758\", \"253807\", \"255737\", \"269202\"]}","task_split":"paper_retrieval"}
{"document_id":"37959","document_content":"# General Board Game Concepts\n## Categories\n- Artificial Intelligence\n## Abstract\nMany games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of \"game concept\", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.01078v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"47061\"], \"outgoing_citations\": [\"47061\", \"67161\", \"76721\", \"77159\", \"77821\", \"100245\", \"118353\", \"178386\", \"178387\", \"186608\", \"194877\", \"218647\"]}","task_split":"paper_retrieval"}
{"document_id":"38028","document_content":"# Reconsidering Dependency Networks from an Information Geometry Perspective\n## Categories\n- Machine Learning\n## Abstract\nDependency networks (Heckerman et al., 2000) are potential probabilistic graphical models for systems comprising a large number of variables. Like Bayesian networks, the structure of a dependency network is represented by a directed graph, and each node has a conditional probability table. Learning and inference are realized locally on individual nodes; therefore, computation remains tractable even with a large number of variables. However, the dependency network's learned distribution is the stationary distribution of a Markov chain called pseudo-Gibbs sampling and has no closed-form expressions. This technical disadvantage has impeded the development of dependency networks. In this paper, we consider a certain manifold for each node. Then, we can interpret pseudo-Gibbs sampling as iterative m-projections onto these manifolds. This interpretation provides a theoretical bound for the location where the stationary distribution of pseudo-Gibbs sampling exists in distribution space. Furthermore, this interpretation involves structure and parameter learning algorithms as optimization problems. In addition, we compare dependency and Bayesian networks experimentally. The results demonstrate that the dependency network and the Bayesian network have roughly the same performance in terms of the accuracy of their learned distributions. The results also show that the dependency network can learn much faster than the Bayesian network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00871v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"357773\"]}","task_split":"paper_retrieval"}
{"document_id":"38037","document_content":"# Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nInducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise that the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00848v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"65457\", \"15406\", \"34142\", \"83792\", \"35918\", \"45824\", \"65740\", \"96305\"], \"outgoing_citations\": [\"29772\", \"39639\", \"41902\", \"60526\", \"81981\", \"83792\", \"95616\", \"96114\", \"104822\", \"115105\", \"115368\", \"143609\", \"153932\", \"155499\", \"159320\", \"160036\", \"162372\", \"163450\", \"163549\", \"163690\", \"164719\", \"164814\", \"165238\", \"171629\", \"172432\", \"183941\", \"185153\", \"200942\", \"201860\", \"207051\", \"213745\", \"216156\", \"229324\", \"236067\", \"240081\", \"255613\", \"277538\", \"279026\", \"302659\", \"335083\", \"357232\"]}","task_split":"paper_retrieval"}
{"document_id":"38073","document_content":"# On Bridging Generic and Personalized Federated Learning for Image Classification\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFederated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributions diverge from each other. This divergence further leads to a dilemma: \"Should we prioritize the learned model's generic performance (for future use at the server) or its personalized performance (for each client)?\" These two, seemingly competing goals have divided the community to focus on one or the other, yet in this paper we show that it is possible to approach both at the same time. Concretely, we propose a novel federated learning framework that explicitly decouples a model's dual duties with two prediction tasks. On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them. On the other hand, we formulate the personalized predictor as a lightweight adaptive module that is learned to minimize each client's empirical risk on top of the generic predictor. With this two-loss, two-predictor framework which we name Federated Robust Decoupling (Fed-RoD), the learned model can simultaneously achieve state-of-the-art generic and personalized performance, essentially bridging the two tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00778v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8820\"], \"outgoing_citations\": [\"10866\", \"67041\", \"69232\", \"69489\", \"70515\", \"80294\", \"85325\", \"96456\", \"100192\", \"102417\", \"106048\", \"107082\", \"109147\", \"110559\", \"112023\", \"113531\", \"117728\", \"118319\", \"118376\", \"119091\", \"125758\", \"133314\", \"134225\", \"134463\", \"135986\", \"136214\", \"137349\", \"139315\", \"140332\", \"141462\", \"142093\", \"142802\", \"142834\", \"143220\", \"148245\", \"148382\", \"148437\", \"149167\", \"151250\", \"153202\", \"158749\", \"160301\", \"160705\", \"161126\", \"162752\", \"164468\", \"164608\", \"164627\", \"166970\", \"167523\", \"172499\", \"177371\", \"177601\", \"180239\", \"180726\", \"182115\", \"183930\", \"198316\", \"200347\", \"200704\", \"202716\", \"205798\", \"207462\", \"229563\", \"230678\", \"237905\", \"241304\", \"253266\", \"256866\", \"259463\", \"260314\", \"260655\", \"262216\", \"263636\", \"265073\", \"282453\", \"291807\", \"297304\", \"307383\", \"308096\", \"350529\", \"360780\"]}","task_split":"paper_retrieval"}
{"document_id":"38211","document_content":"# MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n- Digital Libraries\n- 68T10\n## Abstract\nIdentity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In addition, the published datasets were typically designed only for a subset of document recognition problems, not for a complex identity document analysis. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. For the presented benchmark dataset baselines are provided for such tasks as document location and identification, text fields recognition, and face detection. With 72409 annotated images in total, to the date of publication the proposed dataset is the largest publicly available identity documents dataset with variable artificially generated data, and we believe that it will prove invaluable for advancement of the field of document analysis and recognition. The dataset is available for download at ftp:\/\/smartengines.com\/midv-2020 and http:\/\/l3i-share.univ-lr.fr .","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18287\/2412-6179-CO-1006\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.DL\", \"68T10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Digital Libraries\", \"68T10\"], \"incoming_citations\": [], \"outgoing_citations\": [\"41140\", \"84556\", \"107498\", \"107518\", \"115916\", \"157317\", \"162611\", \"188069\", \"224507\", \"235351\", \"258667\", \"278071\", \"302868\", \"306222\"]}","task_split":"paper_retrieval"}
{"document_id":"38249","document_content":"# Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available. This can allow us to take advantage of environments well suited for training, such as simulators or laboratories, to learn a policy for a real robot in a home or office. To succeed, such policy transfer must overcome both the visual domain gap (e.g. different illumination or background) and the dynamics domain gap (e.g. different robot calibration or modelling error) between source and target environments. However, prior policy transfer approaches either cannot handle a large domain gap or can only address one type of domain gap at a time. In this paper, we propose a novel policy transfer method with iterative \"environment grounding\", IDAPT, that alternates between (1) directly minimizing both visual and dynamics domain gaps by grounding the source environment in the target environment domains, and (2) training a policy on the grounded source environment. This iterative training progressively aligns the domains between the two environments and adapts the policy to the target environment. Once trained, the policy can be directly executed on the target environment. The empirical results on locomotion and robotic manipulation tasks demonstrate that our approach can effectively transfer a policy across visual and dynamics domain gaps with minimal supervision and interaction with the target environment. Videos and code are available at https:\/\/clvrai.com\/idapt .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00339v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"10027\", \"26664\"], \"outgoing_citations\": [\"79812\", \"107895\", \"108017\", \"108018\", \"118301\", \"133560\", \"155930\", \"160599\", \"205568\", \"224411\", \"240817\", \"253042\", \"253045\", \"254597\", \"255139\", \"261786\", \"271030\", \"271975\", \"272030\", \"281760\", \"282684\", \"283289\", \"306513\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"38256","document_content":"# Orthonormal Product Quantization Network for Scalable Face Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nExisting deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations like pose, illumination, and expressions in face images, still pose a challenge for face image retrieval. In light of this, face image retrieval requires sufficiently powerful learning metrics, which are absent in current deep quantization works. Moreover, to tackle the growing unseen identities in the query stage, face image retrieval drives more demands regarding model generalization and system scalability than general image retrieval tasks. This paper integrates product quantization with orthonormal constraints into an end-to-end deep learning framework to effectively retrieve face images. Specifically, a novel scheme that uses predefined orthonormal vectors as codewords is proposed to enhance the quantization informativeness and reduce codewords' redundancy. A tailored loss function maximizes discriminability among identities in each quantization subspace for both the quantized and original features. An entropy-based regularization term is imposed to reduce the quantization error. Experiments are conducted on four commonly-used face datasets under both seen and unseen identities retrieval settings. Our method outperforms all the compared deep hashing\/quantization state-of-the-arts under both settings. Results validate the effectiveness of the proposed orthonormal codewords in improving models' standard retrieval performance and generalization ability. Combing with further experiments on two general image datasets, it demonstrates the broad superiority of our method for scalable image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00327v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62576\", \"140055\", \"173455\", \"191827\", \"200534\", \"200699\", \"239316\", \"243986\", \"249538\", \"252688\", \"264939\", \"267940\", \"278179\", \"284208\", \"303512\", \"316473\"]}","task_split":"paper_retrieval"}
{"document_id":"38332","document_content":"# Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards\n## Categories\n- Computation and Language\n## Abstract\nThe growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities\/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-the-art models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.00176v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"45853\"], \"outgoing_citations\": [\"45853\", \"50181\", \"107584\", \"127753\", \"128932\", \"133009\", \"140175\", \"147514\", \"150522\", \"157706\", \"166959\", \"170657\", \"186316\", \"230212\", \"235232\", \"235463\", \"266671\", \"268902\", \"278954\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"38416","document_content":"# Improving Factual Consistency of Abstractive Summarization on Customer Feedback\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nE-commerce stores collect customer feedback to let sellers learn about customer concerns and enhance customer order experience. Because customer feedback often contains redundant information, a concise summary of the feedback can be generated to help sellers better understand the issues causing customer dissatisfaction. Previous state-of-the-art abstractive text summarization models make two major types of factual errors when producing summaries from customer feedback, which are wrong entity detection (WED) and incorrect product-defect description (IPD). In this work, we introduce a set of methods to enhance the factual consistency of abstractive summarization on customer feedback. We augment the training data with artificially corrupted summaries, and use them as counterparts of the target summaries. We add a contrastive loss term into the training objective so that the model learns to avoid certain factual errors. Evaluation results show that a large portion of WED and IPD errors are alleviated for BART and T5. Furthermore, our approaches do not depend on the structure of the summarization model and thus are generalizable to any abstractive summarization systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.16188v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16503\"], \"outgoing_citations\": [\"91716\", \"93583\", \"93920\", \"96411\", \"125951\", \"132176\", \"136011\", \"139463\", \"150522\", \"159316\", \"170449\", \"187236\", \"250690\", \"261785\", \"298016\"]}","task_split":"paper_retrieval"}
{"document_id":"38417","document_content":"# Reinforcement Learning based Disease Progression Model for Alzheimer's Disease\n## Categories\n- Machine Learning\n## Abstract\nWe model Alzheimer's disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) and the trained RL agent to predict individualized 10-year AD progression using baseline (year 0) features on synthetic and real data. The model was comparable or better at predicting 10-year cognition trajectories than state-of-the-art learning-based models. Our interpretable model demonstrated, and provided insights into, \"recovery\/compensatory\" processes that mitigate the effect of AD, even though those processes were not explicitly encoded in the model. Our framework combines DEs with RL for modelling AD progression and has broad applicability for understanding other neurological disorders.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.16187v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"140704\", \"206239\"]}","task_split":"paper_retrieval"}
{"document_id":"38538","document_content":"# On joint training with interfaces for spoken language understanding\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nSpoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances. SLU systems usually consist of (1) an automatic speech recognition (ASR) module, (2) an interface module that exposes relevant outputs from ASR, and (3) a natural language understanding (NLU) module. Interfaces in SLU systems carry information on text transcriptions or richer information like neural embeddings from ASR to NLU. In this paper, we study how interfaces affect joint-training for spoken language understanding. Most notably, we obtain the state-of-the-art results on the publicly available 50-hr SLURP dataset. We first leverage large-size pretrained ASR and NLU models that are connected by a text interface, and then jointly train both models via a sequence loss function. For scenarios where pretrained models are not utilized, the best results are obtained through a joint sequence loss training using richer neural interfaces. Finally, we show the overall diminishing impact of leveraging pretrained models with increased training data size.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15919v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"49488\", \"55947\", \"57429\", \"69680\", \"69895\", \"80300\", \"84544\", \"89723\", \"106068\", \"109426\", \"134532\", \"143720\", \"191769\", \"197154\", \"216686\", \"233817\", \"248223\", \"248246\", \"308468\", \"347142\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"38564","document_content":"# Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study\n## Categories\n- Artificial Intelligence\n## Abstract\nWe introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster's principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15877v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"4621\", \"21243\", \"37071\"], \"outgoing_citations\": [\"64439\", \"95477\", \"125408\", \"145664\", \"153614\", \"176932\", \"189201\", \"225532\", \"233501\", \"240572\", \"274886\", \"289709\", \"293071\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"38582","document_content":"# The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nAlthough machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to follow a single linear trend when evaluated across a testbed of models. Models that are more accurate on the out-of-distribution data relative to this baseline exhibit \"effective robustness\" and are exceedingly rare. Identifying such models, and understanding their properties, is key to improving out-of-distribution performance. We conduct a thorough empirical investigation of effective robustness during fine-tuning and surprisingly find that models pre-trained on larger datasets exhibit effective robustness during training that vanishes at convergence. We study how properties of the data influence effective robustness, and we show that it increases with the larger size, more diversity, and higher example difficulty of the dataset. We also find that models that display effective robustness are able to correctly classify 10% of the examples that no other current testbed model gets correct. Finally, we discuss several strategies for scaling effective robustness to the high-accuracy regime to improve the out-of-distribution accuracy of state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15831v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"168861\", \"14815\", \"25754\", \"6046\", \"29337\", \"36367\"], \"outgoing_citations\": [\"25754\", \"72058\", \"77680\", \"80696\", \"111560\", \"115360\", \"128088\", \"149671\", \"151836\", \"156893\", \"183711\", \"184651\", \"189419\", \"193797\", \"199022\", \"206200\", \"208243\", \"215646\", \"220228\", \"229604\", \"229879\", \"233440\", \"238070\", \"247927\", \"261608\", \"330696\"]}","task_split":"paper_retrieval"}
{"document_id":"38616","document_content":"# Curvature Graph Neural Network\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing the importance of nodes in the topology structure. We believe that the structural importance of neighboring nodes is closely related to their importance in aggregation. In this paper, we introduce discrete graph curvature (the Ricci curvature) to quantify the strength of structural connection of pairwise nodes. And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature. To improve the adaptability of curvature to various datasets, we explicitly transform curvature into the weights of neighboring nodes by the necessary Negative Curvature Processing Module and Curvature Normalization Module. Then, we conduct numerous experiments on various synthetic datasets and real-world datasets. The experimental results on synthetic datasets show that CGNN effectively exploits the topology structure information, and the performance is improved significantly. CGNN outperforms the baselines on 5 dense node classification benchmark datasets. This study deepens the understanding of how to utilize advanced topology information and assign the importance of neighboring nodes from the perspective of graph curvature and encourages us to bridge the gap between graph theory and neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15762v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"38618","document_content":"# Dual GNNs: Graph Neural Network Learning with Limited Supervision\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph Neural Networks (GNNs) require a relatively large number of labeled nodes and a reliable\/uncorrupted graph connectivity structure in order to obtain good performance on the semi-supervised node classification task. The performance of GNNs can degrade significantly as the number of labeled nodes decreases or the graph connectivity structure is corrupted by adversarial attacks or due to noises in data measurement \/collection. Therefore, it is important to develop GNN models that are able to achieve good performance when there is limited supervision knowledge -- a few labeled nodes and noisy graph structures. In this paper, we propose a novel Dual GNN learning framework to address this challenge task. The proposed framework has two GNN based node prediction modules. The primary module uses the input graph structure to induce regular node embeddings and predictions with a regular GNN baseline, while the auxiliary module constructs a new graph structure through fine-grained spectral clusterings and learns new node embeddings and predictions. By integrating the two modules in a dual GNN learning framework, we perform joint learning in an end-to-end fashion. This general framework can be applied on many GNN baseline models. The experimental results validate that the proposed dual GNN framework can greatly outperform the GNN baseline methods when the labeled nodes are scarce and the graph connectivity structure is noisy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15755v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"90139\", \"90444\", \"98290\", \"117093\", \"117410\", \"118237\", \"118653\", \"134526\", \"136103\", \"174492\", \"178263\", \"182323\", \"182433\", \"191340\", \"197103\", \"204706\", \"240985\", \"244510\", \"252175\", \"261201\", \"295676\", \"311030\"]}","task_split":"paper_retrieval"}
{"document_id":"38642","document_content":"# Deep Multiagent Reinforcement Learning: Challenges and Directions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n- Neural and Evolutionary Computing\n- A.1; I.2.6; I.2.8; J.4\n## Abstract\nThis paper surveys the field of deep multiagent reinforcement learning. The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players' joint actions and (b) the computational complexity increases. We present the most common multiagent problem representations and their main challenges, and identify five research areas that address one or more of these challenges: centralised training and decentralised execution, opponent modelling, communication, efficient coordination, and reward shaping. We find that many computational studies rely on unrealistic assumptions or are not generalisable to other settings; they struggle to overcome the curse of dimensionality or nonstationarity. Approaches from psychology and sociology capture promising relevant behaviours, such as communication and coordination, to help agents achieve better performance in multiagent settings. We suggest that, for multiagent reinforcement learning to be successful, future research should address these challenges with an interdisciplinary approach to open up new possibilities in multiagent reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15691v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\", \"cs.NE\", \"A.1; I.2.6; I.2.8; J.4\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Neural and Evolutionary Computing\", \"A.1; I.2.6; I.2.8; J.4\"], \"incoming_citations\": [\"34882\"], \"outgoing_citations\": [\"38070\", \"40482\", \"44602\", \"64708\", \"71651\", \"79257\", \"88340\", \"88549\", \"89833\", \"108713\", \"110006\", \"114010\", \"115187\", \"117595\", \"121221\", \"121535\", \"135271\", \"135935\", \"141964\", \"143111\", \"143799\", \"151271\", \"154533\", \"155499\", \"156983\", \"161370\", \"163227\", \"166489\", \"173723\", \"179202\", \"180285\", \"181403\", \"186457\", \"189797\", \"194538\", \"195416\", \"195571\", \"196008\", \"198383\", \"198582\", \"200152\", \"201508\", \"206160\", \"212722\", \"213637\", \"214440\", \"214980\", \"215222\", \"229890\", \"231351\", \"231395\", \"235975\", \"237964\", \"240445\", \"240736\", \"241100\", \"242097\", \"244283\", \"246853\", \"247157\", \"247158\", \"247160\", \"247161\", \"255077\", \"256103\", \"256504\", \"258384\", \"262105\", \"262814\", \"263576\", \"264087\", \"264323\", \"264897\", \"265505\", \"269382\", \"270254\", \"271085\", \"274315\", \"277491\", \"280774\", \"284459\", \"284565\", \"284953\", \"287823\", \"291892\", \"292127\", \"297186\", \"302395\", \"309111\", \"317251\", \"335636\", \"350227\"]}","task_split":"paper_retrieval"}
{"document_id":"38653","document_content":"# Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer\n## Categories\n- Neurons and Cognition\n- Machine Learning\n- I.2\n- Artificial Intelligence\n- Robotics\n## Abstract\nBrain science and artificial intelligence have made great progress toward the understanding and engineering of the human mind. The progress has accelerated significantly since the turn of the century thanks to new methods for probing the brain (both structure and function), and rapid development in deep learning research. However, despite these new developments, there are still many open questions, such as how to understand the brain at the system level, and various robustness issues and limitations of deep learning. In this informal essay, I will talk about some of the concepts that are central to brain science and artificial intelligence, such as information and memory, and discuss how a different view on these concepts can help us move forward, beyond current limits of our understanding in these fields.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/B978-0-12-815480-9.00014-1\", \"primary_category\": \"q-bio.NC\", \"categories\": [\"cs.LG\", \"I.2\", \"q-bio.NC\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Neurons and Cognition\", \"categories_human_readable\": [\"Machine Learning\", \"I.2\", \"Neurons and Cognition\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"253708\", \"266182\", \"270823\", \"283391\"]}","task_split":"paper_retrieval"}
{"document_id":"38766","document_content":"# Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically evaluating the quality of image captions can be very challenging since human language is quite flexible that there can be various expressions for the same meaning. Most of the current captioning metrics rely on token level matching between candidate caption and the ground truth label sentences. It usually neglects the sentence-level information. Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric for image captioning, which we call Intrinsic Image Captioning Evaluation($I^2CE$). We develop three progressive model structures to learn the sentence level representations--single branch model, dual branches model, and triple branches model. Our empirical tests show that $I^2CE$ trained with dual branches structure achieves better consistency with human judgments to contemporary image captioning evaluation metrics. Furthermore, We select several state-of-the-art image captioning models and test their performances on the MS COCO dataset concerning both contemporary metrics and the proposed $I^2CE$. Experiment results show that our proposed method can align well with the scores generated from other contemporary metrics. On this concern, the proposed metric could serve as a novel indicator of the intrinsic information between captions, which may be complementary to the existing ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15312v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112250\", \"125947\", \"134875\", \"150464\", \"168543\", \"168616\", \"171096\", \"175035\", \"189542\", \"193328\", \"221153\", \"227741\", \"239242\", \"270154\", \"278604\", \"278954\", \"287504\", \"296681\", \"320761\", \"321493\", \"321682\"]}","task_split":"paper_retrieval"}
{"document_id":"38791","document_content":"# Representation based meta-learning for few-shot spoken intent recognition\n## Categories\n- Computation and Language\n## Abstract\nSpoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick customization of personal devices to new intents. This paper presents a few-shot spoken intent classification approach with task-agnostic representations via meta-learning paradigm. Specifically, we leverage the popular representation-based meta-learning learning to build a task-agnostic representation of utterances, that then use a linear classifier for prediction. We evaluate three such approaches on our novel experimental protocol developed on two popular spoken intent classification datasets: Google Commands and the Fluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel classes, the proposed framework provides an average classification accuracy of 88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent Speech Commands dataset. The performance is comparable to traditionally supervised classification models with abundant training samples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.21437\/Interspeech.2020-3208\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11241\", \"18065\", \"42322\"], \"outgoing_citations\": [\"95623\", \"145654\", \"159890\", \"177892\", \"180179\", \"191732\", \"191769\", \"191898\", \"204765\", \"216686\", \"223002\", \"229907\", \"231222\", \"234262\", \"236235\", \"241146\", \"281176\"]}","task_split":"paper_retrieval"}
{"document_id":"38854","document_content":"# SDL: New data generation tools for full-level annotated document layout\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a novel data generation tool for document processing. The tool focuses on providing a maximal level of visual information in a normal type document, ranging from character position to paragraph-level position. It also enables working with a large dataset on low-resource languages as well as providing a mean of processing thorough full-level information of the documented text. The data generation tools come with a dataset of 320000 Vietnamese synthetic document images and an instruction to generate a dataset of similar size in other languages. The repository can be found at: https:\/\/github.com\/tson1997\/SDL-Document-Image-Generation","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15117v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107516\", \"135263\", \"171596\", \"192471\", \"294023\"]}","task_split":"paper_retrieval"}
{"document_id":"39197","document_content":"# Deep Learning for Technical Document Classification\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nIn large technology companies, the requirements for managing and organizing technical documents created by engineers and managers have increased dramatically in recent years, which has led to a higher demand for more scalable, accurate, and automated document classification. Prior studies have only focused on processing text for classification, whereas technical documents often contain multimodal information. To leverage multimodal information for document classification to improve the model performance, this paper presents a novel multimodal deep learning architecture, TechDoc, which utilizes three types of information, including natural language texts and descriptive images within documents and the associations among the documents. The architecture synthesizes the convolutional neural network, recurrent neural network, and graph neural network through an integrated training process. We applied the architecture to a large multimodal technical document database and trained the model for classifying documents based on the hierarchical International Patent Classification system. Our results show that TechDoc presents a greater classification accuracy than the unimodal methods and other state-of-the-art benchmarks. The trained model can potentially be scaled to millions of real-world multimodal technical documents, which is useful for data and knowledge management in large technology companies and organizations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TEM.2022.3152216\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"6979\", \"9535\"], \"outgoing_citations\": [\"42628\", \"166518\", \"176090\", \"183018\", \"265384\", \"271441\", \"288921\", \"291239\", \"292448\", \"310639\", \"318705\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"39327","document_content":"# UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions. In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require reference captions to evaluate image captions. Based on Vision-and-Language BERT, we train UMIC to discriminate negative captions via contrastive learning. Also, we observe critical problems of the previous benchmark dataset (i.e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions. We validate UMIC on four datasets, including our new dataset, and show that UMIC has a higher correlation than all previous metrics that require multiple references. We release the benchmark dataset and pre-trained models to compute the UMIC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.14019v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"55207\"], \"outgoing_citations\": [\"112250\", \"164927\", \"168082\", \"171096\", \"175035\", \"227741\", \"239242\", \"260926\", \"278954\", \"287504\", \"303627\", \"320761\", \"321493\", \"321621\", \"321682\"]}","task_split":"paper_retrieval"}
{"document_id":"39414","document_content":"# Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nModels that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an extractive rationale provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehensive description of the decision-making process behind a prediction. However, current models that generate the best extractive rationales or NLEs often fall behind the state-of-the-art (SOTA) in terms of task performance. In this work, we bridge this gap by introducing RExC, a self-rationalizing framework that grounds its predictions and two complementary types of explanations (NLEs and extractive rationales) in background knowledge. Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations. Furthermore, a perturbation analysis in RExC shows a high degree of association between explanations and predictions, a necessary property of faithful explanations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.13876v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3645\"], \"outgoing_citations\": [\"44917\", \"50852\", \"52671\", \"80148\", \"80515\", \"91690\", \"94145\", \"95680\", \"96069\", \"96497\", \"98740\", \"99009\", \"114988\", \"115124\", \"121691\", \"123088\", \"123394\", \"128041\", \"128546\", \"129573\", \"131531\", \"132002\", \"147012\", \"157352\", \"157716\", \"162933\", \"163311\", \"164927\", \"170241\", \"171616\", \"175046\", \"181063\", \"182261\", \"183041\", \"183237\", \"185541\", \"189504\", \"189542\", \"200362\", \"202353\", \"207428\", \"208526\", \"211788\", \"212078\", \"215081\", \"217522\", \"218686\", \"222858\", \"242033\", \"266702\", \"272864\", \"287504\", \"290515\", \"295742\", \"307756\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"39485","document_content":"# Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nRecent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded in pathology WSIs beyond what we obtain through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms which are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.13689v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\", \"cs.LG\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"10766\", \"2242\", \"26759\"], \"outgoing_citations\": [\"68578\", \"68578\", \"112883\", \"122236\", \"122236\", \"149360\", \"149360\", \"177516\", \"177516\", \"198577\", \"198711\", \"198711\", \"205779\", \"205779\"]}","task_split":"paper_retrieval"}
{"document_id":"39527","document_content":"# Video Moment Retrieval with Text Query Considering Many-to-Many Correspondence Using Potentially Relevant Pair\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we undertake the task of text-based video moment retrieval from a corpus of videos. To train the model, text-moment paired datasets were used to learn the correct correspondences. In typical training methods, ground-truth text-moment pairs are used as positive pairs, whereas other pairs are regarded as negative pairs. However, aside from the ground-truth pairs, some text-moment pairs should be regarded as positive. In this case, one text annotation can be positive for many video moments. Conversely, one video moment can be corresponded to many text annotations. Thus, there are many-to-many correspondences between the text annotations and video moments. Based on these correspondences, we can form potentially relevant pairs, which are not given as ground truth yet are not negative; effectively incorporating such relevant pairs into training can improve the retrieval performance. The text query should describe what is happening in a video moment. Hence, different video moments annotated with similar texts, which contain a similar action, are likely to hold the similar action, thus these pairs can be considered as potentially relevant pairs. In this paper, we propose a novel training method that takes advantage of potentially relevant pairs, which are detected based on linguistic analysis about text annotation. Experiments on two benchmark datasets revealed that our method improves the retrieval performance both quantitatively and qualitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.13566v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"86252\", \"96188\", \"105055\", \"110610\", \"127728\", \"132887\", \"145702\", \"187454\", \"207818\", \"219151\", \"221984\", \"233407\", \"236468\", \"259318\", \"260926\", \"267223\", \"295117\"]}","task_split":"paper_retrieval"}
{"document_id":"39622","document_content":"# Countering Adversarial Examples: Combining Input Transformation and Noisy Training\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nRecent studies have shown that neural network (NN) based image classifiers are highly vulnerable to adversarial examples, which poses a threat to security-sensitive image recognition task. Prior work has shown that JPEG compression can combat the drop in classification accuracy on adversarial examples to some extent. But, as the compression ratio increases, traditional JPEG compression is insufficient to defend those attacks but can cause an abrupt accuracy decline to the benign images. In this paper, with the aim of fully filtering the adversarial perturbations, we firstly make modifications to traditional JPEG compression algorithm which becomes more favorable for NN. Specifically, based on an analysis of the frequency coefficient, we design a NN-favored quantization table for compression. Considering compression as a data augmentation strategy, we then combine our model-agnostic preprocess with noisy training. We fine-tune the pre-trained model by training with images encoded at different compression levels, thus generating multiple classifiers. Finally, since lower (higher) compression ratio can remove both perturbations and original features slightly (aggressively), we use these trained multiple models for model ensemble. The majority vote of the ensemble of models is adopted as final predictions. Experiments results show our method can improve defense efficiency while maintaining original accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.13394v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116054\", \"138313\", \"147546\", \"188495\", \"192115\", \"199776\", \"201060\", \"201616\", \"206693\", \"206956\", \"238512\", \"239047\", \"239049\", \"241641\", \"242744\", \"243562\", \"247042\", \"251795\", \"260636\", \"265979\", \"267002\", \"287288\", \"303380\"]}","task_split":"paper_retrieval"}
{"document_id":"39697","document_content":"# Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCollaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we present a novel generative replay strategy to address the challenge of data heterogeneity in collaborative learning methods. Different from traditional methods that directly aggregating the model parameters, we leverage generative adversarial learning to aggregate the knowledge from all the local institutions. Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary \"generative replay model\" allows aggregating knowledge from the heterogenous clients. The auxiliary model is then broadcasted to the central sever, to regulate the training of primary model with an unbiased target distribution. Experimental results demonstrate the capability of the proposed method in handling heterogeneous data across institutions. On highly heterogeneous data partitions, our model achieves ~4.88% improvement in the prediction accuracy on a diabetic retinopathy classification dataset, and ~49.8% reduction of mean absolution value on a Bone Age prediction dataset, respectively, compared to the state-of-the art collaborative learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.13208v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37349\", \"42977\"], \"outgoing_citations\": [\"6225\", \"34816\", \"42977\", \"55790\", \"63956\", \"102450\", \"133977\", \"136214\", \"142093\", \"155989\", \"163734\", \"164048\", \"166970\", \"167532\", \"179698\", \"181721\", \"183000\", \"195141\", \"200347\", \"207326\", \"207602\", \"216288\", \"220594\", \"229563\", \"235592\", \"248216\", \"249421\", \"251580\", \"265568\", \"273215\", \"299534\", \"310087\", \"329116\"]}","task_split":"paper_retrieval"}
{"document_id":"39850","document_content":"# Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) have shown excellent performance on graphs that exhibit strong homophily with respect to the node labels i.e. connected nodes have same labels. However, they perform poorly on heterophilic graphs. Recent approaches have typically modified aggregation schemes, designed adaptive graph filters, etc. to address this limitation. In spite of this, the performance on heterophilic graphs can still be poor. We propose a simple alternative method that exploits Truncated Singular Value Decomposition (TSVD) of topological structure and node features. Our approach achieves up to ~30% improvement in performance over state-of-the-art methods on heterophilic graphs. This work is an early investigation into methods that differ from aggregation based approaches. Our experimental results suggest that it might be important to explore other alternatives to aggregation methods for heterophilic setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12807v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"24104\"], \"outgoing_citations\": [\"77179\", \"98076\", \"117308\", \"118719\", \"142571\", \"164464\"]}","task_split":"paper_retrieval"}
{"document_id":"39873","document_content":"# TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration\n## Categories\n- Computation and Language\n- Databases\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nDespite rapid developments in the field of machine learning research, collecting high-quality labels for supervised learning remains a bottleneck for many applications. This difficulty is exacerbated by the fact that state-of-the-art models for NLP tasks are becoming deeper and more complex, often increasing the amount of training data required even for fine-tuning. Weak supervision methods, including data programming, address this problem and reduce the cost of label collection by using noisy label sources for supervision. However, until recently, data programming was only accessible to users who knew how to program. To bridge this gap, the Data Programming by Demonstration framework was proposed to facilitate the automatic creation of labeling functions based on a few examples labeled by a domain expert. This framework has proven successful for generating high-accuracy labeling models for document classification. In this work, we extend the DPBD framework to span-level annotation tasks, arguably one of the most time-consuming NLP labeling tasks. We built a novel tool, TagRuler, that makes it easy for annotators to build span-level labeling functions without programming and encourages them to explore trade-offs between different labeling models and active learning strategies. We empirically demonstrated that an annotator could achieve a higher F1 score using the proposed tool compared to manual labeling for different span-level annotation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12767v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DB\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Databases\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"81272\", \"104521\", \"139761\", \"157587\", \"232584\", \"249126\"]}","task_split":"paper_retrieval"}
{"document_id":"39884","document_content":"# Bayesian Differential Privacy for Linear Dynamical Systems\n## Categories\n- Optimization and Control\n- Systems and Control\n- Systems and Control\n- Cryptography and Security\n## Abstract\nDifferential privacy is a privacy measure based on the difficulty of discriminating between similar input data. In differential privacy analysis, similar data usually implies that their distance does not exceed a predetermined threshold. It, consequently, does not take into account the difficulty of distinguishing data sets that are far apart, which often contain highly private information. This problem has been pointed out in the research on differential privacy for static data, and Bayesian differential privacy has been proposed, which provides a privacy protection level even for outlier data by utilizing the prior distribution of the data. In this study, we introduce this Bayesian differential privacy to dynamical systems, and provide privacy guarantees for distant input data pairs and reveal its fundamental property. For example, we design a mechanism that satisfies the desired level of privacy protection, which characterizes the trade-off between privacy and information utility.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LCSYS.2021.3087096\", \"primary_category\": \"math.OC\", \"categories\": [\"eess.SY\", \"cs.SY\", \"math.OC\", \"cs.CR\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Systems and Control\", \"Systems and Control\", \"Optimization and Control\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"201319\", \"202108\", \"315267\", \"327615\", \"356272\"]}","task_split":"paper_retrieval"}
{"document_id":"39988","document_content":"# Learnt Sparsification for Interpretable Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as inductive biases resulting in flexible and powerful models. However, GNNs remain hard to interpret as the interplay between node features and graph structure is only implicitly learned. In this paper, we propose a novel method called Kedge for explicitly sparsifying the underlying graph by removing unnecessary neighbors. Our key idea is based on a tractable method for sparsification using the Hard Kumaraswamy distribution that can be used in conjugation with any GNN model. Kedge learns edge masks in a modular fashion trained with any GNN allowing for gradient based optimization in an end-to-end fashion. We demonstrate through extensive experiments that our model Kedge can prune a large proportion of the edges with only a minor effect on the test accuracy. Specifically, in the PubMed dataset, Kedge learns to drop more than 80% of the edges with an accuracy drop of merely 2% showing that graph structure has only a small contribution in comparison to node features. Finally, we also show that Kedge effectively counters the over-smoothing phenomena in deep GNNs by maintaining good task performance with increasing GNN layers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12920v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"48804\", \"74797\", \"75999\", \"121171\", \"162259\", \"174492\", \"184400\", \"185541\", \"189496\", \"193345\", \"200141\", \"226383\", \"244510\", \"252576\", \"279575\", \"292407\", \"295676\", \"303863\", \"305707\"]}","task_split":"paper_retrieval"}
{"document_id":"39997","document_content":"# Mixtures of Deep Neural Experts for Automated Speech Scoring\n## Categories\n- Computation and Language\n## Abstract\nThe paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts. The task has significant relevance to the field of computer assisted language learning. The approach presented in the paper relies on two separate modules: (1) an automatic speech recognition system that yields text transcripts of the spoken interactions involved, and (2) a multiple classifier system based on deep learners that ranks the transcripts into proficiency classes. Different deep neural network architectures (both feed-forward and recurrent) are specialized over diverse representations of the texts in terms of: a reference grammar, the outcome of probabilistic language models, several word embeddings, and two bag-of-word models. Combination of the individual classifiers is realized either via a probabilistic pseudo-joint model, or via a neural mixture of experts. Using the data of the third Spoken CALL Shared Task challenge, the highest values to date were obtained in terms of three popular evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.21437\/Interspeech.2020-1055\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"58536\"], \"outgoing_citations\": [\"146052\", \"172694\", \"195047\", \"216557\", \"268380\", \"275599\", \"293171\"]}","task_split":"paper_retrieval"}
{"document_id":"40001","document_content":"# Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThe interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heath-care, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in documents from social media. However, the methods have been poorly studied in data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks, and a transfer learning strategy for gender recognition in documents that are written in informal and formal languages. Models are tested in two different databases consisting of Tweets and call-center conversations. Accuracies of up to 75\\% are achieved for both databases. The results also indicate that it is possible to transfer the knowledge from a system trained on a specific type of expressions or idioms such as those typically used in social media into a more formal type of text data, where the amount of data is more scarce and its structure is completely different.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2107.02759v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"2793\"], \"outgoing_citations\": [\"129260\", \"138930\", \"191564\", \"223084\", \"261421\", \"263108\"]}","task_split":"paper_retrieval"}
{"document_id":"40037","document_content":"# PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nPre-trained language models achieves high performance on machine reading comprehension (MRC) tasks but the results are hard to explain. An appealing approach to make models explainable is to provide rationales for its decision. To investigate whether human rationales can further improve current models and to facilitate supervised learning of human rationales, here we present PALRACE (Pruned And Labeled RACE), a new MRC dataset with human labeled rationales for 800 passages selected from the RACE dataset. We further classified the question to each passage into 6 types. Each passage was read by at least 26 human readers, who labeled their rationales to answer the question. It is demonstrated that models such as RoBERTa-large outperforms human readers in all 6 types of questions, including inference questions, but its performance can be further improved when having access to the human rationales. Simpler models and pre-trained models that are not fine-tuned based on the task benefit more from human rationales, and their performance can be boosted by more than 30% by rationales. With access to human rationales, a simple model based on the GloVe word embedding can reach the performance of BERT-base.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12373v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"35857\"], \"outgoing_citations\": [\"127916\", \"128258\", \"157352\", \"159458\", \"239831\", \"268809\", \"290515\", \"292576\"]}","task_split":"paper_retrieval"}
{"document_id":"40116","document_content":"# Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nTo further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, and then implement and validate it in autonomous driving under various task scenarios. First, an action-conditioned ensemble model with the ability of uncertainty assessment is established as the virtual environment model. Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance. The developed algorithms are then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. The validation results suggest that the proposed UA-MBRL method surpasses the existing model-based and model-free RL approaches, in terms of learning efficiency and achieved performance. The results also demonstrate the good ability of the proposed method with respect to the adaptiveness and robustness, under various autonomous driving scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12194v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55951\", \"92011\", \"124212\", \"142272\", \"144508\", \"150646\", \"152912\", \"171187\", \"179948\", \"196921\", \"206160\", \"208406\", \"225762\", \"240471\", \"251921\", \"259045\", \"265615\", \"269382\", \"292009\", \"297268\"]}","task_split":"paper_retrieval"}
{"document_id":"40165","document_content":"# Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty\n## Categories\n- Robotics\n- Multiagent Systems\n- 93A16\n## Abstract\nThis paper develops a stochastic programming framework for multi-agent systems where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed sub-tasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Due to their inherent flexibility and robustness, multi-agent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multi-agent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk (CVaR) is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12111v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.MA\", \"cs.RO\", \"93A16\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Multiagent Systems\", \"Robotics\", \"93A16\"], \"incoming_citations\": [], \"outgoing_citations\": [\"21125\", \"31190\", \"32195\", \"52259\", \"71288\", \"73237\", \"92387\", \"99956\", \"113637\", \"134737\", \"152015\", \"195425\", \"204971\", \"223485\", \"237680\", \"239684\", \"251975\", \"273166\"]}","task_split":"paper_retrieval"}
{"document_id":"40217","document_content":"# On the Diversity and Limits of Human Explanations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nA growing effort in NLP aims to build datasets of human explanations. However, the term explanation encompasses a broad range of notions, each with different properties and ramifications. Our goal is to provide an overview of diverse types of explanations and human limitations, and discuss implications for collecting and using explanations in NLP. Inspired by prior work in psychology and cognitive sciences, we group existing human explanations in NLP into three categories: proximal mechanism, evidence, and procedure. These three types differ in nature and have implications for the resultant explanations. For instance, procedure is not considered explanations in psychology and connects with a rich body of work on learning from instructions. The diversity of explanations is further evidenced by proxy questions that are needed for annotators to interpret and answer open-ended why questions. Finally, explanations may require different, often deeper, understandings than predictions, which casts doubt on whether humans can provide useful explanations in some tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.11988v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [\"2601\", \"94070\"], \"outgoing_citations\": [\"40191\", \"76684\", \"80515\", \"81829\", \"93011\", \"93929\", \"95395\", \"96031\", \"101670\", \"125553\", \"127454\", \"127494\", \"127851\", \"132372\", \"147166\", \"157125\", \"157352\", \"158133\", \"159123\", \"159864\", \"162419\", \"182261\", \"192608\", \"203187\", \"207428\", \"216563\", \"219560\", \"222858\", \"232584\", \"238372\", \"238986\", \"242033\", \"242879\", \"266702\", \"330049\", \"360333\"]}","task_split":"paper_retrieval"}
{"document_id":"40234","document_content":"# Provably Efficient Representation Learning in Low-rank Markov Decision Processes\n## Categories\n- Machine Learning\n- Optimization and Control\n## Abstract\nThe success of deep reinforcement learning (DRL) is due to the power of learning a representation that is suitable for the underlying exploration and exploitation task. However, existing provable reinforcement learning algorithms with linear function approximation often assume the feature representation is known and fixed. In order to understand how representation learning can improve the efficiency of RL, we study representation learning for a class of low-rank Markov Decision Processes (MDPs) where the transition kernel can be represented in a bilinear form. We propose a provably efficient algorithm called ReLEX that can simultaneously learn the representation and perform exploration. We show that ReLEX always performs no worse than a state-of-the-art algorithm without representation learning, and will be strictly better in terms of sample efficiency if the function class of representations enjoys a certain mild \"coverage'' property over the whole state-action space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.11935v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [\"1235\", \"10707\", \"17678\", \"35680\", \"78473\"], \"outgoing_citations\": [\"57453\", \"69513\", \"78646\", \"80308\", \"80915\", \"85223\", \"96119\", \"100693\", \"110159\", \"116596\", \"117517\", \"117585\", \"118246\", \"119885\", \"121158\", \"121680\", \"138725\", \"139382\", \"156467\", \"160166\", \"162949\", \"176464\", \"182979\", \"184705\", \"184850\", \"187062\", \"199123\", \"201579\", \"276133\", \"277644\", \"281635\"]}","task_split":"paper_retrieval"}
{"document_id":"40266","document_content":"# Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nAccurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-$\\beta$-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12442v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"226421\"], \"outgoing_citations\": [\"99442\", \"116072\", \"124451\", \"131338\", \"133076\", \"134054\", \"134965\", \"138169\", \"139777\", \"157689\", \"157732\", \"170388\", \"174716\", \"174738\", \"191356\", \"199454\", \"206321\", \"211345\", \"214313\", \"227342\", \"229266\", \"232095\", \"237350\", \"249164\", \"266067\", \"268891\", \"303632\"]}","task_split":"paper_retrieval"}
{"document_id":"40358","document_content":"# Reinforcement Learning for Physical Layer Communications\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Networking and Internet Architecture\n- Signal Processing\n## Abstract\nIn this chapter, we will give comprehensive examples of applying RL in optimizing the physical layer of wireless communications by defining different class of problems and the possible solutions to handle them. In Section 9.2, we present all the basic theory needed to address a RL problem, i.e. Markov decision process (MDP), Partially observable Markov decision process (POMDP), but also two very important and widely used algorithms for RL, i.e. the Q-learning and SARSA algorithms. We also introduce the deep reinforcement learning (DRL) paradigm and the section ends with an introduction to the multi-armed bandits (MAB) framework. Section 9.3 focuses on some toy examples to illustrate how the basic concepts of RL are employed in communication systems. We present applications extracted from literature with simplified system models using similar notation as in Section 9.2 of this Chapter. In Section 9.3, we also focus on modeling RL problems, i.e. how action and state spaces and rewards are chosen. The Chapter is concluded in Section 9.4 with a prospective thought on RL trends and it ends with a review of a broader state of the art in Section 9.5.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.11595v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NI\", \"eess.SP\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Networking and Internet Architecture\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112923\", \"135121\", \"200097\", \"213852\", \"232394\", \"238856\", \"241582\", \"253822\", \"257173\", \"260842\", \"261515\", \"269360\", \"273337\", \"273790\", \"352228\"]}","task_split":"paper_retrieval"}
{"document_id":"40491","document_content":"# Dive into Deep Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nThis open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.11342v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3294\", \"3740\", \"3715\", \"21981\", \"27923\", \"30302\", \"65725\", \"77695\", \"99896\", \"101803\", \"2169\", \"13701\", \"20091\", \"22637\", \"27074\", \"40127\", \"39655\", \"47614\", \"49618\", \"68774\", \"71890\", \"102376\", \"114428\", \"119059\", \"81778\", \"106815\", \"134870\", \"144599\", \"156033\", \"176793\", \"154686\", \"155994\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"40597","document_content":"# CLIP2Video: Mastering Video-Text Retrieval via Image CLIP\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and languages from a large-scale video-text dataset. Different from them, we leverage pretrained image-language model, simplify it as a two-stage framework with co-learning of image-text and enhancing temporal relations between video frames and video-text respectively, make it able to train on comparatively small datasets. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pretraining (CLIP) model, our model involves a Temporal Difference Block to capture motions at fine temporal video frames, and a Temporal Alignment Block to re-align the tokens of video clips and phrases and enhance the multi-modal correlation. We conduct thorough ablation studies, and achieve state-of-the-art performance on major text-to-video and video-to-text retrieval benchmarks, including new records of retrieval accuracy on MSR-VTT, MSVD and VATEX.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.11097v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1081\", \"5478\", \"5634\", \"6414\", \"8623\", \"18647\", \"6339\", \"9075\", \"12778\", \"14592\", \"16519\", \"22807\", \"24771\"], \"outgoing_citations\": [\"55118\", \"58876\", \"59838\", \"60135\", \"60673\", \"62053\", \"67133\", \"69945\", \"70426\", \"87013\", \"96230\", \"108985\", \"110610\", \"138177\", \"139259\", \"142128\", \"146888\", \"151327\", \"173627\", \"180862\", \"181897\", \"188114\", \"191861\", \"214015\", \"217562\", \"221984\", \"223871\", \"236468\", \"259318\", \"311742\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"40682","document_content":"# Open-set Label Noise Can Improve Robustness Against Inherent Label Noise\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, we propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the extra capacity of the neural network can be largely consumed in a way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to a flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on Out-of-Distribution detection tasks even in the label noise setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10891v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"16932\", \"43854\", \"111710\", \"15581\"], \"outgoing_citations\": [\"75059\", \"81496\", \"81579\", \"83406\", \"85842\", \"87623\", \"95818\", \"116430\", \"138324\", \"138343\", \"141691\", \"163402\", \"171454\", \"180243\", \"181816\", \"183227\", \"183233\", \"184210\", \"184847\", \"189025\", \"198316\", \"200831\", \"203033\", \"206037\", \"206350\", \"231347\", \"233440\", \"235096\", \"237153\", \"237224\", \"237905\", \"241875\", \"242145\", \"246396\", \"246497\", \"247500\", \"249330\", \"263559\", \"264247\", \"268999\", \"283114\", \"284631\", \"293522\", \"311413\", \"312135\", \"313245\", \"313914\", \"320027\", \"321153\", \"337853\"]}","task_split":"paper_retrieval"}
{"document_id":"40946","document_content":"# Variance-Dependent Best Arm Identification\n## Categories\n- Machine Learning\n## Abstract\nWe study the problem of identifying the best arm in a stochastic multi-armed bandit game. Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is associated with an unknown reward distribution supported on $[0,1]$ with mean $\\theta_i$ and variance $\\sigma_i^2$. Assume $\\theta_1 > \\theta_2 \\geq \\cdots \\geq\\theta_n$. We propose an adaptive algorithm which explores the gaps and variances of the rewards of the arms and makes future decisions based on the gathered information using a novel approach called \\textit{grouped median elimination}. The proposed algorithm guarantees to output the best arm with probability $(1-\\delta)$ and uses at most $O \\left(\\sum_{i = 1}^n \\left(\\frac{\\sigma_i^2}{\\Delta_i^2} + \\frac{1}{\\Delta_i}\\right)(\\ln \\delta^{-1} + \\ln \\ln \\Delta_i^{-1})\\right)$ samples, where $\\Delta_i$ ($i \\geq 2$) denotes the reward gap between arm $i$ and the best arm and we define $\\Delta_1 = \\Delta_2$. This achieves a significant advantage over the variance-independent algorithms in some favorable scenarios and is the first result that removes the extra $\\ln n$ factor on the best arm compared with the state-of-the-art. We further show that $\\Omega \\left( \\sum_{i = 1}^n \\left( \\frac{\\sigma_i^2}{\\Delta_i^2} + \\frac{1}{\\Delta_i} \\right) \\ln \\delta^{-1} \\right)$ samples are necessary for an algorithm to achieve the same goal, thereby illustrating that our algorithm is optimal up to doubly logarithmic terms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10417v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"286178\", \"303524\", \"326956\", \"335987\"]}","task_split":"paper_retrieval"}
{"document_id":"40961","document_content":"# Uncertain Decisions Facilitate Better Preference Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nExisting observational approaches for learning human preferences, such as inverse reinforcement learning, usually make strong assumptions about the observability of the human's environment. However, in reality, people make many important decisions under uncertainty. To better understand preference learning in these cases, we study the setting of inverse decision theory (IDT), a previously proposed framework where a human is observed making non-sequential binary decisions under uncertainty. In IDT, the human's preferences are conveyed through their loss function, which expresses a tradeoff between different types of mistakes. We give the first statistical analysis of IDT, providing conditions necessary to identify these preferences and characterizing the sample complexity -- the number of decisions that must be observed to learn the tradeoff the human is making to a desired precision. Interestingly, we show that it is actually easier to identify preferences when the decision problem is more uncertain. Furthermore, uncertain decision problems allow us to relax the unrealistic assumption that the human is an optimal decision maker but still identify their exact preferences; we give sample complexities in this suboptimal case as well. Our analysis contradicts the intuition that partial observability should make preference learning more difficult. It also provides a first step towards understanding and improving preference learning methods for uncertain and suboptimal humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10394v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55350\", \"74639\", \"116304\", \"116749\", \"142761\", \"162494\", \"179418\", \"214806\", \"219828\", \"229170\", \"247344\", \"251924\", \"284408\"]}","task_split":"paper_retrieval"}
{"document_id":"40967","document_content":"# Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle\n## Categories\n- Machine Learning\n- Data Structures and Algorithms\n## Abstract\nMotivated by applications in crowdsourced entity resolution in database, signed edge prediction in social networks and correlation clustering, Mazumdar and Saha [NIPS 2017] proposed an elegant theoretical model for studying clustering with a faulty oracle. In this model, given a set of $n$ items which belong to $k$ unknown groups (or clusters), our goal is to recover the clusters by asking pairwise queries to an oracle. This oracle can answer the query that ``do items $u$ and $v$ belong to the same cluster?''. However, the answer to each pairwise query errs with probability $\\varepsilon$, for some $\\varepsilon\\in(0,\\frac12)$. Mazumdar and Saha provided two algorithms under this model: one algorithm is query-optimal while time-inefficient (i.e., running in quasi-polynomial time), the other is time efficient (i.e., in polynomial time) while query-suboptimal. Larsen, Mitzenmacher and Tsourakakis [WWW 2020] then gave a new time-efficient algorithm for the special case of $2$ clusters, which is query-optimal if the bias $\\delta:=1-2\\varepsilon$ of the model is large. It was left as an open question whether one can obtain a query-optimal, time-efficient algorithm for the general case of $k$ clusters and other regimes of $\\delta$.   In this paper, we make progress on the above question and provide a time-efficient algorithm with nearly-optimal query complexity (up to a factor of $O(\\log^2 n)$) for all constant $k$ and any $\\delta$ in the regime when information-theoretic recovery is possible. Our algorithm is built on a connection to the stochastic block model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10374v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DS\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Data Structures and Algorithms\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"263056\", \"274754\", \"285460\", \"290903\", \"331060\", \"350032\", \"355567\"]}","task_split":"paper_retrieval"}
{"document_id":"40969","document_content":"# Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scenarios is challenging for real-time strategy (RTS) environments. The RTS environments are characterized by intelligent entities\/non-RL agents cooperating and competing with the RL agents with large state and action spaces over a long period of time, resulting in an infinite space of feasible, but not necessarily realistic, scenarios involving complex interaction among different RL and non-RL agents. Yet, most of the existing simulators rely on randomly generating the environments based on predefined settings\/layouts and offer limited flexibility and control over the environment dynamics for researchers to generate diverse, realistic scenarios as per their demand. To address this issue, for the first time, we formally introduce the benefits of adopting an existing formal scenario specification language, SCENIC, to assist researchers to model and generate diverse scenarios in an RTS environment in a flexible, systematic, and programmatic manner. To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football(GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities. We also show how researchers\/RL practitioners can incorporate their domain knowledge to expedite the training process by intuitively modeling stochastic programmatic policies with SCENIC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10365v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"91372\", \"94535\", \"116312\", \"125052\", \"125165\", \"131003\", \"136359\", \"152918\", \"160036\", \"162155\", \"163467\", \"174408\", \"200334\", \"207051\", \"210077\", \"216651\", \"226544\", \"226635\", \"236067\", \"246118\", \"255531\", \"255613\", \"293995\", \"311595\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"41052","document_content":"# World-GAN: a Generative Model for Minecraft Worlds\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nThis work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator. Our method is motivated by the dense representations used in Natural Language Processing (NLP) introduced with word2vec [1]. The proposed block2vec representations make World-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. Finally, we demonstrate that changing this new representation space allows us to change the generated style of an already trained generator. World-GAN enables its users to generate Minecraft worlds based on parts of their creations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10155v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"47005\"], \"outgoing_citations\": [\"60194\", \"62860\", \"65464\", \"81816\", \"96756\", \"107909\", \"122922\", \"145664\", \"147140\", \"181330\", \"186290\", \"188010\", \"189201\", \"233501\", \"234345\", \"237653\", \"274886\"]}","task_split":"paper_retrieval"}
{"document_id":"41085","document_content":"# It's FLAN time! Summing feature-wise latent representations for interpretability\n## Categories\n- Machine Learning\n## Abstract\nInterpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on the end-user affected by the decision. In many cases, the representational power of deep learning models is not needed, therefore simple and interpretable models (e.g. linear models) should be preferred. However, in high-dimensional and\/or complex domains (e.g. computer vision), the universal approximation capabilities of neural networks are required. Inspired by linear models and the Kolmogorov-Arnold representation theorem, we propose a novel class of structurally-constrained neural networks, which we call FLANs (Feature-wise Latent Additive Networks). Crucially, FLANs process each input feature separately, computing for each of them a representation in a common latent space. These feature-wise latent representations are then simply summed, and the aggregated representation is used for prediction. These constraints (which are at the core of the interpretability of linear models) allow a user to estimate the effect of each individual feature independently from the others, enhancing interpretability. In a set of experiments across different domains, we show how without compromising excessively the test performance, the structural constraints proposed in FLANs indeed facilitates the interpretability of deep learning models. We quantitatively compare FLANs interpretability to post-hoc methods using recently introduced metrics, discussing the advantages of natively interpretable models over a post-hoc analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10086v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54419\", \"63146\", \"111970\", \"124772\", \"128314\", \"137265\", \"145439\", \"164199\", \"165861\", \"172483\", \"202383\", \"226576\", \"227415\", \"242147\", \"250247\", \"253401\", \"262066\", \"272864\", \"279836\", \"291045\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"41101","document_content":"# Contrastive Learning of Generalized Game Representations\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nRepresenting games through their pixels offers a promising approach for building general-purpose and versatile game models. While games are not merely images, neural network models trained on game pixels often capture differences of the visual style of the image rather than the content of the game. As a result, such models cannot generalize well even within similar games of the same genre. In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games. Learning to contrast images of games not only classifies games in a more efficient manner; it also yields models that separate games in a more meaningful fashion by ignoring the visual style and focusing, instead, on their content. Our results in a large dataset of sports video games containing 100k images across 175 games and 10 game genres suggest that contrastive learning is better suited for learning generalized game representations compared to conventional supervised learning. The findings of this study bring us closer to universal visual encoders for games that can be reused across previously unseen games without requiring retraining or fine-tuning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.10060v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"65429\", \"73366\", \"93956\", \"95477\", \"96756\", \"123086\", \"127845\", \"132207\", \"134247\", \"174408\", \"177552\", \"218884\", \"223379\", \"274886\", \"293071\", \"297797\"]}","task_split":"paper_retrieval"}
{"document_id":"41140","document_content":"# Advanced Hough-based method for on-device document localization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution.   In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources.   We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18287\/2412-6179-CO-895\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38211\", \"7430\"], \"outgoing_citations\": [\"107498\", \"115916\", \"224507\"]}","task_split":"paper_retrieval"}
{"document_id":"41199","document_content":"# Identifying intracity freight trip ends from heavy truck GPS trajectories\n## Categories\n- Other Computer Science\n## Abstract\nIntracity heavy truck freight trips are basic data in city freight system planning and management. In the big data era, massive heavy truck GPS trajectories can be acquired cost effectively in real-time. Identifying freight trip ends (origins and destinations) from heavy truck GPS trajectories is an outstanding problem. Although previous studies proposed a variety of trip end identification methods from different perspectives, these studies subjectively defined key threshold parameters and ignored the complex intracity heavy truck travel characteristics. Here, we propose a data-driven trip end identification method in which the speed threshold for identifying truck stops and the multilevel time thresholds for distinguishing temporary stops and freight trip ends are objectively defined. Moreover, an appropriate time threshold level is dynamically selected by considering the intracity activity patterns of heavy trucks. Furthermore, we use urban road networks and point-of-interest (POI) data to eliminate misidentified trip ends to improve method accuracy. The validation results show that the accuracy of the method we propose is 87.45%. Our method incorporates the impact of the city freight context on truck trajectory characteristics, and its results can reflect the spatial distribution and chain patterns of intracity heavy truck freight trips, which have a wide range of practical applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.09881v2\", \"primary_category\": \"cs.OH\", \"categories\": [\"cs.OH\"], \"primary_category_human_readable\": \"Other Computer Science\", \"categories_human_readable\": [\"Other Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150384\", \"281067\"]}","task_split":"paper_retrieval"}
{"document_id":"41243","document_content":"# Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDetecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.09790v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"79082\", \"89391\", \"94122\", \"96249\", \"127595\", \"127659\", \"152396\", \"152656\", \"181063\", \"182669\", \"182683\", \"212078\", \"218115\", \"340958\"]}","task_split":"paper_retrieval"}
{"document_id":"41352","document_content":"# Biomedical Interpretable Entity Representations\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nPre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine. There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine. In this paper, we create a new entity type system and training set from a large corpus of biomedical texts by mapping entities to concepts in a medical ontology, and from these to Wikipedia pages whose categories are our types. From this mapping we derive Biomedical Interpretable Entity Representations(BIERs), in which dimensions correspond to fine-grained entity types, and values are predicted probabilities that a given entity is of the corresponding type. We propose a novel method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. We show that BIERs achieve strong performance in biomedical tasks including named entity disambiguation and entity label classification, and we provide error analysis to highlight the utility of their interpretability, particularly in low-supervision settings. Finally, we provide our induced 68K biomedical type system, the corresponding 37 million triples of derived data used to train BIER models and our best performing model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.09502v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"79296\", \"92185\", \"96019\", \"127709\", \"127760\", \"134662\", \"143080\", \"157563\", \"168097\", \"168951\", \"169340\", \"187755\", \"193725\", \"197659\", \"198222\", \"224723\", \"224786\", \"239137\", \"243397\", \"249483\", \"252936\", \"311696\"]}","task_split":"paper_retrieval"}
{"document_id":"41570","document_content":"# An Intelligent Question Answering System based on Power Knowledge Graph\n## Categories\n- Artificial Intelligence\n## Abstract\nThe intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.09013v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"188675\"]}","task_split":"paper_retrieval"}
{"document_id":"41735","document_content":"# Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nTopic classification systems on spoken documents usually consist of two modules: an automatic speech recognition (ASR) module to convert speech into text and a text topic classification (TTC) module to predict the topic class from the decoded text. In this paper, instead of using the ASR transcripts, the fusion of deep acoustic and linguistic features is used for topic classification on spoken documents. More specifically, a conventional CTC-based acoustic model (AM) using phonemes as output units is first trained, and the outputs of the layer before the linear phoneme classifier in the trained AM are used as the deep acoustic features of spoken documents. Furthermore, these deep acoustic features are fed to a phoneme-to-word (P2W) module to obtain deep linguistic features. Finally, a local multi-head attention module is proposed to fuse these two types of deep features for topic classification. Experiments conducted on a subset selected from Switchboard corpus show that our proposed framework outperforms the conventional ASR+TTC systems and achieves a 3.13% improvement in ACC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.08637v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"168375\", \"186386\", \"221784\", \"240191\", \"241146\", \"249531\", \"270814\", \"302955\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"41796","document_content":"# Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual analytics have played an increasingly critical role in the Internet of Things, where massive visual signals have to be compressed and fed into machines. But facing such big data and constrained bandwidth capacity, existing image\/video compression methods lead to very low-quality representations, while existing feature compression techniques fail to support diversified visual analytics applications\/tasks with low-bit-rate representations. In this paper, we raise and study the novel problem of supporting multiple machine vision analytics tasks with the compressed visual representation, namely, the information compression problem in analytics taxonomy. By utilizing the intrinsic transferability among different tasks, our framework successfully constructs compact and expressive representations at low bit-rates to support a diversified set of machine vision tasks, including both high-level semantic-related tasks and mid-level geometry analytic tasks. In order to impose compactness in the representations, we propose a codebook-based hyperprior, which helps map the representation into a low-dimensional manifold. As it well fits the signal structure of the deep visual feature, it facilitates more accurate entropy estimation, and results in higher compression efficiency. With the proposed framework and the codebook-based hyperprior, we further investigate the relationship of different task features owning different levels of abstraction granularity. Experimental results demonstrate that with the proposed scheme, a set of diversified tasks can be supported at a significantly lower bit-rate, compared with existing compression schemes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.08512v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2018\"], \"outgoing_citations\": [\"40729\", \"49910\", \"87786\", \"110187\", \"147712\", \"147904\", \"234583\", \"238760\", \"243653\", \"251952\", \"267922\", \"298812\", \"316314\", \"321655\", \"328080\"]}","task_split":"paper_retrieval"}
{"document_id":"41804","document_content":"# ICDAR 2021 Competition on Components Segmentation Task of Document Photos\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper describes the short-term competition on the Components Segmentation Task of Document Photos that was prepared in the context of the 16th International Conference on Document Analysis and Recognition (ICDAR 2021). This competition aims to bring together researchers working in the field of identification document image processing and provides them a suitable benchmark to compare their techniques on the component segmentation task of document images. Three challenge tasks were proposed entailing different segmentation assignments to be performed on a provided dataset. The collected data are from several types of Brazilian ID documents, whose personal information was conveniently replaced. There were 16 participants whose results obtained for some or all the three tasks show different rates for the adopted metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different Deep Learning models were applied by the entrants with diverse strategies to achieve the best results in each of the tasks. Obtained results show that the currently applied methods for solving one of the proposed tasks (document boundary detection) are already well established. However, for the other two challenge tasks (text zone and handwritten sign detection) research and development of more robust approaches are still required to achieve acceptable results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.08499v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"110845\", \"113335\", \"122361\", \"123613\", \"130268\", \"133367\", \"150722\", \"155242\", \"165193\", \"171543\", \"179355\", \"191450\", \"208418\", \"231347\", \"261545\", \"265544\"]}","task_split":"paper_retrieval"}
{"document_id":"41806","document_content":"# Improving Entity Linking through Semantic Reinforced Entity Embeddings\n## Categories\n- Computation and Language\n## Abstract\nEntity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2020.acl-main.612\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"2019\", \"64775\", \"80770\", \"121898\", \"4514\", \"84249\"], \"outgoing_citations\": [\"168589\", \"178230\", \"181770\", \"182677\", \"224786\", \"230379\", \"233941\", \"268728\", \"282036\", \"300128\", \"300498\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"41912","document_content":"# Consistency Regularization for Cross-Lingual Fine-Tuning\n## Categories\n- Computation and Language\n## Abstract\nFine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation. In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set. Experimental results on the XTREME benchmark show that our method significantly improves cross-lingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.08226v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18991\", \"28831\", \"4898\", \"21636\", \"23177\", \"23330\"], \"outgoing_citations\": [\"101281\", \"107584\", \"111872\", \"119456\", \"122811\", \"130252\", \"131352\", \"137436\", \"158102\", \"159153\", \"159706\", \"164923\", \"166780\", \"168532\", \"169415\", \"183227\", \"184190\", \"188492\", \"191890\", \"202124\", \"217957\", \"218392\", \"228002\", \"268999\", \"294549\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"41998","document_content":"# A Clinically Inspired Approach for Melanoma classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMelanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evolving) criterion. In practice however, a differential approach wherein outliers (ugly duckling) are detected and used to evaluate nevi\/lesions. Incorporation of differential recognition in Computer Aided Diagnosis (CAD) systems has not been explored but can be beneficial as it can provide a clinical justification for the derived decision. We present a method for identifying and quantifying ugly ducklings by performing Intra-Patient Comparative Analysis (IPCA) of neighboring nevi. This is then incorporated in a CAD system design for melanoma detection. This design ensures flexibility to handle cases where IPCA is not possible. Our experiments on a public dataset show that the outlier information helps boost the sensitivity of detection by at least 4.1 % and specificity by 4.0 % to 8.9 %, depending on the use of a strong (EfficientNet) or moderately strong (VGG or ResNet) classifier.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.08021v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107214\", \"156893\", \"157779\", \"172483\", \"172832\", \"237466\"]}","task_split":"paper_retrieval"}
{"document_id":"42113","document_content":"# rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nReinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.12895v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105270\", \"174408\", \"198383\", \"206160\", \"222669\", \"246118\", \"282841\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"42156","document_content":"# Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe amount of archaeological literature is growing rapidly. Until recently, these data were only accessible through metadata search. We implemented a text retrieval engine for a large archaeological text collection ($\\sim 658$ Million words). In archaeological IR, domain-specific entities such as locations, time periods, and artefacts, play a central role. This motivated the development of a named entity recognition (NER) model to annotate the full collection with archaeological named entities. In this paper, we present ArcheoBERTje, a BERT model pre-trained on Dutch archaeological texts. We compare the model's quality and output on a Named Entity Recognition task to a generic multilingual model and a generic Dutch model. We also investigate ensemble methods for combining multiple BERT models, and combining the best BERT model with a domain thesaurus using Conditional Random Fields (CRF). We find that ArcheoBERTje outperforms both the multilingual and Dutch model significantly with a smaller standard deviation between runs, reaching an average F1 score of 0.735. The model also outperforms ensemble methods combining the three models. Combining ArcheoBERTje predictions and explicit domain knowledge from the thesaurus did not increase the F1 score. We quantitatively and qualitatively analyse the differences between the vocabulary and output of the BERT models on the full collection and provide some valuable insights in the effect of fine-tuning for specific domains. Our results indicate that for a highly specific text domain such as archaeology, further pre-training on domain-specific data increases the model's quality on NER by a much larger margin than shown for other domains in the literature, and that domain-specific pre-training makes the addition of domain knowledge from a thesaurus unnecessary.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07742v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"81241\", \"92590\", \"97100\", \"109896\", \"124265\", \"138298\", \"146697\", \"151090\", \"155428\", \"165339\", \"182313\", \"185852\", \"193725\", \"220951\"]}","task_split":"paper_retrieval"}
{"document_id":"42174","document_content":"# Improving Paraphrase Detection with the Adversarial Paraphrasing Task\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIf two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07691v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"84837\", \"94792\", \"106553\", \"113831\", \"116170\", \"132002\", \"143050\", \"158701\", \"185629\", \"188123\", \"189504\", \"192782\", \"209764\", \"221212\", \"227488\", \"232274\", \"250360\", \"255894\", \"259622\", \"260450\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"42207","document_content":"# Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph\n## Categories\n- Artificial Intelligence\n## Abstract\nDiscriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel Edge Weight Updating Neural Network. Our proposed model when tested on four different datasets achieved state-of-the-art results. We, next, verify our model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical databases, which are widely used named entity normalization datasets in the bioinformatics field. We also tested our model with our own financial named entity normalization dataset to validate the efficacy for more general applications. Using the constructed dataset, we differentiate named entity pairs. Our model achieved the highest named entity normalization performances in terms of various evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07549v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"79296\", \"106440\", \"127709\", \"146640\", \"172372\", \"187797\", \"197910\", \"320009\"]}","task_split":"paper_retrieval"}
{"document_id":"42229","document_content":"# An Empirical Survey of Data Augmentation for Limited Data Learning in NLP\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data. Recently, data augmentation methods have been explored as a means of improving data efficiency in NLP. To date, there has been no systematic empirical overview of data augmentation for NLP in the limited labeled data setting, making it difficult to understand which methods work in which settings. In this paper, we provide an empirical survey of recent progress on data augmentation for NLP in the limited labeled data setting, summarizing the landscape of methods (including token-level augmentations, sentence-level augmentations, adversarial augmentations, and hidden-space augmentations) and carrying out experiments on 11 datasets covering topics\/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. Based on the results, we draw several conclusions to help practitioners choose appropriate augmentations in different settings and discuss the current challenges and future directions for limited data learning in NLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07499v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2567\", \"34358\", \"2024\", \"8723\", \"12125\", \"16544\", \"22194\", \"26384\", \"36045\"], \"outgoing_citations\": [\"22451\", \"45895\", \"51130\", \"75756\", \"86257\", \"91715\", \"91922\", \"96487\", \"96695\", \"96765\", \"97943\", \"99733\", \"100541\", \"105893\", \"111343\", \"117118\", \"122396\", \"126101\", \"127468\", \"128071\", \"128975\", \"129115\", \"129289\", \"129497\", \"130889\", \"131419\", \"132808\", \"133106\", \"137224\", \"138517\", \"143521\", \"146192\", \"146195\", \"157349\", \"157467\", \"159333\", \"163280\", \"164156\", \"164509\", \"164923\", \"166025\", \"166959\", \"170615\", \"176238\", \"179820\", \"180353\", \"182218\", \"182958\", \"184635\", \"187534\", \"188492\", \"189595\", \"194198\", \"200362\", \"200867\", \"202124\", \"202296\", \"205715\", \"212163\", \"213179\", \"216925\", \"218932\", \"219175\", \"220017\", \"226990\", \"228229\", \"231898\", \"234577\", \"234716\", \"234895\", \"235224\", \"235345\", \"240174\", \"240692\", \"242126\", \"243291\", \"247127\", \"250505\", \"251240\", \"251816\", \"251893\", \"252417\", \"253158\", \"254855\", \"255894\", \"257375\", \"258131\", \"258440\", \"258534\", \"260450\", \"260676\", \"260760\", \"267601\", \"268518\", \"268999\", \"272068\", \"272285\", \"272531\", \"272997\", \"280486\", \"282914\", \"283114\", \"290402\", \"290632\", \"291899\", \"292691\", \"302824\", \"302948\", \"307068\", \"320037\", \"320317\", \"324659\", \"328112\"]}","task_split":"paper_retrieval"}
{"document_id":"42317","document_content":"# Cascaded Span Extraction and Response Generation for Document-Grounded Dialog\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second subtask, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07275v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70610\", \"71700\", \"87270\", \"103478\", \"120816\", \"167338\", \"176321\", \"219975\", \"220723\", \"234469\", \"281121\"]}","task_split":"paper_retrieval"}
{"document_id":"42397","document_content":"# Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective\n## Categories\n- Machine Learning\n- Statistics Theory\n- Statistics Theory\n- Methodology\n## Abstract\nSelf-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, $k$-means clustering, and $k$-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07138v4\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.TH\", \"stat.ML\", \"cs.LG\", \"math.ST\", \"stat.ME\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Statistics Theory\", \"Machine Learning\", \"Machine Learning\", \"Statistics Theory\", \"Methodology\"], \"incoming_citations\": [], \"outgoing_citations\": [\"10647\", \"80311\", \"95899\", \"97321\", \"104452\", \"108090\", \"136066\", \"141241\", \"173580\", \"180880\", \"185631\", \"197714\", \"222446\", \"238288\", \"250581\", \"270894\", \"278573\", \"281227\", \"295741\", \"327665\", \"328262\", \"343159\", \"356019\", \"356098\"]}","task_split":"paper_retrieval"}
{"document_id":"42488","document_content":"# Game of GANs: Game-Theoretical Models for Generative Adversarial Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Science and Game Theory\n## Abstract\nGenerative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to its ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative model and improve the GAN's performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each category. Finally, we discuss the remaining challenges in this field and present future research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06976v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.GT\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Science and Game Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"111269\", \"119099\", \"120045\", \"122601\", \"122731\", \"124229\", \"126324\", \"127805\", \"127870\", \"130874\", \"140962\", \"146468\", \"147766\", \"156050\", \"159220\", \"160631\", \"178695\", \"182585\", \"188646\", \"209431\", \"210777\", \"213265\", \"217232\", \"229927\", \"230480\", \"238027\", \"243040\", \"245290\", \"249069\", \"252621\", \"252935\", \"256229\", \"264073\", \"264984\", \"265075\", \"265970\", \"266182\", \"269271\", \"272623\", \"277021\", \"279483\", \"281144\", \"284481\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"42497","document_content":"# Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAttention-based encoder-decoder framework is widely used in the scene text recognition task. However, for the current state-of-the-art(SOTA) methods, there is room for improvement in terms of the efficient usage of local visual and global context information of the input text image, as well as the robust correlation between the scene processing module(encoder) and the text processing module(decoder). In this paper, we propose a Representation and Correlation Enhanced Encoder-Decoder Framework(RCEED) to address these deficiencies and break performance bottleneck. In the encoder module, local visual feature, global context feature, and position information are aligned and fused to generate a small-size comprehensive feature map. In the decoder module, two methods are utilized to enhance the correlation between scene and text feature space. 1) The decoder initialization is guided by the holistic feature and global glimpse vector exported from the encoder. 2) The feature enriched glimpse vector produced by the Multi-Head General Attention is used to assist the RNN iteration and the character prediction at each time step. Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's generalization towards changeable texts. Extensive experiments on the benchmarks demonstrate the advantageous performance of RCEED in scene text recognition tasks, especially the irregular ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06960v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"21983\", \"32623\"], \"outgoing_citations\": [\"111991\", \"135021\", \"163092\", \"192671\", \"203406\", \"205988\", \"211851\", \"229426\", \"232706\", \"256711\", \"294023\", \"296685\", \"296828\", \"309239\", \"324793\", \"328595\", \"356982\"]}","task_split":"paper_retrieval"}
{"document_id":"42554","document_content":"# Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification\n## Categories\n- Machine Learning\n## Abstract\nGraphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks. Prevailing approaches for graph ML typically require abundant labeled instances in achieving satisfactory results, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is limited. Though meta-learning has been applied to different few-shot graph learning problems, most existing efforts predominately assume that all the data from those seen classes is gold-labeled, while those methods may lose their efficacy when the seen data is weakly-labeled with severe label noise. As such, we aim to investigate a novel problem of weakly-supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose a new graph meta-learning framework -- Graph Hallucination Networks (Meta-GHN) in this paper. Based on a new robustness-enhanced episodic training, Meta-GHN is meta-learned to hallucinate clean node representations from weakly-labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GHN over existing graph meta-learning studies on the task of weakly-supervised few-shot node classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06873v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71722\", \"91881\", \"113849\", \"116748\", \"118766\", \"119358\", \"136162\", \"139659\", \"154289\", \"162941\", \"184987\", \"186598\", \"237905\", \"240315\", \"242145\", \"247500\", \"310387\"]}","task_split":"paper_retrieval"}
{"document_id":"42559","document_content":"# A Minimalist Approach to Offline Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOffline reinforcement learning (RL) defines the task of learning from a fixed batch of data. Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms take the approach of constraining or regularizing the policy with the actions contained in the dataset. Built on pre-existing RL algorithms, modifications to make an RL algorithm work offline comes at the cost of additional complexity. Offline RL algorithms introduce new hyperparameters and often leverage secondary components such as generative models, while adjusting the underlying RL algorithm. In this paper we aim to make a deep RL algorithm work while making minimal changes. We find that we can match the performance of state-of-the-art offline RL algorithms by simply adding a behavior cloning term to the policy update of an online RL algorithm and normalizing the data. The resulting algorithm is a simple to implement and tune baseline, while more than halving the overall run time by removing the additional computational overhead of previous methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06860v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"343094\", \"1752\", \"1723\", \"8026\", \"8426\", \"10698\", \"15090\", \"16149\", \"16966\", \"17950\", \"17889\", \"49347\", \"24\", \"23\", \"5856\", \"11091\", \"14104\", \"14761\", \"16398\", \"17658\", \"18400\", \"21756\", \"32449\", \"38148\", \"68900\", \"121735\"], \"outgoing_citations\": [\"45297\", \"59162\", \"59452\", \"63112\", \"80915\", \"89521\", \"100655\", \"110439\", \"111316\", \"115825\", \"118157\", \"120201\", \"120766\", \"127099\", \"131003\", \"141277\", \"152756\", \"154254\", \"162530\", \"163467\", \"164054\", \"168853\", \"172432\", \"178278\", \"182796\", \"206875\", \"212016\", \"229839\", \"231623\", \"240664\", \"240800\", \"247075\", \"254596\", \"254597\", \"255531\", \"260018\"]}","task_split":"paper_retrieval"}
{"document_id":"42645","document_content":"# Graph Neural Networks with Local Graph Parameters\n## Categories\n- Machine Learning\n## Abstract\nVarious recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between $k$-tuples of vertices. The distinguishing power of these \"higher-order'' GNNs is known to be bounded by the $k$-dimensional Weisfeiler-Leman (WL) test, yet their $\\mathcal O(n^k)$ memory requirements limit their applicability. Other proposals infuse GNNs with local higher-order graph structural information from the start, hereby inheriting the desirable $\\mathcal O(n)$ memory requirement from GNNs at the cost of a one-time, possibly non-linear, preprocessing step. We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account. Local graph parameters can be added to any GNN architecture, and are cheap to compute. In terms of expressive power, our proposal lies in the middle of GNNs and their higher-order counterparts. Further, we propose several techniques to aide in choosing the right local graph parameters. Our results connect GNNs with deep results in finite model theory and finite variable logics. Our experimental evaluation shows that adding local graph parameters often has a positive effect for a variety of GNNs, datasets and graph learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06707v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"14352\", \"18144\", \"2056\"], \"outgoing_citations\": [\"82471\", \"97047\", \"110102\", \"114934\", \"115600\", \"115834\", \"118201\", \"119251\", \"126374\", \"127256\", \"127525\", \"132809\", \"134585\", \"136186\", \"137772\", \"138999\", \"142226\", \"143081\", \"143456\", \"156634\", \"160518\", \"177249\", \"183719\", \"184286\", \"184749\", \"186624\", \"186769\", \"192622\", \"201448\", \"204871\", \"210730\", \"215435\", \"240991\", \"249839\", \"267341\", \"269770\", \"279575\"]}","task_split":"paper_retrieval"}
{"document_id":"42711","document_content":"# Evaluating Deep Neural Networks for Image Document Enhancement\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- I.4.3; I.2.10\n## Abstract\nThis work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.15286v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"I.4.3; I.2.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"I.4.3; I.2.10\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74251\", \"149056\", \"182408\", \"198751\", \"202558\", \"203983\", \"220235\", \"229099\", \"269179\", \"278644\", \"283150\", \"297625\", \"327683\"]}","task_split":"paper_retrieval"}
{"document_id":"42715","document_content":"# Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative aggregation of neighbor's\/proximity features by message passing. Its prediction performance has been shown to be strongly bounded by assortative mixing in the graph, a key property wherein nodes with similar attributes mix\/connect with each other. We observe that real world networks exhibit heterogeneous or diverse mixing patterns and the conventional global measurement of assortativity, such as global assortativity coefficient, may not be a representative statistic in quantifying this mixing. We adopt a generalized concept, node-level assortativity, one that is based at the node level to better represent the diverse patterns and accurately quantify the learnability of GNNs. We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity. To break this limit, in this work, we focus on transforming the input graph into a computation graph which contains both proximity and structural information as distinct type of edges. The resulted multi-relational graph has an enhanced level of assortativity and, more importantly, preserves rich information from the original graph. We then propose to run GNNs on this computation graph and show that adaptively choosing between structure and proximity leads to improved performance under diverse mixing. Empirically, we show the benefits of adopting our transformation framework for semi-supervised node classification task on a variety of real world graph learning benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3447548.3467373\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [\"52113\", \"9898\", \"43235\"], \"outgoing_citations\": [\"90641\", \"108588\", \"117308\", \"118719\", \"120374\", \"126374\", \"136103\", \"137772\", \"142571\", \"159315\", \"164464\", \"182282\", \"184434\", \"185055\", \"188300\", \"206470\", \"214311\", \"215435\", \"215669\", \"228663\", \"244510\", \"251741\", \"259418\", \"269202\", \"305707\", \"352372\"]}","task_split":"paper_retrieval"}
{"document_id":"42725","document_content":"# Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems\n## Categories\n- Computation and Language\n## Abstract\nNeural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06566v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117258\", \"128589\", \"303296\"]}","task_split":"paper_retrieval"}
{"document_id":"42799","document_content":"# Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment\n## Categories\n- Computation and Language\n## Abstract\nThe cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences. In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. Specifically, the model first self-labels word alignments for parallel sentences. Then we randomly mask tokens in a bitext pair. Given a masked token, the model uses a pointer network to predict the aligned token in the other language. We alternately perform the above two steps in an expectation-maximization manner. Experimental results show that our method improves cross-lingual transferability on various datasets, especially on the token-level tasks, such as question answering, and structured prediction. Moreover, the model can serve as a pretrained word aligner, which achieves reasonably low error rates on the alignment benchmarks. The code and pretrained parameters are available at https:\/\/github.com\/CZWin32768\/XLM-Align.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06381v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38433\", \"65422\", \"18528\", \"21636\", \"23330\", \"38116\", \"55223\"], \"outgoing_citations\": [\"38433\", \"55223\", \"77611\", \"92112\", \"93942\", \"96365\", \"111872\", \"128057\", \"130391\", \"135112\", \"137436\", \"143273\", \"146003\", \"150830\", \"158546\", \"159706\", \"161377\", \"165388\", \"168995\", \"169415\", \"176600\", \"189792\", \"200678\", \"202124\", \"217957\", \"253718\", \"253840\", \"263927\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"42907","document_content":"# Assessing Political Prudence of Open-domain Chatbots\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nPolitically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06157v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15889\", \"16064\"], \"outgoing_citations\": [\"52612\", \"56816\", \"62534\", \"98838\", \"103544\", \"128396\", \"157164\", \"158493\", \"161329\", \"167648\", \"168228\", \"171356\", \"211664\", \"212046\", \"244479\"]}","task_split":"paper_retrieval"}
{"document_id":"42919","document_content":"# A comprehensive solution to retrieval-based chatbot construction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents. In contrast to most existing research papers in this area where the focus is on solving just one component of a deployable chatbot, we present an end-to-end set of solutions to take the reader from an unlabelled chatlogs to a deployed chatbot. This set of solutions includes creating a self-supervised dataset and a weakly labelled dataset from chatlogs, as well as a systematic approach to selecting a fixed list of canned responses. We present a hierarchical-based RNN architecture for the response selection model, chosen for its ability to cache intermediate utterance embeddings, which helped to meet deployment inference speed requirements. We compare the performance of this architecture across 3 different learning objectives: self-supervised contrastive learning, binary classification, and multi-class classification. We find that using a self-supervised contrastive learning model outperforms training the binary and multi-class classification models on a weakly labelled dataset. Our results validate that the self-supervised contrastive learning approach can be effectively used for a real-world chatbot scenario.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06139v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138862\", \"171936\", \"218515\", \"226959\", \"233026\", \"263034\", \"278671\", \"284357\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"42921","document_content":"# Security and Privacy for Healthcare Blockchains\n## Categories\n- Cryptography and Security\n## Abstract\nHealthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.06136v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"99415\", \"129314\", \"199387\", \"211294\", \"225294\", \"229361\", \"235638\", \"311391\"]}","task_split":"paper_retrieval"}
{"document_id":"43050","document_content":"# A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents\n## Categories\n- Information Retrieval\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn the area of customer support, understanding customers' intents is a crucial step. Machine learning plays a vital role in this type of intent classification. In reality, it is typical to collect confirmation from customer support representatives (CSRs) regarding the intent prediction, though it can unnecessarily incur prohibitive cost to ask CSRs to assign existing or new intents to the mis-classified cases. Apart from the confirmed cases with and without intent labels, there can be a number of cases with no human curation. This data composition (Positives + Unlabeled + multiclass Negatives) creates unique challenges for model development. In response to that, we propose a semi-supervised multi-task learning paradigm. In this manuscript, we share our experience in building text-based intent classification models for a customer support service on an E-commerce website. We improve the performance significantly by evolving the model from multiclass classification to semi-supervised multi-task learning by leveraging the negative cases, domain- and task-adaptively pretrained ALBERT on customer contact texts, and a number of un-curated data with no labels. In the evaluation, the final model boosts the average AUC ROC by almost 20 points compared to the baseline finetuned multiclass classification ALBERT model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2021.ecnlp-1.7\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95602\", \"97207\", \"100541\", \"101281\", \"121866\", \"129503\", \"156893\", \"168995\", \"172001\", \"188123\", \"200764\", \"202124\", \"211028\", \"215828\", \"225872\", \"251585\", \"266003\", \"268809\"]}","task_split":"paper_retrieval"}
{"document_id":"43163","document_content":"# Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThe COVID-19 pandemic has drastically changed accepted norms globally. Within the past year, masks have been used as a public health response to limit the spread of the virus. This sudden change has rendered many face recognition based access control, authentication and surveillance systems ineffective. Official documents such as passports, driving license and national identity cards are enrolled with fully uncovered face images. However, in the current global situation, face matching systems should be able to match these reference images with masked face images. As an example, in an airport or security checkpoint it is safer to match the unmasked image of the identifying document to the masked person rather than asking them to remove the mask. We find that current facial recognition techniques are not robust to this form of occlusion.   To address this unique requirement presented due to the current circumstance, we propose a set of re-purposed datasets and a benchmark for researchers to use. We also propose a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching. We ensure that our method learns robust features to differentiate people across varying data collection scenarios. We achieve this by training over many different datasets and validating our result by testing on various holdout datasets. The specialized weights trained by our method outperform standard face recognition features for masked to unmasked face matching. We believe the provided synthetic mask generating code, our novel training approach and the trained weights from the masked face models will help in adopting existing face recognition systems to operate in the current global environment. We open-source all contributions for broader use by the research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/DICTA52665.2021.9647194\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"2941\"], \"outgoing_citations\": [\"51159\", \"54662\", \"55058\", \"56168\", \"65748\", \"109501\", \"135861\", \"171379\", \"257948\", \"263827\", \"312587\"]}","task_split":"paper_retrieval"}
{"document_id":"43191","document_content":"# DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe present a novel deep neural model for text detection in document images. For robust text detection in noisy scanned documents, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Namely, our proposed model is designed to perform noise reduction and text region enhancement as well as text detection. Moreover, we enrich the training data for the model with synthesized document images that are fully labeled for text detection and enhancement, thus overcome the insufficiency of labeled document image data. For the effective exploitation of the synthetic and real data, the training process is separated in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for the real data is weakly-supervised with information from their detection labels. Our methods are demonstrated in a real document dataset with performances exceeding those of other text detection methods. Moreover, ablations are conducted and the results confirm the effectiveness of the synthetic data, auxiliary task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICPR48806.2021.9412928\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"161220\", \"161530\", \"170332\", \"184301\", \"186238\", \"192471\", \"196529\", \"197308\", \"202558\", \"225795\", \"239002\", \"245899\", \"245970\", \"252159\", \"269205\", \"270668\", \"271112\", \"272481\", \"279950\", \"284871\", \"293979\", \"294606\", \"295622\", \"299461\", \"316457\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"43331","document_content":"# What Would a Teacher Do? Predicting Future Talk Moves\n## Categories\n- Computation and Language\n## Abstract\nRecent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in today's classrooms, NLP systems leveraging question answering and dialog processing techniques can serve as private tutors or participants in classroom discussions to increase student engagement and learning. To progress towards this goal, we use the classroom discourse framework of academically productive talk (APT) to learn strategies that make for the best learning experience. In this paper, we introduce a new task, called future talk move prediction (FTMP): it consists of predicting the next talk move -- an utterance strategy from APT -- given a conversation history with its corresponding talk moves. We further introduce a neural network model for this task, which outperforms multiple baselines by a large margin. Finally, we compare our model's performance on FTMP to human performance and show several similarities between the two.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.05249v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"131150\", \"145384\", \"158493\", \"170044\", \"188123\", \"235090\", \"244130\"]}","task_split":"paper_retrieval"}
{"document_id":"43338","document_content":"# Do Transformers Really Perform Bad for Graph Representation?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers could perform well for graph representation learning. In this paper, we solve this mystery by presenting Graphormer, which is built upon the standard Transformer architecture, and could attain excellent results on a broad range of graph representation learning tasks, especially on the recent OGB Large-Scale Challenge. Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model. To this end, we propose several simple yet effective structural encoding methods to help Graphormer better model graph-structured data. Besides, we mathematically characterize the expressive power of Graphormer and exhibit that with our ways of encoding the structural information of graphs, many popular GNN variants could be covered as the special cases of Graphormer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.05234v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1412\", \"3222\", \"14856\", \"15299\", \"16180\", \"18144\", \"19379\", \"21405\", \"30943\", \"34972\", \"95125\", \"225\", \"425\", \"2910\", \"3628\", \"4995\", \"4897\", \"8067\", \"8403\", \"19268\", \"23232\", \"38709\", \"41897\", \"42026\", \"62582\", \"101873\", \"10668\", \"117616\", \"138999\"], \"outgoing_citations\": [\"39962\", \"43981\", \"50588\", \"59455\", \"61780\", \"62582\", \"67018\", \"67345\", \"67519\", \"76233\", \"79849\", \"80776\", \"83801\", \"96209\", \"101822\", \"101873\", \"103085\", \"117754\", \"118851\", \"120206\", \"127525\", \"131562\", \"138857\", \"138999\", \"141324\", \"142809\", \"147107\", \"155821\", \"157816\", \"164923\", \"181255\", \"183662\", \"183816\", \"184286\", \"200357\", \"218737\", \"219691\", \"239884\", \"249839\", \"256259\", \"284979\"]}","task_split":"paper_retrieval"}
{"document_id":"43409","document_content":"# PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nConveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback; however, such approaches have been challenging to scale since human feedback is very expensive. In this work, we aim to make this process more sample- and feedback-efficient. We present an off-policy, interactive RL algorithm that capitalizes on the strengths of both feedback and off-policy learning. Specifically, we learn a reward model by actively querying a teacher's preferences between two clips of behavior and use it to train an agent. To enable off-policy learning, we relabel all the agent's past experience when its reward model changes. We additionally show that pre-training our agents with unsupervised exploration substantially increases the mileage of its queries. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods, including a variety of locomotion and robotic manipulation skills. We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.05091v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"7093\", \"16536\", \"22668\", \"99547\", \"11431\", \"30094\", \"34461\", \"115937\"], \"outgoing_citations\": [\"64566\", \"68436\", \"119408\", \"126773\", \"133560\", \"151978\", \"160036\", \"177830\", \"181066\", \"190237\", \"199302\", \"199330\", \"206945\", \"209453\", \"209926\", \"212299\", \"214806\", \"215931\", \"220103\", \"222669\", \"223225\", \"226635\", \"230042\", \"241900\", \"253448\", \"254581\", \"263957\", \"266419\", \"269251\", \"272422\", \"275728\", \"280457\", \"282684\", \"283026\", \"283394\", \"289913\", \"291025\", \"291460\", \"296980\", \"306156\", \"355682\"]}","task_split":"paper_retrieval"}
{"document_id":"43489","document_content":"# Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization\n## Categories\n- Machine Learning\n## Abstract\nDomain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. Moreover, privacy concerns regarding the source also require a domain-invariant representation. In this work, we provide theoretical results that link domain invariant representations -- measured by the Wasserstein distance on the joint distributions -- to a practical semi-supervised learning objective based on a cross-entropy classifier and a novel domain critic. Quantitative experiments demonstrate that the proposed approach is indeed able to practically learn such an invariant representation (between two domains), and the latter also supports models with higher predictive accuracy on both domains, comparing favorably to existing techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.04923v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"58225\", \"80696\", \"98419\", \"105831\", \"109342\", \"111166\", \"155339\", \"159053\", \"172059\", \"186913\", \"190734\", \"213601\", \"253776\", \"262064\", \"265523\", \"268999\", \"272139\", \"272827\", \"282655\", \"289913\", \"309807\", \"350529\", \"350766\"]}","task_split":"paper_retrieval"}
{"document_id":"43574","document_content":"# Timestamping Documents and Beliefs\n## Categories\n- Computation and Language\n- Machine Learning\n- I.2.7\n## Abstract\nMost of the textual information available to us are temporally variable. In a world where information is dynamic, time-stamping them is a very important task. Documents are a good source of information and are used for many tasks like, sentiment analysis, classification of reviews etc. The knowledge of creation date of documents facilitates several tasks like summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the web, the time-stamp meta-data is either erroneous or missing. Thus document dating is a challenging problem which requires inference over the temporal structure of the document alongside the contextual information of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document-internal structures. In this paper we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. We also pointed out some limitations of NeuralDater and tried to utilize both context and temporal information in documents in a more flexible and intuitive manner proposing AD3: Attentive Deep Document Dater, an attention-based document dating system. To the best of our knowledge these are the first application of deep learning methods for the task. Through extensive experiments on real-world datasets, we find that our models significantly outperforms state-of-the-art baselines by a significant margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.14622v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152591\", \"166518\", \"200687\", \"261989\", \"263243\", \"266319\", \"268812\", \"271527\", \"289972\", \"303837\", \"304901\", \"311684\", \"316630\", \"320035\"]}","task_split":"paper_retrieval"}
{"document_id":"43748","document_content":"# Segmentation and ABCD rule extraction for skin tumors classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDuring the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion segmentation is proposed. Iterative thresholding is applied to initialize level set automatically. As the detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems, we compare its accuracy with growcut and mean shift algorithms, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification. Relying on visual diagnosis four features: Asymmetry (A), Border (B), Color (C) and Diversity (D) are computed and used to construct a classification module based on artificial neural network for the recognition of malignant melanoma. This framework has been tested on a dermoscopic database [16] of 320 images. The classification results show an increasing true detection rate and a decreasing false positive rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.04372v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"43754","document_content":"# Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n- Computational Complexity\n- Human-Computer Interaction\n## Abstract\nDeep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to overfitting. In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features. Our method employs a spatio-temporal bilinear layer as backbone to capture the motion of facial landmarks during the execution of a facial expression effectively. Besides, it takes advantage of Monte Carlo Dropout to capture the model's uncertainty which is of great importance to analyze and treat uncertain cases. The performance of our method is evaluated on three widely used datasets and it is comparable to that of video-based state-of-the-art methods while it has much less complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.04332v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CC\", \"cs.HC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computational Complexity\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"38438\"], \"outgoing_citations\": [\"56862\", \"87641\", \"88433\", \"110752\", \"163555\", \"231417\", \"239321\", \"255296\", \"265774\", \"287844\", \"303467\"]}","task_split":"paper_retrieval"}
{"document_id":"43897","document_content":"# Coarse-to-Fine Curriculum Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWhen faced with learning challenging new tasks, humans often follow sequences of steps that allow them to incrementally build up the necessary skills for performing these new tasks. However, in machine learning, models are most often trained to solve the target tasks directly.Inspired by human learning, we propose a novel curriculum learning approach which decomposes challenging tasks into sequences of easier intermediate goals that are used to pre-train a model before tackling the target task. We focus on classification tasks, and design the intermediate tasks using an automatically constructed label hierarchy. We train the model at each level of the hierarchy, from coarse labels to fine labels, transferring acquired knowledge across these levels. For instance, the model will first learn to distinguish animals from objects, and then use this acquired knowledge when learning to classify among more fine-grained classes such as cat, dog, car, and truck. Most existing curriculum learning algorithms for supervised learning consist of scheduling the order in which the training examples are presented to the model. In contrast, our approach focuses on the output space of the model. We evaluate our method on several established datasets and show significant performance gains especially on classification problems with many labels. We also evaluate on a new synthetic dataset which allows us to study multiple aspects of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.04072v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107584\", \"133774\", \"152913\", \"170766\", \"193998\", \"208118\", \"222400\", \"232288\", \"235555\", \"247500\", \"253061\", \"255438\", \"261017\", \"271375\", \"320908\", \"329556\"]}","task_split":"paper_retrieval"}
{"document_id":"43904","document_content":"# Graph-MLP: Node Classification without Message Passing in Graph\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Social and Information Networks\n## Abstract\nGraph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data. Both spatial-based and spectral-based GNNs are relying on adjacency matrix to guide message passing among neighbors during feature aggregation. Recent works have mainly focused on powerful message passing modules, however, in this paper, we show that none of the message passing modules is necessary. Instead, we propose a pure multilayer-perceptron-based framework, Graph-MLP with the supervision signal leveraging graph structure, which is sufficient for learning discriminative node representation. In model-level, Graph-MLP only includes multi-layer perceptrons, activation function, and layer normalization. In the loss level, we design a neighboring contrastive (NContrast) loss to bridge the gap between GNNs and MLPs by utilizing the adjacency information implicitly. This design allows our model to be lighter and more robust when facing large-scale graph data and corrupted adjacency information. Extensive experiments prove that even without adjacency information in testing phase, our framework can still reach comparable and even superior performance against the state-of-the-art models in the graph node classification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.04051v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Social and Information Networks\"], \"incoming_citations\": [\"26427\", \"10660\", \"15741\"], \"outgoing_citations\": [\"51004\", \"51275\", \"51636\", \"51767\", \"86320\", \"89486\", \"117866\", \"143875\", \"159292\", \"203857\", \"216376\", \"216618\", \"222661\", \"238422\", \"261876\", \"276530\", \"303167\"]}","task_split":"paper_retrieval"}
{"document_id":"44018","document_content":"# Verifiable and Compositional Reinforcement Learning Systems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task. The framework consists of a high-level model, represented as a parametric Markov decision process (pMDP) which is used to plan and to analyze compositions of subsystems, and of the collection of low-level subsystems themselves. By defining interfaces between the subsystems, the framework enables automatic decompositions of task specifications, e.g., reach a target set of states with a probability of at least 0.95, into individual subtask specifications, i.e. achieve the subsystem's exit conditions with at least some minimum probability, given that its entry conditions are met. This in turn allows for the independent training and testing of the subsystems; if they each learn a policy satisfying the appropriate subtask specification, then their composition is guaranteed to satisfy the overall task specification. Conversely, if the subtask specifications cannot all be satisfied by the learned policies, we present a method, formulated as the problem of finding an optimal set of parameters in the pMDP, to automatically update the subtask specifications to account for the observed shortcomings. The result is an iterative procedure for defining subtask specifications, and for training the subsystems to meet them. As an additional benefit, this procedure allows for particularly challenging or important components of an overall task to be determined automatically, and focused on, during training. Experimental results demonstrate the presented framework's novel capabilities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.05864v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78650\", \"96439\", \"114269\", \"167124\", \"194903\", \"216424\", \"218770\", \"231175\", \"239954\", \"248423\", \"268910\", \"272464\", \"294142\"]}","task_split":"paper_retrieval"}
{"document_id":"44032","document_content":"# X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nTask-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on numerous training data to achieve decent performance but fail to generalize to low-resource target languages or domains. In this paper, we present X2Parser, a transferable Cross-lingual and Cross-domain Parser for TCSP. Unlike previous models that learn to generate the hierarchical representations for nested intents and slots, we propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems. After that, we further propose a fertility-based slot predictor that first learns to dynamically detect the number of labels for each token, and then predicts the slot types. Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains. Furthermore, our model tackles the problem in an efficient non-autoregressive way that reduces the latency by up to 66% compared to the generative model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03777v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23177\"], \"outgoing_citations\": [\"55935\", \"56973\", \"61771\", \"85251\", \"95921\", \"104803\", \"118104\", \"129221\", \"129503\", \"138638\", \"144895\", \"155118\", \"157033\", \"168995\", \"169332\", \"185347\", \"205014\", \"212163\", \"213822\", \"219866\", \"220947\", \"261734\", \"268902\", \"283225\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"44034","document_content":"# Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation: a graphical depiction of the systems generalization and performance in the current game state, how well the agent would play in semantically similar environments, and a narrative explanation of what the graphical information implies. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment. The results demonstrate a statistically significant increase in user trust and acceptance of the AI system with explanation, versus the AI system without explanation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03775v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"187351\", \"206890\", \"254651\"]}","task_split":"paper_retrieval"}
{"document_id":"44064","document_content":"# Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast\n## Categories\n- Machine Learning\n## Abstract\nWe consider graph representation learning in a self-supervised manner. Graph neural networks (GNNs) use neighborhood aggregation as a core component that results in feature smoothing among nodes in proximity. While successful in various prediction tasks, such a paradigm falls short of capturing nodes' similarities over a long distance, which proves to be important for high-quality learning. To tackle this problem, we strengthen the graph with two additional graph views, in which nodes are directly linked to those with the most similar features or local structures. Not restricted by connectivity in the original graph, the generated views allow the model to enhance its expressive power with new and complementary perspectives from which to look at the relationship between nodes. Following a contrastive learning approach, we propose a method that aims to maximize the agreement between representations across generated views and the original graph. We also propose a channel-level contrast approach that greatly reduces computation cost, compared to the commonly used node level contrast, which requires computation cost quadratic in the number of nodes. Extensive experiments on seven assortative graphs and four disassortative graphs demonstrate the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03723v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"66867\", \"86002\", \"92075\", \"113857\", \"114134\", \"115700\", \"117308\", \"117926\", \"119843\", \"120508\", \"142571\", \"144216\", \"161070\", \"172724\", \"173673\", \"194773\", \"216376\", \"227067\", \"228663\", \"239146\", \"240758\", \"242416\", \"252067\", \"252195\", \"261412\", \"269202\", \"289410\", \"295676\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"44161","document_content":"# Identifiability in inverse reinforcement learning\n## Categories\n- Machine Learning\n- Optimization and Control\n- 2020: 49N45, 93B30, 93E12, 93B15, 49N10, 90C40, 60J10, 62M05\n## Abstract\nInverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998] the problem is ill-posed, and the reward function is not identifiable, even under the presence of perfect information about optimal behavior. We provide a resolution to this non-identifiability for problems with entropy regularization. For a given environment, we fully characterize the reward functions leading to a given policy and demonstrate that, given demonstrations of actions for the same reward under two distinct discount factors, or under sufficiently different environments, the unobserved reward can be recovered up to a constant. We also give general necessary and sufficient conditions for reconstruction of time-homogeneous rewards on finite horizons, and for action-independent rewards, generalizing recent results of Kim et al. [2021] and Fu et al. [2018].","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03498v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.OC\", \"2020: 49N45, 93B30, 93E12, 93B15, 49N10, 90C40, 60J10, 62M05\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Optimization and Control\", \"2020: 49N45, 93B30, 93E12, 93B15, 49N10, 90C40, 60J10, 62M05\"], \"incoming_citations\": [\"113646\"], \"outgoing_citations\": [\"86448\", \"233450\", \"251924\", \"266406\", \"272987\", \"280655\", \"297317\", \"299576\"]}","task_split":"paper_retrieval"}
{"document_id":"44176","document_content":"# Multilingual Neural Semantic Parsing for Low-Resourced Languages\n## Categories\n- Computation and Language\n## Abstract\nMultilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-the-art model on the public NLMaps dataset. We also establish a new baseline for zero-shot learning on the TOP dataset. We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03469v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"132812\", \"144895\", \"202124\", \"213822\", \"232288\", \"244755\", \"257206\", \"262535\", \"268859\", \"274095\", \"274271\", \"290632\", \"311464\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"44185","document_content":"# Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patcog.2021.108075\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"28790\", \"30018\", \"30125\", \"60213\", \"74059\", \"141111\", \"141137\", \"168390\", \"169318\", \"172700\", \"173972\", \"187364\", \"195957\", \"202884\", \"246333\", \"254665\", \"256055\"]}","task_split":"paper_retrieval"}
{"document_id":"44220","document_content":"# LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nCross-lingual document representations enable language understanding in multilingual contexts and allow transfer learning from high-resource to low-resource languages at the document level. Recently large pre-trained language models such as BERT, XLM and XLM-RoBERTa have achieved great success when fine-tuned on sentence-level downstream tasks. It is tempting to apply these cross-lingual models to document representation learning. However, there are two challenges: (1) these models impose high costs on long document processing and thus many of them have strict length limit; (2) model fine-tuning requires extra data and computational resources, which is not practical in resource-limited settings. In this work, we address these challenges by proposing unsupervised Language-Agnostic Weighted Document Representations (LAWDR). We study the geometry of pre-trained sentence embeddings and leverage it to derive document representations without fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates comparable performance to state-of-the-art models on benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03379v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"157172\", \"158102\", \"166518\", \"169179\", \"179726\", \"179896\", \"197656\", \"202124\", \"204715\", \"211711\", \"219083\", \"222741\", \"261768\", \"263680\", \"342907\"]}","task_split":"paper_retrieval"}
{"document_id":"44223","document_content":"# Commutative Lie Group VAE for Disentanglement Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. Traditionally, such a structure is fixed to be a vector space with data variations represented by translations along individual latent dimensions. We argue this simple structure is suboptimal since it requires the model to learn to discard the properties (e.g. different scales of changes, different levels of abstractness) of data variations, which is an extra work than equivariance learning. Instead, we propose to encode the data variations with groups, a structure not only can equivariantly represent variations, but can also be adaptively optimized to preserve the properties of data variations. Considering it is hard to conduct training on group structures, we focus on Lie groups and adopt a parameterization using Lie algebra. Based on the parameterization, some disentanglement learning constraints are naturally derived. A simple model named Commutative Lie Group VAE is introduced to realize the group-based disentanglement learning. Experiments show that our model can effectively learn disentangled representations without supervision, and can achieve state-of-the-art performance without extra constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03375v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"115141\"], \"outgoing_citations\": [\"57749\", \"104295\", \"105383\", \"109786\", \"117695\", \"140140\", \"140360\", \"141826\", \"164790\", \"170071\", \"184072\", \"185109\", \"193061\", \"207094\", \"235824\", \"237146\", \"239899\", \"241933\", \"242239\", \"251597\", \"264781\", \"272919\", \"278014\", \"290620\", \"297682\", \"298695\", \"302162\", \"316183\", \"328112\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"44235","document_content":"# DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn E-commerce, vouchers are important marketing tools to enhance users' engagement and boost sales and revenue. The likelihood that a user redeems a voucher is a key factor in voucher distribution decision. User-item Click-Through-Rate (CTR) models are often applied to predict the user-voucher redemption rate. However, the voucher scenario involves more complicated relations among users, items and vouchers. The users' historical behavior in a voucher collection activity reflects users' voucher usage patterns, which is nevertheless overlooked by the CTR-based solutions. In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction. The complex structural user-voucher-item relationships are captured by a User-Behavior Voucher Graph (UVG). User behavior happening both before and after voucher collection is taken into consideration, and a high-level representation is extracted by Higher-order Graph Neural Networks. On top of a sequence of UVGs, an attention network is built which can help to learn users' long-term voucher redemption preference. Extensive experiments on three large-scale production datasets demonstrate the proposed DMBGN model is effective, with 10% to 16% relative AUC improvement over Deep Neural Networks (DNN), and 2% to 4% AUC improvement over Deep Interest Network (DIN). Source code and a sample dataset are made publicly available to facilitate future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3447548.3467191\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"20875\"], \"outgoing_citations\": [\"18009\", \"104522\", \"123124\", \"123543\", \"161522\", \"169328\", \"186198\", \"215435\", \"218374\", \"258393\", \"263158\", \"271665\", \"287781\", \"303167\", \"309082\"]}","task_split":"paper_retrieval"}
{"document_id":"44286","document_content":"# Meta-learning for downstream aware and agnostic pretraining\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNeural network pretraining is gaining attention due to its outstanding performance in natural language processing applications. However, pretraining usually leverages predefined task sequences to learn general linguistic clues. The lack of mechanisms in choosing proper tasks during pretraining makes the learning and knowledge encoding inefficient. We thus propose using meta-learning to select tasks that provide the most informative learning signals in each episode of pretraining. With the proposed method, we aim to achieve better efficiency in computation and memory usage for the pretraining process and resulting networks while maintaining the performance. In this preliminary work, we discuss the algorithm of the method and its two variants, downstream-aware and downstream-agnostic pretraining. Our experiment plan is also summarized, while empirical results will be shared in our future works.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03270v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"83777\", \"124030\", \"188123\"]}","task_split":"paper_retrieval"}
{"document_id":"44352","document_content":"# Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCan we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper presents a hierarchical model that generalizes instructional knowledge from large-scale text corpora and transfers the knowledge to video. Given a portion of an instructional video, our model recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the capabilities of our model, we introduce the \\emph{Tasty Videos Dataset V2}, a collection of 4022 recipes for zero-shot learning, recognition and anticipation. Extensive experiments with various evaluation metrics demonstrate the potential of our method for generalization, given limited video data for training models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03158v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120944\", \"121785\", \"124051\", \"130800\", \"138415\", \"138805\", \"151327\", \"173243\", \"181897\", \"185249\", \"187115\", \"187857\", \"189504\", \"191258\", \"191573\", \"192531\", \"194508\", \"196110\", \"203523\", \"204141\", \"205896\", \"206997\", \"214015\", \"232600\", \"233746\", \"234602\", \"235189\", \"236377\", \"236894\", \"236918\", \"237765\", \"238082\", \"244480\", \"255939\", \"261149\", \"267161\", \"270325\", \"270713\", \"274597\", \"280328\", \"287541\", \"293365\", \"295863\", \"298543\", \"298629\", \"301539\", \"310322\", \"310452\", \"310737\", \"310738\", \"310903\", \"311470\", \"313435\", \"313644\", \"316472\", \"316779\", \"316803\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"44388","document_content":"# Preservation of the Global Knowledge by Not-True Distillation in Federated Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nIn federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data directly, the global model's convergence often suffers from data heterogeneity. This study starts from an analogy to continual learning and suggests that forgetting could be the bottleneck of federated learning. We observe that the global model forgets the knowledge from previous rounds, and the local training induces forgetting the knowledge outside of the local distribution. Based on our findings, we hypothesize that tackling down forgetting will relieve the data heterogeneity problem. To this end, we propose a novel and effective algorithm, Federated Not-True Distillation (FedNTD), which preserves the global perspective on locally available data only for the not-true classes. In the experiments, FedNTD shows state-of-the-art performance on various setups without compromising data privacy or incurring additional communication costs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.03097v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"6786\", \"6799\", \"10866\", \"38297\", \"43448\", \"48683\", \"59584\", \"71707\", \"90584\", \"92241\", \"100192\", \"102417\", \"109503\", \"112023\", \"119058\", \"119091\", \"119873\", \"138175\", \"141358\", \"141512\", \"142093\", \"143263\", \"150255\", \"161269\", \"161521\", \"161758\", \"162752\", \"166970\", \"167523\", \"177601\", \"183930\", \"207676\", \"215646\", \"229563\", \"230678\", \"241392\", \"243820\", \"249234\", \"283045\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"44473","document_content":"# Zero-shot Task Adaptation using Natural Language\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nImitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent. However, as the complexity of tasks grows, it could be beneficial to use both demonstrations and language to communicate with an agent. In this work, we propose a novel setting where an agent is given both a demonstration and a description, and must combine information from both the modalities. Specifically, given a demonstration for a task (the source task), and a natural language description of the differences between the demonstrated task and a related but different task (the target task), our goal is to train an agent to complete the target task in a zero-shot setting, that is, without any demonstrations for the target task. To this end, we introduce Language-Aided Reward and Value Adaptation (LARVA) which, given a source demonstration and a linguistic description of how the target task differs, learns to output a reward \/ value function that accurately describes the target task. Our experiments show that on a diverse set of adaptations, our approach is able to complete more than 95% of target tasks when using template-based descriptions, and more than 70% when using free-form natural language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02972v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74771\", \"92051\", \"98568\", \"108770\", \"152871\", \"177272\", \"181620\", \"191258\", \"196377\", \"208773\", \"215015\", \"228916\", \"249906\", \"251691\", \"251924\", \"259693\", \"268584\", \"290679\", \"297317\", \"314620\", \"315839\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"44609","document_content":"# Neural Auto-Curricula\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nWhen solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population. Within such a process, the update rules of \"who to compete with\" (i.e., the opponent mixture) and \"how to beat them\" (i.e., finding best responses) are underpinned by manually developed game theoretical principles such as fictitious play and Double Oracle. In this paper, we introduce a novel framework -- Neural Auto-Curricula (NAC) -- that leverages meta-gradient descent to automate the discovery of the learning update rule without explicit human design. Specifically, we parameterise the opponent selection module by neural networks and the best-response module by optimisation subroutines, and update their parameters solely via interaction with the game engine, where both players aim to minimise their exploitability. Surprisingly, even without human design, the discovered MARL algorithms achieve competitive or even better performance with the state-of-the-art population-based game solvers (e.g., PSRO) on Games of Skill, differentiable Lotto, non-transitive Mixture Games, Iterated Matching Pennies, and Kuhn Poker. Additionally, we show that NAC is able to generalise from small games to large games, for example training on Kuhn Poker and outperforming PSRO on Leduc Poker. Our work inspires a promising future direction to discover general MARL algorithms solely from data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02745v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"76\", \"4891\", \"29225\", \"63237\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"44781","document_content":"# A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n## Abstract\nConventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such objects occurrences and interactions are equivalently useful and important in this field as they are usually mentioned in the text. Scene graph presentation is a suitable method for the image-text matching challenge and obtained good results due to its ability to capture the inter-relationship information. Both images and text are represented in scene graph levels and formulate the retrieval challenge as a scene graph matching challenge. In this paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model that enhances the state-of-the-art method by integrating an extra graph convolution network to capture the general information of a graph. Specifically, for a pair of scene graphs of an image and its caption, two separate models are used to learn the features of each graph's nodes and edges. Then a Siamese-structure graph convolution model is employed to embed graphs into vector forms. We finally combine the graph-level and the vector-level to calculate the similarity of this image-text pair. The empirical experiments show that our enhancement with the combination of levels can improve the performance of the baseline method by increasing the recall by more than 10% on the Flickr30k dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02400v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"131990\", \"133742\", \"137906\", \"162227\", \"174872\", \"192794\", \"207190\", \"222661\", \"226318\", \"236736\", \"238205\", \"248171\", \"250108\", \"250405\", \"253181\", \"260926\", \"280486\", \"287504\", \"312643\"]}","task_split":"paper_retrieval"}
{"document_id":"44843","document_content":"# Event Classification with Multi-step Machine Learning\n## Categories\n- Machine Learning\n## Abstract\nThe usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of an ML model from several small ML model candidates for each sub-task has been performed by using the idea based on Neural Architecture Search (NAS). In this paper, Differentiable Architecture Search (DARTS) and Single Path One-Shot NAS (SPOS-NAS) are tested, where the construction of loss functions is improved to keep all ML models smoothly learning. Using DARTS and SPOS-NAS as an optimization and selection as well as the connections for multi-step machine learning systems, we find that (1) such a system can quickly and successfully select highly performant model combinations, and (2) the selected models are consistent with baseline algorithms, such as grid search, and their outputs are well controlled.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1051\/epjconf\/202125103036\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74690\", \"149164\", \"188525\", \"192998\", \"193623\", \"214692\", \"237399\", \"242806\", \"243239\", \"251209\", \"258332\", \"266003\"]}","task_split":"paper_retrieval"}
{"document_id":"44848","document_content":"# Cross-language Sentence Selection via Data Augmentation and Rationale Training\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThis paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02293v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"18991\", \"25870\"], \"outgoing_citations\": [\"181826\", \"197416\", \"219516\", \"220022\", \"230181\", \"231863\", \"232022\", \"233462\", \"234811\", \"241616\", \"244373\", \"253559\", \"261389\", \"270020\", \"280884\", \"286324\", \"288943\", \"320039\"]}","task_split":"paper_retrieval"}
{"document_id":"44875","document_content":"# A General Method for Event Detection on Social Media\n## Categories\n- Information Retrieval\n## Abstract\nEvent detection on social media has attracted a number of researches, given the recent availability of large volumes of social media discussions. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from its usual behavior. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect events in time series in a general sense. In the experimental evaluation, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. Our method can be easily implemented and can be treated as a starting point for more specific applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02250v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"330619\"]}","task_split":"paper_retrieval"}
{"document_id":"44891","document_content":"# Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nA good open-domain chatbot should avoid presenting contradictory responses about facts or opinions in a conversational session, known as its consistency capacity. However, evaluating the consistency capacity of a chatbot is still challenging. Employing human judges to interact with chatbots on purpose to check their capacities is costly and low-efficient, and difficult to get rid of subjective bias. In this paper, we propose the Addressing Inquiries about History (AIH), an efficient and practical framework for the consistency evaluation. At the conversation stage, AIH attempts to address appropriate inquiries about the dialogue history to induce the chatbot to redeclare the historical facts or opinions. We carry out the conversation between chatbots, which is more efficient than the human-bot interaction and can also alleviate the subjective bias. In this way, we manage to rapidly obtain a dialog session that contains responses with high contradiction possibilities. At the contradiction recognition stage, we can either employ human judges or a natural language inference (NLI) model to recognize whether the answers to the inquiries are contradictory with history. Finally, we are able to rank chatbots according to the contradiction statistics. Experiments on open-domain chatbots show that our approach can efficiently and reliably assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation. We release the framework and hope to help improve the consistency capacity of chatbots. \\footnote{\\url{https:\/\/github.com\/ictnlp\/AIH}}","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02228v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"44072\"], \"outgoing_citations\": [\"74541\", \"78504\", \"87309\", \"96551\", \"99497\", \"115155\", \"116754\", \"130525\", \"136604\", \"145384\", \"145923\", \"158493\", \"168228\", \"187236\", \"189504\", \"200725\", \"211664\", \"211890\", \"212046\"]}","task_split":"paper_retrieval"}
{"document_id":"44921","document_content":"# Self-supervised Dialogue Learning for Spoken Conversational Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nIn spoken conversational question answering (SCQA), the answer to the corresponding question is generated by retrieving and then analyzing a fixed spoken document, including multi-part conversations. Most SCQA systems have considered only retrieving information from ordered utterances. However, the sequential order of dialogue is important to build a robust spoken conversational question answering system, and the changes of utterances order may severely result in low-quality and incoherent corpora. To this end, we introduce a self-supervised learning approach, including incoherence discrimination, insertion detection, and question prediction, to explicitly capture the coreference resolution and dialogue coherence among spoken documents. Specifically, we design a joint learning framework where the auxiliary self-supervised tasks can enable the pre-trained SCQA systems towards more coherent and meaningful spoken dialogue learning. We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence. Experimental results demonstrate that our proposed method provides more coherent, meaningful, and appropriate responses, yielding superior performance gains compared to the original pre-trained language models. Our method achieves state-of-the-art results on the Spoken-CoQA dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02182v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"45018","document_content":"# Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation\n## Categories\n- Social and Information Networks\n- Artificial Intelligence\n- Machine Learning\n- Computation and Language\n## Abstract\nThe rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence. There is a need to understand the spread of false content through online networks for developing interventions that disrupt misinformation before it achieves virality. Using a Deep Bidirectional Transformer for Language Understanding (BERT) and propagation graphs, this study classifies and visualizes the spread of misinformation on a social media network using publicly available Twitter data. The results confirm prior research around user clusters and the virality of false content while improving the precision of deep learning models for misinformation detection. The study further demonstrates the suitability of BERT for providing a scalable model for false information detection, which can contribute to the development of more timely and accurate interventions to slow the spread of misinformation in online environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.02607v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.AI\", \"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Artificial Intelligence\", \"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138002\", \"166278\", \"219169\", \"244755\", \"270399\"]}","task_split":"paper_retrieval"}
{"document_id":"45038","document_content":"# GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling\n## Categories\n- Computation and Language\n## Abstract\nMulti-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate. Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction layer is proposed to model slot dependency for alleviating uncoordinated slots problem while a global intent-slot graph interaction layer is introduced to model the interaction between multiple intents and all slots in the utterance. Experimental results on two public datasets show that our framework achieves state-of-the-art performance while being 11.5 times faster.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01925v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1340\", \"127897\"], \"outgoing_citations\": [\"50552\", \"65237\", \"78544\", \"81514\", \"92955\", \"95751\", \"95759\", \"96274\", \"129831\", \"166702\", \"168411\", \"168563\", \"178308\", \"204763\", \"205014\", \"209230\", \"219441\", \"230543\", \"254610\", \"263243\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"45119","document_content":"# Causality in Neural Networks -- An Extended Abstract\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCausal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability, causal disentanglement are some important aspects of any machine learning model. Causal explanations are required to believe in a model's decision and causal disentanglement learning is important for transfer learning applications. We exploit the ideas of causality to be used in deep learning models to achieve better and causally explainable models that are useful in fairness, disentangled representation, etc.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3461702.3462467\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"119002\", \"123753\", \"181909\", \"199971\", \"237376\", \"271038\", \"350472\"]}","task_split":"paper_retrieval"}
{"document_id":"45125","document_content":"# Towards urban scenes understanding through polarization cues\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutonomous robotics is critically affected by the robustness of its scene understanding algorithms. We propose a two-axis pipeline based on polarization indices to analyze dynamic urban scenes. As robots evolve in unknown environments, they are prone to encountering specular obstacles. Usually, specular phenomena are rarely taken into account by algorithms which causes misinterpretations and erroneous estimates. By exploiting all the light properties, systems can greatly increase their robustness to events. In addition to the conventional photometric characteristics, we propose to include polarization sensing.   We demonstrate in this paper that the contribution of polarization measurement increases both the performances of segmentation and the quality of depth estimation. Our polarimetry-based approaches are compared here with other state-of-the-art RGB-centric methods showing interest of using polarization imaging.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01717v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"111979\", \"123508\", \"210027\", \"214280\", \"229325\", \"230667\", \"239395\", \"286320\", \"321377\", \"328587\"]}","task_split":"paper_retrieval"}
{"document_id":"45150","document_content":"# Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot's Self-Disclosure in Conversational Recommendations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- I.2\n## Abstract\nUsing chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and relational strategies, such as self-disclosure from a chatbot, may influence users' perception and acceptance of the recommendation. In this work, we designed, implemented, and evaluated a social chatbot capable of performing three different levels of self-disclosure: factual information (low), cognitive opinions (medium), and emotions (high). In the evaluation, we recruited 372 participants to converse with the chatbot on two topics: movies and COVID-19 experiences. In each topic, the chatbot performed small talks and made recommendations relevant to the topic. Participants were randomly assigned to four experimental conditions where the chatbot used factual, cognitive, emotional, and adaptive strategies to perform self-disclosures. By training a text classifier to identify users' level of self-disclosure in real-time, the adaptive chatbot can dynamically match its self-disclosure to the level of disclosure exhibited by the users. Our results show that users reciprocate with higher-level self-disclosure when a recommendation chatbot consistently displays emotions throughout the conversation. Chatbot's emotional disclosure also led to increased interactional enjoyment and more positive interpersonal perception towards the bot, fostering a stronger human-chatbot relationship and thus leading to increased recommendation effectiveness, including a higher tendency to accept the recommendation. We discuss the understandings obtained and implications to future design.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01666v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"I.2\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"I.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"73917\", \"97768\", \"128396\", \"170044\", \"204637\"]}","task_split":"paper_retrieval"}
{"document_id":"45163","document_content":"# Deceptive Level Generation for Angry Birds\n## Categories\n- Artificial Intelligence\n## Abstract\nThe Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01639v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"26642\"], \"outgoing_citations\": [\"172062\", \"239033\", \"243671\", \"278675\", \"316486\"]}","task_split":"paper_retrieval"}
{"document_id":"45196","document_content":"# A Systematic Investigation of KB-Text Embedding Alignment at Scale\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nKnowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way. Separately embedding the individual knowledge sources into vector spaces has demonstrated tremendous successes in encoding the respective knowledge, but how to jointly embed and reason with both knowledge sources to fully leverage the complementary information is still largely an open problem. We conduct a large-scale, systematic investigation of aligning KB and text embeddings for joint reasoning. We set up a novel evaluation framework with two evaluation tasks, few-shot link prediction and analogical reasoning, and evaluate an array of KB-text embedding alignment methods. We also demonstrate how such alignment can infuse textual information into KB embeddings for more accurate link prediction on emerging entities and events, using COVID-19 as a case study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01586v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"182707\", \"193259\", \"197408\", \"205857\", \"210557\", \"248147\", \"261989\", \"268414\", \"280634\", \"300498\", \"308219\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"45199","document_content":"# The Limitations of Limited Context for Constituency Parsing\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIncorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised syntactic parsing. Rapid progress in recent years was arguably spurred on by the empirical success of the Parsing-Reading-Predict architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM of (Shen et al., 2019). Most notably, this is the first time neural approaches were able to successfully perform unsupervised syntactic parsing (evaluated by various metrics like F-1 score).   However, even heuristic (much less fully mathematical) understanding of why and when these architectures work is lagging severely behind. In this work, we answer representational questions raised by the architectures in (Shen et al., 2018a, 2019), as well as some transition-based syntax-aware language models (Dyer et al., 2016): what kind of syntactic structure can current neural approaches to syntax represent? Concretely, we ground this question in the sandbox of probabilistic context-free-grammars (PCFGs), and identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to when forced to make parsing decision. We show that with limited context (either bounded, or unidirectional), there are PCFGs, for which these approaches cannot represent the max-likelihood parse; conversely, if the context is unlimited, they can represent the max-likelihood parse of any PCFG.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01580v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"94152\", \"153933\", \"166223\", \"179201\", \"181752\", \"182116\", \"182542\", \"191740\", \"213362\", \"219836\", \"228464\", \"232243\", \"251565\", \"256948\", \"281225\", \"284357\", \"297648\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"45272","document_content":"# Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nRobots need to be able to work in multiple different environments. Even when performing similar tasks, different behaviour should be deployed to best fit the current environment. In this paper, We propose a new approach to navigation, where it is treated as a multi-task learning problem. This enables the robot to learn to behave differently in visual navigation tasks for different environments while also learning shared expertise across environments. We evaluated our approach in both simulated environments as well as real-world data. Our method allows our system to converge with a 26% reduction in training time, while also increasing accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.01434v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"134247\", \"176597\", \"177381\", \"247418\", \"261316\", \"261983\", \"284565\", \"294142\", \"312170\"]}","task_split":"paper_retrieval"}
{"document_id":"45539","document_content":"# Conversational Question Answering: A Survey\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nQuestion answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on Conversational Question Answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy the user's information needs. Whilst the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey, therefore, is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.00874v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"10232\", \"21167\"], \"outgoing_citations\": [\"54038\", \"54270\", \"58427\", \"63245\", \"65473\", \"109697\", \"121854\", \"123404\", \"139637\", \"161554\", \"162246\", \"162857\", \"167880\", \"168575\", \"169278\", \"169435\", \"170238\", \"171828\", \"172505\", \"173415\", \"173570\", \"178032\", \"181063\", \"182261\", \"183590\", \"186455\", \"196257\", \"206624\", \"212078\", \"213349\", \"215164\", \"216019\", \"216953\", \"219107\", \"219975\", \"220723\", \"220724\", \"221212\", \"232274\", \"235098\", \"237682\", \"239013\", \"243759\", \"247060\", \"250549\", \"259706\", \"260768\", \"266155\", \"266850\", \"268809\", \"268902\", \"270020\", \"279213\", \"279343\", \"281163\", \"285454\", \"290747\", \"294494\", \"298016\", \"307969\", \"308610\", \"309411\", \"311423\", \"311464\", \"313804\"]}","task_split":"paper_retrieval"}
{"document_id":"45669","document_content":"# NewsEmbed: Modeling News through Pre-trained Document Representations\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nEffectively modeling text-rich fresh content such as news articles at document-level is a challenging problem. To ensure a content-based model generalize well to a broad range of applications, it is critical to have a training dataset that is large beyond the scale of human labels while achieving desired quality. In this work, we address those two challenges by proposing a novel approach to mine semantically-relevant fresh documents, and their topic labels, with little human supervision. Meanwhile, we design a multitask model called NewsEmbed that alternatively trains a contrastive learning with a multi-label classification to derive a universal document encoder. We show that the proposed approach can provide billions of high quality organic training examples and can be naturally extended to multilingual setting where texts in different languages are encoded in the same semantic space. We experimentally demonstrate NewsEmbed's competitive performance across multiple natural language understanding tasks, both supervised and unsupervised.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3447548.3467392\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"38858\"], \"outgoing_citations\": [\"92112\", \"95191\", \"114308\", \"124694\", \"129503\", \"135112\", \"143117\", \"152790\", \"169911\", \"176404\", \"179992\", \"182486\", \"190165\", \"202124\", \"204715\", \"211841\", \"212322\", \"219169\", \"219391\", \"222706\", \"238963\", \"259723\", \"302425\", \"317966\"]}","task_split":"paper_retrieval"}
{"document_id":"45681","document_content":"# IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse\n## Categories\n- Machine Learning\n## Abstract\nDespite its success, generative adversarial networks (GANs) still suffer from mode collapse, namely the generator can only map latent variables to a partial set of modes of the target distribution. In this paper, we analyze and try to regularize this issue with an independent and identically distributed (IID) sampling perspective and emphasize that holding the IID property for generation for target distribution (i.e. real distribution) can naturally avoid mode collapse. This is based on the basic IID assumption for real data in machine learning. However, though the source samples $\\{\\mathbf{z}\\}$ obey IID, the generations $\\{G(\\mathbf{z})\\}$ may not necessarily be IID from the target distribution. Based on this observation, we propose a necessary condition of IID generation and provide a new loss to encourage the closeness between the inverse source of real data and the Gaussian source in the latent space to regularize the generation to be IID from the target distribution. The logic is that the inverse samples from target data should also be IID in the source distribution. Experiments on both synthetic and real-world data show the effectiveness of our model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.00563v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"31381\", \"117607\", \"162520\", \"177483\", \"195268\", \"201577\", \"207829\", \"208041\", \"229773\", \"241933\", \"245177\", \"256229\", \"263662\", \"269271\", \"278560\", \"281002\", \"281144\", \"289678\", \"291440\"]}","task_split":"paper_retrieval"}
{"document_id":"45771","document_content":"# Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCross-modal retrieval is an important functionality in modern search engines, as it increases the user experience by allowing queries and retrieved objects to pertain to different modalities. In this paper, we focus on the image-sentence retrieval task, where the objective is to efficiently find relevant images for a given sentence (image-retrieval) or the relevant sentences for a given image (sentence-retrieval). Computer vision literature reports the best results on the image-sentence matching task using deep neural networks equipped with attention and self-attention mechanisms. They evaluate the matching performance on the retrieval task by performing sequential scans of the whole dataset. This method does not scale well with an increasing amount of images or captions. In this work, we explore different preprocessing techniques to produce sparsified deep multi-modal features extracting them from state-of-the-art deep-learning architectures for image-text matching. Our main objective is to lay down the paths for efficient indexing of complex multi-modal descriptions. We use the recently introduced TERN architecture as an image-sentence features extractor. It is designed for producing fixed-size 1024-d vectors describing whole images and sentences, as well as variable-length sets of 1024-d vectors describing the various building components of the two modalities (image regions and sentence words respectively). All these vectors are enforced by the TERN design to lie into the same common space. Our experiments show interesting preliminary results on the explored methods and suggest further experimentation in this important research direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.00358v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37217\"], \"outgoing_citations\": [\"130188\", \"165588\", \"168374\", \"238205\", \"258937\", \"260926\", \"289755\", \"303067\", \"320761\", \"332151\", \"339440\"]}","task_split":"paper_retrieval"}
{"document_id":"45842","document_content":"# Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNamed entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, deep neural networks can hardly scale out to many languages in an industry setting. To tackle this challenge, cross-lingual NER transfers knowledge from a rich-resource language to languages with low resources through pre-trained multilingual language models. Instead of using training data in target languages, cross-lingual NER has to rely on only training data in source languages, and optionally adds the translated training data derived from source languages. However, the existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages, which is relatively easy to collect in industry applications. To address the opportunities and challenges, in this paper we describe our novel practice in Microsoft to leverage such large amounts of unlabeled data in target languages in real production settings. To effectively extract weak supervision signals from the unlabeled data, we develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning. The empirical study on three benchmark data sets verifies that our approach establishes the new state-of-the-art performance with clear edges. Now, the NER techniques reported in this paper are on their way to become a fundamental component for Web ranking, Entity Pane, Answers Triggering, and Question Answering in the Microsoft Bing search engine. Moreover, our techniques will also serve as part of the Spoken Language Understanding module for a commercial voice assistant. We plan to open source the code of the prototype framework after deployment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.00241v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"81199\", \"104803\", \"115664\", \"118617\", \"118926\", \"127811\", \"128230\", \"128868\", \"131993\", \"154905\", \"155428\", \"156364\", \"156893\", \"161064\", \"168995\", \"185443\", \"189792\", \"200678\", \"219866\", \"220186\", \"261734\", \"267233\", \"271158\"]}","task_split":"paper_retrieval"}
{"document_id":"45920","document_content":"# Text Summarization with Latent Queries\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes. When using a summarization system, users often have specific intents with various language realizations, which, depending on the information need, can range from a single keyword to a long narrative composed of multiple questions. Existing summarization systems, however, often either fail to support or act robustly on this query focused summarization task. We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimizes a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Despite learning from only generic summarization data and requiring no further optimization for downstream summarization tasks, our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.00104v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"1442\", \"3037\", \"27254\"], \"outgoing_citations\": [\"89474\", \"93920\", \"127141\", \"130347\", \"136011\", \"157393\", \"170657\", \"181306\", \"183462\", \"219618\", \"220230\", \"220955\", \"244373\", \"267899\", \"268902\", \"298016\", \"307223\", \"309148\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"45982","document_content":"# Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAnnotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not feasible. However, Zero-shot learning methods could aptly be used to recognize unseen\/out-of-lexicon words in such historical document images. Based on previous state-of-the-art method for zero-shot word recognition Pho(SC)Net, we propose a hybrid model based on the CTC framework (Pho(SC)-CTC) that takes advantage of the rich features learned by Pho(SC)Net followed by a connectionist temporal classification (CTC) framework to perform the final classification. Encouraging results were obtained on two publicly available historical document datasets and one synthetic handwritten dataset, which justifies the efficacy of Pho(SC)-CTC and Pho(SC)Net.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.15093v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138593\", \"150132\", \"167090\", \"190296\", \"210634\", \"238591\", \"239643\", \"241864\", \"243311\", \"270842\", \"271625\", \"295459\", \"309239\", \"315231\"]}","task_split":"paper_retrieval"}
{"document_id":"45996","document_content":"# Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data\n## Categories\n- Computation and Language\n## Abstract\nThe scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.15071v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26304\", \"15844\", \"22750\", \"25297\"], \"outgoing_citations\": [\"92489\", \"98946\", \"109640\", \"114334\", \"118104\", \"125894\", \"132595\", \"133020\", \"143551\", \"146003\", \"151737\", \"157109\", \"157172\", \"158546\", \"158913\", \"181686\", \"182716\", \"187452\", \"192335\", \"194844\", \"200263\", \"202124\", \"211745\", \"221514\", \"229813\", \"234635\", \"234807\", \"235275\", \"251816\", \"251974\", \"253840\", \"262361\", \"267524\", \"280486\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"46204","document_content":"# Image-to-Video Generation via 3D Facial Dynamics\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nWe present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to \"imagine\" a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR\/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.14678v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"42347\"], \"outgoing_citations\": [\"50155\", \"109032\", \"140841\", \"171016\", \"171528\", \"184495\", \"185516\", \"188937\", \"190371\", \"194156\", \"199133\", \"202686\", \"202696\", \"213313\", \"219464\", \"221244\", \"221862\", \"223000\", \"223142\", \"223504\", \"227719\", \"247846\", \"248731\", \"249421\", \"261089\", \"267705\", \"267940\", \"268497\", \"268963\", \"269134\", \"270229\", \"270923\", \"274550\", \"287740\", \"291817\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"46513","document_content":"# Recommendations and Results Organization in Netflix Search\n## Categories\n- Information Retrieval\n- Human-Computer Interaction\n## Abstract\nPersonalized recommendations on the Netflix Homepage are based on a user's viewing habits and the behavior of similar users. These recommendations, organized for efficient browsing, enable users to discover the next great video to watch and enjoy without additional input or an explicit expression of their intents or goals. The Netflix Search experience, on the other hand, allows users to take active control of discovering new videos by explicitly expressing their entertainment needs via search queries.   In this talk, we discuss the importance of producing search results that go beyond traditional keyword-matches to effectively satisfy users' search needs in the Netflix entertainment setting. Motivated by users' various search intents, we highlight the necessity to improve Search by applying approaches that have historically powered the Homepage. Specifically, we discuss our approach to leverage recommendations in the context of Search and to effectively organize search results to provide a product experience that meaningfully adds value for our users.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.14134v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.HC\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Information Retrieval\"], \"incoming_citations\": [\"8598\"], \"outgoing_citations\": [\"74866\", \"195545\", \"210902\"]}","task_split":"paper_retrieval"}
{"document_id":"46562","document_content":"# Controllable Abstractive Dialogue Summarization with Sketch Supervision\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary. This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser. 2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case study and show competitive human evaluation results and controllability to human-annotated summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.14064v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"87999\", \"2584\", \"15872\", \"36972\"], \"outgoing_citations\": [\"91125\", \"91128\", \"92955\", \"96769\", \"131150\", \"146323\", \"150522\", \"153936\", \"158493\", \"167338\", \"168405\", \"170657\", \"185347\", \"186043\", \"187236\", \"190394\", \"200414\", \"201080\", \"201867\", \"212057\", \"215247\", \"217759\", \"219618\", \"220695\", \"227985\", \"230212\", \"233407\", \"234436\", \"250505\", \"266671\", \"268902\", \"283618\", \"284149\", \"298016\", \"307223\", \"307651\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"46699","document_content":"# Short-term Maintenance Planning of Autonomous Trucks for Minimizing Economic Risk\n## Categories\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nNew autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.ress.2021.108251\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"46717","document_content":"# Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nAutonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13670v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"10278\"], \"outgoing_citations\": [\"100245\", \"109660\", \"112787\", \"115276\", \"181133\", \"191078\", \"213852\", \"229946\", \"236796\", \"253974\", \"263272\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"46724","document_content":"# Data Augmentation for Text Generation Without Any Augmented Data\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nData augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. Our proposed objective can be efficiently optimized and applied to popular loss functions on text generation tasks with a convergence rate guarantee. Experiments on five datasets of two text generation tasks show that our approach can approximate or even surpass popular data augmentation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13650v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"99613\", \"132808\", \"159333\", \"164509\", \"168405\", \"182218\", \"185283\", \"189715\", \"198316\", \"200867\", \"202498\", \"205715\", \"231898\", \"247500\", \"247577\", \"272068\", \"272531\", \"285557\", \"296248\", \"302824\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"46735","document_content":"# THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMany existing conversation models that are based on the encoder-decoder framework have focused on ways to make the encoder more complicated to enrich the context vectors so as to increase the diversity and informativeness of generated responses. However, these approaches face two problems. First, the decoder is too simple to effectively utilize the previously generated information and tends to generate duplicated and self-contradicting responses. Second, the complex encoder tends to generate diverse but incoherent responses because the complex context vectors may deviate from the original semantics of context. In this work, we proposed a conversation model named \"THINK\" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses. The model simplifies the context vectors and increases the coherence of generated responses in a reasonable way. For this model, we propose Teamwork generation framework and Semantics Extractor. Compared with other baselines, both automatic and human evaluation showed the advantages of our model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13630v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"44204\", \"128257\", \"131723\", \"138581\", \"157151\", \"176971\", \"193900\", \"197057\", \"217366\", \"229813\", \"251348\", \"253593\", \"258494\", \"264149\", \"266955\", \"270040\", \"274567\", \"275597\", \"281269\", \"281508\", \"291102\", \"295863\", \"296250\", \"302948\", \"305200\", \"307969\", \"310740\", \"316165\", \"316328\", \"316424\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"46771","document_content":"# Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach\n## Categories\n- Machine Learning\n- Computer Science and Game Theory\n- General Economics\n- Economics\n## Abstract\nIn e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps advertisers achieve their marketing goals and provides a stream of ad revenue to the platform. The latter content contributes to users' engagement with the platform, which is key to its long-term health. A burning issue for e-commerce platform design is how to blend advertising with content in a way that respects these interactions and balances these multiple business objectives. This paper describes a system developed for this purpose in the context of blending personalized sponsored content with non-sponsored content on the product detail pages of JD.COM, an e-commerce company. This system has three key features: (1) Optimization of multiple competing business objectives through a new virtual bids approach and the expressiveness of the latent, implicit valuation of the platform for the multiple objectives via these virtual bids. (2) Modeling of users' click behavior as a function of their characteristics, the individual characteristics of each sponsored content and the influence exerted by other sponsored and non-sponsored content displayed alongside through a deep learning approach; (3) Consideration of externalities in the allocation of ads, thereby making it directly compatible with a Vickrey-Clarke-Groves (VCG) auction scheme for the computation of payments in the presence of these externalities. The system is currently deployed and serving all traffic through JD.COM's mobile application. Experiments demonstrating the performance and advantages of the system are presented.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13556v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.GT\", \"econ.GN\", \"q-fin.EC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Science and Game Theory\", \"General Economics\", \"Economics\"], \"incoming_citations\": [\"24737\"], \"outgoing_citations\": [\"190609\", \"260407\"]}","task_split":"paper_retrieval"}
{"document_id":"46794","document_content":"# Diagnosing Transformers in Task-Oriented Semantic Parsing\n## Categories\n- Computation and Language\n## Abstract\nModern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models are empirically strong, their specific strengths and weaknesses have largely remained unexplored. In this work, we study BART and XLM-R, two state-of-the-art parsers, across both monolingual and multilingual settings. Our experiments yield several key results: transformer-based parsers struggle not only with disambiguating intents\/slots, but surprisingly also with producing syntactically-valid frames. Though pre-training imbues transformers with syntactic inductive biases, we find the ambiguity of copying utterance spans into frames often leads to tree invalidity, indicating span extraction is a major bottleneck for current parsers. However, as a silver lining, we show transformer-based parsers give sufficient indicators for whether a frame is likely to be correct or incorrect, making them easier to deploy in production settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13496v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21953\", \"36321\", \"55935\"], \"outgoing_citations\": [\"95921\", \"104803\", \"136310\", \"149188\", \"186250\", \"213822\"]}","task_split":"paper_retrieval"}
{"document_id":"46808","document_content":"# Inspecting the concept knowledge graph encoded by modern language models\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nThe field of natural language understanding has experienced exponential progress in the last few years, with impressive results in several tasks. This success has motivated researchers to study the underlying knowledge encoded by these models. Despite this, attempts to understand their semantic capabilities have not been successful, often leading to non-conclusive, or contradictory conclusions among different works. Via a probing classifier, we extract the underlying knowledge graph of nine of the most influential language models of the last years, including word embeddings, text generators, and context encoders. This probe is based on concept relatedness, grounded on WordNet. Our results reveal that all the models encode this knowledge, but suffer from several inaccuracies. Furthermore, we show that the different architectures and training strategies lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging. We hope our insights will motivate the development of models that capture concepts more precisely.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13471v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"23937\", \"24638\"], \"outgoing_citations\": [\"68331\", \"69362\", \"91653\", \"112236\", \"120497\", \"127502\", \"128630\", \"130910\", \"132243\", \"132998\", \"139638\", \"139854\", \"141787\", \"149018\", \"157146\", \"159458\", \"164998\", \"165410\", \"168856\", \"168951\", \"170855\", \"171642\", \"173615\", \"181098\", \"181468\", \"181770\", \"181948\", \"182116\", \"184576\", \"185112\", \"186130\", \"186250\", \"213906\", \"220105\", \"220159\", \"233400\", \"295435\", \"308507\"]}","task_split":"paper_retrieval"}
{"document_id":"46882","document_content":"# RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)\n## Categories\n- Computation and Language\n## Abstract\nMost words are ambiguous--i.e., they convey distinct meanings in different contexts--and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word Sense Disambiguation. However, there are few tasks that directly evaluate how well these contextualized embeddings accommodate the more continuous, dynamic nature of word meaning--particularly in a way that matches human intuitions. We introduce RAW-C, a dataset of graded, human relatedness judgments for 112 ambiguous words in context (with 672 sentence pairs total), as well as human estimates of sense dominance. The average inter-annotator agreement (assessed using a leave-one-annotator-out method) was 0.79. We then show that a measure of cosine distance, computed using contextualized embeddings from BERT and ELMo, correlates with human judgments, but that cosine distance also systematically underestimates how similar humans find uses of the same sense of a word to be, and systematically overestimates how similar humans find uses of different-sense homonyms. Finally, we propose a synthesis between psycholinguistic theories of the mental lexicon and computational models of lexical semantics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13266v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20795\", \"21496\"], \"outgoing_citations\": [\"91501\", \"124264\", \"165410\", \"171633\", \"181125\", \"220098\", \"238321\", \"287281\", \"311013\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"46924","document_content":"# Training Classifiers that are Universally Robust to All Label Noise Levels\n## Categories\n- Machine Learning\n- I.2.0\n## Abstract\nFor classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise levels, they encounter significant performance reduction at high noise levels, or even at medium noise levels when the label noise is asymmetric. To train classifiers that are universally robust to all noise levels, and that are not sensitive to any variation in the noise model, we propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as \"positive\", while the remaining noisy subset is treated as \"unlabeled\". Our framework consists of the following two components: (1) We shall generate, via iterative updates, an augmented clean subset with additional reliable \"positive\" samples filtered from \"unlabeled\" samples; (2) We shall train a teacher model on this larger augmented clean set. With the guidance of the teacher model, we then train a student model on the whole dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic label noise at multiple noise levels for both symmetric and asymmetric noise. The results show that our framework generally outperforms at medium to high noise levels. We also evaluated our framework on Clothing1M, a real-world noisy dataset, and we achieved 2.94% improvement in accuracy over existing state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.13892v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"I.2.0\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"I.2.0\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116430\", \"138343\", \"141691\", \"171454\", \"172805\", \"189025\", \"194631\", \"198316\", \"206182\", \"216164\", \"219108\", \"231347\", \"235096\", \"237224\", \"242145\", \"242973\", \"246497\", \"247500\", \"249957\", \"264889\", \"266873\", \"272109\", \"303724\", \"320027\", \"328627\"]}","task_split":"paper_retrieval"}
{"document_id":"47005","document_content":"# Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nGenerative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses on fixed-size segments combined into a whole level, but individual segments may not fit together cohesively. In contrast, segments in human designed levels are often repeated, directly or with variation, and organized into patterns (the symmetric eagle in Level 1 of The Legend of Zelda, or repeated pipe motifs in Super Mario Bros). Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). CPPNs define latent vector GAN inputs as a function of geometry, organizing segments output by a GAN into complete levels. However, collections of latent vectors can also be evolved directly, producing more chaotic levels. We propose a hybrid approach that evolves CPPNs first, but allows latent vectors to evolve later, combining the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda. We previously demonstrated via a Quality-Diversity algorithm that CPPNs better cover the space of possible levels than directly evolved levels. Here, we show that the hybrid approach (1) covers areas that neither of the other methods can, and (2) achieves comparable or superior QD scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TG.2022.3170730\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"41052\", \"107909\", \"111464\", \"122922\", \"133342\", \"147140\", \"163503\", \"193050\", \"216133\", \"233501\", \"289709\", \"290842\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"47109","document_content":"# Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHandwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a clean and readable form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patcog.2021.108370\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5077\"], \"outgoing_citations\": [\"93552\", \"98454\", \"150014\", \"202558\", \"212915\", \"262398\", \"295622\", \"303489\", \"343204\"]}","task_split":"paper_retrieval"}
{"document_id":"47133","document_content":"# It is rotating leaders who build the swarm: social network determinants of growth for healthcare virtual communities of practice\n## Categories\n- Social and Information Networks\n- J.4; I.2.7; H.4.0\n- Physics and Society\n- Computation and Language\n## Abstract\nPurpose: The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis. By studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use), the authors aim to provide VCoP managers with valuable insights to improve the success of their communities. Design\/methodology\/approach: Communications over a period of seven years (April 2008 to April 2015) and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web platform were analysed. Multilevel regression models were used to reveal the main determinants of community growth over time. Independent variables were derived from social network and semantic analysis measures. Findings: Results show that structural and content-based variables predict the growth of the community. Progressively, more people will join a community if its structure is more centralised, leaders are more dynamic (they rotate more) and the language used in the posts is less complex. Research limitations\/implications: The available data set included one Web platform and a limited number of control variables. To consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs. Originality\/value: The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering, at the same time, the interaction patterns among the community members, the dynamic evolution of these interactions and the use of language. New analytical tools are presented, together with the use of innovative interaction metrics, that can significantly influence community growth, such as rotating leadership.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1108\/JKM-11-2016-0504\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"J.4; I.2.7; H.4.0\", \"physics.soc-ph\", \"cs.CL\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"J.4; I.2.7; H.4.0\", \"Physics and Society\", \"Computation and Language\"], \"incoming_citations\": [\"10123\", \"46969\", \"60376\", \"46965\", \"48123\", \"49162\"], \"outgoing_citations\": [\"48555\"]}","task_split":"paper_retrieval"}
{"document_id":"47195","document_content":"# Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Signal Processing\n## Abstract\nDeep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable artificial intelligence (XAI) based deep learning framework in a federated setting for ECG-based healthcare applications. The federated setting is used to solve issues such as data availability and privacy concerns. Furthermore, the proposed framework setting effectively classifies arrhythmia's using an autoencoder and a classifier, both based on a convolutional neural network (CNN). Additionally, we propose an XAI-based module on top of the proposed classifier to explain the classification results, which help clinical practitioners make quick and reliable decisions. The proposed framework was trained and tested using the MIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.knosys.2021.107763\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"eess.SP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Signal Processing\"], \"incoming_citations\": [\"26435\"], \"outgoing_citations\": [\"142581\", \"143294\", \"143344\", \"156643\", \"175096\", \"196107\", \"199085\", \"215181\", \"222766\", \"226787\", \"236465\", \"261199\", \"263559\", \"283045\"]}","task_split":"paper_retrieval"}
{"document_id":"47366","document_content":"# Big data and big values: When companies need to rethink themselves\n## Categories\n- Social and Information Networks\n- Computation and Language\n- J.4; I.2.7; H.4.0\n- Physics and Society\n## Abstract\nIn order to face the complexity of business environments and detect priorities while triggering contingency strategies, we propose a new methodological approach that combines text mining, social network and big data analytics, with the assessment of stakeholders' attitudes towards company core values. This approach was applied in a case study where we considered the Twitter discourse about core values in Italy. We collected more than 94,000 tweets related to the core values of the firms listed in Fortune's ranking of the World's Most Admired Companies (2013-2017). For the Italian scenario, we found three predominant core values orientations (Customers, Employees and Excellence) - which should be at the basis of any business strategy - and three latent ones (Economic-Financial Growth, Citizenship and Social Responsibility), which need periodic attention. Our contribution is mostly methodological and extends the research on text mining and on online big data analytics applied in complex business contexts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jbusres.2019.10.046\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.CL\", \"J.4; I.2.7; H.4.0\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\", \"J.4; I.2.7; H.4.0\", \"Physics and Society\"], \"incoming_citations\": [\"45160\"], \"outgoing_citations\": [\"48422\", \"337387\"]}","task_split":"paper_retrieval"}
{"document_id":"47368","document_content":"# BASS: Boosting Abstractive Summarization with Unified Semantic Graph\n## Categories\n- Computation and Language\n## Abstract\nAbstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure. Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graph-propagation attention mechanism is developed in the decoder to select salient content into the summary. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.12041v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"14013\", \"48100\"], \"outgoing_citations\": [\"93973\", \"123892\", \"127281\", \"129810\", \"147514\", \"150522\", \"157807\", \"158869\", \"159889\", \"161045\", \"168185\", \"170657\", \"172405\", \"173940\", \"181713\", \"182486\", \"183462\", \"187236\", \"188300\", \"192332\", \"200414\", \"211433\", \"214311\", \"214407\", \"219618\", \"220955\", \"227985\", \"230212\", \"230456\", \"235463\", \"237494\", \"243803\", \"266671\", \"268812\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"47530","document_content":"# Deep Neural Networks and End-to-End Learning for Audio Compression\n## Categories\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nRecent achievements in end-to-end deep learning have encouraged the exploration of tasks dealing with highly structured data with unified deep network models. Having such models for compressing audio signals has been challenging since it requires discrete representations that are not easy to train with end-to-end backpropagation. In this paper, we present an end-to-end deep learning approach that combines recurrent neural networks (RNNs) within the training strategy of variational autoencoders (VAEs) with a binary representation of the latent space. We apply a reparametrization trick for the Bernoulli distribution for the discrete representations, which allows smooth backpropagation. In addition, our approach allows the separation of the encoder and decoder, which is necessary for compression tasks. To our best knowledge, this is the first end-to-end learning for a single audio compression model with RNNs, and our model achieves a Signal to Distortion Ratio (SDR) of 20.54.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.11681v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"93571\", \"118197\", \"127980\", \"180131\", \"212672\", \"226691\", \"227079\", \"227945\", \"250449\", \"252472\", \"291936\", \"311643\", \"321177\", \"328469\"]}","task_split":"paper_retrieval"}
{"document_id":"47534","document_content":"# ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nRecent grid-based document representations like BERTgrid allow the simultaneous encoding of the textual and layout information of a document in a 2D feature map so that state-of-the-art image segmentation and\/or object detection models can be straightforwardly leveraged to extract key information from documents. However, such methods have not achieved comparable performance to state-of-the-art sequence- and graph-based methods such as LayoutLM and PICK yet. In this paper, we propose a new multi-modal backbone network by concatenating a BERTgrid to an intermediate layer of a CNN model, where the input of CNN is a document image and the BERTgrid is a grid of word embeddings, to generate a more powerful grid-based document representation, named ViBERTgrid. Unlike BERTgrid, the parameters of BERT and CNN in our multimodal backbone network are trained jointly. Our experimental results demonstrate that this joint training strategy improves significantly the representation ability of ViBERTgrid. Consequently, our ViBERTgrid-based key information extraction approach has achieved state-of-the-art performance on real-world datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.11672v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"9109\"], \"outgoing_citations\": [\"45488\", \"62258\", \"73803\", \"77979\", \"96466\", \"97694\", \"122763\", \"123526\", \"127548\", \"130903\", \"141395\", \"142227\", \"149004\", \"150132\", \"165784\", \"167422\", \"170447\", \"193222\", \"193543\", \"205569\", \"207432\", \"212253\", \"216765\", \"257785\", \"264315\", \"294693\", \"297147\", \"297150\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"47548","document_content":"# Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles\n## Categories\n- Machine Learning\n- Systems and Control\n- Systems and Control\n## Abstract\nConnected and Automated Hybrid Electric Vehicles have the potential to reduce fuel consumption and travel time in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profiles based upon look-ahead information from connectivity and advanced mapping features. Recently, Deep Reinforcement Learning (DRL) has been applied to the eco-driving problem. While the previous studies synthesize simulators and model-free DRL to reduce online computation, this work proposes a Safe Off-policy Model-Based Reinforcement Learning algorithm for the eco-driving problem. The advantages over the existing literature are three-fold. First, the combination of off-policy learning and the use of a physics-based model improves the sample efficiency. Second, the training does not require any extrinsic rewarding mechanism for constraint satisfaction. Third, the feasibility of trajectory is guaranteed by using a safe set approximated by deep generative models.   The performance of the proposed method is benchmarked against a baseline controller representing human drivers, a previously designed model-free DRL strategy, and the wait-and-see optimal solution. In simulation, the proposed algorithm leads to a policy with a higher average speed and a better fuel economy compared to the model-free agent. Compared to the baseline controller, the learned strategy reduces the fuel consumption by more than 21\\% while keeping the average speed comparable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.11640v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"58585\", \"75578\", \"127099\", \"138174\", \"138823\", \"177658\", \"183351\", \"190756\", \"206875\", \"211425\", \"229890\", \"240129\", \"259045\", \"261780\", \"302857\", \"311683\"]}","task_split":"paper_retrieval"}
{"document_id":"47683","document_content":"# Introducing the Talk Markup Language (TalkML):Adding a little social intelligence to industrial speech interfaces\n## Categories\n- Computation and Language\n- I.2.7; J.5; H.5.2\n## Abstract\nVirtual Personal Assistants like Siri have great potential but such developments hit the fundamental problem of how to make computational devices that understand human speech. Natural language understanding is one of the more disappointing failures of AI research and it seems there is something we computer scientists don't get about the nature of language. Of course philosophers and linguists think quite differently about language and this paper describes how we have taken ideas from other disciplines and implemented them. The background to the work is to take seriously the notion of language as action and look at what people actually do with language using the techniques of Conversation Analysis. The observation has been that human communication is (behind the scenes) about the management of social relations as well as the (foregrounded) passing of information. To claim this is one thing but to implement it requires a mechanism. The mechanism described here is based on the notion of language being intentional - we think intentionally, talk about them and recognise them in others - and cooperative in that we are compelled to help out. The way we are compelled points to a solution to the ever present problem of keeping the human on topic. The approach has led to a recent success in which we significantly improve user satisfaction independent of task completion. Talk Markup Language (TalkML) is a draft alternative to VoiceXML that, we propose, greatly simplifies the scripting of interaction by providing default behaviours for no input and not recognised speech events.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.11294v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7; J.5; H.5.2\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7; J.5; H.5.2\"], \"incoming_citations\": [\"51599\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"48118","document_content":"# Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e. when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.7717\/peerj-cs.467\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"181527\", \"193968\", \"225213\", \"234054\", \"234857\", \"257863\", \"259202\", \"268465\", \"271021\", \"273723\", \"290208\", \"305591\"]}","task_split":"paper_retrieval"}
{"document_id":"48153","document_content":"# Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMachine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffic, finance, sciences) to direct user interactions like advertising and recommendations. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address the knowledgeable user and the application engineer. However, there are users that want to deploy a learned model in a similar way as their washing machine. These stakeholders do not want to spend time in understanding the model, but want to rely on guaranteed properties. What are the relevant properties? How can they be expressed to the stakeholder without presupposing machine learning knowledge? How can they be guaranteed for a certain implementation of a machine learning model? These questions move far beyond the current state of the art and we want to address them here. We propose a unified framework that certifies learning methods via care labels. They are easy to understand and draw inspiration from well-known certificates like textile labels or property cards of electronic devices. Our framework considers both, the machine learning theory and a given implementation. We test the implementation's compliance with theoretical properties and bounds.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/frai.2022.975029\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"45700\"], \"outgoing_citations\": [\"52846\", \"69162\", \"98622\", \"118461\", \"131007\", \"144817\", \"156863\", \"175917\", \"182840\", \"193177\", \"205613\", \"215230\", \"218650\", \"220666\", \"225737\", \"243145\", \"349845\"]}","task_split":"paper_retrieval"}
{"document_id":"48165","document_content":"# Efficient Temporal Piecewise-Linear Numeric Planning with Lazy Consistency Checking\n## Categories\n- Artificial Intelligence\n## Abstract\nTemporal planning often involves numeric effects that are directly proportional to their action's duration. These include continuous effects, where a numeric variable is subjected to a rate of change while the action is being executed, and discrete duration-dependent effects, where the variable is updated instantaneously but the magnitude of such change is computed from the action's duration. When these effects are linear, state--of--the--art temporal planners often make use of Linear Programming to ensure that these numeric updates are consistent with the chosen start times and durations of the plan's actions. This is typically done for each evaluated state as part of the search process. This exhaustive approach is not scalable to solve real-world problems that require long plans, because the linear program's size becomes larger and slower to solve. In this work we propose techniques that minimise this overhead by computing these checks more selectively and formulating linear programs that have a smaller footprint. The effectiveness of these techniques is demonstrated on domains that use a mix of discrete and continuous effects, which is typical of real-world planning problems. The resultant planner also outperforms most state-of-the-art temporal-numeric and hybrid planners, in terms of both coverage and scalability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TAI.2022.3146797\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"334724\", \"334983\"]}","task_split":"paper_retrieval"}
{"document_id":"48168","document_content":"# Explainable Machine Learning with Prior Knowledge: An Overview\n## Categories\n- Machine Learning\n## Abstract\nThis survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elicited research to make them more explainable. However, most explainability methods cannot provide insight beyond the given data, requiring additional information about the context. We propose to harness prior knowledge to improve upon the explanation capabilities of machine learning models. In this paper, we present a categorization of current research into three main categories which either integrate knowledge into the machine learning pipeline, into the explainability method or derive knowledge from explanations. To classify the papers, we build upon the existing taxonomy of informed machine learning and extend it from the perspective of explainability. We conclude with open challenges and research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.10172v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"2096\", \"23966\", \"26880\", \"66837\"], \"outgoing_citations\": [\"84264\", \"84697\", \"86707\", \"127043\", \"129424\", \"131007\", \"136447\", \"144046\", \"145981\", \"147012\", \"151752\", \"152364\", \"163403\", \"164211\", \"176892\", \"179047\", \"179945\", \"182974\", \"185303\", \"185674\", \"191400\", \"193208\", \"193866\", \"195838\", \"199453\", \"204055\", \"207428\", \"210521\", \"238355\", \"249204\", \"251709\", \"254281\", \"257563\", \"259261\", \"271811\", \"274749\", \"277228\", \"279891\", \"295302\", \"301913\", \"309854\", \"310759\"]}","task_split":"paper_retrieval"}
{"document_id":"48235","document_content":"# To do or not to do: finding causal relations in smart homes\n## Categories\n- Artificial Intelligence\n## Abstract\nResearch in Cognitive Science suggests that humans understand and represent knowledge of the world through causal relationships. In addition to observations, they can rely on experimenting and counterfactual reasoning -- i.e. referring to an alternative course of events -- to identify causal relations and explain atypical situations. Different instances of control systems, such as smart homes, would benefit from having a similar causal model, as it would help the user understand the logic of the system and better react when needed. However, while data-driven methods achieve high levels of correlation detection, they mainly fall short of finding causal relations, notably being limited to observations only. Notably, they struggle to identify the cause from the effect when detecting a correlation between two variables. This paper introduces a new way to learn causal models from a mixture of experiments on the environment and observational data. The core of our method is the use of selected interventions, especially our learning takes into account the variables where it is impossible to intervene, unlike other approaches. The causal model we obtain is then used to generate Causal Bayesian Networks, which can be later used to perform diagnostic and predictive inference. We use our method on a smart home simulation, a use case where knowing causal relations pave the way towards explainable systems. Our algorithm succeeds in generating a Causal Bayesian Network close to the simulation's ground truth causal interactions, showing encouraging prospects for application in real-life systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.10058v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"142603\", \"155499\", \"184390\"]}","task_split":"paper_retrieval"}
{"document_id":"48244","document_content":"# A Streaming End-to-End Framework For Spoken Language Understanding\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nEnd-to-end spoken language understanding (SLU) has recently attracted increasing interest. Compared to the conventional tandem-based approach that combines speech recognition and language understanding as separate modules, the new approach extracts users' intentions directly from the speech signals, resulting in joint optimization and low latency. Such an approach, however, is typically designed to process one intention at a time, which leads users to take multiple rounds to fulfill their requirements while interacting with a dialogue system. In this paper, we propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way. The backbone of our framework is a unidirectional RNN trained with the connectionist temporal classification (CTC) criterion. By this design, an intention can be identified when sufficient evidence has been accumulated, and multiple intentions can be identified sequentially. We evaluate our solution on the Fluent Speech Commands (FSC) dataset and the intent detection accuracy is about 97 % on all multi-intent settings. This result is comparable to the performance of the state-of-the-art non-streaming models, but is achieved in an online and incremental way. We also employ our model to a keyword spotting task using the Google Speech Commands dataset and the results are also highly promising.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.10042v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"58416\"], \"outgoing_citations\": [\"87977\", \"97729\", \"107737\", \"157877\", \"191712\", \"191769\", \"210453\", \"216686\", \"220168\", \"236235\", \"285311\", \"309025\"]}","task_split":"paper_retrieval"}
{"document_id":"48248","document_content":"# Improving Generation and Evaluation of Visual Stories via Semantic Consistency\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nStory visualization is an under-explored task that falls at the intersection of many important research directions in both computer vision and natural language processing. In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the captions. Prior work has introduced recurrent generative models which outperform text-to-image synthesis models on this task. However, there is room for improvement of generated images in terms of visual quality, coherence and relevance. We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentially-consistent story visualization, and (3) MART-based transformers to model complex interactions between frames. We present ablation studies to demonstrate the effect of each of these techniques on the generative power of the model for both individual images as well as the entire narrative. Furthermore, due to the complexity and generative nature of the task, standard evaluation metrics do not accurately reflect performance. Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence\/quality of generated characters, the relevance to captions, and the diversity of the generated images. We also present correlation experiments of our proposed automated metrics with human evaluations. Code and data available at: https:\/\/github.com\/adymaharana\/StoryViz","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.10026v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"14791\"], \"outgoing_citations\": [\"96482\", \"106820\", \"125719\", \"192697\", \"195203\", \"197213\", \"205306\", \"206919\", \"208868\", \"212643\", \"231212\", \"234607\", \"236090\", \"249031\", \"250221\", \"254361\", \"262155\", \"262509\", \"269388\"]}","task_split":"paper_retrieval"}
{"document_id":"48306","document_content":"# A practical introduction to the Rational Speech Act modeling framework\n## Categories\n- Computation and Language\n## Abstract\nRecent advances in computational cognitive science (i.e., simulation-based probabilistic programs) have paved the way for significant progress in formal, implementable models of pragmatics. Rather than describing a pragmatic reasoning process in prose, these models formalize and implement one, deriving both qualitative and quantitative predictions of human behavior -- predictions that consistently prove correct, demonstrating the viability and value of the framework. The current paper provides a practical introduction to and critical assessment of the Bayesian Rational Speech Act modeling framework, unpacking theoretical foundations, exploring technological innovations, and drawing connections to issues beyond current applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.09867v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"55769\"], \"outgoing_citations\": [\"125235\", \"151038\", \"192701\", \"194503\", \"197647\", \"215976\", \"228916\", \"235544\", \"270225\", \"286877\", \"295368\", \"304615\"]}","task_split":"paper_retrieval"}
{"document_id":"48472","document_content":"# MLBiNet: A Cross-Sentence Collective Event Detection Network\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nWe consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence. Secondly, an information aggregation module is employed to aggregate sentence-level semantic and event tag information. Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.09458v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"134\", \"47828\", \"48452\", \"59005\"], \"outgoing_citations\": [\"47828\", \"48452\", \"91031\", \"140252\", \"157212\", \"159782\", \"180767\", \"190349\", \"216701\", \"284622\", \"296198\"]}","task_split":"paper_retrieval"}
{"document_id":"48495","document_content":"# Unsupervised learning of text line segmentation by differentiating coarse patterns\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite recent advances in the field of supervised deep learning for text line segmentation, unsupervised deep learning solutions are beginning to gain popularity. In this paper, we present an unsupervised deep learning method that embeds document image patches to a compact Euclidean space where distances correspond to a coarse text line pattern similarity. Once this space has been produced, text line segmentation can be easily implemented using standard techniques with the embedded feature vectors. To train the model, we extract random pairs of document image patches with the assumption that neighbour patches contain a similar coarse trend of text lines, whereas if one of them is rotated, they contain different coarse trends of text lines. Doing well on this task requires the model to learn to recognize the text lines and their salient parts. The benefit of our approach is zero manual labelling effort. We evaluate the method qualitatively and quantitatively on several variants of text line segmentation datasets to demonstrate its effectivity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.09405v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74401\", \"74706\", \"74782\", \"136000\", \"181378\", \"242784\"]}","task_split":"paper_retrieval"}
{"document_id":"48497","document_content":"# Heterogeneous Contrastive Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and are characterized with multiple labels, thus exhibiting the co-existence of multiple types of heterogeneity. Although state-of-the-art techniques are good at modeling complex heterogeneity with sufficient label information, such label information can be quite expensive to obtain in real applications. Recently, researchers pay great attention to contrastive learning due to its prominent performance by utilizing rich unlabeled data. However, existing work on contrastive learning is not able to address the problem of false negative pairs, i.e., some `negative' pairs may have similar representations if they have the same label. To overcome the issues, in this paper, we propose a unified heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity. We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue. Experimental results on real-world data sets demonstrate the effectiveness and the efficiency of the proposed framework modeling multiple types of heterogeneity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.09401v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"13549\"], \"outgoing_citations\": [\"110939\", \"180880\", \"192334\", \"201656\", \"258700\", \"269778\", \"280302\", \"294694\", \"306756\", \"323634\", \"339355\"]}","task_split":"paper_retrieval"}
{"document_id":"48590","document_content":"# Explainable Tsetlin Machine framework for fake news detection with credibility score assessment\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- I.2; I.5; I.7\n## Abstract\nThe proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use the clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5\\%$ in terms of accuracy, with the added benefit of an interpretable logic-based representation. Further, our approach provides higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model's explainability, demonstrating how it decomposes into meaningful words and their negations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.09114v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"I.2; I.5; I.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"I.2; I.5; I.7\"], \"incoming_citations\": [\"76547\", \"29177\", \"101389\"], \"outgoing_citations\": [\"50438\", \"86357\", \"101389\", \"133216\", \"166518\", \"197381\", \"218105\", \"219169\", \"222721\", \"236755\", \"259202\", \"267552\", \"271021\", \"273723\"]}","task_split":"paper_retrieval"}
{"document_id":"48859","document_content":"# Fast Game Content Adaptation Through Bayesian-based Player Modelling\n## Categories\n- Artificial Intelligence\n- Applications\n## Abstract\nIn games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this goal in the context of dynamic difficulty adjustment (DDA). Here the aim is to constantly adapt the content of a game to the skill level of the player, keeping them engaged by avoiding states that are either too difficult or too easy. Current systems for DDA rely on expensive data mining, or on hand-crafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the designer to present content that is purposefully easy or difficult. This paper presents Fast Bayesian Content Adaption (FBCA), a system for DDA that is agnostic to the domain and that can target particular difficulties. We deploy this framework in two different domains: the puzzle game Sudoku, and a simple Roguelike game. By modifying the acquisition function's optimization, we are reliably able to present a content with a bespoke difficulty for players with different skill levels in less than five iterations for Sudoku and fifteen iterations for the simple Roguelike. Our method significantly outperforms simpler DDA heuristics with the added benefit of maintaining a model of the user. These results point towards a promising alternative for content adaption in a variety of different domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.08484v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.AP\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Applications\"], \"incoming_citations\": [\"133218\", \"4621\"], \"outgoing_citations\": [\"94739\", \"112786\", \"115648\", \"124853\", \"163503\", \"173071\", \"233501\", \"264206\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"48905","document_content":"# Online Multimodal Transportation Planning using Deep Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nIn this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination. While traditional planning methods work \"offline\" (i.e., they take decisions for a batch of containers before the transportation starts), the proposed approach is \"online\", in that it can take decisions for individual containers, while transportation is being executed. Planning transportation online helps to effectively respond to unforeseen events that may affect the original transportation plan, thus supporting companies in lowering transportation costs. We implemented different container selection heuristics within the proposed Deep Reinforcement Learning algorithm and we evaluated its performance for each heuristic using data that simulate a realistic scenario, designed on the basis of a real case study at a logistics company. The experimental results revealed that the proposed method was able to learn effective patterns of container assignment. It outperformed tested competitors in terms of total transportation costs and utilization of train capacity by 20.48% to 55.32% for the cost and by 7.51% to 20.54% for the capacity. Furthermore, it obtained results within 2.7% for the cost and 0.72% for the capacity of the optimal solution generated by an Integer Linear Programming solver in an offline setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.08374v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133255\", \"238077\", \"242457\", \"269617\", \"279191\", \"311464\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"48962","document_content":"# PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies\n## Categories\n- Artificial Intelligence\n## Abstract\nWe propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization. PoBRL jointly optimizes over the following three objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multi-objective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies together to produce a summary that is a concise and complete representation of the original input. Our empirical analysis shows state-of-the-art performance on several multi-document datasets. Human evaluation also shows that our method produces high-quality output.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.08244v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"15872\"], \"outgoing_citations\": [\"130347\", \"182486\", \"214407\", \"216554\", \"219618\", \"220955\", \"229185\", \"230212\", \"230464\", \"235232\", \"237477\", \"263243\", \"266671\", \"279351\", \"280557\", \"287823\", \"290789\", \"307969\", \"311464\", \"315546\"]}","task_split":"paper_retrieval"}
{"document_id":"48992","document_content":"# Learning Disentangled Representations for Time Series\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTime-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream applications, the learned representations often lack interpretability and do not expose semantic meanings. Different from previous efforts on the entangled feature space, we aim to extract the semantic-rich temporal correlations in the latent interpretable factorized representation of the data. Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals. To bridge the gap, we propose Disentangle Time Series (DTS), a novel disentanglement enhancement framework for sequential data. Specifically, to generate hierarchical semantic concepts as the interpretable and disentangled representation of time-series, DTS introduces multi-level disentanglement strategies by covering both individual latent factors and group semantic segments. We further theoretically show how to alleviate the KL vanishing problem: DTS introduces a mutual information maximization term, while preserving a heavier penalty on the total correlation and the dimension-wise KL to keep the disentanglement property. Experimental results on various real-world benchmark datasets demonstrate that the representations learned by DTS achieve superior performance in downstream applications, with high interpretability of semantic concepts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.08179v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"48999","document_content":"# Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics\n## Categories\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nThis paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defined by Langevin dynamics on the global log-likelihood of the sequence. This approach parallelizes the sampling process and generalizes to conditional sampling. Using an autoregressive model as a Bayesian prior, we can steer the output of a generative model using a conditional likelihood or constraints. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.08164v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SD\", \"eess.AS\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Audio and Speech Processing\", \"Machine Learning\"], \"incoming_citations\": [\"738\", \"17339\"], \"outgoing_citations\": [\"67541\", \"84446\", \"89315\", \"99468\", \"102682\", \"118296\", \"127980\", \"140596\", \"141466\", \"153034\", \"153962\", \"162797\", \"176382\", \"183000\", \"189263\", \"189504\", \"197583\", \"207908\", \"211222\", \"211342\", \"212248\", \"217178\", \"224090\", \"242468\", \"249044\", \"270288\", \"271912\", \"274838\", \"277384\", \"278850\", \"299534\", \"311388\", \"344185\"]}","task_split":"paper_retrieval"}
{"document_id":"49071","document_content":"# Unknown-box Approximation to Improve Optical Character Recognition Performance\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOptical character recognition (OCR) is a widely used pattern recognition application in numerous domains. There are several feature-rich, general-purpose OCR solutions available for consumers, which can provide moderate to excellent accuracy levels. However, accuracy can diminish with difficult and uncommon document domains. Preprocessing of document images can be used to minimize the effect of domain shift. In this paper, a novel approach is presented for creating a customized preprocessor for a given OCR engine. Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline by applying pixel-level manipulations to the document image. The implementation of the proposed method and the enhanced public datasets are available for download.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.07983v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62258\", \"179057\", \"181721\", \"202558\", \"203087\", \"207712\", \"249222\", \"271783\", \"306751\", \"309239\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"49206","document_content":"# TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nHybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison\/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOPachieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.07624v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33114\", \"23319\", \"29556\", \"39795\", \"24348\", \"47485\"], \"outgoing_citations\": [\"77192\", \"92800\", \"100416\", \"123344\", \"124544\", \"130961\", \"131838\", \"132918\", \"144603\", \"161625\", \"164303\", \"168058\", \"169358\", \"189600\", \"196980\", \"216563\", \"216731\", \"219680\", \"238610\", \"253068\", \"257206\", \"266702\", \"267878\", \"277518\", \"287982\", \"308610\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"49207","document_content":"# Sentence Similarity Based on Contexts\n## Categories\n- Computation and Language\n## Abstract\nExisting methods to measure sentence similarity are faced with two challenges: (1) labeled datasets are usually limited in size, making them insufficient to train supervised neural models; (2) there is a training-test gap for unsupervised language modeling (LM) based models to compute semantic scores between sentences, since sentence-level semantics are not explicitly modeled at training. This results in inferior performances in this task. In this work, we propose a new framework to address these two issues. The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts, and that sentence similarity can be measured by comparing the probabilities of generating two sentences given the same context. The proposed framework is able to generate high-quality, large-scale dataset with semantic similarity scores between two sentences in an unsupervised manner, with which the train-test gap can be largely bridged. Extensive experiments show that the proposed framework achieves significant performance boosts over existing baselines under both the supervised and unsupervised settings across different datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.07623v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25308\", \"26367\"], \"outgoing_citations\": [\"45179\", \"47498\", \"55137\", \"58332\", \"60013\", \"77689\", \"89638\", \"115975\", \"127838\", \"131921\", \"132002\", \"141983\", \"157726\", \"162570\", \"168549\", \"173474\", \"178992\", \"189542\", \"212285\", \"230131\", \"237274\", \"239686\", \"256098\", \"259723\", \"267161\", \"268604\", \"270020\", \"271934\", \"275597\", \"281163\", \"291019\", \"297795\", \"297950\", \"298543\", \"306182\", \"307756\", \"310737\", \"329586\"]}","task_split":"paper_retrieval"}
{"document_id":"49252","document_content":"# Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nAccurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep learning based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured text. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention regulation strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.07542v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"43587\", \"4096\"], \"outgoing_citations\": [\"153944\", \"153949\", \"166316\", \"181285\", \"183050\", \"197416\", \"213345\", \"263454\", \"279891\", \"286253\", \"287747\", \"303053\"]}","task_split":"paper_retrieval"}
{"document_id":"49844","document_content":"# Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAlphaZero has achieved impressive performance in deep reinforcement learning by utilizing an architecture that combines search and training of a neural network in self-play. Many researchers are looking for ways to reproduce and improve results for other games\/tasks. However, the architecture is designed to learn from scratch, tabula rasa, accepting a cold-start problem in self-play. Recently, a warm-start enhancement method for Monte Carlo Tree Search was proposed to improve the self-play starting phase. It employs a fixed parameter $I^\\prime$ to control the warm-start length. Improved performance was reported in small board games. In this paper we present results with an adaptive switch method. Experiments show that our approach works better than the fixed $I^\\prime$, especially for \"deep,\" tactical, games (Othello and Connect Four). We conjecture that the adaptive value for $I^\\prime$ is also influenced by the size of the game, and that on average $I^\\prime$ will increase with game size. We conclude that AlphaZero-like deep reinforcement learning benefits from adaptive rollout based warm-start, as Rapid Action Value Estimate did for rollout-based reinforcement learning 15 years ago.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.06136v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"10078\"], \"outgoing_citations\": [\"118729\", \"128902\", \"197267\", \"214290\", \"258614\"]}","task_split":"paper_retrieval"}
{"document_id":"49871","document_content":"# Good and Bad Optimization Models: Insights from Rockafellians\n## Categories\n- Optimization and Control\n- Machine Learning\n## Abstract\nA basic requirement for a mathematical model is often that its solution (output) shouldn't change much if the model's parameters (input) are perturbed. This is important because the exact values of parameters may not be known and one would like to avoid being mislead by an output obtained using incorrect values. Thus, it's rarely enough to address an application by formulating a model, solving the resulting optimization problem and presenting the solution as the answer. One would need to confirm that the model is suitable, i.e., \"good,\" and this can, at least in part, be achieved by considering a family of optimization problems constructed by perturbing parameters of concern. The resulting sensitivity analysis uncovers troubling situations with unstable solutions, which we referred to as \"bad\" models, and indicates better model formulations. Embedding an actual problem of interest within a family of problems is also a primary path to optimality conditions as well as computationally attractive, alternative problems, which under ideal circumstances, and when properly tuned, may even furnish the minimum value of the actual problem. The tuning of these alternative problems turns out to be intimately tied to finding multipliers in optimality conditions and thus emerges as a main component of several optimization algorithms. In fact, the tuning amounts to solving certain dual optimization problems. In this tutorial, we'll discuss the opportunities and insights afforded by this broad perspective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.06073v3\", \"primary_category\": \"math.OC\", \"categories\": [\"math.OC\", \"cs.LG\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"49939","document_content":"# Identifying Biased Users in Online Social Networks to Enhance the Accuracy of Sentiment Analysis: A User Behavior-Based Approach\n## Categories\n- Social and Information Networks\n## Abstract\nThe development of an automatic way to extract user opinions about products, movies, and foods from online social network (OSN) interactions is among the main interests of sentiment analysis and opinion mining studies. Existing approaches in the sentiment analysis domain mostly do not discriminate the sentences of different types of users, even though some users are always negative and some are always positive. Thus, finding a way to identify these two types of user is significant because their attitudes can change the analysis of user reviews of businesses and products. Due to the complexity of natural language processing, pure text mining methods may lead to misunderstandings about the exact nature of the sentiments expressed in review text. In this study, we propose a neural network classifier to predict the presence of biased users on the basis of users' psychological behaviors. The identification of the psychological behaviors of users allows us to find overly positive and overly negative users and to categorize these users' attitudes regardless of the content of their review texts. The experiment result indicates that the biased users can be predicted based on user behavior at an accuracy rate of 89%, 67% and 81% for three different datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05950v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55115\", \"283923\", \"299491\", \"303025\", \"324761\"]}","task_split":"paper_retrieval"}
{"document_id":"49968","document_content":"# Out of the Box: Embodied Navigation in the Real World\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Robotics\n## Abstract\nThe research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world. Our code and models are available at https:\/\/github.com\/aimagelab\/LoCoNav.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-89128-2_5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"23543\"], \"outgoing_citations\": [\"91301\", \"104828\", \"112104\", \"114824\", \"131180\", \"131814\", \"148172\", \"151367\", \"153902\", \"158547\", \"179954\", \"192738\", \"196393\", \"219658\"]}","task_split":"paper_retrieval"}
{"document_id":"49980","document_content":"# Deep Snapshot HDR Reconstruction Based on the Polarization Camera\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThe recent development of the on-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images with the same ease of operation as a conventional camera. In this paper, we investigate the use of this sensor technology in high-dynamic-range (HDR) imaging. Specifically, observing that natural light can be attenuated differently by varying the orientation of the polarization filter, we treat the multiple images captured by the polarization camera as a set captured under different exposure times. In our approach, we first study the relationship among polarizer orientation, degree and angle of polarization of light to the exposure time of a pixel in the polarization image. Subsequently, we propose a deep snapshot HDR reconstruction framework to recover an HDR image using the polarization images. A polarized HDR dataset is created to train and evaluate our approach. We demonstrate that our approach performs favorably against state-of-the-art HDR reconstruction algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05824v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"85785\", \"133418\", \"189323\", \"239855\", \"252829\", \"259658\"]}","task_split":"paper_retrieval"}
{"document_id":"50136","document_content":"# MT: Multi-Perspective Feature Learning Network for Scene Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text shapes. However, for most intelligent systems, the detector needs to detect arbitrary-shaped texts with high speed and accuracy simultaneously. Thus, in this study, we design an efficient pipeline named as MT, which can detect adhesive arbitrary-shaped texts with only a single binary mask in the inference stage. This paper presents the contributions on three aspects: (1) a light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy; (2) a multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately; (3) a multi-factor constraints IoU minimization loss is introduced for training the proposed model. The effectiveness of MT is evaluated on four real-world scene text datasets, and it surpasses all the state-of-the-art competitors to a large extent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05455v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54412\", \"106579\", \"131896\", \"136466\", \"140478\", \"154995\", \"155242\", \"171543\", \"190708\", \"192471\", \"196854\", \"207348\", \"222451\", \"225795\", \"239002\", \"240965\", \"245591\", \"248133\", \"252159\", \"257197\", \"269205\", \"272481\", \"279950\", \"284214\", \"289389\", \"290957\", \"294023\"]}","task_split":"paper_retrieval"}
{"document_id":"50166","document_content":"# Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known\n## Categories\n- Artificial Intelligence\n## Abstract\nProbabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations - not causal relationships. If observational data is only available and experimentation are infeasible, the correct approach to study the impact of an intervention is to invoke Pearl's causality framework. Even that framework assumes that the underlying causal graph is known, which is seldom the case in practice. When the causal structure is not known, one may use out-of-the-box algorithms to find causal dependencies from observational data. However, there exists no method that also accounts for the decision-maker's prior knowledge when developing the causal structure either. The objective of this paper is to develop rational approaches for making decisions from observational data in the presence of causal graph uncertainty and prior knowledge from the decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to infer set of causal graphs that can represent the data generation process. We provide decisions by computing the expected value and risk of potential interventions explicitly. We demonstrate our approach by applying them in different example contexts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05395v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"348264\", \"348271\", \"348603\"]}","task_split":"paper_retrieval"}
{"document_id":"50218","document_content":"# Doing Natural Language Processing in A Natural Way: An NLP toolkit based on object-oriented knowledge base and multi-level grammar base\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe introduce an NLP toolkit based on object-oriented knowledge base and multi-level grammar base. This toolkit focuses on semantic parsing, it also has abilities to discover new knowledge and grammar automatically, new discovered knowledge and grammar will be identified by human, and will be used to update the knowledge base and grammar base. This process can be iterated many times to improve the toolkit continuously.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05227v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"50345","document_content":"# Conversational Entity Linking: Problem Definition and Datasets\n## Categories\n- Computation and Language\n- Information Retrieval\n- H.3\n## Abstract\nMachine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3404835.3463258\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"H.3\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"H.3\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95304\", \"96431\", \"97185\", \"112804\", \"121381\", \"126671\", \"151897\", \"157181\", \"157255\", \"167753\", \"169192\", \"177825\", \"185889\", \"189127\", \"205857\", \"211664\", \"212046\", \"216019\", \"216850\", \"216953\", \"220723\", \"220724\", \"227746\", \"232454\", \"232596\", \"243397\", \"244479\", \"263254\", \"266411\", \"270020\"]}","task_split":"paper_retrieval"}
{"document_id":"50349","document_content":"# Museum Painting Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTo retrieve images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as feature extraction using machine learning, but over the years different classical methods have been developed. In this paper, we implement a query by example retrieval system for finding paintings in a museum image collection using classic computer vision techniques. Specifically, we study the performance of the color, texture, text and feature descriptors in datasets with different perturbations in the images: noise, overlapping text boxes, color corruption and rotation. We evaluate each of the cases using the Mean Average Precision (MAP) metric, and we obtain results that vary between 0.5 and 1.0 depending on the problem conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04891v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"50398","document_content":"# Cross-Modal Generative Augmentation for Visual Question Answering\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nData augmentation has been shown to effectively improve the performance of multimodal machine learning models. This paper introduces a generative model for data augmentation by leveraging the correlations among multiple modalities. Different from conventional data augmentation approaches that apply low-level operations with deterministic heuristics, our method learns a generator that generates samples of the target modality conditioned on observed modalities in the variational auto-encoder framework. Additionally, the proposed model is able to quantify the confidence of augmented data by its generative probability, and can be jointly optimised with a downstream task. Experiments on Visual Question Answering as downstream task demonstrate the effectiveness of the proposed generative model, which is able to improve strong UpDn-based models to achieve state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04780v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"16013\", \"79890\", \"99962\", \"111053\", \"119547\", \"120414\", \"122474\", \"129809\", \"136840\", \"150930\", \"164927\", \"167256\", \"167539\", \"167930\", \"170603\", \"175031\", \"179219\", \"184872\", \"193573\", \"198870\", \"218853\", \"225791\", \"248620\", \"250183\", \"255298\", \"259007\", \"265384\", \"269122\", \"272531\", \"284092\", \"291430\", \"296279\", \"302943\", \"303011\", \"303029\", \"303837\", \"321621\", \"322022\", \"334299\"]}","task_split":"paper_retrieval"}
{"document_id":"50412","document_content":"# Federated Unbiased Learning to Rank\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nUnbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions. In this framework, ULTR algorithms have to rely on a large amount of user data that are collected, stored, and aggregated by central servers.   In this paper, we consider an on-device search setting, where users search against their personal corpora on their local devices, and the goal is to learn a ranking function from biased user interactions. Due to privacy constraints, users' queries, personal documents, results lists, and raw interaction data will not leave their devices, and ULTR has to be carried out via Federated Learning (FL).   Directly applying existing ULTR algorithms on users' devices could suffer from insufficient training data due to the limited amount of local interactions. To address this problem, we propose the FedIPS algorithm, which learns from user interactions on-device under the coordination of a central server and uses click propensities to remove the position bias in user interactions. Our evaluation of FedIPS on the Yahoo and Istella datasets shows that FedIPS is robust over a range of position biases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04761v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"80263\", \"123668\", \"128415\", \"139315\", \"153331\", \"154097\", \"207491\", \"207827\", \"233684\", \"246939\", \"252956\", \"286532\"]}","task_split":"paper_retrieval"}
{"document_id":"50415","document_content":"# Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and transferring it to other environments, which may be physical or virtual. Recent work in transfer learning achieves encouraging performance by domain adaptation and domain randomization to expose an agent to scenarios that fill the inherent gaps in sim2sim and sim2real approaches. However, it is inefficient to train an agent in environments with randomized conditions to learn the important features of its current state. An agent can use domain knowledge provided by human experts to learn efficiently. We propose a novel approach that uses graph neural networks in conjunction with deep reinforcement learning, enabling decision-making over graphs containing relevant exploration information provided by human experts to predict a robot's optimal sensing action in belief space. The policy, which is trained only in a single simulation environment, offers a real-time, scalable, and transferable decision-making strategy, resulting in zero-shot transfer to other simulation environments and even real-world environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04758v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"4536\"], \"outgoing_citations\": [\"109880\", \"148737\", \"196898\", \"229324\", \"229333\", \"249137\", \"258472\", \"293451\"]}","task_split":"paper_retrieval"}
{"document_id":"50445","document_content":"# Adaptive Policy Transfer in Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nEfficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics. Policy transfer through warm initialization, imitation, or interacting over a large set of agents with randomized instances, have been commonly applied to solve a variety of Reinforcement Learning tasks. However, this seems far from how skill transfer happens in the biological world: Humans and animals are able to quickly adapt the learned behaviors between similar tasks and learn new skills when presented with new situations. Here we seek to answer the question: Will learning to combine adaptation and exploration lead to a more efficient transfer of policies between domains? We introduce a principled mechanism that can \"Adapt-to-Learn\", that is adapt the source policy to learn to solve a target task with significant transition differences and uncertainties. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm with significantly reduced sample complexity in transferring skills between related tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04699v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214302\", \"244554\", \"247903\", \"253045\", \"259045\", \"259762\", \"260284\", \"261780\", \"270925\", \"282841\", \"287362\", \"318666\"]}","task_split":"paper_retrieval"}
{"document_id":"50482","document_content":"# Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity\n## Categories\n- Computation and Language\n## Abstract\nBy using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is much higher than of human translation. We show that this may be explained by the difference in lexical diversity between machine translation and human translation. If machine translation has independent patterns from human translation, automatic metrics which measure the deviation of machine translation from human translation may conflate difference with quality. Our experiment with two different types of automatic metrics shows correlation with the result of the classification task. Therefore, we suggest the difference in lexical diversity between machine translation and human translation be given more attention in machine translation evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04616v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"72477\", \"99810\", \"125563\", \"168405\", \"178113\", \"178575\", \"219720\", \"220720\", \"238926\", \"284962\"]}","task_split":"paper_retrieval"}
{"document_id":"50487","document_content":"# Efficient Self-Supervised Data Collection for Offline Robot Learning\n## Categories\n- Robotics\n- Machine Learning\n- I.2.9; I.2.6; I.2.10\n- Artificial Intelligence\n## Abstract\nA practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data, using some data collection policy, and then learn from this data to perform various tasks, using offline learning algorithms. Previous work focused on manually designing the data collection policy, and on tasks where suitable policies can easily be designed, such as random picking policies for collecting data about object grasping. For more complex tasks, however, it may be difficult to find a data collection policy that explores the environment effectively, and produces data that is diverse enough for the downstream task. In this work, we propose that data collection policies should actively explore the environment to collect diverse data. In particular, we develop a simple-yet-effective goal-conditioned reinforcement-learning method that actively focuses data collection on novel observations, thereby collecting a diverse data-set. We evaluate our method on simulated robot manipulation tasks with visual inputs and show that the improved diversity of active data collection leads to significant improvements in the downstream learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04607v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"I.2.9; I.2.6; I.2.10\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"I.2.9; I.2.6; I.2.10\", \"Artificial Intelligence\"], \"incoming_citations\": [\"343094\", \"10027\"], \"outgoing_citations\": [\"98568\", \"127099\", \"180118\", \"186850\", \"193562\", \"200920\", \"206875\", \"212299\", \"221473\", \"226635\", \"259143\", \"261983\", \"266419\", \"272422\", \"283391\", \"289719\", \"291025\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"50519","document_content":"# A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker Environment\n## Categories\n- Sound\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nIn this work we use deep reinforcement learning to create an autonomous agent that can navigate in a two-dimensional space using only raw auditory sensory information from the environment, a problem that has received very little attention in the reinforcement learning literature. Our experiments show that the agent can successfully identify a particular target speaker among a set of $N$ predefined speakers in a room and move itself towards that speaker, while avoiding collision with other speakers or going outside the room boundaries. The agent is shown to be robust to speaker pitch shifting and it can learn to navigate the environment, even when a limited number of training utterances are available for each speaker.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICASSP39728.2021.9415013\", \"primary_category\": \"cs.SD\", \"categories\": [\"cs.LG\", \"eess.AS\", \"cs.SD\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\", \"Sound\"], \"incoming_citations\": [\"77439\", \"13952\"], \"outgoing_citations\": [\"156378\", \"186927\", \"218770\", \"222993\", \"247650\", \"248736\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"50591","document_content":"# Primitive Representation Learning for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition is a challenging task due to diverse variations of text instances in natural scene images. Conventional methods based on CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully investigate stable and efficient feature representations for multi-oriented scene texts. In this paper, we propose a primitive representation learning method that aims to exploit intrinsic representations of scene text images. We model elements in feature maps as the nodes of an undirected graph. A pooling aggregator and a weighted aggregator are proposed to learn primitive representations, which are transformed into high-level visual text representations by graph convolutional networks. A Primitive REpresentation learning Network (PREN) is constructed to use the visual text representations for parallel decoding. Furthermore, by integrating visual text representations into an encoder-decoder model with the 2D attention mechanism, we propose a framework called PREN2D to alleviate the misalignment problem in attention-based methods. Experimental results on both English and Chinese scene text recognition tasks demonstrate that PREN keeps a balance between accuracy and efficiency, while PREN2D achieves state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04286v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"5857\", \"6379\", \"8109\", \"21983\", \"48901\"], \"outgoing_citations\": [\"111991\", \"123550\", \"134664\", \"135021\", \"136843\", \"144173\", \"150060\", \"172884\", \"180931\", \"192483\", \"205988\", \"207071\", \"207876\", \"211851\", \"217463\", \"232706\", \"250738\", \"256711\", \"257286\", \"272208\", \"294023\", \"294606\", \"296685\", \"296828\", \"309239\", \"311196\", \"328595\", \"350985\"]}","task_split":"paper_retrieval"}
{"document_id":"50635","document_content":"# The Modulo Radon Transform: Theory, Algorithms and Applications\n## Categories\n- Information Theory\n- Computer Vision and Pattern Recognition\n- Signal Processing\n## Abstract\nRecently, experiments have been reported where researchers were able to perform high dynamic range (HDR) tomography in a heuristic fashion, by fusing multiple tomographic projections. This approach to HDR tomography has been inspired by HDR photography and inherits the same disadvantages. Taking a computational imaging approach to the HDR tomography problem, we here suggest a new model based on the Modulo Radon Transform (MRT), which we rigorously introduce and analyze. By harnessing a joint design between hardware and algorithms, we present a single-shot HDR tomography approach, which to our knowledge, is the only approach that is backed by mathematical guarantees.   On the hardware front, instead of recording the Radon Transform projections that my potentially saturate, we propose to measure modulo values of the same. This ensures that the HDR measurements are folded into a lower dynamic range. On the algorithmic front, our recovery algorithms reconstruct the HDR images from folded measurements. Beyond mathematical aspects such as injectivity and inversion of the MRT for different scenarios including band-limited and approximately compactly supported images, we also provide a first proof-of-concept demonstration. To do so, we implement MRT by experimentally folding tomographic measurements available as an open source data set using our custom designed modulo hardware. Our reconstruction clearly shows the advantages of our approach for experimental data. In this way, our MRT based solution paves a path for HDR acquisition in a number of related imaging problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04194v1\", \"primary_category\": \"cs.IT\", \"categories\": [\"cs.CV\", \"eess.SP\", \"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Signal Processing\", \"Information Theory\", \"Information Theory\"], \"incoming_citations\": [\"7459\"], \"outgoing_citations\": [\"49982\", \"131231\", \"267762\"]}","task_split":"paper_retrieval"}
{"document_id":"50710","document_content":"# Societal Biases in Language Generation: Progress and Challenges\n## Categories\n- Computation and Language\n## Abstract\nTechnology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04054v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"30933\", \"4386\", \"15949\", \"23257\", \"24623\", \"27304\", \"39053\", \"55201\"], \"outgoing_citations\": [\"55201\", \"55676\", \"55748\", \"55858\", \"56479\", \"66399\", \"69658\", \"71553\", \"72991\", \"73649\", \"75424\", \"78584\", \"78936\", \"80579\", \"81551\", \"84437\", \"87330\", \"88912\", \"91163\", \"91650\", \"94688\", \"94778\", \"95079\", \"98259\", \"100711\", \"107451\", \"109344\", \"111864\", \"122414\", \"127565\", \"127696\", \"128233\", \"132058\", \"132673\", \"145834\", \"152729\", \"154952\", \"157164\", \"157171\", \"157189\", \"157497\", \"158493\", \"161329\", \"167379\", \"168864\", \"170345\", \"171039\", \"179679\", \"181359\", \"182936\", \"183617\", \"184078\", \"189504\", \"192069\", \"195909\", \"199361\", \"203371\", \"218898\", \"219818\", \"220680\", \"222795\", \"227286\", \"232274\", \"232388\", \"237923\", \"240856\", \"249416\"]}","task_split":"paper_retrieval"}
{"document_id":"50720","document_content":"# Graph Attention Networks with Positional Embeddings\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighboring nodes having similar features and labels--, and therefore may not be at their full potential when dealing with non-homophilic graphs. In this work, we focus on addressing this limitation and enable Graph Attention Networks (GAT), a commonly used variant of GNNs, to explore the structural information within each graph locality. Inspired by the positional encoding in the Transformers, we propose a framework, termed Graph Attentional Networks with Positional Embeddings (GAT-POS), to enhance GATs with positional embeddings which capture structural and positional information of the nodes in the graph. In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node. The model is trained jointly to optimize for the task of node classification as well as the task of predicting graph context. Experimental results show that GAT-POS reaches remarkable improvement compared to strong GNN baselines and recent structural embedding enhanced GNNs on non-homophilic graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.04037v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"29221\"], \"outgoing_citations\": [\"128399\", \"142571\", \"157310\", \"164464\", \"169230\", \"181255\", \"201818\", \"203857\", \"215835\", \"239884\", \"279575\", \"295676\", \"303167\"]}","task_split":"paper_retrieval"}
{"document_id":"50827","document_content":"# Knowledge-based Review Generation by Coherence Enhanced Text Planning\n## Categories\n- Computation and Language\n## Abstract\nAs a natural language generation task, it is challenging to generate informative and coherent review text. In order to enhance the informativeness of the generated text, existing solutions typically learn to copy entities or triples from knowledge graphs (KGs). However, they lack overall consideration to select and arrange the incorporated knowledge, which tends to cause text incoherence.   To address the above issue, we focus on improving entity-centric coherence of the generated reviews by leveraging the semantic structure of KGs. In this paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based on knowledge graphs (KGs) to improve both global and local coherence for review generation. The proposed model learns a two-level text plan for generating a document: (1) the document plan is modeled as a sequence of sentence plans in order, and (2) the sentence plan is modeled as an entity-based subgraph from KG. Local coherence can be naturally enforced by KG subgraphs through intra-sentence correlations between entities. For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs. To our knowledge, we are the first to utilize a KG-based text planning model to enhance text coherence for review generation. Extensive experiments on three datasets confirm the effectiveness of our model on improving the content coherence of generated texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03815v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"48100\"], \"outgoing_citations\": [\"96877\", \"169086\", \"171217\", \"181430\", \"191907\", \"193866\", \"228030\", \"255035\", \"257191\", \"260247\", \"284481\", \"298945\", \"320035\"]}","task_split":"paper_retrieval"}
{"document_id":"50869","document_content":"# Continuous representations of intents for dialogue systems\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIntent modelling has become an important part of modern dialogue systems. With the rapid expansion of practical dialogue systems and virtual assistants, such as Amazon Alexa, Apple Siri, and Google Assistant, the interest has only increased. However, up until recently the focus has been on detecting a fixed, discrete, number of seen intents. Recent years have seen some work done on unseen intent detection in the context of zero-shot learning. This paper continues the prior work by proposing a novel model where intents are continuous points placed in a specialist Intent Space that yields several advantages. First, the continuous representation enables to investigate relationships between the seen intents. Second, it allows any unseen intent to be reliably represented given limited quantities of data. Finally, this paper will show how the proposed model can be augmented with unseen intents without retraining any of the seen ones. Experiments show that the model can reliably add unseen intents with a high accuracy while retaining a high performance on the seen intents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03716v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138059\", \"183006\", \"205014\", \"219441\", \"230543\", \"297304\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"50903","document_content":"# Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nLearning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding (metric) space have been found very popular in RS. A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images to a reference image called as an anchor. Choosing triplets is a difficult task particularly for multi-label RS CBIR, where each training image is annotated by multiple class labels. To address this problem, in this paper we propose a novel triplet sampling method in the framework of deep neural networks (DNNs) defined for multi-label RS CBIR problems. The proposed method selects a small set of the most representative and informative triplets based on two main steps. In the first step, a set of anchors that are diverse to each other in the embedding space is selected from the current mini-batch using an iterative algorithm. In the second step, different sets of positive and negative images are chosen for each anchor by evaluating the relevancy, hardness and diversity of the images among each other based on a novel strategy. Experimental results obtained on two multi-label benchmark archives show that the selection of the most informative and representative triplets in the context of DNNs results in: i) reducing the computational complexity of the training phase of the DNNs without any significant loss on the performance; and ii) an increase in learning speed since informative triplets allow fast convergence. The code of the proposed method is publicly available at https:\/\/git.tu-berlin.de\/rsim\/image-retrieval-from-triplets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TGRS.2021.3124326\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"8790\"], \"outgoing_citations\": [\"88868\", \"133251\", \"189565\", \"191508\", \"192719\", \"194115\", \"195998\", \"198717\", \"214075\", \"221454\", \"239660\", \"263034\", \"280217\", \"302895\", \"314568\", \"320009\"]}","task_split":"paper_retrieval"}
{"document_id":"50907","document_content":"# Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNeural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS Guided Sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations show that, compared with existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG) can generate more diverse text while maintaining comparable quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03641v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"72906\", \"78009\", \"78207\", \"99616\", \"124496\", \"126962\", \"132683\", \"172088\", \"172394\", \"189504\", \"189542\", \"191641\", \"196340\", \"197727\", \"211205\", \"211804\", \"221926\", \"228201\", \"229734\", \"232274\", \"243154\", \"250360\", \"250815\", \"283971\", \"290777\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"50956","document_content":"# Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Sound\n## Abstract\nIn realistic speech enhancement settings for end-user devices, we often encounter only a few speakers and noise types that tend to reoccur in the specific acoustic environment. We propose a novel personalized speech enhancement method to adapt a compact denoising model to the test-time specificity. Our goal in this test-time adaptation is to utilize no clean speech target of the test speaker, thus fulfilling the requirement for zero-shot learning. To complement the lack of clean utterance, we employ the knowledge distillation framework. Instead of the missing clean utterance target, we distill the more advanced denoising results from an overly large teacher model, and use it as the pseudo target to train the small student model. This zero-shot learning procedure circumvents the process of collecting users' clean speech, a process that users are reluctant to comply due to privacy concerns and technical difficulty of recording clean voice. Experiments on various test-time conditions show that the proposed personalization method achieves significant performance gains compared to larger baseline networks trained from a large speaker- and noise-agnostic datasets. In addition, since the compact personalized models can outperform larger general-purpose models, we claim that the proposed method performs model compression with no loss of denoising performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03544v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"eess.AS\", \"cs.SD\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\", \"Sound\"], \"incoming_citations\": [\"8817\", \"10311\", \"15108\", \"58220\"], \"outgoing_citations\": [\"50958\", \"124634\", \"126181\", \"139859\", \"177448\", \"178013\", \"184968\", \"185555\", \"192685\", \"192746\", \"211222\", \"211543\", \"217178\", \"252897\", \"257762\", \"258300\", \"262216\", \"270422\", \"304370\", \"336240\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"51034","document_content":"# Are Pre-trained Convolutions Better than Pre-trained Transformers?\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional models competitive to Transformers when pre-trained? This paper investigates this research question and presents several interesting findings. Across an extensive set of experiments on 8 datasets\/tasks, we find that CNN-based pre-trained models are competitive and outperform their Transformer counterpart in certain scenarios, albeit with caveats. Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. We believe our research paves the way for a healthy amount of optimism in alternative architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03322v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"100684\", \"7265\", \"15609\", \"21804\", \"43784\", \"46629\", \"71813\", \"2169\", \"12685\", \"19960\", \"20091\", \"28652\", \"38189\", \"39925\", \"58486\", \"189867\"], \"outgoing_citations\": [\"88347\", \"95004\", \"100684\", \"127489\", \"136153\", \"136564\", \"169911\", \"172001\", \"187452\", \"195564\", \"201080\", \"211368\", \"212322\", \"235913\", \"259706\", \"264149\", \"266955\", \"281508\", \"281768\"]}","task_split":"paper_retrieval"}
{"document_id":"51175","document_content":"# Learning Controllable Content Generators\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIt has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-specified heuristic. To ensure that these generators' output is sufficiently diverse (that is, not amounting to the reproduction of a single optimal level configuration), the generation process is constrained such that the initial seed results in some variance in the generator's output. However, this results in a loss of control over the generated content for the human user. We propose to train generators capable of producing controllably diverse output, by making them \"goal-aware.\" To this end, we add conditional inputs representing how close a generator is to some heuristic, and also modify the reward mechanism to incorporate that value. Testing on multiple domains, we show that the resulting level generators are capable of exploring the space of possible levels in a targeted, controllable manner, producing levels of comparable quality as their goal-unaware counterparts, that are diverse along designer-specified dimensions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.02993v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"24178\", \"4621\", \"18372\"], \"outgoing_citations\": [\"64439\", \"82943\", \"95477\", \"107442\", \"124122\", \"145053\", \"145664\", \"161468\", \"226544\", \"233501\", \"237119\", \"240572\", \"274886\", \"283001\"]}","task_split":"paper_retrieval"}
{"document_id":"51214","document_content":"# Deep Polarization Imaging for 3D shape and SVBRDF Acquisition\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Machine Learning\n- I.4; I.3\n## Abstract\nWe present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization imaging with deep learning to achieve high quality estimate of 3D object shape (surface normals and depth) and SVBRDF using single-view polarization imaging under frontal flash illumination. In addition to acquired polarization images, we provide our deep network with strong novel cues related to shape and reflectance, in the form of a normalized Stokes map and an estimate of diffuse color. We additionally describe modifications to network architecture and training loss which provide further qualitative improvements. We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.02875v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.LG\", \"I.4; I.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Machine Learning\", \"I.4; I.3\"], \"incoming_citations\": [\"1531\", \"23772\", \"47562\", \"67385\"], \"outgoing_citations\": [\"111407\", \"113806\", \"133689\", \"178720\", \"193877\", \"213300\", \"219267\", \"235442\", \"246024\"]}","task_split":"paper_retrieval"}
{"document_id":"51266","document_content":"# Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems\n## Categories\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by weight decomposition of objectives. This paper proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems. The Pareto front is then built accordingly. Compared with other learning-based methods, our method can greatly shorten the training time of multiple submodels. Due to the rapid and excellent adaptability of the meta-model, more submodels can be derived so as to increase the quality and diversity of the found solutions. The computational experiments on multiobjective traveling salesman problems and multiobjective vehicle routing problem with time windows demonstrate the superiority of our method over most of learning-based and iteration-based approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.02741v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"18639\", \"79509\", \"79517\", \"81909\", \"91898\", \"115883\", \"123490\", \"133255\", \"138001\", \"142482\", \"154998\", \"210039\", \"210938\", \"215015\", \"215989\", \"238077\", \"239655\", \"242457\", \"269617\", \"276858\", \"279191\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"51313","document_content":"# Challenges and Obstacles Towards Deploying Deep Learning Models on Mobile Devices\n## Categories\n- Machine Learning\n- Hardware Architecture\n## Abstract\nFrom computer vision and speech recognition to forecasting trajectories in autonomous vehicles, deep learning approaches are at the forefront of so many domains. Deep learning models are developed using plethora of high-level, generic frameworks and libraries. Running those models on the mobile devices require hardware-aware optimizations and in most cases converting the models to other formats or using a third-party framework. In reality, most of the developed models need to undergo a process of conversion, adaptation, and, in some cases, full retraining to match the requirements and features of the framework that is deploying the model on the target platform. Variety of hardware platforms with heterogeneous computing elements, from wearable devices to high-performance GPU clusters are used to run deep learning models. In this paper, we present the existing challenges, obstacles, and practical solutions towards deploying deep learning models on mobile devices.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.02613v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Hardware Architecture\"], \"incoming_citations\": [], \"outgoing_citations\": [\"161637\", \"328077\"]}","task_split":"paper_retrieval"}
{"document_id":"51399","document_content":"# Inverting Generative Adversarial Renderer for Face Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGiven a monocular face image as input, 3D face geometry reconstruction aims to recover a corresponding 3D face mesh. Recently, both optimization-based and learning-based face reconstruction methods have taken advantage of the emerging differentiable renderer and shown promising results. However, the differentiable renderer, mainly based on graphics rules, simplifies the realistic mechanism of the illumination, reflection, \\etc, of the real world, thus cannot produce realistic images. This brings a lot of domain-shift noise to the optimization or training process. In this work, we introduce a novel Generative Adversarial Renderer (GAR) and propose to tailor its inverted version to the general fitting pipeline, to tackle the above problem. Specifically, the carefully designed neural renderer takes a face normal map and a latent code representing other factors as inputs and renders a realistic face image. Since the GAR learns to model the complicated real-world image, instead of relying on the simplified graphics rules, it is capable of producing realistic images, which essentially inhibits the domain-shift noise in training and optimization. Equipped with the elaborated GAR, we further proposed a novel approach to predict 3D face parameters, in which we first obtain fine initial parameters via Renderer Inverting and then refine it with gradient-based optimizers. Extensive experiments have been conducted to demonstrate the effectiveness of the proposed generative adversarial renderer and the novel optimization-based face reconstruction framework. Our method achieves state-of-the-art performances on multiple face reconstruction datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.02431v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"12184\", \"431\"], \"outgoing_citations\": [\"88918\", \"129244\", \"134076\", \"155623\", \"159596\", \"159914\", \"168545\", \"171294\", \"174537\", \"191310\", \"192020\", \"192689\", \"194402\", \"197493\", \"198793\", \"202710\", \"223428\", \"227856\", \"230415\", \"236050\", \"238562\", \"248005\", \"248572\", \"249836\", \"270134\", \"275879\", \"277948\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"51739","document_content":"# Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments suggest that discourse context is unlikely the cause for the improvement of about +1 BLEU across four language pairs. Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.01691v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2490\"], \"outgoing_citations\": [\"55353\", \"127163\", \"141444\", \"161914\", \"163992\", \"176177\", \"184635\", \"215003\", \"220611\", \"220720\", \"220934\", \"235275\", \"251240\", \"258163\", \"263453\", \"267601\", \"268684\"]}","task_split":"paper_retrieval"}
{"document_id":"52036","document_content":"# Looking for COVID-19 misinformation in multilingual social media texts\n## Categories\n- Computation and Language\n- Databases\n## Abstract\nThis paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03313v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DB\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Databases\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117359\", \"120433\", \"127820\", \"134040\", \"134842\", \"135080\", \"135357\", \"136527\", \"137435\"]}","task_split":"paper_retrieval"}
{"document_id":"52057","document_content":"# Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nRecent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. Reward function is crucial for most of reinforcement learning applications as it can provide the guideline about the optimization. However, current reinforcement-learning-based methods rely on manually-defined reward functions, which cannot adapt to dynamic and noisy environments. Besides, they generally use task-specific reward functions that sacrifice generalization ability. We propose a generative inverse reinforcement learning for user behavioral preference modelling, to address the above issues. Instead of using predefined reward functions, our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN. Our model provides a general way of characterizing and explaining underlying behavioral tendencies, and our experiments show our method outperforms state-of-the-art methods in a variety of scenarios, namely traffic signal control, online recommender systems, and scanpath prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.00822v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"89090\", \"122334\", \"130658\", \"139280\", \"157162\", \"176360\", \"185162\", \"204674\", \"210118\", \"213887\", \"218636\", \"230605\", \"242141\", \"242483\", \"249069\", \"251924\", \"252398\", \"260407\", \"281257\", \"290679\", \"302909\", \"303049\", \"311595\", \"313499\", \"338202\"]}","task_split":"paper_retrieval"}
{"document_id":"52113","document_content":"# Graph Learning: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n- 68T07\n- I.2.6\n## Abstract\nGraphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TAI.2021.3076021\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\", \"68T07\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\", \"68T07\", \"I.2.6\"], \"incoming_citations\": [\"2132\", \"6154\", \"16311\", \"44925\", \"15436\"], \"outgoing_citations\": [\"2132\", \"6661\", \"7683\", \"10337\", \"13814\", \"14302\", \"16311\", \"16928\", \"24963\", \"40582\", \"42715\", \"43042\", \"43059\", \"43369\", \"43609\", \"44516\", \"44925\", \"48505\", \"49353\", \"50036\", \"62817\", \"66588\", \"67813\", \"69784\", \"71193\", \"72028\", \"73530\", \"87085\", \"89373\", \"89956\", \"90518\", \"90641\", \"90935\", \"91654\", \"91881\", \"92075\", \"94952\", \"99280\", \"100975\", \"103085\", \"113226\", \"114415\", \"115700\", \"116748\", \"117093\", \"117926\", \"118201\", \"118237\", \"118349\", \"118653\", \"119293\", \"119843\", \"120001\", \"120508\", \"123835\", \"129410\", \"142936\", \"143456\", \"147107\", \"153306\", \"155900\", \"156910\", \"159315\", \"164674\", \"164939\", \"168172\", \"174492\", \"176611\", \"183231\", \"185584\", \"188492\", \"193345\", \"196521\", \"197103\", \"197726\", \"198305\", \"198382\", \"200357\", \"204706\", \"208440\", \"214311\", \"215435\", \"216376\", \"228120\", \"228992\", \"240268\", \"241029\", \"242399\", \"243791\", \"244510\", \"256259\", \"258578\", \"318072\"]}","task_split":"paper_retrieval"}
{"document_id":"52184","document_content":"# Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server. However, learning a single global model might not work well for all clients participating in the FL under data heterogeneity. Therefore, the personalization of the global model becomes crucial in handling the challenges that arise with statistical heterogeneity and the non-IID distribution of data. Unlike prior works, in this work we propose a new approach for obtaining a personalized model from a client-level objective. This further motivates all clients to participate in federation even under statistical heterogeneity in order to improve their performance, instead of merely being a source of data and model training for the central server. To realize this personalization, we leverage finding a small subnetwork for each client by applying hybrid pruning (combination of structured and unstructured pruning), and unstructured pruning. Through a range of experiments on different benchmarks, we observed that the clients with similar data (labels) share similar personal parameters. By finding a subnetwork for each client ...","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.00562v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"140332\", \"142802\", \"143220\", \"148382\", \"160301\", \"164627\", \"166833\", \"174590\", \"191555\", \"210868\", \"229563\", \"241135\", \"241304\", \"248216\", \"258001\", \"265073\", \"286728\", \"295571\"]}","task_split":"paper_retrieval"}
{"document_id":"52428","document_content":"# Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems\n## Categories\n- Computation and Language\n## Abstract\nBeing able to generate informative and coherent dialogue responses is crucial when designing human-like open-domain dialogue systems. Encoder-decoder-based dialogue models tend to produce generic and dull responses during the decoding step because the most predictable response is likely to be a non-informative response instead of the most suitable one. To alleviate this problem, we propose to train the generation model in a bidirectional manner by adding a backward reasoning step to the vanilla encoder-decoder training. The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction. The advantage of our method is that the forward generation and backward reasoning steps are trained simultaneously through the use of a latent variable to facilitate bidirectional optimization. Our method can improve response quality without introducing side information (e.g., a pre-trained topic model). The proposed bidirectional response generation method achieves state-of-the-art performance for response quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3404835.3463004\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"73917\", \"113020\"], \"outgoing_citations\": [\"126112\", \"132626\", \"158493\", \"161228\", \"168228\", \"201867\", \"204637\", \"206655\", \"218973\", \"219198\", \"244479\", \"253593\", \"267641\", \"275597\", \"278671\", \"289943\", \"290766\", \"291102\", \"292261\", \"305200\", \"309411\", \"310740\"]}","task_split":"paper_retrieval"}
{"document_id":"52533","document_content":"# Evaluating Contrastive Models for Instance-based Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this work, we evaluate contrastive models for the task of image retrieval. We hypothesise that models that are learned to encode semantic similarity among instances via discriminative learning should perform well on the task of image retrieval, where relevancy is defined in terms of instances of the same object. Through our extensive evaluation, we find that representations from models trained using contrastive methods perform on-par with (and outperforms) a pre-trained supervised baseline trained on the ImageNet labels in retrieval tasks under various configurations. This is remarkable given that the contrastive models require no explicit supervision. Thus, we conclude that these models can be used to bootstrap base models to build more robust image retrieval engines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3460426.3463585\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95191\", \"180184\", \"182813\", \"208493\", \"237248\", \"280378\", \"281953\", \"294470\", \"294983\", \"295204\", \"303067\", \"304501\", \"314584\", \"319896\", \"332151\", \"332726\", \"339440\"]}","task_split":"paper_retrieval"}
{"document_id":"52583","document_content":"# The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey\n## Categories\n- Computation and Language\n## Abstract\nRecently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A commonly observed problem with the generated summaries is the distortion or fabrication of factual information in the article. This inconsistency between the original text and the summary has caused various concerns over its applicability, and the previous evaluation methods of text summarization are not suitable for this issue. In response to the above problems, the current research direction is predominantly divided into two categories, one is to design fact-aware evaluation metrics to select outputs without factual inconsistency errors, and the other is to develop new summarization systems towards factual consistency. In this survey, we focus on presenting a comprehensive review of these fact-specific evaluation methods and text summarization models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14839v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22450\", \"36972\", \"5749\"], \"outgoing_citations\": [\"61496\", \"84343\", \"88960\", \"91642\", \"91716\", \"93583\", \"96411\", \"98155\", \"99789\", \"115701\", \"126346\", \"127281\", \"127411\", \"132176\", \"157706\", \"159316\", \"183400\", \"220230\", \"232274\", \"250690\", \"285557\", \"288699\", \"298016\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"52640","document_content":"# Center Prediction Loss for Re-identification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe training loss function that enforces certain training sample distribution patterns plays a critical role in building a re-identification (ReID) system. Besides the basic requirement of discrimination, i.e., the features corresponding to different identities should not be mixed, additional intra-class distribution constraints, such as features from the same identities should be close to their centers, have been adopted to construct losses. Despite the advances of various new loss functions, it is still challenging to strike the balance between the need of reducing the intra-class variation and allowing certain distribution freedom. In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples. The prediction error is then regarded as a loss called Center Prediction Loss (CPL). We show that, without introducing additional hyper-parameters, this new loss leads to a more flexible intra-class distribution constraint while ensuring the between-class samples are well-separated. Extensive experiments on various real-world ReID datasets show that the proposed loss can achieve superior performance and can also be complementary to existing losses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14746v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"84663\", \"109511\", \"131360\", \"131621\", \"136181\", \"140200\", \"141416\", \"147460\", \"175401\", \"179929\", \"187188\", \"187495\", \"187978\", \"190672\", \"192078\", \"195998\", \"205154\", \"210315\", \"212539\", \"216614\", \"232732\", \"235590\", \"236021\", \"237105\", \"237616\", \"240456\", \"241223\", \"244938\", \"249549\", \"260426\", \"266003\", \"270817\", \"275350\", \"302895\"]}","task_split":"paper_retrieval"}
{"document_id":"52662","document_content":"# Adapting Coreference Resolution for Processing Violent Death Narratives\n## Categories\n- Computation and Language\n## Abstract\nCoreference resolution is an important component in analyzing narrative text from administrative data (e.g., clinical or police sources). However, existing coreference models trained on general language corpora suffer from poor transferability due to domain gaps, especially when they are applied to gender-inclusive data with lesbian, gay, bisexual, and transgender (LGBT) individuals. In this paper, we analyzed the challenges of coreference resolution in an exemplary form of administrative text written in English: violent death narratives from the USA's Centers for Disease Control's (CDC) National Violent Death Reporting System. We developed a set of data augmentation rules to improve model performance using a probabilistic data programming framework. Experiments on narratives from an administrative database, as well as existing gender-inclusive coreference datasets, demonstrate the effectiveness of data augmentation in training coreference models that can better handle text data about LGBT individuals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14703v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"158941\", \"191947\", \"214554\", \"234307\", \"235095\", \"235555\", \"238321\", \"249126\", \"260541\"]}","task_split":"paper_retrieval"}
{"document_id":"52717","document_content":"# Unsupervised Layered Image Decomposition into Object Prototypes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present an unsupervised learning framework for decomposing images into layers of automatically discovered object models. Contrary to recent approaches that model image layers with autoencoder networks, we represent them as explicit transformations of a small set of prototypical images. Our model has three main components: (i) a set of object prototypes in the form of learnable images with a transparency channel, which we refer to as sprites; (ii) differentiable parametric functions predicting occlusions and transformation parameters necessary to instantiate the sprites in a given image; (iii) a layered image formation model with occlusion for compositing these instances into complete images including background. By jointly learning the sprites and occlusion\/transformation predictors to reconstruct images, our approach not only yields accurate layered image decompositions, but also identifies object categories and instance parameters. We first validate our approach by providing results on par with the state of the art on standard multi-object synthetic benchmarks (Tetrominoes, Multi-dSprites, CLEVR6). We then demonstrate the applicability of our model to real images in tasks that include clustering (SVHN, GTSRB), cosegmentation (Weizmann Horse) and object discovery from unfiltered social network images. To the best of our knowledge, our approach is the first layered image decomposition algorithm that learns an explicit and shared concept of object type, and is robust enough to be applied to real images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14575v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"18254\", \"8473\", \"20302\", \"35012\", \"42676\"], \"outgoing_citations\": [\"113961\", \"115856\", \"117437\", \"123027\", \"128671\", \"131672\", \"148094\", \"172130\", \"173765\", \"179957\", \"180477\", \"184209\", \"184269\", \"192024\", \"196894\", \"202216\", \"208424\", \"224270\", \"229184\", \"239965\", \"272345\", \"272827\", \"277538\", \"281358\", \"289880\", \"295736\", \"302920\", \"318558\", \"353381\"]}","task_split":"paper_retrieval"}
{"document_id":"52848","document_content":"# A Study into patient similarity through representation learning from medical records\n## Categories\n- Information Retrieval\n- H.1; J.3\n- Machine Learning\n- 68P20\n## Abstract\nPatient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10115-022-01740-2\", \"primary_category\": \"cs.IR\", \"categories\": [\"H.1; J.3\", \"cs.LG\", \"68P20\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"H.1; J.3\", \"Machine Learning\", \"68P20\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42130\", \"96241\", \"172203\", \"182009\", \"186594\", \"198222\", \"214701\", \"225853\", \"226272\", \"233107\", \"287747\", \"298095\", \"298490\", \"308857\", \"329586\", \"345587\"]}","task_split":"paper_retrieval"}
{"document_id":"52891","document_content":"# Actor-centered Representations for Action Localization in Streaming Videos\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent perception tasks such as recognizing and localizing actions in streaming videos are essential for scaling to real-world application contexts. We tackle the problem of learning actor-centered representations through the notion of continual hierarchical predictive learning to localize actions in streaming videos without the need for training labels and outlines for the objects in the video. We propose a framework driven by the notion of hierarchical predictive learning to construct actor-centered features by attention-based contextualization. The key idea is that predictable features or objects do not attract attention and hence do not contribute to the action of interest. Experiments on three benchmark datasets show that the approach can learn robust representations for localizing actions using only one epoch of training, i.e., a single pass through the streaming video. We show that the proposed approach outperforms unsupervised and weakly supervised baselines while offering competitive performance to fully supervised approaches. Additionally, we extend the model to multi-actor settings to recognize group activities while localizing the multiple, plausible actors. We also show that it generalizes to out-of-domain data with limited performance degradation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14131v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"52930","document_content":"# Maneuver-Aware Pooling for Vehicle Trajectory Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nAutonomous vehicles should be able to predict the future states of its environment and respond appropriately. Specifically, predicting the behavior of surrounding human drivers is vital for such platforms to share the same road with humans. Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles. This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We are motivated by improving the prediction accuracy when a surrounding vehicle performs lane change and highway merging maneuvers. We propose a novel pooling strategy to capture the inter-dependencies between the neighbor vehicles. Depending solely on Euclidean trajectory representation, the existing pooling strategies do not model the context information of the maneuvers intended by a surrounding vehicle. In contrast, our pooling mechanism employs polar trajectory representation, vehicles orientation and radial velocity. This results in an implicitly maneuver-aware pooling operation. We incorporated the proposed pooling mechanism into a generative encoder-decoder model, and evaluated our method on the public NGSIM dataset. The results of maneuver-based trajectory predictions demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches. Our \"Pooling Toolbox\" code is available at https:\/\/github.com\/m-hasan-n\/pooling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14079v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162130\", \"194147\", \"223299\", \"227342\", \"232095\", \"237350\", \"241801\", \"244314\", \"268197\", \"311643\", \"320874\", \"321682\", \"341730\"]}","task_split":"paper_retrieval"}
{"document_id":"52949","document_content":"# Multi-Task Learning of Query Intent and Named Entities using Transfer Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNamed entity recognition (NER) has been studied extensively and the earlier algorithms were based on sequence labeling like Hidden Markov Models (HMM) and conditional random fields (CRF). These were followed by neural network based deep learning models. Recently, BERT has shown new state of the art accuracy in sequence labeling tasks like NER. In this short article, we study various approaches to task specific NER. Task specific NER has two components - identifying the intent of a piece of text (like search queries), and then labeling the query with task specific named entities. For example, we consider the task of labeling Target store locations in a search query (which could be entered in a search box or spoken in a device like Alexa or Google Home). Store locations are highly ambiguous and sometimes it is difficult to differentiate between say a location and a non-location. For example, \"pickup my order at orange store\" has \"orange\" as the store location, while \"buy orange at target\" has \"orange\" as a fruit. We explore this difficulty by doing multi-task learning which we call global to local transfer of information. We jointly learn the query intent (i.e. store lookup) and the named entities by using multiple loss functions in our BERT based model and find interesting results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03316v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214692\", \"254576\", \"260314\", \"263636\"]}","task_split":"paper_retrieval"}
{"document_id":"53022","document_content":"# A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nOver the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase patient prognosis. Recently, deep neural networks (DNNs) have become viable to deal with skin cancer detection. In this work, we present a smartphone-based application to assist on skin cancer detection. This application is based on a Convolutional Neural Network(CNN) trained on clinical images and patients demographics, both collected from smartphones. Also, as skin cancer datasets are imbalanced, we present an approach, based on the mutation operator of Differential Evolution (DE) algorithm, to balance data. In this sense, beyond provides a flexible tool to assist doctors on skin cancer screening phase, the method obtains promising results with a balanced accuracy of 85% and a recall of 96%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.14353v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"166581\", \"219115\", \"237466\", \"272645\", \"272909\", \"282613\", \"353154\"]}","task_split":"paper_retrieval"}
{"document_id":"53028","document_content":"# Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nUnsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images. In previous work, pseudo-captions, i.e., sentences that contain the detected object labels, were assigned to a given image. The focus of the previous work was on the alignment of input images and pseudo-captions at the sentence level. However, pseudo-captions contain many words that are irrelevant to a given image. In this work, we investigate the effect of removing mismatched words from image-sentence alignment to determine how they make this task difficult. We propose a simple gating mechanism that is trained to align image features with only the most reliable words in pseudo-captions: the detected object labels. The experimental results show that our proposed method outperforms the previous methods without introducing complex sentence-level learning objectives. Combined with the sentence-level alignment method of previous work, our method further improves its performance. These results confirm the importance of careful alignment in word-level details.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.13872v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5842\"], \"outgoing_citations\": [\"116404\", \"167642\", \"168553\", \"170299\", \"171726\", \"193738\", \"205227\", \"208555\", \"211768\", \"227880\", \"237655\", \"238125\", \"238941\", \"278604\", \"279167\", \"287504\", \"289627\", \"303209\", \"320761\", \"321682\"]}","task_split":"paper_retrieval"}
{"document_id":"53125","document_content":"# On the Unreasonable Effectiveness of Centroids in Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- I.4.9; I.4.10\n## Abstract\nImage retrieval task consists of finding similar images to a query image from a set of gallery (database) images. Such systems are used in various applications e.g. person re-identification (ReID) or visual product search. Despite active development of retrieval models it still remains a challenging task mainly due to large intra-class variance caused by changes in view angle, lighting, background clutter or occlusion, while inter-class variance may be relatively low. A large portion of current research focuses on creating more robust features and modifying objective functions, usually based on Triplet Loss. Some works experiment with using centroid\/proxy representation of a class to alleviate problems with computing speed and hard samples mining used with Triplet Loss. However, these approaches are used for training alone and discarded during the retrieval stage. In this paper we propose to use the mean centroid representation both during training and retrieval. Such an aggregated representation is more robust to outliers and assures more stable features. As each class is represented by a single embedding - the class centroid - both retrieval time and storage requirements are reduced significantly. Aggregating multiple embeddings results in a significant reduction of the search space due to lowering the number of candidate target vectors, which makes the method especially suitable for production deployments. Comprehensive experiments conducted on two ReID and Fashion Retrieval datasets demonstrate effectiveness of our method, which outperforms the current state-of-the-art. We propose centroid training and retrieval as a viable method for both Fashion Retrieval and ReID applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.13643v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"I.4.9; I.4.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"I.4.9; I.4.10\"], \"incoming_citations\": [\"1906\", \"125330\", \"18906\"], \"outgoing_citations\": [\"76974\", \"137767\", \"150821\", \"179929\", \"187978\", \"189920\", \"193735\", \"199956\", \"207941\", \"277635\", \"285255\"]}","task_split":"paper_retrieval"}
{"document_id":"53142","document_content":"# End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nNavigating through intersections is one of the main challenging tasks for an autonomous vehicle. However, for the majority of intersections regulated by traffic lights, the problem could be solved by a simple rule-based method in which the autonomous vehicle behavior is closely related to the traffic light states. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step. We demonstrate that agents learn both the basic rules needed to handle intersections by understanding the priorities of other learners inside the environment, and to drive safely along their paths. Moreover, a comparison between our system and a rule-based method proves that our model achieves better results especially with dense traffic conditions. Finally, we test our system on real world scenarios using real recorded traffic data, proving that our module is able to generalize both to unseen environments and to different traffic conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.13617v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"25760\"], \"outgoing_citations\": [\"85572\", \"125326\", \"148658\", \"155761\", \"196584\", \"204750\", \"206571\", \"207983\", \"226166\", \"238960\", \"248327\", \"253953\", \"255272\", \"267428\", \"278081\", \"283410\", \"283732\", \"293864\", \"301005\"]}","task_split":"paper_retrieval"}
{"document_id":"53332","document_content":"# LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThree-dimensional reconstruction of objects from shading information is a challenging task in computer vision. As most of the approaches facing the Photometric Stereo problem use simplified far-field assumptions, real-world scenarios have essentially more complex physical effects that need to be handled for accurately reconstructing the 3D shape. An increasing number of methods have been proposed to address the problem when point light sources are assumed to be nearby the target object. The proximity of the light sources complicates the modeling of the image formation as the light behaviour requires non-linear parameterisation to describe its propagation and attenuation.   To understand the capability of the approaches dealing with this near-field scenario, the literature till now has used synthetically rendered photometric images or minimal and very customised real-world data. In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials. A device counting 52 LEDs has been designed to lit each object positioned 10 to 30 centimeters away from the camera. Together with the raw images, in order to evaluate the 3D reconstructions, the dataset includes both normal and depth maps for comparing different features of the retrieved 3D geometry. Furthermore, we evaluate the performance of the latest near-field Photometric Stereo algorithms on the proposed dataset to assess the SOTA method with respect to actual close range effects and object materials.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.13135v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"101039\", \"106554\", \"111509\", \"211407\", \"219800\", \"223643\", \"255625\", \"262122\", \"339203\"]}","task_split":"paper_retrieval"}
{"document_id":"53577","document_content":"# GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Multimedia\n## Abstract\nTraditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding the text-based query and the video effectively are both important for the task of multi-modal video summarization. In this work, a new method is proposed that uses a specialized attention network and contextualized word representations to tackle this task. The proposed model consists of a contextualized video summary controller, multi-modal attention mechanisms, an interactive attention network, and a video summary generator. Based on the evaluation of the existing multi-modal video summarization benchmark, experimental results show that the proposed model is effective with the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared with the state-of-the-art method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.12465v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"16805\", \"48256\", \"53571\", \"153482\"], \"outgoing_citations\": [\"53571\", \"75310\", \"89845\", \"132411\", \"147596\", \"169179\", \"188669\", \"190086\", \"194308\", \"199343\", \"208886\", \"221163\", \"225315\", \"227740\", \"229881\", \"246292\", \"249441\", \"250228\", \"256019\", \"257295\", \"264136\", \"267568\", \"271120\", \"283780\", \"291817\", \"296780\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"53603","document_content":"# Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students\n## Categories\n- Computation and Language\n## Abstract\nAlthough Natural Language Processing (NLP) is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2021, both face-to-face and online.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.12422v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"53609\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"53666","document_content":"# Reranking Machine Translation Hypotheses with Structured and Web-based Language Models\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system. These language models, developed from Constraint Dependency Grammar parses, tightly integrate knowledge of words, morphological and lexical features, and syntactic dependency constraints. Two structured language models are applied for N-best rescoring, one is an almost-parsing language model, and the other utilizes more syntactic features by explicitly modeling syntactic dependencies between words. We also investigate effective and efficient language modeling methods to use N-grams extracted from up to 1 teraword of web documents. We apply all these language models for N-best re-ranking on the NIST and DARPA GALE program 2006 and 2007 machine translation evaluation tasks and find that the combination of these language models increases the BLEU score up to 1.6% absolutely on blind test sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ASRU.2007.4430102\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"261049\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"53672","document_content":"# User Preference-aware Fake News Detection\n## Categories\n- Social and Information Networks\n- Computation and Language\n## Abstract\nDisinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on mining news content and\/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he\/she decides to spread a piece of fake news or not is ignored. The confirmation bias theory has indicated that a user is more likely to spread a piece of fake news when it confirms his\/her existing beliefs\/preferences. Users' historical, social engagements such as posts provide rich information about users' preferences toward news and have great potential to advance fake news detection. However, the work on exploring user preference for fake news detection is somewhat limited. Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. We release our code and data as a benchmark for GNN-based fake news detection: https:\/\/github.com\/safe-graph\/GNN-FakeNews.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.12259v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.CL\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\"], \"incoming_citations\": [\"11343\", \"12462\", \"42371\"], \"outgoing_citations\": [\"74426\", \"96445\", \"106017\", \"113687\", \"129247\", \"139587\", \"141447\", \"194215\", \"199511\", \"207715\", \"219169\", \"227067\", \"234054\", \"236257\", \"259202\", \"267552\", \"271021\", \"305094\"]}","task_split":"paper_retrieval"}
{"document_id":"53750","document_content":"# Parallel Scale-wise Attention Network for Effective Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nThe paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.12076v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"3813\", \"861\", \"49205\"], \"outgoing_citations\": [\"90841\", \"96775\", \"134664\", \"139664\", \"148316\", \"150060\", \"172884\", \"180931\", \"190056\", \"205988\", \"211851\", \"217463\", \"232706\", \"250738\", \"256711\", \"294023\", \"296685\", \"309239\", \"311196\", \"320148\", \"320837\", \"328595\", \"350985\"]}","task_split":"paper_retrieval"}
{"document_id":"53764","document_content":"# Math Operation Embeddings for Open-ended Solution Analysis and Feedback\n## Categories\n- Machine Learning\n- Computers and Society\n## Abstract\nFeedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually constructing cognitive models and anticipating student errors for each question. This process requires significant human effort and does not scale to most questions used in homework and practices that do not come with this information. In this paper, we analyze students' step-by-step solution processes to equation solving questions in an attempt to scale up error diagnostics and feedback mechanisms developed for a small number of questions to a much larger number of questions. Leveraging a recent math expression encoding method, we represent each math operation applied in solution steps as a transition in the math embedding vector space. We use a dataset that contains student solution steps in the Cognitive Tutor system to learn implicit and explicit representations of math operations. We explore whether these representations can i) identify math operations a student intends to perform in each solution step, regardless of whether they did it correctly or not, and ii) select the appropriate feedback type for incorrect steps. Experimental results show that our learned math operation representations generalize well across different data distributions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.12047v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"94434\", \"161669\", \"192616\", \"200464\", \"312437\", \"318882\", \"359981\"]}","task_split":"paper_retrieval"}
{"document_id":"53826","document_content":"# Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn many real-world scenarios where extrinsic rewards to the agent are extremely sparse, curiosity has emerged as a useful concept providing intrinsic rewards that enable the agent to explore its environment and acquire information to achieve its goals. Despite their strong performance on many sparse-reward tasks, existing curiosity approaches rely on an overly holistic view of state transitions, and do not allow for a structured understanding of specific aspects of the environment. In this paper, we formulate curiosity based on grounded question answering by encouraging the agent to ask questions about the environment and be curious when the answers to these questions change. We show that natural language questions encourage the agent to uncover specific knowledge about their environment such as the physical properties of objects as well as their spatial relationships with other objects, which serve as valuable curiosity rewards to solve sparse-reward tasks more efficiently.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.11902v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"113565\", \"180270\", \"181532\", \"212299\", \"215409\", \"221473\", \"260018\", \"266419\", \"266825\", \"277538\", \"280457\", \"291025\"]}","task_split":"paper_retrieval"}
{"document_id":"53833","document_content":"# Automatic Description Construction for Math Expression via Topic Relation Graph\n## Categories\n- Information Retrieval\n## Abstract\nMath expressions are important parts of scientific and educational documents, but some of them may be challenging for junior scholars or students to understand. Nevertheless, constructing textual descriptions for math expressions is nontrivial. In this paper, we explore the feasibility to automatically construct descriptions for math expressions. But there are two challenges that need to be addressed: 1) finding relevant documents since a math equation understanding usually requires several topics, but these topics are often explained in different documents. 2) the sparsity of the collected relevant documents making it difficult to extract reasonable descriptions. Different documents mainly focus on different topics which makes model hard to extract salient information and organize them to form a description of math expressions. To address these issues, we propose a hybrid model (MathDes) which contains two important modules: Selector and Summarizer. In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions. TRG is a graph built according to the citations between expressions. In the Summarizer, a summarization model under the Integer Linear Programming (ILP) framework is proposed. This module constructs the final description with the help of a timeline that is extracted from TRG. The experimental results demonstrate that our methods are promising for this task and outperform the baselines in all aspects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.11890v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"153887\", \"204334\", \"220230\", \"261042\", \"309926\"]}","task_split":"paper_retrieval"}
{"document_id":"53848","document_content":"# Class-Incremental Experience Replay for Continual Learning under Concept Drift\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- I.4.0; I.5.0\n## Abstract\nModern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.11861v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"I.4.0; I.5.0\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"I.4.0; I.5.0\"], \"incoming_citations\": [\"32565\"], \"outgoing_citations\": [\"111955\", \"112604\", \"131526\", \"133677\", \"166959\", \"182889\", \"183426\", \"193041\", \"195562\", \"208243\", \"217663\", \"241392\", \"244665\", \"250875\", \"259175\", \"272788\", \"289616\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"53849","document_content":"# A Multi-Size Neural Network with Attention Mechanism for Answer Selection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSemantic matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks with attention to generate sentences representations in a way that information from pair sentences can mutually influence the computation of representations. In this work, an effective architecture,multi-size neural network with attention mechanism (AM-MSNN),is introduced into the answer selection task. This architecture captures more levels of language granularities in parallel, because of the various sizes of filters comparing with single-layer CNN and multi-layer CNNs. Meanwhile it extends the sentence representations by attention mechanism, thus containing more information for different types of questions. The empirical study on three various benchmark tasks of answer selection demonstrates the efficacy of the proposed model in all the benchmarks and its superiority over competitors. The experimental results show that (1) multi-size neural network (MSNN) is a more useful method to capture abstract features on different levels of granularities than single\/multi-layer CNNs; (2) the attention mechanism (AM) is a better strategy to derive more informative representations; (3) AM-MSNN is a better architecture for the answer selection task for the moment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.03278v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"115010\", \"123340\", \"140972\", \"271934\", \"298513\", \"301397\", \"303469\", \"306182\", \"308414\", \"316165\", \"320868\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"54027","document_content":"# Automated News Summarization Using Transformers\n## Categories\n- Computation and Language\n## Abstract\nThe amount of text data available online is increasing at a very fast pace hence text summarization has become essential. Most of the modern recommender and text classification systems require going through a huge amount of data. Manually generating precise and fluent summaries of lengthy articles is a very tiresome and time-consuming task. Hence generating automated summaries for the data and using it to train machine learning models will make these models space and time-efficient. Extractive summarization and abstractive summarization are two separate methods of generating summaries. The extractive technique identifies the relevant sentences from the original document and extracts only those from the text. Whereas in abstractive summarization techniques, the summary is generated after interpreting the original text, hence making it more complicated. In this paper, we will be presenting a comprehensive comparison of a few transformer architecture based pre-trained models for text summarization. For analysis and comparison, we have used the BBC news dataset that contains text data that can be used for summarization and human generated summaries for evaluating and comparing the summaries generated by machine learning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2108.01064v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150522\", \"187236\", \"207215\", \"261785\", \"341730\"]}","task_split":"paper_retrieval"}
{"document_id":"54112","document_content":"# Knowledge Triggering, Extraction and Storage via Human-Robot Verbal Interaction\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nThis article describes a novel approach to expand in run-time the knowledge base of an Artificial Conversational Agent. A technique for automatic knowledge extraction from the user's sentence and four methods to insert the new acquired concepts in the knowledge base have been developed and integrated into a system that has already been tested for knowledge-based conversation between a social humanoid robot and residents of care homes. The run-time addition of new knowledge allows overcoming some limitations that affect most robots and chatbots: the incapability of engaging the user for a long time due to the restricted number of conversation topics. The insertion in the knowledge base of new concepts recognized in the user's sentence is expected to result in a wider range of topics that can be covered during an interaction, making the conversation less repetitive. Two experiments are presented to assess the performance of the knowledge extraction technique, and the efficiency of the developed insertion methods when adding several concepts in the Ontology.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.robot.2021.103938\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"31456\"], \"outgoing_citations\": [\"163973\", \"238119\", \"259284\", \"334905\"]}","task_split":"paper_retrieval"}
{"document_id":"54218","document_content":"# Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans\n## Categories\n- Cryptography and Security\n- Image and Video Processing\n- Machine Learning\n## Abstract\nMedical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy. The blockchain ledger technology provides the decentralization of federated learning model without any central server. The proposed homomorphic encryption scheme encrypts and decrypts the gradients of model to preserve the privacy. More precisely, the proposed framework: i) train the local model by a novel capsule network to segmentation and classify COVID-19 images, ii) then use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, and finally the model is shared over a decentralized platform through proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing in the decentralized network. The conducted experimental resultsdemonstrate the performance of the proposed scheme.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10903v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"eess.IV\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68302\", \"72995\", \"85147\", \"112927\", \"123218\", \"146915\", \"159224\", \"183106\", \"194178\", \"205519\", \"221592\", \"235911\", \"249603\"]}","task_split":"paper_retrieval"}
{"document_id":"54286","document_content":"# Meta-learning for skin cancer detection using Deep Learning Techniques\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. The aim of this study is to explore the benefits of the generalization of the knowledge extracted from non-medical data in the classification performance of medical data and the impact of the distribution shift problem within limited data by using a simple class and distribution balancer algorithm. In this study, a small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data. The results show an increase in performance on detecting melanoma, malignant (skin cancer), and benign moles with the prior knowledge obtained from images of everyday objects from the ImageNet dataset by 20 points. These findings suggest that features from non-medical images can be used towards the classification of skin moles and that the distribution of the data affects the performance of the model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10775v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162565\", \"207612\", \"264002\", \"283778\"]}","task_split":"paper_retrieval"}
{"document_id":"54471","document_content":"# Improving Biomedical Pretrained Language Models with Knowledge\n## Categories\n- Computation and Language\n## Abstract\nPretrained language models have shown success in many natural language processing tasks. Many works explore incorporating knowledge into language models. In the biomedical domain, experts have taken decades of effort on building large-scale knowledge bases. For example, the Unified Medical Language System (UMLS) contains millions of entities with their synonyms and defines hundreds of relations among entities. Leveraging this knowledge can benefit a variety of downstream tasks such as named entity recognition and relation extraction. To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases. Specifically, we extract entities from PubMed abstracts and link them to UMLS. We then train a knowledge-aware language model that firstly applies a text-only encoding layer to learn entity representation and applies a text-entity fusion encoding to aggregate entity representation. Besides, we add two training objectives as entity detection and entity linking. Experiments on the named entity recognition and relation extraction from the BLURB benchmark demonstrate the effectiveness of our approach. Further analysis on a collected probing dataset shows that our model has better ability to model medical knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10344v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"70327\", \"36467\", \"55453\", \"16012\", \"21082\", \"24520\", \"55949\", \"62216\"], \"outgoing_citations\": [\"88816\", \"92814\", \"94701\", \"94756\", \"95827\", \"108689\", \"131012\", \"156620\", \"166443\", \"167753\", \"168951\", \"179992\", \"180997\", \"185889\", \"191942\", \"192402\", \"193725\", \"198222\"]}","task_split":"paper_retrieval"}
{"document_id":"54472","document_content":"# Sensitivity as a Complexity Measure for Sequence Classification Tasks\n## Categories\n- Computation and Language\n- Computational Complexity\n- Machine Learning\n## Abstract\nWe introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a function, given a distribution over input sequences, quantifies the number of disjoint subsets of the input sequence that can each be individually changed to change the output. We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult. To that end, we show analytically that simple lexical classifiers can only express functions of bounded sensitivity, and we show empirically that low-sensitivity functions are easier to learn for LSTMs. We then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks, and that sensitivity predicts the performance both of simple lexical classifiers and of vanilla BiLSTMs without pretrained contextualized embeddings. Within a task, sensitivity predicts which inputs are hard for such simple models. Our results suggest that the success of massively pretrained contextual representations stems in part because they provide representations from which information can be extracted by low-sensitivity decoders.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10343v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computational Complexity\", \"Machine Learning\"], \"incoming_citations\": [\"2436\", \"55256\"], \"outgoing_citations\": [\"94302\", \"129277\", \"132633\", \"132773\", \"136604\", \"153048\", \"164659\", \"180511\", \"182542\", \"184691\", \"188123\", \"195989\", \"201065\", \"204791\", \"220137\", \"221316\", \"225899\", \"227085\", \"229768\", \"231077\", \"233416\", \"239831\", \"241034\", \"244755\", \"259723\", \"260450\", \"281225\", \"302516\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"54505","document_content":"# Compact and Effective Representations for Sketch-based Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets; the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10278v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7434\", \"148035\"], \"outgoing_citations\": [\"271313\", \"278179\"]}","task_split":"paper_retrieval"}
{"document_id":"54516","document_content":"# Network Defense is Not a Game\n## Categories\n- Cryptography and Security\n- Artificial Intelligence\n- Computer Science and Game Theory\n## Abstract\nResearch seeks to apply Artificial Intelligence (AI) to scale and extend the capabilities of human operators to defend networks. A fundamental problem that hinders the generalization of successful AI approaches -- i.e., beating humans at playing games -- is that network defense cannot be defined as a single game with a fixed set of rules. Our position is that network defense is better characterized as a collection of games with uncertain and possibly drifting rules. Hence, we propose to define network defense tasks as distributions of network environments, to: (i) enable research to apply modern AI techniques, such as unsupervised curriculum learning and reinforcement learning for network defense; and, (ii) facilitate the design of well-defined challenges that can be used to compare approaches for autonomous cyberdefense.   To demonstrate that an approach for autonomous network defense is practical it is important to be able to reason about the boundaries of its applicability. Hence, we need to be able to define network defense tasks that capture sets of adversarial tactics, techniques, and procedures (TTPs); quality of service (QoS) requirements; and TTPs available to defenders. Furthermore, the abstractions to define these tasks must be extensible; must be backed by well-defined semantics that allow us to reason about distributions of environments; and should enable the generation of data and experiences from which an agent can learn.   Our approach named Network Environment Design for Autonomous Cyberdefense inspired the architecture of FARLAND, a Framework for Advanced Reinforcement Learning for Autonomous Network Defense, which we use at MITRE to develop RL network defenders that perform blue actions from the MITRE Shield matrix against attackers with TTPs that drift from MITRE ATT&CK TTPs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10262v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.AI\", \"cs.GT\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Science and Game Theory\", \"Cryptography and Security\"], \"incoming_citations\": [\"26539\"], \"outgoing_citations\": [\"63330\", \"82943\", \"138782\", \"140299\", \"180897\", \"184592\", \"213308\", \"221083\", \"240233\", \"266162\"]}","task_split":"paper_retrieval"}
{"document_id":"54563","document_content":"# Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nAs autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictions of multiple objects are required for effective route planning. There has been a critical need for high-quality motion data that is rich in both interactions and annotation to develop motion planning models. In this work, we introduce the most diverse interactive motion dataset to our knowledge, and provide specific labels for interacting objects suitable for developing joint prediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, our new dataset contains more than 570 hours of unique data over 1750 km of roadways. It was collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States. We use a high-accuracy 3D auto-labeling system to generate high quality 3D bounding boxes for each road agent, and provide corresponding high definition 3D maps for each scene. Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models. Finally, we provide strong baseline models for individual-agent prediction and joint-prediction. We hope that this new large-scale interactive motion dataset will provide new opportunities for advancing motion forecasting models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10133v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"4129\", \"4576\", \"6518\", \"11990\", \"15272\", \"15731\", \"16755\", \"23459\", \"25792\", \"5124\", \"23190\", \"28279\", \"32512\", \"33187\", \"39249\", \"40291\", \"41845\", \"47655\"], \"outgoing_citations\": [\"54626\", \"64356\", \"74563\", \"75080\", \"75097\", \"105216\", \"109433\", \"110101\", \"116072\", \"126156\", \"137715\", \"147880\", \"151836\", \"154697\", \"157689\", \"158269\", \"161127\", \"161689\", \"162130\", \"164133\", \"173508\", \"174738\", \"179692\", \"187849\", \"217456\", \"232331\", \"268891\", \"287020\"]}","task_split":"paper_retrieval"}
{"document_id":"54585","document_content":"# Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nCardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10085v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2214\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"54595","document_content":"# T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nText-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text-video similarities in a joint embedding space. However, most existing methods only consider the global cross-modal similarity and overlook the local details. Some works incorporate the local comparisons through cross-modal local matching and reasoning. These complex operations introduce tremendous computation. In this paper, we design an efficient global-local alignment method. The multi-modal video sequences and text features are adaptively aggregated with a set of shared semantic centers. The local cross-modal similarities are computed between the video feature and text feature within the same center. This design enables the meticulous local comparison and reduces the computational cost of the interaction between each text-video pair. Moreover, a global alignment method is proposed to provide a global cross-modal measurement that is complementary to the local perspective. The global aggregated visual features also provide additional supervision, which is indispensable to the optimization of the learnable semantic centers. We achieve consistent improvements on three standard text-video retrieval benchmarks and outperform the state-of-the-art by a clear margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.10054v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"4359\", \"5687\", \"8845\", \"10271\", \"12778\", \"13933\", \"24771\", \"46080\", \"58876\", \"60135\"], \"outgoing_citations\": [\"87013\", \"110610\", \"139259\", \"151327\", \"172394\", \"173627\", \"181897\", \"192531\", \"213240\", \"214015\", \"217562\", \"221984\", \"236468\", \"238205\", \"247535\", \"256883\", \"260926\", \"267523\", \"269276\", \"280328\", \"282943\", \"283649\", \"287103\", \"313189\", \"319169\", \"320761\", \"321798\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"54658","document_content":"# An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- I.4; I.5\n## Abstract\nMelanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a high time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no proposed system allows both the selection of the tumoral region and the prediction of the diagnosis as benign or malignant. Motivated by this, we propose a novel end-to-end weakly-supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we perform extensive experiments on a private skin database with spitzoid lesions. Test results reach an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. Besides, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist due to the huge workload.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09878v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"I.4; I.5\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"I.4; I.5\", \"Image and Video Processing\"], \"incoming_citations\": [\"39530\"], \"outgoing_citations\": [\"48019\", \"48053\", \"104617\", \"115463\", \"125856\", \"149360\", \"167310\", \"242311\", \"243478\", \"282613\", \"290145\"]}","task_split":"paper_retrieval"}
{"document_id":"54670","document_content":"# Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nRecently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible applications, graph-level unsupervised learning has not received much attention yet. This might be mainly attributed to the high representation complexity of graphs, which can be represented by n! equivalent adjacency matrices, where n is the number of nodes. In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data. Our proposed model indirectly learns to match the node ordering of input and output graph, without imposing a particular node ordering or performing expensive graph matching. We demonstrate the effectiveness of our proposed model on various graph reconstruction and generation tasks and evaluate the expressive power of extracted representations for downstream graph-level classification and regression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09856v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"36192\"], \"outgoing_citations\": [\"71722\", \"103085\", \"115435\", \"117868\", \"139133\", \"147374\", \"161189\", \"173673\", \"183456\", \"186789\", \"189263\", \"192794\", \"194309\", \"215835\", \"227067\", \"228992\", \"229927\", \"237455\", \"239581\", \"241029\", \"242150\", \"242416\", \"242430\", \"242739\", \"249614\", \"253807\", \"255737\", \"261085\", \"272626\", \"279653\", \"283078\", \"303167\", \"305707\"]}","task_split":"paper_retrieval"}
{"document_id":"54705","document_content":"# Identifying Helpful Sentences in Product Reviews\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09792v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"23542\"], \"outgoing_citations\": [\"110335\", \"127889\", \"157807\", \"180935\", \"214407\", \"220188\", \"220230\", \"233391\", \"237274\", \"246513\"]}","task_split":"paper_retrieval"}
{"document_id":"54810","document_content":"# A Framework using Contrastive Learning for Classification with Noisy Labels\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies such as pseudo-labeling, sample selection with Gaussian Mixture models, weighted supervised contrastive learning have been combined into a fine-tuning phase following the pre-training. This paper provides an extensive empirical study showing that a preliminary contrastive learning step brings a significant gain in performance when using different loss functions: non-robust, robust, and early-learning regularized. Our experiments performed on standard benchmarks and real-world datasets demonstrate that: i) the contrastive pre-training increases the robustness of any loss function to noisy labels and ii) the additional fine-tuning phase can further improve accuracy but at the cost of additional complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09563v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"12409\"], \"outgoing_citations\": [\"78494\", \"81908\", \"83411\", \"86611\", \"95191\", \"97123\", \"100194\", \"103100\", \"111710\", \"115038\", \"116430\", \"138181\", \"138343\", \"141691\", \"152790\", \"163402\", \"171454\", \"183136\", \"185155\", \"188169\", \"189025\", \"203033\", \"215865\", \"231347\", \"233440\", \"235096\", \"242145\", \"246497\", \"247500\", \"258956\", \"263559\"]}","task_split":"paper_retrieval"}
{"document_id":"54820","document_content":"# Temporal Query Networks for Fine-grained Video Understanding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOur objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. We cast this into a query-response mechanism, where each query addresses a particular question, and has its own response label set. We make the following four contributions: (I) We propose a new model - a Temporal Query Network - which enables the query-response functionality, and a structural understanding of fine-grained actions. It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query. (ii) We propose a new way - stochastic feature bank update - to train a network on videos of various lengths with the dense sampling required to respond to fine-grained queries. (iii) We compare the TQN to other architectures and text supervision methods, and analyze their pros and cons. Finally, (iv) we evaluate the method extensively on the FineGym and Diving48 benchmarks for fine-grained action classification and surpass the state-of-the-art using only RGB features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09496v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"97097\", \"4582\", \"5626\", \"16624\", \"8520\", \"12199\", \"34329\"], \"outgoing_citations\": [\"87013\", \"107688\", \"110579\", \"110610\", \"110933\", \"111461\", \"115673\", \"119349\", \"131199\", \"131988\", \"132450\", \"139259\", \"142809\", \"151327\", \"151602\", \"152275\", \"164410\", \"172394\", \"173627\", \"180920\", \"181885\", \"181897\", \"188308\", \"189965\", \"191546\", \"192531\", \"206227\", \"206483\", \"206940\", \"209299\", \"216043\", \"216920\", \"217021\", \"229164\", \"229179\", \"236377\", \"247535\", \"249567\", \"260730\", \"261223\", \"263185\", \"265650\", \"267223\", \"268871\", \"277625\", \"287286\", \"287541\", \"288919\", \"294503\", \"295099\", \"295117\", \"312423\", \"315200\"]}","task_split":"paper_retrieval"}
{"document_id":"54876","document_content":"# A Two-stage Deep Network for High Dynamic Range Image Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMapping a single exposure low dynamic range (LDR) image into a high dynamic range (HDR) is considered among the most strenuous image to image translation tasks due to exposure-related missing information. This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network. Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings. Therefore, we aim to perform image enhancement task like denoising, exposure correction, etc., in the first stage. Additionally, the second stage of our deep network learns tone mapping and bit-expansion from a convex set of data samples. The qualitative and quantitative comparisons demonstrate that the proposed method can outperform the existing LDR to HDR works with a marginal difference. Apart from that, we collected an LDR image dataset incorporating different camera systems. The evaluation with our collected real-world LDR images illustrates that the proposed method can reconstruct plausible HDR images without presenting any visual artefacts. Code available: https:\/\/github. com\/sharif-apu\/twostageHDR_NTIRE21.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09386v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"15803\", \"41356\", \"45270\"], \"outgoing_citations\": [\"45270\", \"54868\", \"133418\", \"142474\", \"149056\", \"149650\", \"239855\", \"244617\", \"244729\", \"252829\", \"269393\"]}","task_split":"paper_retrieval"}
{"document_id":"54928","document_content":"# Continual Learning with Fully Probabilistic Models\n## Categories\n- Machine Learning\n## Abstract\nWe present an approach for continual learning (CL) that is based on fully probabilistic (or generative) models of machine learning. In contrast to, e.g., GANs that are \"generative\" in the sense that they can generate samples, fully probabilistic models aim at modeling the data distribution directly. Consequently, they provide functionalities that are highly relevant for continual learning, such as density estimation (outlier detection) and sample generation. As a concrete realization of generative continual learning, we propose Gaussian Mixture Replay (GMR). GMR is a pseudo-rehearsal approach using a Gaussian Mixture Model (GMM) instance for both generator and classifier functionalities. Relying on the MNIST, FashionMNIST and Devanagari benchmarks, we first demonstrate unsupervised task boundary detection by GMM density estimation, which we also use to reject untypical generated samples. In addition, we show that GMR is capable of class-conditional sampling in the way of a cGAN. Lastly, we verify that GMR, despite its simple structure, achieves state-of-the-art performance on common class-incremental learning problems at very competitive time and memory complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09240v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"54929\", \"97411\", \"102469\", \"131008\", \"166134\", \"185559\", \"194365\", \"197283\", \"207676\", \"226506\", \"228062\", \"229773\", \"230690\", \"231847\", \"241392\", \"244665\", \"245957\", \"250354\", \"252176\", \"259175\", \"262842\", \"265568\", \"270171\", \"270623\", \"271679\", \"275087\", \"280109\"]}","task_split":"paper_retrieval"}
{"document_id":"54967","document_content":"# Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification\/Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace identification\/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification\/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification\/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09145v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"50029\", \"54633\"], \"outgoing_citations\": [\"150214\", \"188889\", \"197748\", \"250292\", \"260701\", \"270106\", \"270923\", \"302701\", \"305707\", \"311030\", \"328120\"]}","task_split":"paper_retrieval"}
{"document_id":"55016","document_content":"# Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection\n## Categories\n- Computation and Language\n## Abstract\nDespite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09061v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2767\", \"26645\", \"5749\", \"16503\", \"22443\", \"50478\"], \"outgoing_citations\": [\"57244\", \"91840\", \"93583\", \"94834\", \"94994\", \"97363\", \"126346\", \"127538\", \"128009\", \"132773\", \"136604\", \"159316\", \"170449\", \"170657\", \"173940\", \"220230\", \"241070\", \"298016\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"55073","document_content":"# Generating explanations for answer set programming applications\n## Categories\n- Artificial Intelligence\n## Abstract\nWe present an explanation system for applications that leverage Answer Set Programming (ASP). Given a program P, an answer set A of P, and an atom a in the program P, our system generates all explanation graphs of a which help explain why a is true (or false) given the program P and the answer set A. We illustrate the functionality of the system using some examples from the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08963v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"22900\"], \"outgoing_citations\": [\"108030\", \"321505\"]}","task_split":"paper_retrieval"}
{"document_id":"55148","document_content":"# Best Practices for Noise-Based Augmentation to Improve the Performance of Emotion Recognition \"In the Wild\"\n## Categories\n- Sound\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nEmotion recognition as a key component of high-stake downstream applications has been shown to be effective, such as classroom engagement or mental health assessments. These systems are generally trained on small datasets collected in single laboratory environments, and hence falter when tested on data that has different noise characteristics. Multiple noise-based data augmentation approaches have been proposed to counteract this challenge in other speech domains. But, unlike speech recognition and speaker verification, in emotion recognition, noise-based data augmentation may change the underlying label of the original emotional sample. In this work, we generate realistic noisy samples of a well known emotion dataset (IEMOCAP) using multiple categories of environmental and synthetic noise. We evaluate how both human and machine emotion perception changes when noise is introduced. We find that some commonly used augmentation techniques for emotion recognition significantly change human perception, which may lead to unreliable evaluation metrics such as evaluating efficiency of adversarial attack. We also find that the trained state-of-the-art emotion recognition models fail to classify unseen noise-augmented samples, even when trained on noise augmented datasets. This finding demonstrates the brittleness of these systems in real-world conditions. We propose a set of recommendations for noise-based augmentation of emotion datasets and for how to deploy these emotion recognition systems \"in the wild\".","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08806v1\", \"primary_category\": \"cs.SD\", \"categories\": [\"eess.AS\", \"cs.SD\", \"cs.LG\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"165847\", \"170441\", \"193438\", \"194666\", \"203897\", \"219936\", \"227432\", \"234829\", \"245835\", \"247658\", \"250979\", \"252161\", \"255020\", \"257868\", \"261323\", \"294549\"]}","task_split":"paper_retrieval"}
{"document_id":"55200","document_content":"# Unsupervised Deep Keyphrase Generation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nKeyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated a remarkable success in this task, capable of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of annotated data. In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any human annotation. Motivated by the observation that an absent keyphrase in one document can appear in other places, in whole or in part, we first construct a phrase bank by pooling all phrases in a corpus. With this phrase bank, we then draw candidate absent keyphrases for each document through a partial matching process. To rank both types of candidates, we combine their lexical- and semantic-level similarities to the input document. Moreover, we utilize these top-ranked candidates as to train a deep generative model for more absent keyphrases. Extensive experiments demonstrate that AutoKeyGen outperforms all unsupervised baselines and can even beat strong supervised methods in certain cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08729v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3368\"], \"outgoing_citations\": [\"61270\", \"99320\", \"130503\", \"157672\", \"167979\", \"185679\", \"214542\", \"220295\", \"220788\", \"237494\", \"245154\", \"268251\", \"268902\", \"273995\", \"288174\", \"298627\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"55216","document_content":"# Intent Features for Rich Natural Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nComplex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. However, these models are often created from scratch, for specific clients and use cases, and require the annotation of large datasets. This encourages the sharing of annotated data across multiple clients. To facilitate this we introduce the idea of intent features: domain and topic agnostic properties of intents that can be learned from the syntactic cues only, and hence can be shared. We introduce a new neural network architecture, the Global-Local model, that shows significant improvement over strong baselines for identifying these features in a deployed, multi-intent natural language understanding module, and, more generally, in a classification setting where a part of an utterance has to be classified utilizing the whole context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08701v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95581\", \"97725\", \"260541\", \"297150\", \"350985\"]}","task_split":"paper_retrieval"}
{"document_id":"55223","document_content":"# MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs\n## Categories\n- Computation and Language\n## Abstract\nMultilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on eight multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08692v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2585\", \"38433\", \"41905\", \"127275\", \"31574\", \"21636\", \"23330\", \"30029\", \"39468\", \"42799\"], \"outgoing_citations\": [\"38433\", \"39468\", \"42799\", \"77611\", \"77651\", \"92112\", \"93942\", \"96126\", \"111872\", \"130391\", \"135112\", \"137436\", \"139463\", \"143273\", \"146003\", \"150522\", \"150830\", \"158546\", \"159706\", \"161377\", \"165388\", \"168995\", \"169415\", \"176600\", \"187236\", \"187452\", \"189792\", \"200678\", \"202124\", \"204715\", \"217957\", \"253840\"]}","task_split":"paper_retrieval"}
{"document_id":"55287","document_content":"# Machine learning-assisted surrogate construction for full-core fuel performance analysis\n## Categories\n- Computational Engineering, Finance, and Science\n- Machine Learning\n- I.2.6, I.6.3\n## Abstract\nAccurately predicting the behavior of a nuclear reactor requires multiphysics simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics. The fuel thermo-mechanical response provides essential information for operational limits and safety analysis. Traditionally, fuel performance analysis is performed standalone, using calculated spatial-temporal power distribution and thermal boundary conditions from the coupled neutronics-thermal-hydraulics simulation as input. Such one-way coupling is result of the high cost induced by the full-core fuel performance analysis, which provides more realistic and accurate prediction of the core-wide response than the \"peak rod\" analysis. It is therefore desirable to improve the computational efficiency of full-core fuel performance modeling by constructing fast-running surrogate, such that fuel performance modeling can be utilized in the core reload design optimization. This work presents methodologies for full-core surrogate construction based on several realistic equilibrium PWR core designs. As a fast and conventional approach, look-up tables are only effective for certain fuel performance quantities of interest (QoIs). Several representative machine-learning algorithms are introduced to capture the complicated physics for other fuel performance QoIs. Rule-based model is useful as a feature extraction technique to account for the spatial-temporal complexity of operating conditions. Constructed surrogates achieve at least ten thousand time acceleration with satisfying prediction accuracy. Current work lays foundation for tighter coupling of fuel performance modeling into the core design optimization framework. It also sets stage for full-core fuel performance analysis with BISON where the computational cost becomes more burdensome.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.09499v1\", \"primary_category\": \"cs.CE\", \"categories\": [\"cs.CE\", \"cs.LG\", \"I.2.6, I.6.3\"], \"primary_category_human_readable\": \"Computational Engineering, Finance, and Science\", \"categories_human_readable\": [\"Computational Engineering, Finance, and Science\", \"Machine Learning\", \"I.2.6, I.6.3\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"55293","document_content":"# Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nObjective: Today's neural machine translation (NMT) can achieve near human-level translation quality and greatly facilitates international communications, but the lack of parallel corpora poses a key problem to the development of translation systems for highly specialized domains, such as biomedicine. This work presents an unsupervised algorithm for deriving parallel corpora from document-level translations by using sentence alignment and explores how training materials affect the performance of biomedical NMT systems. Materials and Methods: Document-level translations are mixed to train bilingual word embeddings (BWEs) for the evaluation of cross-lingual word similarity, and sentence distance is defined by combining semantic and positional similarities of the sentences. The alignment of sentences is formulated as an extended earth mover's distance problem. A Chinese-English biomedical parallel corpus is derived with the proposed algorithm using bilingual articles from UpToDate and translations of PubMed abstracts, which is then used for the training and evaluation of NMT. Results: On two manually aligned translation datasets, the proposed algorithm achieved accurate sentence alignment in the 1-to-1 cases and outperformed competing algorithms in the many-to-many cases. The NMT model fine-tuned on biomedical data significantly improved the in-domain translation quality (zh-en: +17.72 BLEU; en-zh: +17.02 BLEU). Both the size of the training data and the combination of different corpora can significantly affect the model's performance. Conclusion: The proposed algorithm relaxes the assumption for sentence alignment and effectively generates accurate translation pairs that facilitate training high quality biomedical NMT models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08588v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"124249\", \"168300\", \"187690\", \"219175\", \"220720\", \"238926\", \"253559\", \"263299\", \"263927\", \"330941\"]}","task_split":"paper_retrieval"}
{"document_id":"55330","document_content":"# The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech\n## Categories\n- Computation and Language\n## Abstract\nIn recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting human ratings of the quality of learner productions, and benchmarking L2 development. In contrast, there is comparatively little work in the area of speaking, particularly with respect to fully automated approaches to assessing L2 spontaneous speech. While the importance of a well-performing ASR system is widely recognized, little research has been conducted to investigate the impact of its performance on subsequent automatic text analysis. In this paper, we focus on this issue and examine the impact of using a state-of-the-art ASR system for subsequent automatic analysis of linguistic complexity in spontaneously produced L2 speech. A set of 30 selected measures were considered, falling into four categories: syntactic, lexical, n-gram frequency, and information-theoretic measures. The agreement between the scores for these measures obtained on the basis of ASR-generated vs. manual transcriptions was determined through correlation analysis. A more differential effect of ASR performance on specific types of complexity measures when controlling for task type effects is also presented.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08529v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133484\", \"192576\"]}","task_split":"paper_retrieval"}
{"document_id":"55332","document_content":"# Multilingual and Cross-Lingual Intent Detection from Spoken Data\n## Categories\n- Computation and Language\n## Abstract\nWe present a systematic study on multilingual and cross-lingual intent detection from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) can yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., zero-shot versus few-shot learning, translation direction, and impact of speech recognition. We see this work as an important step towards more inclusive development and evaluation of multilingual intent detectors from spoken data, in a much wider spectrum of languages compared to prior work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08524v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22045\"], \"outgoing_citations\": [\"33683\", \"74373\", \"91922\", \"113832\", \"114308\", \"116282\", \"127665\", \"128121\", \"129921\", \"135112\", \"137492\", \"157249\", \"167138\", \"168247\", \"168621\", \"175691\", \"176839\", \"182577\", \"189532\", \"195286\", \"197945\", \"200764\", \"202124\", \"211737\", \"212163\", \"212322\", \"226011\", \"230543\", \"294494\"]}","task_split":"paper_retrieval"}
{"document_id":"55346","document_content":"# A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn this work we explore an auxiliary loss useful for reinforcement learning in environments where strong performing agents are required to be able to navigate a spatial environment. The auxiliary loss proposed is to minimize the classification error of a neural network classifier that predicts whether or not a pair of states sampled from the agents current episode trajectory are in order. The classifier takes as input a pair of states as well as the agent's memory. The motivation for this auxiliary loss is that there is a strong correlation with which of a pair of states is more recent in the agents episode trajectory and which of the two states is spatially closer to the agent. Our hypothesis is that learning features to answer this question encourages the agent to learn and internalize in memory representations of states that facilitate spatial reasoning. We tested this auxiliary loss on a navigation task in a gridworld and achieved 9.6% increase in accumulative episode reward compared to a strong baseline approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08492v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179594\", \"182108\", \"207834\", \"219180\", \"225125\", \"245731\", \"260314\", \"262593\", \"280296\", \"280681\", \"285677\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"55353","document_content":"# Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy. However, it shows poor performance in the translation of long sentences, which is a major issue in low-resource languages. It is assumed that this issue is caused by insufficient number of long sentences in the training data. Therefore, this study proposes a simple data augmentation method to handle long sentences. In this method, we use only the given parallel corpora as the training data and generate long sentences by concatenating two sentences. Based on the experimental results, we confirm improvements in long sentence translation by the proposed data augmentation method, despite its simplicity. Moreover, the translation quality is further improved by the proposed method, when combined with back-translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08478v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23339\", \"51739\"], \"outgoing_citations\": [\"128975\", \"220611\", \"263927\", \"273586\", \"281076\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"55400","document_content":"# LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nDocument layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use layout information only consider textual contents, and overlook the existence of contents in other modalities such as images. Additionally, spatial interactions of presented contents in a layout were never really fully exploited. To bridge this gap, we parse a document into content blocks (eg. text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document. Our LAMPreT encodes each block with a multimodal transformer in the lower-level and aggregates the block-level representations and connections utilizing a specifically designed transformer at the higher-level. We design hierarchical pretraining objectives where the lower-level model is trained similarly to multimodal grounding models, and the higher-level model is trained with our proposed novel layout-aware objectives. We evaluate the proposed model on two layout-aware tasks -- text block filling and image suggestion and show the effectiveness of our proposed hierarchical architecture as well as pretraining techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08405v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"15859\", \"9109\", \"26093\"], \"outgoing_citations\": [\"94101\", \"96466\", \"97694\", \"109314\", \"115124\", \"121700\", \"122763\", \"130903\", \"131401\", \"147374\", \"149004\", \"167422\", \"170603\", \"172370\", \"216765\", \"264315\"]}","task_split":"paper_retrieval"}
{"document_id":"55418","document_content":"# Concadia: Towards Image-Based Text Generation with a Purpose\n## Categories\n- Computation and Language\n## Abstract\nCurrent deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice. We argue that to close this gap, it is vital to distinguish descriptions from captions based on their distinct communicative roles. Descriptions focus on visual features and are meant to replace an image (often to increase accessibility), whereas captions appear alongside an image to supply additional information. To motivate this distinction and help people put it into practice, we introduce the publicly available Wikipedia-based dataset Concadia consisting of 96,918 images with corresponding English-language descriptions, captions, and surrounding context. Using insights from Concadia, models trained on it, and a preregistered human-subjects experiment with human- and model-generated texts, we characterize the commonalities and differences between descriptions and captions. In addition, we show that, for generating both descriptions and captions, it is useful to augment image-to-text models with representations of the textual context in which the image appeared.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08376v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55207\", \"65751\", \"68626\", \"77306\", \"79138\", \"131401\", \"135255\", \"141196\", \"152418\", \"189795\", \"192648\", \"223815\", \"233685\", \"237923\", \"278604\", \"287504\", \"296049\", \"315108\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"55420","document_content":"# Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nAutonomous driving consists of a multitude of interacting modules, where each module must contend with errors from the others. Typically, the motion prediction module depends upon a robust tracking system to capture each agent's past movement. In this work, we systematically explore the importance of the tracking module for the motion prediction task and ultimately conclude that the overall motion prediction performance is highly sensitive to the tracking module's imperfections. We explicitly compare models that use tracking information to models that do not across multiple scenarios and conditions. We find that the tracking information plays an essential role and improves motion prediction performance in noise-free conditions. However, in the presence of tracking noise, it can potentially affect the overall performance if not studied thoroughly. We thus argue practitioners should be mindful of noise when developing and testing motion\/tracking modules, or that they should consider tracking free alternatives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08368v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71655\", \"74563\", \"78890\", \"106160\", \"115066\", \"116072\", \"121363\", \"122175\", \"126156\", \"126790\", \"131665\", \"136203\", \"139777\", \"146899\", \"160770\", \"162130\", \"177460\", \"178883\", \"179871\", \"181133\", \"187849\", \"191356\", \"206321\", \"217456\", \"221071\", \"237350\", \"268891\", \"302659\"]}","task_split":"paper_retrieval"}
{"document_id":"55431","document_content":"# An Analysis of a BERT Deep Learning Strategy on a Technology Assisted Review Task\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nDocument screening is a central task within Evidenced Based Medicine, which is a clinical discipline that supplements scientific proof to back medical decisions. Given the recent advances in DL (Deep Learning) methods applied to Information Retrieval tasks, I propose a DL document classification approach with BERT or PubMedBERT embeddings and a DL similarity search path using SBERT embeddings to reduce physicians' tasks of screening and classifying immense amounts of documents to answer clinical queries. I test and evaluate the retrieval effectiveness of my DL strategy on the 2017 and 2018 CLEF eHealth collections. I find that the proposed DL strategy works, I compare it to the recently successful BM25 plus RM3 model, and conclude that the suggested method accomplishes advanced retrieval performance in the initial ranking of the articles with the aforementioned datasets, for the CLEF eHealth Technologically Assisted Reviews in Empirical Medicine Task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08340v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"51985\"], \"outgoing_citations\": [\"93798\", \"108689\", \"131569\", \"163052\", \"193627\", \"316977\"]}","task_split":"paper_retrieval"}
{"document_id":"55544","document_content":"# Citations are not opinions: a corpus linguistics approach to understanding how citations are made\n## Categories\n- Digital Libraries\n- Computation and Language\n## Abstract\nCitation content analysis seeks to understand citations based on the language used during the making of a citation. A key issue in citation content analysis is looking for linguistic structures that characterize distinct classes of citations for the purposes of understanding the intent and function of a citation. Previous works have focused on modeling linguistic features first and drawn conclusions on the language structures unique to each class of citation function based on the performance of a classification task or inter-annotator agreement. In this study, we start with a large sample of a pre-classified citation corpus, 2 million citations from each class of the scite Smart Citation dataset (supporting, disputing, and mentioning citations), and analyze its corpus linguistics in order to reveal the unique and statistically significant language structures belonging to each type of citation. By generating comparison tables for each citation type we present a number of interesting linguistic features that uniquely characterize citation type. What we find is that within citation collocates, there is very low correlation between citation type and sentiment. Additionally, we find that the subjectivity of citation collocates across classes is very low. These findings suggest that the sentiment of collocates is not a predictor of citation function and that due to their low subjectivity, an opinion-expressing mode of understanding citations, implicit in previous citation sentiment analysis literature, is inappropriate. Instead, we suggest that citations can be better understood as claims-making devices where the citation type can be explained by understanding how two claims are being compared. By presenting this approach, we hope to inspire similar corpus linguistic studies on citations that derive a more robust theory of citation from an empirical basis using citation corpora","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.08087v1\", \"primary_category\": \"cs.DL\", \"categories\": [\"cs.DL\", \"cs.CL\"], \"primary_category_human_readable\": \"Digital Libraries\", \"categories_human_readable\": [\"Digital Libraries\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"103389\"]}","task_split":"paper_retrieval"}
{"document_id":"55703","document_content":"# Rethinking Text Line Recognition Models\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this paper, we study the problem of text line recognition. Unlike most approaches targeting specific domains such as scene-text or handwritten documents, we investigate the general problem of developing a universal architecture that can extract text from any image, regardless of source or input modality. We consider two decoder families (Connectionist Temporal Classification and Transformer) and three encoder modules (Bidirectional LSTMs, Self-Attention, and GRCLs), and conduct extensive experiments to compare their accuracy and performance on widely used public datasets of scene and handwritten text. We find that a combination that so far has received little attention in the literature, namely a Self-Attention encoder coupled with the CTC decoder, when compounded with an external language model and trained on both public and internal data, outperforms all the others in accuracy and computational complexity. Unlike the more common Transformer-based models, this architecture can handle inputs of arbitrary length, a requirement for universal line recognition. Using an internal dataset collected from multiple sources, we also expose the limitations of current public datasets in evaluating the accuracy of line recognizers, as the relatively narrow image width and sequence length distributions do not allow to observe the quality degradation of the Transformer approach when applied to the transcription of long lines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07787v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"17060\", \"21983\"], \"outgoing_citations\": [\"93345\", \"122798\", \"126431\", \"128052\", \"134664\", \"135021\", \"148908\", \"150060\", \"152219\", \"162477\", \"163092\", \"168175\", \"189751\", \"192483\", \"194746\", \"205988\", \"210634\", \"211768\", \"211851\", \"223954\", \"252159\", \"258514\", \"274252\", \"293655\", \"294023\", \"296685\", \"309239\", \"320837\", \"328595\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"55744","document_content":"# Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling\n## Categories\n- Computation and Language\n## Abstract\nThe goal of semantic role labelling (SRL) is to recognise the predicate-argument structure of a sentence. Recent models have shown that syntactic information can enhance the SRL performance, but other syntax-agnostic approaches achieved reasonable performance. The best way to encode syntactic information for the SRL task is still an open question. In this paper, we propose the Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) architecture, which encodes the syntactic structure with a novel way to input graph relations as embeddings directly into the self-attention mechanism of Transformer. This approach adds a soft bias towards attention patterns that follow the syntactic structure but also allows the model to use this information to learn alternative patterns. We evaluate our model on both dependency-based and span-based SRL datasets, and outperform all previous syntax-aware and syntax-agnostic models in both in-domain and out-of-domain settings, on the CoNLL 2005 and CoNLL 2009 datasets. Our architecture is general and can be applied to encode any graph information for a desired downstream task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07704v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16648\", \"23643\"], \"outgoing_citations\": [\"98029\", \"134352\", \"157323\", \"158797\", \"162910\", \"168146\", \"168549\", \"169520\", \"177443\", \"195384\", \"202775\", \"215434\", \"232290\", \"239884\", \"248279\", \"268198\", \"269973\", \"271527\", \"276493\", \"291941\"]}","task_split":"paper_retrieval"}
{"document_id":"55793","document_content":"# Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models\n## Categories\n- Computation and Language\n## Abstract\nWhile vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what aspects of sentence-level syntax are captured by these representations, nor how (if at all) they are built along the stacked layers of the network. In this paper, we aim to address such questions with a general class of interventional, input perturbation-based analyses of representations from pretrained language models. Importing from computational and cognitive neuroscience the notion of representational invariance, we perform a series of probes designed to test the sensitivity of these representations to several kinds of structure in sentences. Each probe involves swapping words in a sentence and comparing the representations from perturbed sentences against the original. We experiment with three different perturbations: (1) random permutations of n-grams of varying width, to test the scale at which a representation is sensitive to word position; (2) swapping of two spans which do or do not form a syntactic phrase, to test sensitivity to global phrase structure; and (3) swapping of two adjacent words which do or do not break apart a syntactic phrase, to test sensitivity to local phrase structure.   Results from these probes collectively suggest that Transformers build sensitivity to larger parts of the sentence along their layers, and that hierarchical phrase structure plays a role in this process. More broadly, our results also indicate that structured input perturbations widens the scope of analyses that can be performed on often-opaque deep learning systems, and can serve as a complement to existing tools (such as supervised linear probes) for interpreting complex black-box models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07578v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"89761\", \"9935\", \"56222\", \"77760\"], \"outgoing_citations\": [\"56222\", \"76238\", \"77800\", \"121685\", \"122609\", \"144956\", \"157157\", \"168097\", \"172001\", \"172611\", \"173615\", \"181575\", \"182116\", \"186250\", \"189595\", \"194308\", \"195989\", \"219156\", \"220611\", \"227957\", \"251240\", \"251816\", \"251974\", \"253066\", \"256062\", \"260450\", \"263444\", \"298543\"]}","task_split":"paper_retrieval"}
{"document_id":"55802","document_content":"# Rethinking Automatic Evaluation in Sentence Simplification\n## Categories\n- Computation and Language\n## Abstract\nAutomatic evaluation remains an open research question in Natural Language Generation. In the context of Sentence Simplification, this is particularly challenging: the task requires by nature to replace complex words with simpler ones that shares the same meaning. This limits the effectiveness of n-gram based metrics like BLEU. Going hand in hand with the recent advances in NLG, new metrics have been proposed, such as BERTScore for Machine Translation. In summarization, the QuestEval metric proposes to automatically compare two texts by questioning them.   In this paper, we first propose a simple modification of QuestEval allowing it to tackle Sentence Simplification. We then extensively evaluate the correlations w.r.t. human judgement for several metrics including the recent BERTScore and QuestEval, and show that the latter obtain state-of-the-art correlations, outperforming standard metrics like BLEU and SARI. More importantly, we also show that a large part of the correlations are actually spurious for all the metrics. To investigate this phenomenon further, we release a new corpus of evaluated simplifications, this time not generated by systems but instead, written by humans. This allows us to remove the spurious correlations and draw very different conclusions from the original ones, resulting in a better understanding of these metrics. In particular, we raise concerns about very low correlations for most of traditional metrics. Our results show that the only significant measure of the Meaning Preservation is our adaptation of QuestEval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07560v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15974\", \"15519\", \"61182\"], \"outgoing_citations\": [\"61182\", \"120280\", \"127613\", \"140414\", \"163045\", \"168744\", \"172006\", \"183694\", \"212887\", \"214312\", \"260587\", \"268339\"]}","task_split":"paper_retrieval"}
{"document_id":"55823","document_content":"# FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server. But since real federated settings are resource-constrained, FL is encountered with systems heterogeneity which causes a lot of stragglers directly and then leads to significantly accuracy reduction indirectly. To solve the problems caused by systems heterogeneity, we introduce a novel self-adaptive federated framework FedSAE which adjusts the training task of devices automatically and selects participants actively to alleviate the performance degradation. In this work, we 1) propose FedSAE which leverages the complete information of devices' historical training tasks to predict the affordable training workloads for each device. In this way, FedSAE can estimate the reliability of each device and self-adaptively adjust the amount of training load per client in each round. 2) combine our framework with Active Learning to self-adaptively select participants. Then the framework accelerates the convergence of the global model. In our framework, the server evaluates devices' value of training based on their training loss. Then the server selects those clients with bigger value for the global model to reduce communication overhead. The experimental result indicates that in a highly heterogeneous system, FedSAE converges faster than FedAvg, the vanilla FL framework. Furthermore, FedSAE outperforms than FedAvg on several federated datasets - FedSAE improves test accuracy by 26.7% and reduces stragglers by 90.3% on average.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07515v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"66296\"], \"outgoing_citations\": [\"119748\", \"148245\", \"164572\", \"174590\", \"177601\", \"178024\", \"184652\", \"200347\", \"200704\", \"205577\", \"207462\", \"208297\", \"210868\", \"224282\", \"229563\", \"232435\", \"234581\", \"238685\", \"252956\", \"265073\", \"335904\"]}","task_split":"paper_retrieval"}
{"document_id":"55913","document_content":"# Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReconstructing an object's high-quality 3D shape with inherent spectral reflectance property, beyond typical device-dependent RGB albedos, opens the door to applications requiring a high-fidelity 3D model in terms of both geometry and photometry. In this paper, we propose a novel Multi-View Inverse Rendering (MVIR) method called Spectral MVIR for jointly reconstructing the 3D shape and the spectral reflectance for each point of object surfaces from multi-view images captured using a standard RGB camera and low-cost lighting equipment such as an LED bulb or an LED projector. Our main contributions are twofold: (i) We present a rendering model that considers both geometric and photometric principles in the image formation by explicitly considering camera spectral sensitivity, light's spectral power distribution, and light source positions. (ii) Based on the derived model, we build a cost-optimization MVIR framework for the joint reconstruction of the 3D shape and the per-vertex spectral reflectance while estimating the light source positions and the shadows. Different from most existing spectral-3D acquisition methods, our method does not require expensive special equipment and cumbersome geometric calibration. Experimental results using both synthetic and real-world data demonstrate that our Spectral MVIR can acquire a high-quality 3D model with accurate spectral reflectance property.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07308v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"111407\", \"170721\"], \"outgoing_citations\": [\"79703\", \"111407\", \"170721\"]}","task_split":"paper_retrieval"}
{"document_id":"55926","document_content":"# Consistency Training with Virtual Adversarial Discrete Perturbation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nConsistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar. Previous studies have proposed various augmentation methods for the perturbation but are limited in that they are agnostic to the training model. Thus, the perturbed samples may not aid in regularization due to their ease of classification from the model. In this context, we propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions. This virtual adversarial discrete noise obtained by replacing a small portion of tokens while keeping original semantics as much as possible efficiently pushes a training model's decision boundary. Experimental results show that our proposed method outperforms other consistency training baselines with text editing, paraphrasing, or a continuous noise on semi-supervised text classification tasks and a robustness benchmark","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07284v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"40367\", \"41974\"], \"outgoing_citations\": [\"615\", \"41974\", \"51130\", \"99332\", \"100386\", \"100521\", \"128206\", \"128975\", \"129807\", \"129858\", \"130252\", \"132773\", \"133106\", \"157349\", \"158701\", \"169347\", \"171039\", \"181676\", \"182218\", \"185283\", \"188492\", \"189696\", \"192811\", \"194985\", \"195794\", \"200867\", \"205715\", \"216925\", \"220017\", \"220285\", \"231898\", \"234340\", \"234716\", \"238986\", \"241611\", \"247127\", \"260450\", \"268604\", \"268999\", \"272068\", \"283114\", \"290407\", \"290778\", \"291899\", \"302824\", \"307068\", \"307756\"]}","task_split":"paper_retrieval"}
{"document_id":"55962","document_content":"# Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling\n## Categories\n- Computation and Language\n## Abstract\nTask-oriented semantic parsing models typically have high resource requirements: to support new ontologies (i.e., intents and slots), practitioners crowdsource thousands of samples for supervised fine-tuning. Partly, this is due to the structure of de facto copy-generate parsers; these models treat ontology labels as discrete entities, relying on parallel data to extrinsically derive their meaning. In our work, we instead exploit what we intrinsically know about ontology labels; for example, the fact that SL:TIME_ZONE has the categorical type \"slot\" and language-based span \"time zone\". Using this motivation, we build our approach with offline and online stages. During preprocessing, for each ontology label, we extract its intrinsic properties into a component, and insert each component into an inventory as a cache of sorts. During training, we fine-tune a seq2seq, pre-trained transformer to map utterances and inventories to frames, parse trees comprised of utterance and ontology tokens. Our formulation encourages the model to consider ontology labels as a union of its intrinsic properties, therefore substantially bootstrapping learning in low-resource settings. Experiments show our model is highly sample efficient: using a low-resource benchmark derived from TOPv2, our inventory parser outperforms a copy-generate parser by +15 EM absolute (44% relative) when fine-tuning on 10 samples from an unseen domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07224v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24691\", \"27041\", \"36321\", \"55174\"], \"outgoing_citations\": [\"95921\", \"104803\", \"124544\", \"170083\", \"180447\", \"198784\", \"213822\", \"219814\", \"239655\", \"261770\", \"290632\"]}","task_split":"paper_retrieval"}
{"document_id":"55981","document_content":"# An Alignment-Agnostic Model for Chinese Text Error Correction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which is common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters errors, but they cannot deal with missing or redundant characters. The reason is that lengths of sentences before and after correction are not the same, leading to the inconsistence between model inputs and outputs. Although the Seq2Seq-based or sequence tagging methods provide solutions to the problem and achieved relatively good results on English context, but they do not perform well in Chinese context according to our experimental results. In our work, we propose a novel detect-correct framework which is alignment-agnostic, meaning that it can handle both text aligned and non-aligned occasions, and it can also serve as a cold start model when there are no annotated data provided. Experimental results on three datasets demonstrate that our method is effective and achieves the best performance among existing published models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.07190v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"89164\", \"122918\", \"124955\", \"127352\", \"128949\", \"162976\", \"168922\", \"169184\", \"191276\", \"196990\", \"225890\", \"235400\", \"263927\"]}","task_split":"paper_retrieval"}
{"document_id":"56108","document_content":"# Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis\n## Categories\n- Artificial Intelligence\n## Abstract\nThe potential presented by Artificial Intelligence (AI) for healthcare has long been recognised by the technical community. More recently, this potential has been recognised by policymakers, resulting in considerable public and private investment in the development of AI for healthcare across the globe. Despite this, excepting limited success stories, real-world implementation of AI systems into front-line healthcare has been limited. There are numerous reasons for this, but a main contributory factor is the lack of internationally accepted, or formalised, regulatory standards to assess AI safety and impact and effectiveness. This is a well-recognised problem with numerous ongoing research and policy projects to overcome it. Our intention here is to contribute to this problem-solving effort by seeking to set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. We do this by conducting a systematic search across Scopus, PubMed and Google Scholar to identify all the relevant literature published between January 1970 and November 2020 related to the evaluation of: output performance; efficacy; and real-world use of AI systems, and synthesising the key themes according to the stages of evaluation: pre-clinical (theoretical phase); exploratory phase; definitive phase; and post-market surveillance phase (monitoring). The result is a framework to guide AI system developers, policymakers, and regulators through a sufficient evaluation of an AI system designed for use in healthcare.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.06910v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"14727\", \"186232\"]}","task_split":"paper_retrieval"}
{"document_id":"56138","document_content":"# Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption\n## Categories\n- Cryptography and Security\n## Abstract\nWith the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between $k<N-1$ participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.06824v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"20178\", \"30490\", \"30901\"], \"outgoing_citations\": [\"81222\", \"173134\", \"179698\", \"199440\", \"229563\", \"232524\", \"240048\", \"246939\", \"273221\", \"280506\"]}","task_split":"paper_retrieval"}
{"document_id":"56186","document_content":"# A Weakly Supervised Model for Solving Math word Problems\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSolving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solve MWPs require full supervision in the form of intermediate equations. However, labeling every math word problem with its corresponding equations is a time-consuming and expensive task. In order to address this challenge of equation annotation, we propose a weakly supervised model for solving math word problems by requiring only the final answer as supervision. We approach this problem by first learning to generate the equation using the problem description and the final answer, which we then use to train a supervised MWP solver. We propose and compare various weakly supervised techniques to learn to generate equations directly from the problem description and answer. Through extensive experiment, we demonstrate that even without using equations for supervision, our approach achieves an accuracy of 56.0 on the standard Math23K dataset. We also curate and release a new dataset for MWPs in English consisting of 10227 instances suitable for training weakly supervised models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.06722v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"26187\"], \"outgoing_citations\": [\"210184\", \"211865\", \"220655\", \"246516\", \"278863\", \"283758\", \"287181\"]}","task_split":"paper_retrieval"}
{"document_id":"56270","document_content":"# Should Semantic Vector Composition be Explicit? Can it be Linear?\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nVector representations have become a central element in semantic language modelling, leading to mathematical overlaps with many fields including quantum theory. Compositionality is a core goal for such representations: given representations for 'wet' and 'fish', how should the concept 'wet fish' be represented?   This position paper surveys this question from two points of view. The first considers the question of whether an explicit mathematical representation can be successful using only tools from within linear algebra, or whether other mathematical tools are needed. The second considers whether semantic vector composition should be explicitly described mathematically, or whether it can be a model-internal side-effect of training a neural network.   A third and newer question is whether a compositional model can be implemented on a quantum computer. Given the fundamentally linear nature of quantum mechanics, we propose that these questions are related, and that this survey may help to highlight candidate operations for future quantum implementation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.06555v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"9851\"], \"outgoing_citations\": [\"64678\", \"66952\", \"72781\", \"75959\", \"92814\", \"94905\", \"125845\", \"131601\", \"167796\", \"234811\", \"241536\", \"265434\", \"301504\", \"313529\", \"316774\", \"325323\", \"328199\", \"333603\"]}","task_split":"paper_retrieval"}
{"document_id":"56433","document_content":"# Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution\n## Categories\n- Artificial Intelligence\n- I.2.1\n## Abstract\nVideo game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3449639.3459290\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"I.2.1\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.1\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95477\", \"163503\", \"199895\", \"207051\", \"226544\", \"233501\", \"239076\", \"274886\", \"302948\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"56568","document_content":"# Evaluating Saliency Methods for Neural Language Models\n## Categories\n- Computation and Language\n## Abstract\nSaliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05824v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9635\", \"24197\", \"55164\", \"75603\", \"13325\", \"25725\", \"26549\", \"26821\", \"44174\"], \"outgoing_citations\": [\"87757\", \"93644\", \"94934\", \"98602\", \"117118\", \"127086\", \"132402\", \"157352\", \"165928\", \"179179\", \"182218\", \"186130\", \"186250\", \"188812\", \"188933\", \"200043\", \"201425\", \"205161\", \"215081\", \"216217\", \"220137\", \"226532\", \"232097\", \"233400\", \"234716\", \"235095\", \"252114\", \"259145\", \"263108\", \"263942\", \"277288\", \"281176\", \"283971\", \"286594\", \"289752\", \"290677\", \"311845\", \"319947\", \"322867\"]}","task_split":"paper_retrieval"}
{"document_id":"56635","document_content":"# Common Limitations of Image Processing Metrics: A Picture Story\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05642v6\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2016\", \"10437\", \"12414\", \"43091\", \"48398\", \"90200\", \"50893\", \"71462\"], \"outgoing_citations\": [\"13914\", \"59460\", \"63883\", \"106227\", \"136550\", \"161488\", \"209201\", \"222360\", \"225754\", \"229108\", \"246082\", \"261545\"]}","task_split":"paper_retrieval"}
{"document_id":"56639","document_content":"# Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress has been made in the past few years in dealing with the challenge of correcting for differing behavior between the data collection and learned policies. However, little attention has been paid to potentially changing dynamics when transferring a policy to the online setting, where performance can be up to 90% reduced for existing methods. In this paper we address this problem with Augmented World Models (AugWM). We augment a learned dynamics model with simple transformations that seek to capture potential changes in physical properties of the robot, leading to more robust policies. We not only train our policy in this new setting, but also provide it with the sampled augmentation as a context, allowing it to adapt to changes in the environment. At test time we learn the context in a self-supervised fashion by approximating the augmentation which corresponds to the new environment. We rigorously evaluate our approach on over 100 different changed dynamics settings, and show that this simple approach can significantly improve the zero-shot generalization of a recent state-of-the-art baseline, often achieving successful policies where the baseline fails.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05632v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8658\", \"17957\", \"6628\", \"13406\", \"38678\"], \"outgoing_citations\": [\"22860\", \"75808\", \"91231\", \"91372\", \"97096\", \"106317\", \"112116\", \"112851\", \"113327\", \"113870\", \"116687\", \"119726\", \"120201\", \"122699\", \"125479\", \"127099\", \"128392\", \"131003\", \"132207\", \"143663\", \"152469\", \"152912\", \"154254\", \"155499\", \"161071\", \"174232\", \"179948\", \"188486\", \"194494\", \"196921\", \"205477\", \"206160\", \"206875\", \"210517\", \"217919\", \"219180\", \"222669\", \"229890\", \"229919\", \"237229\", \"240495\", \"250362\", \"253045\", \"258580\", \"261786\", \"263272\", \"271030\", \"272659\", \"274445\", \"297268\", \"317861\", \"341343\"]}","task_split":"paper_retrieval"}
{"document_id":"56667","document_content":"# Survey on reinforcement learning for language processing\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10462-022-10205-5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8099\"], \"outgoing_citations\": [\"31739\", \"99255\", \"103709\", \"116433\", \"126444\", \"127819\", \"127984\", \"128934\", \"130329\", \"131723\", \"137555\", \"146658\", \"155499\", \"173805\", \"176426\", \"176753\", \"177387\", \"177541\", \"181620\", \"185001\", \"190578\", \"197853\", \"212832\", \"216953\", \"220186\", \"220866\", \"223301\", \"230774\", \"233720\", \"237274\", \"238099\", \"238926\", \"240090\", \"245831\", \"250322\", \"251736\", \"255035\", \"259693\", \"260644\", \"264937\", \"267161\", \"268410\", \"270020\", \"271085\", \"271497\", \"272513\", \"272752\", \"273062\", \"273400\", \"277773\", \"281472\", \"283451\", \"283908\", \"284481\", \"290615\", \"291102\", \"295884\", \"298651\", \"300001\", \"301313\", \"303349\", \"304241\", \"310370\", \"310737\", \"311470\", \"329586\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"56816","document_content":"# On Unifying Misinformation Detection\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2's learned representation is helpful for few-shot learning of unseen misinformation tasks\/datasets and model's generalizability to unseen events.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05243v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"42907\", \"55169\"], \"outgoing_citations\": [\"111821\", \"163109\", \"168387\", \"168707\", \"200764\", \"209764\", \"217496\", \"227354\", \"228585\", \"238986\", \"246910\", \"263636\", \"267552\", \"268466\", \"273723\", \"288699\", \"302653\", \"312389\", \"319035\"]}","task_split":"paper_retrieval"}
{"document_id":"56882","document_content":"# Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models\n## Categories\n- Computation and Language\n## Abstract\nPre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05115v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18913\"], \"outgoing_citations\": [\"73415\", \"89638\", \"124496\", \"126962\", \"153217\", \"157122\", \"164127\", \"192755\", \"204715\", \"221478\", \"233400\", \"235345\", \"237157\", \"238963\", \"250035\", \"250360\", \"259723\", \"267161\", \"272531\"]}","task_split":"paper_retrieval"}
{"document_id":"57063","document_content":"# A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDisplay technology must offer high dynamic range (HDR) contrast-based depth induction and 3D personalization simultaneously. Efficient algorithms to compress HDR stereo data is critical. Direct capturing of HDR content is complicated due to the high expense and scarcity of HDR cameras. The HDR 3D images could be generated in low-cost by fusing low-dynamic-range (LDR) images acquired using a stereo camera with various exposure settings. In this paper, an efficient scheme for coding multi-exposure stereo images is proposed based on a tensor low-rank approximation scheme. The multi-exposure fusion can be realized to generate HDR stereo output at the decoder for increased realism and exaggerated binocular 3D depth cues.   For exploiting spatial redundancy in LDR stereo images, the stack of multi-exposure stereo images is decomposed into a set of projection matrices and a core tensor following an alternating least squares Tucker decomposition model. The compact, low-rank representation of the scene, thus, generated is further processed by 3D extension of High Efficiency Video Coding standard. The encoding with 3D-HEVC enhance the proposed scheme efficiency by exploiting intra-frame, inter-view and the inter-component redundancies in low-rank approximated representation. We consider constant luminance property of IPT and Y'CbCr color space to precisely approximate intensity prediction and perceptually minimize the encoding distortion. Besides, the proposed scheme gives flexibility to adjust the bitrate of tensor latent components by changing the rank of core tensor and its quantization. Extensive experiments on natural scenes demonstrate that the proposed scheme outperforms state-of-the-art JPEG-XT and 3D-HEVC range coding standards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.04726v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189323\", \"208629\", \"221421\", \"226538\", \"248795\", \"249439\", \"252829\"]}","task_split":"paper_retrieval"}
{"document_id":"57263","document_content":"# Counter-Strike Deathmatch with Large-Scale Behavioural Cloning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nThis paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input. The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike play style. Unlike much prior work in games, no API is available for CSGO, so algorithms must train and run in real-time. This limits the quantity of on-policy data that can be generated, precluding many reinforcement learning algorithms. Our solution uses behavioural cloning - training on a large noisy dataset scraped from human play on online servers (4 million frames, comparable in size to ImageNet), and a smaller dataset of high-quality expert demonstrations. This scale is an order of magnitude larger than prior work on imitation learning in FPS games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.04258v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78586\", \"127099\", \"133479\", \"151271\", \"173914\", \"176738\", \"183941\", \"218435\", \"238967\", \"273716\", \"278195\", \"284468\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"57556","document_content":"# Extended Parallel Corpus for Amharic-English Machine Translation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine translation systems; we trained statistical and neural machine translation models using the corpus. In the experiments, we also used a large monolingual corpus for the language model of statistical machine translation and back-translation of neural machine translation. In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points. Besides, among the neural machine translation models, the subword models outperform the word-based models by three to four BLEU points. Moreover, two other relevant automatic evaluation metrics, Translation Edit Rate on Character Level and Better Evaluation as Ranking, reflect corresponding differences among the trained models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.03543v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"44334\"], \"outgoing_citations\": [\"39552\", \"42337\", \"183976\", \"184677\", \"219840\", \"225119\", \"234469\", \"234807\", \"237091\", \"262535\", \"263927\", \"281472\", \"283908\", \"290402\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"57604","document_content":"# Nutribullets Hybrid: Multi-document Health Summarization\n## Categories\n- Computation and Language\n## Abstract\nWe present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health -- rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.03465v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21951\", \"55239\", \"56297\"], \"outgoing_citations\": [\"61496\", \"125731\", \"143501\", \"159316\", \"161027\", \"164142\", \"168499\", \"168613\", \"181930\", \"182486\", \"183161\", \"192332\", \"199671\", \"219789\", \"220955\", \"227993\", \"244373\", \"250505\", \"254855\", \"267980\", \"296029\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"57637","document_content":"# Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs\n## Categories\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nA major focus of recent research in spoken language understanding (SLU) has been on the end-to-end approach where a single model can predict intents directly from speech inputs without intermediate transcripts. However, this approach presents some challenges. First, since speech can be considered as personally identifiable information, in some cases only automatic speech recognition (ASR) transcripts are accessible. Second, intent-labeled speech data is scarce. To address the first challenge, we propose a novel system that can predict intents from flexible types of inputs: speech, ASR transcripts, or both. We demonstrate strong performance for either modality separately, and when both speech and ASR transcripts are available, through system combination, we achieve better results than using a single input modality. To address the second challenge, we leverage a semantically robust pre-trained BERT model and adopt a cross-modal system that co-trains text embeddings and acoustic embeddings in a shared latent space. We further enhance this system by utilizing an acoustic module pre-trained on LibriSpeech and domain-adapting the text module on our target datasets. Our experiments show significant advantages for these pre-training and fine-tuning strategies, resulting in a system that achieves competitive intent-classification performance on Snips SLU and Fluent Speech Commands datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.05752v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"6598\"], \"outgoing_citations\": [\"80300\", \"86254\", \"95623\", \"106294\", \"191769\", \"212359\", \"216686\", \"241146\"]}","task_split":"paper_retrieval"}
{"document_id":"57739","document_content":"# Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nHigh quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations. This poses high constraints on the optical hash or low cost applications. Although one can utilize algorithmic reconstruction to remove the artifacts of low-end lenses, the degeneration from optical aberrations is spatially varying and the computation has to trade off efficiency for performance. For example, we need to conduct patch-wise optimization or train a large set of local deep neural networks to achieve high reconstruction performance across the whole image. In this paper, we propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors, thus leading to a universal and flexible optical aberration correction method. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters, which largely alleviates the time and memory consumption of model learning. The approach is of high efficiency in both training and testing stages. Extensive results verify the promising applications of our proposed approach for compact low-end cameras.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.03078v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"22621\"], \"outgoing_citations\": [\"62379\", \"114342\", \"134150\", \"134152\", \"135332\", \"172265\", \"186459\", \"191876\", \"253572\", \"270288\", \"280029\", \"280852\", \"316657\"]}","task_split":"paper_retrieval"}
{"document_id":"57769","document_content":"# RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we study the compositional learning of images and texts for image retrieval. The query is given in the form of an image and text that describes the desired modifications to the image; the goal is to retrieve the target image that satisfies the given modifications and resembles the query by composing information in both the text and image modalities. To remedy this, we propose a novel architecture designed for the image-text composition task and show that the proposed structure can effectively encode the differences between the source and target images conditioned on the text. Furthermore, we introduce a new joint training technique based on the graph convolutional network that is generally applicable for any existing composition methods in a plug-and-play manner. We found that the proposed technique consistently improves performance and achieves state-of-the-art scores on various benchmarks. To avoid misleading experimental results caused by trivial training hyper-parameters, we reproduce all individual baselines and train models with a unified training environment. We expect this approach to suppress undesirable effects from irrelevant components and emphasize the image-text composition module's ability. Also, we achieve the state-of-the-art score without restricting the training environment, which implies the superiority of our method considering the gains from hyper-parameter tuning. The code, including all the baseline methods, are released https:\/\/github.com\/nashory\/rtic-gcn-pytorch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.03015v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"30600\", \"33627\", \"112422\", \"115043\", \"117426\", \"134661\", \"167782\", \"172896\", \"176580\", \"183623\", \"191813\", \"200744\", \"205616\", \"233661\", \"255134\", \"258888\", \"266120\", \"270817\", \"278894\", \"287454\", \"291119\", \"303106\"]}","task_split":"paper_retrieval"}
{"document_id":"57938","document_content":"# Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations\n## Categories\n- Computation and Language\n## Abstract\nThe growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specifically, in the shopping domain, customers tend to use implicit utterances (e.g., \"organic milk\") rather than explicit names, leading to a large number of candidate products. Meanwhile, for the same query, different customers may expect different results. For example, with \"add milk to my cart\", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase. To address these issues, we propose a new framework that leverages personalized features to improve the accuracy of product ranking. We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings. After that, we incorporate product, customer, and history representations into a neural reranking model to predict which candidate is most likely to be purchased for a specific customer. Experiments show that our model substantially improves the accuracy of the top ranked candidates by 24.6% compared to the state-of-the-art product search model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02667v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"126388\", \"132445\", \"138857\", \"139651\", \"152591\", \"157181\", \"165378\", \"180267\", \"186848\", \"194773\", \"197408\", \"201962\", \"209228\", \"220553\", \"237944\", \"248226\", \"253807\", \"289972\", \"306182\", \"308546\", \"320035\"]}","task_split":"paper_retrieval"}
{"document_id":"58041","document_content":"# Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews\n## Categories\n- Software Engineering\n- Audio and Speech Processing\n- D.2.1; D.2.2\n- Machine Learning\n- Sound\n- 68N30\n## Abstract\nCapturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data (F1=0.72), and that voice features alone are sufficiently effective (F1=0.71). Our work contributes with one the first studies in requirements engineering in which biometrics are used to identify emotions. This is also the first study in software engineering that considers voice analysis. The usage of voice features could be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02410v4\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\", \"eess.AS\", \"D.2.1; D.2.2\", \"cs.LG\", \"cs.SD\", \"68N30\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\", \"Audio and Speech Processing\", \"D.2.1; D.2.2\", \"Machine Learning\", \"Sound\", \"68N30\"], \"incoming_citations\": [], \"outgoing_citations\": [\"67508\", \"69813\", \"145677\", \"180680\", \"195918\", \"210264\", \"243781\", \"257966\", \"326118\"]}","task_split":"paper_retrieval"}
{"document_id":"58044","document_content":"# ProsoBeast Prosody Annotation Tool\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nThe labelling of speech corpora is a laborious and time-consuming process. The ProsoBeast Annotation Tool seeks to ease and accelerate this process by providing an interactive 2D representation of the prosodic landscape of the data, in which contours are distributed based on their similarity. This interactive map allows the user to inspect and label the utterances. The tool integrates several state-of-the-art methods for dimensionality reduction and feature embedding, including variational autoencoders. The user can use these to find a good representation for their data. In addition, as most of these methods are stochastic, each can be used to generate an unlimited number of different prosodic maps. The web app then allows the user to seamlessly switch between these alternative representations in the annotation process. Experiments with a sample prosodically rich dataset have shown that the tool manages to find good representations of varied data and is helpful both for annotation and label correction. The tool is released as free software for use by the community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02397v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227099\", \"320032\", \"334040\"]}","task_split":"paper_retrieval"}
{"document_id":"58082","document_content":"# A hybrid ensemble method with negative correlation learning for regression\n## Categories\n- Machine Learning\n## Abstract\nHybrid ensemble, an essential branch of ensembles, has flourished in the regression field, with studies confirming diversity's importance. However, previous ensembles consider diversity in the sub-model training stage, with limited improvement compared to single models. In contrast, this study automatically selects and weights sub-models from a heterogeneous model pool. It solves an optimization problem using an interior-point filtering linear-search algorithm. The objective function innovatively incorporates negative correlation learning as a penalty term, with which a diverse model subset can be selected. The best sub-models from each model class are selected to build the NCL ensemble, which performance is better than the simple average and other state-of-the-art weighting methods. It is also possible to improve the NCL ensemble with a regularization term in the objective function. In practice, it is difficult to conclude the optimal sub-model for a dataset prior due to the model uncertainty. Regardless, our method would achieve comparable accuracy as the potential optimal sub-models. In conclusion, the value of this study lies in its ease of use and effectiveness, allowing the hybrid ensemble to embrace diversity and accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02317v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"39918\", \"100310\", \"159031\", \"213350\", \"240388\", \"267911\"]}","task_split":"paper_retrieval"}
{"document_id":"58154","document_content":"# Robust Classification Under $\\ell 0$ Attack for the Gaussian Mixture Model\n## Categories\n- Machine Learning\n## Abstract\nIt is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major progress in designing attacks and defenses for various adversarial settings, many fundamental and theoretical problems are yet to be resolved. In this paper, we consider classification in the presence of $\\ell_0$-bounded adversarial perturbations, a.k.a. sparse attacks. This setting is significantly different from other $\\ell_p$-adversarial settings, with $p\\geq 1$, as the $\\ell_0$-ball is non-convex and highly non-smooth. Under the assumption that data is distributed according to the Gaussian mixture model, our goal is to characterize the optimal robust classifier and the corresponding robust classification error as well as a variety of trade-offs between robustness, accuracy, and the adversary's budget. To this end, we develop a novel classification algorithm called FilTrun that has two main modules: Filtration and Truncation. The key idea of our method is to first filter out the non-robust coordinates of the input and then apply a carefully-designed truncated inner product for classification. By analyzing the performance of FilTrun, we derive an upper bound on the optimal robust classification error. We also find a lower bound by designing a specific adversarial strategy that enables us to derive the corresponding robust classifier and its achieved error. For the case that the covariance matrix of the Gaussian mixtures is diagonal, we show that as the input's dimension gets large, the upper and lower bounds converge; i.e. we characterize the asymptotically-optimal robust classifier. Throughout, we discuss several examples that illustrate interesting behaviors such as the existence of a phase transition for adversary's budget determining whether the effect of adversarial perturbation can be fully neutralized.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02189v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"58316","document_content":"# A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world to stay connected. With the advent of technology, digital media has become more relevant and widely used than ever before and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe a novel Fake News Detection system that automatically identifies whether a news item is \"real\" or \"fake\", as an extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network , along with a novel heuristic algorithm by incorporating various attributes present in news items or tweets like source, username handles, URL domains and authors as statistical feature. Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task. We have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet dataset to show the effectiveness of the proposed algorithm on detecting fake news in short news content as well as in news articles. We obtained a best F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9073 on the FakeNewsNet dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01791v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75448\", \"75850\", \"76125\", \"76199\", \"76628\", \"79024\", \"88648\", \"120762\", \"135296\", \"219169\", \"230998\", \"246910\", \"271021\", \"329586\"]}","task_split":"paper_retrieval"}
{"document_id":"58318","document_content":"# Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nClinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01787v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214701\", \"286253\", \"287747\", \"298095\"]}","task_split":"paper_retrieval"}
{"document_id":"58471","document_content":"# Influencing Reinforcement Learning through Natural Language Guidance\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nInteractive reinforcement learning agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that is either positive or negative. While informative, this information can be difficult to generalize on its own. In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known Interactive reinforcement learning technique. Usually policy shaping employs a human feedback policy to help an agent to learn more about how to achieve its goal. In our case, we replace this human feedback policy with policy generated based on natural language advice. We aim to inspect if the generated natural language reasoning provides support to a deep reinforcement learning agent to decide its actions successfully in any given environment. So, we design our model with three networks: first one is the experience driven, next is the advice generator and third one is the advice driven. While the experience driven reinforcement learning agent chooses its actions being influenced by the environmental reward, the advice driven neural network with generated feedback by the advice generator for any new state selects its actions to assist the reinforcement learning agent to better policy shaping.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01506v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"203187\", \"212704\", \"260107\"]}","task_split":"paper_retrieval"}
{"document_id":"58507","document_content":"# Modeling and Development of Operation Guidelines for Leader-Follower Autonomous Truck-Mounted Attenuator Vehicles\n## Categories\n- Robotics\n- Systems and Control\n- Systems and Control\n## Abstract\nMobile and slow moving operations, such as striping, sweeping, bridge flushing and pothole patching, are critical for efficient and safe operation of the highway transportation system. A successfully implemented leader follower autonomous truck mounted attenuators system will eliminate all injuries to DOT employees in follow truck provided appropriate Statutory authority. The leader follower system design imposes more requirements to the lead truck drivers in order to ensure a safe and smooth system operation. The driver is now required to make driving decisions not only from the lead truck's perspective, but also consider the potential implications of his decisions to the follow truck. This project aims to develop a set of rules and clear instructions for ATMA system operation","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.13140\/RG.2.2.13108.99203\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.SY\", \"eess.SY\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Systems and Control\", \"Systems and Control\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"58539","document_content":"# Convex Aggregation for Opinion Summarization\n## Categories\n- Computation and Language\n## Abstract\nRecent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs. More specifically, they perform the aggregation via simple average. However, little is known about how the vector aggregation step affects the generation quality. In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries. We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration. To overcome this issue, we develop a framework Coop, which searches input combinations for the latent vector aggregation using input-output word overlap. Experimental results show that Coop successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks. Code is available at \\url{https:\/\/github.com\/megagonlabs\/coop}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01371v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16339\"], \"outgoing_citations\": [\"80578\", \"81917\", \"127015\", \"127930\", \"129810\", \"133034\", \"144231\", \"157807\", \"168247\", \"169037\", \"181279\", \"183462\", \"189715\", \"193900\", \"214407\", \"220188\", \"220230\", \"221478\", \"266671\", \"272531\", \"278954\", \"281205\", \"286225\", \"296029\", \"298945\", \"302948\", \"307223\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"58613","document_content":"# Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep neural networks achieve high prediction accuracy when the train and test distributions coincide. In practice though, various types of corruptions occur which deviate from this setup and cause severe performance degradations. Few methods have been proposed to address generalization in the presence of unforeseen domain shifts. In particular, digital noise corruptions arise commonly in practice during the image acquisition stage and present a significant challenge for current robustness approaches. In this paper, we propose a diverse Gaussian noise consistency regularization method for improving robustness of image classifiers under a variety of noise corruptions while still maintaining high clean accuracy. We derive bounds to motivate and understand the behavior of our Gaussian noise consistency regularization using a local loss landscape analysis. We show that this simple approach improves robustness against various unforeseen noise corruptions by 4.2-18.4% over adversarial training and other strong diverse data augmentation baselines across several benchmarks. Furthermore, when combined with state-of-the-art diverse data augmentation techniques, experiments against state-of-the-art show our method further improves robustness accuracy by 3.7% and uncertainty calibration by 5.5% for all common corruptions on several image classification benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01231v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"13559\", \"78906\", \"115360\", \"117916\", \"137849\", \"152544\", \"170768\", \"177462\", \"182179\", \"184364\", \"190017\", \"201258\", \"201710\", \"208923\", \"209785\", \"220228\", \"229886\", \"230755\", \"239299\", \"248017\", \"248524\", \"249085\", \"249303\"]}","task_split":"paper_retrieval"}
{"document_id":"58635","document_content":"# grASP: A Graph Based ASP-Solver and Justification System\n## Categories\n- Artificial Intelligence\n## Abstract\nAnswer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. We propose a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. Performance results from an implementation are also reported. Our work paves the way for computing answer sets without grounding a program.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01190v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"22891\"], \"outgoing_citations\": [\"233740\", \"257131\", \"329643\"]}","task_split":"paper_retrieval"}
{"document_id":"58691","document_content":"# Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training\n## Categories\n- Sound\n- Computation and Language\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nSelf-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at https:\/\/github.com\/pytorch\/fairseq.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01027v2\", \"primary_category\": \"cs.SD\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"3030\", \"8834\", \"10223\", \"13523\", \"16135\", \"16304\", \"17557\", \"18221\", \"23544\", \"25197\", \"36606\", \"38145\", \"1147\", \"6640\", \"15023\", \"16918\", \"20271\", \"27235\", \"36323\", \"43032\"], \"outgoing_citations\": [\"77367\", \"84456\", \"92204\", \"116282\", \"133484\", \"143604\", \"145027\", \"150817\", \"151274\", \"154976\", \"157113\", \"162127\", \"164998\", \"190961\", \"191898\", \"191994\", \"228240\", \"232327\", \"239780\", \"255129\"]}","task_split":"paper_retrieval"}
{"document_id":"58695","document_content":"# Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nA key challenge in Imitation Learning (IL) is that optimal state actions demonstrations are difficult for the teacher to provide. For example in robotics, providing kinesthetic demonstrations on a robotic manipulator requires the teacher to control multiple degrees of freedom at once. The difficulty of requiring optimal state action demonstrations limits the space of problems where the teacher can provide quality feedback. As an alternative to state action demonstrations, the teacher can provide corrective feedback such as their preferences or rewards. Prior work has created algorithms designed to learn from specific types of noisy feedback, but across teachers and tasks different forms of feedback may be required. Instead we propose that in order to learn from a diversity of scenarios we need to learn from a variety of feedback. To learn from a variety of feedback we make the following insight: the teacher's cost function is latent and we can model a stream of feedback as a stream of loss functions. We then use any online learning algorithm to minimize the sum of these losses. With this insight we can learn from a diversity of feedback that is weakly correlated with the teacher's true cost function. We unify prior work into a general corrective feedback meta-algorithm and show that regardless of feedback we can obtain the same regret bounds. We demonstrate our approach by learning to perform a household navigation task on a robotic racecar platform. Our results show that our approach can learn quickly from a variety of noisy feedback.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.01021v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"170764\", \"209859\", \"219658\", \"228998\", \"263957\", \"272501\", \"275728\", \"300596\"]}","task_split":"paper_retrieval"}
{"document_id":"58799","document_content":"# Choice-Aware User Engagement Modeling andOptimization on Social Media\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nWe address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform. We formulate the engagement forecasting task as a multi-label classification problem that captures choice behavior on an unsupervised clustering of tweet-topics. We propose a neural network architecture that incorporates user engagement history and predicts choice conditional on this context. We study the impact of recommend-ing tweets on engagement outcomes by solving an appropriately defined sweet optimization problem based on the proposed model using a large dataset obtained from Twitter.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.00801v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"118688\", \"138104\", \"291158\", \"313371\"]}","task_split":"paper_retrieval"}
{"document_id":"58876","document_content":"# Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOur objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that the available large scale video-text training datasets, such as HowTo100M, are noisy and hence competitive performance is achieved only at scale through large amounts of compute. We address both these challenges in this paper. We propose an end-to-end trainable model that is designed to take advantage of both large-scale image and video captioning datasets. Our model is an adaptation and extension of the recent ViT and Timesformer architectures, and consists of attention in both space and time. The model is flexible and can be trained on both image and video text datasets, either independently or in conjunction. It is trained with a curriculum learning schedule that begins by treating images as 'frozen' snapshots of video, and then gradually learns to attend to increasing temporal context when trained on video datasets. We also provide a new video-text pretraining dataset WebVid-2M, comprised of over two million videos with weak captions scraped from the internet. Despite training on datasets that are an order of magnitude smaller, we show that this approach yields state-of-the-art results on standard downstream video-retrieval benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.00650v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7637\", \"1081\", \"2212\", \"2284\", \"4359\", \"4348\", \"5478\", \"5634\", \"5960\", \"7218\", \"8438\", \"8991\", \"12103\", \"33472\", \"43252\", \"55118\", \"69816\", \"5726\", \"7309\", \"7454\", \"7938\", \"10348\", \"12778\", \"16210\", \"16519\", \"24771\", \"38350\", \"40597\", \"43276\", \"43644\", \"46080\", \"55460\", \"89020\", \"173844\"], \"outgoing_citations\": [\"54595\", \"60135\", \"68626\", \"69945\", \"70049\", \"70426\", \"76974\", \"81390\", \"87013\", \"96230\", \"110610\", \"115367\", \"118223\", \"119114\", \"126167\", \"127728\", \"131401\", \"137906\", \"138177\", \"142128\", \"151327\", \"153131\", \"157310\", \"164410\", \"164927\", \"173627\", \"175441\", \"180863\", \"181897\", \"189263\", \"207941\", \"212776\", \"214015\", \"219048\", \"221984\", \"236468\", \"236918\", \"238205\", \"247535\", \"248734\", \"249241\", \"259318\", \"260926\", \"266992\", \"267523\", \"269276\", \"270325\", \"284230\", \"295117\", \"296681\", \"303011\", \"315108\", \"319169\", \"320332\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"59076","document_content":"# Less is More: Accelerating Faster Neural Networks Straight from JPEG\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost image data available are often stored in a compressed format, from which JPEG is the most widespread. To feed this data on a convolutional neural network (CNN), a preliminary decoding process is required to obtain RGB pixels, demanding a high computational load and memory usage. For this reason, the design of CNNs for processing JPEG compressed data has gained attention in recent years. In most existing works, typical CNN architectures are adapted to facilitate the learning with the DCT coefficients rather than RGB pixels. Although they are effective, their architectural changes either raise the computational costs or neglect relevant information from DCT inputs. In this paper, we examine different ways of speeding up CNNs designed for DCT inputs, exploiting learning strategies to reduce the computational complexity by taking full advantage of DCT inputs. Our experiments were conducted on the ImageNet dataset. Results show that learning how to combine all DCT inputs in a data-driven fashion is better than discarding them by hand, and its combination with a reduction of layers has proven to be effective for reducing the computational costs while retaining accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-93420-0_23\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78359\", \"170451\", \"190197\", \"204367\"]}","task_split":"paper_retrieval"}
{"document_id":"59105","document_content":"# Misinformation detection in Luganda-English code-mixed social media text\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nThe increasing occurrence, forms, and negative effects of misinformation on social media platforms has necessitated more misinformation detection tools. Currently, work is being done addressing COVID-19 misinformation however, there are no misinformation detection tools for any of the 40 distinct indigenous Ugandan languages. This paper addresses this gap by presenting basic language resources and a misinformation detection data set based on code-mixed Luganda-English messages sourced from the Facebook and Twitter social media platforms. Several machine learning methods are applied on the misinformation detection data set to develop classification models for detecting whether a code-mixed Luganda-English message contains misinformation or not. A 10-fold cross validation evaluation of the classification methods in an experimental misinformation detection task shows that a Discriminative Multinomial Naive Bayes (DMNB) method achieves the highest accuracy and F-measure of 78.19% and 77.90% respectively. Also, Support Vector Machine and Bagging ensemble classification models achieve comparable results. These results are promising since the machine learning models are based on n-gram features from only the misinformation detection dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.00124v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"88648\", \"108176\", \"123410\", \"125690\", \"128148\", \"199511\", \"259202\", \"348596\"]}","task_split":"paper_retrieval"}
{"document_id":"59138","document_content":"# SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network\n## Categories\n- Machine Learning\n## Abstract\nTo capture spatial relationships and temporal dynamics in traffic data, spatio-temporal models for traffic forecasting have drawn significant attention in recent years. Most of the recent works employed graph neural networks(GNN) with multiple layers to capture the spatial dependency. However, road junctions with different hop-distance can carry distinct traffic information which should be exploited separately but existing multi-layer GNNs are incompetent to discriminate between their impact. Again, to capture the temporal interrelationship, recurrent neural networks are common in state-of-the-art approaches that often fail to capture long-range dependencies. Furthermore, traffic data shows repeated patterns in a daily or weekly period which should be addressed explicitly. To address these limitations, we have designed a Simplified Spatio-temporal Traffic forecasting GNN(SST-GNN) that effectively encodes the spatial dependency by separately aggregating different neighborhood representations rather than with multiple layers and capture the temporal dependency with a simple yet effective weighted spatio-temporal aggregation mechanism. We capture the periodic traffic patterns by using a novel position encoding scheme with historical and current data in two different models. With extensive experimental analysis, we have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets from the Performance Measurement System (PeMS).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.00055v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117308\", \"153564\", \"183174\", \"261876\"]}","task_split":"paper_retrieval"}
{"document_id":"59341","document_content":"# Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nWe are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments. Critical to this is the robot's ability to acquire some metric of task success or reward, which is necessary for reinforcement learning, planning, or knowing when to ask for help. For a general-purpose robot operating in the real world, this reward function must also be able to generalize broadly across environments, tasks, and objects, while depending only on on-board sensor observations (e.g. RGB images). While deep learning on large and diverse datasets has shown promise as a path towards such generalization in computer vision and natural language, collecting high quality datasets of robotic interaction at scale remains an open challenge. In contrast, \"in-the-wild\" videos of humans (e.g. YouTube) contain an extensive collection of people doing interesting tasks across a diverse range of settings. In this work, we propose a simple approach, Domain-agnostic Video Discriminator (DVD), that learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task, and can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos. We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.16817v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"8658\", \"20414\", \"6628\", \"21975\", \"22978\", \"26076\", \"154511\"], \"outgoing_citations\": [\"87181\", \"87299\", \"92122\", \"93419\", \"106556\", \"117896\", \"128799\", \"140124\", \"149198\", \"151978\", \"154954\", \"158215\", \"159949\", \"160036\", \"164743\", \"181484\", \"185610\", \"190237\", \"192592\", \"207601\", \"210517\", \"211133\", \"222669\", \"224142\", \"224840\", \"226419\", \"226635\", \"230042\", \"237605\", \"243236\", \"245255\", \"251921\", \"251924\", \"254396\", \"261522\", \"263838\", \"268248\", \"272498\", \"277564\", \"283391\", \"297317\", \"306156\", \"309396\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"59422","document_content":"# A Kinematic Model for Trajectory Prediction in General Highway Scenarios\n## Categories\n- Robotics\n## Abstract\nHighway driving invariably combines high speeds with the need to interact closely with other drivers. Prediction methods enable autonomous vehicles (AVs) to anticipate drivers' future trajectories and plan accordingly. Kinematic methods for prediction have traditionally ignored the presence of other drivers, or made predictions only for a limited set of scenarios. Data-driven approaches fill this gap by learning from large datasets to predict trajectories in general scenarios. While they achieve high accuracy, they also lose the interpretability and tools for model validation enjoyed by kinematic methods. This letter proposes a novel kinematic model to describe car-following and lane change behavior, and extends it to predict trajectories in general scenarios. Experiments on highway datasets under varied sensing conditions demonstrate that the proposed method outperforms state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.16673v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"82376\", \"109433\", \"126156\", \"142189\", \"153754\", \"161193\", \"162130\", \"182082\", \"191356\", \"206321\", \"214529\", \"232095\", \"237350\", \"241801\", \"244664\", \"265546\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"59465","document_content":"# Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOur objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformer-based models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and re-ranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.16553v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1081\", \"2580\", \"21001\", \"22962\", \"23886\", \"32344\", \"10271\", \"13933\", \"16210\", \"44873\", \"173844\"], \"outgoing_citations\": [\"61497\", \"87013\", \"96230\", \"111811\", \"115367\", \"117945\", \"119349\", \"127728\", \"131401\", \"133524\", \"139259\", \"139906\", \"151327\", \"152680\", \"165195\", \"170603\", \"171465\", \"172370\", \"172394\", \"181897\", \"191861\", \"197653\", \"205340\", \"209299\", \"217562\", \"222332\", \"236468\", \"247616\", \"263034\", \"269128\", \"272109\", \"298750\", \"303011\", \"310233\", \"312643\", \"313189\", \"315108\", \"321486\", \"321708\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"59514","document_content":"# Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nMost chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots -- humans intrinsically understand the effect their responses have on the interlocutor and often respond with an intention such as proposing an optimistic view to make the interlocutor feel better. This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans. The guiding chatbot is assigned an intention and learns to induce the interlocutor to reply with responses matching the intention, for example, long responses, joyful responses, responses with specific words, etc. We examined our framework using three experimental setups and evaluated the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages. Additionally, we performed trials with human interlocutors to substantiate the guiding chatbot's effectiveness in influencing the responses of humans to a certain extent. Code will be made available to the public.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.16429v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"100574\", \"145384\", \"158493\", \"174067\", \"175474\", \"212046\", \"217027\", \"234808\", \"236477\", \"243154\", \"246394\", \"249219\", \"251892\", \"256632\", \"267551\", \"309411\", \"310903\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"59579","document_content":"# Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nPredicting future trajectories of surrounding obstacles is a crucial task for autonomous driving cars to achieve a high degree of road safety. There are several challenges in trajectory prediction in real-world traffic scenarios, including obeying traffic rules, dealing with social interactions, handling traffic of multi-class movement, and predicting multi-modal trajectories with probability. Inspired by people's natural habit of navigating traffic with attention to their goals and surroundings, this paper presents a unique dynamic graph attention network to solve all those challenges. The network is designed to model the dynamic social interactions among agents and conform to traffic rules with a semantic map. By extending the anchor-based method to multiple types of agents, the proposed method can predict multi-modal trajectories with probabilities for multi-class movements using a single model. We validate our approach on the proprietary autonomous driving dataset for the logistic delivery scenario and two publicly available datasets. The results show that our method outperforms state-of-the-art techniques and demonstrates the potential for trajectory prediction in real-world traffic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.16273v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"23743\"], \"outgoing_citations\": [\"133076\", \"139777\", \"147880\", \"157689\", \"162130\", \"177460\", \"206321\", \"217456\", \"229266\", \"237350\", \"249164\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"59753","document_content":"# Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nKnowledge is captured in the form of entities and their relationships and stored in knowledge graphs. Knowledge graphs enhance the capabilities of applications in many different areas including Web search, recommendation, and natural language understanding. This is mainly because, entities enable machines to understand things that go beyond simple tokens. Many modern algorithms use learned entity embeddings from these structured representations. However, building a knowledge graph takes time and effort, hence very costly and nontrivial. On the other hand, many Web sources describe entities in some structured format and therefore, finding ways to get them into useful entity knowledge is advantageous. We propose an approach that processes entity centric textual knowledge sources to learn entity embeddings and in turn avoids the need for a traditional knowledge graph. We first extract triples into the new representation format that does not use traditional complex triple extraction methods defined by pre-determined relationship labels. Then we learn entity embeddings through this new type of triples. We show that the embeddings learned from our approach are: (i) high quality and comparable to a known knowledge graph-based embeddings and can be used to improve them further, (ii) better than a contextual language model-based entity embeddings, and (iii) easy to compute and versatile in domain-specific applications where a knowledge graph is not readily available","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15950v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152591\", \"166443\", \"166518\", \"185889\", \"195502\", \"261989\", \"279522\", \"289536\", \"289972\", \"298945\", \"304901\", \"313944\", \"318126\", \"320035\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"59761","document_content":"# Shaping Advice in Deep Multi-Agent Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nMulti-agent reinforcement learning involves multiple agents interacting with each other and a shared environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feedback on the quality of actions that they take, thereby affecting learning of policies. In this paper, we propose a method called Shaping Advice in deep Multi-agent reinforcement learning (SAM) to augment the reward signal from the environment with an additional reward termed shaping advice. The shaping advice is given by a difference of potential functions at consecutive time-steps. Each potential function is a function of observations and actions of the agents. The shaping advice needs to be specified only once at the start of training, and can be easily provided by non-experts. We show through theoretical analyses and experimental validation that shaping advice provided by SAM does not distract agents from completing tasks specified by the environment reward. Theoretically, we prove that convergence of policy gradients and value functions when using SAM implies convergence of these quantities in the absence of SAM. Experimentally, we evaluate SAM on three tasks in the multi-agent Particle World environment that have sparse rewards. We observe that using SAM results in agents learning policies to complete tasks faster, and obtain higher rewards than: i) using sparse rewards alone; ii) a state-of-the-art reward redistribution method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15941v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"13755\", \"25013\"], \"outgoing_citations\": [\"91719\", \"146535\", \"175265\", \"201508\", \"215235\", \"237193\", \"263576\", \"269382\", \"272788\", \"274315\", \"302395\", \"335110\"]}","task_split":"paper_retrieval"}
{"document_id":"59780","document_content":"# pH-RL: A personalization architecture to bring reinforcement learning to health practice\n## Categories\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nWhile reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life scenarios successfully. This paper presents pH-RL (personalization in e-Health with RL) a general RL architecture for personalization to bring RL to health practice. pH-RL allows for various levels of personalization in health applications and allows for online and batch learning. Furthermore, we provide a general-purpose implementation framework that can be integrated with various healthcare applications. We describe a step-by-step guideline for the successful deployment of RL policies in a mobile application. We implemented our open-source RL architecture and integrated it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules. We then performed a comprehensive study with human participants over a sustained period. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data. Furthermore, we empirically demonstrate the stability of the learned policies during the study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15908v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [\"43959\"], \"outgoing_citations\": [\"121735\", \"188486\", \"258656\"]}","task_split":"paper_retrieval"}
{"document_id":"59818","document_content":"# Privacy and Trust Redefined in Federated Machine Learning\n## Categories\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n- Machine Learning\n- Computers and Society\n## Abstract\nA common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries\/Indy\/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/make3020017\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.DC\", \"cs.LG\", \"cs.CY\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\", \"Computers and Society\"], \"incoming_citations\": [\"9036\", \"49762\", \"2007\", \"56605\", \"58924\"], \"outgoing_citations\": [\"86126\", \"86155\", \"101296\", \"105970\", \"115911\", \"117519\", \"121218\", \"130373\", \"155989\", \"161269\", \"172559\", \"192406\", \"198581\", \"200347\", \"205882\", \"207018\", \"208013\", \"210718\", \"210832\", \"210936\", \"225706\", \"226146\", \"229332\", \"229877\", \"234581\", \"237069\", \"237561\", \"245467\", \"247042\", \"253391\", \"255133\", \"257486\", \"264157\", \"265979\", \"273215\", \"280506\", \"285037\", \"291088\", \"291918\", \"297857\", \"298682\", \"303380\", \"319945\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"59900","document_content":"# Augmenting Automated Game Testing with Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGeneral game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement learning (DRL), we introduce a self-learning mechanism to the game testing framework. With DRL, the framework is capable of exploring and\/or exploiting the game mechanics based on a user-defined, reinforcing reward signal. As a result, test coverage is increased and unintended game play mechanics, exploits and bugs are discovered in a multitude of game types. In this paper, we show that DRL can be used to increase test coverage, find exploits, test map difficulty, and to detect common problems that arise in the testing of first-person shooter (FPS) games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15819v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1269\", \"38103\", \"60722\", \"64439\"], \"outgoing_citations\": [\"166489\", \"173071\", \"193790\", \"238967\", \"266419\", \"280681\", \"284468\"]}","task_split":"paper_retrieval"}
{"document_id":"59937","document_content":"# Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nThis paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets. Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level structure and the game's character class parameters as input and the gameplay outcomes as output. The model is trained on a large corpus of game data from simulations with artificial agents in random sets of levels and class parameters. The model is then used to generate classes for specific levels and for a desired game outcome, such as balanced matches of short duration. Findings in this paper show that the system can be expressive and can generate classes for both computer generated and human authored levels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3235765.3235816\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"61575\", \"95477\", \"61740\"], \"outgoing_citations\": [\"274886\", \"297233\"]}","task_split":"paper_retrieval"}
{"document_id":"59948","document_content":"# Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nRobust face reconstruction from monocular image in general lighting conditions is challenging. Methods combining deep neural network encoders with differentiable rendering have opened up the path for very fast monocular reconstruction of geometry, lighting and reflectance. They can also be trained in self-supervised manner for increased robustness and better generalization. However, their differentiable rasterization based image formation models, as well as underlying scene parameterization, limit them to Lambertian face reflectance and to poor shape details. More recently, ray tracing was introduced for monocular face reconstruction within a classic optimization-based framework and enables state-of-the art results. However optimization-based approaches are inherently slow and lack robustness. In this paper, we build our work on the aforementioned approaches and propose a new method that greatly improves reconstruction quality and robustness in general scenes. We achieve this by combining a CNN encoder with a differentiable ray tracer, which enables us to base the reconstruction on much more advanced personalized diffuse and specular albedos, a more sophisticated illumination model and a plausible representation of self-shadows. This enables to take a big leap forward in reconstruction quality of shape, appearance and lighting even in scenes with difficult illumination. With consistent face attributes reconstruction, our method leads to practical applications such as relighting and self-shadows removal. Compared to state-of-the-art methods, our results show improved accuracy and validity of the approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15432v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"3093\", \"41321\"], \"outgoing_citations\": [\"58977\", \"75585\", \"112307\", \"124310\", \"132772\", \"133529\", \"134076\", \"163543\", \"168874\", \"185981\", \"191310\", \"205494\", \"236050\", \"237024\", \"248005\", \"248572\", \"254971\", \"270134\", \"270923\", \"278966\", \"284248\"]}","task_split":"paper_retrieval"}
{"document_id":"60004","document_content":"# Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMost unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences. We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of multi-mode codebook embeddings to capture different semantic facets of its meaning. The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space. We introduce an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence during test time. Our experiments show that the per-sentence codebook embeddings significantly improve the performances in unsupervised sentence similarity and extractive summarization benchmarks. In phrase similarity experiments, we discover that the multi-facet embeddings provide an interpretable semantic representation but do not outperform the single-facet baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15330v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"40685\", \"55145\", \"60000\", \"59998\"], \"outgoing_citations\": [\"59998\", \"60000\", \"124270\", \"127741\", \"155664\", \"173321\", \"181817\", \"190973\", \"199671\", \"200162\", \"200268\", \"206516\", \"215835\", \"219920\", \"225253\", \"237494\", \"247007\", \"250494\", \"250815\", \"253345\", \"255228\", \"259723\", \"267868\", \"272080\", \"285181\", \"296273\", \"300128\", \"302930\", \"310737\", \"311084\", \"311423\", \"311696\", \"313947\", \"320008\", \"325323\", \"340248\", \"357899\"]}","task_split":"paper_retrieval"}
{"document_id":"60037","document_content":"# One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCan deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capable of simultaneously learning multiple tasks from a combined data set, for a variety of methods for representing tasks -- for example, when the distinct tasks are encoded by well-separated clusters or decision trees over certain task-code attributes. More concretely, we present a novel analysis that shows that families of simple programming-like constructs for the codes encoding the tasks are learnable by two-layer neural networks with standard training. We study more generally how the complexity of learning such combined tasks grows with the complexity of the task codes; we find that combining many tasks may incur a sample complexity penalty, even though the individual tasks are easy to learn. We provide empirical support for the usefulness of the learning bounds by training networks on clusters, decision trees, and SQL-style aggregation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15261v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169364\", \"176571\", \"197007\", \"198526\", \"201703\", \"210391\", \"215486\", \"227402\", \"260314\", \"261209\", \"263620\", \"275599\", \"280486\"]}","task_split":"paper_retrieval"}
{"document_id":"60102","document_content":"# Explaining Representation by Mutual Information\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nScience is used to discover the law of world. Machine learning can be used to discover the law of data. In recent years, there are more and more research about interpretability in machine learning community. We hope the machine learning methods are safe, interpretable, and they can help us to find meaningful pattern in data. In this paper, we focus on interpretability of deep representation. We propose a interpretable method of representation based on mutual information, which summarizes the interpretation of representation into three types of information between input data and representation. We further proposed MI-LR module, which can be inserted into the model to estimate the amount of information to explain the model's representation. Finally, we verify the method through the visualization of the prototype network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.15114v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148797\", \"173580\", \"241933\", \"245283\", \"278987\", \"301835\", \"316314\"]}","task_split":"paper_retrieval"}
{"document_id":"60271","document_content":"# LSTM Based Sentiment Analysis for Cryptocurrency Prediction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nRecent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14804v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"17797\", \"29256\", \"31119\", \"37674\", \"69406\"], \"outgoing_citations\": [\"37674\", \"82464\", \"82485\", \"94262\", \"175814\", \"220402\"]}","task_split":"paper_retrieval"}
{"document_id":"60277","document_content":"# Learning Efficient Photometric Feature Transform for Multi-view Stereo\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nWe present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction. Both the illumination conditions during acquisition and the subsequent per-pixel feature transform can be jointly optimized in a differentiable fashion. Our framework automatically adapts to and makes efficient use of the geometric information available in different forms of input data. High-quality 3D reconstructions of a variety of challenging objects are demonstrated on the data captured with an illumination multiplexing device, as well as a point light. Our results compare favorably with state-of-the-art techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14794v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"146592\", \"211407\", \"223643\", \"224621\", \"263034\", \"314520\", \"324494\"]}","task_split":"paper_retrieval"}
{"document_id":"60281","document_content":"# A Comprehensive Review of the Video-to-Text Problem\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nResearch in the Vision and Language area encompasses challenging topics that seek to connect visual and textual information. When the visual information is related to videos, this takes us into Video-Text Research, which includes several challenging tasks such as video question answering, video summarization with natural language, and video-to-text and text-to-video conversion. This paper reviews the video-to-text problem, in which the goal is to associate an input video with its textual description. This association can be mainly made by retrieving the most relevant descriptions from a corpus or generating a new one given a context video. These two ways represent essential tasks for Computer Vision and Natural Language Processing communities, called text retrieval from video task and video captioning\/description task. These two tasks are substantially more complex than predicting or retrieving a single sentence from an image. The spatiotemporal information present in videos introduces diversity and complexity regarding the visual content and the structure of associated language descriptions. This review categorizes and describes the state-of-the-art techniques for the video-to-text problem. It covers the main video-to-text methods and the ways to evaluate their performance. We analyze twenty-six benchmark datasets, showing their drawbacks and strengths for the problem requirements. We also show the progress that researchers have made on each dataset, we cover the challenges in the field, and we discuss future research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14785v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"53205\", \"89825\", \"99401\", \"115939\", \"120547\", \"120890\", \"125719\", \"134024\", \"139259\", \"139932\", \"146928\", \"151327\", \"152680\", \"169352\", \"170020\", \"170027\", \"172394\", \"176538\", \"181897\", \"182940\", \"187303\", \"187914\", \"190241\", \"191680\", \"191861\", \"192531\", \"194686\", \"197355\", \"198870\", \"204751\", \"205750\", \"214015\", \"217244\", \"217562\", \"226421\", \"227052\", \"229734\", \"234222\", \"234325\", \"234374\", \"235531\", \"236468\", \"236918\", \"237150\", \"237201\", \"238229\", \"240071\", \"240605\", \"246482\", \"247535\", \"248813\", \"248855\", \"249241\", \"249748\", \"252097\", \"254361\", \"256883\", \"257266\", \"259121\", \"259318\", \"260810\", \"260926\", \"267523\", \"269640\", \"269646\", \"270292\", \"270325\", \"279024\", \"279337\", \"279676\", \"279740\", \"282943\", \"285973\", \"286427\", \"286978\", \"287504\", \"290549\", \"292192\", \"293986\", \"294503\", \"294898\", \"295117\", \"295122\", \"301767\", \"302815\", \"303593\", \"304481\", \"311742\", \"312423\", \"312453\", \"313189\", \"313435\", \"315108\", \"316564\", \"316803\", \"317032\", \"317379\", \"319169\", \"320003\", \"320332\", \"320761\", \"321493\", \"321621\", \"321682\", \"321708\", \"322022\", \"326606\", \"329586\", \"332079\", \"336175\", \"339440\", \"347142\"]}","task_split":"paper_retrieval"}
{"document_id":"60291","document_content":"# Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users' Requests: a Case Study of the Android Operating System\n## Categories\n- Software Engineering\n## Abstract\nText mining approaches are being used increasingly for business analytics. In particular, such approaches are now central to understanding users' feedback regarding systems delivered via online application distribution platforms such as Google Play. In such settings, large volumes of reviews of potentially numerous apps and systems means that it is infeasible to use manual mechanisms to extract insights and knowledge that could inform product improvement. In this context of identifying software system improvement options, text mining techniques are used to reveal the features that are mentioned most often as being in need of correction (e.g., GPS), and topics that are associated with features perceived as being defective (e.g., inaccuracy of GPS). Other approaches may supplement such techniques to provide further insights for online communities and solution providers. In this work we augment text mining approaches with social network analysis to demonstrate the utility of using multiple techniques. Our outcomes suggest that text mining approaches may indeed be supplemented with other methods to deliver a broader range of insights.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/HICSS.2016.145\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\"], \"incoming_citations\": [\"174735\"], \"outgoing_citations\": [\"40834\", \"66445\", \"67091\", \"67093\", \"69880\", \"348303\"]}","task_split":"paper_retrieval"}
{"document_id":"60321","document_content":"# Focused LRP: Explainable AI for Face Morphing Attack Detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nThe task of detecting morphed face images has become highly relevant in recent years to ensure the security of automatic verification systems based on facial images, e.g. automated border control gates. Detection methods based on Deep Neural Networks (DNN) have been shown to be very suitable to this end. However, they do not provide transparency in the decision making and it is not clear how they distinguish between genuine and morphed face images. This is particularly relevant for systems intended to assist a human operator, who should be able to understand the reasoning. In this paper, we tackle this problem and present Focused Layer-wise Relevance Propagation (FLRP). This framework explains to a human inspector on a precise pixel level, which image regions are used by a Deep Neural Network to distinguish between a genuine and a morphed face image. Additionally, we propose another framework to objectively analyze the quality of our method and compare FLRP to other DNN interpretability methods. This evaluation framework is based on removing detected artifacts and analyzing the influence of these changes on the decision of the DNN. Especially, if the DNN is uncertain in its decision or even incorrect, FLRP performs much better in highlighting visible artifacts compared to other methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14697v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"30580\", \"37448\"], \"outgoing_citations\": [\"89191\", \"113588\", \"129348\", \"148504\", \"160442\", \"225647\", \"262955\"]}","task_split":"paper_retrieval"}
{"document_id":"60347","document_content":"# Training a Task-Specific Image Reconstruction Loss\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThe choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should encourage natural and perceptually pleasing results. A popular choice for a loss is a pre-trained network, such as VGG, which is used as a feature extractor for computing the difference between restored and reference images. However, such an approach has multiple drawbacks: it is computationally expensive, requires regularization and hyper-parameter tuning, and involves a large network trained on an unrelated task. Furthermore, it has been observed that there is no single loss function that works best across all applications and across different datasets. In this work, we instead propose to train a set of loss functions that are application specific in nature. Our loss function comprises a series of discriminators that are trained to detect and penalize the presence of application-specific artifacts. We show that a single natural image and corresponding distortions are sufficient to train our feature extractor that outperforms state-of-the-art loss functions in applications like single image super resolution, denoising, and JPEG artifact removal. Finally, we conclude that an effective loss function does not have to be a good predictor of perceived image quality, but instead needs to be specialized in identifying the distortions for a given restoration method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14616v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"80011\", \"111861\", \"124917\", \"152833\", \"188010\", \"207680\", \"219505\", \"247788\", \"250250\", \"269384\", \"269393\", \"277357\", \"298700\", \"299975\", \"303174\", \"310922\", \"312178\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"60365","document_content":"# Correcting Automated and Manual Speech Transcription Errors using Warped Language Models\n## Categories\n- Computation and Language\n## Abstract\nMasked language models have revolutionized natural language processing systems in the past few years. A recently introduced generalization of masked language models called warped language models are trained to be more robust to the types of errors that appear in automatic or manual transcriptions of spoken language by exposing the language model to the same types of errors during training. In this work we propose a novel approach that takes advantage of the robustness of warped language models to transcription noise for correcting transcriptions of spoken language. We show that our proposed approach is able to achieve up to 10% reduction in word error rates of both automatic and manual transcriptions of spoken language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14580v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20620\"], \"outgoing_citations\": [\"89247\", \"142446\", \"145190\", \"157701\", \"159846\", \"160457\", \"191541\"]}","task_split":"paper_retrieval"}
{"document_id":"60381","document_content":"# Unsupervised Document Embedding via Contrastive Augmentation\n## Categories\n- Computation and Language\n## Abstract\nWe present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-supervised learning algorithms used for image and NLP pretraining, we hypothesize that high-quality document embedding should be invariant to diverse paraphrases that preserve the semantics of the original document. With different backbones and contrastive learning frameworks, our study reveals the enormous benefits of contrastive augmentation for document representation learning with two additional insights: 1) including data augmentation in a contrastive way can substantially improve the embedding quality in unsupervised document representation learning, and 2) in general, stochastic augmentations generated by simple word-level manipulation work much better than sentence-level and document-level ones. We plug our method into a classifier and compare it with a broad range of baseline methods on six benchmark datasets. Our method can decrease the classification error rate by up to 6.4% over the SOTA approaches on the document classification task, matching or even surpassing fully-supervised methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14542v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"28479\", \"14819\"], \"outgoing_citations\": [\"77689\", \"92075\", \"93612\", \"97943\", \"109314\", \"114239\", \"120760\", \"159142\", \"164044\", \"165126\", \"181966\", \"184635\", \"188492\", \"205715\", \"212285\", \"217775\", \"220611\", \"234731\", \"239686\", \"261768\", \"267161\", \"267601\", \"279688\", \"298543\", \"302824\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"60412","document_content":"# Spatial Dual-Modality Graph Reasoning for Key Information Extraction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nKey information extraction from document images is of paramount importance in office automation. Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual and textual features of detected text regions, and edges of which represent the spatial relations between neighboring text regions. The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes. In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild. It contains 25 key information categories, a total of about 69000 text boxes, and is about 2 times larger than the existing public datasets. Extensive experiments validate that all information including visual features, textual features and spatial relations can benefit key information extraction. It has been shown that SDMG-R can effectively extract key information from document images of unseen templates, and obtain new state-of-the-art results on the recent popular benchmark SROIE and our WildReceipt. Our code and dataset will be publicly released.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14470v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"29640\"], \"outgoing_citations\": [\"169444\", \"193543\", \"200744\", \"216765\", \"226124\", \"229179\", \"242322\", \"246959\", \"247477\", \"257785\", \"258746\", \"276130\", \"284449\", \"291892\", \"296060\", \"296356\", \"297147\", \"297150\", \"302500\", \"305707\"]}","task_split":"paper_retrieval"}
{"document_id":"60505","document_content":"# Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLearning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14267v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17715\", \"21162\", \"25985\", \"1429\", \"2532\", \"2912\", \"7059\", \"10628\", \"104603\", \"3184\", \"34463\", \"37284\"], \"outgoing_citations\": [\"98277\", \"107032\", \"112093\", \"133680\", \"135196\", \"137349\", \"140213\", \"151954\", \"152642\", \"160705\", \"180239\", \"191218\", \"194055\", \"202716\", \"206710\", \"225092\", \"237905\", \"253266\", \"260655\", \"278526\", \"286172\", \"301306\", \"308096\"]}","task_split":"paper_retrieval"}
{"document_id":"60526","document_content":"# ACRE: Abstract Causal REasoning Beyond Covariation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nCausal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Causal REasoning (ACRE) dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario: direct, indirect, screening-off, and backward-blocking, intentionally going beyond the simple strategy of inducing causal relationships by covariation. By analyzing visual reasoning architectures on this testbed, we notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning. These deficiencies call for future research in models with a more comprehensive capability of causal induction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.14232v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"7311\", \"85385\", \"19640\", \"38037\", \"60528\", \"80307\"], \"outgoing_citations\": [\"60528\", \"117403\", \"119357\", \"129004\", \"130231\", \"137359\", \"144119\", \"153300\", \"153518\", \"154335\", \"162372\", \"163549\", \"164378\", \"164814\", \"188867\", \"196150\", \"200715\", \"206171\", \"215409\", \"224981\", \"228679\", \"240081\", \"251911\", \"264533\", \"277538\", \"291800\", \"295435\", \"296366\"]}","task_split":"paper_retrieval"}
{"document_id":"60770","document_content":"# JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nWith the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some comparable methods which perform the previous two tasks separately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TMM.2023.3240880\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"2613\", \"60697\", \"80496\", \"83295\", \"92268\", \"93576\", \"94389\", \"134378\", \"136638\", \"194014\", \"197211\", \"208434\", \"211273\", \"223755\", \"224859\", \"234815\", \"248969\", \"259660\", \"262816\", \"268494\", \"270024\", \"286661\", \"317946\", \"321145\"]}","task_split":"paper_retrieval"}
{"document_id":"60966","document_content":"# On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Theory\n- Information Theory\n## Abstract\nUser preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, one representing a user with his\/her preferences and another one representing an agent that has to learn these preferences. The user with his\/her behaviour is modeled by a parametric preference function. To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning. The agent presents two proposals to the user for evaluation, and the user rates them based on his\/her preference function. We show that the optimum agent strategy for data collection and preference learning is a result of maximin optimization of the normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions. The resulting value of KL-divergence, which we also call remaining system uncertainty (RSU), provides an efficient performance metric in the absence of the ground truth. This metric characterises how well the agent can predict user and, thus, the quality of the underlying learned user (preference) model. Our proposed agent comprises sequential mechanisms for user model inference and proposal generation. To infer the user model (preference function), Bayesian approximate inference is used in the agent. The data collection strategy is to generate proposals, responses to which help resolving uncertainty associated with prediction of the user responses the most. The efficiency of our approach is validated by numerical simulations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.13192v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"328549\"]}","task_split":"paper_retrieval"}
{"document_id":"60994","document_content":"# Behavior coordination for self-adaptive robots using constraint-based configuration\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nAutonomous robots may be able to adapt their behavior in response to changes in the environment. This is useful, for example, to efficiently handle limited resources or to respond appropriately to unexpected events such as faults. The architecture of a self-adaptive robot is complex because it should include automatic mechanisms to dynamically configure the elements that control robot behaviors. To facilitate the construction of this type of architectures, it is useful to have general solutions in the form of software tools that may be applicable to different robotic systems. This paper presents an original algorithm to dynamically configure the control architecture, which is applicable to the development of self-adaptive autonomous robots. This algorithm uses a constraint-based configuration approach to decide which basic robot behaviors should be activated in response to both reactive and deliberative events. The algorithm uses specific search heuristics and initialization procedures to achieve the performance required by robotic systems. The solution has been implemented as a software development tool called Behavior Coordinator CBC (Constraint-Based Configuration), which is based on ROS and open source, available to the general public. This tool has been successfully used for building multiple applications of autonomous aerial robots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.13128v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"103093\"], \"outgoing_citations\": [\"63735\", \"93358\"]}","task_split":"paper_retrieval"}
{"document_id":"61181","document_content":"# Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks\n## Categories\n- Machine Learning\n## Abstract\nLearning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of data to train a decent model on a particular task, and the model often fails to generalize to new tasks that have a different distribution. In practice, demonstrations from new tasks will be continuously observed and the data might be unlabeled or only partially labeled. Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available. In this work, we build an adaptable imitation learning model based on the integration of Meta-learning and Adversarial Inverse Reinforcement Learning (Meta-AIRL). We exploit the adversarial learning and inverse reinforcement learning mechanisms to learn policies and reward functions simultaneously from available training tasks and then adapt them to new tasks with the meta-learning framework. Simulation results show that the adapted policy trained with Meta-AIRL can effectively learn from limited number of demonstrations, and quickly reach the performance comparable to that of the experts on unseen tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.12694v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"165811\", \"229737\", \"230669\", \"230962\", \"239655\", \"251924\", \"264051\", \"280202\", \"290679\", \"293864\"]}","task_split":"paper_retrieval"}
{"document_id":"61394","document_content":"# Human-like Controllable Image Captioning with Verb-specific Semantic Roles\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Multimedia\n## Abstract\nControllable Image Captioning (CIC) -- generating image descriptions following designated control signals -- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective properties, such as contents of interest or descriptive patterns. However, we argue that almost all existing objective control signals have overlooked two indispensable characteristics of an ideal control signal: 1) Event-compatible: all visual contents referred to in a single sentence should be compatible with the described activity. 2) Sample-suitable: the control signals should be suitable for a specific image sample. To this end, we propose a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists of a verb and some semantic roles, which represents a targeted activity and the roles of entities involved in this activity. Given a designated VSR, we first train a grounded semantic role labeling (GSRL) model to identify and ground all entities for each role. Then, we propose a semantic structure planner (SSP) to learn human-like descriptive semantic structures. Lastly, we use a role-shift captioning model to generate the captions. Extensive experiments and ablations demonstrate that our framework can achieve better controllability than several strong baselines on two challenging CIC benchmarks. Besides, we can generate multi-level diverse captions easily. The code is available at: https:\/\/github.com\/mad-red\/VSR-guided-CIC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.12204v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"35427\", \"134765\", \"4014\", \"4453\", \"113677\", \"15902\", \"43997\"], \"outgoing_citations\": [\"5781\", \"74508\", \"83854\", \"101699\", \"110219\", \"112250\", \"127403\", \"139263\", \"152418\", \"170604\", \"189964\", \"191177\", \"193328\", \"195170\", \"208615\", \"209504\", \"213042\", \"223302\", \"225101\", \"229734\", \"231647\", \"236907\", \"237199\", \"238125\", \"239242\", \"241060\", \"249972\", \"253934\", \"258592\", \"268356\", \"270154\", \"271189\", \"271234\", \"276436\", \"278604\", \"278883\", \"278954\", \"280246\", \"283076\", \"287504\", \"305473\", \"312643\", \"312703\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"61450","document_content":"# Extracting the Unknown from Long Math Problems\n## Categories\n- Computation and Language\n- History and Overview\n## Abstract\nIn problem solving, understanding the problem that one seeks to solve is an essential initial step. In this paper, we propose computational methods for facilitating problem understanding through the task of recognizing the unknown in specifications of long Math problems. We focus on the topic of Probability. Our experimental results show that learning models yield strong results on the task, a promising first step towards human interpretable, modular approaches to understanding long Math problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.12048v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"math.HO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"History and Overview\"], \"incoming_citations\": [], \"outgoing_citations\": [\"144766\", \"182026\", \"183403\", \"185524\", \"219841\", \"238610\", \"284082\", \"300421\", \"301316\", \"303348\"]}","task_split":"paper_retrieval"}
{"document_id":"61497","document_content":"# Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nCurrent state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross-modal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach which combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine-tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross-encoders.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.11920v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"1081\", \"23886\", \"13644\", \"24382\", \"59465\"], \"outgoing_citations\": [\"35135\", \"43298\", \"70049\", \"74373\", \"77354\", \"77428\", \"81870\", \"83776\", \"92550\", \"93824\", \"96287\", \"114308\", \"114755\", \"119547\", \"121147\", \"131401\", \"131921\", \"132146\", \"157249\", \"158750\", \"164127\", \"164927\", \"166854\", \"168031\", \"170603\", \"170926\", \"171465\", \"174872\", \"182577\", \"183076\", \"200155\", \"203064\", \"212322\", \"217125\", \"222741\", \"234808\", \"238205\", \"250405\", \"252903\", \"260373\", \"260926\", \"269128\", \"270817\", \"272822\", \"281407\", \"293418\", \"312643\"]}","task_split":"paper_retrieval"}
{"document_id":"61515","document_content":"# BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nStandard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson's r correlation with human judgments compared to previous metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.11878v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133219\", \"153822\", \"169344\", \"186239\", \"192811\", \"193367\", \"215003\", \"220720\", \"221522\", \"238926\", \"251684\", \"262509\", \"263927\", \"268098\", \"307969\", \"310737\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"61605","document_content":"# Polarization Guided Specular Reflection Separation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSince specular reflection often exists in the real captured images and causes deviation between the recorded color and intrinsic color, specular reflection separation can bring advantages to multiple applications that require consistent object surface appearance. However, due to the color of an object is significantly influenced by the color of the illumination, the existing researches still suffer from the near-duplicate challenge, that is, the separation becomes unstable when the illumination color is close to the surface color. In this paper, we derive a polarization guided model to incorporate the polarization information into a designed iteration optimization separation strategy to separate the specular reflection. Based on the analysis of polarization, we propose a polarization guided model to generate a polarization chromaticity image, which is able to reveal the geometrical profile of the input image in complex scenarios, such as diversity of illumination. The polarization chromaticity image can accurately cluster the pixels with similar diffuse color. We further use the specular separation of all these clusters as an implicit prior to ensure that the diffuse components will not be mistakenly separated as the specular components. With the polarization guided model, we reformulate the specular reflection separation into a unified optimization function which can be solved by the ADMM strategy. The specular reflection will be detected and separated jointly by RGB and polarimetric information. Both qualitative and quantitative experimental results have shown that our method can faithfully separate the specular reflection, especially in some challenging scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2021.3104188\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"174146\", \"150998\"], \"outgoing_citations\": [\"145617\", \"182873\", \"193319\", \"301506\", \"302215\", \"338908\"]}","task_split":"paper_retrieval"}
{"document_id":"61750","document_content":"# Natural Perturbed Training for General Robustness of Neural Network Classifiers\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe focus on the robustness of neural networks for classification. To permit a fair comparison between methods to achieve robustness, we first introduce a standard based on the mensuration of a classifier's degradation. Then, we propose natural perturbed training to robustify the network. Natural perturbations will be encountered in practice: the difference of two images of the same object may be approximated by an elastic deformation (when they have slightly different viewing angles), by occlusions (when they hide differently behind objects), or by saturation, Gaussian noise etc. Training some fraction of the epochs on random versions of such variations will help the classifier to learn better. We conduct extensive experiments on six datasets of varying sizes and granularity. Natural perturbed learning show better and much faster performance than adversarial training on clean, adversarial as well as natural perturbed images. It even improves general robustness on perturbations not seen during the training. For Cifar-10 and STL-10 natural perturbed training even improves the accuracy for clean data and reaches the state of the art performance. Ablation studies verify the effectiveness of natural perturbed training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.11372v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8665\", \"34396\"], \"outgoing_citations\": [\"115110\", \"115360\", \"147546\", \"149501\", \"161781\", \"165521\", \"168664\", \"179674\", \"184953\", \"201060\", \"201258\", \"201710\", \"222222\", \"229604\", \"229879\", \"229886\", \"235885\", \"248017\", \"249403\", \"252519\", \"262216\", \"263162\", \"267121\", \"268999\", \"302645\", \"309109\"]}","task_split":"paper_retrieval"}
{"document_id":"61785","document_content":"# An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Multimedia\n## Abstract\nIn this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2104.02605v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Multimedia\"], \"incoming_citations\": [\"11273\"], \"outgoing_citations\": [\"149704\", \"167256\", \"190250\", \"193442\", \"238205\", \"248171\", \"250183\", \"250405\", \"253341\", \"260926\", \"280249\", \"281407\", \"296681\", \"303534\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"61976","document_content":"# Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information content shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago, where we replace human annotators with language models. We also view these metrics as an extension of BLANC, a recently proposed approach to summary quality measurement based on the performance of a language model with and without the help of a summary. Using transformer based language models, we empirically verify that our metrics achieve state-of-the-art correlation with human judgement of the summary quality dimensions of both coherence and relevance, as well as competitive correlation with human judgement of consistency and fluency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.10918v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22977\", \"23776\", \"56864\"], \"outgoing_citations\": [\"94486\", \"103740\", \"109884\", \"126362\", \"140642\", \"152729\", \"159316\", \"166082\", \"167338\", \"168405\", \"168744\", \"169142\", \"170449\", \"187236\", \"189542\", \"190030\", \"203452\", \"214294\", \"233710\", \"244102\", \"268339\", \"307651\", \"310738\", \"311423\", \"321493\"]}","task_split":"paper_retrieval"}
{"document_id":"62053","document_content":"# MDMMT: Multidomain Multimodal Transformer for Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a new state-of-the-art on the text to video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved with a single model on two datasets without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that training on different datasets can improve test results of each other. Additionally we check intersection between many popular datasets and found that MSRVTT has a significant overlap between the test and the train parts, and the same situation is observed for ActivityNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPRW53098.2021.00374\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4348\", \"12103\", \"34089\", \"39753\", \"43252\", \"55118\", \"8129\", \"8845\", \"10348\", \"17403\", \"24771\", \"40597\", \"46080\", \"89020\"], \"outgoing_citations\": [\"53205\", \"67133\", \"67133\", \"92613\", \"92613\", \"96230\", \"96230\", \"110610\", \"110610\", \"118223\", \"118223\", \"151327\", \"151327\", \"172394\", \"173627\", \"173627\", \"181897\", \"181897\", \"188114\", \"192147\", \"192147\", \"192531\", \"192531\", \"217562\", \"217562\", \"221984\", \"221984\", \"232882\", \"232882\", \"233440\", \"236468\", \"236468\", \"247535\", \"247535\", \"256883\", \"256883\", \"263838\", \"263838\", \"267523\", \"267523\", \"282943\", \"282943\", \"283649\", \"283649\", \"283914\", \"283914\", \"294898\", \"294898\", \"316564\", \"316564\", \"319169\", \"319169\", \"328051\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"62296","document_content":"# Learning Multimodal Affinities for Textual Editing in Images\n## Categories\n- Computer Vision and Pattern Recognition\n- I.7.1; I.5.3; I.5.4; I.2.6\n## Abstract\nNowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, document-images contain a significant amount of text with critical semantics and complicated layouts. In this work, we devise a generic unsupervised technique to learn multimodal affinities between textual entities in a document-image, considering their visual style, the content of their underlying text and their geometric context within the image. We then use these learned affinities to automatically cluster the textual entities in the image into different semantic groups. The core of our approach is a deep optimization scheme dedicated for an image provided by the user that detects and leverages reliable pairwise connections in the multimodal representation of the textual elements in order to properly learn the affinities. We show that our technique can operate on highly varying images spanning a wide range of documents and demonstrate its applicability for various editing operations manipulating the content, appearance and geometry of the image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3451340\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"I.7.1; I.5.3; I.5.4; I.2.6\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"I.7.1; I.5.3; I.5.4; I.2.6\"], \"incoming_citations\": [], \"outgoing_citations\": [\"173606\", \"221803\", \"223189\", \"234815\", \"238083\", \"240073\", \"248620\", \"254795\", \"264315\", \"268390\", \"270202\", \"278894\", \"294674\", \"295999\", \"302955\", \"320837\", \"321708\"]}","task_split":"paper_retrieval"}
{"document_id":"62373","document_content":"# Situated Language Learning via Interactive Narratives\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nThis paper provides a roadmap that explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language in service of achieving a goal. We hypothesize that two key components in creating such agents are interactivity and environment grounding, shown to be vital parts of language learning in humans, and posit that interactive narratives should be the environments of choice for such training these agents. These games are simulations in which an agent interacts with the world through natural language -- \"perceiving\", \"acting upon\", and \"talking to\" the world using textual descriptions, commands, and dialogue -- and as such exist at the intersection of natural language processing, storytelling, and sequential decision making. We discuss the unique challenges a text games' puzzle-like structure combined with natural language state-and-action spaces provides: knowledge representation, commonsense reasoning, and exploration. Beyond the challenges described so far, progress in the realm of interactive narratives can be applied in adjacent problem domains. These applications provide interesting challenges of their own as well as extensions to those discussed so far. We describe three of them in detail: (1) evaluating AI system's commonsense understanding by automatically creating interactive narratives; (2) adapting abstract text-based policies to include other modalities such as vision; and (3) enabling multi-agent and human-AI collaboration in shared, situated worlds.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09977v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"18344\"], \"outgoing_citations\": [\"73556\", \"95814\", \"97289\", \"119028\", \"127451\", \"128664\", \"145329\", \"145784\", \"145805\", \"152871\", \"166985\", \"167303\", \"187527\", \"192738\", \"196049\", \"207273\", \"210613\", \"218923\", \"222669\", \"226321\", \"226327\", \"247418\", \"263874\", \"280296\", \"282827\", \"295435\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"62391","document_content":"# Impact of Facial Tattoos and Paintings on Face Recognition Systems\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the past years, face recognition technologies have shown impressive recognition performance, mainly due to recent developments in deep convolutional neural networks. Notwithstanding those improvements, several challenges which affect the performance of face recognition systems remain. In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems. To this end, we first collected an appropriate database containing image-pairs of individuals with and without facial tattoos or paintings. The assembled database was used to evaluate how facial tattoos and paintings affect the detection, quality estimation, as well as the feature extraction and comparison modules of a face recognition system. The impact on these modules was evaluated using state-of-the-art open-source and commercial systems. The obtained results show that facial tattoos and paintings affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings. Our work is an initial case-study and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1049\/bme2.12032\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"102723\", \"117348\", \"120075\", \"120894\", \"135770\", \"138418\", \"146630\", \"148895\", \"189528\", \"191293\", \"205223\", \"209150\", \"235160\", \"255205\", \"260279\", \"318826\"]}","task_split":"paper_retrieval"}
{"document_id":"62410","document_content":"# A Survey on Spatio-temporal Data Analytics Systems\n## Categories\n- Machine Learning\n## Abstract\nDue to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09883v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"81821\", \"104381\", \"110246\", \"132508\", \"177055\", \"224868\", \"250628\", \"278471\", \"321047\", \"326683\", \"327607\"]}","task_split":"paper_retrieval"}
{"document_id":"62450","document_content":"# Training GANs with Stronger Augmentations via Contrastive Discriminator\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations in training. In this paper, we propose a novel way to address these questions by incorporating a recent contrastive representation learning scheme into the GAN discriminator, coined ContraD. This \"fusion\" enables the discriminators to work with much stronger augmentations without increasing their training instability, thereby preventing the discriminator overfitting issue in GANs more effectively. Even better, we observe that the contrastive learning itself also benefits from our GAN training, i.e., by maintaining discriminative features between real and fake samples, suggesting a strong coherence between the two worlds: good contrastive representations are also good for GAN discriminators, and vice versa. Our experimental results show that GANs with ContraD consistently improve FID and IS compared to other recent techniques incorporating data augmentations, still maintaining highly discriminative features in the discriminator in terms of the linear evaluation. Finally, as a byproduct, we also show that our GANs trained in an unsupervised manner (without labels) can induce many conditional generative models via a simple latent sampling, leveraging the learned features of ContraD. Code is available at https:\/\/github.com\/jh-jeong\/ContraD.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09742v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1131\", \"4508\", \"5322\", \"7000\", \"36385\", \"43327\", \"61906\", \"105167\", \"12157\", \"35691\", \"43660\", \"59382\"], \"outgoing_citations\": [\"95455\", \"97123\", \"113223\", \"116782\", \"117601\", \"119380\", \"119954\", \"121168\", \"123821\", \"133043\", \"137050\", \"139499\", \"142822\", \"152821\", \"159636\", \"174140\", \"182813\", \"185155\", \"186427\", \"188010\", \"201585\", \"208408\", \"224849\", \"226061\", \"240034\", \"242055\", \"245177\", \"249069\", \"272783\", \"290679\", \"337853\"]}","task_split":"paper_retrieval"}
{"document_id":"62499","document_content":"# Code Word Detection in Fraud Investigations using a Deep-Learning Approach\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn modern litigation, fraud investigators often face an overwhelming number of documents that must be reviewed throughout a matter. In the majority of legal cases, fraud investigators do not know beforehand, exactly what they are looking for, nor where to find it. In addition, fraudsters may use deception to hide their behaviour and intentions by using code words. Effectively, this means fraud investigators are looking for a needle in the haystack without knowing what the needle looks like.   As part of a larger research program, we use a framework to expedite the investigation process applying text-mining and machine learning techniques. We structure this framework using three well-known methods in fraud investigations: (i) the fraud triangle (ii) the golden (\"W\") investigation questions, and (iii) the analysis of competing hypotheses. With this framework, it is possible to automatically organize investigative data, so it is easier for investigators to find answers to typical investigative questions.   In this research, we focus on one of the components of this framework: the identification of the usage of code words by fraudsters. Here for, a novel (annotated) synthetic data set is created containing such code words, hidden in normal email communication. Subsequently, a range of machine learning techniques are employed to detect such code words. We show that the state-of-the-art BERT model significantly outperforms other methods on this task. With this result, we demonstrate that deep neural language models can reliably (F1 score of 0.9) be applied in fraud investigations for the detection of code words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09606v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175763\", \"271361\"]}","task_split":"paper_retrieval"}
{"document_id":"62503","document_content":"# On the Role of Images for Analyzing Claims in Social Media\n## Categories\n- Social and Information Networks\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nFake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09602v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"22430\", \"28679\"], \"outgoing_citations\": [\"72912\", \"76206\", \"102002\", \"102258\", \"110664\", \"111820\", \"123836\", \"124916\", \"137403\", \"139404\", \"152680\", \"169458\", \"185940\", \"268140\"]}","task_split":"paper_retrieval"}
{"document_id":"62674","document_content":"# A Multilingual African Embedding for FAQ Chatbots\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSearching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages. In this paper, we introduce an Artificial Intelligence Powered chatbot for crisis communication that would be omnichannel, multilingual and multi dialectal. We present our work on modified StarSpace embedding tailored for African dialects for the question-answering task along with the architecture of the proposed chatbot system and a description of the different layers. English, French, Arabic, Tunisian, Igbo,Yor\\`ub\\'a, and Hausa are used as languages and dialects. Quantitative and qualitative evaluation results are obtained for our real deployed Covid-19 chatbot. Results show that users are satisfied and the conversation with the chatbot is meeting customer needs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09185v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128148\", \"145384\", \"181032\"]}","task_split":"paper_retrieval"}
{"document_id":"62696","document_content":"# Learning to increase matching efficiency in identifying additional b-jets in the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process\n## Categories\n- High Energy Physics - Experiment\n- Machine Learning\n## Abstract\nThe $\\text{t}\\bar{\\text{t}}\\text{H}(\\text{b}\\bar{\\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process, which produces a top quark pair in association with a b quark pair. Therefore, understanding the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process is crucial for improving the sensitivity of a search for the $\\text{t}\\bar{\\text{t}}\\text{H}(\\text{b}\\bar{\\text{b}})$ process. To this end, when measuring the differential cross-section of the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process, we need to distinguish the b-jets originated from top quark decays, and additional b-jets originated from gluon splitting. Since there are no simple identification rules, we adopt deep learning methods to learn from data to identify the additional b-jets from the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ events. Specifically, by exploiting the special structure of the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ event data, we propose several loss functions that can be minimized to directly increase the matching efficiency, the accuracy of identifying additional b-jets. We discuss the difference between our method and another deep learning-based approach based on binary classification arXiv:1910.14535 using synthetic data. We then verify that additional b-jets can be identified more accurately by increasing matching efficiency directly rather than the binary classification accuracy, using simulated $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ event data in the lepton+jets channel from pp collision at $\\sqrt{s}$ = 13 TeV.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.09129v1\", \"primary_category\": \"hep-ex\", \"categories\": [\"cs.LG\", \"hep-ex\"], \"primary_category_human_readable\": \"High Energy Physics - Experiment\", \"categories_human_readable\": [\"Machine Learning\", \"High Energy Physics - Experiment\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225392\"]}","task_split":"paper_retrieval"}
{"document_id":"62785","document_content":"# KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nEntity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (OOV) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named \\emph{KGSynNet}. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed \\emph{fusion gate} to adaptively absorb the entities' knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our \\emph{KGSynNet} in leveraging the knowledge graph. The experimental results show that the \\emph{KGSynNet} improves the state-of-the-art methods by 14.7\\% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3\\% in the positive feedback rate of an online A\/B test on the entity linking module of a question answering system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.08893v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"55646\", \"49213\"], \"outgoing_citations\": [\"79296\", \"127709\", \"179582\", \"185889\", \"209766\", \"210500\", \"217754\", \"284357\", \"300867\", \"309148\", \"311942\"]}","task_split":"paper_retrieval"}
{"document_id":"62806","document_content":"# Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThis paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (MultiHowTo100M) for pre-training. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available at http:\/\/github.com\/berniebear\/Multi-HT100M.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.08849v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"23886\", \"32678\", \"20409\", \"43276\", \"43644\", \"48259\", \"96230\"], \"outgoing_citations\": [\"96230\", \"119114\", \"125894\", \"126586\", \"127811\", \"132146\", \"135112\", \"136352\", \"137412\", \"138177\", \"139259\", \"151327\", \"159706\", \"161377\", \"164107\", \"168031\", \"168995\", \"172394\", \"173627\", \"180873\", \"181897\", \"182594\", \"188114\", \"188119\", \"188920\", \"189792\", \"191861\", \"197656\", \"200678\", \"202124\", \"217562\", \"221514\", \"221984\", \"230727\", \"257375\", \"259706\", \"260373\", \"260926\", \"274183\", \"278894\", \"291073\", \"302943\", \"310322\", \"316472\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"63045","document_content":"# Mention-centered Graph Neural Network for Document-level Relation Extraction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDocument-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.08200v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"125340\", \"134513\", \"164858\", \"169298\", \"180210\", \"180767\", \"181307\", \"192327\", \"210593\", \"214572\", \"216419\", \"219946\", \"220475\", \"230636\", \"258746\", \"268414\", \"284622\", \"313962\"]}","task_split":"paper_retrieval"}
{"document_id":"63076","document_content":"# Detection and Localization of Facial Expression Manipulations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConcern regarding the wide-spread use of fraudulent images\/videos in social media necessitates precise detection of such fraud. The importance of facial expressions in communication is widely known, and adversarial attacks often focus on manipulating the expression related features. Thus, it is important to develop methods that can detect manipulations in facial expressions, and localize the manipulated regions. To address this problem, we propose a framework that is able to detect manipulations in facial expression using a close combination of facial expression recognition and image manipulation methods. With the addition of feature maps extracted from the facial expression recognition framework, our manipulation detector is able to localize the manipulated region. We show that, on the Face2Face dataset, where there is abundant expression manipulation, our method achieves over 3% higher accuracy for both classification and localization of manipulations compared to state-of-the-art methods. In addition, results on the NeuralTextures dataset where the facial expressions corresponding to the mouth regions have been modified, show 2% higher accuracy in both classification and localization of manipulation. We demonstrate that the method performs at-par with the state-of-the-art methods in cases where the expression is not manipulated, but rather the identity is changed, thus ensuring generalizability of the approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.08134v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"40777\"], \"outgoing_citations\": [\"71804\", \"109032\", \"146691\", \"147899\", \"148972\", \"160879\", \"163461\", \"164494\", \"167088\", \"180443\", \"188099\", \"188623\", \"196242\", \"205223\", \"206993\", \"211893\", \"211897\", \"212878\", \"219266\", \"228884\", \"231497\", \"232230\", \"237252\", \"237855\", \"244613\", \"258664\", \"262250\", \"265282\", \"271011\", \"271584\", \"319938\"]}","task_split":"paper_retrieval"}
{"document_id":"63124","document_content":"# Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nWe present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics. In contrast, SCT explicitly takes the agent's dynamics model into consideration, and aims to accurately capture how well the agent has approximated the fastest navigation behavior afforded by its dynamics. While several embodied navigation works use point-turn dynamics, we focus on unicycle-cart dynamics for our agent, which better exemplifies the dynamics model of popular mobile robotics platforms (e.g., LoCoBot, TurtleBot, Fetch, etc.). We also present RRT*-Unicycle, an algorithm for unicycle dynamics that estimates the fastest collision-free path and completion time from a starting pose to a goal location in an environment containing obstacles. We experiment with deep reinforcement learning and reward shaping to train and compare the navigation performance of agents with different dynamics models. In evaluating these agents, we show that in contrast to SPL, SCT is able to capture the advantages in navigation speed a unicycle model has over a simpler point-turn model of dynamics. Lastly, we show that we can successfully deploy our trained models and algorithms outside of simulation in the real world. We embody our agents in an real robot to navigate an apartment, and show that they can generalize in a zero-shot manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.08022v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"21900\", \"18840\", \"56552\"], \"outgoing_citations\": [\"90871\", \"91301\", \"114824\", \"116592\", \"131814\", \"158547\", \"192738\", \"219658\", \"224142\", \"224223\"]}","task_split":"paper_retrieval"}
{"document_id":"63204","document_content":"# Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMultimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future. Previous frameworks can be divided into three categories: regression, generation and classification frameworks. However, these frameworks have weaknesses in different aspects so that they cannot model the multimodal prediction task comprehensively. In this paper, we present a novel insight along with a brand-new prediction framework by formulating multimodal prediction into three steps: modality clustering, classification and synthesis, and address the shortcomings of earlier frameworks. Exhaustive experiments on popular benchmarks have demonstrated that our proposed method surpasses state-of-the-art works even without introducing social and map information. Specifically, we achieve 19.2% and 20.8% improvement on ADE and FDE respectively on ETH\/UCY dataset. Our code will be made publicly availabe.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.07854v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7698\", \"38000\"], \"outgoing_citations\": [\"65095\", \"111800\", \"119784\", \"124451\", \"128967\", \"129703\", \"133076\", \"133489\", \"139777\", \"151320\", \"154697\", \"162130\", \"185315\", \"199454\", \"207713\", \"211345\", \"212031\", \"214313\", \"229266\", \"237350\", \"253435\", \"268891\", \"279059\", \"356259\"]}","task_split":"paper_retrieval"}
{"document_id":"63219","document_content":"# Dynamically Switching Human Prediction Models for Efficient Planning\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nAs environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously; a more complex one might predict that the human will act optimally, disregarding the robot; whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.07815v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"54054\"], \"outgoing_citations\": [\"147365\", \"166003\", \"167540\", \"186194\", \"231418\", \"243185\", \"252398\", \"260731\", \"274823\"]}","task_split":"paper_retrieval"}
{"document_id":"63298","document_content":"# Improving Diversity of Neural Text Generation via Inverse Probability Weighting\n## Categories\n- Computation and Language\n## Abstract\nThe neural text generation suffers from the text degeneration issue such as repetition. Traditional stochastic sampling methods only focus on truncating the unreliable \"tail\" of the distribution, and do not address the \"head\" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops. They also do not consider the issue that human text does not always favor high-probability words. Inspired by these, in this work we propose a heuristic sampling method. We propose to use interquartile range of the predicted distribution to determine the \"head\" part, then permutate and rescale the \"head\" with inverse probability. This aims at decreasing the probability for the tedious and possibly repetitive candidates with higher probability, and increasing the probability for the rational but more surprising candidates with lower probability. The proposed algorithm provides a reasonable permutation on the predicted distribution which enhances diversity without compromising rationality of the distribution. We use pre-trained language model to compare our algorithm with traditional methods. Results show that our algorithm can effectively increase the diversity of generated samples while achieving close resemblance to human text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.07649v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"27291\", \"42357\"], \"outgoing_citations\": [\"108978\", \"128009\", \"189504\", \"192158\", \"231949\", \"232274\", \"243154\", \"260247\", \"265309\", \"291102\"]}","task_split":"paper_retrieval"}
{"document_id":"63305","document_content":"# Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nConsider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classification problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efficient approach to learn the classifier and generalize it with a minimal amount of data. A high-fidelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classifier, and for each branch of the decision tree, we reason about the confidence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain confidence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2105.05019v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"160607\", \"188493\"]}","task_split":"paper_retrieval"}
{"document_id":"63306","document_content":"# Simpson's Bias in NLP Training\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn most machine learning tasks, we evaluate a model $M$ on a given data population $S$ by measuring a population-level metric $F(S;M)$. Examples of such evaluation metric $F$ include precision\/recall for (binary) recognition, the F1 score for multi-class classification, and the BLEU metric for language generation. On the other hand, the model $M$ is trained by optimizing a sample-level loss $G(S_t;M)$ at each learning step $t$, where $S_t$ is a subset of $S$ (a.k.a. the mini-batch). Popular choices of $G$ include cross-entropy loss, the Dice loss, and sentence-level BLEU scores. A fundamental assumption behind this paradigm is that the mean value of the sample-level loss $G$, if averaged over all possible samples, should effectively represent the population-level metric $F$ of the task, such as, that $\\mathbb{E}[ G(S_t;M) ] \\approx F(S;M)$.   In this paper, we systematically investigate the above assumption in several NLP tasks. We show, both theoretically and experimentally, that some popular designs of the sample-level loss $G$ may be inconsistent with the true population-level metric $F$ of the task, so that models trained to optimize the former can be substantially sub-optimal to the latter, a phenomenon we call it, Simpson's bias, due to its deep connections with the classic paradox known as Simpson's reversal paradox in statistics and social sciences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.11795v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"157384\", \"157587\", \"220186\", \"234469\", \"250577\", \"287823\", \"301840\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"63347","document_content":"# Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning\n## Categories\n- Computation and Language\n## Abstract\nFew-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a technique particularly suitable for training with limited data -- for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average.   To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2021.naacl-main.434\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24125\", \"24590\", \"51130\"], \"outgoing_citations\": [\"121633\", \"122950\", \"147344\", \"186466\", \"188492\", \"189595\", \"200867\", \"213179\", \"220611\", \"226232\", \"242601\", \"290778\", \"292687\", \"298513\", \"302824\", \"302895\"]}","task_split":"paper_retrieval"}
{"document_id":"63488","document_content":"# Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models' Transferability\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThis paper investigates whether the power of the models pre-trained on text data, such as BERT, can be transferred to general token sequence classification applications. To verify pre-trained models' transferability, we test the pre-trained models on text classification tasks with meanings of tokens mismatches, and real-world non-text token sequence classification data, including amino acid, DNA, and music. We find that even on non-text data, the models pre-trained on text converge faster, perform better than the randomly initialized models, and only slightly worse than the models using task-specific knowledge. We also find that the representations of the text and non-text pre-trained models share non-trivial similarities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.07162v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"79010\", \"97224\", \"97908\", \"119541\", \"128002\", \"130590\", \"154444\", \"190543\", \"212525\", \"227957\", \"228204\"]}","task_split":"paper_retrieval"}
{"document_id":"63736","document_content":"# Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCausal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of-the-art local causal structure learning algorithms, a set of state-of-the-art MB learning algorithms, and functions for evaluating algorithms. The data generation part of Causal Learner is written in R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to provide researchers and practitioners with an open-source platform for causal learning from data and for the development and evaluation of new causal learning algorithms. The Causal Learner project is available at http:\/\/bigdata.ahu.edu.cn\/causal-learner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.06544v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"346695\"]}","task_split":"paper_retrieval"}
{"document_id":"63817","document_content":"# Energy Decay Network (EDeN)\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nThis paper and accompanying Python and C++ Framework is the product of the authors perceived problems with narrow (Discrimination based) AI. (Artificial Intelligence) The Framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation\/exchange value (energy) to create a model whereby neural architecture and all unit processes are co-dependently developed by genetic and real time signal processing influences; successful routes are defined by stability of the spike distribution per epoch which is influenced by genetically encoded morphological development biases.These principles are aimed towards creating a diverse and robust network that is capable of adapting to general tasks by training within a simulation designed for transfer learning to other mediums at scale.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.31224\/osf.io\/dfyzn\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"63870","document_content":"# CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis\n## Categories\n- Machine Learning\n## Abstract\nModern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing `creative' designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.06242v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"14780\", \"54725\"], \"outgoing_citations\": [\"100546\", \"105669\", \"108739\", \"119380\", \"123049\", \"126852\", \"140390\", \"178695\", \"196971\", \"231729\", \"240914\", \"241846\", \"242554\", \"263146\", \"271257\"]}","task_split":"paper_retrieval"}
{"document_id":"63992","document_content":"# How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution\n## Categories\n- Computation and Language\n## Abstract\nAutomatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, the continuous evolution process would generate unprecedented fake news and cheat the original model. We present the Fake News Evolution (FNE) dataset: a new dataset tracking the fake news evolution process. Our dataset is composed of 950 paired data, each of which consists of articles representing the three significant phases of the evolution process, which are the truth, the fake news, and the evolved fake news. We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.05944v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"154507\", \"163109\", \"168163\", \"183694\", \"219169\", \"259202\", \"267552\", \"297117\", \"333086\"]}","task_split":"paper_retrieval"}
{"document_id":"64122","document_content":"# Challenges for Reinforcement Learning in Healthcare\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- J.3; I.2\n## Abstract\nMany healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.05612v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"J.3; I.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"J.3; I.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"186313\", \"202934\", \"206875\", \"208939\", \"214701\", \"221387\", \"229839\", \"241329\", \"242732\", \"248533\", \"249233\", \"252895\", \"265649\", \"268399\", \"282777\", \"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"64196","document_content":"# Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, a new task is proposed, namely, weather translation, which refers to transferring weather conditions of the image from one category to another. It is important for photographic style transfer. Although lots of approaches have been proposed in traditional image translation tasks, few of them can handle the multi-category weather translation task, since weather conditions have rich categories and highly complex semantic structures. To address this problem, we develop a multi-domain weather translation approach based on generative adversarial networks (GAN), denoted as Weather GAN, which can achieve the transferring of weather conditions among sunny, cloudy, foggy, rainy and snowy. Specifically, the weather conditions in the image are determined by various weather-cues, such as cloud, blue sky, wet ground, etc. Therefore, it is essential for weather translation to focus the main attention on weather-cues. To this end, the generator of Weather GAN is composed of an initial translation module, an attention module and a weather-cue segmentation module. The initial translation module performs global translation during generation procedure. The weather-cue segmentation module identifies the structure and exact distribution of weather-cues. The attention module learns to focus on the interesting areas of the image while keeping other areas unaltered. The final generated result is synthesized by these three parts. This approach suppresses the distortion and deformation caused by weather translation. our approach outperforms the state-of-the-arts has been shown by a large number of experiments and evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.05422v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"18536\", \"35499\", \"34948\"], \"outgoing_citations\": [\"87695\", \"92173\", \"135967\", \"189179\", \"204590\", \"227816\", \"229037\", \"232133\", \"235769\", \"237705\", \"238566\", \"241758\", \"245963\", \"248595\", \"248730\", \"249153\", \"270879\", \"272553\", \"284565\"]}","task_split":"paper_retrieval"}
{"document_id":"64314","document_content":"# Improving Document-Level Sentiment Classification Using Importance of Sentences\n## Categories\n- Computation and Language\n## Abstract\nPrevious researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/e22121336\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"70490\", \"77332\"], \"outgoing_citations\": [\"167795\", \"250376\", \"315890\"]}","task_split":"paper_retrieval"}
{"document_id":"64337","document_content":"# Model Complexity of Deep Learning: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nModel complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity. We review the existing studies on those two categories along four important factors, including model framework, model size, optimization process and data complexity. We also discuss the applications of deep learning model complexity including understanding model generalization, model optimization, and model selection and design. We conclude by proposing several interesting future directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.05127v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"19284\", \"2461\", \"24349\"], \"outgoing_citations\": [\"118315\", \"150465\", \"152688\", \"183435\", \"183846\", \"184118\", \"196916\", \"201578\", \"210391\", \"210936\", \"212644\", \"217274\", \"229898\", \"231186\", \"234807\", \"241034\", \"242806\", \"246259\", \"248564\", \"251258\", \"251401\", \"251608\", \"251913\", \"256582\", \"256736\", \"262817\", \"271981\", \"281199\", \"284631\", \"290234\", \"290235\", \"297210\", \"297358\", \"316785\", \"320016\", \"333968\", \"336193\", \"348722\"]}","task_split":"paper_retrieval"}
{"document_id":"64489","document_content":"# Cluster-based Input Weight Initialization for Echo State Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nEcho State Networks (ESNs) are a special type of recurrent neural networks (RNNs), in which the input and recurrent connections are traditionally generated randomly, and only the output weights are trained. Despite the recent success of ESNs in various tasks of audio, image and radar recognition, we postulate that a purely random initialization is not the ideal way of initializing ESNs. The aim of this work is to propose an unsupervised initialization of the input connections using the $K$-Means algorithm on the training data. We show that for a large variety of datasets this initialization performs equivalently or superior than a randomly initialized ESN whilst needing significantly less reservoir neurons. Furthermore, we discuss that this approach provides the opportunity to estimate a suitable size of the reservoir based on prior knowledge about the data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2022.3145565\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"64458\"], \"outgoing_citations\": [\"163877\", \"193355\", \"226050\", \"232836\", \"238246\", \"274275\"]}","task_split":"paper_retrieval"}
{"document_id":"64504","document_content":"# Learning Graph Neural Networks with Positive and Unlabeled Nodes\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes. To enable graph neural network learning, existing works typically assume that labeled nodes, from two or multiple classes, are provided, so that a discriminative classifier can be learned from the labeled data. In reality, this assumption might be too restrictive for applications, as users may only provide labels of interest in a single class for a small number of nodes. In addition, most GNN models only aggregate information from short distances (e.g., 1-hop neighbors) in each round, and fail to capture long distance relationship in graphs. In this paper, we propose a novel graph neural network framework, long-short distance aggregation networks (LSDAN), to overcome these limitations. By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs. The direct neighbors are captured via a short-distance attention mechanism, and neighbors with long distance are captured by a long distance attention mechanism. Two novel risk estimators are further employed to aggregate long-short-distance networks, for PU learning and the loss is back-propagated for model learning. Experimental results on real-world datasets demonstrate the effectiveness of our algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3450316\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"168780\", \"8265\", \"28136\"], \"outgoing_citations\": [\"71722\", \"86320\", \"117093\", \"157816\", \"180610\", \"204003\", \"228992\", \"234692\", \"238422\", \"242416\", \"245486\", \"253504\", \"272618\", \"289473\", \"305707\", \"311030\", \"313371\"]}","task_split":"paper_retrieval"}
{"document_id":"64560","document_content":"# Asymptotics of Ridge Regression in Convolutional Models\n## Categories\n- Machine Learning\n## Abstract\nUnderstanding generalization and estimation error of estimators for simple models such as linear and generalized linear models has attracted a lot of attention recently. This is in part due to an interesting observation made in machine learning community that highly over-parameterized neural networks achieve zero training error, and yet they are able to generalize well over the test samples. This phenomenon is captured by the so called double descent curve, where the generalization error starts decreasing again after the interpolation threshold. A series of recent works tried to explain such phenomenon for simple models. In this work, we analyze the asymptotics of estimation error in ridge estimators for convolutional linear models. These convolutional inverse problems, also known as deconvolution, naturally arise in different fields such as seismology, imaging, and acoustics among others. Our results hold for a large class of input distributions that include i.i.d. features as a special case. We derive exact formulae for estimation error of ridge estimators that hold in a certain high-dimensional regime. We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.04557v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116050\", \"117812\", \"120115\", \"127742\", \"152688\", \"156464\", \"157359\", \"158352\", \"184118\", \"193433\", \"194532\", \"194690\", \"204540\", \"204836\", \"209138\", \"209785\", \"210734\", \"210798\", \"215486\", \"222357\", \"222593\", \"253651\", \"261109\", \"282918\"]}","task_split":"paper_retrieval"}
{"document_id":"64631","document_content":"# Adaptive Agent Architecture for Real-time Human-Agent Teaming\n## Categories\n- Robotics\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nTeamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players' skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.04439v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"151271\", \"189416\", \"202683\", \"214395\", \"218899\", \"231267\", \"255077\", \"258472\", \"264940\"]}","task_split":"paper_retrieval"}
{"document_id":"64708","document_content":"# Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nReward function, as an incentive representation that recognizes humans' agency and rationalizes humans' actions, is particularly appealing for modeling human behavior in human-robot interaction. Inverse Reinforcement Learning is an effective way to retrieve reward functions from demonstrations. However, it has always been challenging when applying it to multi-agent settings since the mutual influence between agents has to be appropriately modeled. To tackle this challenge, previous work either exploits equilibrium solution concepts by assuming humans as perfectly rational optimizers with unbounded intelligence or pre-assigns humans' interaction strategies a priori. In this work, we advocate that humans are bounded rational and have different intelligence levels when reasoning about others' decision-making process, and such an inherent and latent characteristic should be accounted for in reward learning algorithms. Hence, we exploit such insights from Theory-of-Mind and propose a new multi-agent Inverse Reinforcement Learning framework that reasons about humans' latent intelligence levels during learning. We validate our approach in both zero-sum and general-sum games with synthetic agents and illustrate a practical application to learning human drivers' reward functions from real driving data. We compare our approach with two baseline algorithms. The results show that by reasoning about humans' latent intelligence levels, the proposed approach has more flexibility and capability to retrieve reward functions that explain humans' driving behaviors better.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.04289v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"38642\", \"26056\"], \"outgoing_citations\": [\"102575\", \"105013\", \"117065\", \"123164\", \"164133\", \"173723\", \"201508\", \"203923\", \"214395\", \"214806\", \"218635\", \"221960\", \"280655\", \"285682\", \"344078\"]}","task_split":"paper_retrieval"}
{"document_id":"64775","document_content":"# Improving Zero-Shot Entity Retrieval through Effective Dense Representations\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- I.2.7; H.3.3\n## Abstract\nEntity Linking (EL) seeks to align entity mentions in text to entries in a knowledge-base and is usually comprised of two phases: candidate generation and candidate ranking. While most methods focus on the latter, it is the candidate generation phase that sets an upper bound to both time and accuracy performance of the overall EL system. This work's contribution is a significant improvement in candidate generation which thus raises the performance threshold for EL, by generating candidates that include the gold entity in the least candidate set (top-K). We propose a simple approach that efficiently embeds mention-entity pairs in dense space through a BERT-based bi-encoder. Specifically, we extend (Wu et al., 2020) by introducing a new pooling function and incorporating entity type side-information. We achieve a new state-of-the-art 84.28% accuracy on top-50 candidates on the Zeshel dataset, compared to the previous 82.06% on the top-64 of (Wu et al., 2020). We report the results from extensive experimentation using our proposed model on both seen and unseen entity datasets. Our results suggest that our method could be a useful complement to existing EL approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.04156v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"I.2.7; H.3.3\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"I.2.7; H.3.3\"], \"incoming_citations\": [\"24055\"], \"outgoing_citations\": [\"41806\", \"72124\", \"94753\", \"96431\", \"148404\", \"157181\", \"157255\", \"165378\", \"175482\", \"180267\", \"191177\", \"208638\", \"224723\", \"243397\", \"268728\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"64858","document_content":"# Simultaneously Localize, Segment and Rank the Camouflaged Objects\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCamouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [35]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. And, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.04011v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"71347\", \"11345\", \"68066\", \"8088\", \"12780\", \"39693\", \"40346\", \"41576\", \"47168\", \"57952\", \"58541\", \"63540\"], \"outgoing_citations\": [\"48476\", \"54400\", \"57952\", \"58541\", \"68066\", \"118919\", \"129697\", \"131537\", \"136059\", \"136373\", \"137184\", \"154210\", \"189585\", \"189915\", \"197308\", \"230440\", \"232376\", \"235068\", \"237454\", \"239061\", \"251965\", \"258037\", \"259523\", \"261572\", \"270340\", \"270629\", \"328505\"]}","task_split":"paper_retrieval"}
{"document_id":"64908","document_content":"# Measuring Mathematical Problem Solving With the MATH Dataset\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nMany intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics. Even though we are able to increase accuracy on MATH, our results show that accuracy remains relatively low, even with enormous Transformer models. Moreover, we find that simply increasing budgets and model parameter counts will be impractical for achieving strong mathematical reasoning if scaling trends continue. While scaling Transformers is automatically solving most other text-based tasks, scaling is not currently solving MATH. To have more traction on mathematical problem solving we will likely need new algorithmic advancements from the broader research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.03874v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"24984\", \"26489\", \"5954\", \"9241\", \"12817\", \"13400\", \"13374\", \"13464\", \"18243\", \"25667\", \"36325\", \"37895\", \"45914\", \"48280\", \"61074\", \"60990\", \"63858\"], \"outgoing_citations\": [\"29470\", \"36889\", \"38612\", \"72058\", \"75254\", \"90800\", \"101855\", \"101870\", \"111747\", \"120213\", \"120762\", \"125659\", \"127002\", \"127513\", \"129503\", \"145957\", \"153185\", \"156112\", \"183403\", \"188123\", \"191993\", \"192616\", \"229553\", \"266702\"]}","task_split":"paper_retrieval"}
{"document_id":"65100","document_content":"# DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery\n## Categories\n- Machine Learning\n- Multiagent Systems\n## Abstract\nWith the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results show that the proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery time and fuel consumption.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.03450v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"86277\", \"96717\", \"104270\", \"109449\", \"117542\", \"158912\", \"161370\", \"186457\", \"195771\", \"235762\", \"237193\", \"263576\", \"265133\"]}","task_split":"paper_retrieval"}
{"document_id":"65339","document_content":"# Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing\n## Categories\n- Software Engineering\n- Computer Vision and Pattern Recognition\n## Abstract\nSynthesising photo-realistic images from natural language is one of the challenging problems in computer vision. Over the past decade, a number of approaches have been proposed, of which the improved Stacked Generative Adversarial Network (StackGAN-v2) has proven capable of generating high resolution images that reflect the details specified in the input text descriptions. In this paper, we aim to assess the robustness and fault-tolerance capability of the StackGAN-v2 model by introducing variations in the training data. However, due to the working principle of Generative Adversarial Network (GAN), it is difficult to predict the output of the model when the training data are modified. Hence, in this work, we adopt Metamorphic Testing technique to evaluate the robustness of the model with a variety of unexpected training dataset. As such, we first implement StackGAN-v2 algorithm and test the pre-trained model provided by the original authors to establish a ground truth for our experiments. We then identify a metamorphic relation, from which test cases are generated. Further, metamorphic relations were derived successively based on the observations of prior test results. Finally, we synthesise the results from our experiment of all the metamorphic relations and found that StackGAN-v2 algorithm is susceptible to input images with obtrusive objects, even if it overlaps with the main object minimally, which was not reported by the authors and users of StackGAN-v2 model. The proposed metamorphic relations can be applied to other text-to-image synthesis models to not only verify the robustness but also to help researchers understand and interpret the results made by the machine learning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MET52542.2021.00008\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\", \"cs.CV\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150453\", \"245825\", \"267121\", \"294611\"]}","task_split":"paper_retrieval"}
{"document_id":"65390","document_content":"# Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nAutonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real-world being developed. In the context of the complete autonomous vehicle control pipeline, subsystems downstream of perception need to be tested with accurate recreations of the perception system output, rather than focusing on subjective visual realism of the input - whether in simulation or the real world. This study develops the untapped potential of a lightweight weather augmentation method in an autonomous racing vehicle - focusing not on visual accuracy, but rather the effect upon perception subsystem performance in real time. With minimal adjustment, the prototype developed in this study can replicate the effects of water droplets on the camera lens, and fading light conditions. This approach introduces a latency of less than 8 ms using compute hardware well suited to being carried in the vehicle - rendering it ideal for real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.02760v4\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25795\"], \"outgoing_citations\": [\"105905\", \"115276\", \"137519\", \"146773\", \"152373\", \"169927\", \"186256\", \"187778\", \"201953\", \"203887\", \"204030\", \"217374\", \"239575\", \"266485\", \"275747\"]}","task_split":"paper_retrieval"}
{"document_id":"65673","document_content":"# Augmentation Strategies for Learning with Noisy Labels\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the \"learning with noisy labels\" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset Clothing1M. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset.   (K. Nishi and Y. Ding contributed equally to this work)","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.02130v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6534\", \"7999\", \"10877\", \"45728\", \"4960\", \"5724\", \"7727\", \"7861\", \"14381\", \"27701\", \"38726\", \"60132\"], \"outgoing_citations\": [\"115038\", \"125914\", \"141691\", \"152544\", \"186598\", \"188492\", \"189025\", \"194631\", \"203033\", \"206182\", \"228948\", \"235096\", \"237224\", \"247500\", \"251743\", \"263559\", \"264247\", \"275596\", \"290407\", \"320027\"]}","task_split":"paper_retrieval"}
{"document_id":"65753","document_content":"# MultiSubs: A Large-scale Multimodal and Multilingual Dataset\n## Categories\n- Computation and Language\n## Abstract\nThis paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. The dataset is a valuable resource as (i) the images are aligned to text fragments rather than whole sentences; (ii) multiple images are possible for a text fragment and a sentence; (iii) the sentences are free-form and real-world like; (iv) the parallel texts are multilingual. We set up a fill-in-the-blank game for humans to evaluate the quality of the automatic image selection process of our dataset. We show the utility of the dataset on two automatic tasks: (i) fill-in-the-blank; (ii) lexical translation. Results of the human evaluation and automatic models demonstrate that images can be a useful complement to the textual context. The dataset will benefit research on visual grounding of words especially in the context of free-form sentences, and can be obtained from https:\/\/doi.org\/10.5281\/zenodo.5034604 under a Creative Commons licence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.01910v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23886\", \"25136\"], \"outgoing_citations\": [\"214285\", \"227757\", \"252903\", \"265384\", \"268169\", \"279518\", \"293418\", \"295598\", \"296049\", \"300099\", \"313440\", \"315108\", \"321682\"]}","task_split":"paper_retrieval"}
{"document_id":"65799","document_content":"# The Rediscovery Hypothesis: Language Models Need to Meet Linguistics\n## Categories\n- Computation and Language\n## Abstract\nThere is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether linguistic knowledge is a necessary condition for the good performance of modern language models, which we call the \\textit{rediscovery hypothesis}. In the first place, we show that language models that are significantly compressed but perform well on their pretraining objectives retain good scores when probed for linguistic structures. This result supports the rediscovery hypothesis and leads to the second contribution of our paper: an information-theoretic framework that relates language modeling objectives with linguistic information. This framework also provides a metric to measure the impact of linguistic information on the word prediction task. We reinforce our analytical results with various experiments, both on synthetic and on real NLP tasks in English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.1.12788\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26685\", \"53602\"], \"outgoing_citations\": [\"70750\", \"87953\", \"95883\", \"96531\", \"100467\", \"108090\", \"110036\", \"118253\", \"121728\", \"124850\", \"127101\", \"127502\", \"127590\", \"127850\", \"127922\", \"128059\", \"128882\", \"130821\", \"132635\", \"134662\", \"136604\", \"138515\", \"139638\", \"141303\", \"161070\", \"168097\", \"168951\", \"182116\", \"186130\", \"186250\", \"201292\", \"214624\", \"220838\", \"233400\", \"237283\", \"237900\", \"250815\", \"259706\", \"281205\", \"281225\", \"283971\", \"286594\", \"302478\", \"317627\"]}","task_split":"paper_retrieval"}
{"document_id":"65859","document_content":"# Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nEarly detection of skin cancers like melanoma is crucial to ensure high chances of survival for patients. Clinical application of Deep Learning (DL)-based Decision Support Systems (DSS) for skin cancer screening has the potential to improve the quality of patient care. The majority of work in the medical AI community focuses on a diagnosis setting that is mainly relevant for autonomous operation. Practical decision support should, however, go beyond plain diagnosis and provide explanations. This paper provides an overview of works towards explainable, DL-based decision support in medical applications with the example of skin cancer diagnosis from clinical, dermoscopic and histopathologic images. Analysis reveals that comparably little attention is payed to the explanation of histopathologic skin images and that current work is dominated by visual relevance maps as well as dermoscopic feature identification. We conclude that future work should focus on meeting the stakeholder's cognitive concepts, providing exhaustive explanations that combine global and local approaches and leverage diverse modalities. Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.05112v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"99192\", \"124097\", \"126969\", \"127184\", \"128588\", \"134283\", \"135443\", \"144046\", \"164211\", \"168243\", \"171212\", \"171647\", \"189887\", \"190836\", \"211583\", \"211753\", \"215081\", \"218317\", \"229865\", \"261733\", \"262955\", \"270624\", \"271595\", \"272491\"]}","task_split":"paper_retrieval"}
{"document_id":"66048","document_content":"# A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness\n## Categories\n- Machine Learning\n## Abstract\nAlongside the well-publicized accomplishments of deep neural networks there has emerged an apparent bug in their success on tasks such as object recognition: with deep models trained using vanilla methods, input images can be slightly corrupted in order to modify output predictions, even when these corruptions are practically invisible. This apparent lack of robustness has led researchers to propose methods that can help to prevent an adversary from having such capabilities. The state-of-the-art approaches have incorporated the robustness requirement into the loss function, and the training process involves taking stochastic gradient descent steps not using original inputs but on adversarially-corrupted ones. In this paper we propose a multiclass boosting framework to ensure adversarial robustness. Boosting algorithms are generally well-suited for adversarial scenarios, as they were classically designed to satisfy a minimax guarantee. We provide a theoretical foundation for this methodology and describe conditions under which robustness can be achieved given a weak training oracle. We show empirically that adversarially-robust multiclass boosting not only outperforms the state-of-the-art methods, it does so at a fraction of the training time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.01276v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92070\", \"147546\", \"156825\", \"160650\", \"181746\", \"181808\", \"188495\", \"199776\", \"201710\", \"212366\", \"218549\", \"224235\", \"230854\", \"238966\", \"242744\", \"243562\", \"244005\", \"245835\", \"247127\", \"251596\", \"252103\", \"258100\", \"263720\", \"265979\", \"270002\", \"311813\"]}","task_split":"paper_retrieval"}
{"document_id":"66175","document_content":"# A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSpiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training high accuracy models. A major thread of research on SNNs is on converting a pre-trained convolutional neural network (CNN) to an SNN of the same structure. State-of-the-art conversion methods are approaching the accuracy limit, i.e., the near-zero accuracy loss of SNN against the original CNN. However, we note that this is made possible only when significantly more energy is consumed to process an input. In this paper, we argue that this trend of \"energy for accuracy\" is not necessary -- a little energy can go a long way to achieve the near-zero accuracy loss. Specifically, we propose a novel CNN-to-SNN conversion method that is able to use a reasonably short spike train (e.g., 256 timesteps for CIFAR10 images) to achieve the near-zero accuracy loss. The new conversion method, named as explicit current control (ECC), contains three techniques (current normalisation, thresholding for residual elimination, and consistency maintenance for batch-normalisation), in order to explicitly control the currents flowing through the SNN when processing inputs. We implement ECC into a tool nicknamed SpKeras, which can conveniently import Keras CNN models and convert them into SNNs. We conduct an extensive set of experiments with the tool -- working with VGG16 and various datasets such as CIFAR10 and CIFAR100 -- and compare with state-of-the-art conversion methods. Results show that ECC is a promising method that can optimise over energy consumption and accuracy loss simultaneously.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/fnins.2022.759900\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42483\", \"66387\", \"127058\", \"140136\", \"140546\", \"190034\", \"195058\", \"230061\", \"242996\", \"247330\", \"270473\", \"285642\"]}","task_split":"paper_retrieval"}
{"document_id":"66240","document_content":"# Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn this paper, we present a multi-lingual sentence encoder that can be used in search engines as a query and document encoder. This embedding enables a semantic similarity score between queries and documents that can be an important feature in document ranking and relevancy. To train such a customized sentence encoder, it is beneficial to leverage users search data in the form of query-document clicked pairs however, we must avoid relying too much on search click data as it is biased and does not cover many unseen cases. The search data is heavily skewed towards short queries and for long queries is small and often noisy. The goal is to design a universal multi-lingual encoder that works for all cases and covers both short and long queries. We select a number of public NLI datasets in different languages and translation data and together with user search data we train a language model using a multi-task approach. A challenge is that these datasets are not homogeneous in terms of content, size and the balance ratio. While the public NLI datasets are usually two-sentence based with the same portion of positive and negative pairs, the user search data can contain multi-sentence documents and only positive pairs. We show how multi-task training enables us to leverage all these datasets and exploit knowledge sharing across these tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2106.07719v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92779\", \"111872\", \"135112\", \"146533\", \"165441\", \"176600\", \"176659\", \"183076\", \"185112\", \"206198\", \"212322\", \"217957\", \"237274\", \"267161\", \"267351\", \"279343\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"66252","document_content":"# RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nEntity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majority of the existing embeddings-based entity alignment methods embed entities and relations into a vector space based on relation triples of KGs for local alignment. As these methods insufficiently consider the multiple relations between entities, the structure information of KGs has not been fully leveraged. In this paper, we propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities. Furthermore, we propose a global alignment algorithm to make one-to-one entity alignments with a fine-grained similarity matrix. Experiments on three real-world cross-lingual datasets show that our framework outperforms the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.00791v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"37040\"], \"outgoing_citations\": [\"105369\", \"109626\", \"125610\", \"129719\", \"145482\", \"145839\", \"165809\", \"166443\", \"170709\", \"258410\", \"280634\", \"313465\"]}","task_split":"paper_retrieval"}
{"document_id":"66310","document_content":"# Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires players to make multiple decisions during the game. Decision-making in Monopoly involves many real-world elements such as strategizing, luck, and modeling of opponent's policies. In this paper, we present novel representations for the state and action space for the full version of Monopoly and define an improved reward function. Using these, we show that our deep reinforcement learning agent can learn winning strategies for Monopoly against different fixed-policy agents. In Monopoly, players can take multiple actions even if it is not their turn to roll the dice. Some of these actions occur more frequently than others, resulting in a skewed distribution that adversely affects the performance of the learning agent. To tackle the non-uniform distribution of actions, we propose a hybrid approach that combines deep reinforcement learning (for frequent but complex decisions) with a fixed policy approach (for infrequent but straightforward decisions). Experimental results show that our hybrid agent outperforms a standard deep reinforcement learning agent by 30% in the number of games won against fixed-policy agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.00683v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"161794\", \"168583\", \"191707\", \"193066\", \"236923\", \"253974\", \"302857\", \"303049\", \"309498\"]}","task_split":"paper_retrieval"}
{"document_id":"66380","document_content":"# Exploring the social influence of Kaggle virtual community on the M5 competition\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nOne of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online platform of data scientists and machine learning practitioners. Kaggle provides a gathering place, or virtual community, for web users who are interested in the M5 competition. Users can share code, models, features, loss functions, etc. through online notebooks and discussion forums. This paper aims to study the social influence of virtual community on user behaviors in the M5 competition. We first research the content of the M5 virtual community by topic modeling and trend analysis. Further, we perform social media analysis to identify the potential relationship network of the virtual community. We study the roles and characteristics of some key participants that promote the diffusion of information within the M5 virtual community. Overall, this study provides in-depth insights into the mechanism of the virtual community's influence on the participants and has potential implications for future online competitions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.00501v3\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"124346\"]}","task_split":"paper_retrieval"}
{"document_id":"66487","document_content":"# N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking\n## Categories\n- Computation and Language\n## Abstract\nAugmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. Unlike other augmentation strategies, it operates with as few as five examples. Our augmentation strategy yields significant improvements when both adapting a DST model to a new domain, and when adapting a language model to the DST task, on evaluations with TRADE and TOD-BERT models. Further analysis shows that our model performs better on seen values during training, and it is also more robust to unseen values. We conclude that exploiting belief state annotations enhances dialogue augmentation and results in improved models in n-shot training scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.00293v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"51130\", \"75004\", \"90522\", \"91631\", \"106036\", \"124328\", \"127406\", \"129221\", \"130905\", \"131150\", \"140759\", \"152623\", \"170825\", \"185347\", \"195886\", \"200867\", \"216019\", \"216265\", \"224315\", \"225791\", \"231442\", \"231898\", \"234577\", \"246324\", \"267601\", \"272068\", \"294494\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"66570","document_content":"# Meta-Learning with Graph Neural Networks: Methods and Applications\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2103.00137v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"102775\"], \"outgoing_citations\": [\"49630\", \"51479\", \"65047\", \"66264\", \"67661\", \"68391\", \"69111\", \"71722\", \"80992\", \"113849\", \"116748\", \"117213\", \"118766\", \"119358\", \"131702\", \"136162\", \"139659\", \"139881\", \"140855\", \"142226\", \"150166\", \"150176\", \"156858\", \"162941\", \"164957\", \"168796\", \"181255\", \"182115\", \"182533\", \"184987\", \"187232\", \"193831\", \"194480\", \"197245\", \"197982\", \"200764\", \"210119\", \"213046\", \"221106\", \"238422\", \"240758\", \"241565\", \"255737\", \"269617\", \"312389\", \"351617\"]}","task_split":"paper_retrieval"}
{"document_id":"66671","document_content":"# Gradient-guided Loss Masking for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nTo mitigate the negative effect of low quality training data on the performance of neural machine translation models, most existing strategies focus on filtering out harmful data before training starts. In this paper, we explore strategies that dynamically optimize data usage during the training process using the model's gradients on a small set of clean data. At each training step, our algorithm calculates the gradient alignment between the training data and the clean data to mask out data with negative alignment. Our method has a natural intuition: good training data should update the model parameters in a similar direction as the clean data. Experiments on three WMT language pairs show that our method brings significant improvement over strong baselines, and the improvements are generalizable across test data from different domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.13549v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21985\", \"23217\", \"24536\"], \"outgoing_citations\": [\"131195\", \"154778\", \"219516\", \"219573\", \"229845\", \"234469\", \"237905\", \"259541\"]}","task_split":"paper_retrieval"}
{"document_id":"66697","document_content":"# FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity is a fact and constitutes a primary problem for fairness, training performance and accuracy. Although significant efforts have been made into tackling statistical data heterogeneity, the diversity in the processing capabilities and network bandwidth of clients, termed as system heterogeneity, has remained largely unexplored. Current solutions either disregard a large portion of available devices or set a uniform limit on the model's capacity, restricted by the least capable participants. In this work, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in deep neural networks (DNNs) and enables the extraction of lower footprint submodels without the need of retraining. We further show that for linear maps our Ordered Dropout is equivalent to SVD. We employ this technique, along with a self-distillation methodology, in the realm of FL in a framework called FjORD. FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities. Extensive evaluation on both CNNs and RNNs across diverse modalities shows that FjORD consistently leads to significant performance gains over state-of-the-art baselines, while maintaining its nested structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.13451v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"11900\", \"2562\", \"8820\", \"52814\", \"6737\", \"12938\", \"18713\", \"20459\", \"35424\", \"43437\"], \"outgoing_citations\": [\"66969\", \"69230\", \"69232\", \"80364\", \"106879\", \"109184\", \"112023\", \"117458\", \"117635\", \"118278\", \"142093\", \"147242\", \"148382\", \"161637\", \"161758\", \"162023\", \"162752\", \"164048\", \"164701\", \"177109\", \"179016\", \"183930\", \"184372\", \"184652\", \"185818\", \"196107\", \"200347\", \"205577\", \"207462\", \"209139\", \"212047\", \"213207\", \"215408\", \"226146\", \"228475\", \"232524\", \"234581\", \"235592\", \"246939\", \"248216\", \"252956\", \"265073\", \"286522\", \"334128\", \"353750\"]}","task_split":"paper_retrieval"}
{"document_id":"66846","document_content":"# PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task.   In this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/computers12010017\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162693\", \"165339\", \"179953\", \"191177\", \"201647\", \"205022\", \"242126\", \"243880\", \"260541\", \"297147\", \"302500\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"66858","document_content":"# Task-Agnostic Morphology Evolution\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nDeep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form. So, how should one go about finding a morphology fit for solving tasks in a given environment? Current approaches that co-adapt morphology and behavior use a specific task's reward as a signal for morphology optimization. However, this often requires expensive policy optimization and results in task-dependent morphologies that are not built to generalize. In this work, we propose a new approach, Task-Agnostic Morphology Evolution (TAME), to alleviate both of these issues. Without any task or reward specification, TAME evolves morphologies by only applying randomly sampled action primitives on a population of agents. This is accomplished using an information-theoretic objective that efficiently ranks agents by their ability to reach diverse states in the environment and the causality of their actions. Finally, we empirically demonstrate that across 2D, 3D, and manipulation environments TAME can evolve morphologies that match the multi-task performance of those learned with task supervised algorithms. Our code and videos can be found at https:\/\/sites.google.com\/view\/task-agnostic-evolution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.13100v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"18174\", \"11658\"], \"outgoing_citations\": [\"106733\", \"107693\", \"113076\", \"124165\", \"156143\", \"160036\", \"177830\", \"181042\", \"198914\", \"214945\", \"215435\", \"229333\", \"234575\", \"241900\", \"245955\", \"246118\", \"263377\", \"279790\", \"303049\", \"305802\", \"306153\"]}","task_split":"paper_retrieval"}
{"document_id":"66865","document_content":"# Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation\n## Categories\n- Machine Learning\n## Abstract\nKnowledge distillation is classically a procedure where a neural network is trained on the output of another network along with the original targets in order to transfer knowledge between the architectures. The special case of self-distillation, where the network architectures are identical, has been observed to improve generalization accuracy. In this paper, we consider an iterative variant of self-distillation in a kernel regression setting, in which successive steps incorporate both model outputs and the ground-truth targets. This allows us to provide the first theoretical results on the importance of using the weighted ground-truth targets in self-distillation. Our focus is on fitting nonlinear functions to training data with a weighted mean square error objective function suitable for distillation, subject to $\\ell_2$ regularization of the model parameters. We show that any such function obtained with self-distillation can be calculated directly as a function of the initial fit, and that infinite distillation steps yields the same optimization problem as the original with amplified regularization. Furthermore, we provide a closed form solution for the optimal choice of weighting parameter at each step, and show how to efficiently estimate this weighting parameter for deep learning and significantly reduce the computational requirements compared to a grid search.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.13088v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46940\", \"99426\", \"121907\", \"131467\", \"134943\", \"142405\", \"143263\", \"149786\", \"152688\", \"160122\", \"182142\", \"184210\", \"184968\", \"185555\", \"188780\", \"190971\", \"191248\", \"192746\", \"198526\", \"212497\", \"227402\", \"232071\", \"232302\", \"236228\", \"240363\", \"247616\", \"252897\", \"278175\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"66906","document_content":"# Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?\n## Categories\n- Computation and Language\n## Abstract\nDevelopment of language proficiency models for non-native learners has been an active area of interest in NLP research for the past few years. Although language proficiency is multidimensional in nature, existing research typically considers a single \"overall proficiency\" while building models. Further, existing approaches also considers only one language at a time. This paper describes our experiments and observations about the role of pre-trained and fine-tuned multilingual embeddings in performing multi-dimensional, multilingual language proficiency classification. We report experiments with three languages -- German, Italian, and Czech -- and model seven dimensions of proficiency ranging from vocabulary control to sociolinguistic appropriateness. Our results indicate that while fine-tuned embeddings are useful for multilingual proficiency modeling, none of the features achieve consistently best performance for all dimensions of language proficiency. All code, data and related supplementary material can be found at: https:\/\/github.com\/nishkalavallabhi\/MultidimCEFRScoring.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.12971v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72244\"], \"outgoing_citations\": [\"132661\", \"201994\", \"204715\", \"235168\", \"244579\", \"290471\"]}","task_split":"paper_retrieval"}
{"document_id":"66923","document_content":"# Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed as federated x learning, where x includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.12920v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"44872\", \"17383\", \"36560\"], \"outgoing_citations\": [\"93444\", \"105490\", \"107082\", \"109147\", \"113531\", \"117000\", \"119099\", \"120533\", \"125428\", \"126324\", \"129194\", \"129740\", \"135674\", \"139315\", \"140332\", \"142093\", \"156183\", \"156643\", \"157890\", \"160301\", \"161126\", \"161758\", \"162301\", \"162752\", \"164627\", \"177601\", \"183930\", \"184652\", \"185690\", \"200704\", \"205620\", \"206720\", \"226146\", \"265073\", \"282410\"]}","task_split":"paper_retrieval"}
{"document_id":"67124","document_content":"# Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSeveral works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.12463v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33220\", \"41462\"], \"outgoing_citations\": [\"61579\", \"94739\", \"96756\", \"104574\", \"104584\", \"109729\", \"111464\", \"112786\", \"117983\", \"124853\", \"128518\", \"133342\", \"133812\", \"137670\", \"139814\", \"152647\", \"176932\", \"177032\", \"181115\", \"189206\", \"200920\", \"218887\", \"224233\", \"228285\", \"233501\", \"261405\", \"265896\", \"274886\", \"289709\", \"297233\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"67582","document_content":"# Exploring Supervised and Unsupervised Rewards in Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nReinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time. When applied to neural Machine Translation (MT), it minimises the mismatch between the cross-entropy loss and non-differentiable evaluation metrics like BLEU. However, the suitability of these metrics as reward function at training time is questionable: they tend to be sparse and biased towards the specific words used in the reference texts. We propose to address this problem by making models less reliant on such metrics in two ways: (a) with an entropy-regularised RL method that does not only maximise a reward function but also explore the action space to avoid peaky distributions; (b) with a novel RL method that explores a dynamic unsupervised reward function to balance between exploration and exploitation. We base our proposals on the Soft Actor-Critic (SAC) framework, adapting the off-policy maximum entropy model for language generation applications such as MT. We demonstrate that SAC with BLEU reward tends to overfit less to the training data and performs better on out-of-domain data. We also show that our dynamic unsupervised reward can lead to better translation of ambiguous words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.11403v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"100492\", \"126362\", \"161476\", \"177775\", \"206160\", \"230362\", \"230774\", \"233811\", \"241900\", \"252903\", \"263927\", \"271637\", \"272987\", \"284481\", \"286225\", \"287823\", \"301840\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"67665","document_content":"# Federated $f$-Differential Privacy\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nFederated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on record level: it provides the privacy guarantee on each individual record of one client's data against adversaries. We then propose a generic private federated learning framework {PriFedSync} that accommodates a large family of state-of-the-art FL algorithms, which provably achieves federated $f$-differential privacy. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by {PriFedSync} in computer vision tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.11158v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CR\", \"cs.CV\", \"stat.ML\", \"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70556\", \"70680\", \"105947\", \"108682\", \"117029\", \"117635\", \"120773\", \"124827\", \"130313\", \"133867\", \"135251\", \"142628\", \"151798\", \"170810\", \"207518\", \"230382\", \"281424\", \"282453\", \"326583\"]}","task_split":"paper_retrieval"}
{"document_id":"67666","document_content":"# Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data\n## Categories\n- Computation and Language\n## Abstract\nThis paper explores the difficulties of annotating transcribed spoken Dutch-Frisian code-switch utterances into Universal Dependencies. We make use of data from the FAME! corpus, which consists of transcriptions and audio data. Besides the usual annotation difficulties, this dataset is extra challenging because of Frisian being low-resource, the informal nature of the data, code-switching and non-standard sentence segmentation. As a starting point, two annotators annotated 150 random utterances in three stages of 50 utterances. After each stage, disagreements where discussed and resolved. An increase of 7.8 UAS and 10.5 LAS points was achieved between the first and third round. This paper will focus on the issues that arise when annotating a transcribed speech corpus. To resolve these issues several solutions are proposed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.11152v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129611\", \"168129\", \"234621\", \"270085\"]}","task_split":"paper_retrieval"}
{"document_id":"67671","document_content":"# Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nDeep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of CNN to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. We can match CNN feature activations to nodes in our external knowledge base. This supports knowledge-based interpretation of the features associated with model decisions. To demonstrate our approach, we build two separate graphs. We use an entity alignment method to align the feature nodes in a CNN with the nodes in a ConceptNet based knowledge graph. We then measure the proximity of CNN graph nodes to semantically meaningful knowledge base nodes. Our results show that in the aligned embedding space, nodes from the knowledge graph are close to the CNN feature nodes that have similar meanings, indicating that nodes from an external knowledge base can act as explanatory semantic references for features in the model. We analyse a variety of graph building methods in order to improve the results from our embedding space. We further demonstrate that by using hierarchical relationships from our external knowledge base, we can locate new unseen classes outside the CNN training set in our embeddings space, based on visual feature activations. This suggests that we can adapt our approach to identify unseen classes based on CNN feature activations. Our demonstrated approach of aligning a CNN with an external knowledge base paves the way to reason about and beyond the trained model, with future adaptations to explainable models and zero-shot learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.11132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"184117\", \"187128\", \"238203\", \"250142\", \"258410\", \"268506\", \"271562\", \"277968\"]}","task_split":"paper_retrieval"}
{"document_id":"67682","document_content":"# User Factor Adaptation for User Embedding via Multitask Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLanguage varies across users and their interested fields in social media data: words authored by a user across his\/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.11103v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"127554\", \"132077\", \"153344\", \"178989\", \"182713\", \"207670\", \"244339\", \"267631\", \"289057\", \"298945\", \"303025\", \"322294\"]}","task_split":"paper_retrieval"}
{"document_id":"67913","document_content":"# Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nFrom the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those variations with separate dimensions. To discover the factors and learn disentangled representation, previous methods typically leverage an extra regularization term when learning to generate realistic images. However, the term usually results in a trade-off between disentanglement and generation quality. For the generative models pretrained without any disentanglement term, the generated images show semantically meaningful variations when traversing along different directions in the latent space. Based on this observation, we argue that it is possible to mitigate the trade-off by $(i)$ leveraging the pretrained generative models with high generation quality, $(ii)$ focusing on discovering the traversal directions as factors for disentangled representation learning. To achieve this, we propose Disentaglement via Contrast (DisCo) as a framework to model the variations based on the target disentangled representations, and contrast the variations to jointly discover disentangled directions and learn disentangled representations. DisCo achieves the state-of-the-art disentangled representation learning and distinct direction discovering, given pretrained non-disentangled generative models including GAN, VAE, and Flow. Source code is at https:\/\/github.com\/xrenaa\/DisCo.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.10543v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"5936\", \"27322\"], \"outgoing_citations\": [\"33264\", \"55012\", \"63187\", \"69932\", \"91429\", \"95191\", \"108736\", \"112375\", \"123820\", \"125833\", \"129244\", \"132835\", \"143164\", \"147421\", \"174537\", \"175835\", \"180811\", \"181909\", \"185155\", \"208041\", \"222958\", \"225328\", \"241933\", \"241941\", \"242239\", \"251597\", \"271035\", \"290620\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"67939","document_content":"# A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.10499v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"52741\"], \"outgoing_citations\": [\"88915\", \"94582\", \"97999\", \"138875\", \"188938\", \"189222\", \"192118\", \"193667\", \"212191\", \"260689\", \"302916\", \"303465\", \"310867\", \"317684\", \"319781\"]}","task_split":"paper_retrieval"}
{"document_id":"67990","document_content":"# NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe understanding of the human language is quantified by identifying intents and entities. Even though classification methods that rely on labeled information are often used for the comprehension of language understanding, it is incredibly time consuming and tedious process to generate high propensity supervised datasets. In this paper, we present the generation of accurate intents for the corresponding Roman Urdu unstructured data and integrate this corpus in RASA NLU module for intent classification. We embed knowledge graph with RASA Framework to maintain the dialog history for semantic based natural language mechanism for chatbot communication. We compare results of our work with existing linguistic systems combined with semantic technologies. Minimum accuracy of intents generation is 64 percent of confidence and in the response generation part minimum accuracy is 82.1 percent and maximum accuracy gain is 96.7 percent. All the scores refers to log precision, recall, and f1 measure for each intents once summarized for all. Furthermore, it creates a confusion matrix represents that which intents are ambiguously recognized by approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.10410v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"68031","document_content":"# Physical Reasoning Using Dynamics-Aware Models\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nA common approach to solving physical reasoning tasks is to train a value learner on example tasks. A limitation of such an approach is that it requires learning about object dynamics solely from reward values assigned to the final state of a rollout of the environment. This study aims to address this limitation by augmenting the reward value with self-supervised signals about object dynamics. Specifically, we train the model to characterize the similarity of two environment rollouts, jointly with predicting the outcome of the reasoning task. This similarity can be defined as a distance measure between the trajectory of objects in the two rollouts, or learned directly from pixels using a contrastive formulation. Empirically, we find that this approach leads to substantial performance improvements on the PHYRE benchmark for physical reasoning (Bakhtin et al., 2019), establishing a new state-of-the-art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.10336v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107635\", \"117602\", \"171629\", \"255134\"]}","task_split":"paper_retrieval"}
{"document_id":"68053","document_content":"# Towards Building A Group-based Unsupervised Representation Disentanglement Framework\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nDisentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. A well-defined theoretical guarantee still lacks for the VAE-based unsupervised methods, which are a set of popular methods to achieve unsupervised disentanglement. The Group Theory based definition of representation disentanglement mathematically connects the data transformations to the representations using the formalism of group. In this paper, built on the group-based definition and inspired by the n-th dihedral group, we first propose a theoretical framework towards achieving unsupervised representation disentanglement. We then propose a model, based on existing VAE-based methods, to tackle the unsupervised learning problem of the framework. In the theoretical framework, we prove three sufficient conditions on model, group structure, and data respectively in an effort to achieve, in an unsupervised way, disentangled representation per group-based definition. With the first two of the conditions satisfied and a necessary condition derived for the third one, we offer additional constraints, from the perspective of the group-based definition, for the existing VAE-based models. Experimentally, we train 1800 models covering the most prominent VAE-based methods on five datasets to verify the effectiveness of our theoretical framework. Compared to the original VAE-based methods, these Groupified VAEs consistently achieve better mean performance with smaller variances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.10303v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"70171\", \"91429\", \"105383\", \"110743\", \"116650\", \"131445\", \"141826\", \"147421\", \"165707\", \"168312\", \"176649\", \"180811\", \"183257\", \"183732\", \"193061\", \"196894\", \"206907\", \"207094\", \"208041\", \"212169\", \"241933\", \"242239\", \"251597\", \"278987\", \"295435\"]}","task_split":"paper_retrieval"}
{"document_id":"68235","document_content":"# Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFine-grained image recognition is very challenging due to the difficulty of capturing both semantic global features and discriminative local features. Meanwhile, these two features are not easy to be integrated, which are even conflicting when used simultaneously. In this paper, a retrieval-based coarse-to-fine framework is proposed, where we re-rank the TopN classification results by using the local region enhanced embedding features to improve the Top1 accuracy (based on the observation that the correct category usually resides in TopN results). To obtain the discriminative regions for distinguishing the fine-grained images, we introduce a weakly-supervised method to train a box generating branch with only image-level labels. In addition, to learn more effective semantic global features, we design a multi-level loss over an automatically constructed hierarchical category structure. Experimental results show that our method achieves state-of-the-art performance on three benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. Also, visualizations and analysis are provided for better understanding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09875v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"43969\", \"63146\", \"14239\", \"114215\"], \"outgoing_citations\": [\"114215\", \"132785\", \"137866\", \"140481\", \"157286\", \"165064\", \"166636\", \"196126\", \"219483\", \"227817\", \"235068\", \"259166\", \"267587\", \"268934\", \"270185\", \"297315\", \"302732\", \"302806\", \"327036\", \"328477\", \"343358\"]}","task_split":"paper_retrieval"}
{"document_id":"68321","document_content":"# Back Translation Survey for Improving Text Augmentation\n## Categories\n- Computation and Language\n## Abstract\nNatural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this requirement, text augmentation should be looked at as a way to expand your current dataset and to generalize your models. One text augmentation we will look at is translation augmentation. We take an English sentence and translate it to another language before translating it back to English. In this paper, we look at the effect of 108 different language back translations on various metrics and text embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09708v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"77542\", \"96712\", \"200867\", \"288174\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"68333","document_content":"# SQAPlanner: Generating Data-Informed Software Quality Improvement Plans\n## Categories\n- Software Engineering\n- Machine Learning\n## Abstract\nSoftware Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, defect prediction models have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable-i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners' perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rule-based explanations for the predictions of defect prediction models. Finally, we develop and evaluate an information visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80% of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09687v2\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.LG\", \"cs.SE\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Machine Learning\", \"Software Engineering\"], \"incoming_citations\": [\"64244\", \"8168\", \"63530\", \"67336\", \"83143\"], \"outgoing_citations\": [\"63530\", \"67336\", \"75758\", \"83143\", \"101786\", \"172258\", \"226776\", \"230290\", \"236631\", \"239072\", \"243145\", \"243780\", \"243781\", \"243783\", \"290227\"]}","task_split":"paper_retrieval"}
{"document_id":"68487","document_content":"# Reduced-Order Neural Network Synthesis with Robustness Guarantees\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nIn the wake of the explosive growth in smartphones and cyberphysical systems, there has been an accelerating shift in how data is generated away from centralised data towards on-device generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware limited, devices to improve user privacy, reduce latency and be more energy efficient. However, our understanding of how these device orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input\/output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semi-definite programme that minimises the worst-case approximation error with respect to the larger network. Worst-case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g. pruning, is the inclusion of the worst-case approximation error directly within the training cost function, which should add robustness. Numerical examples highlight the potential of the proposed approach. The overriding goal of this paper is to generalise recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09284v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"83721\"], \"outgoing_citations\": [\"92234\", \"138248\", \"171364\", \"180710\", \"184826\", \"196610\", \"197641\", \"211749\", \"214530\", \"270473\", \"280047\", \"285659\", \"302059\", \"302879\", \"302932\", \"319905\"]}","task_split":"paper_retrieval"}
{"document_id":"68524","document_content":"# Minimizing false negative rate in melanoma detection and providing insight into the causes of classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- I.4.9; J.3\n## Abstract\nOur goal is to bridge human and machine intelligence in melanoma detection. We develop a classification system exploiting a combination of visual pre-processing, deep learning, and ensembling for providing explanations to experts and to minimize false negative rate while maintaining high accuracy in melanoma detection. Source images are first automatically segmented using a U-net CNN. The result of the segmentation is then used to extract image sub-areas and specific parameters relevant in human evaluation, namely center, border, and asymmetry measures. These data are then processed by tailored neural networks which include structure searching algorithms. Partial results are then ensembled by a committee machine. Our evaluation on the largest skin lesion dataset which is publicly available today, ISIC-2019, shows improvement in all evaluated metrics over a baseline using the original images only. We also showed that indicative scores computed by the feature classifiers can provide useful insight into the various features on which the decision can be based.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09199v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"I.4.9; J.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"I.4.9; J.3\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162635\", \"172832\", \"237466\"]}","task_split":"paper_retrieval"}
{"document_id":"68544","document_content":"# Robust and Differentially Private Mean Estimation\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Information Theory\n- Information Theory\n## Abstract\nIn statistical learning and analysis from shared data, which is increasingly widely adopted in platforms such as federated learning and meta-learning, there are two major concerns: privacy and robustness. Each participating individual should be able to contribute without the fear of leaking one's sensitive information. At the same time, the system should be robust in the presence of malicious participants inserting corrupted data. Recent algorithmic advances in learning from shared data focus on either one of these threats, leaving the system vulnerable to the other. We bridge this gap for the canonical problem of estimating the mean from i.i.d. samples. We introduce PRIME, which is the first efficient algorithm that achieves both privacy and robustness for a wide range of distributions. We further complement this result with a novel exponential time algorithm that improves the sample complexity of PRIME, achieving a near-optimal guarantee and matching a known lower bound for (non-robust) private mean estimation. This proves that there is no extra statistical cost to simultaneously guaranteeing privacy and robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09159v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [\"4734\", \"7318\", \"7989\", \"10744\", \"14347\", \"33753\", \"44699\", \"11585\", \"39649\", \"44933\", \"54065\"], \"outgoing_citations\": [\"93009\", \"108651\", \"116176\", \"116546\", \"118021\", \"119210\", \"119376\", \"121393\", \"123994\", \"125294\", \"140838\", \"140839\", \"146153\", \"154090\", \"154750\", \"156427\", \"166018\", \"167824\", \"178810\", \"181322\", \"183433\", \"183954\", \"186739\", \"194595\", \"197217\", \"199224\", \"208996\", \"217003\", \"217193\", \"229728\", \"230053\", \"233633\", \"234737\", \"239601\", \"239702\", \"241743\", \"247399\", \"249858\", \"249932\", \"269037\", \"271485\", \"272542\", \"280717\", \"280981\", \"293961\", \"294070\", \"302442\", \"311096\", \"311599\", \"316531\", \"320379\", \"326583\", \"327473\", \"338290\", \"356499\"]}","task_split":"paper_retrieval"}
{"document_id":"68562","document_content":"# Entity-level Factual Consistency of Abstractive Text Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nA key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.09130v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"9385\", \"26779\", \"16503\", \"22443\", \"24846\", \"31578\", \"50478\", \"55204\", \"91716\"], \"outgoing_citations\": [\"150522\", \"157151\", \"159316\", \"170449\", \"211890\", \"220230\", \"233710\", \"250505\", \"250690\", \"298016\"]}","task_split":"paper_retrieval"}
{"document_id":"68638","document_content":"# Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAs the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place lives of people at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets. Additionally, we propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data. Cross-SEAN partially generalises to emerging fake news as it learns from relevant external knowledge. We compare Cross-SEAN with seven state-of-the-art fake news detection methods. We observe that it achieves $0.95$ F1 Score on CTF, outperforming the best baseline by $9\\%$. We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.08924v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22517\", \"63247\", \"19514\", \"34289\", \"82909\"], \"outgoing_citations\": [\"88560\", \"88648\", \"95568\", \"101677\", \"123466\", \"128075\", \"128975\", \"141447\", \"176918\", \"179458\", \"182314\", \"188492\", \"199511\", \"207715\", \"234368\", \"259202\", \"268465\", \"271021\", \"291899\", \"303997\", \"314946\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"68665","document_content":"# ShaRF: Shape-conditioned Radiance Fields from a Single View\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nWe present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object and its use as a guide for the reconstruction of the underlying radiance field. Our formulation is based on a generative process that first maps a latent code to a voxelized shape, and then renders it to an image, with the object appearance being controlled by a second latent code. During inference, we optimize both the latent codes and the networks to fit a test image of a new object. The explicit disentanglement of shape and appearance allows our model to be fine-tuned given a single image. We can then render new views in a geometrically consistent manner and they represent faithfully the input object. Additionally, our method is able to generalize to images outside of the training domain (more realistic renderings and even real photographs). Finally, the inferred geometric scaffold is itself an accurate estimate of the object's 3D shape. We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.08860v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"5932\", \"6623\", \"7942\", \"8410\", \"17233\", \"32828\", \"35874\", \"7189\", \"35012\", \"41676\", \"44669\", \"46786\", \"56324\", \"58870\", \"60741\", \"71214\", \"81334\", \"84424\", \"95454\"], \"outgoing_citations\": [\"55756\", \"58870\", \"66863\", \"79808\", \"82893\", \"87011\", \"114053\", \"118040\", \"118898\", \"132347\", \"132835\", \"140591\", \"149905\", \"150515\", \"150974\", \"154346\", \"156067\", \"173162\", \"182539\", \"185839\", \"188623\", \"206909\", \"213394\", \"235794\", \"236720\", \"260887\", \"268404\", \"268963\", \"278937\", \"284874\", \"286586\", \"295402\", \"302827\"]}","task_split":"paper_retrieval"}
{"document_id":"68717","document_content":"# Ego-based Entropy Measures for Structural Representations on Graphs\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nMachine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on the node homophily, i.e neighboring nodes share similar characteristics. However, in many complex networks, nodes that lie to distant parts of the graph share structurally equivalent characteristics and exhibit similar roles (e.g chemical properties of distant atoms in a molecule, type of social network users). A growing literature proposed representations that identify structurally equivalent nodes. However, most of the existing methods require high time and space complexity. In this paper, we propose VNEstruct, a simple approach, based on entropy measures of the neighborhood's topology, for generating low-dimensional structural representations, that is time-efficient and robust to graph perturbations. Empirically, we observe that VNEstruct exhibits robustness on structural role identification tasks. Moreover, VNEstruct can achieve state-of-the-art performance on graph classification, without incorporating the graph structure information in the optimization, in contrast to GNN competitors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.08735v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"151458\", \"164732\", \"176236\", \"186769\", \"210426\", \"212561\", \"230004\", \"252195\", \"255737\", \"269202\"]}","task_split":"paper_retrieval"}
{"document_id":"68895","document_content":"# Quantifying the effects of environment and population diversity in multi-agent reinforcement learning\n## Categories\n- Multiagent Systems\n- Artificial Intelligence\n## Abstract\nGeneralization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players? In this paper, we investigate and quantify the relationship between generalization and diversity in the multi-agent domain. Across the range of multi-agent environments considered here, procedurally generating training levels significantly improves agent performance on held-out levels. However, agent performance on the specific levels used in training sometimes declines as a result. To better understand the effects of co-player variation, our experiments introduce a new environment-agnostic measure of behavioral diversity. Results demonstrate that population size and intrinsic motivation are both effective methods of generating greater population diversity. In turn, training with a diverse set of co-players strengthens agent performance in some (but not all) cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10458-022-09548-8\", \"primary_category\": \"cs.MA\", \"categories\": [\"cs.MA\", \"cs.AI\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Multiagent Systems\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"63173\", \"75515\", \"80268\", \"87092\", \"130071\", \"130841\", \"134858\", \"136046\", \"138270\", \"143799\", \"152918\", \"161959\", \"164729\", \"200264\", \"201876\", \"203756\", \"205643\", \"207051\", \"218124\", \"226544\", \"226697\", \"237964\", \"241900\", \"251601\", \"260667\", \"264323\", \"272987\"]}","task_split":"paper_retrieval"}
{"document_id":"69001","document_content":"# Recommender Systems for Configuration Knowledge Engineering\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n## Abstract\nThe knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and maintenance processes. We discuss a couple of scenarios for the application of recommender systems in knowledge engineering and report the results of empirical studies which show the importance of user-centered configuration knowledge organization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.08113v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"67182\", \"69725\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"69016","document_content":"# An AutoML-based Approach to Multimodal Image Sentiment Analysis\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nSentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/IJCNN52387.2021.9533552\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"102540\", \"173324\", \"198498\", \"221186\", \"227801\", \"260475\", \"271934\", \"291045\", \"292925\"]}","task_split":"paper_retrieval"}
{"document_id":"69075","document_content":"# Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach\n## Categories\n- Networking and Internet Architecture\n- Signal Processing\n- Machine Learning\n## Abstract\nWe consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wireless edge employing over-the-air computing, a common subset of k-coordinates of the gradient updates across edge devices are selected by the receiver in each iteration, and then transmitted simultaneously over k sub-carriers, each experiencing time-varying channel conditions. We characterize the impact of communication error and compression, in terms of the resulting gradient bias and mean squared error, on the convergence of the proposed algorithms. We then study learning-driven communication error minimization via joint optimization of power allocation and learning rates. Our findings reveal that optimal power allocation across different sub-carriers should take into account both the gradient values and channel conditions, thus generalizing the widely used water-filling policy. We also develop sub-optimal distributed solutions amenable to implementation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07972v1\", \"primary_category\": \"cs.NI\", \"categories\": [\"eess.SP\", \"cs.LG\", \"cs.NI\"], \"primary_category_human_readable\": \"Networking and Internet Architecture\", \"categories_human_readable\": [\"Signal Processing\", \"Machine Learning\", \"Networking and Internet Architecture\"], \"incoming_citations\": [\"24319\", \"429\", \"2965\", \"10304\", \"41431\"], \"outgoing_citations\": [\"169253\", \"170935\", \"174862\", \"176239\", \"177426\", \"180455\", \"195432\", \"201282\", \"203005\", \"204040\", \"204345\", \"204421\", \"207036\", \"210478\", \"216321\", \"217132\", \"219461\", \"227271\", \"230678\", \"242402\", \"265771\", \"268702\", \"278523\", \"283126\", \"353620\"]}","task_split":"paper_retrieval"}
{"document_id":"69125","document_content":"# A Hidden Challenge of Link Prediction: Which Pairs to Check?\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nThe traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is no test set: the ground-truth is not known, so the number of possible pairs to predict over is quadratic in the number of nodes in the graph. Moreover, because graphs are sparse, most of these possible pairs will not be links. Thus, link prediction methods, which often rely on proximity-preserving embeddings or heuristic notions of node similarity, face a vast search space, with many pairs that are in close proximity, but that should not be linked. To mitigate this issue, we introduce LinkWaldo, a framework for choosing from this quadratic, massively-skewed search space of node pairs, a concise set of candidate pairs that, in addition to being in close proximity, also structurally resemble the observed edges. This allows it to ignore some high-proximity but low-resemblance pairs, and also identify high-resemblance, lower-proximity pairs. Our framework is built on a model that theoretically combines Stochastic Block Models (SBMs) with node proximity models. The block structure of the SBM maps out where in the search space new links are expected to fall, and the proximity identifies the most plausible links within these blocks, using locality sensitive hashing to avoid expensive exhaustive search. LinkWaldo can use any node representation learning or heuristic definition of proximity, and can generate candidate pairs for any link prediction method, allowing the representation power of current and future methods to be realized for link prediction in practice. We evaluate LinkWaldo on 13 networks across multiple domains, and show that on average it returns candidate sets containing 7-33% more missing and future links than both embedding-based and heuristic baselines' sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICDM50108.2020.00092\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"75437\"], \"outgoing_citations\": [\"66660\", \"115715\", \"133426\", \"186336\", \"240758\", \"241837\", \"252195\", \"253807\", \"255737\", \"269202\", \"270341\", \"308627\", \"316095\", \"325820\"]}","task_split":"paper_retrieval"}
{"document_id":"69143","document_content":"# Meta Back-translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nBack-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality of the pseudo-parallel data does not necessarily lead to better final translation models, while lower-quality but more diverse data often yields stronger results. In this paper, we propose a novel method to generate pseudo-parallel data from a pre-trained back-translation model. Our method is a meta-learning algorithm which adapts a pre-trained back-translation model so that the pseudo-parallel data it generates would train a forward-translation model to do well on a validation set. In our evaluations in both the standard datasets WMT En-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our method leads to significant improvements over strong baselines. Our code will be made available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07847v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"51287\"], \"outgoing_citations\": [\"115192\", \"127679\", \"131195\", \"132406\", \"135281\", \"150871\", \"154778\", \"164156\", \"183673\", \"185523\", \"187452\", \"194577\", \"199569\", \"220017\", \"221514\", \"235275\", \"251974\", \"271545\", \"281472\", \"290402\", \"302824\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"69210","document_content":"# Does the Adam Optimizer Exacerbate Catastrophic Forgetting?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- I.2.6\n## Abstract\nCatastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despite the extensive amount of work on catastrophic forgetting, we argue that it is still unclear how exactly the phenomenon should be quantified, and, moreover, to what degree all of the choices we make when designing learning systems affect the amount of catastrophic forgetting. We use various testbeds from the reinforcement learning and supervised learning literature to (1) provide evidence that the choice of which modern gradient-based optimization algorithm is used to train an ANN has a significant impact on the amount of catastrophic forgetting and show that-surprisingly-in many instances classical algorithms such as vanilla SGD experience less catastrophic forgetting than the more modern algorithms such as Adam. We empirically compare four different existing metrics for quantifying catastrophic forgetting and (2) show that the degree to which the learning systems experience catastrophic forgetting is sufficiently sensitive to the metric used that a change from one principled metric to another is enough to change the conclusions of a study dramatically. Our results suggest that a much more rigorous experimental methodology is required when looking at catastrophic forgetting. Based on our results, we recommend inter-task forgetting in supervised learning must be measured with both retention and relearning metrics concurrently, and intra-task forgetting in reinforcement learning must-at the very least-be measured with pairwise interference.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07686v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\", \"I.2.6\"], \"incoming_citations\": [], \"outgoing_citations\": [\"119224\", \"136513\", \"139573\", \"188169\", \"212647\", \"230690\", \"243454\", \"259175\", \"266696\", \"270623\", \"271679\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"69226","document_content":"# Consistency-based Merging of Variability Models\n## Categories\n- Artificial Intelligence\n## Abstract\nGlobally operating enterprises selling large and complex products and services often have to deal with situations where variability models are locally developed to take into account the requirements of local markets. For example, cars sold on the U.S. market are represented by variability models in some or many aspects different from European ones. In order to support global variability management processes, variability models and the underlying knowledge bases often need to be integrated. This is a challenging task since an integrated knowledge base should not produce results which are different from those produced by the individual knowledge bases. In this paper, we introduce an approach to variability model integration that is based on the concepts of contextual modeling and conflict detection. We present the underlying concepts and the results of a corresponding performance analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07643v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"69271","document_content":"# Cooperation and Reputation Dynamics with Reinforcement Learning\n## Categories\n- Multiagent Systems\n- Artificial Intelligence\n## Abstract\nCreating incentives for cooperation is a challenge in natural and artificial systems. One potential answer is reputation, whereby agents trade the immediate cost of cooperation for the future benefits of having a good reputation. Game theoretical models have shown that specific social norms can make cooperation stable, but how agents can independently learn to establish effective reputation mechanisms on their own is less understood. We use a simple model of reinforcement learning to show that reputation mechanisms generate two coordination problems: agents need to learn how to coordinate on the meaning of existing reputations and collectively agree on a social norm to assign reputations to others based on their behavior. These coordination problems exhibit multiple equilibria, some of which effectively establish cooperation. When we train agents with a standard Q-learning algorithm in an environment with the presence of reputation mechanisms, convergence to undesirable equilibria is widespread. We propose two mechanisms to alleviate this: (i) seeding a proportion of the system with fixed agents that steer others towards good equilibria; and (ii), intrinsic rewards based on the idea of introspection, i.e., augmenting agents' rewards by an amount proportionate to the performance of their own strategy against themselves. A combination of these simple mechanisms is successful in stabilizing cooperation, even in a fully decentralized version of the problem where agents learn to use and assign reputations simultaneously. We show how our results relate to the literature in Evolutionary Game Theory, and discuss implications for artificial, human and hybrid systems, where reputations can be used as a way to establish trust and cooperation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07523v1\", \"primary_category\": \"cs.MA\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"183135\", \"194538\", \"199688\", \"209279\", \"213637\", \"237964\", \"256103\", \"256504\"]}","task_split":"paper_retrieval"}
{"document_id":"69287","document_content":"# On the Impact of Device and Behavioral Heterogeneity in Federated Learning\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n- Performance\n## Abstract\nFederated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployment in production environments, and it has been adopted in services such as virtual keyboards, auto-completion, item recommendation, and several IoT applications. However, FL comes with the challenge of performing training over largely heterogeneous datasets, devices, and networks that are out of the control of the centralized FL server. Motivated by this inherent setting, we make a first step towards characterizing the impact of device and behavioral heterogeneity on the trained model. We conduct an extensive empirical study spanning close to 1.5K unique configurations on five popular FL benchmarks. Our analysis shows that these sources of heterogeneity have a major impact on both model performance and fairness, thus sheds light on the importance of considering heterogeneity in FL system design.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07500v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"cs.PF\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Performance\"], \"incoming_citations\": [\"11900\", \"13363\", \"14535\"], \"outgoing_citations\": [\"87343\", \"109190\", \"123767\", \"164468\", \"164627\", \"184652\", \"196107\", \"197515\", \"200347\", \"200704\", \"206873\", \"207462\", \"207518\", \"210718\", \"210868\", \"226146\", \"232524\", \"234581\", \"234850\", \"242007\", \"248032\", \"252956\", \"265073\"]}","task_split":"paper_retrieval"}
{"document_id":"69343","document_content":"# MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents a self-supervised learning method for pointer-generator networks to improve spoken-text normalization. Spoken-text normalization that converts spoken-style text into style normalized text is becoming an important technology for improving subsequent processing such as machine translation and summarization. The most successful spoken-text normalization method to date is sequence-to-sequence (seq2seq) mapping using pointer-generator networks that possess a copy mechanism from an input sequence. However, these models require a large amount of paired data of spoken-style text and style normalized text, and it is difficult to prepare such a volume of data. In order to construct spoken-text normalization model from the limited paired data, we focus on self-supervised learning which can utilize unpaired text data to improve seq2seq models. Unfortunately, conventional self-supervised learning methods do not assume that pointer-generator networks are utilized. Therefore, we propose a novel self-supervised learning method, MAsked Pointer-Generator Network (MAPGN). The proposed method can effectively pre-train the pointer-generator network by learning to fill masked tokens using the copy mechanism. Our experiments demonstrate that MAPGN is more effective for pointer-generator networks than the conventional self-supervised learning methods in two spoken-text normalization tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07380v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"40154\"], \"outgoing_citations\": [\"146003\", \"163579\", \"170712\", \"187452\", \"230212\", \"232141\", \"240068\", \"266671\", \"282914\"]}","task_split":"paper_retrieval"}
{"document_id":"69349","document_content":"# Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification\n## Categories\n- Computation and Language\n## Abstract\nIntent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies the intents. There are also studies of end-to-end system that takes acoustic features as input and classifies the intents directly. Such systems don't take advantage of relevant linguistic information, and suffer from limited training data. In this work, we propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We use knowledge distillation technique to map the acoustic embeddings towards linguistic embeddings. We perform fusion of both acoustic and linguistic embeddings through cross-attention approach to classify intents. With the proposed method, we achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.07370v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"13152\", \"11202\", \"20622\", \"31268\", \"54462\", \"58416\"], \"outgoing_citations\": [\"95623\", \"114312\", \"168375\", \"178445\", \"183080\", \"191769\", \"216686\", \"232847\", \"241146\", \"258300\", \"266955\", \"281534\", \"301814\", \"308468\", \"310601\"]}","task_split":"paper_retrieval"}
{"document_id":"69807","document_content":"# Transformer Language Models with LSTM-based Cross-utterance Information Representation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe effective incorporation of cross-utterance information has the potential to improve language models (LMs) for automatic speech recognition (ASR). To extract more powerful and robust cross-utterance representations for the Transformer LM (TLM), this paper proposes the R-TLM which uses hidden states in a long short-term memory (LSTM) LM. To encode the cross-utterance information, the R-TLM incorporates an LSTM module together with a segment-wise recurrence in some of the Transformer blocks. In addition to the LSTM module output, a shortcut connection using a fusion layer that bypasses the LSTM module is also investigated. The proposed system was evaluated on the AMI meeting corpus, the Eval2000 and the RT03 telephone conversation evaluation sets. The best R-TLM achieved 0.9%, 0.6%, and 0.8% absolute WER reductions over the single-utterance TLM baseline, and 0.5%, 0.3%, 0.2% absolute WER reductions over a strong cross-utterance TLM baseline on the AMI evaluation set, Eval2000 and RT03 respectively. Improvements on Eval2000 and RT03 were further supported by significance tests. R-TLMs were found to have better LM scores on words where recognition errors are more likely to occur. The R-TLM WER can be further reduced by interpolation with an LSTM-LM.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.06474v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"11281\", \"41834\", \"54500\", \"58184\"], \"outgoing_citations\": [\"105108\", \"156880\", \"176397\", \"178701\", \"185638\", \"186906\", \"189263\", \"209770\", \"221967\", \"223083\", \"239884\", \"258039\", \"258109\", \"281534\", \"282492\"]}","task_split":"paper_retrieval"}
{"document_id":"69882","document_content":"# Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nWith the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.06314v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"23894\", \"40560\", \"21555\"], \"outgoing_citations\": [\"113687\", \"123410\", \"149300\", \"187975\", \"194215\", \"199511\", \"219169\", \"228585\", \"257863\", \"262722\", \"262842\", \"271021\"]}","task_split":"paper_retrieval"}
{"document_id":"69932","document_content":"# Disentangled Representations from Non-Disentangled Models\n## Categories\n- Machine Learning\n## Abstract\nConstructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is currently to train a generative model that separates different factors of variation in its latent space. This separation is typically enforced by training with specific regularization terms in the model's objective function. These terms, however, introduce additional hyperparameters responsible for the trade-off between disentanglement and generation quality. While tuning these hyperparameters is crucial for proper disentanglement, it is often unclear how to tune them without external supervision.   This paper investigates an alternative route to disentangled representations. Namely, we propose to extract such representations from the state-of-the-art generative models trained without disentangling terms in their objectives. This paradigm of post hoc disentanglement employs little or no hyperparameters when learning representations while achieving results on par with existing state-of-the-art, as shown by comparison in terms of established disentanglement metrics, fairness, and the abstract reasoning task. All our code and models are publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.06204v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"2522\", \"67913\", \"118821\"], \"outgoing_citations\": [\"91004\", \"104295\", \"112375\", \"132835\", \"133858\", \"138086\", \"143164\", \"174537\", \"180811\", \"181909\", \"183257\", \"183732\", \"192020\", \"206218\", \"206907\", \"207094\", \"208041\", \"224981\", \"236093\", \"236595\", \"241933\", \"242142\", \"242239\", \"251597\", \"256309\", \"264219\", \"271038\", \"278987\", \"280232\", \"281848\", \"284874\", \"312484\"]}","task_split":"paper_retrieval"}
{"document_id":"70112","document_content":"# Predicting Customer Lifetime Values -- ecommerce use case\n## Categories\n- Machine Learning\n- Applications\n## Abstract\nPredicting customer future purchases and lifetime value is a key metrics for managing marketing campaigns and optimizing marketing spend. This task is specifically challenging when the relationships between the customer and the firm are of a noncontractual nature and therefore the future purchases need to be predicted based mostly on historical purchases. This work compares two approaches to predict customer future purchases, first using a 'buy-till-you-die' statistical model to predict customer behavior and later using a neural network on the same dataset and comparing the results. This comparison will lead to both quantitative and qualitative analysis of those two methods as well as recommendation on how to proceed in different cases and opportunities for future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05771v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.AP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Applications\"], \"incoming_citations\": [], \"outgoing_citations\": [\"272063\"]}","task_split":"paper_retrieval"}
{"document_id":"70118","document_content":"# Customizing Contextualized Language Models forLegal Document Reviews\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nInspired by the inductive transfer learning on computer vision, many efforts have been made to train contextualized language models that boost the performance of natural language processing tasks. These models are mostly trained on large general-domain corpora such as news, books, or Wikipedia.Although these pre-trained generic language models well perceive the semantic and syntactic essence of a language structure, exploiting them in a real-world domain-specific scenario still needs some practical considerations to be taken into account such as token distribution shifts, inference time, memory, and their simultaneous proficiency in multiple tasks. In this paper, we focus on the legal domain and present how different language model strained on general-domain corpora can be best customized for multiple legal document reviewing tasks. We compare their efficiencies with respect to task performances and present practical considerations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05757v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"50782\"], \"outgoing_citations\": [\"96350\", \"143978\", \"158505\", \"170021\", \"181776\", \"195157\", \"200155\", \"252430\", \"303469\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"70171","document_content":"# Addressing the Topological Defects of Disentanglement via Distributed Operators\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces of a model's latent representation. However, this approach has shown limited empirical success to date. Here, we show that, for a broad family of transformations acting on images--encompassing simple affine transformations such as rotations and translations--this approach to disentanglement introduces topological defects (i.e. discontinuities in the encoder). Motivated by classical results from group representation theory, we study an alternative, more flexible approach to disentanglement which relies on distributed latent operators, potentially acting on the entire latent space. We theoretically and empirically demonstrate the effectiveness of this approach to disentangle affine transformations. Our work lays a theoretical foundation for the recent success of a new generation of models using distributed operators for disentanglement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05623v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33203\", \"25951\", \"68053\"], \"outgoing_citations\": [\"92137\", \"113839\", \"117673\", \"118898\", \"140140\", \"141826\", \"143581\", \"143629\", \"152148\", \"152573\", \"176649\", \"182154\", \"182563\", \"183257\", \"193061\", \"201551\", \"208041\", \"208277\", \"211397\", \"228186\", \"236895\", \"295435\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"70180","document_content":"# Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nSystematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition. In this work, we study systematic generalization of NNs in forecasting future time series of dependent variables in a dynamical system, conditioned on past time series of dependent variables, and past and future control variables. We focus on systematic generalization wherein the NN-based forecasting model should perform well on previously unseen combinations or regimes of control variables after being trained on a limited set of the possible regimes. For NNs to depict such out-of-distribution generalization, they should be able to disentangle the various dependencies between control variables and dependent variables. We hypothesize that a modular NN architecture guided by the readily-available knowledge of independence of control variables as a potentially useful inductive bias to this end. Through extensive empirical evaluation on a toy dataset and a simulated electric motor dataset, we show that our proposed modular NN architecture serves as a simple yet highly effective inductive bias that enabling better forecasting of the dependent variables up to large horizons in contrast to standard NNs, and indeed capture the true dependency relations between the dependent and the control variables.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05602v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95713\", \"97560\", \"117374\", \"137263\", \"137359\", \"153932\", \"160617\", \"200986\", \"207866\", \"242322\", \"251911\", \"262990\", \"271008\", \"271717\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"70191","document_content":"# A Witness Two-Sample Test\n## Categories\n- Machine Learning\n## Abstract\nThe Maximum Mean Discrepancy (MMD) has been the state-of-the-art nonparametric test for tackling the two-sample problem. Its statistic is given by the difference in expectations of the witness function, a real-valued function defined as a weighted sum of kernel evaluations on a set of basis points. Typically the kernel is optimized on a training set, and hypothesis testing is performed on a separate test set to avoid overfitting (i.e., control type-I error). That is, the test set is used to simultaneously estimate the expectations and define the basis points, while the training set only serves to select the kernel and is discarded. In this work, we propose to use the training data to also define the weights and the basis points for better data efficiency. We show that 1) the new test is consistent and has a well-controlled type-I error; 2) the optimal witness function is given by a precision-weighted mean in the reproducing kernel Hilbert space associated with the kernel; and 3) the test power of the proposed test is comparable or exceeds that of the MMD and other modern tests, as verified empirically on challenging synthetic and real problems (e.g., Higgs data).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05573v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"13014\"], \"outgoing_citations\": [\"117759\", \"121271\", \"140964\", \"161791\", \"165099\", \"264948\", \"280505\", \"292098\", \"298807\"]}","task_split":"paper_retrieval"}
{"document_id":"70337","document_content":"# Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe design a simple reinforcement learning (RL) agent that implements an optimistic version of $Q$-learning and establish through regret analysis that this agent can operate with some level of competence in any environment. While we leverage concepts from the literature on provably efficient RL, we consider a general agent-environment interface and provide a novel agent design and analysis. This level of generality positions our results to inform the design of future agents for operation in complex real environments. We establish that, as time progresses, our agent performs competitively relative to policies that require longer times to evaluate. The time it takes to approach asymptotic performance is polynomial in the complexity of the agent's state representation and the time required to evaluate the best policy that the agent can represent. Notably, there is no dependence on the complexity of the environment. The ultimate per-period performance loss of the agent is bounded by a constant multiple of a measure of distortion introduced by the agent's state representation. This work is the first to establish that an algorithm approaches this asymptotic condition within a tractable time frame.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05261v7\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"70365","document_content":"# Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nThis paper is concerned with polarimetric dense map reconstruction based on a polarization camera with the help of relative depth information as a prior. In general, polarization imaging is able to reveal information about surface normal such as azimuth and zenith angles, which can support the development of solutions to the problem of dense reconstruction, especially in texture-poor regions. However, polarimetric shape cues are ambiguous due to two types of polarized reflection (specular\/diffuse). Although methods have been proposed to address this issue, they either are offline and therefore not practical in robotics applications, or use incomplete polarimetric cues, leading to sub-optimal performance. In this paper, we propose an online reconstruction method that uses full polarimetric cues available from the polarization camera. With our online method, we can propagate sparse depth values both along and perpendicular to iso-depth contours. Through comprehensive experiments on challenging image sequences, we demonstrate that our method is able to significantly improve the accuracy of the depthmap as well as increase its density, specially in regions of poor texture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05212v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"174028\", \"174678\", \"177927\", \"192017\", \"193319\", \"225068\", \"229003\", \"288753\", \"291362\", \"294622\", \"295255\", \"326774\"]}","task_split":"paper_retrieval"}
{"document_id":"70389","document_content":"# Decontextualization: Making Sentences Stand-Alone\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nModels for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05169v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33114\", \"2673\", \"2746\", \"5579\", \"16513\", \"18368\", \"21236\", \"61369\", \"1055\", \"55199\", \"77341\"], \"outgoing_citations\": [\"114545\", \"128107\", \"143089\", \"174612\", \"182486\", \"183076\", \"270020\", \"295884\", \"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"70399","document_content":"# Enhancing Audio Augmentation Methods with Consistency Learning\n## Categories\n- Sound\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nData augmentation is an inexpensive way to increase training data diversity and is commonly achieved via transformations of existing data. For tasks such as classification, there is a good case for learning representations of the data that are invariant to such transformations, yet this is not explicitly enforced by classification losses such as the cross-entropy loss. This paper investigates the use of training objectives that explicitly impose this consistency constraint and how it can impact downstream audio classification tasks. In the context of deep convolutional neural networks in the supervised setting, we show empirically that certain measures of consistency are not implicitly captured by the cross-entropy loss and that incorporating such measures into the loss function can improve the performance of audio classification systems. Put another way, we demonstrate how existing augmentation methods can further improve learning by enforcing consistency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05151v3\", \"primary_category\": \"cs.SD\", \"categories\": [\"cs.LG\", \"eess.AS\", \"cs.SD\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\", \"Sound\"], \"incoming_citations\": [], \"outgoing_citations\": [\"106726\", \"106987\", \"150055\", \"152544\", \"182405\", \"188492\", \"190961\", \"251231\", \"268999\", \"286557\", \"294549\"]}","task_split":"paper_retrieval"}
{"document_id":"70439","document_content":"# SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer a task-specific latent structure and then apply a GNN to the inferred graph. Unfortunately, the space of possible graph structures grows super-exponentially with the number of nodes and so the task-specific supervision may be insufficient for learning both the structure and the GNN parameters. In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision. A comprehensive experimental study demonstrates that SLAPS scales to large graphs with hundreds of thousands of nodes and outperforms several models that have been proposed to learn a task-specific graph structure on established benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.05034v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"66588\", \"82307\", \"7139\", \"16014\", \"65259\"], \"outgoing_citations\": [\"97557\", \"110109\", \"113099\", \"115700\", \"117308\", \"117866\", \"118237\", \"119293\", \"121235\", \"123835\", \"126374\", \"127525\", \"137772\", \"142936\", \"147107\", \"147374\", \"150059\", \"150834\", \"155997\", \"163822\", \"164674\", \"168172\", \"183456\", \"183816\", \"184180\", \"184400\", \"187908\", \"191735\", \"193345\", \"198121\", \"200461\", \"208815\", \"229027\", \"229324\", \"231277\", \"231858\", \"238422\", \"241029\", \"245486\", \"250796\", \"278531\", \"280296\"]}","task_split":"paper_retrieval"}
{"document_id":"70811","document_content":"# Quantifying and Mitigating Privacy Risks of Contrastive Learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nData is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive to collect. To leverage large-scale unlabeled data, self-supervised learning, represented by contrastive learning, is introduced. The objective of contrastive learning is to map different views derived from a training sample (e.g., through data augmentation) closer in their representation space, while different views derived from different samples more distant. In this way, a contrastive model learns to generate informative representations for data samples, which are then used to perform downstream ML tasks. Recent research has shown that machine learning models are vulnerable to various privacy attacks. However, most of the current efforts concentrate on models trained with supervised learning. Meanwhile, data samples' informative representations learned with contrastive learning may cause severe privacy risks as well.   In this paper, we perform the first privacy analysis of contrastive learning through the lens of membership inference and attribute inference. Our experimental results show that contrastive models trained on image datasets are less vulnerable to membership inference attacks but more vulnerable to attribute inference attacks compared to supervised models. The former is due to the fact that contrastive models are less prone to overfitting, while the latter is caused by contrastive models' capability of representing data samples expressively. To remedy this situation, we propose the first privacy-preserving contrastive learning mechanism, Talos, relying on adversarial training. Empirical results show that Talos can successfully mitigate attribute inference risks for contrastive models while maintaining their membership privacy and model utility.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.04140v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [\"2760\", \"9511\", \"10538\", \"71528\", \"137962\", \"12869\", \"27709\"], \"outgoing_citations\": [\"70251\", \"80579\", \"92075\", \"99280\", \"108776\", \"109212\", \"111676\", \"114155\", \"118617\", \"120760\", \"126916\", \"126927\", \"133855\", \"135273\", \"150794\", \"159473\", \"165358\", \"167831\", \"168866\", \"178647\", \"184045\", \"184737\", \"192804\", \"206929\", \"207518\", \"211944\", \"220007\", \"220838\", \"224486\", \"229332\", \"232283\", \"232524\", \"237069\", \"240981\", \"242128\", \"251337\", \"255133\", \"256827\", \"258100\", \"264914\", \"265979\", \"272912\", \"280659\", \"285037\", \"303062\"]}","task_split":"paper_retrieval"}
{"document_id":"70890","document_content":"# Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i.e. learning to transfer in few-shot scenario.) or meta-learning. However, as the base classes have no overlap to the novel set, simply transferring whole knowledge from base data is not an optimal solution since some knowledge in the base model may be biased or even harmful to the novel class. In this paper, we propose to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model. Specifically, layers will be imposed different learning rates if they are chosen to be fine-tuned, to control the extent of preserved transferability. To determine which layers to be recast and what values of learning rates for them, we introduce an evolutionary search based method that is efficient to simultaneously locate the target layers and determine their individual learning rates. We conduct extensive experiments on CUB and mini-ImageNet to demonstrate the effectiveness of our proposed method. It achieves the state-of-the-art performance on both meta-learning and non-meta based frameworks. Furthermore, we extend our method to the conventional pre-training + fine-tuning paradigm and obtain consistent improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.03983v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"21010\", \"5532\", \"4767\", \"4834\", \"60469\", \"113803\"], \"outgoing_citations\": [\"134943\", \"191555\", \"191732\", \"206595\", \"212301\", \"213370\", \"230792\", \"234262\", \"238070\", \"242426\", \"246393\", \"247038\", \"250796\", \"251701\", \"259762\", \"261577\", \"264016\", \"272361\", \"272497\", \"272636\", \"290426\"]}","task_split":"paper_retrieval"}
{"document_id":"70911","document_content":"# Iconographic Image Captioning for Artworks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning implies automatically generating textual descriptions of images based only on the visual input. Although this has been an extensively addressed research topic in recent years, not many contributions have been made in the domain of art historical data. In this particular context, the task of image captioning is confronted with various challenges such as the lack of large-scale datasets of image-text pairs, the complexity of meaning associated with describing artworks and the need for expert-level annotations. This work aims to address some of those challenges by utilizing a novel large-scale dataset of artwork images annotated with concepts from the Iconclass classification system designed for art and iconography. The annotations are processed into clean textual description to create a dataset suitable for training a deep neural network model on the image captioning task. Motivated by the state-of-the-art results achieved in generating captions for natural images, a transformer-based vision-language pre-trained model is fine-tuned using the artwork image dataset. Quantitative evaluation of the results is performed using standard image captioning metrics. The quality of the generated captions and the model's capacity to generalize to new data is explored by employing the model on a new collection of paintings and performing an analysis of the relation between commonly generated captions and the artistic genre. The overall results suggest that the model can generate meaningful captions that exhibit a stronger relevance to the art historical context, particularly in comparison to captions obtained from models trained only on natural image datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.03942v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"68570\"], \"outgoing_citations\": [\"103568\", \"133936\", \"135561\", \"136078\", \"138796\", \"165195\", \"177110\", \"196177\", \"213333\", \"244371\"]}","task_split":"paper_retrieval"}
{"document_id":"71004","document_content":"# Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering\n## Categories\n- Computation and Language\n## Abstract\nRetrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them. However, in this paper, we point out the intrinsic defect of such methods. Instead, we propose a new architecture that models paragraphs as sequential data and considers multi-hop information retrieval as a kind of sequence labeling task. Specifically, we design a rewritable external memory to model the dependency among paragraphs. Moreover, a threshold gate mechanism is proposed to eliminate the distraction of noise paragraphs. We evaluate our method on both full wiki and distractor subtask of HotpotQA, a public textual multi-hop QA dataset requiring multi-hop information retrieval. Experiments show that our method achieves significant improvement over the published state-of-the-art method in retrieval and downstream QA task performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.03741v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25328\"], \"outgoing_citations\": [\"154601\", \"159864\", \"161554\", \"166283\", \"182026\", \"185429\", \"185805\", \"185952\", \"186337\", \"186432\", \"216563\", \"218944\", \"219849\", \"235408\", \"238610\", \"253068\", \"270020\", \"290747\"]}","task_split":"paper_retrieval"}
{"document_id":"71388","document_content":"# Generalized Zero-shot Intent Detection via Commonsense Knowledge\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIdentifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the scarcely available training data and overfit to seen intents data, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents and that RIDE outperforms the state-of-the-art model for unseen intents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.02925v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"75121\"], \"outgoing_citations\": [\"75121\", \"108680\", \"112804\", \"114145\", \"118285\", \"143927\", \"149188\", \"150360\", \"167138\", \"194488\", \"205014\", \"219441\", \"222680\", \"230543\", \"237274\", \"242267\", \"262216\", \"271625\", \"285317\", \"289972\", \"292616\", \"336240\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"71397","document_content":"# How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nDeep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time,real world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn; as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1177\/0278364920987859\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"16116\", \"18240\", \"34264\", \"170092\", \"14\", \"18008\", \"20192\", \"28137\", \"39352\", \"64940\", \"4052\", \"4718\", \"8041\", \"10673\", \"11038\", \"11658\", \"19709\", \"20726\", \"26872\", \"28513\", \"37058\", \"38610\", \"39481\", \"41661\", \"45066\", \"49013\", \"51162\", \"65571\", \"86382\", \"89850\"], \"outgoing_citations\": [\"90361\", \"90837\", \"92451\", \"116233\", \"118301\", \"131417\", \"149380\", \"152041\", \"156707\", \"159949\", \"162997\", \"164054\", \"164478\", \"164957\", \"166705\", \"176738\", \"177094\", \"177272\", \"180884\", \"182796\", \"187966\", \"188953\", \"190237\", \"191065\", \"194494\", \"196671\", \"197663\", \"199156\", \"199193\", \"201429\", \"201681\", \"204750\", \"205568\", \"206160\", \"206781\", \"206875\", \"207601\", \"212299\", \"213161\", \"214302\", \"214692\", \"215102\", \"215931\", \"216429\", \"226635\", \"227078\", \"233440\", \"234025\", \"235189\", \"235628\", \"236393\", \"237229\", \"237605\", \"238577\", \"240495\", \"240506\", \"244294\", \"250066\", \"250492\", \"251209\", \"253042\", \"253045\", \"254596\", \"254685\", \"255139\", \"255435\", \"255525\", \"255939\", \"260018\", \"261780\", \"261786\", \"261983\", \"262486\", \"263733\", \"263957\", \"265053\", \"266419\", \"268248\", \"268563\", \"270925\", \"271030\", \"271952\", \"272030\", \"272437\", \"272585\", \"272987\", \"274445\", \"277386\", \"277822\", \"279790\", \"280570\", \"280870\", \"283318\", \"283391\", \"283427\", \"288338\", \"291025\", \"293864\", \"296980\", \"297268\", \"297317\", \"298281\", \"300847\", \"302952\", \"302967\", \"306156\", \"306266\", \"315055\", \"318666\"]}","task_split":"paper_retrieval"}
{"document_id":"71652","document_content":"# Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nWord vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities. In this paper, we propose a novel word vector space estimation by unsupervised learning on lattices output by an automatic speech recognition (ASR) system. We encode each word in confusion2vec vector space by its constituent subword character n-grams. We show the subword encoding helps better represent the acoustic perceptual ambiguities in human spoken language via information modeled on lattice structured ASR output. The usefulness of the proposed Confusion2vec representation is evaluated using semantic, syntactic and acoustic analogy and word similarity tasks. We also show the benefits of subword modeling for acoustic ambiguity representation on the task of spoken language intent detection. The results significantly outperform existing word vector representations when evaluated on erroneous ASR outputs. We demonstrate that Confusion2vec subword modeling eliminates the need for retraining\/adapting the natural language understanding models on ASR transcripts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1371\/journal.pone.0264488\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"71667","document_content":"# DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation\n## Categories\n- Computation and Language\n## Abstract\nHaving engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present \\textbf{DiSCoL} (\\textbf{Di}alogue \\textbf{S}ystems through \\textbf{Co}versational \\textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly \\textbf{convlines}) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to \\textit{control} the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.02191v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"44409\", \"48927\"], \"outgoing_citations\": [\"93004\", \"99445\", \"116754\", \"127856\", \"158107\", \"158493\", \"189217\", \"192322\", \"197924\", \"200324\", \"205147\", \"210155\", \"272513\", \"276382\", \"295863\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"71715","document_content":"# On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generative model provides a local access to the MDP: The planner can ask for random transitions from previously returned states and arbitrary actions, and features are only accessible for states that are encountered in this process. As opposed to previous work (e.g. Lattimore et al. (2020)) where linear realizability of all policies was assumed, we consider the significantly relaxed assumption of a single linearly realizable (deterministic) policy. A recent lower bound by Weisz et al. (2020) established that the related problem when the action-value function of the optimal policy is linearly realizable requires an exponential number of queries, either in $H$ (the horizon of the MDP) or $d$ (the dimension of the feature mapping). Their construction crucially relies on having an exponentially large action set. In contrast, in this work, we establish that poly$(H,d)$ planning is possible with state value function realizability whenever the action set has a constant size. In particular, we present the TensorPlan algorithm which uses poly$((dH\/\\delta)^A)$ simulator queries to find a $\\delta$-optimal policy relative to any deterministic policy for which the value function is linearly realizable with some bounded parameter. This is the first algorithm to give a polynomial query complexity guarantee using only linear-realizability of a single competing value function. Whether the computation cost is similarly bounded remains an open question. We extend the upper bound to the near-realizable case and to the infinite-horizon discounted setup. We also present a lower bound in the infinite-horizon episodic setting: Planners that achieve constant suboptimality need exponentially many queries, either in $d$ or the number of actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.02049v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"18847\", \"30034\", \"28205\", \"42123\", \"49056\", \"77413\", \"18847\"], \"outgoing_citations\": [\"49056\", \"80518\", \"92132\", \"96944\", \"112531\", \"121680\", \"152062\", \"155667\", \"155743\", \"160166\", \"162949\", \"176464\", \"180703\", \"199123\", \"240335\", \"269378\", \"281635\", \"350403\"]}","task_split":"paper_retrieval"}
{"document_id":"71722","document_content":"# Learning Graph Representations\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nSocial and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc. The Graph Neural Networks have evolved over the last few years. Graph Neural Networks (GNNs) are efficient ways to get insight into large and dynamic graph datasets capturing relationships among billions of entities also known as knowledge graphs.   In this paper, we discuss the graph convolutional neural networks graph autoencoders and spatio-temporal graph neural networks. The representations of the graph in lower dimensions can be learned using these methods. The representations in lower dimensions can be used further for downstream machine learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.02026v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [\"137613\", \"17315\", \"47526\", \"53265\", \"60450\", \"66165\", \"86665\", \"118617\", \"129299\", \"138382\", \"168780\", \"587\", \"1070\", \"1432\", \"2954\", \"7165\", \"14080\", \"16309\", \"25542\", \"43424\", \"47163\", \"48316\", \"52615\", \"65306\", \"66358\", \"66762\", \"80211\", \"89473\", \"95125\", \"101061\", \"141153\", \"155125\", \"164948\", \"167800\", \"206470\", \"230611\", \"8235\", \"11470\", \"12740\", \"15131\", \"19894\", \"23606\", \"26074\", \"27805\", \"32565\", \"34177\", \"37202\", \"40445\", \"42554\", \"43231\", \"46267\", \"46889\", \"49965\", \"50020\", \"50193\", \"53364\", \"54670\", \"59279\", \"61263\", \"64504\", \"66570\", \"67557\", \"67661\", \"68819\", \"69048\", \"74821\", \"75163\", \"76018\", \"76945\", \"77151\", \"66660\", \"162785\", \"84204\", \"95944\", \"107034\", \"108652\", \"108589\", \"113289\", \"117122\", \"120986\", \"146864\", \"202135\", \"155678\", \"164796\", \"203061\", \"208548\", \"64686\", \"75089\", \"203291\", \"100584\", \"102365\", \"102511\", \"103340\", \"104515\", \"105486\", \"105754\", \"106574\", \"110778\", \"111140\", \"113849\", \"114406\", \"116748\", \"119836\", \"119969\", \"122186\", \"126374\", \"127915\", \"127940\", \"132167\", \"134249\", \"134585\", \"138627\", \"134564\", \"141707\", \"148469\", \"152813\", \"155550\", \"168318\", \"171736\", \"180470\", \"181169\", \"184180\", \"184533\", \"184971\", \"185389\", \"203765\", \"204003\", \"205283\", \"218866\", \"218758\", \"146267\", \"248444\", \"153424\", \"135521\", \"131251\", \"160564\", \"161681\", \"162032\", \"159863\", \"164215\", \"159624\", \"163358\", \"170364\", \"171249\", \"172184\", \"172579\", \"172724\", \"176942\", \"177402\", \"141756\", \"179302\", \"179767\", \"181999\", \"182698\", \"180610\", \"184356\", \"186056\", \"186218\", \"169526\", \"218197\", \"187568\", \"189544\", \"189984\", \"191208\", \"189994\", \"196521\", \"198858\", \"201149\", \"203062\", \"204719\", \"210546\", \"213446\", \"218737\", \"220275\", \"232750\", \"234468\", \"237896\", \"241252\", \"242520\", \"248960\", \"249497\", \"279988\", \"207044\", \"214137\", \"215171\", \"218799\", \"218825\", \"221972\", \"142585\", \"229936\", \"230155\", \"230194\", \"232665\", \"232752\", \"180806\", \"239146\", \"239929\", \"242416\", \"243887\", \"249614\", \"249747\", \"250764\", \"252363\", \"253807\", \"255202\", \"267030\", \"251756\", \"252978\", \"253007\", \"253843\", \"255737\", \"256259\", \"260400\", \"263488\", \"266346\", \"269202\", \"271521\", \"274201\", \"277316\", \"281539\"], \"outgoing_citations\": [\"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"71759","document_content":"# Mind the Gap: Assessing Temporal Generalization in Neural Language Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nOur world is open-ended, non-stationary, and constantly evolving; thus what we talk about and how we talk about it change over time. This inherent dynamic nature of language contrasts with the current static language modelling paradigm, which trains and evaluates models on utterances from overlapping time periods. Despite impressive recent progress, we demonstrate that Transformer-XL language models perform worse in the realistic setup of predicting future utterances from beyond their training period, and that model performance becomes increasingly worse with time. We find that, while increasing model size alone -- a key driver behind recent progress -- does not solve this problem, having models that continually update their knowledge with new information can indeed mitigate this performance degradation over time. Hence, given the compilation of ever-larger language modelling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our language models, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world. We publicly release our dynamic, streaming language modelling benchmarks for WMT and arXiv to facilitate language model evaluation that takes temporal dynamics into account.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01951v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"7420\", \"14596\", \"38858\", \"56554\", \"7038\"], \"outgoing_citations\": [\"41029\", \"54467\", \"55508\", \"71526\", \"76037\", \"77542\", \"77747\", \"83604\", \"107494\", \"123394\", \"127553\", \"129503\", \"131414\", \"131526\", \"133706\", \"145957\", \"147374\", \"158621\", \"158701\", \"168047\", \"168120\", \"168163\", \"168612\", \"169401\", \"176837\", \"181279\", \"182313\", \"182764\", \"183694\", \"189263\", \"190048\", \"206315\", \"206940\", \"227367\", \"253658\", \"255253\", \"262842\", \"266850\", \"268671\", \"279213\", \"290041\", \"291574\", \"311464\", \"336720\", \"341730\", \"357878\"]}","task_split":"paper_retrieval"}
{"document_id":"71787","document_content":"# LinkLouvain: Link-Aware A\/B Testing and Its Application on Online Marketing Campaign\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nA lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A\/B testing is usually conducted for such needs, whereas the existence of user interaction can introduce interference to normal A\/B testing. With the help of link prediction, we design a network A\/B testing method LinkLouvain to minimize graph interference and it gives an accurate and sound estimate of the campaign's ATE. In this paper, we analyze the network A\/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data. Our method achieves significant performance compared with others and is deployed in the online marketing campaign.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01902v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"240758\", \"344307\"]}","task_split":"paper_retrieval"}
{"document_id":"71837","document_content":"# The Ethical Implications of Shared Medical Decision Making without Providing Adequate Computational Support to the Care Provider and to the Patient\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- J.3; I.2.1\n## Abstract\nThere is a clear need to involve patients in medical decisions. However, cognitive psychological research has highlighted the cognitive limitations of humans with respect to 1. Probabilistic assessment of the patient state and of potential outcomes of various decisions, 2. Elicitation of the patient utility function, and 3. Integration of the probabilistic knowledge and of patient preferences to determine the optimal strategy. Therefore, without adequate computational support, current shared decision models have severe ethical deficiencies. An informed consent model unfairly transfers the responsibility to a patient who does not have the necessary knowledge, nor the integration capability. A paternalistic model endows with exaggerated power a physician who might not be aware of the patient preferences, is prone to multiple cognitive biases, and whose computational integration capability is bounded. Recent progress in Artificial Intelligence suggests adding a third agent: a computer, in all deliberative medical decisions: Non emergency medical decisions in which more than one alternative exists, the patient preferences can be elicited, the therapeutic alternatives might be influenced by these preferences, medical knowledge exists regarding the likelihood of the decision outcomes, and there is sufficient decision time. Ethical physicians should exploit computational decision support technologies, neither making the decisions solely on their own, nor shirking their duty and shifting the responsibility to patients in the name of informed consent. The resulting three way (patient, care provider, computer) human machine model that we suggest emphasizes the patient preferences, the physician knowledge, and the computational integration of both aspects, does not diminish the physician role, but rather brings out the best in human and machine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01811v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"J.3; I.2.1\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"J.3; I.2.1\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"71863","document_content":"# Vehicle trajectory prediction in top-view image sequences based on deep learning method\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nAnnually, a large number of injuries and deaths around the world are related to motor vehicle accidents. This value has recently been reduced to some extent, via the use of driver-assistance systems. Developing driver-assistance systems (i.e., automated driving systems) can play a crucial role in reducing this number. Estimating and predicting surrounding vehicles' movement is essential for an automated vehicle and advanced safety systems. Moreover, predicting the trajectory is influenced by numerous factors, such as drivers' behavior during accidents, history of the vehicle's movement and the surrounding vehicles, and their position on the traffic scene. The vehicle must move over a safe path in traffic and react to other drivers' unpredictable behaviors in the shortest time. Herein, to predict automated vehicles' path, a model with low computational complexity is proposed, which is trained by images taken from the road's aerial image. Our method is based on an encoder-decoder model that utilizes a social tensor to model the effect of the surrounding vehicles' movement on the target vehicle. The proposed model can predict the vehicle's future path in any freeway only by viewing the images related to the history of the target vehicle's movement and its neighbors. Deep learning was used as a tool for extracting the features of these images. Using the HighD database, an image dataset of the road's aerial image was created, and the model's performance was evaluated on this new database. We achieved the RMSE of 1.91 for the next 5 seconds and found that the proposed method had less error than the best path-prediction methods in previous studies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01749v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"97521\", \"149568\", \"182082\", \"196618\", \"196758\", \"199604\", \"205045\", \"209241\", \"212031\", \"214529\", \"217456\", \"218337\", \"221071\", \"232095\", \"232096\", \"244314\", \"265546\", \"292984\", \"293864\"]}","task_split":"paper_retrieval"}
{"document_id":"71976","document_content":"# Face Recognition using 3D CNNs\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe area of face recognition is one of the most widely researched areas in the domain of computer vision and biometric. This is because, the non-intrusive nature of face biometric makes it comparatively more suitable for application in area of surveillance at public places such as airports. The application of primitive methods in face recognition could not give very satisfactory performance. However, with the advent of machine and deep learning methods and their application in face recognition, several major breakthroughs were obtained. The use of 2D Convolution Neural networks(2D CNN) in face recognition crossed the human face recognition accuracy and reached to 99%. Still, robust face recognition in the presence of real world conditions such as variation in resolution, illumination and pose is a major challenge for researchers in face recognition. In this work, we used video as input to the 3D CNN architectures for capturing both spatial and time domain information from the video for face recognition in real world environment. For the purpose of experimentation, we have developed our own video dataset called CVBL video dataset. The use of 3D CNN for face recognition in videos shows promising results with DenseNets performing the best with an accuracy of 97% on CVBL dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01441v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"267940\", \"278526\", \"294503\", \"315200\", \"320918\"]}","task_split":"paper_retrieval"}
{"document_id":"72134","document_content":"# RectiNet-v2: A stacked network architecture for document image dewarping\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the advent of mobile and hand-held cameras, document images have found their way into almost every domain. Dewarping of these images for the removal of perspective distortions and folds is essential so that they can be understood by document recognition algorithms. For this, we propose an end-to-end CNN architecture that can produce distortion free document images from warped documents it takes as input. We train this model on warped document images simulated synthetically to compensate for lack of enough natural data. Our method is novel in the use of a bifurcated decoder with shared weights to prevent intermingling of grid coordinates, in the use of residual networks in the U-Net skip connections to allow flow of data from different receptive fields in the model, and in the use of a gated network to help the model focus on structure and line level detail of the document image. We evaluate our method on the DocUNet dataset, a benchmark in this domain, and obtain results comparable to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.01120v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107649\", \"110950\", \"176337\", \"199366\", \"225289\", \"291404\"]}","task_split":"paper_retrieval"}
{"document_id":"72369","document_content":"# TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting\n## Categories\n- Social and Information Networks\n- Computation and Language\n## Abstract\nWe present TruthBot, an all-in-one multilingual conversational chatbot designed for seeking truth (trustworthy and verified information) on specific topics. It helps users to obtain information specific to certain topics, fact-check information, and get recent news. The chatbot learns the intent of a query by training a deep neural network from the data of the previous intents and responds appropriately when it classifies the intent in one of the classes above. Each class is implemented as a separate module that uses either its own curated knowledge-base or searches the web to obtain the correct information. The topic of the chatbot is currently set to COVID-19. However, the bot can be easily customized to any topic-specific responses. Our experimental results show that each module performs significantly better than its closest competitor, which is verified both quantitatively and through several user-based surveys in multiple languages. TruthBot has been deployed in June 2020 and is currently running.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.00509v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"146683\", \"181527\", \"207715\", \"245831\", \"259202\", \"268841\"]}","task_split":"paper_retrieval"}
{"document_id":"72454","document_content":"# Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nGenerative Adversarial Networks (GANs) can generate levels for a variety of games. This paper focuses on combining GAN-generated segments in a snaking pattern to create levels for Mega Man. Adjacent segments in such levels can be orthogonally adjacent in any direction, meaning that an otherwise fine segment might impose a barrier between its neighbor depending on what sorts of segments in the training set are being most closely emulated: horizontal, vertical, or corner segments. To pick appropriate segments, multiple GANs were trained on different types of segments to ensure better flow between segments. Flow was further improved by evolving the latent vectors for the segments being joined in the level to maximize the length of the level's solution path. Using multiple GANs to represent different types of segments results in significantly longer solution paths than using one GAN for all segment types, and a human subject study verifies that these levels are more fun and have more human-like design than levels produced by one GAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2102.00337v2\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33220\", \"41462\"], \"outgoing_citations\": [\"74578\", \"95477\", \"111464\", \"112786\", \"133342\", \"133812\", \"147140\", \"163503\", \"244263\", \"289709\"]}","task_split":"paper_retrieval"}
{"document_id":"72649","document_content":"# CD2CR: Co-reference Resolution Across Documents and Domains\n## Categories\n- Computation and Language\n- I.2.7\n## Abstract\nCross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD$^2$CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.12637v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7\"], \"incoming_citations\": [\"17320\", \"26026\", \"55145\"], \"outgoing_citations\": [\"99007\", \"126188\", \"127851\", \"128380\", \"168085\", \"168495\", \"174612\", \"182484\", \"218235\", \"235555\", \"243397\", \"260541\", \"311384\"]}","task_split":"paper_retrieval"}
{"document_id":"72660","document_content":"# Does injecting linguistic structure into language models lead to better alignment with brain recordings?\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNeuroscientists evaluate deep neural networks for natural language processing as possible candidate models for how language is processed in the brain. These models are often trained without explicit linguistic supervision, but have been shown to learn some linguistic structure in the absence of such supervision (Manning et al., 2020), potentially questioning the relevance of symbolic linguistic theories in modeling such cognitive processes (Warstadt and Bowman, 2020). We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms. Using structure from dependency or minimal recursion semantic annotations, we find alignments improve significantly for one of the datasets. For another dataset, we see more mixed results. We present an extensive analysis of these results. Our proposed approach enables the evaluation of more targeted hypotheses about the composition of meaning in the brain, expanding the range of possible scientific inferences a neuroscientist could make, and opens up new opportunities for cross-pollination between computational neuroscience and linguistics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.12608v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112305\", \"123094\", \"129611\", \"159055\", \"159954\", \"163634\", \"168201\", \"182580\", \"184006\", \"188169\", \"194308\", \"210435\", \"220137\", \"229558\", \"234631\", \"244357\", \"254522\", \"269890\", \"284943\", \"320039\"]}","task_split":"paper_retrieval"}
{"document_id":"72973","document_content":"# ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nPractical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classification performance. Further, we apply data augmentation in conjunction with meta-learning to reduce sampling bias. We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task. We explore augmentation in the sentence embedding space as well as prototypical embedding space. Combining meta-learning with augmentation provides upto 6.49% and 8.53% relative F1-score improvements over the best performing systems in the 5-shot and 10-shot learning, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11753v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"41812\", \"13600\", \"15943\"], \"outgoing_citations\": [\"162565\", \"166305\", \"213822\", \"228280\", \"230543\", \"231496\", \"231898\", \"240292\", \"244924\", \"267601\", \"272531\", \"273761\", \"290426\", \"290800\", \"300141\", \"302500\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"73003","document_content":"# LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHandwritten document image binarization is challenging due to high variability in the written content and complex background attributes such as page style, paper quality, stains, shadow gradients, and non-uniform illumination. While the traditional thresholding methods do not effectively generalize on such challenging real-world scenarios, deep learning-based methods have performed relatively well when provided with sufficient training data. However, the existing datasets are limited in size and diversity. This work proposes LS-HDIB - a large-scale handwritten document image binarization dataset containing over a million document images that span numerous real-world scenarios. Additionally, we introduce a novel technique that uses a combination of adaptive thresholding and seamless cloning methods to create the dataset with accurate ground truths. Through an extensive quantitative and qualitative evaluation over eight different deep learning based models, we demonstrate the enhancement in the performance of these models when trained on the LS-HDIB dataset and tested on unseen images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11674v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"212915\", \"228675\", \"230546\", \"258854\", \"263731\", \"296980\"]}","task_split":"paper_retrieval"}
{"document_id":"73046","document_content":"# Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Multimedia\n## Abstract\nWe introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action descriptions contain words not encountered during training. We also extend our approach to the more challenging Generalized Zero-Shot Learning (GZSL) problem via a confidence-based gating mechanism. We are the first to present zero-shot skeleton action recognition results on the large-scale NTU-60 and NTU-120 skeleton action datasets with multiple splits. Our results demonstrate SynSE's state of the art performance in both ZSL and GZSL settings compared to strong baselines on the NTU-60 and NTU-120 datasets. The code and pretrained models are available at https:\/\/github.com\/skelemoa\/synse-zsl","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11530v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Multimedia\"], \"incoming_citations\": [\"26190\", \"28057\"], \"outgoing_citations\": [\"133957\", \"154266\", \"172394\", \"186694\", \"193900\", \"204870\", \"234901\", \"247760\", \"248411\", \"250258\", \"257077\", \"271260\", \"292386\", \"294875\", \"315231\", \"323833\", \"336312\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"73107","document_content":"# Combat Data Shift in Few-shot Learning with Knowledge Graph\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMany few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within\/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11354v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128\", \"41397\", \"50396\", \"65940\", \"143919\", \"145837\", \"167398\", \"186261\", \"187797\", \"189952\", \"191555\", \"199208\", \"207031\", \"224094\", \"226118\", \"228212\", \"230024\", \"230792\", \"234262\", \"234599\", \"238203\", \"244888\", \"246393\", \"250265\", \"250796\", \"251002\", \"263051\", \"265386\", \"273777\", \"277822\", \"286181\", \"288545\", \"288929\", \"290800\", \"292142\", \"292262\", \"302756\", \"305359\", \"311030\", \"317764\", \"336185\", \"339440\"]}","task_split":"paper_retrieval"}
{"document_id":"73143","document_content":"# Language Modelling as a Multi-Task Problem\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11287v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"24928\", \"46654\"], \"outgoing_citations\": [\"87179\", \"112305\", \"168415\", \"185730\", \"186250\", \"194308\", \"196722\", \"208903\", \"219670\", \"220137\", \"220159\", \"237283\", \"263620\", \"272944\", \"303003\", \"339440\", \"357245\"]}","task_split":"paper_retrieval"}
{"document_id":"73149","document_content":"# VisualMRC: Machine Reading Comprehension on Document Images\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent studies on machine reading comprehension have focused on text-level understanding but have not yet reached the level of human understanding of the visual layout and content of real-world documents. In this study, we introduce a new visual machine reading comprehension dataset, named VisualMRC, wherein given a question and a document image, a machine reads and comprehends texts in the image to answer the question in natural language. Compared with existing visual question answering (VQA) datasets that contain texts in images, VisualMRC focuses more on developing natural language understanding and generation abilities. It contains 30,000+ pairs of a question and an abstractive answer for 10,000+ document images sourced from multiple domains of webpages. We also introduce a new model that extends existing sequence-to-sequence models, pre-trained with large-scale text corpora, to take into account the visual layout and content of documents. Experiments with VisualMRC show that this model outperformed the base sequence-to-sequence models and a state-of-the-art VQA model. However, its performance is still below that of humans on most automatic evaluation metrics. The dataset will facilitate research aimed at connecting vision and language understanding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11272v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"53473\", \"9109\", \"40383\", \"51060\", \"55549\"], \"outgoing_citations\": [\"114913\", \"115759\", \"128027\", \"140469\", \"144625\", \"149004\", \"152680\", \"156340\", \"165195\", \"171465\", \"183268\", \"189847\", \"216765\", \"219292\", \"239884\", \"241203\", \"244278\", \"247060\", \"252885\", \"264315\", \"278894\"]}","task_split":"paper_retrieval"}
{"document_id":"73173","document_content":"# Automatic image annotation base on Naive Bayes and Decision Tree classifiers using MPEG-7\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently it has become essential to search for and retrieve high-resolution and efficient images easily due to swift development of digital images, many present annotation algorithms facing a big challenge which is the variance for represent the image where high level represent image semantic and low level illustrate the features, this issue is known as semantic gab. This work has been used MPEG-7 standard to extract the features from the images, where the color feature was extracted by using Scalable Color Descriptor (SCD) and Color Layout Descriptor (CLD), whereas the texture feature was extracted by employing Edge Histogram Descriptor (EHD), the CLD produced high dimensionality feature vector therefore it is reduced by Principal Component Analysis (PCA). The features that have extracted by these three descriptors could be passing to the classifiers (Naive Bayes and Decision Tree) for training. Finally, they annotated the query image. In this study TUDarmstadt image bank had been used. The results of tests and comparative performance evaluation indicated better precision and executing time of Naive Bayes classification in comparison with Decision Tree classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11222v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"80872\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"73178","document_content":"# Towards Robustness to Label Noise in Text Classification via Noise Modeling\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLarge datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to capture this noise through an auxiliary noise model over the classifier. We first assign a probability score to each training sample of having a noisy label, through a beta mixture model fitted on the losses at an early epoch of training. Then, we use this score to selectively guide the learning of the noise model and classifier. Our empirical evaluation on two text classification tasks shows that our approach can improve over the baseline accuracy, and prevent over-fitting to the noise.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459637.3482204\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22036\"], \"outgoing_citations\": [\"184385\", \"189025\", \"192033\", \"194712\", \"218209\", \"222824\", \"247500\", \"320027\"]}","task_split":"paper_retrieval"}
{"document_id":"73265","document_content":"# A Comparison of Approaches to Document-level Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nDocument-level machine translation conditions on surrounding sentences to produce coherent translations. There has been much recent work in this area with the introduction of custom model architectures and decoding algorithms. This paper presents a systematic comparison of selected approaches from the literature on two benchmarks for which document-level phenomena evaluation suites exist. We find that a simple method based purely on back-translating monolingual document-level data performs as well as much more elaborate alternatives, both in terms of document-level metrics as well as human evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11040v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21968\"], \"outgoing_citations\": [\"146003\", \"163902\", \"168854\", \"171617\", \"176177\", \"186239\", \"215003\", \"216217\", \"219083\", \"219720\", \"220017\", \"230556\", \"234469\", \"238926\", \"250879\", \"258163\", \"266955\", \"268684\", \"268909\", \"280916\", \"283971\", \"302824\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"73279","document_content":"# Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem\n## Categories\n- Optimization and Control\n- Machine Learning\n- Systems and Control\n- Machine Learning\n- Systems and Control\n## Abstract\nThis paper considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each agent locally observes a set of data points, and the agents' goal is to compute a linear model that best fits the collective data points observed by all the agents. We consider a server-based distributed architecture where the agents interact with a common server to solve the problem; however, the server cannot access the agents' data points. We consider a practical scenario wherein the system either has observation noise, i.e., the data points observed by the agents are corrupted, or has process noise, i.e., the computations performed by the server and the agents are corrupted. In noise-free systems, the recently proposed distributed linear regression algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method, has been claimed to converge faster than related methods. In this paper, we study the robustness of the IPG method, against both the observation noise and the process noise. We empirically show that the robustness of the IPG method compares favorably to the state-of-the-art algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LCSYS.2020.3045533\", \"primary_category\": \"math.OC\", \"categories\": [\"cs.LG\", \"eess.SY\", \"stat.ML\", \"cs.SY\", \"math.OC\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Machine Learning\", \"Systems and Control\", \"Machine Learning\", \"Systems and Control\", \"Optimization and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107418\", \"136930\", \"259364\", \"282938\"]}","task_split":"paper_retrieval"}
{"document_id":"73408","document_content":"# Probability Trajectory: One New Movement Description for Trajectory Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTrajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Currently, most of existing work treat the pedestrian trajectory as a series of fixed two-dimensional coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observed fact, also considering other movement characteristics of pedestrians, we propose one simple and intuitive movement description, probability trajectory, which maps the coordinate points of pedestrian trajectory into two-dimensional Gaussian distribution in images. Based on this unique description, we develop one novel trajectory prediction method, called social probability. The method combines the new probability trajectory and powerful convolution recurrent neural networks together. Both the input and output of our method are probability trajectories, which provide the recurrent neural network with sufficient spatial and random information of moving pedestrians. And the social probability extracts spatio-temporal features directly on the new movement description to generate robust and accurate predicted results. The experiments on public benchmark datasets show the effectiveness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10595v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129703\", \"151320\", \"157011\", \"158269\", \"162130\", \"174738\", \"181757\", \"196132\", \"199454\", \"229266\", \"237350\", \"250081\", \"253435\", \"296681\", \"311030\", \"311643\", \"317379\", \"320874\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"73411","document_content":"# Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology\n## Categories\n- Computation and Language\n## Abstract\nTools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year. Many such tools provide users the ability to search for specific entities (e.g. proteins, diseases) by tracking their mentions in papers. PubMed, the most well known database of biomedical papers, relies on human curators to add these annotations. This can take several weeks for new papers, and not all papers get tagged. Machine learning models have been developed to facilitate the semantic indexing of scientific papers. However their performance on the more comprehensive ontologies of biomedical concepts does not reach the levels of typical entity recognition problems studied in NLP. In large part this is due to their low resources, where the ontologies are large, there is a lack of descriptive text defining most entities, and labeled data can only cover a small portion of the ontology. In this paper, we develop a new model that overcomes these challenges by (1) generalizing to entities unseen at training time, and (2) incorporating linking predictions into the mention segmentation decisions. Our approach achieves new state-of-the-art results for the UMLS ontology in both traditional recognition\/linking (+8 F1 pts) as well as semantic indexing-based evaluation (+10 F1 pts).","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459930.3469524\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"92416\", \"92449\", \"118229\", \"154917\", \"178230\", \"180267\", \"197659\", \"198222\", \"220553\", \"224723\", \"243397\", \"249726\", \"250346\", \"295323\", \"297147\"]}","task_split":"paper_retrieval"}
{"document_id":"73448","document_content":"# Continual Learning of Visual Concepts for Robots through Limited Supervision\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nFor many real-world robotics applications, robots need to continually adapt and learn new concepts. Further, robots need to learn through limited data because of scarcity of labeled data in the real-world environments. To this end, my research focuses on developing robots that continually learn in dynamic unseen environments\/scenarios, learn from limited human supervision, remember previously learned knowledge and use that knowledge to learn new concepts. I develop machine learning models that not only produce State-of-the-results on benchmark datasets but also allow robots to learn new objects and scenes in unconstrained environments which lead to a variety of novel robotics applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10509v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75729\", \"104618\", \"112025\", \"115945\", \"139615\", \"154102\", \"158630\", \"183426\", \"187087\", \"192027\", \"207755\", \"221473\", \"223417\", \"241436\", \"271988\"]}","task_split":"paper_retrieval"}
{"document_id":"73530","document_content":"# Identity-aware Graph Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nMessage passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Altogether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10320v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [\"52113\", \"18135\", \"18144\", \"18515\", \"26661\", \"36313\", \"37770\", \"2056\", \"4995\", \"13814\", \"15576\", \"19609\", \"25687\", \"39747\", \"41578\", \"42514\", \"90277\", \"82471\"], \"outgoing_citations\": [\"86320\", \"90139\", \"103085\", \"112378\", \"118201\", \"127525\", \"159199\", \"160169\", \"181255\", \"183719\", \"184286\", \"191502\", \"195767\", \"196221\", \"198364\", \"204871\", \"215435\", \"218754\", \"228992\", \"229324\", \"240758\", \"241029\", \"242430\", \"245763\", \"249839\", \"261201\", \"279575\"]}","task_split":"paper_retrieval"}
{"document_id":"73536","document_content":"# The Role of Cost in the Integration of Security Features in Integrated Circuits for Smart Cards\n## Categories\n- Risk Management\n- Economics\n- Cryptography and Security\n- General Economics\n## Abstract\nThis essay investigates the role of cost in the development and production of secure integrated circuits. Initially, I make a small introduction on hardware attacks on smart cards and some of the reasons behind them. Subsequently, I introduce the production phases of chips that are integrated to smart cards and try to identify the costs affecting each one of them. I proceed to identify how adding security features on such integrated circuits may affect the costs of their development and production. I then make a more thorough investigation on the costs of developing a hardware attack for such chips and try to estimate the potential damages and losses of such an attack. I also go on to examine potential ways of reducing the cost of production for secure chips, while identifying the difficulties in adopting them.   This essay ends with the conclusion that adding security features to chips meant to be used for secure applications is well worth it, because the costs of developing attacks are of comparable amounts to the costs of developing and producing a chip and the potential damages and losses caused by such attacks can be way higher than these costs. Therefore, although the production and development of integrated circuits come at a certain cost and security introduces further additional costs, security is inherently unavoidable in such chips. Finally, I additionally identify that security is an evolving concept and does not aim to make a chip totally impenetrable, as this may be impossible, but to lower the potential risks, including that of being compromised, to acceptable levels. Thus, a balance needs be found between the level of security and the levels of cost and risk.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10293v1\", \"primary_category\": \"q-fin.RM\", \"categories\": [\"q-fin.EC\", \"q-fin.RM\", \"cs.CR\", \"econ.GN\"], \"primary_category_human_readable\": \"Risk Management\", \"categories_human_readable\": [\"Economics\", \"Risk Management\", \"Cryptography and Security\", \"General Economics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"73563","document_content":"# Unified Framework for Feature Extraction based on Contrastive Learning\n## Categories\n- Machine Learning\n## Abstract\nFeature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. As a popular self-supervised learning method, contrastive learning has recently garnered considerable attention. In this study, we proposed a unified framework based on a new perspective of contrastive learning (CL) that is suitable for both unsupervised and supervised feature extraction. The proposed framework first constructed two CL graph for uniquely defining the positive and negative pairs. Subsequently, the projection matrix was determined by minimizing the contrastive loss function. In addition, the proposed framework considered both similar and dissimilar samples to unify unsupervised and supervised feature extraction. Moreover, we propose the three specific methods: unsupervised contrastive learning method, supervised contrastive learning method 1 ,and supervised contrastive learning method 2. Finally, the numerical experiments on five real datasets demonstrated the superior performance of the proposed framework in comparison to the existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.11703v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"255202\"]}","task_split":"paper_retrieval"}
{"document_id":"73582","document_content":"# Cognitive Perspectives on Context-based Decisions and Explanations\n## Categories\n- Artificial Intelligence\n## Abstract\nWhen human cognition is modeled in Philosophy and Cognitive Science, there is a pervasive idea that humans employ mental representations in order to navigate the world and make predictions about outcomes of future actions. By understanding how these representational structures work, we not only understand more about human cognition but also gain a better understanding for how humans rationalise and explain decisions. This has an influencing effect on explainable AI, where the goal is to provide explanations of computer decision-making for a human audience. We show that the Contextual Importance and Utility method for XAI share an overlap with the current new wave of action-oriented predictive representational structures, in ways that makes CIU a reliable tool for creating explanations that humans can relate to and trust.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10179v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"73754","document_content":"# Context-Specific Likelihood Weighting\n## Categories\n- Artificial Intelligence\n## Abstract\nSampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.09791v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"195361\", \"229738\", \"345528\", \"346873\", \"346895\", \"348963\", \"349241\", \"349518\", \"349552\", \"349562\", \"350216\"]}","task_split":"paper_retrieval"}
{"document_id":"73757","document_content":"# Evaluating Models of Robust Word Recognition with Serial Reproduction\n## Categories\n- Computation and Language\n## Abstract\nSpoken communication occurs in a \"noisy channel\" characterized by high levels of environmental noise, variability within and between speakers, and lexical and syntactic ambiguity. Given these properties of the received linguistic input, robust spoken word recognition -- and language processing more generally -- relies heavily on listeners' prior knowledge to evaluate whether candidate interpretations of that input are more or less likely. Here we compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. Serial reproduction, an experimental paradigm where spoken utterances are reproduced by successive participants similar to the children's game of \"Telephone,\" is used to elicit a sample that reflects the linguistic expectations of English-speaking adults. When we evaluate a suite of probabilistic generative language models against the yielded chains of utterances, we find that those models that make use of abstract representations of preceding linguistic context (i.e., phrase structure) best predict the changes made by people in the course of serial reproduction. A logistic regression model predicting which words in an utterance are most likely to be lost or changed in the course of spoken transmission corroborates this result. We interpret these findings in light of research highlighting the interaction of memory-based constraints and representations in language processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.cognition.2020.104553\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"166082\", \"195989\", \"211421\", \"224182\", \"237283\", \"258109\", \"281225\", \"297648\", \"308310\", \"320202\", \"322412\", \"324793\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"74000","document_content":"# Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance\n## Categories\n- Systems and Control\n- Machine Learning\n## Abstract\nThis paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.09298v2\", \"primary_category\": \"eess.SY\", \"categories\": [\"cs.SY\", \"eess.SY\", \"cs.LG\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129079\", \"163901\", \"170237\", \"194332\", \"196535\", \"238132\", \"265615\", \"287003\"]}","task_split":"paper_retrieval"}
{"document_id":"74109","document_content":"# Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nSegmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.08993v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"221998\", \"233579\", \"264858\", \"287717\"]}","task_split":"paper_retrieval"}
{"document_id":"74508","document_content":"# Macroscopic Control of Text Generation for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite the fact that image captioning models have been able to generate impressive descriptions for a given image, challenges remain: (1) the controllability and diversity of existing models are still far from satisfactory; (2) models sometimes may produce extremely poor-quality captions. In this paper, two novel methods are introduced to solve the problems respectively. Specifically, for the former problem, we introduce a control signal which can control the macroscopic sentence attributes, such as sentence quality, sentence length, sentence tense and number of nouns etc. With such a control signal, the controllability and diversity of existing captioning models are enhanced. For the latter problem, we innovatively propose a strategy that an image-text matching model is trained to measure the quality of sentences generated in both forward and backward directions and finally choose the better one. As a result, this strategy can effectively reduce the proportion of poorquality sentences. Our proposed methods can be easily applie on most image captioning models to improve their overall performance. Based on the Up-Down model, the experimental results show that our methods achieve BLEU- 4\/CIDEr\/SPICE scores of 37.5\/120.3\/21.5 on MSCOCO Karpathy test split with cross-entropy training, which surpass the results of other state-of-the-art methods trained by cross-entropy loss.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.08000v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"61394\"], \"outgoing_citations\": [\"117131\", \"133695\", \"139263\", \"147001\", \"161715\", \"171096\", \"174872\", \"189964\", \"190585\", \"207034\", \"208615\", \"209504\", \"223302\", \"223314\", \"229734\", \"231619\", \"233685\", \"237199\", \"238205\", \"250183\", \"269128\", \"271234\", \"278604\", \"278954\", \"280246\", \"281152\", \"287504\", \"296681\"]}","task_split":"paper_retrieval"}
{"document_id":"74578","document_content":"# Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nGenerative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The quality diversity algorithm MAP-Elites was used to explore the set of quality levels that could be produced by each GAN, where quality was defined as being beatable and having the longest solution path possible. Interestingly, a GAN trained on only 20 levels generated the largest set of diverse beatable levels while a GAN trained on 150 levels generated the smallest set of diverse beatable levels, thus challenging the notion that more is always better when training GANs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.07868v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"2630\", \"41462\", \"72454\"], \"outgoing_citations\": [\"107909\", \"112786\", \"133342\", \"133812\", \"147140\", \"163503\", \"233501\", \"289709\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"74714","document_content":"# Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nThe widespread deployment of surveillance cameras for facial recognition gives rise to many privacy concerns. This study proposes a privacy-friendly alternative to large scale facial recognition. While there are multiple techniques to preserve privacy, our work is based on the minimization principle which implies minimizing the amount of collected personal data. Instead of running facial recognition software on all video data, we propose to automatically extract a high quality snapshot of each detected person without revealing his or her identity. This snapshot is then encrypted and access is only granted after legal authorization. We introduce a novel unsupervised face image quality assessment method which is used to select the high quality snapshots. For this, we train a variational autoencoder on high quality face images from a publicly available dataset and use the reconstruction probability as a metric to estimate the quality of each face crop. We experimentally confirm that the reconstruction probability can be used as biometric quality predictor. Unlike most previous studies, we do not rely on a manually defined face quality metric as everything is learned from data. Our face quality assessment method outperforms supervised, unsupervised and general image quality assessment methods on the task of improving face verification performance by rejecting low quality images. The effectiveness of the whole system is validated qualitatively on still images and videos.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.07529v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120894\", \"135770\", \"148748\", \"222731\", \"235160\", \"252688\", \"255790\", \"262487\", \"268379\", \"328183\", \"346465\"]}","task_split":"paper_retrieval"}
{"document_id":"74748","document_content":"# Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAs a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this paper, we propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent MORL model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally, get the Pareto frontier. Experimental results on the actual DL-PBS systems show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.07437v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"74755\", \"170765\", \"242350\"]}","task_split":"paper_retrieval"}
{"document_id":"74756","document_content":"# Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- I.4.0; I.4.9\n## Abstract\nSurveillance scenarios are prone to several problems since they usually involve low-resolution footage, and there is no control of how far the subjects may be from the camera in the first place. This situation is suitable for the application of upsampling (super-resolution) algorithms since they may be able to recover the discriminant properties of the subjects involved. While general super-resolution approaches were proposed to enhance image quality for human-level perception, biometrics super-resolution methods seek the best \"computer perception\" version of the image since their focus is on improving automatic recognition performance. Convolutional neural networks and deep learning algorithms, in general, have been applied to computer vision tasks and are now state-of-the-art for several sub-domains, including image classification, restoration, and super-resolution. However, no work has evaluated the effects that the latest proposed super-resolution methods may have upon the accuracy and face verification performance in low-resolution \"in-the-wild\" data. This project aimed at evaluating and adapting different deep neural network architectures for the task of face super-resolution driven by face recognition performance in real-world low-resolution images. The experimental results in a real-world surveillance and attendance datasets showed that general super-resolution architectures might enhance face verification performance of deep neural networks trained on high-resolution faces. Also, since neural networks are function approximators and can be trained based on specific objective functions, the use of a customized loss function optimized for feature extraction showed promising results for recovering discriminant features in low-resolution face images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10845v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"I.4.0; I.4.9\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"I.4.0; I.4.9\"], \"incoming_citations\": [], \"outgoing_citations\": [\"170700\", \"174767\", \"193626\", \"196746\", \"198751\", \"198793\", \"225289\", \"229236\", \"230095\", \"235160\", \"237237\", \"241758\", \"244327\", \"248969\", \"252688\", \"270229\", \"274513\", \"275999\", \"287403\", \"296675\", \"300053\"]}","task_split":"paper_retrieval"}
{"document_id":"74771","document_content":"# Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics. Unlike previous work, Messenger does not assume prior knowledge connecting text and state observations $-$ the control policy must simultaneously ground the game manual to entity symbols and dynamics in the environment. We develop a new model, EMMA (Entity Mapper with Multi-modal Attention) which uses an entity-conditioned attention module that allows for selective focus over relevant descriptions in the manual for each entity in the environment. EMMA is end-to-end differentiable and learns a latent grounding of entities and dynamics from text to observations using only environment rewards. EMMA achieves successful zero-shot generalization to unseen games with new dynamics, obtaining a 40% higher win rate compared to multiple baselines. However, win rate on the hardest stage of Messenger remains low (10%), demonstrating the need for additional work in this direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.07393v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"14861\", \"16824\", \"44473\"], \"outgoing_citations\": [\"102494\", \"116357\", \"124122\", \"160530\", \"161143\", \"163895\", \"181620\", \"183049\", \"208773\", \"209452\", \"250567\", \"259693\", \"261378\", \"263270\", \"263650\", \"267779\", \"281106\", \"282900\", \"307569\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"74797","document_content":"# Dissonance Between Human and Machine Understanding\n## Categories\n- Artificial Intelligence\n- I.2.10\n## Abstract\nComplex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this paper, we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well-performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3359158\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"I.2.10\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.10\"], \"incoming_citations\": [\"81443\", \"21628\", \"24169\", \"39988\", \"48804\", \"75999\", \"208204\", \"71025\", \"96085\", \"97778\", \"129344\", \"245009\"], \"outgoing_citations\": [\"220228\", \"229203\", \"243731\", \"251010\", \"271811\", \"290227\", \"298307\", \"303951\"]}","task_split":"paper_retrieval"}
{"document_id":"74948","document_content":"# Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). However, when applying DRL to tasks in a real-world environment, designing an appropriate reward is difficult. Rewards obtained via actual hardware sensors may include noise, misinterpretation, or failed observations. The learning instability caused by these unstable signals is a problem that remains to be solved in DRL. In this work, we propose an approach that extends existing DRL models by adding a subtask to directly estimate the variance contained in the reward signal. The model then takes the feature map learned by the subtask in a critic network and sends it to the actor network. This enables stable learning that is robust to the effects of potential noise. The results of experiments in the Atari game domain with unstable reward signals show that our method stabilizes training convergence. We also discuss the extensibility of the model by visualizing feature maps. This approach has the potential to make DRL more practical for use in noisy, real-world scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.06906v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"137934\", \"185544\", \"204825\", \"209786\", \"232723\", \"247487\", \"265652\", \"281062\", \"296980\", \"315055\", \"361329\"]}","task_split":"paper_retrieval"}
{"document_id":"75121","document_content":"# Linguistically-Enriched and Context-Aware Zero-shot Slot Filling\n## Categories\n- Computation and Language\n## Abstract\nSlot filling is identifying contiguous spans of words in an utterance that correspond to certain parameters (i.e., slots) of a user request\/query. Slot filling is one of the most important challenges in modern task-oriented dialog systems. Supervised learning approaches have proven effective at tackling this challenge, but they need a significant amount of labeled training data in a given domain. However, new domains (i.e., unseen in training) may emerge after deployment. Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains -- unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts. This setting is commonly referred to as zero-shot slot filling. Little work has focused on this setting, with limited experimental evaluation. Existing models that mainly rely on context-independent embedding-based similarity measures fail to detect slot values in unseen domains or do so only partially. We propose a new zero-shot slot filling neural model, LEONA, which works in three steps. Step one acquires domain-oblivious, context-aware representations of the utterance word by exploiting (a) linguistic features; (b) named entity recognition cues; (c) contextual embeddings from pre-trained language models. Step two fine-tunes these rich representations and produces slot-independent tags for each word. Step three exploits generalizable context-aware utterance-slot similarity features at the word level, uses slot-independent tags, and contextualizes them to produce slot-specific predictions for each word. Our thorough evaluation on four diverse public datasets demonstrates that our approach consistently outperforms the SOTA models by 17.52%, 22.15%, 17.42%, and 17.95% on average for unseen domains on SNIPS, ATIS, MultiWOZ, and SGD datasets, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.06514v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"71388\"], \"outgoing_citations\": [\"71388\", \"108680\", \"112804\", \"114145\", \"118285\", \"119773\", \"129221\", \"129574\", \"143927\", \"150360\", \"167138\", \"169527\", \"169709\", \"180447\", \"195286\", \"197286\", \"205014\", \"205899\", \"219814\", \"230543\", \"232057\", \"248279\", \"255999\", \"260613\", \"261770\", \"281163\", \"281169\", \"285317\", \"297147\", \"300449\"]}","task_split":"paper_retrieval"}
{"document_id":"75125","document_content":"# Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nMany existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.06507v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"85918\", \"23954\", \"42569\"], \"outgoing_citations\": [\"2771\", \"98849\", \"111608\", \"120998\", \"123494\", \"139931\", \"147546\", \"154504\", \"159704\", \"162451\", \"167415\", \"170131\", \"178686\", \"188495\", \"194324\", \"205519\", \"215022\", \"222222\", \"231780\", \"234369\", \"239450\", \"242604\", \"247960\", \"251701\", \"257861\", \"261128\", \"272361\", \"281001\", \"287288\", \"288416\", \"291989\", \"303380\"]}","task_split":"paper_retrieval"}
{"document_id":"75455","document_content":"# AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nPedestrian trajectory prediction is a critical yet challenging task, especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size. In this work, we propose a novel method, AVGCN, for trajectory prediction utilizing graph convolutional networks (GCN) based on human attention (A denotes attention, V denotes visual field constraints). First, we train an attention network that estimates the importance of neighboring pedestrians, using gaze data collected as subjects perform a bird's eye view crowd navigation task. Then, we incorporate the learned attention weights modulated by constraints on the pedestrian's visual field into a trajectory prediction network that uses a GCN to aggregate information from neighbors efficiently. AVGCN also considers the stochastic nature of pedestrian trajectories by taking advantage of variational trajectory prediction. Our approach achieves state-of-the-art performance on several trajectory prediction benchmarks, and the lowest average prediction error over all considered benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.05682v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"100535\"], \"outgoing_citations\": [\"127485\", \"136834\", \"165422\", \"177460\", \"181757\", \"189614\", \"190049\", \"196132\", \"214313\", \"228899\", \"229266\", \"237350\", \"253435\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"75518","document_content":"# ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSocial media platforms are vulnerable to fake news dissemination, which causes negative consequences such as panic and wrong medication in the healthcare domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news. We propose a framework modeling those features by using BERT language model and external sources, namely Simple English Wikipedia and source reliability tags. The conducted experiments on CONSTRAINT datasets demonstrated the benefit of integrating these features for the early detection of fake news in the healthcare domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.05499v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"82006\", \"88648\", \"95139\", \"103491\", \"106772\", \"125450\", \"133216\", \"142020\", \"145400\", \"168163\", \"192050\", \"207715\", \"208471\", \"219405\", \"259202\", \"267552\", \"268465\"]}","task_split":"paper_retrieval"}
{"document_id":"75533","document_content":"# Text Augmentation in a Multi-Task View\n## Categories\n- Computation and Language\n## Abstract\nTraditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective -- a multi-task view (MTV) of data augmentation -- in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger levels of augmentation. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that the MTV leads to higher and more robust performance improvements than traditional augmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.05469v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"51130\"], \"outgoing_citations\": [\"200867\", \"213379\", \"220611\", \"231898\", \"234577\", \"239335\", \"272068\", \"290778\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"75628","document_content":"# Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Econometrics\n## Abstract\nAirlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before. The latter is not observable. Moreover, the expected impact can be small in relative value. In this paper, we cast the problem as counterfactual prediction of unobserved revenue. The impact on revenue is then the difference between the observed and the estimated revenue. The originality of this work lies in the innovative application of econometric methods proposed for macroeconomic applications to a new problem setting. Broadly applicable, the approach benefits from only requiring revenue data observed for origin-destination pairs in the network of the airline at each day, before and after a change in the system is applied. We report results using real large-scale data from Air Canada. We compare a deep neural network counterfactual predictions model with econometric models. They achieve respectively 1% and 1.1% of error on the counterfactual revenue predictions, and allow to accurately estimate small impacts (in the order of 2%).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.10249v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"econ.EM\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Econometrics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"75750","document_content":"# Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records\n## Categories\n- Machine Learning\n## Abstract\nWith the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems. However, due to the heterogeneity of EHRs, models trained on different patient groups suffer from poor generalizability. How to mitigate domain shifts between the source patient group where the model is built upon and the target one where the model will be deployed becomes a critical issue. In this paper, we propose a data augmentation method to facilitate domain adaptation, which leverages knowledge from the source patient group when training model on the target one. Specifically, adversarially generated samples are used during domain adaptation to fill the generalization gap between the two patient groups. The proposed method is evaluated by a case study on different predictive modeling tasks on MIMIC-III EHR dataset. Results confirm the effectiveness of our method and the generality on different tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.04853v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"150118\", \"172700\", \"187557\", \"201710\", \"204327\", \"206956\", \"207787\", \"217091\", \"222222\", \"229886\", \"264024\", \"265793\"]}","task_split":"paper_retrieval"}
{"document_id":"76021","document_content":"# Learning to Ignore: Fair and Task Independent Representations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTraining fair machine learning models, aiming for their interpretability and solving the problem of domain shift has gained a lot of interest in the last years. There is a vast amount of work addressing these topics, mostly in separation. In this work we show that they can be seen as a common framework of learning invariant representations. The representations should allow to predict the target while at the same time being invariant to sensitive attributes which split the dataset into subgroups. Our approach is based on the simple observation that it is impossible for any learning algorithm to differentiate samples if they have the same feature representation. This is formulated as an additional loss (regularizer) enforcing a common feature representation across subgroups. We apply it to learn fair models and interpret the influence of the sensitive attribute. Furthermore it can be used for domain adaptation, transferring knowledge and learning effectively from very few examples. In all applications it is essential not only to learn to predict the target, but also to learn what to ignore.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.04047v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116447\", \"179390\", \"199971\", \"214146\", \"233306\", \"249428\", \"251849\", \"254577\", \"260707\", \"262364\", \"264914\", \"265530\", \"277050\", \"281848\", \"307383\"]}","task_split":"paper_retrieval"}
{"document_id":"76023","document_content":"# Evaluating Disentanglement of Structured Representations\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce the first metric for evaluating disentanglement at individual hierarchy levels of a structured latent representation. Applied to object-centric generative models, this offers a systematic, unified approach to evaluating (i) object separation between latent slots (ii) disentanglement of object properties inside individual slots (iii) disentanglement of intrinsic and extrinsic object properties. We theoretically show that for structured representations, our framework gives stronger guarantees of selecting a good model than previous disentanglement metrics. Experimentally, we demonstrate that viewing object compositionality as a disentanglement problem addresses several issues with prior visual metrics of object separation. As a core technical component, we present the first representation probing algorithm handling slot permutation invariance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.04041v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"27322\"], \"outgoing_citations\": [\"9733\", \"70382\", \"80015\", \"84985\", \"111212\", \"114553\", \"115856\", \"141033\", \"148094\", \"163549\", \"173765\", \"192689\", \"196894\", \"202065\", \"208041\", \"236595\", \"241060\", \"241933\", \"242142\", \"242239\", \"251597\", \"277538\", \"289210\", \"289880\"]}","task_split":"paper_retrieval"}
{"document_id":"76033","document_content":"# Evaluating Deep Learning Approaches for Covid19 Fake News Detection\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nSocial media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41\\% on the Covid-19 Fake news detection dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-73696-5_15\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"3480\", \"12329\", \"15439\", \"19514\", \"33521\", \"34289\", \"35460\", \"61725\"], \"outgoing_citations\": [\"88648\", \"124916\", \"230998\", \"259202\", \"267552\"]}","task_split":"paper_retrieval"}
{"document_id":"76084","document_content":"# Analysis of skin lesion images with deep learning\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nSkin cancer is the most common cancer worldwide, with melanoma being the deadliest form. Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of skin cancer compared to visual examination without support. We evaluate the current state of the art in the classification of dermoscopic images based on the ISIC-2019 Challenge for the classification of skin lesions and current literature. Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set comprised of publicly available dermoscopic and clinical images of skin lesions using transfer learning and model fine-tuning. The performance and applicability of these models for the detection of eight classes of skin lesions are examined. Real-time data augmentation, which uses random rotation, translation, shear, and zoom within specified bounds is used to increase the number of available training samples. Model predictions are multiplied by inverse class frequencies and normalized to better approximate actual probability distributions. Overall prediction accuracy is further increased by using the arithmetic mean of the predictions of several independently trained models. The best single model has been published as a web service.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.03814v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"72064\"], \"outgoing_citations\": [\"172832\", \"182065\", \"202716\", \"215865\", \"219227\", \"233279\", \"237466\", \"237660\", \"248564\", \"253266\", \"257861\", \"260655\", \"264232\", \"298556\", \"320829\"]}","task_split":"paper_retrieval"}
{"document_id":"76110","document_content":"# Deep Learning-based Face Super-Resolution: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, then present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field. A curated list of papers and resources to face super-resolution are available at \\url{https:\/\/github.com\/junjun-jiang\/Face-Hallucination-Benchmark}","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.03749v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26693\", \"22967\", \"56280\", \"59128\", \"92268\"], \"outgoing_citations\": [\"26693\", \"28002\", \"37459\", \"46052\", \"49720\", \"49874\", \"50155\", \"71292\", \"76015\", \"83295\", \"83477\", \"83603\", \"92268\", \"94977\", \"99898\", \"100258\", \"103761\", \"108335\", \"109766\", \"111131\", \"125816\", \"127680\", \"130065\", \"134076\", \"134378\", \"134658\", \"137881\", \"139332\", \"140312\", \"142045\", \"142121\", \"143372\", \"149113\", \"149962\", \"152993\", \"154701\", \"155416\", \"158246\", \"160915\", \"164523\", \"166256\", \"167661\", \"170700\", \"174140\", \"180372\", \"184965\", \"186054\", \"187775\", \"190354\", \"193626\", \"198751\", \"198793\", \"205139\", \"205408\", \"209098\", \"210477\", \"211273\", \"218033\", \"219346\", \"221777\", \"222451\", \"222871\", \"223755\", \"225426\", \"226546\", \"230252\", \"230415\", \"235729\", \"236344\", \"238254\", \"241941\", \"245319\", \"248005\", \"248019\", \"248731\", \"248969\", \"249421\", \"252221\", \"252688\", \"256279\", \"256389\", \"258886\", \"259660\", \"262168\", \"262816\", \"265203\", \"267136\", \"267940\", \"269027\", \"270024\", \"270105\", \"270132\", \"270134\", \"270795\", \"270923\", \"271035\", \"279655\", \"279992\", \"281201\", \"284716\", \"288238\", \"290675\", \"296033\", \"301276\", \"302868\", \"303360\", \"311691\", \"319578\", \"320837\", \"321122\", \"321145\", \"345469\"]}","task_split":"paper_retrieval"}
{"document_id":"76196","document_content":"# Summaformers @ LaySumm 20, LongSumm 20\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nAutomatic text summarization has been widely studied as an important task in natural language processing. Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization. Recently, deep learning based, specifically Transformer-based systems have been immensely popular. Summarization is a cognitively challenging task - extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. We differentiate between two types of summaries, namely, (a) LaySumm: A very short summary that captures the essence of the research paper in layman terms restricting overtly specific technical jargon and (b) LongSumm: A much longer detailed summary aimed at providing specific insights into various ideas touched upon in the paper. While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries of the two types described above. Evaluations against gold standard summaries using ROUGE metrics prove the effectiveness of our approach. On blind test corpora, our system ranks first and third for the LongSumm and LaySumm tasks respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2020.sdp-1.39\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"70000\", \"78200\"], \"outgoing_citations\": [\"149557\", \"149557\", \"149562\", \"149562\", \"165585\", \"165585\", \"166268\", \"166268\", \"168185\", \"168185\", \"169645\", \"170657\", \"170657\", \"182634\", \"182634\", \"186043\", \"186043\", \"193848\", \"193848\", \"235463\", \"235463\", \"261785\", \"261785\", \"263905\", \"263905\", \"280557\", \"280557\"]}","task_split":"paper_retrieval"}
{"document_id":"76199","document_content":"# A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become more relevant and widely used than ever before, and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is \"real\" or \"fake\", as a part of CONSTRAINT COVID19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models that has helped us achieve a joint 8th position on the leader board. We have achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion of the competition, we have been able to drastically improve our system by incorporating a novel heuristic algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art results on the given dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.03545v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22517\", \"58316\", \"34289\"], \"outgoing_citations\": [\"88648\", \"120762\", \"199511\", \"230998\", \"271021\"]}","task_split":"paper_retrieval"}
{"document_id":"76250","document_content":"# Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies\n## Categories\n- Mathematical Finance\n- Computational Finance\n- Machine Learning\n## Abstract\nOver the past decades, researchers have been pushing the limits of Deep Reinforcement Learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, while others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, I am particularly interested in testing out if any of the recent technology developed by the leads in the field can be readily applied to a class of optimal trading problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated and tested on strategic games: from AlphaGo to AlphaStar and at about the same time, OpenAI Five. Thus, in this writing, I want to show precisely how to use a DRL library that is initially built for games in a fundamental trading problem; mean reversion. And by introducing a framework that incorporates economically-motivated function properties, I also demonstrate, through the library, a highly-performant and convergent DRL solution to decision-making financial problems in general.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3905\/fjds.2022.1.094\", \"primary_category\": \"q-fin.MF\", \"categories\": [\"q-fin.MF\", \"q-fin.CP\", \"cs.LG\"], \"primary_category_human_readable\": \"Mathematical Finance\", \"categories_human_readable\": [\"Mathematical Finance\", \"Computational Finance\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225419\"]}","task_split":"paper_retrieval"}
{"document_id":"76272","document_content":"# Learning Better Sentence Representation with Syntax Information\n## Categories\n- Computation and Language\n## Abstract\nSentence semantic understanding is a key topic in the field of natural language processing. Recently, contextualized word representations derived from pre-trained language models such as ELMO and BERT have shown significant improvements for a wide range of semantic tasks, e.g. question answering, text classification and sentiment analysis. However, how to add external knowledge to further improve the semantic modeling capability of model is worth probing. In this paper, we propose a novel approach to combining syntax information with a pre-trained language model. In order to evaluate the effect of the pre-training model, first, we introduce RNN-based and Transformer-based pre-trained language models; secondly, to better integrate external knowledge, such as syntactic information integrate with the pre-training model, we propose a dependency syntax expansion (DSE) model. For evaluation, we have selected two subtasks: sentence completion task and biological relation extraction task. The experimental results show that our model achieves 91.2\\% accuracy, outperforming the baseline model by 37.8\\% on sentence completion task. And it also gets competitive performance by 75.1\\% $F_{1}$ score on relation extraction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.03343v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"185889\", \"186130\", \"189726\", \"194089\", \"194308\", \"203064\", \"300508\", \"300593\", \"304229\", \"310099\"]}","task_split":"paper_retrieval"}
{"document_id":"76426","document_content":"# Octave Mix: Data augmentation using frequency decomposition for activity recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the research field of activity recognition, although it is difficult to collect a large amount of measured sensor data, there has not been much discussion about data augmentation (DA). In this study, I propose Octave Mix as a new synthetic-style DA method for sensor-based activity recognition. Octave Mix is a simple DA method that combines two types of waveforms by intersecting low and high frequency waveforms using frequency decomposition. In addition, I propose a DA ensemble model and its training algorithm to acquire robustness to the original sensor data while remaining a wide variety of feature representation. I conducted experiments to evaluate the effectiveness of my proposed method using four different benchmark datasets of sensing-based activity recognition. As a result, my proposed method achieved the best estimation accuracy. Furthermore, I found that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, make it possible to achieve higher accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.02882v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"165972\", \"191257\", \"193971\", \"195349\", \"212960\", \"217861\", \"222828\", \"257987\", \"261491\", \"264752\", \"279737\", \"284931\"]}","task_split":"paper_retrieval"}
{"document_id":"76623","document_content":"# Architectural Patterns for the Design of Federated Learning Systems\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n- Software Engineering\n## Abstract\nFederated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client devices participate in federated learning. Designing a federated learning system requires software system design thinking apart from machine learning knowledge. Although much effort has been put into federated learning from the machine learning technique aspects, the software architecture design concerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, and four model aggregation patterns. The patterns are associated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.02373v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"cs.SE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Software Engineering\"], \"incoming_citations\": [\"52799\", \"29448\", \"40369\"], \"outgoing_citations\": [\"40369\", \"102145\", \"106037\", \"114599\", \"140052\", \"145648\", \"151488\", \"152891\", \"157855\", \"162376\", \"164701\", \"170831\", \"177426\", \"178024\", \"180726\", \"185996\", \"186022\", \"194178\", \"195752\", \"196107\", \"199085\", \"200347\", \"208297\", \"265073\"]}","task_split":"paper_retrieval"}
{"document_id":"76820","document_content":"# Reinforcement Learning with Latent Flow\n## Categories\n- Machine Learning\n## Abstract\nTemporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture temporal information present in the image observations. This heuristic is in contrast to the current paradigm in video classification architectures, which utilize explicit encodings of temporal information through methods such as optical flow and two-stream architectures to achieve state-of-the-art performance. Inspired by leading video classification architectures, we introduce the Flow of Latents for Reinforcement Learning (Flare), a network architecture for RL that explicitly encodes temporal information through latent vector differences. We show that Flare (i) recovers optimal performance in state-based RL without explicit access to the state velocity, solely with positional state information, (ii) achieves state-of-the-art performance on pixel-based challenging continuous control tasks within the DeepMind control benchmark suite, namely quadruped walk, hopper hop, finger turn hard, pendulum swing, and walker run, and is the most sample efficient model-free pixel-based RL algorithm, outperforming the prior model-free state-of-the-art by 1.9X and 1.5X on the 500k and 1M step benchmarks, respectively, and (iv), when augmented over rainbow DQN, outperforms this state-of-the-art level baseline on 5 of 8 challenging Atari games at 100M time step benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.01857v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"25651\"], \"outgoing_citations\": [\"93350\", \"100693\", \"127845\", \"128392\", \"132207\", \"134247\", \"151271\", \"152912\", \"163705\", \"192738\", \"203355\", \"209152\", \"210517\", \"214302\", \"222669\", \"237486\", \"243235\", \"253974\", \"254597\", \"260583\", \"262381\", \"280296\", \"283427\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"76825","document_content":"# Node2Seq: Towards Trainable Convolutions in Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nInvestigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features. While great performance has been achieved, the weights learning for different neighboring nodes is still less explored. In this work, we propose a novel graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores. Experimental results demonstrate the effectiveness of our proposed Node2Seq layer and show that the proposed adaptively non-local information learning can improve the performance of feature learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.01849v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"19379\", \"68697\", \"70398\"], \"outgoing_citations\": [\"77699\", \"92953\", \"93054\", \"110697\", \"122212\", \"164464\", \"177342\", \"190159\", \"210119\", \"221587\", \"227067\", \"228663\", \"292407\", \"311119\"]}","task_split":"paper_retrieval"}
{"document_id":"77117","document_content":"# Advances in Electron Microscopy with Deep Learning\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Materials Science\n- Computer Vision and Pattern Recognition\n## Abstract\nThis doctoral thesis covers some of my advances in electron microscopy with deep learning. Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative adversarial networks for exit wavefunction reconstruction from single transmission electron micrographs. This thesis adds to my publications by presenting their relationships, reflections, and holistic conclusions. This version of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of Philosophy in Physics is typeset for physical printing and binding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5281\/zenodo.4399748\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.LG\", \"cond-mat.mtrl-sci\", \"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Materials Science\", \"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"86892\", \"99462\", \"102547\", \"106304\", \"109729\", \"109731\", \"115704\", \"116687\", \"118936\", \"119969\", \"120620\", \"122153\", \"123525\", \"123627\", \"123725\", \"123946\", \"126706\", \"128391\", \"129594\", \"132717\", \"132740\", \"134750\", \"138534\", \"139082\", \"141077\", \"142497\", \"143878\", \"146642\", \"147746\", \"148099\", \"149052\", \"154958\", \"156516\", \"157188\", \"157650\", \"157876\", \"160203\", \"162067\", \"162801\", \"163614\", \"164305\", \"164784\", \"164935\", \"164955\", \"166975\", \"170131\", \"171090\", \"171112\", \"172261\", \"172483\", \"172766\", \"173324\", \"174630\", \"175336\", \"175919\", \"178656\", \"179609\", \"179649\", \"179822\", \"179903\", \"180015\", \"180089\", \"180474\", \"181346\", \"182121\", \"183254\", \"183406\", \"183417\", \"183438\", \"185417\", \"185563\", \"187962\", \"188180\", \"188293\", \"188297\", \"188392\", \"188497\", \"188578\", \"189870\", \"190684\", \"191822\", \"191908\", \"192169\", \"193129\", \"193433\", \"194445\", \"196125\", \"196586\", \"197515\", \"197612\", \"198355\", \"198751\", \"199083\", \"199913\", \"200862\", \"201126\", \"201258\", \"201262\", \"201994\", \"202296\", \"202567\", \"203414\", \"203834\", \"205889\", \"205941\", \"206218\", \"206945\", \"208406\", \"208446\", \"209138\", \"209380\", \"210264\", \"210288\", \"210925\", \"212301\", \"213507\", \"213852\", \"216298\", \"216607\", \"219077\", \"219850\", \"220538\", \"220877\", \"221186\", \"221777\", \"222765\", \"222856\", \"222941\", \"223084\", \"223224\", \"224192\", \"224849\", \"226488\", \"226638\", \"226786\", \"226862\", \"227935\", \"228070\", \"228591\", \"229645\", \"230177\", \"230291\", \"231271\", \"231517\", \"232128\", \"232243\", \"233177\", \"233733\", \"234815\", \"236117\", \"236190\", \"239125\", \"239560\", \"239971\", \"241616\", \"242554\", \"242604\", \"242744\", \"244673\", \"245271\", \"245355\", \"246146\", \"246396\", \"246604\", \"247042\", \"247158\", \"247194\", \"247774\", \"248557\", \"248564\", \"249065\", \"250976\", \"251209\", \"251913\", \"251965\", \"252381\", \"252945\", \"254185\", \"255050\", \"255562\", \"256582\", \"257015\", \"258199\", \"258809\", \"259486\", \"259513\", \"261608\", \"261612\", \"264252\", \"264308\", \"264627\", \"264988\", \"265398\", \"265677\", \"266419\", \"266833\", \"266960\", \"271294\", \"271578\", \"271890\", \"272553\", \"272987\", \"273351\", \"273563\", \"275763\", \"276296\", \"276462\", \"276973\", \"277258\", \"277386\", \"277393\", \"279997\", \"281258\", \"283078\", \"283100\", \"283807\", \"284157\", \"284468\", \"285757\", \"286335\", \"286488\", \"287597\", \"288174\", \"289412\", \"290092\", \"290341\", \"290426\", \"292186\", \"292359\", \"295566\", \"296021\", \"297625\", \"297667\", \"298802\", \"299375\", \"300739\", \"301502\", \"302009\", \"302803\", \"302916\", \"303384\", \"305359\", \"306196\", \"307557\", \"307989\", \"309148\", \"309807\", \"309886\", \"310922\", \"311813\", \"312519\", \"313550\", \"315023\", \"319427\", \"319781\", \"321611\", \"327563\", \"327963\", \"331074\", \"333448\", \"336188\", \"336246\", \"350283\", \"351685\", \"358045\"]}","task_split":"paper_retrieval"}
{"document_id":"77246","document_content":"# Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn Recurrent Neural Networks (RNNs), encoding information in a suboptimal or erroneous way can impact the quality of representations based on later elements in the sequence and subsequently lead to wrong predictions and a worse model performance. In humans, challenging cases like garden path sentences (an instance of this being the infamous \"The horse raced past the barn fell\") can lead their language understanding astray. However, they are still able to correct their representation accordingly and recover when new information is encountered. Inspired by this, I propose an augmentation to standard RNNs in form of a gradient-based correction mechanism: This way I hope to enable such models to dynamically adapt their inner representation of a sentence, adding a way to correct deviations as soon as they occur. This could therefore lead to more robust models using more flexible representations, even during inference time.   I conduct different experiments in the context of language modeling, where the impact of using such a mechanism is examined in detail. To this end, I look at modifications based on different kinds of time-dependent error signals and how they influence the model performance. Furthermore, this work contains a study of the model's confidence in its predictions during training and for challenging test samples and the effect of the manipulation thereof. Lastly, I also study the difference in behavior of these novel models compared to a standard LSTM baseline and investigate error cases in detail to identify points of future research. I show that while the proposed approach comes with promising theoretical guarantees and an appealing intuition, it is only able to produce minor improvements over the baseline due to challenges in its practical application and the efficacy of the tested model variants.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.00674v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175763\", \"195989\", \"217402\", \"218361\", \"219670\", \"220460\", \"220865\", \"228238\", \"228604\", \"230051\", \"230246\", \"233400\", \"235858\", \"237229\", \"237283\", \"240665\", \"241503\", \"246396\", \"249109\", \"255253\", \"271935\", \"281225\", \"295435\", \"328545\"]}","task_split":"paper_retrieval"}
{"document_id":"77310","document_content":"# A Multilayer Correlated Topic Model\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation\n- Machine Learning\n- Methodology\n## Abstract\nWe proposed a novel multilayer correlated topic model (MCTM) to analyze how the main ideas inherit and vary between a document and its different segments, which helps understand an article's structure. The variational expectation-maximization (EM) algorithm was derived to estimate the posterior and parameters in MCTM. We introduced two potential applications of MCTM, including the paragraph-level document analysis and market basket data analysis. The effectiveness of MCTM in understanding the document structure has been verified by the great predictive performance on held-out documents and intuitive visualization. We also showed that MCTM could successfully capture customers' popular shopping patterns in the market basket analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.02028v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\", \"stat.CO\", \"stat.ML\", \"stat.ME\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Computation\", \"Machine Learning\", \"Methodology\"], \"incoming_citations\": [], \"outgoing_citations\": [\"184805\", \"297260\", \"335884\", \"350478\", \"357095\"]}","task_split":"paper_retrieval"}
{"document_id":"77389","document_content":"# SDA: Improving Text Generation with Self Data Augmentation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nData augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision. However, less work has been done in the context of text, partially due to its discrete nature and the complexity of natural languages. In this paper, we propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, which are only applied to specific models, our method is more general and could be easily adapted to any MLE-based training procedure. In addition, our framework allows task-specific evaluation metrics to be designed to flexibly control the generated sentences, for example, in terms of controlling vocabulary usage and avoiding nontrivial repetitions. Extensive experimental results demonstrate the superiority of our method on two synthetic and several standard real datasets, significantly improving related baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.03236v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128396\", \"157151\", \"170273\", \"172088\", \"189504\", \"200867\", \"211205\", \"212478\", \"223324\", \"227991\", \"230014\", \"231898\", \"231949\", \"234342\", \"235238\", \"243154\", \"255035\", \"265309\", \"267601\", \"278954\", \"284481\", \"287823\", \"290679\", \"297317\", \"302815\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"77413","document_content":"# When Is Generalizable Reinforcement Learning Tractable?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAgents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tractable generalization is only possible when the environments are similar or close in some sense. To capture this, we introduce Weak Proximity, a natural structural condition that requires the environments to have highly similar transition and reward functions and share a policy providing optimal value. Despite such shared structure, we prove that tractable generalization is impossible in the worst case. This holds even when each individual environment can be efficiently solved to obtain an optimal linear policy, and when the agent possesses a generative model. Our lower bound applies to the more complex task of representation learning for the purpose of efficient generalization to multiple environments. On the positive side, we introduce Strong Proximity, a strengthened condition which we prove is sufficient for efficient generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.00300v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"31401\", \"42123\", \"44049\"], \"outgoing_citations\": [\"71715\", \"71947\", \"75606\", \"89586\", \"116586\", \"117376\", \"117599\", \"121684\", \"152469\", \"152918\", \"155106\", \"155667\", \"155743\", \"160036\", \"162949\", \"166022\", \"180703\", \"181677\", \"183690\", \"187062\", \"192827\", \"194494\", \"207051\", \"212511\", \"216076\", \"227301\", \"236067\", \"237229\", \"238577\", \"255531\", \"272043\", \"283410\", \"302952\", \"339851\", \"356588\", \"357220\"]}","task_split":"paper_retrieval"}
{"document_id":"77428","document_content":"# VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nText-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.00265v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"61497\"], \"outgoing_citations\": [\"98266\", \"111384\", \"128171\", \"131401\", \"146087\", \"152680\", \"157249\", \"160128\", \"167256\", \"171465\", \"172370\", \"174872\", \"236696\", \"238205\", \"244479\", \"248171\", \"260926\", \"280249\", \"281407\", \"312643\"]}","task_split":"paper_retrieval"}
{"document_id":"77467","document_content":"# Transformer based Automatic COVID-19 Fake News Detection System\n## Categories\n- Computation and Language\n## Abstract\nRecent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news. Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles. Quick detection of fake news can reduce the spread of panic and confusion among the public. For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our best approach is based on an ensemble of three transformer models (BERT, ALBERT, and XLNET) to detecting fake news. This model was trained and evaluated in the context of the ConstraintAI 2021 shared task COVID19 Fake News Detection in English. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.00180v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20082\", \"34289\", \"42587\"], \"outgoing_citations\": [\"84522\", \"88313\", \"88648\", \"108176\", \"117359\", \"119855\", \"123410\", \"227367\"]}","task_split":"paper_retrieval"}
{"document_id":"77530","document_content":"# Towards Modelling Coherence in Spoken Discourse\n## Categories\n- Computation and Language\n## Abstract\nWhile there has been significant progress towards modelling coherence in written discourse, the work in modelling spoken discourse coherence has been quite limited. Unlike the coherence in text, coherence in spoken discourse is also dependent on the prosodic and acoustic patterns in speech. In this paper, we model coherence in spoken discourse with audio-based coherence models. We perform experiments with four coherence-related tasks with spoken discourses. In our experiments, we evaluate machine-generated speech against the speech delivered by expert human speakers. We also compare the spoken discourses generated by human language learners of varying language proficiency levels. Our results show that incorporating the audio modality along with the text benefits the coherence models in performing downstream coherence related tasks with spoken discourses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.00056v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9000\", \"112289\", \"6424\", \"26881\", \"77371\"], \"outgoing_citations\": [\"123777\", \"124607\", \"169244\", \"174159\", \"183969\", \"190961\", \"227746\", \"230322\", \"232214\", \"233043\", \"250568\", \"280892\", \"291100\"]}","task_split":"paper_retrieval"}
{"document_id":"77548","document_content":"# A Tale of Two Efficient and Informative Negative Sampling Distributions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Data Structures and Algorithms\n- Information Retrieval\n## Abstract\nSoftmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full softmax is costly from the computational and energy perspective. There have been various sampling approaches to overcome this challenge, popularly known as negative sampling (NS). Ideally, NS should sample negative classes from a distribution that is dependent on the input data, the current parameters, and the correct positive class. Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently. Therefore, alternative heuristics like random sampling, static frequency-based sampling, or learning-based biased sampling, which primarily trade either the sampling cost or the adaptivity of samples per iteration are adopted. In this paper, we show two classes of distributions where the sampling scheme is truly adaptive and provably generates negative samples in near-constant time. Our implementation in C++ on CPU is significantly superior, both in terms of wall-clock time and accuracy, compared to the most optimized TensorFlow implementations of other popular negative sampling approaches on powerful NVIDIA V100 GPU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.15843v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.DS\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Data Structures and Algorithms\", \"Information Retrieval\"], \"incoming_citations\": [\"11375\", \"12907\"], \"outgoing_citations\": [\"64862\", \"142159\", \"152693\", \"159293\", \"174552\", \"196041\", \"198536\", \"217775\", \"219705\", \"241435\", \"253891\", \"271428\", \"297570\", \"319828\", \"320814\", \"328700\", \"329290\"]}","task_split":"paper_retrieval"}
{"document_id":"77697","document_content":"# Text-Free Image-to-Speech Synthesis Using Learned Segmental Units\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nIn this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. Instead, we connect the image captioning module and the speech synthesis module with a set of discrete, sub-word speech units that are discovered with a self-supervised visual grounding task. We conduct experiments on the Flickr8k spoken caption dataset in addition to a novel corpus of spoken audio captions collected for the popular MSCOCO dataset, demonstrating that our generated captions also capture diverse visual semantics of the images they describe. We investigate several different intermediate speech representations, and empirically find that the representation must satisfy several important properties to serve as drop-in replacements for text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.15454v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"18300\", \"22962\", \"53303\", \"16315\", \"41730\", \"72108\"], \"outgoing_citations\": [\"91944\", \"114658\", \"128408\", \"145196\", \"154976\", \"157113\", \"162127\", \"163466\", \"163801\", \"164931\", \"166011\", \"166110\", \"167882\", \"171057\", \"172088\", \"173580\", \"182857\", \"184199\", \"188951\", \"189504\", \"190961\", \"191994\", \"192212\", \"193442\", \"198064\", \"199724\", \"201626\", \"205750\", \"206418\", \"210764\", \"211804\", \"212248\", \"212631\", \"212672\", \"214014\", \"222868\", \"229074\", \"230159\", \"231949\", \"232274\", \"235904\", \"236236\", \"236348\", \"236582\", \"236772\", \"237023\", \"237655\", \"237906\", \"237918\", \"239658\", \"242192\", \"246746\", \"247325\", \"252801\", \"253934\", \"254061\", \"255129\", \"260141\", \"260669\", \"263945\", \"268936\", \"269817\", \"270234\", \"270711\", \"274549\", \"275409\", \"278604\", \"278954\", \"279002\", \"282735\", \"287504\", \"303546\", \"310601\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"77774","document_content":"# Robustness Testing of Language Understanding in Task-Oriented Dialog\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMost language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in task-oriented dialog. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.15262v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"3823\", \"15935\", \"16678\", \"1178\", \"8726\", \"43919\"], \"outgoing_citations\": [\"89723\", \"96515\", \"119809\", \"124982\", \"126188\", \"131150\", \"139637\", \"142784\", \"166702\", \"169780\", \"171356\", \"171740\", \"174139\", \"180447\", \"189948\", \"200867\", \"202296\", \"204763\", \"206156\", \"216953\", \"217486\", \"218853\", \"234716\", \"235345\", \"247127\", \"255138\", \"270018\", \"285317\", \"293671\", \"294780\", \"296198\", \"301814\"]}","task_split":"paper_retrieval"}
{"document_id":"77841","document_content":"# Enhancing Pre-trained Language Model with Lexical Simplification\n## Categories\n- Computation and Language\n## Abstract\nFor both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity, and therefore to improve the understandability of sentences. In this paper, we leverage LS and propose a novel approach which can effectively improve the performance of PrLMs in text classification. A rule-based simplification process is applied to a given sentence. PrLMs are encouraged to predict the real label of the given sentence with auxiliary inputs from the simplified version. Using strong PrLMs (BERT and ELECTRA) as baselines, our approach can still further improve the performance in various text classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.15070v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"97943\", \"129858\", \"131475\", \"176154\", \"200867\", \"205715\", \"220017\", \"297242\"]}","task_split":"paper_retrieval"}
{"document_id":"78011","document_content":"# Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nGoal-oriented conversational agents are becoming prevalent in our daily lives. For these systems to engage users and achieve their goals, they need to exhibit appropriate social behavior as well as provide informative replies that guide users through tasks. The first component of the research in this paper applies statistical modeling techniques to understand conversations between users and human agents for customer service. Analyses show that social language used by human agents is associated with greater users' responsiveness and task completion. The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14653v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"205147\", \"231145\", \"234391\", \"245831\", \"265309\", \"272531\", \"302056\", \"305200\", \"309411\", \"310740\", \"310903\", \"316328\", \"343228\"]}","task_split":"paper_retrieval"}
{"document_id":"78137","document_content":"# General Mechanism of Evolution Shared by Proteins and Words\n## Categories\n- Populations and Evolution\n- Biological Physics\n- Soft Condensed Matter\n- Computation and Language\n## Abstract\nComplex systems, such as life and languages, are governed by principles of evolution. The analogy and comparison between biology and linguistics\\cite{alphafold2, RoseTTAFold, lang_virus, cell language, faculty1, language of gene, Protein linguistics, dictionary, Grammar of pro_dom, complexity, genomics_nlp, InterPro, language modeling, Protein language modeling} provide a computational foundation for characterizing and analyzing protein sequences, human corpora, and their evolution. However, no general mathematical formula has been proposed so far to illuminate the origin of quantitative hallmarks shared by life and language. Here we show several new statistical relationships shared by proteins and words, which inspire us to establish a general mechanism of evolution with explicit formulations that can incorporate both old and new characteristics. We found natural selection can be quantified via the entropic formulation by the principle of least effort to determine the sequence variation that survives in evolution. Besides, the origin of power law behavior and how changes in the environment stimulate the emergence of new proteins and words can also be explained via the introduction of function connection network. Our results demonstrate not only the correspondence between genetics and linguistics over their different hierarchies but also new fundamental physical properties for the evolution of complex adaptive systems. We anticipate our statistical tests can function as quantitative criteria to examine whether an evolution theory of sequence is consistent with the regularity of real data. In the meantime, their correspondence broadens the bridge to exchange existing knowledge, spurs new interpretations, and opens Pandora's box to release several potentially revolutionary challenges. For example, does linguistic arbitrariness conflict with the dogma that structure determines function?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14309v2\", \"primary_category\": \"q-bio.PE\", \"categories\": [\"physics.bio-ph\", \"cond-mat.soft\", \"cs.CL\", \"q-bio.PE\"], \"primary_category_human_readable\": \"Populations and Evolution\", \"categories_human_readable\": [\"Biological Physics\", \"Soft Condensed Matter\", \"Computation and Language\", \"Populations and Evolution\"], \"incoming_citations\": [], \"outgoing_citations\": [\"327561\"]}","task_split":"paper_retrieval"}
{"document_id":"78152","document_content":"# Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nSkin lesions segmentation is an important step in the process of automated diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular shapes, unclear boundaries, and different skin colors. Our proposed approach helps in improving the accuracy of skin lesion segmentation. Firstly, we have introduced the coordinate convolutional layer before passing the input image into the encoder. This layer helps the network to decide on the features related to translation invariance which further improves the generalization capacity of the model. Secondly, we have leveraged the properties of deep residual units along with the convolutional layers. At last, instead of using only cross-entropy or Dice-loss, we have combined the two-loss functions to optimize the training metrics which helps in converging the loss more quickly and smoothly. After training and validating the proposed model on ISIC 2018 (60% as train set + 20% as validation set), we tested the robustness of our trained model on various other datasets like ISIC 2018 (20% as test-set) ISIC 2017, 2016 and PH2 dataset. The results show that the proposed model either outperform or at par with the existing skin lesion segmentation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14249v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"218308\", \"225289\", \"237466\", \"254665\", \"270887\", \"271426\", \"271531\", \"272645\", \"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"78165","document_content":"# Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nFake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.01142v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"40620\"], \"outgoing_citations\": [\"104013\", \"164328\", \"181527\", \"219169\", \"234054\", \"245590\", \"249420\", \"259202\", \"270399\", \"283722\", \"316774\", \"326852\", \"355287\"]}","task_split":"paper_retrieval"}
{"document_id":"78200","document_content":"# On Generating Extended Summaries of Long Documents\n## Categories\n- Computation and Language\n## Abstract\nPrior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in some cases to know more detailed information about its salient points that can't fit in a short summary. This is typically the case for longer documents such as a research paper, legal document, or a book. In this paper, we present a new method for generating extended summaries of long papers. Our method exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach. We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method outperforms or matches the performance of strong baselines. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for long-form summary generation task. Our analysis shows that our multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections. Our datasets, and codes are publicly available at https:\/\/github.com\/Georgetown-IR-Lab\/ExtendedSumm","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14136v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19290\", \"21737\", \"38565\"], \"outgoing_citations\": [\"76196\", \"96411\", \"97507\", \"127753\", \"150522\", \"158869\", \"166268\", \"167795\", \"170657\", \"182634\", \"186316\", \"225556\", \"235463\", \"263905\", \"268334\", \"280557\"]}","task_split":"paper_retrieval"}
{"document_id":"78210","document_content":"# Syntax-Enhanced Pre-trained Model\n## Categories\n- Computation and Language\n## Abstract\nWe study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14116v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"5798\", \"12125\", \"16716\", \"47815\", \"56248\"], \"outgoing_citations\": [\"104905\", \"136604\", \"141406\", \"143089\", \"143992\", \"149018\", \"150253\", \"156620\", \"166853\", \"167753\", \"169278\", \"171817\", \"181948\", \"183694\", \"185889\", \"224786\", \"250529\", \"261389\", \"265786\", \"268671\", \"281163\", \"294249\"]}","task_split":"paper_retrieval"}
{"document_id":"78351","document_content":"# How Far Can We Get with Neural Networks Straight from JPEG?\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learning robust representations of the data directly from the RGB pixels. However, most image data are usually available in compressed format, from which the JPEG is the most widely used due to transmission and storage purposes demanding a preliminary decoding process that have a high computational load and memory usage. For this reason, deep learning methods capable of leaning directly from the compressed domain have been gaining attention in recent years. These methods adapt typical CNNs to work on the compressed domain, but the common architectural modifications lead to an increase in computational complexity and the number of parameters. In this paper, we investigate the usage of CNNs that are designed to work directly with the DCT coefficients available in JPEG compressed images, proposing a handcrafted and data-driven techniques for reducing the computational complexity and the number of parameters for these models in order to keep their computational cost similar to their RGB baselines. We make initial ablation studies on a subset of ImageNet in order to analyse the impact of different frequency ranges, image resolution, JPEG quality and classification task difficulty on the performance of the models. Then, we evaluate the models on the complete ImageNet dataset. Our results indicate that DCT models are capable of obtaining good performance, and that it is possible to reduce the computational complexity and the number of parameters from these models while retaining a similar classification accuracy through the use of our proposed techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.14426v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"32011\", \"78359\", \"79054\", \"86299\", \"100596\", \"119756\", \"130705\", \"139611\", \"170451\", \"174787\", \"190197\", \"204367\", \"217518\", \"258412\", \"270915\", \"281358\"]}","task_split":"paper_retrieval"}
{"document_id":"78355","document_content":"# Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nGenerative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https:\/\/github.com\/pshams55\/GAN-Case-Study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13736v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"35415\", \"7490\", \"34190\", \"54640\"], \"outgoing_citations\": [\"58715\", \"106870\", \"107310\", \"109534\", \"109907\", \"115787\", \"124575\", \"132995\", \"136348\", \"144345\", \"144882\", \"146468\", \"151061\", \"157815\", \"158820\", \"161937\", \"163258\", \"167541\", \"176382\", \"178695\", \"181193\", \"182585\", \"189785\", \"190170\", \"193736\", \"195141\", \"195652\", \"196658\", \"198247\", \"199669\", \"205404\", \"205980\", \"214841\", \"216097\", \"217027\", \"217232\", \"217640\", \"217644\", \"217694\", \"217729\", \"217982\", \"218395\", \"219541\", \"219742\", \"220270\", \"220538\", \"222303\", \"222900\", \"223275\", \"224125\", \"224849\", \"224941\", \"225333\", \"225463\", \"226432\", \"226593\", \"226839\", \"227341\", \"227750\", \"228615\", \"229074\", \"230013\", \"233382\", \"233623\", \"235467\", \"237705\", \"237891\", \"239219\", \"239595\", \"241758\", \"241846\", \"243040\", \"243612\", \"244890\", \"245038\", \"247081\", \"247713\", \"248036\", \"248413\", \"248586\", \"248730\", \"248853\", \"249031\", \"249251\", \"249326\", \"250193\", \"250198\", \"250353\", \"250524\", \"252107\", \"252224\", \"252935\", \"255208\", \"255272\", \"255521\", \"256746\", \"256813\", \"257649\", \"258227\", \"258510\", \"259486\", \"259846\", \"261762\", \"262011\", \"264243\", \"264436\", \"264685\", \"265203\", \"266719\", \"268494\", \"269388\", \"269443\", \"270105\", \"270245\", \"271135\", \"271419\", \"271503\", \"272220\", \"272553\", \"273246\", \"274632\", \"275665\", \"276476\", \"276542\", \"276764\", \"277021\", \"279030\", \"279574\", \"280050\", \"280996\", \"281002\", \"281472\", \"281618\", \"282032\", \"284986\", \"289678\", \"290378\", \"291302\", \"291440\", \"292385\", \"294023\", \"296021\", \"296355\", \"300739\", \"310922\"]}","task_split":"paper_retrieval"}
{"document_id":"78362","document_content":"# Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPhotometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions. In this paper, we present a complete framework, including a multilight source illumination and acquisition hardware system and a two-stage convolutional neural network (CNN) architecture, to construct inter- and intraframe representations for accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter- and intraframe feature extraction modules for the photometric stereo problem. Moreover, we propose utilizing the easily obtained object mask to eliminate adverse interference from invalid background regions in intraframe spatial convolutions, thus effectively improving the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that the proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favourably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.optlaseng.2021.106838\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"106554\", \"109661\", \"186960\", \"194752\", \"219800\", \"223643\", \"240584\", \"248813\", \"249732\", \"268521\", \"270532\"]}","task_split":"paper_retrieval"}
{"document_id":"78376","document_content":"# Translating Natural Language Instructions to Computer Programs for Robot Manipulation\n## Categories\n- Robotics\n- Machine Learning\n- Computation and Language\n## Abstract\nIt is highly desirable for robots that work alongside humans to be able to understand instructions in natural language. Existing language conditioned imitation learning models directly predict the actuator commands from the image observation and the instruction text. Rather than directly predicting actuator commands, we propose translating the natural language instruction to a Python function which queries the scene by accessing the output of the object detector and controls the robot to perform the specified task. This enables the use of non-differentiable modules such as a constraint solver when computing commands to the robot. Moreover, the labels in this setup are significantly more informative computer programs that capture the intent of the expert rather than teleoperated demonstrations. We show that the proposed method performs better than training a neural network to directly predict the robot actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13695v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"78369\", \"78372\", \"78380\", \"78416\", \"120826\", \"132918\", \"141361\", \"188484\", \"190487\", \"192383\", \"204038\", \"211133\", \"228542\", \"243236\", \"253448\", \"254184\", \"261617\", \"261784\", \"296701\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"78402","document_content":"# Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards\n## Categories\n- Machine Learning\n## Abstract\nA major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action spaces. Intuitively, if the reinforcement signal is very scarce, the agent should rely on some form of short-term memory in order to cover its environment efficiently. We propose a new exploration method, based on two intuitions: (1) the choice of the next exploratory action should depend not only on the (Markovian) state of the environment, but also on the agent's trajectory so far, and (2) the agent should utilize a measure of spread in the state space to avoid getting stuck in a small region. Our method leverages concepts often used in statistical physics to provide explanations for the behavior of simplified (polymer) chains in order to generate persistent (locally self-avoiding) trajectories in state space. We discuss the theoretical properties of locally self-avoiding walks and their ability to provide a kind of short-term memory through a decaying temporal correlation within the trajectory. We provide empirical evaluations of our approach in a simulated 2D navigation task, as well as higher-dimensional MuJoCo continuous control locomotion tasks with sparse rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13658v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"26464\", \"56286\"], \"outgoing_citations\": [\"159327\", \"160673\", \"172746\", \"201462\", \"239080\", \"241496\", \"242202\", \"254596\", \"261983\", \"262381\", \"264419\", \"264941\", \"271358\", \"272422\", \"272437\", \"272987\", \"273607\", \"291460\", \"350185\"]}","task_split":"paper_retrieval"}
{"document_id":"78444","document_content":"# Towards a Universal Continuous Knowledge Base\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn artificial intelligence (AI), knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base (CKB) that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Experiments on text classification show promising results: the CKB imports knowledge from a single model and then exports the knowledge to a new model, achieving comparable performance with the original model. More interesting, we import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model, achieving a higher accuracy than the original model. With the CKB, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in various neural networks trained for different AI tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13568v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"42396\"], \"outgoing_citations\": [\"92103\", \"122591\", \"139638\", \"153748\", \"165441\", \"168951\", \"169182\", \"232479\", \"250035\", \"256171\", \"269127\", \"277968\", \"278175\", \"281225\", \"292448\", \"310639\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"78462","document_content":"# Brain-inspired Search Engine Assistant based on Knowledge Graph\n## Categories\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nSearch engines can quickly response a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q and A) system attempt to assist search engines by providing simple, accurate and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this paper, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of human and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot firstly constructs a multi-layer query graph by splitting a complex multi-constraint query into several ordered constraints. Then it models the constraint reasoning process as subgraph search process inspired by the spreading activation model of cognitive science. In the end, novel features of the subgraph will be extracted for decision-making. The corresponding reasoning subgraph and answer confidence will be derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate the answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13529v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"136450\", \"169936\", \"195838\", \"206349\", \"211723\", \"232728\", \"233732\", \"261379\", \"304934\", \"358121\"]}","task_split":"paper_retrieval"}
{"document_id":"78473","document_content":"# Towards Continual Reinforcement Learning: A Review and Perspectives\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13490v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8658\", \"2206\", \"3429\", \"4353\", \"10698\", \"15121\", \"16608\", \"26377\", \"44495\", \"76756\", \"3273\", \"9455\", \"12745\", \"15055\", \"29720\", \"31901\", \"33029\", \"35136\", \"47830\", \"53458\", \"58958\", \"63604\", \"65204\", \"67803\", \"69915\"], \"outgoing_citations\": [\"3273\", \"3429\", \"4353\", \"4921\", \"10740\", \"12640\", \"12667\", \"13348\", \"13461\", \"13877\", \"15121\", \"16608\", \"16872\", \"17678\", \"18748\", \"21656\", \"21768\", \"22169\", \"22537\", \"24689\", \"26377\", \"29720\", \"31487\", \"33029\", \"37164\", \"39730\", \"40234\", \"42946\", \"43745\", \"44495\", \"46570\", \"46780\", \"49394\", \"53458\", \"63163\", \"65204\", \"65294\", \"66091\", \"67873\", \"69202\", \"69616\", \"69948\", \"70116\", \"72233\", \"76342\", \"82278\", \"88577\", \"89958\", \"90800\", \"91613\", \"91767\", \"95616\", \"97906\", \"100762\", \"102469\", \"111431\", \"111603\", \"113044\", \"113866\", \"114456\", \"115845\", \"115958\", \"116339\", \"116497\", \"117376\", \"117585\", \"117623\", \"118362\", \"119726\", \"124615\", \"125378\", \"125475\", \"127099\", \"127978\", \"129569\", \"129809\", \"131702\", \"134149\", \"136046\", \"136936\", \"137062\", \"137119\", \"137294\", \"137546\", \"139834\", \"139874\", \"140801\", \"142151\", \"143204\", \"143504\", \"143630\", \"145957\", \"146533\", \"148857\", \"148987\", \"151613\", \"151618\", \"152918\", \"153357\", \"160036\", \"161071\", \"162581\", \"162949\", \"164077\", \"164505\", \"166134\", \"167562\", \"167748\", \"172432\", \"176551\", \"179114\", \"179610\", \"181081\", \"181093\", \"182602\", \"183390\", \"183710\", \"186101\", \"187278\", \"188515\", \"189411\", \"192812\", \"192827\", \"194494\", \"194729\", \"196008\", \"196671\", \"197982\", \"198921\", \"200574\", \"200656\", \"200682\", \"200753\", \"200920\", \"201452\", \"201579\", \"203756\", \"205477\", \"207011\", \"207051\", \"207480\", \"207676\", \"208243\", \"208585\", \"209279\", \"209779\", \"210195\", \"210293\", \"212511\", \"212556\", \"212647\", \"212772\", \"214135\", \"214159\", \"214238\", \"214440\", \"214993\", \"215924\", \"216076\", \"218114\", \"218124\", \"221473\", \"223225\", \"224873\", \"226544\", \"227065\", \"228303\", \"228540\", \"228561\", \"229806\", \"230669\", \"231351\", \"231847\", \"233926\", \"235092\", \"235228\", \"236067\", \"238475\", \"239080\", \"239450\", \"240177\", \"240600\", \"241185\", \"241392\", \"241499\", \"241900\", \"242190\", \"242281\", \"242292\", \"243235\", \"247002\", \"248423\", \"248805\", \"250078\", \"251499\", \"251797\", \"251957\", \"252334\", \"253658\", \"255531\", \"255613\", \"256040\", \"256103\", \"258332\", \"258580\", \"261577\", \"261983\", \"262591\", \"262842\", \"263650\", \"266182\", \"266419\", \"269087\", \"271358\", \"271375\", \"272290\", \"272530\", \"273608\", \"275087\", \"277472\", \"279790\", \"280202\", \"280296\", \"280681\", \"280870\", \"281106\", \"281175\", \"282065\", \"284134\", \"284468\", \"284567\", \"289331\", \"291025\", \"291460\", \"293071\", \"293876\", \"296999\", \"298562\", \"302967\", \"303049\", \"305802\", \"306812\", \"312389\", \"341343\", \"349544\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"78493","document_content":"# ThamizhiUDp: A Dependency Parser for Tamil\n## Categories\n- Computation and Language\n## Abstract\nThis paper describes how we developed a neural-based dependency parser, namely ThamizhiUDp, which provides a complete pipeline for the dependency parsing of the Tamil language text using Universal Dependency formalism. We have considered the phases of the dependency parsing pipeline and identified tools and resources in each of these phases to improve the accuracy and to tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech (POS) and Morphological annotations, and uuparser with multilingual training for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the Stanza, trained with Amrita POS-tagged corpus. It is the current state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our morphological analyzer, ThamizhiMorph is a rule-based system with a very good coverage of Tamil. Our dependency parser ThamizhiUDp was trained using multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points higher than the current best achieved for Tamil dependency parsing. Therefore, we show that breaking up the dependency parsing pipeline to accommodate existing tools and resources is a viable approach for low-resource languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13436v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"122192\"], \"outgoing_citations\": [\"136604\", \"218881\", \"225852\"]}","task_split":"paper_retrieval"}
{"document_id":"78505","document_content":"# Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback\n## Categories\n- Artificial Intelligence\n## Abstract\nExploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users' provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users' perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13387v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"79100\", \"130931\", \"195041\", \"202683\", \"208079\", \"232696\", \"235041\", \"241070\", \"280557\", \"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"78527","document_content":"# Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nPurpose: Lesion segmentation in medical imaging is key to evaluating treatment response. We have recently shown that reinforcement learning can be applied to radiological images for lesion localization. Furthermore, we demonstrated that reinforcement learning addresses important limitations of supervised deep learning; namely, it can eliminate the requirement for large amounts of annotated training data and can provide valuable intuition lacking in supervised approaches. However, we did not address the fundamental task of lesion\/structure-of-interest segmentation. Here we introduce a method combining unsupervised deep learning clustering with reinforcement learning to segment brain lesions on MRI.   Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image. The user then selected the best mask for each of 10 training images. We then trained a reinforcement learning algorithm to select the masks. We tested the corresponding trained deep Q network on a separate testing set of 10 images. For comparison, we also trained and tested a U-net supervised deep learning network on the same set of training\/testing images.   Results: Whereas the supervised approach quickly overfit the training data and predictably performed poorly on the testing set (16% average Dice score), the unsupervised deep clustering and reinforcement learning achieved an average Dice score of 83%.   Conclusion: We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.13321v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"41234\", \"1002\", \"71406\"], \"outgoing_citations\": [\"92661\", \"107467\", \"153970\", \"242720\", \"294674\"]}","task_split":"paper_retrieval"}
{"document_id":"78682","document_content":"# Privacy Preservation for Wireless Sensor Networks in Healthcare: State of the Art, and Open Research Challenges\n## Categories\n- Cryptography and Security\n- Networking and Internet Architecture\n## Abstract\nThe advent of miniature biosensors has generated numerous opportunities for deploying wireless sensor networks in healthcare. However, an important barrier is that acceptance by healthcare stakeholders is influenced by the effectiveness of privacy safeguards for personal and intimate information which is collected and transmitted over the air, within and beyond these networks. In particular, these networks are progressing beyond traditional sensors, towards also using multimedia sensors, which raise further privacy concerns. Paradoxically, less research has addressed privacy protection, compared to security. Nevertheless, privacy protection has gradually evolved from being assumed an implicit by-product of security measures, and it is maturing into a research concern in its own right. However, further technical and socio-technical advances are needed. As a contribution towards galvanising further research, the hallmarks of this paper include: (i) a literature survey explicitly anchored on privacy preservation, it is underpinned by untangling privacy goals from security goals, to avoid mixing privacy and security concerns, as is often the case in other papers; (ii) a critical survey of privacy preservation services for wireless sensor networks in healthcare, including threat analysis and assessment methodologies; it also offers classification trees for the multifaceted challenge of privacy protection in healthcare, and for privacy threats, attacks and countermeasures; (iii) a discussion of technical advances complemented by reflection over the implications of regulatory frameworks; (iv) a discussion of open research challenges, leading onto offers of directions for future research towards unlocking the door onto privacy protection which is appropriate for healthcare in the twenty-first century.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.12958v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.NI\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Networking and Internet Architecture\"], \"incoming_citations\": [], \"outgoing_citations\": [\"306159\", \"349973\"]}","task_split":"paper_retrieval"}
{"document_id":"78739","document_content":"# Principled network extraction from images\n## Categories\n- Computer Vision and Pattern Recognition\n- Adaptation and Self-Organizing Systems\n- Physics and Society\n## Abstract\nImages of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mold and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labeled ones, while also giving high performance in extracting networks from images of rivers and slime mold for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1098\/rsos.210025\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"nlin.AO\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Adaptation and Self-Organizing Systems\", \"Physics and Society\"], \"incoming_citations\": [\"26126\", \"42360\"], \"outgoing_citations\": [\"127117\", \"242649\", \"300639\", \"317284\"]}","task_split":"paper_retrieval"}
{"document_id":"78933","document_content":"# Video Influencers: Unboxing the Mystique\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Sound\n- Audio and Speech Processing\n## Abstract\nInfluencer marketing has become a very popular tool to reach customers. Despite the rapid growth in influencer videos, there has been little research on the effectiveness of their constituent elements in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using a novel \"interpretable deep learning\" framework that accomplishes both goals of prediction and interpretation. Our prediction-based approach analyzes unstructured data and finds that \"what is said\" in words (text) is more influential than \"how it is said\" in imagery (images) followed by acoustics (audio). Our interpretation-based approach is implemented after completion of model prediction by analyzing the same source of unstructured data to measure importance attributed to the video elements. We eliminate several spurious and confounded relationships, and identify a smaller subset of theory-based relationships. We uncover novel findings that establish distinct effects for measures of shallow and deep engagement which are based on the dual-system framework of human thinking. Our approach is validated using simulated data, and we discuss the learnings from our findings for influencers and brands.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.12311v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"171988\", \"310738\", \"315200\"]}","task_split":"paper_retrieval"}
{"document_id":"79140","document_content":"# Alleviating Noisy Data in Image Captioning with Cooperative Distillation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nImage captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images. Unfortunately, scarce availability of such cleanly labeled data results in trained algorithms producing captions that can be terse and idiosyncratically specific to details in the image. We propose a new technique, cooperative distillation that combines clean curated datasets with the web-scale automatically extracted captions of the Google Conceptual Captions dataset (GCC), which can have poor descriptions of images, but is abundant in size and therefore provides a rich vocabulary resulting in more expressive captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.11691v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"79138\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"79143","document_content":"# Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval\n## Categories\n- Information Retrieval\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNeural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.11685v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"54873\", \"86961\"], \"outgoing_citations\": [\"74866\", \"86961\", \"94572\", \"110713\", \"114755\", \"119959\", \"122094\", \"125854\", \"128437\", \"128592\", \"150157\", \"151964\", \"177069\", \"189744\", \"190051\", \"190612\", \"194675\", \"203064\", \"223874\", \"224566\", \"226499\", \"232699\", \"233213\", \"235841\", \"237414\", \"241493\", \"245908\", \"246808\", \"248784\", \"249381\", \"249530\", \"253162\", \"255679\", \"258809\", \"260668\", \"261608\", \"262355\", \"262420\", \"263265\", \"265057\", \"265786\", \"266569\", \"267351\", \"267775\", \"268845\", \"269018\", \"273876\", \"274567\", \"276174\", \"276882\", \"279343\", \"281358\", \"281524\", \"281915\", \"284733\", \"285950\", \"285951\", \"286057\", \"286532\", \"289564\", \"289654\", \"289666\", \"289779\", \"290013\", \"290385\", \"290387\", \"291855\", \"291925\", \"292922\", \"293551\", \"294532\", \"295863\", \"295955\", \"296289\", \"297950\", \"298750\", \"299043\", \"301397\", \"301447\", \"302067\", \"302507\", \"302777\", \"303266\", \"303827\", \"309886\", \"310601\", \"310717\", \"310738\", \"310740\", \"310903\", \"311423\", \"311684\", \"314050\", \"314240\", \"316165\", \"316774\", \"316977\", \"317213\", \"317401\", \"320006\", \"320814\", \"321994\", \"322527\", \"327963\", \"328327\", \"328529\", \"331356\", \"333603\", \"333968\", \"336326\", \"336991\", \"341355\", \"341730\", \"357185\", \"357549\"]}","task_split":"paper_retrieval"}
{"document_id":"79427","document_content":"# Path Planning and Obstacle Avoidance Scheme for Autonomous Robots using Raspberry Pi\n## Categories\n- Robotics\n## Abstract\nWith the incremental development of robotic platforms to automate the manual processes, path planning has become a critical domain with or without the knowledge of the indoor and outdoor environment. The algorithms can be intelligent or pre-structured and should optimally reach the destination efficiently. The major challenge in this domain is to find a path which is free from static obstacles as well as dynamic obstacles. In this paper, a methodology is proposed with the implementation details of the robotic platform to cover the critical key points and to arrive at the original key point in a dynamic environment. The main computation is happening inside a Raspberry Pi B+ module, and compass, wheel encoders and ultrasonic sensors were used in the implementation for the localization of the robot to relevant key points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.10863v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"79467","document_content":"# Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nIncreasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of different brightness levels and dynamic range. Such metrics should be trained and validated on a sufficiently large subjective image quality dataset to ensure robust performance. As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4,000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets. The realigned quality scores share the same unified quality scale across all datasets. Such realignment was achieved by collecting additional cross-dataset quality comparisons and re-scaling data with a psychometric scaling method. Images in the proposed dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. We use the new dataset to retrain existing HDR metrics and show that the dataset is sufficiently large for training deep architectures. We show the utility of the dataset on brightness aware image compression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TMM.2021.3076298\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"131580\", \"146383\", \"161814\", \"229099\", \"247788\", \"255790\", \"260172\", \"275583\", \"280679\", \"283372\", \"298109\", \"303697\"]}","task_split":"paper_retrieval"}
{"document_id":"79536","document_content":"# Learning by Fixing: Solving Math Word Problems with Weak Supervision\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nPrevious neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a \\textit{weakly-supervised} paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate various solutions for a single problem. To boost weakly-supervised learning, we propose a novel \\textit{learning-by-fixing} (LBF) framework, which corrects the misperceptions of the neural network via symbolic reasoning. Specifically, for an incorrect solution tree generated by the neural network, the \\textit{fixing} mechanism propagates the error from the root node to the leaf nodes and infers the most probable fix that can be executed to get the desired answer. To generate more diverse solutions, \\textit{tree regularization} is applied to guide the efficient shrinkage and exploration of the solution space, and a \\textit{memory buffer} is designed to track and save the discovered various fixes for each problem. Experimental results on the Math23K dataset show the proposed LBF framework significantly outperforms reinforcement learning baselines in weakly-supervised learning. Furthermore, it achieves comparable top-1 and much better top-3\/5 answer accuracies than fully-supervised methods, demonstrating its strength in producing diverse solutions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.10582v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"26187\", \"32042\", \"32821\", \"4107\", \"12817\", \"37796\", \"50647\", \"78252\"], \"outgoing_citations\": [\"114455\", \"119357\", \"129004\", \"130231\", \"142951\", \"183403\", \"196150\", \"196980\", \"198373\", \"210184\", \"211865\", \"225551\", \"233912\", \"266702\", \"267980\", \"278863\", \"301993\", \"340914\"]}","task_split":"paper_retrieval"}
{"document_id":"79555","document_content":"# A 3D GAN for Improved Large-pose Facial Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to learn robustness to intra-class variation. In practice, such datasets are difficult to obtain, particularly those containing adequate variation of pose. Generative Adversarial Networks (GANs) provide a potential solution to this problem due to their ability to generate realistic, synthetic images. However, recent studies have shown that current methods of disentangling pose from identity are inadequate. In this work we incorporate a 3D morphable model into the generator of a GAN in order to learn a nonlinear texture model from in-the-wild images. This allows generation of new, synthetic identities, and manipulation of pose, illumination and expression without compromising the identity. Our synthesised data is used to augment training of facial recognition networks with performance evaluated on the challenging CFP and CPLFW datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.10545v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3173\", \"17613\", \"43659\", \"43812\"], \"outgoing_citations\": [\"79549\", \"99587\", \"103082\", \"112307\", \"126722\", \"133736\", \"133828\", \"143198\", \"168545\", \"188069\", \"191310\", \"198793\", \"202449\", \"205494\", \"236050\", \"236073\", \"240261\", \"241968\", \"247558\", \"248005\", \"268915\", \"274313\", \"275879\", \"296061\", \"321117\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"79849","document_content":"# A Generalization of Transformer Networks to Graphs\n## Categories\n- Machine Learning\n## Abstract\nWe propose a generalization of transformer neural network architecture for arbitrary graphs. The original transformer was designed for Natural Language Processing (NLP), which operates on fully connected graphs representing all connections between the words in a sequence. Such architecture does not leverage the graph connectivity inductive bias, and can perform poorly when the graph topology is important and has not been encoded into the node features. We introduce a graph transformer with four new properties compared to the standard model. First, the attention mechanism is a function of the neighborhood connectivity for each node in the graph. Second, the positional encoding is represented by the Laplacian eigenvectors, which naturally generalize the sinusoidal positional encodings often used in NLP. Third, the layer normalization is replaced by a batch normalization layer, which provides faster training and better generalization performance. Finally, the architecture is extended to edge feature representation, which can be critical to tasks s.a. chemistry (bond type) or link prediction (entity relationship in knowledge graphs). Numerical experiments on a graph benchmark demonstrate the performance of the proposed graph transformer architecture. This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs. As our architecture is simple and generic, we believe it can be used as a black box for future applications that wish to consider transformer and graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09699v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"1778\", \"4979\", \"13585\", \"14856\", \"16180\", \"16424\", \"18756\", \"30943\", \"46302\", \"67685\", \"865\", \"1162\", \"2910\", \"3989\", \"5069\", \"8403\", \"13274\", \"15396\", \"40588\", \"43122\", \"43338\", \"43845\", \"43981\", \"46560\", \"64937\", \"65259\", \"117616\", \"138999\"], \"outgoing_citations\": [\"103085\", \"127594\", \"138857\", \"138999\", \"147107\", \"149704\", \"157816\", \"167136\", \"181255\", \"196221\", \"199511\", \"228663\", \"249839\", \"279575\", \"292407\"]}","task_split":"paper_retrieval"}
{"document_id":"79859","document_content":"# Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nGenerative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the $G$ are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09673v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"34985\"], \"outgoing_citations\": [\"138674\", \"165442\", \"181239\", \"182695\", \"192020\", \"199960\", \"224747\", \"228631\", \"232018\", \"238966\", \"240246\", \"263859\", \"264436\", \"265075\", \"271487\", \"279798\", \"281093\", \"284631\", \"320051\"]}","task_split":"paper_retrieval"}
{"document_id":"80015","document_content":"# Measuring Disentanglement: A Review of Metrics\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning to disentangle and represent factors of variation in data is an important problem in AI. While many advances have been made to learn these representations, it is still unclear how to quantify disentanglement. While several metrics exist, little is known on their implicit assumptions, what they truly measure, and their limits. In consequence, it is difficult to interpret results when comparing different representations. In this work, we survey supervised disentanglement metrics and thoroughly analyze them. We propose a new taxonomy in which all metrics fall into one of three families: intervention-based, predictor-based and information-based. We conduct extensive experiments in which we isolate properties of disentangled representations, allowing stratified comparison along several axes. From our experiment results and analysis, we provide insights on relations between disentangled representation properties. Finally, we share guidelines on how to measure disentanglement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09276v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5274\", \"7546\", \"26622\", \"27322\", \"76023\", \"12936\"], \"outgoing_citations\": [\"118768\", \"130767\", \"140360\", \"147158\", \"154101\", \"170071\", \"183257\", \"183696\", \"183732\", \"187863\", \"200197\", \"205697\", \"206907\", \"208041\", \"212169\", \"214014\", \"236093\", \"236595\", \"241933\", \"242142\", \"242239\", \"251597\", \"265526\", \"277856\", \"290620\", \"353174\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"80091","document_content":"# Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review\n## Categories\n- Artificial Intelligence\n## Abstract\nKnowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying exiting research and by identifying gaps and opportunities for further research. To do that, we have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but seems to be still far from its peak.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09049v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"51608\"], \"outgoing_citations\": [\"25031\", \"139084\", \"144530\", \"184180\", \"185616\", \"204585\", \"222780\", \"256171\", \"267030\", \"316630\", \"324651\"]}","task_split":"paper_retrieval"}
{"document_id":"80112","document_content":"# Discovering New Intents with Deep Aligned Clustering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDiscovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods. The codes are released at https:\/\/github.com\/thuiar\/DeepAligned-Clustering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08987v7\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"24089\", \"53240\", \"65237\"], \"outgoing_citations\": [\"105764\", \"106272\", \"117646\", \"137492\", \"155266\", \"168563\", \"168621\", \"169539\", \"170106\", \"183006\", \"204126\", \"204763\", \"249143\", \"277590\", \"282580\", \"294674\", \"302955\"]}","task_split":"paper_retrieval"}
{"document_id":"80172","document_content":"# A Lightweight Neural Model for Biomedical Entity Linking\n## Categories\n- Computation and Language\n## Abstract\nBiomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods have advanced the state-of-the-art by allowing for rich representations of word sequences. However, they often have hundreds of millions of parameters and require heavy computing resources, which limits their applications in resource-limited scenarios. Here, we propose a lightweight neural method for biomedical entity linking, which needs just a fraction of the parameters of a BERT model and much less computing resources. Our method uses a simple alignment layer with attention mechanisms to capture the variations between mention and entity names. Yet, we show that our model is competitive with previous work on standard evaluation benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08844v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"37082\", \"25606\"], \"outgoing_citations\": [\"132687\", \"165441\", \"172372\", \"191145\", \"281119\", \"284357\", \"316774\"]}","task_split":"paper_retrieval"}
{"document_id":"80211","document_content":"# Graph Neural Networks: Taxonomy, Advances and Trends\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3495161\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"25810\", \"53381\"], \"outgoing_citations\": [\"42239\", \"66588\", \"67813\", \"71722\", \"72717\", \"72929\", \"77699\", \"78815\", \"88420\", \"90935\", \"91794\", \"92075\", \"92363\", \"99280\", \"100584\", \"102511\", \"103085\", \"111211\", \"115700\", \"116758\", \"116933\", \"117926\", \"119843\", \"120001\", \"124702\", \"127525\", \"128110\", \"136153\", \"137181\", \"137234\", \"137772\", \"138857\", \"138999\", \"139259\", \"139263\", \"140828\", \"142226\", \"142571\", \"144216\", \"145025\", \"146390\", \"147107\", \"147572\", \"148465\", \"148848\", \"149230\", \"149338\", \"150818\", \"153932\", \"154502\", \"155210\", \"155900\", \"157011\", \"157339\", \"157816\", \"158776\", \"159199\", \"159292\", \"161400\", \"162941\", \"164732\", \"165082\", \"168038\", \"168159\", \"168186\", \"168411\", \"168575\", \"169242\", \"169509\", \"170154\", \"170660\", \"170968\", \"171200\", \"171886\", \"172724\", \"173570\", \"173673\", \"174492\", \"175093\", \"176579\", \"177249\", \"178508\", \"180210\", \"181255\", \"182282\", \"182687\", \"183231\", \"183245\", \"183437\", \"183447\", \"183816\", \"184749\", \"185313\", \"185389\", \"185584\", \"185699\", \"185805\", \"185952\", \"186000\", \"186063\", \"186137\", \"186151\", \"186164\", \"186432\", \"186624\", \"186789\", \"186848\", \"186854\", \"187276\", \"187797\", \"188300\", \"188398\", \"189083\", \"189505\", \"190159\", \"190866\", \"191295\", \"191735\", \"191813\", \"192124\", \"192174\", \"192332\", \"192501\", \"192935\", \"193246\", \"193523\", \"193533\", \"193816\", \"194708\", \"194773\", \"194789\", \"195553\", \"195650\", \"195676\", \"195767\", \"196221\", \"196318\", \"196416\", \"196605\", \"196618\", \"197726\", \"197748\", \"198002\", \"198364\", \"198382\", \"199504\", \"199832\", \"200461\", \"200687\", \"203681\", \"203765\", \"203857\", \"204706\", \"204871\", \"206468\", \"206470\", \"208884\", \"209797\", \"210119\", \"211646\", \"211939\", \"211951\", \"212032\", \"212054\", \"213046\", \"213061\", \"213697\", \"214311\", \"214405\", \"215435\", \"216376\", \"216419\", \"216477\", \"216618\", \"216701\", \"217775\", \"217873\", \"218179\", \"218329\", \"218729\", \"218779\", \"218944\", \"219849\", \"220105\", \"220537\", \"220996\", \"221106\", \"221186\", \"221587\", \"222661\", \"223897\", \"225459\", \"226124\", \"226318\", \"226756\", \"227067\", \"228120\", \"228663\", \"228849\", \"228992\", \"229027\", \"229197\", \"229324\", \"229333\", \"229343\", \"229927\", \"230155\", \"230239\", \"230604\", \"230611\", \"230673\", \"230880\", \"231240\", \"231277\", \"231422\", \"232980\", \"232981\", \"234587\", \"234692\", \"236736\", \"236917\", \"237151\", \"237267\", \"237455\", \"237582\", \"238077\", \"238203\", \"238297\", \"238422\", \"238725\", \"239456\", \"239581\", \"240268\", \"240758\", \"240985\", \"241029\", \"241565\", \"242237\", \"242322\", \"242416\", \"242422\", \"242430\", \"242648\", \"242739\", \"243437\", \"243529\", \"243791\", \"244510\", \"245486\", \"245763\", \"249133\", \"249190\", \"249839\", \"250142\", \"250241\", \"250796\", \"251741\", \"252105\", \"252175\", \"252315\", \"255946\", \"258746\", \"261189\", \"261876\", \"263068\", \"263378\", \"263488\", \"264326\", \"264532\", \"264533\", \"265828\", \"267919\", \"268287\", \"268812\", \"269273\", \"269515\", \"269617\", \"271527\", \"271957\", \"272182\", \"272568\", \"273869\", \"276530\", \"277410\", \"277968\", \"278935\", \"279009\", \"279026\", \"279191\", \"279575\", \"279653\", \"279891\", \"284449\", \"284701\", \"291461\", \"291892\", \"292407\", \"296060\", \"297245\", \"300593\", \"303167\", \"303203\", \"303300\", \"303863\", \"305550\", \"305707\", \"307969\", \"311030\", \"316774\", \"317213\", \"327963\", \"336185\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"80286","document_content":"# FoggySight: A Scheme for Facial Lookup Privacy\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nAdvances in deep learning algorithms have enabled better-than-human performance on face recognition tasks. In parallel, private companies have been scraping social media and other public websites that tie photos to identities and have built up large databases of labeled face images. Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users. In this work, we tackle the problem of providing privacy from such face recognition systems. We propose and evaluate FoggySight, a solution that applies lessons learned from the adversarial examples literature to modify facial photos in a privacy-preserving manner before they are uploaded to social media. FoggySight's core feature is a community protection strategy where users acting as protectors of privacy for others upload decoy photos generated by adversarial machine learning algorithms. We explore different settings for this scheme and find that it does enable protection of facial privacy -- including against a facial recognition service with unknown internals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08588v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"79556\", \"38954\", \"55225\", \"92858\", \"4332\", \"31225\"], \"outgoing_citations\": [\"74554\", \"125144\", \"135947\", \"141283\", \"141295\", \"144649\", \"150162\", \"165358\", \"170530\", \"187557\", \"189775\", \"191481\", \"232283\", \"236933\", \"243986\", \"246211\", \"247399\", \"251596\", \"252688\", \"257486\", \"257949\", \"260424\", \"265949\", \"267940\", \"270382\", \"280873\", \"302157\", \"320918\", \"321122\", \"328183\"]}","task_split":"paper_retrieval"}
{"document_id":"80322","document_content":"# Bayes Meets Entailment and Prediction: Commonsense Reasoning with Non-monotonicity, Paraconsistency and Predictive Accuracy\n## Categories\n- Artificial Intelligence\n## Abstract\nThe recent success of Bayesian methods in neuroscience and artificial intelligence gives rise to the hypothesis that the brain is a Bayesian machine. Since logic and learning are both practices of the human brain, it leads to another hypothesis that there is a Bayesian interpretation underlying both logical reasoning and machine learning. In this paper, we introduce a generative model of logical consequence relations. It formalises the process of how the truth value of a sentence is probabilistically generated from the probability distribution over states of the world. We show that the generative model characterises a classical consequence relation, paraconsistent consequence relation and nonmonotonic consequence relation. In particular, the generative model gives a new consequence relation that outperforms them in reasoning with inconsistent knowledge. We also show that the generative model gives a new classification algorithm that outperforms several representative algorithms in predictive accuracy and complexity on the Kaggle Titanic dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08479v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"80375","document_content":"# Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSpatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. To overcome those limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer, SFTGNN could handle long sequences. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09641v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"229745\", \"216\", \"4726\", \"9956\", \"27383\", \"27697\", \"37836\", \"73199\", \"1592\", \"5069\", \"5070\", \"5008\", \"5290\", \"5429\", \"21187\", \"24176\", \"30816\", \"41438\", \"42843\"], \"outgoing_citations\": [\"147936\", \"183174\", \"185584\", \"238422\"]}","task_split":"paper_retrieval"}
{"document_id":"80468","document_content":"# Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nPrecision medicine has the potential to revolutionize healthcare, but much of the data for patients is locked away in unstructured free-text, limiting research and delivery of effective personalized treatments. Generating large annotated datasets for information extraction from clinical notes is often challenging and expensive due to the high level of expertise needed for high quality annotations. To enable natural language processing for small dataset sizes, we develop a novel enriched hierarchical annotation scheme and algorithm, Supervised Line Attention (SLA), and apply this algorithm to predicting categorical tumor attributes from kidney and colon cancer pathology reports from the University of California San Francisco (UCSF). Whereas previous work only annotated document level labels, we in addition ask the annotators to enrich the traditional label by asking them to also highlight the relevant line or potentially lines for the final label, which leads to a 20% increase of annotation time required per document. With the enriched annotations, we develop a simple and interpretable machine learning algorithm that first predicts the relevant lines in the document and then predicts the tumor attribute. Our results show across the small dataset sizes of 32, 64, 128, and 186 labeled documents per cancer, SLA only requires half the number of labeled documents as state-of-the-art methods to achieve similar or better micro-f1 and macro-f1 scores for the vast majority of comparisons that we made. Accounting for the increased annotation time, this leads to a 40% reduction in total annotation time over the state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08113v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"191145\", \"197416\", \"277007\", \"284752\"]}","task_split":"paper_retrieval"}
{"document_id":"80538","document_content":"# Odd-One-Out Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nThe effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations. Recent work in disentangled representation learning has shown that unsupervised representation learning approaches rely on fully supervised disentanglement metrics, which assume access to labels for ground-truth factors of variation. In many real-world cases ground-truth factors are expensive to collect, or difficult to model, such as for perception. Here we empirically show that a weakly-supervised downstream task based on odd-one-out observations is suitable for model selection by observing high correlation on a difficult downstream abstract visual reasoning task. We also show that a bespoke metric-learning VAE model which performs highly on this task also out-performs other standard unsupervised and a weakly-supervised disentanglement model across several metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.07966v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"143581\", \"182775\", \"183696\", \"183732\", \"187863\", \"206218\", \"208041\", \"220865\", \"224981\", \"241933\", \"242142\", \"242419\", \"251597\", \"297838\", \"311052\"]}","task_split":"paper_retrieval"}
{"document_id":"80728","document_content":"# Intrinsic Image Captioning Evaluation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe image captioning task is about to generate suitable descriptions from images. For this task there can be several challenges such as accuracy, fluency and diversity. However there are few metrics that can cover all these properties while evaluating results of captioning models.In this paper we first conduct a comprehensive investigation on contemporary metrics. Motivated by the auto-encoder mechanism and the research advances of word embeddings we propose a learning based metrics for image captioning, which we call Intrinsic Image Captioning Evaluation(I2CE). We select several state-of-the-art image captioning models and test their performances on MS COCO dataset with respects to both contemporary metrics and the proposed I2CE. Experiment results show that our proposed method can keep robust performance and give more flexible scores to candidate captions when encountered with semantic similar expression or less aligned semantics. On this concern the proposed metric could serve as a novel indicator on the intrinsic information between captions, which may be complementary to the existing ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.07333v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"193328\", \"227741\", \"234753\", \"237655\", \"239242\", \"249394\", \"253934\", \"256347\", \"258345\", \"269030\", \"271234\", \"277419\", \"278604\", \"278954\", \"287504\", \"311824\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"80791","document_content":"# Privacy-preserving Decentralized Aggregation for Federated Learning\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning is a promising framework for learning over decentralized data spanning multiple regions. This approach avoids expensive central training data aggregation cost and can improve privacy because distributed sites do not have to reveal privacy-sensitive data. In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning. We formulate the distributed aggregation protocol with the Alternating Direction Method of Multiplier (ADMM) and examine its privacy weakness. Unlike prior work that use Differential Privacy or homomorphic encryption for privacy, we develop a protocol that controls communication among participants in each round of aggregation to minimize privacy leakage. We establish its privacy guarantee against an honest-but-curious adversary. We also propose an efficient algorithm to construct such a communication pattern, inspired by combinatorial block design theory. Our secure aggregation protocol based on this novel group communication pattern design leads to an efficient algorithm for federated training with privacy guarantees. We evaluate our federated training algorithm on image classification and next-word prediction applications over benchmark datasets with 9 and 15 distributed sites. Evaluation results show that our algorithm performs comparably to the standard centralized federated learning method while preserving privacy; the degradation in test accuracy is only up to 0.73%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.07183v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"27377\"], \"outgoing_citations\": [\"151488\", \"177179\", \"204176\", \"206770\", \"207462\", \"207518\", \"210718\", \"219797\", \"229053\", \"232524\", \"261273\", \"270409\", \"273803\"]}","task_split":"paper_retrieval"}
{"document_id":"80802","document_content":"# Mask-Align: Self-Supervised Neural Word Alignment\n## Categories\n- Computation and Language\n## Abstract\nWord alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.07162v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"46950\", \"59165\"], \"outgoing_citations\": [\"127908\", \"127964\", \"128057\", \"129826\", \"147110\", \"168604\", \"169200\", \"179179\", \"181468\", \"181964\", \"189696\", \"200809\", \"232603\", \"288943\", \"289285\", \"290995\", \"299936\", \"311845\"]}","task_split":"paper_retrieval"}
{"document_id":"80837","document_content":"# Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTransformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. In GET, a Global Enhanced Encoder is designed for the embedding of the global feature, and a Global Adaptive Decoder are designed for the guidance of the caption generation. The former models intra- and inter-layer global representation by taking advantage of the proposed Global Enhanced Attention and a layer-wise fusion module. The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation. Extensive experiments on MS COCO dataset demonstrate the superiority of our GET over many state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.07061v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"6697\", \"15902\"], \"outgoing_citations\": [\"112250\", \"133695\", \"133970\", \"135934\", \"139529\", \"140486\", \"140526\", \"141949\", \"147001\", \"169417\", \"171096\", \"180838\", \"186189\", \"207034\", \"213165\", \"217310\", \"278604\", \"278954\", \"281152\", \"284230\", \"287504\"]}","task_split":"paper_retrieval"}
{"document_id":"80899","document_content":"# Adversarial Permutation Guided Node Representations for Link Prediction\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Physics and Society\n## Abstract\nAfter observing a snapshot of a social network, a link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate a score for currently non-neighboring node pairs, and rank them by this score. Recent LP systems compute this score by comparing dense, low dimensional vector representations of nodes. Graph neural networks (GNNs), in particular graph convolutional networks (GCNs), are popular examples. For two nodes to be meaningfully compared, their embeddings should be indifferent to reordering of their neighbors. GNNs typically use simple, symmetric set aggregators to ensure this property, but this design decision has been shown to produce representations with limited expressive power. Sequence encoders are more expressive, but are permutation sensitive by design. Recent efforts to overcome this dilemma turn out to be unsatisfactory for LP tasks. In response, we propose PermGNN, which aggregates neighbor features using a recurrent, order-sensitive aggregator and directly minimizes an LP loss while it is `attacked' by adversarial generator of neighbor permutations. By design, PermGNN{} has more expressive power compared to earlier symmetric aggregators. Next, we devise an optimization framework to map PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds up reporting the top-$K$ most likely edges for the LP task. Our experiments on diverse datasets show that \\our outperforms several state-of-the-art link predictors by a significant margin, and can predict the most likely edges fast.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.08974v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Physics and Society\"], \"incoming_citations\": [\"113797\", \"28034\"], \"outgoing_citations\": [\"91503\", \"142226\", \"165624\", \"176367\", \"181255\", \"185047\", \"185055\", \"192469\", \"196221\", \"201584\", \"211417\", \"215835\", \"228663\", \"240758\", \"241060\", \"280499\"]}","task_split":"paper_retrieval"}
{"document_id":"80924","document_content":"# Semi-supervised reward learning for offline reinforcement learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such as healthcare and robotics interactions with the environment are either expensive or unethical. Training agents usually requires reward functions, but unfortunately, rewards are seldom available in practice and their engineering is challenging and laborious. To overcome this, we investigate reward learning under the constraint of minimizing human reward annotations. We consider two types of supervision: timestep annotations and demonstrations. We propose semi-supervised learning algorithms that learn from limited annotations and incorporate unlabelled data. In our experiments with a simulated robotic arm, we greatly improve upon behavioural cloning and closely approach the performance achieved with ground truth rewards. We further investigate the relationship between the quality of the reward model and the final policies. We notice, for example, that the reward models do not need to be perfect to result in useful policies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06899v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"43417\", \"36644\", \"61195\"], \"outgoing_citations\": [\"84270\", \"111316\", \"115825\", \"121735\", \"127099\", \"141277\", \"158515\", \"159565\", \"163689\", \"164054\", \"164743\", \"184304\", \"190237\", \"206875\", \"209859\", \"240800\", \"251924\", \"260583\", \"261617\", \"261805\", \"270529\", \"272618\", \"277564\", \"286689\", \"297317\", \"316584\"]}","task_split":"paper_retrieval"}
{"document_id":"80979","document_content":"# Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06777v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17221\", \"17233\", \"13293\"], \"outgoing_citations\": [\"95673\", \"101039\", \"106554\", \"109661\", \"134561\", \"134658\", \"146592\", \"154173\", \"155419\", \"186960\", \"194752\", \"200338\", \"211407\", \"219800\", \"223643\", \"240584\", \"248572\", \"249732\", \"258566\", \"274898\", \"313371\"]}","task_split":"paper_retrieval"}
{"document_id":"80992","document_content":"# A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. However, generating node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is an open problem. We propose a novel meta-learning strategy capable of producing multi-task node embeddings. Our method avoids the difficulties arising when learning to perform multiple tasks concurrently by, instead, learning to quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks singularly. We show that the embeddings produced by our method can be used to perform multiple tasks with comparable or higher performance than classically trained models. Our method is model-agnostic and task-agnostic, thus applicable to a wide variety of multi-task domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06755v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"74969\", \"66570\"], \"outgoing_citations\": [\"111508\", \"126374\", \"131702\", \"137073\", \"150176\", \"162941\", \"165886\", \"166877\", \"167398\", \"184987\", \"185730\", \"187797\", \"189848\", \"218329\", \"231489\", \"250796\", \"251209\", \"255737\"]}","task_split":"paper_retrieval"}
{"document_id":"81011","document_content":"# On Duality Gap as a Measure for Monitoring GAN Training\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGenerative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging task. This is often attributed to the lack of correlation between the training progress and the trajectory of the generator and discriminator losses and the need for the GAN's subjective evaluation. A recently proposed measure inspired by game theory - the duality gap, aims to bridge this gap. However, as we demonstrate, the duality gap's capability remains constrained due to limitations posed by its estimation process. This paper presents a theoretical understanding of this limitation and proposes a more dependable estimation process for the duality gap. At the crux of our approach is the idea that local perturbations can help agents in a zero-sum game escape non-Nash saddle points efficiently. Through exhaustive experimentation across GAN models and datasets, we establish the efficacy of our approach in capturing the GAN training progress with minimal increase to the computational complexity. Further, we show that our estimate, with its ability to identify model convergence\/divergence, is a potential performance measure that can be used to tune the hyperparameters of a GAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06723v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"50390\"], \"outgoing_citations\": [\"140669\", \"177863\", \"190533\", \"200533\", \"201084\", \"204041\", \"208948\", \"210224\", \"215384\", \"221328\", \"229730\", \"242751\", \"246852\", \"249069\", \"252621\", \"265604\", \"272543\", \"276542\", \"303913\", \"321079\"]}","task_split":"paper_retrieval"}
{"document_id":"81039","document_content":"# Protective Policy Transfer\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nBeing able to transfer existing skills to new situations is a key capability when training robots to operate in unpredictable real-world environments. A successful transfer algorithm should not only minimize the number of samples that the robot needs to collect in the new environment, but also prevent the robot from damaging itself or the surrounding environment during the transfer process. In this work, we introduce a policy transfer algorithm for adapting robot motor skills to novel scenarios while minimizing serious failures. Our algorithm trains two control policies in the training environment: a task policy that is optimized to complete the task of interest, and a protective policy that is dedicated to keep the robot from unsafe events (e.g. falling to the ground). To decide which policy to use during execution, we learn a safety estimator model in the training environment that estimates a continuous safety level of the robot. When used with a set of thresholds, the safety estimator becomes a classifier for switching between the protective policy and the task policy. We evaluate our approach on four simulated robot locomotion problems and a 2D navigation problem and show that our method can achieve successful transfer to notably different environments while taking the robot's safety into consideration.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06662v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"90837\", \"96091\", \"119743\", \"121230\", \"133560\", \"159108\", \"161304\", \"164478\", \"167687\", \"178846\", \"193579\", \"194332\", \"196228\", \"196576\", \"201681\", \"214403\", \"214419\", \"230215\", \"234025\", \"250066\", \"253045\", \"265053\", \"265615\", \"271030\", \"271989\", \"307158\", \"327091\", \"334213\"]}","task_split":"paper_retrieval"}
{"document_id":"81388","document_content":"# Improving healthcare access management by predicting patient no-show behaviour\n## Categories\n- Machine Learning\n- Applications\n## Abstract\nLow attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogot\\'a, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.dss.2020.113398\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.AP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Applications\"], \"incoming_citations\": [\"74566\"], \"outgoing_citations\": [\"263108\", \"266513\", \"294007\", \"306238\"]}","task_split":"paper_retrieval"}
{"document_id":"81402","document_content":"# Imitating Interactive Intelligence\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nA common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. This setting nevertheless integrates a number of the central challenges of artificial intelligence (AI) research: complex visual perception and goal-directed physical control, grounded language comprehension and production, and multi-agent social interaction. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. However, this is presently impractical. Therefore, we approximate the role of the human with another learned agent, and use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour. Rigorously evaluating our agents poses a great challenge, so we develop a variety of behavioural tests, including evaluation by humans who watch videos of agents or interact directly with them. These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone. Further, we demonstrate that agent capabilities generalise beyond literal experiences in the dataset. Finally, we train evaluation models whose ratings of agents agree well with human judgement, thus permitting the evaluation of new agent models without additional effort. Taken together, our results in this virtual environment provide evidence that large-scale human behavioural imitation is a promising tool to create intelligent, interactive agents, and the challenge of reliably evaluating such agents is possible to surmount.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05672v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"32344\", \"79803\", \"16039\", \"25667\", \"30277\", \"33224\", \"37989\", \"39416\", \"43987\", \"45297\", \"47996\", \"64268\", \"71164\"], \"outgoing_citations\": [\"86146\", \"107839\", \"145957\", \"151495\", \"157804\", \"163689\", \"163895\", \"164743\", \"185155\", \"209299\", \"209859\", \"230068\", \"239884\", \"243235\", \"248742\", \"249906\", \"255134\", \"261805\", \"263264\", \"263270\", \"263957\", \"270529\", \"272298\", \"280655\", \"341730\"]}","task_split":"paper_retrieval"}
{"document_id":"81481","document_content":"# An Event Correlation Filtering Method for Fake News Detection\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nNowadays, social network platforms have been the prime source for people to experience news and events due to their capacities to spread information rapidly, which inevitably provides a fertile ground for the dissemination of fake news. Thus, it is significant to detect fake news otherwise it could cause public misleading and panic. Existing deep learning models have achieved great progress to tackle the problem of fake news detection. However, training an effective deep learning model usually requires a large amount of labeled news, while it is expensive and time-consuming to provide sufficient labeled news in actual applications. To improve the detection performance of fake news, we take advantage of the event correlations of news and propose an event correlation filtering method (ECFM) for fake news detection, mainly consisting of the news characterizer, the pseudo label annotator, the event credibility updater, and the news entropy selector. The news characterizer is responsible for extracting textual features from news, which cooperates with the pseudo label annotator to assign pseudo labels for unlabeled news by fully exploiting the event correlations of news. In addition, the event credibility updater employs adaptive Kalman filter to weaken the credibility fluctuations of events. To further improve the detection performance, the news entropy selector automatically discovers high-quality samples from pseudo labeled news by quantifying their news entropy. Finally, ECFM is proposed to integrate them to detect fake news in an event correlation filtering manner. Extensive experiments prove that the explainable introduction of the event correlations of news is beneficial to improve the detection performance of fake news.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05491v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"149300\", \"159882\", \"194215\", \"210466\", \"243331\", \"271021\", \"273029\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"81503","document_content":"# Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nVision-and-language navigation (VLN) is a challenging task that requires an agent to navigate in real-world environments by understanding natural language instructions and visual information received in real-time. Prior works have implemented VLN tasks on continuous environments or physical robots, all of which use a fixed camera configuration due to the limitations of datasets, such as 1.5 meters height, 90 degrees horizontal field of view (HFOV), etc. However, real-life robots with different purposes have multiple camera configurations, and the huge gap in visual information makes it difficult to directly transfer the learned navigation model between various robots. In this paper, we propose a visual perception generalization strategy based on meta-learning, which enables the agent to fast adapt to a new camera configuration with a few shots. In the training phase, we first locate the generalization problem to the visual perception module, and then compare two meta-learning algorithms for better generalization in seen and unseen environments. One of them uses the Model-Agnostic Meta-Learning (MAML) algorithm that requires a few shot adaptation, and the other refers to a metric-based meta-learning method with a feature-wise affine transformation layer. The experiment results show that our strategy successfully adapts the learned navigation model to a new camera configuration, and the two algorithms show their advantages in seen and unseen environments respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05446v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"27522\"], \"outgoing_citations\": [\"88444\", \"88552\", \"93950\", \"114991\", \"132720\", \"145837\", \"155676\", \"191570\", \"192738\", \"203398\", \"207499\", \"208773\", \"228916\", \"238287\", \"249906\", \"255575\"]}","task_split":"paper_retrieval"}
{"document_id":"81508","document_content":"# Developing Motion Code Embedding for Action Recognition in Videos\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes. These motion codes provide a robust motion representation, and they are obtained using a hierarchy of features called the motion taxonomy. We developed and trained a deep neural network model that combines visual and semantic features to identify the features found in our motion taxonomy to embed or annotate videos with motion codes. To demonstrate the potential of motion codes as features for machine learning tasks, we integrated the extracted features from the motion embedding model into the current state-of-the-art action recognition model. The obtained model achieved higher accuracy than the baseline model for the verb classification task on egocentric videos from the EPIC-KITCHENS dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICPR48806.2021.9413030\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"2116\"], \"outgoing_citations\": [\"108649\", \"112330\", \"163912\", \"172394\", \"179535\", \"196906\", \"200208\", \"204141\", \"219374\", \"225607\", \"225608\", \"226028\", \"236377\", \"237671\", \"271868\", \"294046\", \"294503\", \"309902\", \"315231\"]}","task_split":"paper_retrieval"}
{"document_id":"81514","document_content":"# Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nIn many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of performance degradation. One is the reduction of annotated entities and the other is treating unlabeled entities as negative instances. The first cause has less impact than the second one and can be mitigated by adopting pretraining language models. The second cause seriously misguides a model in training and greatly affects its performances. Based on the above observations, we propose a general approach, which can almost eliminate the misguidance brought by unlabeled entities. The key idea is to use negative sampling that, to a large extent, avoids training NER models with unlabeled entities. Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines. On well-annotated datasets, our model is competitive with the state-of-the-art method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05426v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24314\", \"27492\", \"17804\", \"29323\", \"45038\", \"55965\", \"81520\", \"77619\"], \"outgoing_citations\": [\"125061\", \"157803\", \"159862\", \"182622\", \"218407\", \"268382\", \"273995\", \"297147\", \"297150\"]}","task_split":"paper_retrieval"}
{"document_id":"81532","document_content":"# Infusing Finetuning with Semantic Dependencies\n## Categories\n- Computation and Language\n## Abstract\nFor natural language processing systems, two kinds of evidence support the use of text representations from neural language models \"pretrained\" on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia). On the other hand, the lack of grounded supervision calls into question how well these representations can ever capture meaning (Bender and Koller, 2020). We apply novel probes to recent language models -- specifically focusing on predicate-argument structure as operationalized by semantic dependencies (Ivanova et al., 2012) -- and find that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning, yielding benefits to natural language understanding (NLU) tasks in the GLUE benchmark. This approach demonstrates the potential for general-purpose (rather than task-specific) linguistic supervision, above and beyond conventional pretraining and finetuning. Several diagnostics help to localize the benefits of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1162\/tacl_a_00363\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2946\", \"12125\", \"20519\", \"24554\", \"45609\", \"46654\", \"51308\"], \"outgoing_citations\": [\"122609\", \"127479\", \"142153\", \"157349\", \"159924\", \"166223\", \"166307\", \"168549\", \"169687\", \"170855\", \"171817\", \"181468\", \"186130\", \"194308\", \"200362\", \"205161\", \"216925\", \"219714\", \"225852\", \"229768\", \"232339\", \"234631\", \"235377\", \"249109\", \"259723\", \"268263\", \"268812\", \"271527\", \"274183\", \"281121\", \"284357\", \"286594\", \"290777\", \"291019\", \"297226\", \"300593\"]}","task_split":"paper_retrieval"}
{"document_id":"81593","document_content":"# MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computers and Society\n- Machine Learning\n## Abstract\nTo detect bias in face recognition networks, it can be useful to probe a network under test using samples in which only specific attributes vary in some controlled way. However, capturing a sufficiently large dataset with specific control over the attributes of interest is difficult. In this work, we describe a simulator that applies specific head pose and facial expression adjustments to images of previously unseen people. The simulator first fits a 3D morphable model to a provided image, applies the desired head pose and facial expression controls, then renders the model into an image. Next, a conditional Generative Adversarial Network (GAN) conditioned on the original image and the rendered morphable model is used to produce the image of the original person with the new facial expression and head pose. We call this conditional GAN -- MorphGAN. Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression. Images generated by MorphGAN can also serve as data augmentation when training data are scarce. We show that by augmenting small datasets of faces with new poses and expressions improves the recognition performance by up to 9% depending on the augmentation and data scarcity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05225v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"40071\", \"43659\"], \"outgoing_citations\": [\"103082\", \"129244\", \"129267\", \"133828\", \"138014\", \"142067\", \"150970\", \"152821\", \"158820\", \"168874\", \"174537\", \"185516\", \"185981\", \"187059\", \"192689\", \"192730\", \"193656\", \"197839\", \"218905\", \"220842\", \"223142\", \"223229\", \"223504\", \"227994\", \"230031\", \"238472\", \"247558\", \"248273\", \"252688\", \"270923\", \"307557\", \"312178\"]}","task_split":"paper_retrieval"}
{"document_id":"81622","document_content":"# Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTexts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks -- text-based visual question answering and text-based image captioning, with a text extension from existing vision-language applications, are catching on rapidly. To address these problems, many sophisticated multi-modality encoding frameworks (such as heterogeneous graph structure) are being used. In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. Under this mechanism, we simply split OCR token features into separate visual- and linguistic-attention branches, and send them to a popular Transformer decoder to generate answers or captions. Surprisingly, we find this simple baseline model is rather strong -- it consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of ST-VQA, although these SOTA models use far more complex encoding mechanisms. Transferring it to text-based image captioning, we also surpass the TextCaps Challenge 2020 winner. We wish this work to set the new baseline for this two OCR text related applications and to inspire new thinking of multi-modality encoder design. Code is available at https:\/\/github.com\/ZephyrZhuQi\/ssbaseline","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.05153v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"1156\", \"10369\", \"28596\", \"31502\"], \"outgoing_citations\": [\"110053\", \"121818\", \"134013\", \"135255\", \"156340\", \"166370\", \"166606\", \"178095\", \"182255\", \"183268\", \"189847\", \"252885\", \"257286\", \"277538\", \"287504\", \"290213\", \"299461\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"81639","document_content":"# Interactive Search Based on Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWith the continuous development of machine learning technology, major e-commerce platforms have launched recommendation systems based on it to serve a large number of customers with different needs more efficiently. Compared with traditional supervised learning, reinforcement learning can better capture the user's state transition in the decision-making process, and consider a series of user actions, not just the static characteristics of the user at a certain moment. In theory, it will have a long-term perspective, producing a more effective recommendation. The special requirements of reinforcement learning for data make it need to rely on an offline virtual system for training. Our project mainly establishes a virtual user environment for offline training. At the same time, we tried to improve a reinforcement learning algorithm based on bi-clustering to expand the action space and recommended path space of the recommendation agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.06052v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167468\", \"230605\", \"244898\"]}","task_split":"paper_retrieval"}
{"document_id":"81752","document_content":"# Understanding Action Sequences based on Video Captioning for Learning-from-Observation\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nLearning actions from human demonstration video is promising for intelligent robotic systems. Extracting the exact section and re-observing the extracted video section in detail is important for imitating complex skills because human motions give valuable hints for robots. However, the general video understanding methods focus more on the understanding of the full frame,lacking consideration on extracting accurate sections and aligning them with the human's intent. We propose a Learning-from-Observation framework that splits and understands a video of a human demonstration with verbal instructions to extract accurate action sequences. The splitting is done based on local minimum points of the hand velocity, which align human daily-life actions with object-centered face contact transitions required for generating robot motion. Then, we extract a motion description on the split videos using video captioning techniques that are trained from our new daily-life action video dataset. Finally, we match the motion descriptions with the verbal instructions to understand the correct human intent and ignore the unintended actions inside the video. We evaluate the validity of hand velocity-based video splitting and demonstrate that it is effective. The experimental results on our new video captioning dataset focusing on daily-life human actions demonstrate the effectiveness of the proposed method. The source code, trained models, and the dataset will be made available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2101.05061v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"65629\"], \"outgoing_citations\": [\"107913\", \"111462\", \"112330\", \"125719\", \"154204\", \"167684\", \"170283\", \"174896\", \"194045\", \"228841\", \"236918\", \"254396\", \"266172\", \"287302\", \"300351\", \"313435\", \"361203\"]}","task_split":"paper_retrieval"}
{"document_id":"81864","document_content":"# Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nModel-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to predict the environment dynamics and expected reward from interaction and use this predictive model to plan and perform the task. However, MBRL methods vary in their fundamental design choices, and there is no strong consensus in the literature on how these design decisions affect performance. In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. A big exception to this finding is that predicting future observations (i.e., images) leads to significant task performance improvement compared to only predicting rewards. We also empirically find that image prediction accuracy, somewhat surprisingly, correlates more strongly with downstream task performance than reward prediction accuracy. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks (that require exploration) will perform the same as the best-performing models when trained on the same training data. Simultaneously, in the absence of exploration, models that fit the data better usually perform better on the downstream task as well, but surprisingly, these are often not the same models that perform the best when learning and exploring from scratch. These findings suggest that performance and exploration place important and potentially contradictory requirements on the model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04603v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"39704\", \"48758\", \"57862\"], \"outgoing_citations\": [\"125475\", \"127099\", \"152912\", \"154254\", \"155499\", \"158006\", \"159949\", \"170695\", \"176738\", \"182108\", \"182141\", \"196563\", \"196921\", \"203355\", \"206875\", \"207601\", \"208939\", \"210517\", \"220103\", \"228270\", \"228466\", \"229890\", \"236752\", \"241365\", \"246118\", \"251921\", \"253268\", \"254494\", \"261089\", \"261493\", \"264963\", \"271821\", \"274441\", \"281110\", \"283391\", \"288746\", \"289229\", \"291461\", \"292009\", \"306266\", \"310624\", \"315055\", \"317745\"]}","task_split":"paper_retrieval"}
{"document_id":"81887","document_content":"# Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nMillions of unsolicited medical inquiries are received by pharmaceutical companies every year. It has been hypothesized that these inquiries represent a treasure trove of information, potentially giving insight into matters regarding medicinal products and the associated medical treatments. However, due to the large volume and specialized nature of the inquiries, it is difficult to perform timely, recurrent, and comprehensive analyses. Here, we propose a machine learning approach based on natural language processing and unsupervised learning to automatically discover key topics in real-world medical inquiries from customers. This approach does not require ontologies nor annotations. The discovered topics are meaningful and medically relevant, as judged by medical information specialists, thus demonstrating that unsolicited medical inquiries are a source of valuable customer insights. Our work paves the way for the machine-learning-driven analysis of medical inquiries in the pharmaceutical industry, which ultimately aims at improving patient care.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/fcomp.2021.672867\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105074\", \"126216\", \"136869\", \"138895\", \"165848\", \"172844\", \"190719\", \"191942\", \"198222\", \"213344\", \"214235\", \"261618\"]}","task_split":"paper_retrieval"}
{"document_id":"81948","document_content":"# CrossNER: Evaluating Cross-Domain Named Entity Recognition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nCross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https:\/\/github.com\/zliucr\/CrossNER.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04373v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"25829\", \"27783\", \"76075\"], \"outgoing_citations\": [\"97664\", \"101194\", \"129221\", \"129503\", \"131150\", \"134700\", \"138638\", \"142316\", \"155118\", \"157033\", \"159867\", \"174612\", \"176632\", \"214221\", \"228348\", \"234384\", \"249726\", \"251816\", \"251974\", \"266198\", \"271158\", \"281169\", \"297147\", \"297150\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"81958","document_content":"# Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nDeep Learning has become interestingly popular in computer vision, mostly attaining near or above human-level performance in various vision tasks. But recent work has also demonstrated that these deep neural networks are very vulnerable to adversarial examples (adversarial examples - inputs to a model which are naturally similar to original data but fools the model in classifying it into a wrong class). Humans are very robust against such perturbations; one possible reason could be that humans do not learn to classify based on an error between \"target label\" and \"predicted label\" but possibly due to reinforcements that they receive on their predictions. In this work, we proposed a novel method to train deep learning models on an image classification task. We used a reward-based optimization function, similar to the vanilla policy gradient method used in reinforcement learning, to train our model instead of conventional cross-entropy loss. An empirical evaluation on the cifar10 dataset showed that our method learns a more robust classifier than the same model architecture trained using cross-entropy loss function (on adversarial training). At the same time, our method shows a better generalization with the difference in test accuracy and train accuracy $< 2\\%$ for most of the time compared to the cross-entropy one, whose difference most of the time remains $> 2\\%$.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04353v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"153528\", \"182797\", \"183227\", \"208591\"]}","task_split":"paper_retrieval"}
{"document_id":"81968","document_content":"# StacMR: Scene-Text Aware Cross-Modal Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent models for cross-modal retrieval have benefited from an increasingly rich understanding of visual scenes, afforded by scene graphs and object interactions to mention a few. This has resulted in an improved matching between the visual representation of an image and the textual representation of its caption. Yet, current visual representations overlook a key aspect: the text appearing in images, which may contain crucial information for retrieval. In this paper, we first propose a new dataset that allows exploration of cross-modal retrieval where images contain scene-text instances. Then, armed with this dataset, we describe several approaches which leverage scene text, including a better scene-text aware cross-modal retrieval method which uses specialized representations for text from the captions and text from the visual scene, and reconcile them in a common embedding space. Extensive experiments confirm that cross-modal retrieval approaches benefit from scene text and highlight interesting research questions worth exploring further. Dataset and code are available at http:\/\/europe.naverlabs.com\/stacmr","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04329v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"18648\"], \"outgoing_citations\": [\"99456\", \"111384\", \"126431\", \"134664\", \"135255\", \"137906\", \"147253\", \"152418\", \"162227\", \"162242\", \"168374\", \"183268\", \"189847\", \"190708\", \"205616\", \"220129\", \"236696\", \"238205\", \"239535\", \"245591\", \"260926\", \"268829\", \"269205\", \"281407\", \"299461\", \"303011\", \"313435\", \"314050\", \"315108\", \"320003\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"81981","document_content":"# CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nHumans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04293v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9040\", \"17103\", \"38037\"], \"outgoing_citations\": [\"12898\", \"44099\", \"56489\", \"59458\", \"84568\", \"96580\", \"154152\", \"162372\", \"163549\", \"164814\", \"171629\", \"174944\", \"182209\", \"188920\", \"204561\", \"215627\", \"218357\", \"219048\", \"237923\", \"239633\", \"248813\", \"277538\", \"278894\", \"297162\", \"301767\", \"303251\", \"303476\", \"303603\", \"313143\", \"313440\", \"323775\"]}","task_split":"paper_retrieval"}
{"document_id":"81985","document_content":"# CTRLsum: Towards Generic Controllable Text Summarization\n## Categories\n- Computation and Language\n## Abstract\nCurrent summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts. Using a single unified model, CTRLsum is able to achieve a broad scope of summary manipulation at inference time without requiring additional human annotations or pre-defining a set of control aspects during training. We quantitatively demonstrate the effectiveness of our approach on three domains of summarization datasets and five control aspects: 1) entity-centric and 2) length-controllable summarization, 3) contribution summarization on scientific papers, 4) invention purpose summarization on patent filings, and 5) question-guided summarization on news articles in a reading comprehension setting. Moreover, when used in a standard, uncontrolled summarization setting, CTRLsum achieves state-of-the-art results on the CNN\/DailyMail dataset. Code and model checkpoints are available at https:\/\/github.com\/salesforce\/ctrl-sum","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04281v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9385\", \"88725\", \"15996\", \"16293\", \"16339\", \"25100\", \"52568\", \"5701\", \"17825\", \"20171\", \"20781\", \"23447\", \"24581\", \"50834\", \"55781\", \"66399\", \"79038\"], \"outgoing_citations\": [\"30911\", \"47624\", \"77463\", \"93920\", \"109884\", \"115344\", \"127930\", \"134393\", \"143126\", \"146323\", \"150522\", \"167338\", \"170449\", \"174612\", \"181713\", \"184148\", \"189542\", \"192811\", \"193848\", \"210155\", \"219618\", \"219789\", \"227323\", \"231203\", \"233710\", \"235175\", \"235463\", \"241070\", \"250035\", \"250505\", \"266671\", \"266955\", \"268166\", \"268902\", \"272531\", \"279213\", \"280557\", \"281554\", \"285811\", \"289061\", \"296029\", \"307223\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"82006","document_content":"# Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nThe dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies mainly focused on finding clues from news content or diffusion path. However, the required features of previous models are often unavailable or insufficient in early detection scenarios, resulting in poor performance. Thus, early fake news detection remains a tough challenge. Intuitively, the news from trusted and authoritative sources or shared by many users with a good reputation is more reliable than other news. Using the credibility of publishers and users as prior weakly supervised information, we can quickly locate fake news in massive news and detect them in the early stages of dissemination.   In this paper, we propose a novel Structure-aware Multi-head Attention Network (SMAN), which combines the news content, publishing, and reposting relations of publishers and users, to jointly optimize the fake news detection and credibility prediction tasks. In this way, we can explicitly exploit the credibility of publishers and users for early fake news detection. We conducted experiments on three real-world datasets, and the results show that SMAN can detect fake news in 4 hours with an accuracy of over 91%, which is much faster than the state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04233v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"14416\", \"11296\", \"50683\", \"75518\", \"76628\"], \"outgoing_citations\": [\"167608\", \"189715\", \"210668\", \"246910\", \"273723\", \"316774\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"82109","document_content":"# Improving Clinical Document Understanding on COVID-19 Research with Spark NLP\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nFollowing the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events in addition to other commonly used clinical and biomedical entities. Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient. Third, the deep learning models used are more accurate than previously available, leveraging an integrated pipeline of state-of-the-art pretrained named entity recognition models, and improving on the previous best performing benchmarks for assertion status detection. We illustrate extracting trends and insights, e.g. most frequent disorders and symptoms, and most common vital signs and EKG findings, from the COVID-19 Open Research Dataset (CORD-19). The system is built using the Spark NLP library which natively supports scaling to use distributed clusters, leveraging GPUs, configurable and reusable NLP pipelines, healthcare specific embeddings, and the ability to train models to support new entity types or human languages with no code changes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.04005v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"886\", \"73310\"], \"outgoing_citations\": [\"129596\", \"159867\", \"193702\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"82223","document_content":"# An Enhanced MeanSum Method For Generating Hotel Multi-Review Summarizations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMulti-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-document summarizers are mostly supervised extractive. However, supervised methods require datasets of large, paired document-summary examples which are rare and expensive to produce. In 2018, an unsupervised multi-document abstractive summarization method(Meansum) was proposed by Chu and Liu, and demonstrated competitive performances comparing to extractive methods. Despite good evaluation results on automatic metrics, Meansum has multiple limitations, notably the inability of dealing with multiple aspects. The aim of this work was to use Multi-Aspect Masker(MAM) as content selector to address the issue with multi-aspect. Moreover, we propose a regularizer to control the length of the generated summaries. Through a series of experiments on the hotel dataset from Trip Advisor, we validate our assumption and show that our improved model achieves higher ROUGE, Sentiment Accuracy than the original Meansum method and also beats\/ comprarable\/close to the supervised baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.03656v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"283618\"]}","task_split":"paper_retrieval"}
{"document_id":"82242","document_content":"# Structural Text Segmentation of Legal Documents\n## Categories\n- Computation and Language\n## Abstract\nThe growing complexity of legal cases has lead to an increasing interest in legal information retrieval systems that can effectively satisfy user-specific information needs. However, such downstream systems typically require documents to be properly formatted and segmented, which is often done with relatively simple pre-processing steps, disregarding topical coherence of segments. Systems generally rely on representations of individual sentences or paragraphs, which may lack crucial context, or document-level representations, which are too long for meaningful search results. To address this issue, we propose a segmentation system that can predict topical coherence of sequential text segments spanning several paragraphs, effectively segmenting a document and providing a more balanced representation for downstream applications. We build our model on top of popular transformer networks and formulate structural text segmentation as topical change detection, by performing a series of independent classifications that allow for efficient fine-tuning on task-specific data. We crawl a novel dataset consisting of roughly $74,000$ online Terms-of-Service documents, including hierarchical topic annotations, which we use for training. Results show that our proposed system significantly outperforms baselines, and adapts well to structural peculiarities of legal documents. We release both data and trained models to the research community for future work.https:\/\/github.com\/dennlinger\/TopicalChange","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3462757.3466085\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"1502\", \"148632\", \"177166\", \"184851\", \"237812\", \"293087\"]}","task_split":"paper_retrieval"}
{"document_id":"82325","document_content":"# NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification\n## Categories\n- Machine Learning\n## Abstract\nMessage passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems, i.e., (i) lack of interpretability to identify node features significant to the prediction of GNNs, and (ii) feature over-mixing that leads to the over-smoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily. In this paper, we propose a Node-level Capsule Graph Neural Network (NCGNN) to address these problems with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of node-level capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. NCGNN aggregates only the advantageous capsules and restrains irrelevant messages to avoid over-mixing features of interacting nodes. Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily. Furthermore, our proposed message passing scheme is inherently interpretable and exempt from complex post-hoc explanations, as the graph filter and the dynamic routing procedure identify a subset of node features that are most significant to the model prediction from the extracted subgraph. Extensive experiments on synthetic as well as real-world graphs demonstrate that NCGNN can well address the over-smoothing issue and produce better node representations for semisupervised node classification. It outperforms the state of the arts under both homophily and heterophily.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.03476v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"36605\", \"56223\", \"70398\", \"71193\", \"80220\", \"88103\", \"94864\", \"111211\", \"114193\", \"114415\", \"117308\", \"119214\", \"120543\", \"120789\", \"121171\", \"122728\", \"136103\", \"142571\", \"142797\", \"154215\", \"156781\", \"157487\", \"159315\", \"164464\", \"164732\", \"168172\", \"174492\", \"183245\", \"188300\", \"191340\", \"191735\", \"195767\", \"198002\", \"198382\", \"210119\", \"214311\", \"218737\", \"221587\", \"228165\", \"228663\", \"231240\", \"244510\", \"252315\", \"261412\", \"269202\", \"269273\", \"279575\", \"287380\", \"305707\", \"319949\"]}","task_split":"paper_retrieval"}
{"document_id":"82328","document_content":"# Attention-based Saliency Hashing for Ophthalmic Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nDeep hashing methods have been proved to be effective for the large-scale medical image search assisting reference-based diagnosis for clinicians. However, when the salient region plays a maximal discriminative role in ophthalmic image, existing deep hashing methods do not fully exploit the learning ability of the deep network to capture the features of salient regions pointedly. The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions. To address this issue, we propose a novel end-to-end network, named Attention-based Saliency Hashing (ASH), for learning compact hash-code to represent ophthalmic images. ASH embeds a spatial-attention module to focus more on the representation of salient regions and highlights their essential role in differentiating ophthalmic images. Benefiting from the spatial-attention module, the information of salient regions can be mapped into the hash-code for similarity calculation. In the training stage, we input the image pairs to share the weights of the network, and a pairwise loss is designed to maximize the discriminability of the hash-code. In the retrieval stage, ASH obtains the hash-code by inputting an image with an end-to-end manner, then the hash-code is used to similarity calculation to return the most similar images. Extensive experiments on two different modalities of ophthalmic image datasets demonstrate that the proposed ASH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods due to the huge contributions of the spatial-attention module.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/BIBM49941.2020.9313536\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"81782\", \"48708\"], \"outgoing_citations\": [\"193667\", \"220608\", \"225824\", \"231483\", \"246058\", \"263387\", \"280246\", \"287103\", \"291400\", \"303512\", \"314568\", \"315233\"]}","task_split":"paper_retrieval"}
{"document_id":"82385","document_content":"# A Weighted Solution to SVM Actionability and Interpretability\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nResearch in machine learning has successfully developed algorithms to build accurate classification models. However, in many real-world applications, such as healthcare, customer satisfaction, and environment protection, we want to be able to use the models to decide what actions to take.   We investigate the concept of actionability in the context of Support Vector Machines. Actionability is as important as interpretability or explainability of machine learning models, an ongoing and important research topic. Actionability is the task that gives us ways to act upon machine learning models and their predictions.   This paper finds a solution to the question of actionability on both linear and non-linear SVM models. Additionally, we introduce a way to account for weighted actions that allow for more change in certain features than others. We propose a gradient descent solution on the linear, RBF, and polynomial kernels, and we test the effectiveness of our models on both synthetic and real datasets. We are also able to explore the model's interpretability through the lens of actionability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.03372v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"272046\", \"290515\"]}","task_split":"paper_retrieval"}
{"document_id":"82387","document_content":"# Competition in Cross-situational Word Learning: A Computational Study\n## Categories\n- Computation and Language\n- Machine Learning\n- 68T50, 91F20, 68T05\n- I.2.7; I.2.6; G.3; J.4\n## Abstract\nChildren learn word meanings by tapping into the commonalities across different situations in which words are used and overcome the high level of uncertainty involved in early word learning experiences. We propose a modeling framework to investigate the role of mutual exclusivity bias - asserting one-to-one mappings between words and their meanings - in reducing uncertainty in word learning. In a set of computational studies, we show that to successfully learn word meanings in the face of uncertainty, a learner needs to use two types of competition: words competing for association to a referent when learning from an observation and referents competing for a word when the word is used. Our work highlights the importance of an algorithmic-level analysis to shed light on the utility of different mechanisms that can implement the same computational-level theory.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.03370v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"68T50, 91F20, 68T05\", \"I.2.7; I.2.6; G.3; J.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"68T50, 91F20, 68T05\", \"I.2.7; I.2.6; G.3; J.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"121079\", \"137220\", \"273440\", \"287521\"]}","task_split":"paper_retrieval"}
{"document_id":"82662","document_content":"# Delexicalized Paraphrase Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots, we apply convolutional neural networks (CNN) prior to pooling on slot values and use pointers to locate slots in the output. We show empirically that the generated paraphrases are of high quality, leading to an additional 1.29% exact match on live utterances. We also show that natural language understanding (NLU) tasks, such as intent classification and named entity recognition, can benefit from data augmentation using automatically generated paraphrases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02763v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116170\", \"144895\", \"163280\", \"251669\", \"255894\", \"282914\", \"302948\"]}","task_split":"paper_retrieval"}
{"document_id":"82712","document_content":"# A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering\n## Categories\n- Software Engineering\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nChatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge.   Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs' performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure > 84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. Our results also show that all NLUs, except for Dialogflow, generally provide trustable confidence scores. For entity extraction, Microsoft LUIS and IBM Watson outperform other NLUs in the two SE tasks. Our results provide guidance to software engineering practitioners when deciding which NLU to use in their chatbots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TSE.2021.3078384\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.AI\", \"cs.SE\", \"cs.CL\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Software Engineering\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"131007\", \"185942\", \"205548\", \"208865\", \"242862\"]}","task_split":"paper_retrieval"}
{"document_id":"82800","document_content":"# Learning Node Representations from Noisy Graph Structures\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nLearning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. While existing methods tend to focus on preserving structural properties, the robustness of the learned representations against noises is generally ignored. In this paper, we propose a novel framework to learn noise-free node representations and eliminate noises simultaneously. Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting. On the one hand, the graph generator serves as a unified scheme to incorporate any useful graph prior knowledge to generate normal structures. We illustrate the generative process with community structures and power-law degree distributions as examples. On the other hand, the noise generator generates graph noises not only satisfying some fundamental properties but also in an adaptive way. Thus, real noises with arbitrary distributions can be handled successfully. Finally, in order to eliminate noises and obtain noise-free node representations, two generators need to be optimized jointly, and through maximum likelihood estimation, we equivalently convert the model into imposing different regularization constraints on the true graph and noises respectively. Our model is evaluated on both real-world and synthetic data. It outperforms other strong baselines for node classification and graph reconstruction tasks, demonstrating its ability to eliminate graph noises.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02434v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169526\", \"208440\", \"242416\", \"249614\", \"249747\"]}","task_split":"paper_retrieval"}
{"document_id":"82846","document_content":"# Understanding Guided Image Captioning Performance across Domains\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nImage captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativeness, and information overload. On the other hand, VQA models generally lack the ability to provide long descriptive answers, while expecting the textual question to be quite precise. We present a method to control the concepts that an image caption should focus on, using an additional input called the guiding text that refers to either groundable or ungroundable concepts in the image. Our model consists of a Transformer-based multimodal encoder that uses the guiding text together with global and object-level image features to derive early-fusion representations used to generate the guided caption. While models trained on Visual Genome data have an in-domain advantage of fitting well when guided with automatic object labels, we find that guided captioning models trained on Conceptual Captions generalize better on out-of-domain images and guiding texts. Our human-evaluation results indicate that attempting in-the-wild guided image captioning requires access to large, unrestricted-domain training datasets, and that increased style diversity (even without increasing the number of unique tokens) is a key factor for improved performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02339v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"16013\", \"24290\", \"28644\"], \"outgoing_citations\": [\"139263\", \"152418\", \"165195\", \"168596\", \"170603\", \"198983\", \"208615\", \"209504\", \"211768\", \"229734\", \"237655\", \"268356\", \"278243\", \"278948\", \"278954\", \"279002\", \"280004\", \"296681\", \"302621\", \"313196\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"82935","document_content":"# Fast-reactive probabilistic motion planning for high-dimensional robots\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nMany real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02118v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167985\", \"211053\", \"211389\", \"249613\", \"255779\", \"260429\", \"264965\", \"283956\", \"313631\", \"334194\"]}","task_split":"paper_retrieval"}
{"document_id":"82943","document_content":"# Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nA wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a significant amount of developer time and effort. We propose Unsupervised Environment Design (UED) as an alternative paradigm, where developers provide environments with unknown parameters, and these parameters are used to automatically produce a distribution over valid, solvable environments. Existing approaches to automatically generating environments suffer from common failure modes: domain randomization cannot generate structure or adapt the difficulty of the environment to the agent's learning progress, and minimax adversarial training leads to worst-case environments that are often unsolvable. To generate structured, solvable environments for our protagonist agent, we introduce a second, antagonist agent that is allied with the environment-generating adversary. The adversary is motivated to generate environments which maximize regret, defined as the difference between the protagonist and antagonist agent's return. We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED). Our experiments demonstrate that PAIRED produces a natural curriculum of increasingly complex environments, and PAIRED agents achieve higher zero-shot transfer performance when tested in highly novel environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02096v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"8658\", \"64\", \"18344\", \"35634\", \"40573\", \"40861\", \"84594\", \"106764\", \"38561\", \"6028\", \"8582\", \"18748\", \"20726\", \"32620\", \"33080\", \"35932\", \"38947\", \"47091\", \"51175\", \"54516\", \"63330\", \"64937\", \"65728\", \"71387\", \"95739\", \"97320\", \"142583\"], \"outgoing_citations\": [\"116987\", \"136046\", \"145405\", \"166489\", \"184592\", \"191364\", \"196801\", \"203756\", \"207642\", \"222669\", \"228299\", \"234025\", \"235526\", \"241900\", \"262325\", \"268248\", \"269253\", \"271375\", \"272659\", \"274445\", \"280570\", \"283216\", \"358915\", \"359369\"]}","task_split":"paper_retrieval"}
{"document_id":"82999","document_content":"# DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL). Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames. We begin with an analysis of requirements that such an AI system should satisfy in order to be practically applicable in video game development, and identify the elements of the DRL model used in the DeepCrawl prototype. The successes and limitations of DeepCrawl are documented through a series of playability tests performed on the final game. We believe that the techniques we propose offer insight into innovative new avenues for the development of behaviors for non-player characters in video games, as they offer the potential to overcome critical issues with","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.01914v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"54347\", \"82750\", \"82289\", \"185834\"], \"outgoing_citations\": [\"200264\", \"218770\", \"220513\", \"258472\", \"263957\", \"272464\", \"280202\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"83044","document_content":"# Interpretability and Explainability: A Machine Learning Zoo Mini-tour\n## Categories\n- Machine Learning\n## Abstract\nIn this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have escaped a clear universal definition, many techniques motivated by these properties have been developed over the recent 30 years with the focus currently shifting towards deep learning methods. In this review, we emphasise the divide between interpretability and explainability and illustrate these two different research directions with concrete examples of the state-of-the-art. The review is intended for a general machine learning audience with interest in exploring the problems of interpretation and explanation beyond logistic regression or random forest variable importance. This work is not an exhaustive literature survey, but rather a primer focusing selectively on certain lines of research which the authors found interesting or informative.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.01805v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"3449\", \"15913\", \"34352\", \"65176\", \"67705\", \"394\", \"22260\", \"41539\", \"47321\", \"78936\"], \"outgoing_citations\": [\"95709\", \"113213\", \"117574\", \"119292\", \"121589\", \"128098\", \"134870\", \"139553\", \"140127\", \"142171\", \"148263\", \"152364\", \"154865\", \"163507\", \"168652\", \"177312\", \"180864\", \"181717\", \"181921\", \"184183\", \"184259\", \"185674\", \"188030\", \"197416\", \"204327\", \"206218\", \"208041\", \"219203\", \"223886\", \"227415\", \"229866\", \"239658\", \"242322\", \"243106\", \"251709\", \"253926\", \"255129\", \"265119\", \"304847\", \"316549\", \"317469\", \"317771\", \"320501\", \"321467\"]}","task_split":"paper_retrieval"}
{"document_id":"83081","document_content":"# Learning Class-Transductive Intent Representations for Zero-shot Intent Detection\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nZero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage. To address this problem, we propose a novel framework that utilizes unseen class labels to learn Class-Transductive Intent Representations (CTIR). Specifically, we allow the model to predict unseen intents during training, with the corresponding label names serving as input utterances. On this basis, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately. CTIR is easy to implement and can be integrated with existing methods. Experiments on two real-world datasets show that CTIR brings considerable improvement to the baseline systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.01721v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"168621\", \"183006\", \"193137\", \"219441\", \"230543\", \"238203\", \"285317\", \"292616\", \"318839\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"83160","document_content":"# On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering\n## Categories\n- Machine Learning\n## Abstract\nRecommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand behavioral traits of users and use that to predict future behavior. In this work, we present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters. We demonstrate the performance of the proposed method on the next attribute prediction task on two real world datasets and show that it outperforms standard baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.01577v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"55722\"], \"outgoing_citations\": [\"177138\", \"183341\", \"204102\", \"213594\", \"242002\", \"257855\", \"281570\", \"335884\", \"357189\"]}","task_split":"paper_retrieval"}
{"document_id":"83295","document_content":"# Learning Spatial Attention for Face Super-Resolution\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGeneral image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (e.g., $128\\times128$), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e.g., $16\\times16$). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (i.e., $512\\times512$). We show that SPARNetHD trained with synthetic data cannot only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2020.3043093\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60770\", \"76110\", \"8330\", \"18974\", \"139332\", \"996\", \"1773\", \"8303\", \"10533\", \"13328\", \"22967\", \"92268\"], \"outgoing_citations\": [\"134378\", \"170700\", \"211273\", \"235729\", \"236344\", \"248019\", \"248969\", \"250250\", \"250712\", \"258886\", \"259660\", \"267940\", \"270923\", \"280246\", \"281201\", \"287403\", \"288238\", \"294693\", \"327963\"]}","task_split":"paper_retrieval"}
{"document_id":"83564","document_content":"# Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture\n## Categories\n- Computer Vision and Pattern Recognition\n- Computer Science and Game Theory\n## Abstract\nEstimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across different image regions can be used to localize splicing in double JPEG tampered images. Traditional model-based approaches work under specific assumptions on the relationship between the first and second compression qualities and on the alignment of the JPEG grid. Recently, a deep learning-based estimator capable to work under a wide variety of conditions has been proposed, that outperforms tailored existing methods in most of the cases. The method is based on a Convolutional Neural Network (CNN) that is trained to solve the estimation as a standard regression problem. By exploiting the integer nature of the quantization coefficients, in this paper, we propose a deep learning technique that performs the estimation by resorting to a simil-classification architecture. The CNN is trained with a loss function that takes into account both the accuracy and the Mean Square Error (MSE) of the estimation. Results confirm the superior performance of the proposed technique, compared to the state-of-the art methods based on statistical analysis and, in particular, deep learning regression. Moreover, the capability of the method to work under general operative conditions, regarding the alignment of the second compression grid with the one of first compression and the combinations of the JPEG qualities of former and second compression, is very relevant in practical applications, where these information are unknown a priori.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.00468v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GT\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computer Science and Game Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"107263\", \"259495\", \"285257\"]}","task_split":"paper_retrieval"}
{"document_id":"83641","document_content":"# Patient similarity: methods and applications\n## Categories\n- Machine Learning\n## Abstract\nPatient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes the pairwise similarity between patients. Procedures of typical a patient similarity study can be divided into several steps including data integration, similarity measurement, and neighborhood identification. And according to an analysis of patient similarity, doctors can easily find the most suitable treatments. There are many methods to analyze the similarity such as cluster analysis. And during machine learning become more and more popular, Using neural networks such as CNN is a new hot topic. This review summarizes representative methods used in each step and discusses applications of patient similarity networks especially in the context of precision medicine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.01976v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"110450\", \"258976\", \"315644\", \"328112\", \"343026\", \"349562\"]}","task_split":"paper_retrieval"}
{"document_id":"83703","document_content":"# Statistical patterns of word frequency suggesting the probabilistic nature of human languages\n## Categories\n- Computation and Language\n- Computational Physics\n## Abstract\nTraditional linguistic theories have largely regard language as a formal system composed of rigid rules. However, their failures in processing real language, the recent successes in statistical natural language processing, and the findings of many psychological experiments have suggested that language may be more a probabilistic system than a formal system, and thus cannot be faithfully modeled with the either\/or rules of formal linguistic theory. The present study, based on authentic language data, confirmed that those important linguistic issues, such as linguistic universal, diachronic drift, and language variations can be translated into probability and frequency patterns in parole. These findings suggest that human language may well be probabilistic systems by nature, and that statistical may well make inherent properties of human languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.00187v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"physics.comp-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computational Physics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225709\", \"351470\"]}","task_split":"paper_retrieval"}
{"document_id":"83881","document_content":"# Reducing Road Vehicle Fuel Consumption by Exploiting Connectivity and Automation: A Literature Survey\n## Categories\n- Systems and Control\n## Abstract\nThis paper examines the degree to which connectivity and automation can potentially reduce the overall fuel consumption of on-road vehicles. The paper begins with a simulation study highlighting the tradeoff between: (i) the fuel that a vehicle can save through speed trajectory shaping, versus (ii) the additional inter-vehicle spacing needed for this trajectory shaping to be feasible. This study shows that connectivity and automation are essential, rather than merely useful, for substantial reductions in the fuel consumed by fixed on-road vehicle powertrain\/chassis configurations in traffic. Motivated by this insight, we survey the literature on the fuel savings achievable through different connected\/automated vehicle technologies. This includes optimal vehicle routing, eco-arrival\/departure at intersections, platooning, speed trajectory optimization, predictive driveline disengagement, predictive gear shifting, and predictive powertrain accessory control. This survey shows that the ability to shape vehicle speed trajectories collaboratively plays a dominant role in reducing urban\/suburban fuel consumption, while platooning plays a dominant role in influencing the attainable fuel savings on the highway. Moreover, the survey shows that the degree to which connectivity\/automation can reduce on-road vehicle fuel consumption, in both urban\/suburban and highway settings, depends critically on the integration of powertrain- and chassis-level control.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14805v1\", \"primary_category\": \"eess.SY\", \"categories\": [\"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"300678\", \"304663\", \"309656\", \"313444\"]}","task_split":"paper_retrieval"}
{"document_id":"83901","document_content":"# A Comprehensive Review on Recent Methods and Challenges of Video Description\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nVideo description involves the generation of the natural language description of actions, events, and objects in the video. There are various applications of video description by filling the gap between languages and vision for visually impaired people, generating automatic title suggestion based on content, browsing of the video based on the content and video-guided machine translation [86] etc.In the past decade, several works had been done in this field in terms of approaches\/methods for video description, evaluation metrics,and datasets. For analyzing the progress in the video description task, a comprehensive survey is needed that covers all the phases of video description approaches with a special focus on recent deep learning approaches. In this work, we report a comprehensive survey on the phases of video description approaches, the dataset for video description, evaluation metrics, open competitions for motivating the research on the video description, open challenges in this field, and future research directions. In this survey, we cover the state-of-the-art approaches proposed for each and every dataset with their pros and cons. For the growth of this research domain,the availability of numerous benchmark dataset is a basic need. Further, we categorize all the dataset into two classes: open domain dataset and domain-specific dataset. From our survey, we observe that the work in this field is in fast-paced development since the task of video description falls in the intersection of computer vision and natural language processing. But still, the work in the video description is far from saturation stage due to various challenges like the redundancy due to similar frames which affect the quality of visual features, the availability of dataset containing more diverse content and availability of an effective evaluation metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14752v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120547\", \"120890\", \"139932\", \"157944\", \"161232\", \"182088\", \"191861\", \"192210\", \"197355\", \"205750\", \"215181\", \"223293\", \"227052\", \"234602\", \"236918\", \"237150\", \"237201\", \"238229\", \"240071\", \"240605\", \"248855\", \"249241\", \"252706\", \"259121\", \"267523\", \"268408\", \"270325\", \"278604\", \"278954\", \"279337\", \"279740\", \"285973\", \"287504\", \"292371\", \"295117\", \"295122\", \"303593\", \"304481\", \"311742\", \"313189\", \"313435\", \"316564\", \"316803\", \"319169\", \"320332\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"83911","document_content":"# Exploration of Whether Skylight Polarization Patterns Contain Three-dimensional Attitude Information\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Optics\n## Abstract\nOur previous work has demonstrated that Rayleigh model, which is widely used in polarized skylight navigation to describe skylight polarization patterns, does not contain three-dimensional (3D) attitude information [1]. However, it is still necessary to further explore whether the skylight polarization patterns contain 3D attitude information. So, in this paper, a social spider optimization (SSO) method is proposed to estimate three Euler angles, which considers the difference of each pixel among polarization images based on template matching (TM) to make full use of the captured polarization information. In addition, to explore this problem, we not only use angle of polarization (AOP) and degree of polarization (DOP) information, but also the light intensity (LI) information. So, a sky model is established, which combines Berry model and Hosek model to fully describe AOP, DOP, and LI information in the sky, and considers the influence of four neutral points, ground albedo, atmospheric turbidity, and wavelength. The results of simulation show that the SSO algorithm can estimate 3D attitude and the established sky model contains 3D attitude information. However, when there are measurement noise or model error, the accuracy of 3D attitude estimation drops significantly. Especially in field experiment, it is very difficult to estimate 3D attitude. Finally, the results are discussed in detail.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.09154v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\", \"physics.optics\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\", \"Optics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"328424\"]}","task_split":"paper_retrieval"}
{"document_id":"83960","document_content":"# Automatic Mathematical Information Retrieval to Perform Translations up to Computer Algebra Systems\n## Categories\n- Information Retrieval\n- Mathematical Software\n## Abstract\nIn mathematics, LaTeX is the de facto standard to prepare documents, e.g., scientific publications. While some formulae are still developed using pen and paper, more complicated mathematical expressions used more and more often with computer algebra systems. Mathematical expressions are often manually transcribed to computer algebra systems. The goal of my doctoral thesis is to improve the efficiency of this workflow. My envisioned method will automatically semantically enrich mathematical expressions so that they can be imported to computer algebra systems and other systems that can take advantage of the semantics, such as search engines or automatic plagiarism detection systems. These imports should preserve the essential semantic features of the expression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14616v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.MS\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Mathematical Software\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"22732\", \"235696\", \"313266\", \"327641\", \"330602\", \"340922\"]}","task_split":"paper_retrieval"}
{"document_id":"83962","document_content":"# SoMin.ai: Personality-Driven Content Generation Platform\n## Categories\n- Multimedia\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for the enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin.ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns. The real-time user feedback to the platform via the platform's GUI fine-tunes the content generation model and the evaluation results demonstrate the promising performance of the proposed multi-view personality profiling framework when being applied in the content generation scenario. By leveraging content generation at a large scale, marketers will be able to execute more effective digital marketing campaigns at a lower cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3437963.3441714\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.IR\", \"cs.MM\", \"cs.LG\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Information Retrieval\", \"Multimedia\", \"Machine Learning\"], \"incoming_citations\": [\"40799\"], \"outgoing_citations\": [\"144022\", \"217526\", \"249433\"]}","task_split":"paper_retrieval"}
{"document_id":"83992","document_content":"# A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper aims to provide a selective survey about knowledge distillation(KD) framework for researchers and practitioners to take advantage of it for developing new optimized models in the deep neural network field. To this end, we give a brief overview of knowledge distillation and some related works including learning using privileged information(LUPI) and generalized distillation(GD). Even though knowledge distillation based on the teacher-student architecture was initially devised as a model compression technique, it has found versatile applications over various frameworks.   In this paper, we review the characteristics of knowledge distillation from the hypothesis that the three important ingredients of knowledge distillation are distilled knowledge and loss,teacher-student paradigm, and the distillation process. In addition, we survey the versatility of the knowledge distillation by studying its direct applications and its usage in combination with other deep learning paradigms. Finally we present some future works in knowledge distillation including explainable knowledge distillation where the analytical analysis of the performance gain is studied and the self-supervised learning which is a hot research topic in deep learning community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14554v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"46940\", \"114288\", \"135921\", \"143263\", \"144069\", \"152638\", \"153402\", \"153853\", \"165441\", \"179355\", \"179872\", \"181769\", \"185155\", \"186590\", \"192494\", \"199616\", \"217680\", \"224790\", \"233092\", \"242046\", \"252610\", \"263687\", \"264787\", \"275545\", \"278692\", \"301197\", \"302585\", \"303553\", \"303707\", \"304173\", \"317797\", \"320127\", \"327639\", \"334281\", \"336193\", \"343694\", \"344236\"]}","task_split":"paper_retrieval"}
{"document_id":"84037","document_content":"# Intrinsic Decomposition of Document Images In-the-Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to a significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 26% improvement of character error rate (CER), thus, proving the practical applicability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14447v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"13051\", \"78663\"], \"outgoing_citations\": [\"95913\", \"100813\", \"189499\", \"208009\", \"236990\", \"240274\", \"248392\", \"250881\", \"276409\", \"277902\", \"291404\", \"296203\", \"301868\", \"305321\", \"310241\", \"314354\"]}","task_split":"paper_retrieval"}
{"document_id":"84194","document_content":"# Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n## Abstract\nWhile Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high demands on data and computation. We show that state-of-the-art GAN models -- such as they are being publicly released by researchers and industry -- can be used for a range of applications beyond unconditional image generation. We achieve this by an iterative scheme that also allows gaining control over the image generation process despite the highly non-linear latent spaces of the latest GAN models. We demonstrate that this opens up the possibility to re-use state-of-the-art, difficult to train, pre-trained GANs with a high level of control even if only black-box access is granted. Our work also raises concerns and awareness that the use cases of a published GAN model may well reach beyond the creators' intention, which needs to be taken into account before a full public release. Code is available at https:\/\/github.com\/a514514772\/hijackgan.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.14107v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\"], \"incoming_citations\": [\"21787\", \"75482\", \"13058\", \"22548\", \"31665\", \"7301\", \"46371\", \"59382\"], \"outgoing_citations\": [\"107590\", \"108739\", \"124846\", \"128113\", \"132835\", \"133828\", \"133858\", \"134150\", \"143164\", \"152821\", \"154169\", \"174537\", \"175835\", \"179226\", \"181076\", \"192020\", \"193736\", \"208915\", \"254254\", \"280274\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"84313","document_content":"# Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n- 68T45, 68T10, 68T07, 68U10\n- I.2.10; I.4.8; I.5.1; J.3; I.4.1; K.4.2\n## Abstract\nDue to the boom in technical compute in the last few years, the world has seen massive advances in artificially intelligent systems solving diverse real-world problems. But a major roadblock in the ubiquitous acceptance of these models is their enormous computational complexity and memory footprint. Hence efficient architectures and training techniques are required for deployment on extremely low resource inference endpoints. This paper proposes an architecture for detection of alphabets in American Sign Language on an ARM Cortex-M7 microcontroller having just 496 KB of framebuffer RAM. Leveraging parameter quantization is a common technique that might cause varying drops in test accuracy. This paper proposes using interpolation as augmentation amongst other techniques as an efficient method of reducing this drop, which also helps the model generalize well to previously unseen noisy data. The proposed model is about 185 KB post-quantization and inference speed is 20 frames per second.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/RAICS51191.2020.9332480\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\", \"cs.HC\", \"68T45, 68T10, 68T07, 68U10\", \"I.2.10; I.4.8; I.5.1; J.3; I.4.1; K.4.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"68T45, 68T10, 68T07, 68U10\", \"I.2.10; I.4.8; I.5.1; J.3; I.4.1; K.4.2\"], \"incoming_citations\": [\"32595\", \"32598\", \"45109\", \"83869\"], \"outgoing_citations\": [\"151144\", \"231775\", \"244656\", \"251498\", \"252985\"]}","task_split":"paper_retrieval"}
{"document_id":"84383","document_content":"# A survey of benchmarking frameworks for reinforcement learning\n## Categories\n- Machine Learning\n## Abstract\nReinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18489\/sacj.v32i2.746\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177658\", \"188486\", \"214945\", \"224746\", \"226321\", \"231175\", \"240602\", \"248552\", \"253045\", \"253974\", \"255531\", \"255613\", \"258472\", \"258834\", \"259045\", \"260284\", \"260583\", \"261983\", \"265084\", \"266342\", \"266416\", \"269458\", \"271868\", \"272930\", \"279333\", \"293995\", \"298222\", \"302028\", \"308711\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"84389","document_content":"# Joint Extraction of Entity and Relation with Information Redundancy Elimination\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nTo solve the problem of redundant information and overlapping relations of the entity and relation extraction model, we propose a joint extraction model. This model can directly extract multiple pairs of related entities without generating unrelated redundant information. We also propose a recurrent neural network named Encoder-LSTM that enhances the ability of recurrent units to model sentences. Specifically, the joint model includes three sub-modules: the Named Entity Recognition sub-module consisted of a pre-trained language model and an LSTM decoder layer, the Entity Pair Extraction sub-module which uses Encoder-LSTM network to model the order relationship between related entity pairs, and the Relation Classification sub-module including Attention mechanism. We conducted experiments on the public datasets ADE and CoNLL04 to evaluate the effectiveness of our model. The results show that the proposed model achieves good performance in the task of entity and relation extraction and can greatly reduce the amount of redundant information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13565v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"166360\", \"205022\", \"234772\", \"254706\", \"260349\", \"264389\", \"291192\", \"300593\", \"308507\"]}","task_split":"paper_retrieval"}
{"document_id":"84473","document_content":"# Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nQualitative analysis of verbal data is of central importance in the learning sciences. It is labor-intensive and time-consuming, however, which limits the amount of data researchers can include in studies. This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning. We start with a set of lab reports from an undergraduate biology course, scored by a four-level scheme that considers the complexity of argument structure, the scope of evidence, and the care and nuance of conclusions. Using this set of labeled data, we show that a popular natural language modeling processing pipeline, namely vector representation of words, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) model for capturing language generation as a state-space model, is able to quantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK) prediction score, when trained in via a novel contrastive learning set-up. We show that the ML algorithm approached the inter-rater reliability of human analysis. Ultimately, we conclude, that machine learning (ML) for natural language processing (NLP) holds promise for assisting learning sciences researchers in conducting qualitative studies at much larger scales than is currently possible.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13384v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"643\", \"40426\"], \"outgoing_citations\": [\"187201\", \"291617\", \"297955\"]}","task_split":"paper_retrieval"}
{"document_id":"84479","document_content":"# Understand Watchdogs: Discover How Game Bot Get Discovered\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nThe game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13374v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"172296\", \"244701\"]}","task_split":"paper_retrieval"}
{"document_id":"84503","document_content":"# Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n- Image and Video Processing\n## Abstract\nSemantic Segmentation (SS) is promising for outdoor scene perception in safety-critical applications like autonomous vehicles, assisted navigation and so on. However, traditional SS is primarily based on RGB images, which limits the reliability of SS in complex outdoor scenes, where RGB images lack necessary information dimensions to fully perceive unconstrained environments. As preliminary investigation, we examine SS in an unexpected obstacle detection scenario, which demonstrates the necessity of multimodal fusion. Thereby, in this work, we present EAFNet, an Efficient Attention-bridged Fusion Network to exploit complementary information coming from different optical sensors. Specifically, we incorporate polarization sensing to obtain supplementary information, considering its optical characteristics for robust representation of diverse materials. By using a single-shot polarization sensor, we build the first RGB-P dataset which consists of 394 annotated pixel-aligned RGB-Polarization images. A comprehensive variety of experiments shows the effectiveness of EAFNet to fuse polarization and RGB information, as well as the flexibility to be adapted to other sensor combination scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1364\/OE.416130\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\", \"Image and Video Processing\"], \"incoming_citations\": [\"28488\", \"28977\", \"36975\", \"64098\"], \"outgoing_citations\": [\"104948\", \"134022\", \"137377\", \"137553\", \"140351\", \"166380\", \"171558\", \"171631\", \"174337\", \"174431\", \"174686\", \"184822\", \"192386\", \"194418\", \"198176\", \"208122\", \"219256\", \"237959\", \"284665\"]}","task_split":"paper_retrieval"}
{"document_id":"84506","document_content":"# A Metric for Linear Symmetry-Based Disentanglement\n## Categories\n- Machine Learning\n## Abstract\nThe definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation that captures the symmetries of data. However, it is not clear how to measure the degree to which a data representation fulfills these properties. We propose a metric for the evaluation of the level of LSBD that a data representation achieves. We provide a practical method to evaluate this metric and use it to evaluate the disentanglement of the data representations obtained for three datasets with underlying $SO(2)$ symmetries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13306v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"141826\", \"181388\", \"183732\", \"190015\", \"193061\", \"201588\", \"207094\", \"220865\", \"231018\", \"252884\", \"319797\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"84522","document_content":"# Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nThe rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of truth in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13253v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"2364\", \"3480\", \"10531\", \"20082\", \"46313\", \"70727\", \"75367\", \"77467\", \"79024\", \"88648\"], \"outgoing_citations\": [\"114043\", \"132259\", \"132687\", \"134993\", \"209764\", \"228156\", \"238986\", \"250722\", \"261539\"]}","task_split":"paper_retrieval"}
{"document_id":"84541","document_content":"# Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe study the problem of integrating syntactic information from constituency trees into a neural model in Frame-semantic parsing sub-tasks, namely Target Identification (TI), FrameIdentification (FI), and Semantic Role Labeling (SRL). We use a Graph Convolutional Network to learn specific representations of constituents, such that each constituent is profiled as the production grammar rule it corresponds to. We leverage these representations to build syntactic features for each word in a sentence, computed as the sum of all the constituents on the path between a word and a task-specific node in the tree, e.g. the target predicate for SRL. Our approach improves state-of-the-art results on the TI and SRL of ~1%and~3.5% points, respectively (+2.5% additional points are gained with BERT as input), when tested on FrameNet 1.5, while yielding comparable results on the CoNLL05 dataset to other syntax-aware systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13210v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"21139\"], \"outgoing_citations\": [\"153933\", \"165653\", \"183096\", \"202775\", \"213527\", \"215434\", \"216701\", \"218179\", \"219714\", \"226756\", \"232290\", \"233407\", \"234587\", \"234631\", \"235377\", \"238321\", \"248279\", \"262592\", \"268812\", \"271527\", \"291941\"]}","task_split":"paper_retrieval"}
{"document_id":"84559","document_content":"# Saliency-based segmentation of dermoscopic images using color information\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how color information, besides saliency, can be used to determine the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency in order to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the input image data distribution. As a means of refining the accuracy of the proposed method, the segmentation step is preceded by a pre-processing aimed at reducing the computation burden, removing artifacts, and improving contrast. We have assessed the method on two public databases, including 1497 dermoscopic images. We have also compared its performance with classical and recent saliency-based methods designed explicitly for dermoscopic images. The qualitative and quantitative evaluation indicates that the proposed method is promising since it produces an accurate skin lesion segmentation and performs satisfactorily compared to other existing saliency-based segmentation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13179v3\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"84770\", \"271920\", \"299339\"]}","task_split":"paper_retrieval"}
{"document_id":"84578","document_content":"# Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers. When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity. In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output. Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our Refuel as well as several baseline models. We release source code for our models and experiments at https:\/\/github.com\/amzn\/refuel-open-domain-qa.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13137v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15846\", \"12125\", \"55375\", \"68754\"], \"outgoing_citations\": [\"94773\", \"114545\", \"123394\", \"129610\", \"140784\", \"143089\", \"167467\", \"175365\", \"181023\", \"183076\", \"200155\", \"266850\", \"270020\"]}","task_split":"paper_retrieval"}
{"document_id":"84625","document_content":"# Squared $\\ell 2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations\n## Categories\n- Machine Learning\n## Abstract\nData augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmented samples, a torrent of methods regularizing the distance between embeddings\/representations of the original samples and their augmented counterparts have been introduced. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. Our analysis suggests the ideal choices of regularization correspond to various assumptions. With an invariance test, we argue that regularization is important if the model is to be used in a broader context than the accuracy-driven setting because non-regularized approaches are limited in learning the concept of invariance, despite equally high accuracy. Finally, we also show that the generic approach we identified (squared $\\ell_2$ norm regularized augmentation) outperforms several recent methods, which are each specially designed for one task and significantly more complicated than ours, over three different tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.13052v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"11110\"], \"outgoing_citations\": [\"129807\", \"152544\", \"159333\", \"164734\", \"174490\", \"178843\", \"178859\", \"182110\", \"183874\", \"183921\", \"186466\", \"187243\", \"188943\", \"196864\", \"198870\", \"201551\", \"210300\", \"224289\", \"226925\", \"228536\", \"238702\", \"238777\", \"277021\", \"281227\", \"290407\", \"294549\", \"297682\"]}","task_split":"paper_retrieval"}
{"document_id":"84673","document_content":"# A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Image and Video Processing\n- Signal Processing\n- 68T45\n- I.4.8\n## Abstract\nIn open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data. Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning. This paper formalizes various open-world learning problems including open-world learning without labels. These open-world problems can be addressed with modifications to known elements, we present a new framework that enables agents to combine various modules for novelty-detection, novelty-characterization, incremental learning, and instance management to learn new classes from a stream of unlabeled data in an unsupervised manner, survey how to adapt a few state-of-the-art techniques to fit the framework and use them to define seven baselines for performance on the open-world learning without labels problem. We then discuss open-world learning quality and analyze how that can improve instance management. We also discuss some of the general ambiguity issues that occur in open-world learning without labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.12906v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"eess.IV\", \"eess.SP\", \"68T45\", \"I.4.8\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Image and Video Processing\", \"Signal Processing\", \"68T45\", \"I.4.8\"], \"incoming_citations\": [], \"outgoing_citations\": [\"65445\", \"69142\", \"71110\", \"71785\", \"72659\", \"75729\", \"85277\", \"87722\", \"95818\", \"105340\", \"111723\", \"113961\", \"115945\", \"126851\", \"128432\", \"130159\", \"131637\", \"132880\", \"133624\", \"133677\", \"134324\", \"135957\", \"139615\", \"140045\", \"140473\", \"142406\", \"146858\", \"157773\", \"158737\", \"170086\", \"170106\", \"171865\", \"178420\", \"182120\", \"183426\", \"186791\", \"189924\", \"191218\", \"192024\", \"192740\", \"194598\", \"196311\", \"197038\", \"204870\", \"206350\", \"206443\", \"207069\", \"208243\", \"209242\", \"210697\", \"212176\", \"217632\", \"219227\", \"220890\", \"222680\", \"223417\", \"225766\", \"230254\", \"235875\", \"236957\", \"238423\", \"242006\", \"247760\", \"249018\", \"249143\", \"257884\", \"258770\", \"260716\", \"262216\", \"262777\", \"264232\", \"267895\", \"271625\", \"281741\", \"310844\", \"318558\", \"320186\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"84766","document_content":"# XTQA: Span-Level Explanations of the Textbook Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- 68T07\n- I.1.2\n## Abstract\nTextbook Question Answering (TQA) is a task that one should answer a diagram\/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams. We argue that the explainability of this task should place students as a key aspect to be considered. To address this issue, we devise a novel architecture towards span-level eXplanations of the TQA (XTQA) based on our proposed coarse-to-fine grained algorithm, which can provide not only the answers but also the span-level evidences to choose them for students. This algorithm first coarsely chooses top $M$ paragraphs relevant to questions using the TF-IDF method, and then chooses top $K$ evidence spans finely from all candidate spans within these paragraphs by computing the information gain of each span to questions. Experimental results shows that XTQA significantly improves the state-of-the-art performance compared with baselines. The source code is available at https:\/\/github.com\/keep-smile-001\/opentqa","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.12662v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"68T07\", \"I.1.2\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"68T07\", \"I.1.2\"], \"incoming_citations\": [\"5014\"], \"outgoing_citations\": [\"125462\", \"166283\", \"172318\", \"179017\", \"186432\", \"188867\", \"208172\", \"215409\", \"215928\", \"231300\", \"259346\", \"266120\", \"303837\"]}","task_split":"paper_retrieval"}
{"document_id":"84962","document_content":"# Domain-Transferable Method for Named Entity Recognition Task\n## Categories\n- Computation and Language\n## Abstract\nNamed Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.12170v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"157681\", \"159862\", \"172044\", \"185852\", \"192143\", \"205022\", \"218407\", \"315447\"]}","task_split":"paper_retrieval"}
{"document_id":"85120","document_content":"# When Machine Learning Meets Privacy: A Survey and Outlook\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Cryptography and Security\n## Abstract\nThe newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.11819v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Cryptography and Security\"], \"incoming_citations\": [\"5092\", \"6150\", \"37430\", \"86916\", \"18971\", \"37702\", \"63205\", \"4271\", \"6524\", \"14024\", \"18622\", \"55158\", \"63529\", \"119868\", \"191461\"], \"outgoing_citations\": [\"102972\", \"112045\", \"137962\", \"168905\", \"179397\", \"181177\", \"182994\", \"188167\", \"189775\", \"194008\", \"207518\", \"207602\", \"207623\", \"207882\", \"208390\", \"220953\", \"225706\", \"228463\", \"228714\", \"229332\", \"230009\", \"232283\", \"232524\", \"234037\", \"239622\", \"240013\", \"241210\", \"242128\", \"242258\", \"242744\", \"245925\", \"248101\", \"248388\", \"249425\", \"251337\", \"252956\", \"255133\", \"255443\", \"255678\", \"256057\", \"256827\", \"257949\", \"258657\", \"265829\", \"268358\", \"269045\", \"270119\", \"270382\", \"271122\", \"272203\", \"273215\", \"273221\", \"275209\", \"281860\", \"282410\", \"285037\", \"285517\", \"287568\", \"291989\", \"293141\", \"298309\", \"310922\", \"314498\", \"317401\", \"319813\", \"332751\", \"343470\", \"355805\"]}","task_split":"paper_retrieval"}
{"document_id":"85278","document_content":"# STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present a novel multi-modal deep neural network architecture that uses speech and text entanglement for learning phonetically sound spoken-word representations. STEPs-RL is trained in a supervised manner to predict the phonetic sequence of a target spoken-word using its contextual spoken word's speech and text, such that the model encodes its meaningful latent representations. Unlike existing work, we have used text along with speech for auditory representation learning to capture semantical and syntactical information along with the acoustic and temporal information. The latent representations produced by our model were not only able to predict the target phonetic sequences with an accuracy of 89.47% but were also able to achieve competitive results to textual word representation models, Word2Vec & FastText (trained on textual transcripts), when evaluated on four widely used word similarity benchmark datasets. In addition, investigation of the generated vector space also demonstrated the capability of the proposed model to capture the phonetic structure of the spoken-words. To the best of our knowledge, none of the existing works use speech and text entanglement for learning spoken-word representation, which makes this work first of its kind.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.11387v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"112617\", \"121188\", \"178277\", \"188295\", \"190961\", \"201626\", \"211976\", \"221967\", \"237678\", \"246088\", \"255129\", \"283764\", \"289854\", \"301814\", \"308796\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"85317","document_content":"# AutoGraph: Automated Graph Neural Network\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nGraphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs. To fully utilize the power of the deep neural network, some deep GNNs have been proposed recently. However, the design of deep GNNs requires significant architecture engineering. In this work, we propose a method to automate the deep GNNs design. In our proposed method, we add a new type of skip connection to the GNNs search space to encourage feature reuse and alleviate the vanishing gradient problem. We also allow our evolutionary algorithm to increase the layers of GNNs during the evolution to generate deeper networks. We evaluate our method in the graph node classification task. The experiments show that the GNNs generated by our method can obtain state-of-the-art results in Cora, Citeseer, Pubmed and PPI datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-63833-7_16\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [\"365\", \"20588\", \"168186\", \"742\", \"27860\", \"66279\"], \"outgoing_citations\": [\"144037\", \"168186\", \"171841\", \"182323\", \"184273\", \"189505\", \"221587\", \"227067\", \"243437\", \"249497\", \"272497\"]}","task_split":"paper_retrieval"}
{"document_id":"85327","document_content":"# Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNatural language processing (NLP) techniques have become mainstream in the recent decade. Most of these advances are attributed to the processing of a single language. More recently, with the extensive growth of social media platforms focus has shifted to code-mixed text. The code-mixed text comprises text written in more than one language. People naturally tend to combine local language with global languages like English. To process such texts, current NLP techniques are not sufficient. As a first step, the text is processed to identify the language of the words in the text. In this work, we focus on language identification in code-mixed sentences for Hindi-English mixed text. The task of language identification is formulated as a token classification task. In the supervised setting, each word in the sentence has an associated language label. We evaluate different deep learning models and input representation combinations for this task. Mainly, character, sub-word, and word embeddings are considered in combination with CNN and LSTM based models. We show that sub-word representation along with the LSTM model gives the best results. In general sub-word representations perform significantly better than other input representations. We report the best accuracy of 94.52% using a single layer LSTM model on the standard SAIL ICON 2017 test set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-981-16-3690-5_73\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"15439\"], \"outgoing_citations\": [\"95512\", \"128892\", \"214843\", \"220710\", \"233817\", \"238587\"]}","task_split":"paper_retrieval"}
{"document_id":"85341","document_content":"# An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare\n## Categories\n- Machine Learning\n## Abstract\nReinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data. In practice, successful RL relies on informative latent states derived from sequential observations to develop optimal treatment strategies. To date, how best to construct such states in a healthcare setting is an open question. In this paper, we perform an empirical study of several information encoding architectures using data from septic patients in the MIMIC-III dataset to form representations of a patient state. We evaluate the impact of representation dimension, correlations with established acuity scores, and the treatment policies derived from them. We find that sequentially formed state representations facilitate effective policy learning in batch settings, validating a more thoughtful approach to representation learning that remains faithful to the sequential and partial nature of healthcare data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.11235v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"74313\", \"17932\", \"22978\", \"33819\", \"43376\"], \"outgoing_citations\": [\"124309\", \"136894\", \"163467\", \"165848\", \"185440\", \"185773\", \"201685\", \"206875\", \"208939\", \"229923\", \"230082\", \"233926\", \"249233\", \"268399\", \"291026\", \"296713\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"85376","document_content":"# A Decade Survey of Content Based Image Retrieval using Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Multimedia\n## Abstract\nThe content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TCSVT.2021.3080920\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Multimedia\"], \"incoming_citations\": [\"20193\", \"21001\", \"32688\", \"5469\", \"20231\", \"21005\", \"48131\", \"48791\", \"59092\", \"67784\"], \"outgoing_citations\": [\"91347\", \"119839\", \"133188\", \"134085\", \"138613\", \"139753\", \"139775\", \"145738\", \"146595\", \"155366\", \"170190\", \"180184\", \"182055\", \"187156\", \"189084\", \"189997\", \"191881\", \"193175\", \"198711\", \"200516\", \"200699\", \"227654\", \"227937\", \"234601\", \"236817\", \"236991\", \"243056\", \"247969\", \"249538\", \"251473\", \"251855\", \"259061\", \"259070\", \"260176\", \"262169\", \"263387\", \"264939\", \"267404\", \"269814\", \"270624\", \"271313\", \"272805\", \"274846\", \"274926\", \"277635\", \"278179\", \"278480\", \"281953\", \"285259\", \"287103\", \"287562\", \"288218\", \"291400\", \"294367\", \"294470\", \"294983\", \"295204\", \"296534\", \"296798\", \"296862\", \"297319\", \"300357\", \"300743\", \"302895\", \"303067\", \"303372\", \"303512\", \"303664\", \"307882\", \"308236\", \"310303\", \"314231\", \"314520\", \"314568\", \"316473\", \"317841\", \"318535\", \"319454\", \"320009\", \"330936\", \"331445\", \"332151\", \"343159\"]}","task_split":"paper_retrieval"}
{"document_id":"85426","document_content":"# QuerYD: A video dataset with high-quality text and audio narrations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video. A unique feature of our dataset is the availability of two audio tracks for each video: the original audio, and a high-quality spoken description of the visual content. The dataset is based on YouDescribe, a volunteer project that assists visually-impaired people by attaching voiced narrations to existing YouTube videos. This ever-growing collection of videos contains highly detailed, temporally aligned audio and text annotations. The content descriptions are more relevant than dialogue, and more detailed than previous description attempts, which can be observed to contain many superficial or uninformative descriptions. To demonstrate the utility of the QuerYD dataset, we show that it can be used to train and benchmark strong models for retrieval and event localisation. Data, code and models are made publicly available, and we hope that QuerYD inspires further research on video understanding with written and spoken natural language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.11071v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1081\", \"2284\", \"2460\", \"6047\", \"10669\", \"16315\", \"51492\", \"55460\", \"62318\", \"118223\"], \"outgoing_citations\": [\"126167\", \"151327\", \"165268\", \"173627\", \"181897\", \"191861\", \"194508\", \"214015\", \"229604\", \"236377\", \"236468\", \"247535\", \"259318\", \"264533\", \"267223\", \"267523\", \"270325\", \"316803\", \"319169\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"85428","document_content":"# Modelling Compositionality and Structure Dependence in Natural Language\n## Categories\n- Computation and Language\n## Abstract\nHuman beings possess the most sophisticated computational machinery in the known universe. We can understand language of rich descriptive power, and communicate in the same environment with astonishing clarity. Two of the many contributors to the interest in natural language - the properties of Compositionality and Structure Dependence, are well documented, and offer a vast space to ask interesting modelling questions. The first step to begin answering these questions is to ground verbal theory in formal terms. Drawing on linguistics and set theory, a formalisation of these ideas is presented in the first half of this thesis. We see how cognitive systems that process language need to have certain functional constraints, viz. time based, incremental operations that rely on a structurally defined domain. The observations that result from analysing this formal setup are examined as part of a modelling exercise. Using the advances of word embedding techniques, a model of relational learning is simulated with a custom dataset to demonstrate how a time based role-filler binding mechanism satisfies some of the constraints described in the first section. The model's ability to map structure, along with its symbolic-connectionist architecture makes for a cognitively plausible implementation. The formalisation and simulation are together an attempt to recognise the constraints imposed by linguistic theory, and explore the opportunities presented by a cognitive model of relation learning to realise these constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2012.02038v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"218179\"]}","task_split":"paper_retrieval"}
{"document_id":"85467","document_content":"# Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nThe emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given language instruction. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments; second, during the training process, the agent usually imitate the shortest-path to the target location. Due to the discrepancy of action selection between training and inference, the agent solely on the basis of imitation learning does not perform well. Sampling the next action from its predicted probability distribution during the training process allows the agent to explore diverse routes from the environments, yielding higher success rates. Nevertheless, without being presented with the shortest navigation paths during the training process, the agent may arrive at the target location through an unexpected longer route. To overcome these challenges, we design a cross-modal grounding module, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities. We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference. We further exploit the advantages of both these two learning schemes via adversarial learning. Extensive experimental results on the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning scheme is generalized and complementary to prior arts. Our method performs well against state-of-the-art approaches in terms of effectiveness and efficiency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TCSVT.2020.3039522\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5947\", \"83134\", \"27522\", \"66217\", \"153902\"], \"outgoing_citations\": [\"127852\", \"139037\", \"140318\", \"155676\", \"168528\", \"171882\", \"172416\", \"177351\", \"179017\", \"182215\", \"183049\", \"191394\", \"191570\", \"196218\", \"196525\", \"203398\", \"208773\", \"224223\", \"228916\", \"231300\", \"235895\", \"238287\", \"244272\", \"248742\", \"249906\", \"250195\", \"255575\", \"261095\", \"269136\", \"277832\", \"280681\", \"281749\", \"284565\", \"311470\", \"315839\"]}","task_split":"paper_retrieval"}
{"document_id":"85490","document_content":"# Hierachical Delta-Attention Method for Multimodal Fusion\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nIn vision and linguistics; the main input modalities are facial expressions, speech patterns, and the words uttered. The issue with analysis of any one mode of expression (Visual, Verbal or Vocal) is that lot of contextual information can get lost. This asks researchers to inspect multiple modalities to get a thorough understanding of the cross-modal dependencies and temporal context of the situation to analyze the expression. This work attempts at preserving the long-range dependencies within and across different modalities, which would be bottle-necked by the use of recurrent networks and adds the concept of delta-attention to focus on local differences per modality to capture the idiosyncrasy of different people. We explore a cross-attention fusion technique to get the global view of the emotion expressed through these delta-self-attended modalities, in order to fuse all the local nuances and global context together. The addition of attention is new to the multi-modal fusion field and currently being scrutinized for on what stage the attention mechanism should be used, this work achieves competitive accuracy for overall and per-class classification which is close to the current state-of-the-art with almost half number of parameters.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10916v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105929\", \"114236\", \"115458\", \"141620\", \"153516\", \"165591\", \"168375\", \"170617\", \"172012\", \"177167\", \"179209\", \"183080\", \"205435\", \"209002\", \"243427\", \"243430\", \"265384\"]}","task_split":"paper_retrieval"}
{"document_id":"85511","document_content":"# Deep learning for video game genre classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- I.4.9; I.2.6\n## Abstract\nVideo game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many of which are not concretely defined. Second, video game covers vary in many different ways such as colors, styles, textual information, etc, even for games of the same genre. Third, cover designs and textual descriptions may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the video game industry, the cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The computer-based automatic video game genre classification systems become a particularly exciting research topic in recent years. In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold. First, we compiles a large dataset consisting of 50,000 video games from 21 genres made of cover images, description text, and title text and the genre information. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of genre classification for video games. Third, we developed an efficient and salable multi-modal framework based on both images and texts. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework outperforms the current state-of-the-art image-based or text-based models. Several challenges are outlined for this task. More efforts and resources are needed for this classification task in order to reach a satisfactory level.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.12143v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"I.4.9; I.2.6\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"I.4.9; I.2.6\"], \"incoming_citations\": [\"50037\"], \"outgoing_citations\": [\"86814\", \"149432\", \"182313\", \"206470\", \"222766\", \"230308\", \"237274\", \"240756\", \"245188\", \"261112\", \"276064\", \"281706\", \"283091\", \"297743\"]}","task_split":"paper_retrieval"}
{"document_id":"85594","document_content":"# Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe are interested in learning models of non-stationary environments, which can be framed as a multi-task learning problem. Model-free reinforcement learning algorithms can achieve good asymptotic performance in multi-task learning at a cost of extensive sampling, due to their approach, which requires learning from scratch. While model-based approaches are among the most data efficient learning algorithms, they still struggle with complex tasks and model uncertainties. Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task. In this paper, we propose a meta-reinforcement learning approach to learn the dynamic model of a non-stationary environment to be used for meta-policy optimization later. Due to the sample efficiency of model-based learning methods, we are able to simultaneously train both the meta-model of the non-stationary environment and the meta-policy until dynamic model convergence. Then, the meta-learned dynamic model of the environment will generate simulated data for meta-policy optimization. Our experiment demonstrates that our proposed method can meta-learn the policy in a non-stationary environment with the data efficiency of model-based learning approaches while achieving the high asymptotic performance of model-free meta-reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10714v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"20262\"], \"outgoing_citations\": [\"161071\", \"188486\", \"205477\", \"213370\", \"214135\", \"217919\", \"237229\", \"239655\", \"240471\", \"240495\", \"244112\", \"249463\", \"253658\", \"255139\", \"259045\", \"259762\", \"261577\", \"263288\", \"280202\", \"280870\", \"283289\", \"283410\", \"290426\", \"297268\", \"302295\", \"306165\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"85600","document_content":"# Explainable Composition of Aggregated Assistants\n## Categories\n- Artificial Intelligence\n## Abstract\nA new design of an AI assistant that has become increasingly popular is that of an \"aggregated assistant\" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks. In this paper, we will talk about the role of planning in the automated composition of such assistants and explore how concepts in automated planning can help to establish transparency of the inner workings of the assistant to the end-user.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10707v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"109600\", \"109601\", \"148027\", \"170627\"]}","task_split":"paper_retrieval"}
{"document_id":"85708","document_content":"# OAK: Ontology-Based Knowledge Map Model for Digital Agriculture\n## Categories\n- Databases\n- Artificial Intelligence\n## Abstract\nNowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining processes, the scalability still remains an open issue. In this study, we propose a knowledge representation model, called an ontology-based knowledge map, which can collect knowledge from different sources, store it, and exploit either directly by stakeholders or as an input to the knowledge discovery process (Data Mining). The proposed model consists of two stages, 1) build an ontology as a knowledge base for a specific domain and data mining concepts, and 2) build the ontology-based knowledge map model for representing and storing the knowledge mined on the crop datasets. A framework of the proposed model has been implemented in agriculture domain. It is an efficient and scalable model, and it can be used as knowledge repository a digital agriculture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-63924-2_14\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.AI\", \"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Databases\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152998\", \"160185\", \"209811\", \"270332\"]}","task_split":"paper_retrieval"}
{"document_id":"85766","document_content":"# Learning Synthetic to Real Transfer for Localization and Navigational Tasks\n## Categories\n- Robotics\n- Artificial Intelligence\n- I.2.9\n- Computer Vision and Pattern Recognition\n## Abstract\nAutonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10274v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"I.2.9\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"I.2.9\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120259\", \"123031\", \"131814\", \"155441\", \"164936\", \"170986\", \"176738\", \"181081\", \"195772\", \"196898\", \"203998\", \"206901\", \"207509\", \"224223\", \"225036\", \"231414\", \"233167\", \"234517\", \"240323\", \"249777\", \"249906\", \"251924\", \"255463\", \"266419\", \"272930\", \"274102\", \"280681\", \"281110\", \"284565\"]}","task_split":"paper_retrieval"}
{"document_id":"85782","document_content":"# Shuffle and Learn: Minimizing Mutual Information for Unsupervised Hashing\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n## Abstract\nUnsupervised binary representation allows fast data retrieval without any annotations, enabling practical application like fast person re-identification and multimedia retrieval. It is argued that conflicts in binary space are one of the major barriers to high-performance unsupervised hashing as current methods failed to capture the precise code conflicts in the full domain. A novel relaxation method called Shuffle and Learn is proposed to tackle code conflicts in the unsupervised hash. Approximated derivatives for joint probability and the gradients for the binary layer are introduced to bridge the update from the hash to the input. Proof on $\\epsilon$-Convergence of joint probability with approximated derivatives is provided to guarantee the preciseness on update applied on the mutual information. The proposed algorithm is carried out with iterative global updates to minimize mutual information, diverging the code before regular unsupervised optimization. Experiments suggest that the proposed method can relax the code optimization from local optimum and help to generate binary representations that are more discriminative and informative without any annotations. Performance benchmarks on image retrieval with the unsupervised binary code are conducted on three open datasets, and the model achieves state-of-the-art accuracy on image retrieval task for all those datasets. Datasets and reproducible code are provided.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10239v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"136015\", \"160792\", \"170190\", \"187156\", \"227654\", \"239316\", \"247969\", \"268390\", \"272805\", \"274846\", \"276451\"]}","task_split":"paper_retrieval"}
{"document_id":"85785","document_content":"# Deep Snapshot HDR Imaging Using Multi-Exposure Color Filter Array\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Image and Video Processing\n## Abstract\nIn this paper, we propose a deep snapshot high dynamic range (HDR) imaging framework that can effectively reconstruct an HDR image from the RAW data captured using a multi-exposure color filter array (ME-CFA), which consists of a mosaic pattern of RGB filters with different exposure levels. To effectively learn the HDR image reconstruction network, we introduce the idea of luminance normalization that simultaneously enables effective loss computation and input data normalization by considering relative local contrasts in the \"normalized-by-luminance\" HDR domain. This idea makes it possible to equally handle the errors in both bright and dark areas regardless of absolute luminance levels, which significantly improves the visual image quality in a tone-mapped domain. Experimental results using two public HDR image datasets demonstrate that our framework outperforms other snapshot methods and produces high-quality HDR images with fewer visual artifacts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10232v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Image and Video Processing\"], \"incoming_citations\": [\"9660\", \"49980\"], \"outgoing_citations\": [\"189323\", \"205608\", \"228199\", \"235857\", \"239014\", \"239855\", \"244729\", \"246985\", \"249439\", \"252829\", \"264079\"]}","task_split":"paper_retrieval"}
{"document_id":"85803","document_content":"# Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper focuses on weakly-supervised action alignment, where only the ordered sequence of video-level actions is available for training. We propose a novel Duration Network, which captures a short temporal window of the video and learns to predict the remaining duration of a given action at any point in time with a level of granularity based on the type of that action. Further, we introduce a Segment-Level Beam Search to obtain the best alignment, that maximizes our posterior probability. Segment-Level Beam Search efficiently aligns actions by considering only a selected set of frames that have more confident predictions. The experimental results show that our alignments for long videos are more robust than existing models. Moreover, the proposed method achieves state of the art results in certain cases on the popular Breakfast and Hollywood Extended datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.10190v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17178\", \"30657\"], \"outgoing_citations\": [\"164400\", \"182786\", \"194508\", \"203523\", \"206567\", \"231686\", \"236894\", \"237402\", \"247497\", \"264711\", \"268934\", \"269276\", \"269646\", \"270713\", \"271891\", \"277625\", \"280328\", \"283115\", \"285965\", \"287541\", \"307001\", \"310322\", \"310452\", \"312423\", \"316472\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"85897","document_content":"# DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Software Engineering\n## Abstract\nDeep neural networks (DNNs) are being widely applied for various real-world applications across domains due to their high performance (e.g., high accuracy on image classification). Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise etc. Hence, it poses a rather important problem for the DNNs' real-world applications: how to repair the deployed DNNs for correcting the failure samples (i.e., incorrect prediction) under the deployed operational environment while not harming their capability of handling normal or clean data. The number of failure samples we can collect in practice, caused by the noise factors in the operational environment, is often limited. Therefore, It is rather challenging how to repair more similar failures based on the limited failure samples we can collect.   In this paper, we propose a style-guided data augmentation for repairing DNN in the operational environment. We propose a style transfer method to learn and introduce the unknown failure patterns within the failure data into the training data via data augmentation. Moreover, we further propose the clustering-based failure data generation for much more effective style-guided data augmentation. We conduct a large-scale evaluation with fifteen degradation factors that may happen in the real world and compare with four state-of-the-art data augmentation methods and two DNN repairing methods, demonstrating that our method can significantly enhance the deployed DNNs on the corrupted data in the operational environment, and with even better accuracy on clean datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.09884v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.SE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68771\", \"92489\", \"95479\", \"99705\", \"99707\", \"99712\", \"112155\", \"121259\", \"126437\", \"127360\", \"139986\", \"143285\", \"146514\", \"151271\", \"152154\", \"152544\", \"157109\", \"157172\", \"159696\", \"160956\", \"167037\", \"179903\", \"180568\", \"194041\", \"195652\", \"198651\", \"198795\", \"201503\", \"205350\", \"220346\", \"222669\", \"223079\", \"226119\", \"232331\", \"238354\", \"241749\", \"248102\", \"250376\", \"257512\", \"262106\", \"266128\", \"267151\", \"270879\", \"313499\", \"317642\"]}","task_split":"paper_retrieval"}
{"document_id":"86002","document_content":"# Node Similarity Preserving Graph Convolutional Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGraph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within node neighborhoods. However, through theoretical and empirical analysis, we reveal that the aggregation process of GNNs tends to destroy node similarity in the original feature space. There are many scenarios where node similarity plays a crucial role. Thus, it has motivated the proposed framework SimP-GCN that can effectively and efficiently preserve node similarity while exploiting graph structure. Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features. Furthermore, we employ self-supervised learning to explicitly capture the complex feature similarity and dissimilarity relations between nodes. We validate the effectiveness of SimP-GCN on seven benchmark datasets including three assortative and four disassorative graphs. The results demonstrate that SimP-GCN outperforms representative baselines. Further probe shows various advantages of the proposed framework. The implementation of SimP-GCN is available at \\url{https:\/\/github.com\/ChandlerBang\/SimP-GCN}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.09643v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"67813\", \"9898\", \"16062\", \"28688\", \"66588\", \"122212\", \"204706\", \"8529\", \"13257\", \"13470\", \"24144\", \"30024\", \"30916\", \"29739\", \"43235\", \"44064\", \"98256\", \"188800\"], \"outgoing_citations\": [\"92075\", \"111211\", \"114193\", \"115602\", \"117866\", \"118237\", \"119843\", \"122212\", \"123835\", \"125407\", \"134148\", \"139118\", \"142571\", \"166272\", \"174492\", \"192819\", \"193345\", \"196521\", \"206691\", \"228663\", \"231277\", \"240758\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"86043","document_content":"# A Sequence-to-Sequence Approach to Dialogue State Tracking\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nThis paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.09553v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"20119\", \"12766\", \"23248\", \"26534\", \"74318\"], \"outgoing_citations\": [\"75427\", \"99219\", \"103646\", \"112804\", \"126664\", \"127463\", \"162766\", \"167138\", \"169079\", \"177332\", \"177825\", \"185347\", \"186662\", \"197154\", \"210245\", \"213349\", \"224315\", \"230543\", \"231442\", \"233268\", \"245042\", \"246324\", \"251348\", \"285317\", \"290588\", \"294494\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"86098","document_content":"# Topology of Word Embeddings: Singularities Reflect Polysemy\n## Categories\n- Computation and Language\n## Abstract\nThe manifold hypothesis suggests that word vectors live on a submanifold within their ambient vector space. We argue that we should, more accurately, expect them to live on a pinched manifold: a singular quotient of a manifold obtained by identifying some of its points. The identified, singular points correspond to polysemous words, i.e. words with multiple meanings. Our point of view suggests that monosemous and polysemous words can be distinguished based on the topology of their neighbourhoods. We present two kinds of empirical evidence to support this point of view: (1) We introduce a topological measure of polysemy based on persistent homology that correlates well with the actual number of meanings of a word. (2) We propose a simple, topologically motivated solution to the SemEval-2010 task on Word Sense Induction & Disambiguation that produces competitive results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.09413v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"52669\"], \"outgoing_citations\": [\"189726\", \"244590\", \"259706\", \"268619\", \"282036\", \"300128\", \"313947\", \"348198\"]}","task_split":"paper_retrieval"}
{"document_id":"86112","document_content":"# LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization\n## Categories\n- Computation and Language\n## Abstract\nReinforcement learning (RL) can enable task-oriented dialogue systems to steer the conversation towards successful task completion. In an end-to-end setting, a response can be constructed in a word-level sequential decision making process with the entire system vocabulary as action space. Policies trained in such a fashion do not require expert-defined action spaces, but they have to deal with large action spaces and long trajectories, making RL impractical. Using the latent space of a variational model as action space alleviates this problem. However, current approaches use an uninformed prior for training and optimize the latent distribution solely on the context. It is therefore unclear whether the latent representation truly encodes the characteristics of different actions. In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning. We choose response auto-encoding as the auxiliary task, as this captures the generative factors of dialogue responses while requiring low computational cost and neither additional data nor labels. Our approach yields a more action-characterized latent representations which support end-to-end dialogue policy optimization and achieves state-of-the-art success rates. These results warrant a more wide-spread use of RL in end-to-end dialogue models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.09378v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24760\", \"6635\", \"36920\", \"63830\", \"23423\", \"43184\", \"44832\", \"70413\"], \"outgoing_citations\": [\"119299\", \"127463\", \"128897\", \"133105\", \"150633\", \"154597\", \"162696\", \"171571\", \"174786\", \"177825\", \"183017\", \"197853\", \"210245\", \"216019\", \"232285\", \"234624\", \"234681\", \"291916\", \"292379\", \"294494\", \"295863\", \"302948\", \"303003\", \"309411\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"86408","document_content":"# Neural Semi-supervised Learning for Text Classification Under Large-Scale Pretraining\n## Categories\n- Computation and Language\n## Abstract\nThe goal of semi-supervised learning is to utilize the unlabeled, in-domain dataset U to improve models trained on the labeled dataset D. Under the context of large-scale language-model (LM) pretraining, how we can make the best use of U is poorly understood: is semi-supervised learning still beneficial with the presence of large-scale pretraining? should U be used for in-domain LM pretraining or pseudo-label generation? how should the pseudo-label based semi-supervised model be actually implemented? how different semi-supervised strategies affect performances regarding D of different sizes, U of different sizes, etc. In this paper, we conduct comprehensive studies on semi-supervised learning in the task of text classification under the context of large-scale LM pretraining. Our studies shed important lights on the behavior of semi-supervised learning methods: (1) with the presence of in-domain pretraining LM on U, open-domain LM pretraining is unnecessary; (2) both the in-domain pretraining strategy and the pseudo-label based strategy introduce significant performance boosts, with the former performing better with larger U, the latter performing better with smaller U, and the combination leading to the largest performance boost; (3) self-training (pretraining first on pseudo labels D' and then fine-tuning on D) yields better performances when D is small, while joint training on the combination of pseudo labels D' and the original dataset D yields better performances when D is large. Using semi-supervised learning strategies, we are able to achieve a performance of around 93.8% accuracy with only 50 training data points on the IMDB dataset, and a competitive performance of 96.6% with the full IMDB dataset. Our work marks an initial step in understanding the behavior of semi-supervised learning models under the context of large-scale pretraining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.08626v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23822\", \"27090\"], \"outgoing_citations\": [\"119265\", \"124040\", \"138517\", \"139463\", \"143508\", \"157467\", \"164156\", \"164349\", \"165025\", \"169215\", \"174612\", \"181686\", \"182943\", \"182961\", \"184190\", \"184635\", \"185283\", \"188119\", \"191383\", \"191477\", \"192602\", \"194123\", \"194198\", \"195794\", \"200867\", \"205715\", \"216925\", \"220017\", \"228002\", \"230755\", \"231898\", \"235096\", \"238809\", \"245531\", \"251743\", \"265273\", \"267601\", \"267692\", \"272068\", \"280884\", \"281076\", \"283114\", \"290402\", \"290407\", \"291899\", \"302088\", \"302824\", \"303997\", \"309807\", \"320027\"]}","task_split":"paper_retrieval"}
{"document_id":"86447","document_content":"# Structural and Functional Decomposition for Personality Image Captioning in a Communication Game\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nPersonality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener. The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits. In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits. In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC. This enables the speaker and listener to benefit from the language encoding capacity of GPT2. Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.08543v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"125091\", \"170603\", \"174277\", \"180838\", \"207034\", \"213042\", \"229967\", \"231647\", \"233440\", \"235974\", \"238125\", \"239242\", \"244479\", \"262814\", \"268356\", \"277491\", \"278954\", \"305473\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"86550","document_content":"# Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nWe present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by representation and reinforcement learning. The framework is based on the idea of latent variable models that represent high-dimensional observations using low-dimensional latent variables. The resulting policy consists of two levels of hierarchy: (i) a planning module that reasons a sequence of latent intentions that would lead to an optimistic future and (ii) a feedback control policy, shared across the tasks, that executes the inferred intention. Because the planning is performed in low-dimensional latent space, the learned policy can immediately be used to solve or adapt to new tasks without additional training. We demonstrate the proposed framework can learn compact representations (3- and 1-dimensional latent states and commands for a humanoid with 197- and 36-dimensional state features and actions) while solving a small number of imitation tasks, and the resulting policy is directly applicable to other types of tasks, i.e., navigation in cluttered environments. Video: https:\/\/youtu.be\/HQsQysUWOhg","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.08345v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"73951\"], \"outgoing_citations\": [\"139633\", \"177830\", \"184951\", \"196387\", \"196921\", \"208234\", \"208932\", \"210517\", \"212130\", \"219180\", \"225626\", \"228899\", \"229890\", \"231175\", \"233450\", \"236352\", \"236393\", \"241496\", \"241900\", \"249613\", \"253197\", \"260429\", \"262566\", \"269251\", \"283547\", \"292185\", \"298651\", \"313186\", \"317745\"]}","task_split":"paper_retrieval"}
{"document_id":"86583","document_content":"# A Probabilistic Approach in Historical Linguistics Word Order Change in Infinitival Clauses: from Latin to Old French\n## Categories\n- Computation and Language\n## Abstract\nThis research offers a new interdisciplinary approach to the field of Linguistics by using Computational Linguistics, NLP, Bayesian Statistics and Sociolinguistics methods. This thesis investigates word order change in infinitival clauses from Object-Verb (OV) to Verb-Object (VO) in the history of Latin and Old French. By applying a variationist approach, I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I also show that in discourse-configurational languages, such as Latin and Early Old French, it is possible to identify pragmatically neutral contexts by using information structure annotation. I further argue that by mapping pragmatic categories into a syntactic structure, we can detect how word order change unfolds. For this investigation, the data are extracted from annotated corpora spanning several centuries of Latin and Old French and from additional resources created by using computational linguistic methods. The data are then further codified for various pragmatic, semantic, syntactic and sociolinguistic factors. This study also evaluates previous factors proposed to account for word order alternation and change. I show how information structure and syntactic constraints change over time and propose a method that allows researchers to differentiate a stable word order alternation from alternation indicating a change. Finally, I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.08262v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"86824","document_content":"# DORB: Dynamically Optimizing Multiple Rewards with Bandits\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nPolicy gradients-based reinforcement learning has proven to be a promising approach for directly optimizing non-differentiable evaluation metrics for language generation tasks. However, optimizing for a specific metric reward leads to improvements in mostly that metric only, suggesting that the model is gaming the formulation of that metric in a particular way without often achieving real qualitative improvements. Hence, it is more beneficial to make the model optimize multiple diverse metric rewards jointly. While appealing, this is challenging because one needs to manually decide the importance and scaling weights of these metric rewards. Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time. Considering the above aspects, in our work, we automate the optimization of multiple metric rewards simultaneously via a multi-armed bandit approach (DORB), where at each round, the bandit chooses which metric reward to optimize next, based on expected arm gains. We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit). We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks: question generation and data-to-text generation, including on an unseen-test transfer setup. Finally, we present interpretable analyses of the learned bandit curriculum over the optimized rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.07635v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"166959\", \"171516\", \"191589\", \"199499\", \"201861\", \"201921\", \"210320\", \"213229\", \"214686\", \"218826\", \"219774\", \"221304\", \"227488\", \"230212\", \"231981\", \"235232\", \"236918\", \"237494\", \"248855\", \"256956\", \"259121\", \"259145\", \"260247\", \"266671\", \"267246\", \"267694\", \"268083\", \"269030\", \"269253\", \"269583\", \"270048\", \"274450\", \"278954\", \"279002\", \"290201\", \"291102\", \"295815\", \"295920\", \"296167\", \"296198\", \"298016\", \"302815\", \"305585\", \"307195\", \"307223\", \"308381\", \"309411\", \"310903\", \"311464\", \"313427\", \"313435\", \"359369\"]}","task_split":"paper_retrieval"}
{"document_id":"86983","document_content":"# Meaningful Answer Generation of E-Commerce Question-Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nIn e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we focus on the task of product-aware answer generation, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews. We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.07307v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"756\", \"733\", \"13997\", \"73917\", \"80700\"], \"outgoing_citations\": [\"88937\", \"98380\", \"125951\", \"137534\", \"140423\", \"156647\", \"159173\", \"161027\", \"165983\", \"169799\", \"172076\", \"181172\", \"184455\", \"188746\", \"189326\", \"202015\", \"203907\", \"206114\", \"207427\", \"212117\", \"217522\", \"217881\", \"219618\", \"221086\", \"227586\", \"232543\", \"247262\", \"249315\", \"254855\", \"263691\", \"266671\", \"276382\", \"279343\", \"281163\", \"288385\", \"289943\", \"290747\", \"292261\", \"296198\", \"297133\", \"301143\", \"305200\", \"307223\", \"310639\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"87249","document_content":"# Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nPredicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction models on standard metrics. We propose a loss function which enhances such models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric by penalizing trajectories that oppose the ascribed heading (flow direction) of a driving lane, and we show this metric to be differentiable and therefore suitable as an auxiliary loss function. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models, achieving improved results on the nuScenes prediction benchmark by predicting trajectories which better conform to the lane-following rules of the road.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06679v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"5860\", \"21785\", \"61453\", \"148683\"], \"outgoing_citations\": [\"99442\", \"109433\", \"110101\", \"120207\", \"121146\", \"126790\", \"131366\", \"147880\", \"148683\", \"154697\", \"157689\", \"161127\", \"162130\", \"166501\", \"170388\", \"173508\", \"177460\", \"186194\", \"187849\", \"189614\", \"191356\", \"214011\", \"214313\", \"217456\", \"229266\", \"232095\", \"232096\", \"237350\", \"268891\", \"317200\"]}","task_split":"paper_retrieval"}
{"document_id":"87279","document_content":"# Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems\n## Categories\n- Cryptography and Security\n- Computer Science and Game Theory\n- Artificial Intelligence\n## Abstract\nWe model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters on the degree of suboptimality of security outcomes due to behavioral decision-making.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06933v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.GT\", \"cs.AI\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computer Science and Game Theory\", \"Artificial Intelligence\"], \"incoming_citations\": [\"65135\", \"83468\"], \"outgoing_citations\": [\"133114\", \"147844\", \"196875\", \"231807\", \"274237\", \"304259\"]}","task_split":"paper_retrieval"}
{"document_id":"87306","document_content":"# Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Multimedia\n## Abstract\nContent-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image. This scenario is called instance or object retrieval and requires matching fine-grained visual patterns between images. Semantics, however, do not play a crucial role. This brings rise to the question: Do the recent advances in instance retrieval transfer to more generic image retrieval scenarios? To answer this question, we first provide a brief overview of the most relevant milestones of instance retrieval. We then apply them to a semantic image retrieval task and find that they perform inferior to much less sophisticated and more generic methods in a setting that requires image understanding. Following this, we review existing approaches to closing this so-called semantic gap by integrating prior world knowledge. We conclude that the key problem for the further advancement of semantic image retrieval lies in the lack of a standardized task definition and an appropriate benchmark dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06490v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [\"1733\", \"73146\"], \"outgoing_citations\": [\"110044\", \"115435\", \"137943\", \"147155\", \"156399\", \"172160\", \"180184\", \"180562\", \"198922\", \"216478\", \"218842\", \"220896\", \"235575\", \"237248\", \"242250\", \"251473\", \"251572\", \"277635\", \"281953\", \"303067\", \"320036\", \"332151\"]}","task_split":"paper_retrieval"}
{"document_id":"87328","document_content":"# A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nLive fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for navigation based on the observed and stored facts in live fire environments. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine which enables the interaction of the agent with the environment. The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences. The agent trained under this deep Q-learning approach outperforms agents trained through alternative path planning systems and demonstrates this methodology as a promising foundation on which to build a path planning navigation assistant capable of safely guiding fire fighters through live fire environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5220\/0010267102670277\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"99147\", \"107989\", \"122470\", \"144508\", \"162750\", \"266485\", \"285351\"]}","task_split":"paper_retrieval"}
{"document_id":"87371","document_content":"# Biomedical Named Entity Recognition at Scale\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNamed entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status detection, entity resolution, relation extraction, and de-identification. Reimplementing a Bi-LSTM-CNN-Char deep learning architecture on top of Apache Spark, we present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks without using heavy contextual embeddings like BERT. This includes improving BC4CHEMD to 93.72% (4.1% gain), Species800 to 80.91% (4.6% gain), and JNLPBA to 81.29% (5.2% gain). In addition, this model is freely available within a production-grade code base as part of the open-source Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java; and can be extended to support other human languages with no code changes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06315v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"54796\", \"73310\"], \"outgoing_citations\": [\"108689\", \"109085\", \"124255\", \"159867\", \"180997\", \"191942\", \"198222\", \"228681\", \"237660\", \"243880\", \"297150\", \"302500\", \"324793\"]}","task_split":"paper_retrieval"}
{"document_id":"87458","document_content":"# I-POST: Intelligent Point of Sale and Transaction System\n## Categories\n- Machine Learning\n## Abstract\nWe propose a novel solution for the cashier problem. Current cashier system\/Point of Sale (POS) terminals can be inefficient, cumbersome and time-consuming for the users. There is a need for a solution dependent on modern technology and ubiquitous computing resources. We present I-POST (Intelligent Point of Sale and Transaction) as a software system that uses smart devices, mobile phone and state of the art machine learning algorithms to process the user transactions in automated and real time manner. I-POST is an automated checkout system that allows the user to walk in a store, collect his items and exit the store. There is no need to stand and wait in a queue. The system uses object detection and facial recognition algorithm to process the authentication of the client and the state of the object. At point of exit, the classifier sends the data to the backend server which execute the payments. The system uses Convolution Neural Network (CNN) for the image recognition and processing. CNN is a supervised learning model that has found major application in pattern recognition problem. The current implementation uses two classifiers that work intrinsically to authenticate the user and track the items. The model accuracy for object recognition is 97%, the loss is 9.3%. We expect that such systems can bring efficiency to the market and has the potential for broad and diverse applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06144v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"87490","document_content":"# Quantifying and Learning Linear Symmetry-Based Disentanglement\n## Categories\n- Machine Learning\n## Abstract\nThe definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD. Such a metric is crucial to evaluate LSBD methods and to compare to previous understandings of disentanglement. We propose $\\mathcal{D}_\\mathrm{LSBD}$, a mathematically sound metric to quantify LSBD, and provide a practical implementation for $\\mathrm{SO}(2)$ groups. Furthermore, from this metric we derive LSBD-VAE, a semi-supervised method to learn LSBD representations. We demonstrate the utility of our metric by showing that (1) common VAE-based disentanglement methods don't learn LSBD representations, (2) LSBD-VAE as well as other recent methods can learn LSBD representations, needing only limited supervision on transformations, and (3) various desirable properties expressed by existing disentanglement metrics are also achieved by LSBD representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.06070v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105383\", \"116650\", \"141826\", \"143581\", \"193061\", \"201588\", \"207094\", \"208041\", \"236093\", \"241933\", \"242142\", \"242239\", \"251597\", \"278014\", \"319797\", \"333469\", \"335083\"]}","task_split":"paper_retrieval"}
{"document_id":"87603","document_content":"# Survey on 3D face reconstruction from uncalibrated images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a list of the most popular publicly available 3D facial models. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, becoming the standard choice in replacement of the widespread statistical model fitting. Unlike the other two strategies, photometry-based methods have decreased in number due to the need for strong underlying assumptions that limit the quality of their reconstructions compared to statistical model fitting and deep learning methods. The review also identifies current challenges and suggests avenues for future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.05740v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26422\", \"29692\", \"82106\"], \"outgoing_citations\": [\"109032\", \"109928\", \"112307\", \"134076\", \"134658\", \"136226\", \"137181\", \"137769\", \"159733\", \"168874\", \"174524\", \"185981\", \"187327\", \"191310\", \"191463\", \"191844\", \"194156\", \"194402\", \"197211\", \"197493\", \"197839\", \"198793\", \"199069\", \"205494\", \"216762\", \"218034\", \"219961\", \"222599\", \"223229\", \"227150\", \"227856\", \"229345\", \"236050\", \"237024\", \"237222\", \"238254\", \"247495\", \"248005\", \"248572\", \"248918\", \"254971\", \"256630\", \"258386\", \"259000\", \"260755\", \"260916\", \"267940\", \"268405\", \"268703\", \"270042\", \"270134\", \"270235\", \"270795\", \"273729\", \"275879\", \"277948\", \"280394\", \"284716\", \"290715\", \"294694\", \"296027\", \"299047\", \"302701\", \"306261\", \"321486\", \"335418\", \"362118\"]}","task_split":"paper_retrieval"}
{"document_id":"87615","document_content":"# Reinforcement Learning with Time-dependent Goals for Robotic Musicians\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nReinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a temporal dimension. In this paper, we address robotic musicianship by introducing a temporal extension to goal-conditioned reinforcement learning: Time-dependent goals. We demonstrate that these can be used to train a robotic musician to play the theremin instrument. We train the robotic agent in simulation and transfer the acquired policy to a real-world robotic thereminist. Supplemental video: https:\/\/youtu.be\/jvC9mPzdQN4","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.05715v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"77439\"], \"outgoing_citations\": [\"126444\", \"178857\", \"185001\", \"217577\", \"261983\"]}","task_split":"paper_retrieval"}
{"document_id":"87635","document_content":"# Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nA document which elaborates opinions and arguments related to the previous court cases is known as a legal opinion text. Lawyers and legal officials have to spend considerable effort and time to obtain the required information manually from those documents when dealing with new legal cases. Hence, it provides much convenience to those individuals if there is a way to automate the process of extracting information from legal opinion texts. Party-based sentiment analysis will play a key role in the automation system by identifying opinion values with respect to each legal parties in legal texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICTer51097.2020.9325435\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"10302\"], \"outgoing_citations\": [\"182224\", \"215532\", \"233152\", \"264287\", \"264409\", \"291177\"]}","task_split":"paper_retrieval"}
{"document_id":"87663","document_content":"# Reinforcement Learning with Dual-Observation for General Video Game Playing\n## Categories\n- Artificial Intelligence\n## Abstract\nReinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforcement learning algorithms. The General Video Game AI Learning Competition aims to develop agents capable of learning to play different game levels that were unseen during training. This paper summarises the five years' General Video Game AI Learning Competition editions. At each edition, three new games were designed. The training and test levels were designed separately in the first three editions. Since 2020, three test levels of each game were generated by perturbing or combining two training levels. Then, we present a novel reinforcement learning technique with dual-observation for general video game playing, assuming that it is more likely to observe similar local information in different levels rather than global information. Instead of directly inputting a single, raw pixel-based screenshot of the current game screen, our proposed general technique takes the encoded, transformed global and local observations of the game screen as two simultaneous inputs, aiming at learning local information for playing new levels. Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition. Ablation studies show the outstanding performance of using encoded, transformed global and local observations as input.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TG.2022.3164242\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95477\", \"111431\", \"117801\", \"127845\", \"128392\", \"145405\", \"152918\", \"153614\", \"155499\", \"196921\", \"200264\", \"207051\", \"212511\", \"226544\", \"229002\", \"240572\", \"268226\", \"274886\", \"281257\", \"293071\", \"302029\"]}","task_split":"paper_retrieval"}
{"document_id":"87928","document_content":"# Input Bias in Rectified Gradients and Modified Saliency Maps\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nInterpretation and improvement of deep neural networks relies on better understanding of their underlying mechanisms. In particular, gradients of classes or concepts with respect to the input features (e.g., pixels in images) are often used as importance scores or estimators, which are visualized in saliency maps. Thus, a family of saliency methods provide an intuitive way to identify input features with substantial influences on classifications or latent concepts. Several modifications to conventional saliency maps, such as Rectified Gradients and Layer-wise Relevance Propagation (LRP), have been introduced to allegedly denoise and improve interpretability. While visually coherent in certain cases, Rectified Gradients and other modified saliency maps introduce a strong input bias (e.g., brightness in the RGB space) because of inappropriate uses of the input features. We demonstrate that dark areas of an input image are not highlighted by a saliency map using Rectified Gradients, even if it is relevant for the class or concept. Even in the scaled images, the input bias exists around an artificial point in color spectrum. Our modification, which simply eliminates multiplication with input features, removes this bias. This showcases how a visual criteria may not align with true explainability of deep learning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.05002v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"159191\", \"199083\", \"215081\", \"284708\", \"300739\"]}","task_split":"paper_retrieval"}
{"document_id":"88217","document_content":"# CapWAP: Captioning with a Purpose\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nThe traditional image captioning task uses generic reference captions to provide textual information about images. Different user populations, however, will care about different visual aspects of images. In this paper, we propose a new task, Captioning with a Purpose (CapWAP). Our goal is to develop systems that can be tailored to be useful for the information needs of an intended population, rather than merely provide generic information about an image. In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation. We show that it is possible to use reinforcement learning to directly optimize for the intended information need, by rewarding outputs that allow a question answering model to provide correct answers to sampled user questions. We convert several visual question answering datasets into CapWAP datasets, and demonstrate that under a variety of scenarios our purposeful captioning system learns to anticipate and fulfill specific information needs better than its generic counterparts, as measured by QA performance on user questions from unseen images, when using the caption alone as context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.04264v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79138\", \"27110\"], \"outgoing_citations\": [\"132176\", \"165195\", \"168744\", \"168908\", \"171096\", \"172370\", \"173805\", \"174612\", \"181023\", \"182982\", \"183065\", \"192342\", \"197653\", \"207755\", \"217955\", \"238125\", \"239242\", \"241070\", \"241203\", \"244082\", \"244102\", \"249972\", \"253934\", \"266671\", \"268166\", \"271234\", \"277941\", \"278894\", \"278948\", \"278954\", \"302815\", \"303603\"]}","task_split":"paper_retrieval"}
{"document_id":"88245","document_content":"# Automorphic Equivalence-aware Graph Neural Network\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDistinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e.g., computational biologist and social network analysis. However, existing graph neural networks (GNNs) fail to capture such an important property. To make GNN aware of automorphic equivalence, we first introduce a localized variant of this concept -- ego-centered automorphic equivalence (Ego-AE). Then, we design a novel variant of GNN, i.e., GRAPE, that uses learnable AE-aware aggregators to explicitly differentiate the Ego-AE of each node's neighbors with the aids of various subgraph templates. While the design of subgraph templates can be hard, we further propose a genetic algorithm to automatically search them from graph data. Moreover, we theoretically prove that GRAPE is expressive in terms of generating distinct representations for nodes with different Ego-AE features, which fills in a fundamental gap of existing GNN variants. Finally, we empirically validate our model on eight real-world graph data, including social network, e-commerce co-purchase network, and citation network, and show that it consistently outperforms existing GNNs. The source code is public available at https:\/\/github.com\/tsinghua-fib-lab\/GRAPE.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.04218v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"7776\"], \"outgoing_citations\": [\"34974\", \"66588\", \"67813\", \"103085\", \"114134\", \"118201\", \"131562\", \"138212\", \"142226\", \"143456\", \"176579\", \"181255\", \"184749\", \"188300\", \"212171\", \"215435\", \"243437\", \"252195\", \"269202\", \"272361\", \"277250\", \"350606\"]}","task_split":"paper_retrieval"}
{"document_id":"88333","document_content":"# FairLens: Auditing Black-box Clinical Decision Support Systems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n- 68U99, 68T99\n- J.3; K.4.2\n## Abstract\nThe pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how our tool can be used to audit a fictional commercial black-box model acting as a clinical decision support system. In this scenario, the healthcare facility experts can use FairLens on their own historical data to discover the model's biases before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to attributes such as age, ethnicity, gender and insurance; it then assesses the model performance on such subgroups of patients identifying those in need of expert evaluation. Finally, building on recent state-of-the-art XAI (eXplainable Artificial Intelligence) techniques, FairLens explains which elements in patients' clinical history drive the model error in the selected subgroup. Therefore, FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitute potential fairness issues.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.04049v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\", \"68U99, 68T99\", \"J.3; K.4.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\", \"68U99, 68T99\", \"J.3; K.4.2\"], \"incoming_citations\": [\"1127\", \"19599\", \"45724\", \"80572\"], \"outgoing_citations\": [\"99183\", \"210204\", \"215523\", \"244340\", \"250187\", \"250523\", \"284408\", \"291026\", \"303053\", \"305328\", \"320503\"]}","task_split":"paper_retrieval"}
{"document_id":"88899","document_content":"# Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answering systems is still under exploration in both research academia and industry. In this paper, we propose a novel question-answering method by integrating multiple knowledge sources, i.e. ConceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance. More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources. Afterward, we use a pre-trained language model to encode the question, retrieved knowledge and choices, and propose an answer choice-aware attention mechanism to fuse all hidden representations of the previous modules. Finally, the linear classifier for specific tasks is used to predict the answer. Experimental results on the CommonsenseQA dataset show that our method significantly outperforms other competitive methods and achieves the new state-of-the-art. In addition, further ablation studies demonstrate the effectiveness of our graph-based iterative knowledge retrieval module and the answer choice-aware attention module in retrieving and synthesizing background knowledge from multiple knowledge sources.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.02705v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2158\", \"4851\", \"5072\", \"81791\"], \"outgoing_citations\": [\"127513\", \"127522\", \"145454\", \"154601\", \"157144\", \"161554\", \"162246\", \"164923\", \"165107\", \"167880\", \"168575\", \"171165\", \"174555\", \"180575\", \"182261\", \"186337\", \"189515\", \"211788\", \"212078\", \"216563\", \"218698\", \"220723\", \"234624\", \"238962\", \"259723\", \"266850\", \"267878\", \"270020\"]}","task_split":"paper_retrieval"}
{"document_id":"88924","document_content":"# Deep Active Learning with Augmentation-based Consistency Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn active learning, the focus is mainly on the selection strategy of unlabeled data for enhancing the generalization capability of the next learning cycle. For this, various uncertainty measurement methods have been proposed. On the other hand, with the advent of data augmentation metrics as the regularizer on general deep learning, we notice that there can be a mutual influence between the method of unlabeled data selection and the data augmentation-based regularization techniques in active learning scenarios. Through various experiments, we confirmed that consistency-based regularization from analytical learning theory could affect the generalization capability of the classifier in combination with the existing uncertainty measurement method. By this fact, we propose a methodology to improve generalization ability, by applying data augmentation-based techniques to an active learning scenario. For the data augmentation-based regularization loss, we redefined cutout (co) and cutmix (cm) strategies as quantitative metrics and applied at both model training and unlabeled data selection steps. We have shown that the augmentation-based regularizer can lead to improved performance on the training step of active learning, while that same approach can be effectively combined with the uncertainty measurement metrics proposed so far. We used datasets such as FashionMNIST, CIFAR10, CIFAR100, and STL10 to verify the performance of the proposed active learning technique for multiple image classification tasks. Our experiments show consistent performance gains for each dataset and budget scenario.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.02666v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"155531\", \"187087\", \"228536\", \"241443\", \"241444\", \"259604\", \"271988\"]}","task_split":"paper_retrieval"}
{"document_id":"89191","document_content":"# Face Morphing Attack Generation & Detection: A Comprehensive Survey\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Computers and Society\n## Abstract\nThe vulnerability of Face Recognition System (FRS) to various kind of attacks (both direct and in-direct attacks) and face morphing attacks has received a great interest from the biometric community. The goal of a morphing attack is to subvert the FRS at Automatic Border Control (ABC) gates by presenting the Electronic Machine Readable Travel Document (eMRTD) or e-passport that is obtained based on the morphed face image. Since the application process for the e-passport in the majority countries requires a passport photo to be presented by the applicant, a malicious actor and the accomplice can generate the morphed face image and to obtain the e-passport. An e-passport with a morphed face images can be used by both the malicious actor and the accomplice to cross the border as the morphed face image can be verified against both of them. This can result in a significant threat as a malicious actor can cross the border without revealing the track of his\/her criminal background while the details of accomplice are recorded in the log of the access control system. This survey aims to present a systematic overview of the progress made in the area of face morphing in terms of both morph generation and morph detection. In this paper, we describe and illustrate various aspects of face morphing attacks, including different techniques for generating morphed face images but also the state-of-the-art regarding Morph Attack Detection (MAD) algorithms based on a stringent taxonomy and finally the availability of public databases, which allow to benchmark new MAD algorithms in a reproducible manner. The outcomes of competitions\/benchmarking, vulnerability assessments and performance evaluation metrics are also provided in a comprehensive manner. Furthermore, we discuss the open challenges and potential future works that need to be addressed in this evolving field of biometrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.02045v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [\"6530\", \"7210\", \"7690\", \"13646\", \"27985\", \"37448\", \"102490\", \"60321\"], \"outgoing_citations\": [\"113588\", \"113950\", \"119444\", \"129348\", \"148504\", \"152969\", \"192020\", \"201625\", \"209503\", \"225647\", \"228434\"]}","task_split":"paper_retrieval"}
{"document_id":"89316","document_content":"# Representation Matters: Improving Perception and Exploration for Robotics\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nProjecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important to understand how the representation will be used by the agent and what properties such a 'good' representation should have. In this paper we systematically evaluate a number of common learnt and hand-engineered representations in the context of three robotics tasks: lifting, stacking and pushing of 3D blocks. The representations are evaluated in two use-cases: as input to the agent, or as a source of auxiliary tasks. Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement. We can significantly improve performance in both use-cases and demonstrate that some representations can perform commensurate to simulator states as agent inputs. Finally, our results challenge common intuitions by demonstrating that: 1) dimensionality strongly matters for task generation, but is negligible for inputs, 2) observability of task-relevant aspects mostly affects the input representation use-case, and 3) disentanglement leads to better auxiliary tasks, but has only limited benefits for input representations. This work serves as a step towards a more systematic understanding of what makes a 'good' representation for control in robotics, enabling practitioners to make more informed choices for developing new learned or hand-engineered representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01758v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"22761\", \"35918\", \"46081\"], \"outgoing_citations\": [\"108757\", \"124909\", \"132207\", \"152912\", \"154419\", \"160036\", \"160599\", \"162568\", \"177483\", \"177830\", \"178094\", \"178846\", \"179899\", \"196921\", \"202065\", \"202216\", \"203756\", \"207082\", \"207094\", \"208041\", \"224840\", \"225801\", \"226635\", \"230068\", \"231175\", \"240506\", \"241900\", \"246118\", \"259143\", \"260136\", \"261017\", \"261316\", \"261535\", \"261780\", \"261983\", \"266419\", \"269253\", \"270823\", \"279790\", \"280296\", \"280681\", \"281110\", \"293570\"]}","task_split":"paper_retrieval"}
{"document_id":"89428","document_content":"# BioNerFlair: biomedical named entity recognition using flair embedding and sequence tagger\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nMotivation: The proliferation of Biomedical research articles has made the task of information retrieval more important than ever. Scientists and Researchers are having difficulty in finding articles that contain information relevant to them. Proper extraction of biomedical entities like Disease, Drug\/chem, Species, Gene\/protein, can considerably improve the filtering of articles resulting in better extraction of relevant information. Performance on BioNer benchmarks has progressively improved because of progression in transformers-based models like BERT, XLNet, OpenAI, GPT2, etc. These models give excellent results; however, they are computationally expensive and we can achieve better scores for domain-specific tasks using other contextual string-based models and LSTM-CRF based sequence tagger. Results: We introduce BioNerFlair, a method to train models for biomedical named entity recognition using Flair plus GloVe embeddings and Bidirectional LSTM-CRF based sequence tagger. With almost the same generic architecture widely used for named entity recognition, BioNerFlair outperforms previous state-of-the-art models. I performed experiments on 8 benchmarks datasets for biomedical named entity recognition. Compared to current state-of-the-art models, BioNerFlair achieves the best F1-score of 90.17 beyond 84.72 on the BioCreative II gene mention (BC2GM) corpus, best F1-score of 94.03 beyond 92.36 on the BioCreative IV chemical and drug (BC4CHEMD) corpus, best F1-score of 88.73 beyond 78.58 on the JNLPBA corpus, best F1-score of 91.1 beyond 89.71 on the NCBI disease corpus, best F1-score of 85.48 beyond 78.98 on the Species-800 corpus, while near best results was observed on BC5CDR-chem, BC3CDR-disease, and LINNAEUS corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01504v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105587\", \"198222\", \"213077\", \"243880\", \"249726\", \"269294\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"89474","document_content":"# WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nIn the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results in Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01421v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"1442\", \"157393\", \"3068\", \"19613\", \"25615\", \"45920\", \"53276\", \"77963\", \"87030\"], \"outgoing_citations\": [\"87030\", \"91732\", \"109314\", \"132665\", \"154905\", \"157013\", \"170657\", \"203630\", \"211966\", \"244373\", \"266671\", \"267899\", \"279343\", \"298016\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"89480","document_content":"# Sampling and Recovery of Graph Signals based on Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n- Signal Processing\n## Abstract\nWe propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. To take informative measurements, we propose a new graph neural sampling module, which aims to select those vertices that maximally express their corresponding neighborhoods. Such expressiveness can be quantified by the mutual information between vertices' features and neighborhoods' features, which are estimated via a graph neural network. To reconstruct an original graph signal from the sampled measurements, we propose a graph neural recovery module based on the algorithm-unrolling technique. Compared to previous analytical sampling and recovery, the proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks; compared to previous neural-network-based sampling and recovery, the proposed methods are designed through exploiting specific graph properties and provide interpretability. We further design a new multiscale graph neural network, which is a trainable multiscale graph filter bank and can handle various graph-related learning tasks. The multiscale network leverages the proposed graph neural sampling and recovery modules to achieve multiscale representations of a graph. In the experiments, we illustrate the effects of the proposed graph neural sampling and recovery modules and find that the modules can flexibly adapt to various graph structures and graph signals. In the task of active-sampling-based semi-supervised learning, the graph neural sampling module improves the classification accuracy over 10% in Cora dataset. We further validate the proposed multiscale graph neural network on several standard datasets for both vertex and graph classification. The results show that our method consistently improves the classification accuracies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01412v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"eess.SP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"136834\", \"163169\", \"181265\", \"190159\", \"216376\", \"217873\", \"227067\", \"229462\", \"233653\", \"243791\", \"269273\", \"270231\", \"291301\", \"292407\", \"301369\", \"303246\", \"305647\", \"314178\", \"314327\", \"316551\", \"321216\", \"322618\", \"329554\", \"338271\", \"341149\", \"353328\"]}","task_split":"paper_retrieval"}
{"document_id":"89488","document_content":"# Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nNavigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localisation and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1080\/13875868.2020.1857386\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"263724\", \"275080\", \"290110\", \"333595\", \"334305\", \"334905\"]}","task_split":"paper_retrieval"}
{"document_id":"89490","document_content":"# GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nGraph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Graph neural network models generate node embeddings by merging nodes features with the aggregated neighboring nodes information. Most existing GNN models exploit a single type of aggregator (e.g., mean-pooling) to aggregate neighboring nodes information, and then add or concatenate the output of aggregator to the current representation vector of the center node. However, using only a single type of aggregator is difficult to capture the different aspects of neighboring information and the simple addition or concatenation update methods limit the expressive capability of GNNs. Not only that, existing supervised or semi-supervised GNN models are trained based on the loss function of the node label, which leads to the neglect of graph structure information. In this paper, we propose a novel graph neural network architecture, Graph Attention \\& Interaction Network (GAIN), for inductive learning on graphs. Unlike the previous GNN models that only utilize a single type of aggregation method, we use multiple types of aggregators to gather neighboring information in different aspects and integrate the outputs of these aggregators through the aggregator-level attention mechanism. Furthermore, we design a graph regularized loss to better capture the topological relationship of the nodes in the graph. Additionally, we first present the concept of graph feature interaction and propose a vector-wise explicit feature interaction mechanism to update the node embeddings. We conduct comprehensive experiments on two node-classification benchmarks and a real-world financial news dataset. The experiments demonstrate our GAIN model outperforms current state-of-the-art performances on all the tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01393v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"185584\", \"212054\", \"216376\", \"238422\", \"239029\", \"243437\", \"243791\", \"248444\", \"255946\", \"258393\", \"261201\", \"261876\", \"267030\", \"271665\", \"281163\", \"289623\", \"292407\", \"303167\", \"303863\", \"305707\", \"311030\", \"316095\", \"336185\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"89522","document_content":"# Useful Policy Invariant Shaping from Arbitrary Advice\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary RL research is to discover how to learn with less data. Previous work has shown that domain information can be successfully used to shape the reward; by adding additional reward information, the agent can learn with much less data. Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered. While such potential-based reward shaping (PBRS) holds promise, it is limited by the need for a well-defined potential function. Ideally, we would like to be able to take arbitrary advice from a human or other agent and improve performance without affecting the optimal policy. The recently introduced dynamic potential based advice (DPBA) method tackles this challenge by admitting arbitrary advice from a human or other agent and improves performance without affecting the optimal policy. The main contribution of this paper is to expose, theoretically and empirically, a flaw in DPBA. Alternatively, to achieve the ideal goals, we present a simple method called policy invariant explicit shaping (PIES) and show theoretically and empirically that PIES succeeds where DPBA fails.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01297v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"99547\", \"64051\", \"71959\", \"114431\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"89554","document_content":"# Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe availability of different pre-trained semantic models enabled the quick development of machine learning components for downstream applications. Despite the availability of abundant text data for low resource languages, only a few semantic models are publicly available. Publicly available pre-trained models are usually built as a multilingual version of semantic models that can not fit well for each language due to context variations. In this work, we introduce different semantic models for Amharic. After we experiment with the existing pre-trained semantic models, we trained and fine-tuned nine new different models using a monolingual text corpus. The models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and investigate their impact. We find that newly trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from RoBERTA perform better than the word2Vec models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/fi13110275\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42337\", \"87107\", \"133866\", \"137490\", \"147791\", \"154791\", \"186250\", \"191536\", \"241616\", \"241862\", \"255202\", \"255737\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"89612","document_content":"# Causal Campbell-Goodhart's law and Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- General Economics\n- Economics\n## Abstract\nCampbell-Goodhart's law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it. This is a well known error in Economics and Political Science but not widely labelled in Artificial Intelligence research. Through a simple example, we show how off-the-shelf deep Reinforcement Learning (RL) algorithms are not necessarily immune to this cognitive error. The off-policy learning method is tricked, whilst the on-policy method is not. The practical implication is that naive application of RL to complex real life problems can result in the same types of policy errors that humans make. Great care should be taken around understanding the causal model that underpins a solution derived from Reinforcement Learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.01010v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"econ.GN\", \"q-fin.EC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"General Economics\", \"Economics\"], \"incoming_citations\": [\"65457\"], \"outgoing_citations\": [\"143609\", \"209989\", \"239187\", \"239539\"]}","task_split":"paper_retrieval"}
{"document_id":"89629","document_content":"# Diverse Image Captioning with Context-Object Split Latent Spaces\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDiverse image captioning models aim to learn one-to-many mappings that are innate to cross-domain datasets, such as of images and texts. Current methods for this task are based on generative latent variable models, e.g. VAEs with structured latent spaces. Yet, the amount of multimodality captured by prior work is limited to that of the paired training data -- the true diversity of the underlying generative process is not fully captured. To address this limitation, we leverage the contextual descriptions in the dataset that explain similar contexts in different visual scenes. To this end, we introduce a novel factorization of the latent space, termed context-object split, to model diversity in contextual descriptions across images and texts within the dataset. Our framework not only enables diverse captioning through context-based pseudo supervision, but extends this to images with novel objects and without paired captions in the training data. We evaluate our COS-CVAE approach on the standard COCO dataset and on the held-out COCO dataset consisting of images with novel objects, showing significant gains in accuracy and diversity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00966v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"35427\", \"17988\", \"35257\", \"59530\"], \"outgoing_citations\": [\"141978\", \"167838\", \"170604\", \"171096\", \"171902\", \"189018\", \"193328\", \"205227\", \"227880\", \"228859\", \"229734\", \"236042\", \"237655\", \"249394\", \"249972\", \"258345\", \"260926\", \"262828\", \"270154\", \"278604\", \"278948\", \"287504\", \"289627\", \"295744\", \"302621\", \"303209\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"89686","document_content":"# An ontology-based chatbot for crises management: use case coronavirus\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nToday is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term \"disaster management\" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.02340v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"145384\", \"235725\", \"245831\"]}","task_split":"paper_retrieval"}
{"document_id":"89691","document_content":"# Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics\n## Categories\n- Computation and Language\n## Abstract\nBuilding robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality---indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00834v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2946\"], \"outgoing_citations\": [\"122841\", \"127834\", \"129611\", \"136096\", \"166000\", \"197253\", \"214570\", \"232245\", \"233612\", \"236032\", \"269890\", \"274095\", \"289267\"]}","task_split":"paper_retrieval"}
{"document_id":"89719","document_content":"# Actor and Action Modular Network for Text-based Video Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \\emph{semantic asymmetry}. The \\emph{semantic asymmetry} implies that two modalities contain different amounts of semantic information during the multi-modal fusion process. To alleviate this problem, we propose a novel actor and action modular network that individually localizes the actor and its action in two separate modules. Specifically, we first learn the actor-\/action-related content from the video and textual query, and then match them in a symmetrical manner to localize the target tube. The target tube contains the desired actor and action which is then fed into a fully convolutional network to predict segmentation masks of the actor. Our method also establishes the association of objects cross multiple frames with the proposed temporal proposal aggregation mechanism. This enables our method to segment the video effectively and keep the temporal consistency of predictions. The whole model is allowed for joint learning of the actor-action matching and segmentation, as well as achieves the state-of-the-art performance for both single-frame segmentation and full video segmentation on A2D Sentences and J-HMDB Sentences datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00786v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6553\"], \"outgoing_citations\": [\"41744\", \"49435\", \"70547\", \"130336\", \"134341\", \"150722\", \"152254\", \"158795\", \"166245\", \"172256\", \"179422\", \"189743\", \"191371\", \"196763\", \"202277\", \"206311\", \"211534\", \"216895\", \"223735\", \"225578\", \"226421\", \"235053\", \"238366\", \"244272\", \"248215\", \"259318\", \"260926\", \"267223\", \"269276\", \"270767\", \"273696\", \"275873\", \"278453\", \"279177\", \"280345\", \"287377\", \"287427\", \"294046\", \"295707\", \"296243\", \"300351\", \"300421\", \"300777\", \"303833\", \"328601\"]}","task_split":"paper_retrieval"}
{"document_id":"89759","document_content":"# Cross-Lingual Document Retrieval with Smooth Learning\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nCross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00701v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"58383\"], \"outgoing_citations\": [\"297041\", \"307136\", \"316977\", \"342907\"]}","task_split":"paper_retrieval"}
{"document_id":"89875","document_content":"# Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nComplex, multi-task problems have proven to be difficult to solve efficiently in a sparse-reward reinforcement learning setting. In order to be sample efficient, multi-task learning requires reuse and sharing of low-level policies. To facilitate the automatic decomposition of hierarchical tasks, we propose the use of step-by-step human demonstrations in the form of natural language instructions and action trajectories. We introduce a dataset of such demonstrations in a crafting-based grid world. Our model consists of a high-level language generator and low-level policy, conditioned on language. We find that human demonstrations help solve the most complex tasks. We also find that incorporating natural language allows the model to generalize to unseen tasks in a zero-shot setting and to learn quickly from a few demonstrations. Generalization is not only reflected in the actions of the agent, but also in the generated natural language instructions in unseen tasks. Our approach also gives our trained agent interpretable behaviors because it is able to generate a sequence of high-level descriptions of its actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00517v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"8658\", \"16789\"], \"outgoing_citations\": [\"161143\", \"173161\", \"180270\", \"181620\", \"182870\", \"206489\", \"209452\", \"240336\", \"241900\", \"243782\", \"247002\", \"248742\", \"249906\", \"251691\", \"259143\", \"259693\", \"263104\", \"263650\", \"266182\", \"269251\", \"281106\", \"282503\", \"311272\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"89900","document_content":"# Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels\n## Categories\n- Computation and Language\n## Abstract\nIn natural languages, words are used in association to construct sentences. It is not words in isolation, but the appropriate combination of hierarchical structures that conveys the meaning of the whole sentence. Neural networks can capture expressive language features; however, insights into the link between words and sentences are difficult to acquire automatically. In this work, we design a deep neural network architecture that explicitly wires lower and higher linguistic components; we then evaluate its ability to perform the same task at different hierarchical levels. Settling on broad text classification tasks, we show that our model, MHAL, learns to simultaneously solve them at different levels of granularity by fluidly transferring knowledge between hierarchies. Using a multi-head attention mechanism to tie the representations between single words and full sentences, MHAL systematically outperforms equivalent models that are not incentivized towards developing compositional representations. Moreover, we demonstrate that, with the proposed architecture, the sentence information flows naturally to individual words, allowing the model to behave like a sequence labeller (which is a lower, word-level task) even without any word supervision, in a zero-shot fashion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00470v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20888\"], \"outgoing_citations\": [\"169401\", \"170543\", \"192043\", \"210078\", \"210090\", \"220137\", \"221529\", \"224756\", \"233064\", \"265814\", \"267692\", \"268157\", \"271158\", \"272944\", \"278531\", \"281169\", \"294248\", \"295483\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"89918","document_content":"# A Parallel Approach for Real-Time Face Recognition from a Large Database\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nWe present a new facial recognition system, capable of identifying a person, provided their likeness has been previously stored in the system, in real time. The system is based on storing and comparing facial embeddings of the subject, and identifying them later within a live video feed. This system is highly accurate, and is able to tag people with their ID in real time. It is able to do so, even when using a database containing thousands of facial embeddings, by using a parallelized searching technique. This makes the system quite fast and allows it to be highly scalable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00443v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.DC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"89930","document_content":"# Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDeveloping high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often require large annotated datasets, which is not available in the biomedical domain. Transfer learning and multi-task learning have been shown to improve performance for low-resource domains. However, the applications of these methods are relatively scarce in the biomedical domain, and a theoretical understanding of why these methods improve the performance is lacking. In this study, we performed an extensive analysis to understand the transferability between different biomedical entity datasets. We found useful measures to predict transferability between these datasets. Besides, we propose combining transfer learning and multi-task learning to improve the performance of biomedical named entity recognition systems, which is not applied before to the best of our knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00425v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"129792\"], \"outgoing_citations\": [\"127479\", \"243880\", \"261027\", \"272944\", \"278531\"]}","task_split":"paper_retrieval"}
{"document_id":"89955","document_content":"# A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks\n## Categories\n- Medical Physics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction. We show that both models are capable of generating a reasonable uncertainty map, and, with our proposed scaling technique, creating interpretable uncertainties and bounds on the prediction and any relevant metrics. Performance-wise, bagging provides statistically significant reduced loss value and errors in most of the metrics investigated in this study. The addition of bagging was able to further reduce errors by another 0.34% for Dmean and 0.19% for Dmax, on average, when compared to the baseline framework. Overall, the bagging framework provided significantly lower MAE of 2.62, as opposed to the baseline framework's MAE of 2.87. The usefulness of bagging, from solely a performance standpoint, does highly depend on the problem and the acceptable predictive error, and its high upfront computational cost during training should be factored in to deciding whether it is advantageous to use it. In terms of deployment with uncertainty estimations turned on, both frameworks offer the same performance time of about 12 seconds. As an ensemble-based metaheuristic, bagging can be used with existing machine learning architectures to improve stability and performance, and MCDO can be applied to any deep learning models that have dropout as part of their architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00388v2\", \"primary_category\": \"physics.med-ph\", \"categories\": [\"physics.med-ph\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Medical Physics\", \"categories_human_readable\": [\"Medical Physics\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"12636\"], \"outgoing_citations\": [\"84205\", \"117389\", \"171556\", \"205655\", \"224323\", \"230457\", \"254791\"]}","task_split":"paper_retrieval"}
{"document_id":"89958","document_content":"# A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nA fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, each agent is itself constantly learning, leading to natural non-stationarity in the distribution of experiences encountered. In this paper, we propose a novel meta-multiagent policy gradient theorem that directly accounts for the non-stationary policy dynamics inherent to multiagent learning settings. This is achieved by modeling our gradient updates to consider both an agent's own non-stationary policy dynamics and the non-stationary policy dynamics of other agents in the environment. We show that our theoretically grounded approach provides a general solution to the multiagent learning problem, which inherently comprises all key aspects of previous state of the art approaches on this topic. We test our method on a diverse suite of multiagent benchmarks and demonstrate a more efficient ability to adapt to new agents as they learn than baseline methods across the full spectrum of mixed incentive, competitive, and cooperative domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00382v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"8026\", \"23578\", \"67873\", \"78473\", \"76\", \"3186\", \"3273\", \"3848\", \"31593\", \"43066\"], \"outgoing_citations\": [\"109379\", \"131702\", \"137119\", \"140801\", \"146533\", \"167748\", \"181403\", \"183710\", \"201508\", \"201680\", \"209279\", \"212647\", \"227728\", \"234177\", \"240780\", \"242097\", \"242190\", \"253658\", \"256103\", \"259927\", \"261577\", \"265505\", \"280202\", \"280870\", \"281257\", \"284459\", \"293995\", \"311595\"]}","task_split":"paper_retrieval"}
{"document_id":"89971","document_content":"# Is Transfer Learning Necessary for Protein Landscape Prediction?\n## Categories\n- Biomolecules\n- Machine Learning\n## Abstract\nRecently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein representation learning as a model's hidden layers produce potentially useful vector embeddings. TAPE introduced a number of benchmark tasks and showed that semi-supervised learning, via pretraining language models on a large protein corpus, improved performance on downstream tasks. Two of the tasks (fluorescence prediction and stability prediction) involve learning fitness landscapes. In this paper, we show that CNN models trained solely using supervised learning both compete with and sometimes outperform the best models from TAPE that leverage expensive pretraining on large protein datasets. These CNN models are sufficiently simple and small that they can be trained using a Google Colab notebook. We also find for the fluorescence task that linear regression outperforms our models and the TAPE models. The benchmarking tasks proposed by TAPE are excellent measures of a model's ability to predict protein function and should be used going forward. However, we believe it is important to add baselines from simple models to put the performance of the semi-supervised models that have been reported so far into perspective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.03443v1\", \"primary_category\": \"q-bio.BM\", \"categories\": [\"q-bio.BM\", \"cs.LG\"], \"primary_category_human_readable\": \"Biomolecules\", \"categories_human_readable\": [\"Biomolecules\", \"Machine Learning\"], \"incoming_citations\": [\"24943\", \"53659\", \"57182\"], \"outgoing_citations\": [\"115797\", \"141073\", \"179955\", \"197921\"]}","task_split":"paper_retrieval"}
{"document_id":"90074","document_content":"# Deep Reactive Planning in Dynamic Environments\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to cases where the robot's goal can change during execution. This is something that humans are naturally able to do. However, it is difficult for robots to learn such reflexes (i.e., to naturally respond to dynamic environments), especially when the goal location is not explicitly provided to the robot, and instead needs to be perceived through a vision sensor. In the current work, we present a method that can achieve such behavior by combining traditional kinematic planning, deep learning, and deep reinforcement learning in a synergistic fashion to generalize to arbitrary environments. We demonstrate the proposed approach for several reaching and pick-and-place tasks in simulation, as well as on a real system of a 6-DoF industrial manipulator. A video describing our work could be found \\url{https:\/\/youtu.be\/hE-Ew59GRPQ}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00155v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"75000\"], \"outgoing_citations\": [\"118301\", \"138752\", \"155441\", \"160156\", \"163705\", \"195243\", \"213622\", \"216429\", \"226166\", \"238323\", \"253600\", \"271850\", \"277386\", \"277822\", \"287020\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"90077","document_content":"# Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, where color is an important discriminative feature. In this paper, we propose an adaptive color augmentation technique to amplify data expression and model performance, while regulating color difference and saturation to minimize the risks of using synthetic data. Through deep visualization, we qualitatively identify and verify the semantic structural features learned by the network for discriminating skin lesions against normal skin tissue. The overall system achieves a Dice Ratio of 0.891 with 0.943 sensitivity and 0.932 specificity on the ISIC 2018 Testing Set for segmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISBI45749.2020.9098344\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"199630\", \"201512\", \"209140\", \"220335\", \"229319\", \"254665\", \"271814\", \"279331\", \"310759\"]}","task_split":"paper_retrieval"}
{"document_id":"90103","document_content":"# Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments\n## Categories\n- Machine Learning\n## Abstract\nFor mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed multi-domain measurements such as diagnostic symptoms and patient functioning scores. Additionally, substantial heterogeneity in the disease diagnosis between patients needs to be addressed for optimizing individualized treatment policy in order to achieve precision medicine. To address these challenges, we propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual. This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states). It allows incorporation of the multivariate pre- and post-treatment outcomes as well as biological measures while preserving the invariant structure for representing patients' latent mental states. A multi-layer neural network is used to allow complex treatment effect heterogeneity. Optimal treatment policy can be inferred for future patients by comparing their potential mental states under different treatments given the observed multi-domain pre-treatment measurements. Experiments on simulated data and a real-world clinical trial data show that the learned treatment polices compare favorably to alternative methods on heterogeneous treatment effects, and have broad utilities which lead to better patient outcomes on multiple domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.00094v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"90341\"], \"outgoing_citations\": [\"182335\", \"244024\", \"263409\", \"265530\", \"290547\"]}","task_split":"paper_retrieval"}
{"document_id":"90152","document_content":"# Wide-angle Image Rectification: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nWide field-of-view (FOV) cameras, which capture a larger scene area than narrow FOV cameras, are used in many applications including 3D reconstruction, autonomous driving, and video surveillance. However, wide-angle images contain distortions that violate the assumptions underlying pinhole camera models, resulting in object distortion, difficulties in estimating scene distance, area, and direction, and preventing the use of off-the-shelf deep models trained on undistorted images for downstream computer vision tasks. Image rectification, which aims to correct these distortions, can solve these problems. In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods. Specifically, we first present a detailed description and discussion of the camera models used in different approaches. Then, we summarize several distortion models including radial distortion and projection distortion. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods, where the former formulate distortion parameter estimation as an optimization problem and the latter treat it as a regression problem by leveraging the power of deep neural networks. We evaluate the performance of state-of-the-art methods on public datasets and show that although both kinds of methods can achieve good results, these methods only work well for specific camera models and distortion types. We also provide a strong baseline model and carry out an empirical study of different distortion models on synthetic datasets and real-world wide-angle images. Finally, we discuss several potential research directions that are expected to further advance this area in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.12108v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"83963\"], \"outgoing_citations\": [\"83963\", \"111995\", \"134989\", \"156244\", \"166956\", \"168050\", \"174399\", \"189469\", \"198751\", \"221925\", \"223570\", \"231268\", \"235751\", \"244327\", \"268068\", \"279420\", \"285393\", \"286320\"]}","task_split":"paper_retrieval"}
{"document_id":"90277","document_content":"# Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN into a joint node set representation. In this paper, we show a fundamental constraint of such an approach, namely the inability to capture the dependence between nodes in the node set, and argue that directly aggregating individual node representations does not lead to an effective joint representation for multiple nodes. Then, we notice that a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, and ID-GNN, all used node labeling. These methods first label nodes in the graph according to their relationships with the target node set before applying a GNN. Then, the node representations obtained in the labeled graph are aggregated into a node set representation. By investigating their inner mechanisms, we unify these node labeling techniques into a single and most general form -- labeling trick. We prove that with labeling trick a sufficiently expressive GNN learns the most expressive node set representations, thus in principle solves any joint learning tasks over node sets. Experiments on one important two-node representation learning task, link prediction, verified our theory. Our work explains the superior performance of previous node-labeling-based methods, and establishes a theoretical foundation of using GNNs for multi-node representation learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.16103v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"42979\", \"7220\"], \"outgoing_citations\": [\"42514\", \"43770\", \"47106\", \"73530\", \"117690\", \"127525\", \"143456\", \"156074\", \"159292\", \"163945\", \"177249\", \"181255\", \"183719\", \"184286\", \"188760\", \"215435\", \"227067\", \"240758\", \"269202\", \"296356\", \"316630\", \"350606\"]}","task_split":"paper_retrieval"}
{"document_id":"90284","document_content":"# Health improvement framework for planning actionable treatment process using surrogate Bayesian model\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nClinical decision making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. However, the remaining prominent issue is the development of objective treatment processes in clinical situations. This study proposes a novel framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the \"actionability\" for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluated the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework was applied to an actual health checkup dataset comprising data from 3,132 participants, to improve systolic blood pressure values at the individual level. We confirmed that the computed treatment processes are actionable and consistent with clinical knowledge for lowering blood pressure. These results demonstrate that our framework could contribute toward decision making in the medical field, providing clinicians with deeper insights.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1038\/s41467-021-23319-1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177711\", \"217462\", \"246394\", \"246729\", \"251709\", \"354944\"]}","task_split":"paper_retrieval"}
{"document_id":"90328","document_content":"# Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nHoney Encryption is an approach to encrypt the messages using low min-entropy keys, such as weak passwords, OTPs, PINs, credit card numbers. The ciphertext is produces, when decrypted with any number of incorrect keys, produces plausible-looking but bogus plaintext called \"honey messages\". But the current techniques used in producing the decoy plaintexts do not model human language entirely. A gibberish, random assortment of words is not enough to fool an attacker; that will not be acceptable and convincing, whether or not the attacker knows some information of the genuine source.   In this paper, I focus on the plaintexts which are some non-numeric informative messages. In order to fool the attacker into believing that the decoy message can actually be from a certain source, we need to capture the empirical and contextual properties of the language. That is, there should be no linguistic difference between real and fake message, without revealing the structure of the real message. I employ natural language processing and generalized differential privacy to solve this problem. Mainly I focus on machine learning methods like keyword extraction, context classification, bags-of-words, word embeddings, transformers for text processing to model privacy for text documents. Then I prove the security of this approach with e-differential privacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.15985v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [\"19098\"], \"outgoing_citations\": [\"183694\", \"208709\"]}","task_split":"paper_retrieval"}
{"document_id":"90476","document_content":"# Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent cameras, i.e., the Dynamic and Active-pixel Vision Sensor (DAVIS) ones, capture the intensity changes in the scene and generates a stream of events in an asynchronous fashion. The output rate of such cameras can reach up to 10 million events per second in high dynamic environments. DAVIS cameras use novel vision sensors that mimic human eyes. Their attractive attributes, such as high output rate, High Dynamic Range (HDR), and high pixel bandwidth, make them an ideal solution for applications that require high-frequency tracking. Moreover, applications that operate in challenging lighting scenarios can exploit the high HDR of event cameras, i.e., 140 dB compared to 60 dB of traditional cameras. In this paper, a novel asynchronous corner tracking method is proposed that uses both events and intensity images captured by a DAVIS camera. The Harris algorithm is used to extract features, i.e., frame-corners from keyframes, i.e., intensity images. Afterward, a matching algorithm is used to extract event-corners from the stream of events. Events are solely used to perform asynchronous tracking until the next keyframe is captured. Neighboring events, within a window size of 5x5 pixels around the event-corner, are used to calculate the velocity and direction of extracted event-corners by fitting the 2D planar using a randomized Hough transform algorithm. Experimental evaluation showed that our approach is able to update the location of the extracted corners up to 100 times during the blind time of traditional cameras, i.e., between two consecutive intensity images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.15510v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25099\", \"52214\"], \"outgoing_citations\": [\"281870\"]}","task_split":"paper_retrieval"}
{"document_id":"90641","document_content":"# On Graph Neural Networks versus Graph-Augmented MLPs\n## Categories\n- Machine Learning\n- Combinatorics\n## Abstract\nFrom the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion. From the perspective of graph isomorphism testing, we show both theoretically and numerically that GA-MLPs with suitable operators can distinguish almost all non-isomorphic graphs, just like the Weifeiler-Lehman (WL) test. However, by viewing them as node-level functions and examining the equivalence classes they induce on rooted graphs, we prove a separation in expressive power between GA-MLPs and GNNs that grows exponentially in depth. In particular, unlike GNNs, GA-MLPs are unable to count the number of attributed walks. We also demonstrate via community detection experiments that GA-MLPs can be limited by their choice of operator family, as compared to GNNs with higher flexibility in learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.15116v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.CO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Combinatorics\", \"Machine Learning\"], \"incoming_citations\": [\"89083\", \"52113\", \"7929\", \"48316\", \"12967\", \"32247\", \"42715\"], \"outgoing_citations\": [\"103085\", \"114193\", \"118201\", \"118851\", \"120001\", \"129410\", \"132795\", \"137772\", \"140399\", \"142226\", \"143081\", \"143456\", \"159315\", \"159599\", \"177249\", \"181255\", \"182323\", \"183719\", \"184286\", \"184400\", \"184749\", \"185055\", \"186624\", \"186769\", \"191735\", \"201448\", \"214311\", \"215435\", \"228663\", \"243382\", \"328683\", \"343291\"]}","task_split":"paper_retrieval"}
{"document_id":"90653","document_content":"# Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- I.2.6, I.5.4\n- I.2.6; I.5.4\n## Abstract\nIdentifying anomalies in the fuel consumption of the vehicles of a fleet is a crucial aspect for optimizing consumption and reduce costs. However, this information alone is insufficient, since fleet operators need to know the causes behind anomalous fuel consumption. We combine unsupervised anomaly detection techniques, domain knowledge and interpretable Machine Learning models for explaining potential causes of abnormal fuel consumption in terms of feature relevance. The explanations are used for generating recommendations about fuel optimization, that are adjusted according to two different user profiles: fleet managers and fleet operators. Results are evaluated over real-world data from telematics devices connected to diesel and petrol vehicles from different types of industrial fleets. We measure the proposal regarding model performance, and using Explainable AI metrics that compare the explanations in terms of representativeness, fidelity, stability, contrastiveness and consistency with apriori beliefs. The potential fuel reductions that can be achieved is round 35%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.16051v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"I.2.6, I.5.4\", \"I.2.6; I.5.4\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"I.2.6, I.5.4\", \"I.2.6; I.5.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"44549\", \"119438\", \"155090\", \"165861\", \"168243\", \"179578\", \"179919\", \"200101\", \"206349\", \"218100\", \"227415\", \"242562\"]}","task_split":"paper_retrieval"}
{"document_id":"90803","document_content":"# Deep Learning for Individual Heterogeneity: An Automatic Inference Framework\n## Categories\n- Econometrics\n- Statistics Theory\n- Machine Learning\n- Statistics Theory\n- Machine Learning\n## Abstract\nWe develop methodology for estimation and inference using machine learning to enrich economic models. Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or complex observable characteristics. These \"parameter functions\" retain the interpretability, economic meaning, and discipline of classical parameters. Deep learning is particularly well-suited to structured modeling of heterogeneity in economics. We show how to design the network architecture to match the structure of the economic model, delivering novel methodology that moves deep learning beyond prediction. We prove convergence rates for the estimated parameter functions. These functions are the key inputs into the finite-dimensional parameter of inferential interest. We obtain inference based on a novel influence function calculation that covers any second-stage parameter and any machine-learning-enriched model that uses a smooth per-observation loss function. No additional derivations are required. The score can be taken directly to data, using automatic differentiation if needed. The researcher need only define the original model and define the parameter of interest. A key insight is that we need not write down the influence function in order to evaluate it on the data. Our framework gives new results for a host of contexts, covering such diverse examples as price elasticities, willingness-to-pay, and surplus measures in binary or multinomial choice models, effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production functions, and more. We apply our methodology to a large scale advertising experiment for short-term loans. We show how economically meaningful estimates and inferences can be made that would be unavailable without our results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14694v2\", \"primary_category\": \"econ.EM\", \"categories\": [\"math.ST\", \"econ.EM\", \"stat.ML\", \"stat.TH\", \"cs.LG\"], \"primary_category_human_readable\": \"Econometrics\", \"categories_human_readable\": [\"Statistics Theory\", \"Econometrics\", \"Machine Learning\", \"Statistics Theory\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"77761\", \"100891\", \"112539\", \"131984\", \"184808\", \"201661\", \"216470\", \"228249\", \"237872\", \"242676\", \"257973\", \"259015\", \"283379\", \"293473\", \"319606\"]}","task_split":"paper_retrieval"}
{"document_id":"90853","document_content":"# Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications\n## Categories\n- Robotics\n- Machine Learning\n- Systems and Control\n- Systems and Control\n## Abstract\nReinforcement Learning (RL) is widely utilized in the field of robotics, and as such, it is gradually being implemented in the Hybrid Electric Vehicle (HEV) supervisory control. Even though RL exhibits excellent performance in terms of fuel consumption minimization in simulation, the large learning iteration number needs a long learning time, making it hardly applicable in real-world vehicles. In addition, the fuel consumption of initial learning phases is much worse than baseline controls. This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods. Different from previous studies, which initiated Q-learning with zero or random Q values, this study initiates the Q-learning with different supervisory controls (i.e., Equivalent Consumption Minimization Strategy control and heuristic control), and detailed analysis is given. The results show that the proposed warm start Q-learning requires 68.8% fewer iterations than cold start Q-learning. The trained Q-learning is validated in two different driving cycles, and the results show 10-16% MPG improvement when compared to Equivalent Consumption Minimization Strategy control. Furthermore, real-time feasibility is analyzed, and the guidance of vehicle implementation is provided. The results of this study can be used to facilitate the deployment of RL in vehicle supervisory control applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TTE.2020.3019009.\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"eess.SY\", \"cs.RO\", \"cs.SY\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Systems and Control\", \"Robotics\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"198781\", \"227991\", \"268739\", \"283410\", \"296214\"]}","task_split":"paper_retrieval"}
{"document_id":"90871","document_content":"# Unsupervised Domain Adaptation for Visual Navigation\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nAdvances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide variety of tasks involving structural and semantic reasoning. However, most learning-based navigation policies are trained and tested in simulation environments. In order for these policies to be practically useful, they need to be transferred to the real-world. In this paper, we propose an unsupervised domain adaptation method for visual navigation. Our method translates the images in the target domain to the source domain such that the translation is consistent with the representations learned by the navigation policy. The proposed method outperforms several baselines across two different navigation tasks in simulation. We further show that our method can be used to transfer the navigation policies learned in simulation to the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14543v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"16494\", \"40343\", \"63124\"], \"outgoing_citations\": [\"114824\", \"117896\", \"123031\", \"131814\", \"149759\", \"158547\", \"160530\", \"167688\", \"179954\", \"180898\", \"185747\", \"192738\", \"195772\", \"196393\", \"198524\", \"207499\", \"208042\", \"208773\", \"214195\", \"219658\", \"224223\", \"228916\", \"231955\", \"234287\", \"243597\", \"245731\", \"247418\", \"247749\", \"247900\", \"248225\", \"248730\", \"248742\", \"249906\", \"255139\", \"255575\", \"263104\", \"263270\", \"269388\", \"271419\", \"272553\", \"274102\", \"276597\", \"277386\", \"280681\", \"280996\", \"281110\", \"284468\", \"284565\", \"302720\"]}","task_split":"paper_retrieval"}
{"document_id":"90880","document_content":"# COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nReinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amount of robot data we can collect for any single task is limited by time and cost considerations, the learned behavior is typically narrow: the policy can only execute the task in a handful of scenarios that it was trained on. What if there was a way to incorporate a large amount of prior data, either from previously solved tasks or from unsupervised or undirected environment interaction, to extend and generalize learned behaviors? While most prior work on extending robotic skills using pre-collected data focuses on building explicit hierarchies or skill decompositions, we show in this paper that we can reuse prior data to extend new skills simply through dynamic programming. We show that even when the prior data does not actually succeed at solving the new task, it can still be utilized for learning a better policy, by providing the agent with a broader understanding of the mechanics of its environment. We demonstrate the effectiveness of our approach by chaining together several behaviors seen in prior datasets for solving a new task, with our hardest experimental setting involving composing four robotic skills in a row: picking, placing, drawer opening, and grasping, where a +1\/0 sparse reward is provided only on task completion. We train our policies in an end-to-end fashion, mapping high-dimensional image observations to low-level robot control commands, and present results in both simulated and real world domains. Additional materials and source code can be found on our project website: https:\/\/sites.google.com\/view\/cog-rl","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14500v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"4258\", \"14284\", \"17889\", \"4444\", \"10424\", \"15055\", \"19780\", \"21756\", \"22978\", \"27991\", \"32240\", \"34081\", \"38148\", \"55721\", \"61683\", \"63685\", \"63851\", \"68900\"], \"outgoing_citations\": [\"118157\", \"120201\", \"122699\", \"127099\", \"129794\", \"137034\", \"154254\", \"156612\", \"164054\", \"167147\", \"176738\", \"178278\", \"181081\", \"182796\", \"190237\", \"191065\", \"197390\", \"206160\", \"206875\", \"207601\", \"222669\", \"224142\", \"224293\", \"226635\", \"227314\", \"237605\", \"254597\", \"280383\", \"283026\", \"283391\", \"283394\", \"283427\", \"296980\", \"306156\"]}","task_split":"paper_retrieval"}
{"document_id":"90890","document_content":"# One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL\n## Categories\n- Machine Learning\n## Abstract\nWhile reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations are not explicitly provided during training. One natural approach to this problem is to train agents with manually specified variation in the training task or environment. However, this may be infeasible in practical situations, either because making perturbations is not possible, or because it is unclear how to choose suitable perturbation strategies without sacrificing performance. The key insight of this work is that learning diverse behaviors for accomplishing a task can directly lead to behavior that generalizes to varying environments, without needing to perform explicit perturbations during training. By identifying multiple solutions for the task in a single environment during training, our approach can generalize to new situations by abandoning solutions that are no longer effective and adopting those that are. We theoretically characterize a robustness set of environments that arises from our algorithm and empirically find that our diversity-driven approach can extrapolate to various changes in the environment and task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14484v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"43980\", \"17388\", \"23112\", \"32551\", \"35834\", \"63523\", \"79803\", \"4456\", \"15055\", \"26894\", \"35179\", \"45631\", \"50413\"], \"outgoing_citations\": [\"152041\", \"157288\", \"159298\", \"161071\", \"162581\", \"163862\", \"164077\", \"177830\", \"184592\", \"192966\", \"201518\", \"207051\", \"208234\", \"215780\", \"216076\", \"225023\", \"228299\", \"235092\", \"237229\", \"241496\", \"241900\", \"247801\", \"270529\", \"272030\", \"272032\", \"274445\", \"274487\", \"274762\", \"279790\", \"280570\", \"280870\", \"283289\", \"283410\", \"311650\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"90967","document_content":"# Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nCross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages. We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC. Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model's cross-lingual ability. Meanwhile, the produced single multilingual model is applicable to all target languages, which saves the cost of training, inference, and maintenance for multiple models. Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14271v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"4248\"], \"outgoing_citations\": [\"128230\", \"129799\", \"133328\", \"157641\", \"159706\", \"161064\", \"161377\", \"166781\", \"168995\", \"169239\", \"177377\", \"182594\", \"184190\", \"197295\", \"217191\", \"217957\", \"218485\", \"220570\", \"234577\", \"250547\", \"251798\", \"266850\", \"267031\", \"278104\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"91004","document_content":"# A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation\n## Categories\n- Machine Learning\n## Abstract\nThe idea behind the \\emph{unsupervised} learning of \\emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over $14000$ models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties \"encouraged\" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered \"disentangled\" and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14766v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"315\", \"38128\", \"483\", \"18189\", \"65221\", \"66630\", \"69932\", \"78548\", \"118768\", \"69639\", \"118821\"], \"outgoing_citations\": [\"143581\", \"147210\", \"160471\", \"176649\", \"181909\", \"183257\", \"183696\", \"183732\", \"186021\", \"187863\", \"206218\", \"207094\", \"210434\", \"218829\", \"224840\", \"225801\", \"228466\", \"231027\", \"233751\", \"239658\", \"241933\", \"242142\", \"242239\", \"251597\", \"253197\", \"255129\", \"260136\", \"261512\", \"264782\", \"264963\", \"265526\", \"280734\", \"292209\", \"295435\", \"297838\", \"300606\", \"311052\", \"311482\", \"316183\", \"319797\", \"320031\", \"321465\", \"333469\", \"353174\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"91061","document_content":"# Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection\n## Categories\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nFrequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user\\u2019s query that is likely to reoccur frequently. To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure. In this paper, we extend this line of research by combining the clues gathered from the q-Q similarity measure and the q-A relevance measure and meanwhile injecting extra word interaction information, distilled from a generic (open domain) knowledge base, into a contextual language model for inferring the q-A relevance. Furthermore, we also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner, acting as a surrogate to the supervised domain-specific knowledge base information. As such, it enables the model to equip sentence representations with the knowledge about domain-specific and topically-relevant relations among words, thereby providing a better q-A relevance measure. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset, and further apply and contextualize it to a large-scale question-matching task, which aims to search questions from a QA dataset that have a similar intent as an input query. Extensive experimental results on these two datasets confirm the promising performance of the proposed approach in relation to some state-of-the-art ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.14049v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"144530\", \"166443\", \"168182\", \"185889\", \"187337\", \"196257\", \"349865\"]}","task_split":"paper_retrieval"}
{"document_id":"91095","document_content":"# Predict and Use Latent Patterns for Short-Text Conversation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMany neural network models nowadays have achieved promising performances in Chit-chat settings. The majority of them rely on an encoder for understanding the post and a decoder for generating the response. Without given assigned semantics, the models lack the fine-grained control over responses as the semantic mapping between posts and responses is hidden on the fly within the end-to-end manners. Some previous works utilize sampled latent words as a controllable semantic form to drive the generated response around the work, but few works attempt to use more complex semantic patterns to guide the generation. In this paper, we propose to use more detailed semantic forms, including latent responses and part-of-speech sequences sampled from the corresponding distributions, as the controllable semantics to guide the generation. Our results show that the richer semantics are not only able to provide informative and diverse responses, but also increase the overall performance of response quality, including fluency and coherence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13982v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"104906\", \"127566\", \"154886\", \"166480\", \"174687\", \"184148\", \"207427\", \"210158\", \"211746\", \"221363\", \"227586\", \"254855\", \"256448\", \"270040\", \"275597\", \"279543\", \"282136\", \"289061\", \"289943\", \"291102\", \"299981\", \"302815\", \"305200\", \"310740\", \"310903\", \"316328\", \"325200\"]}","task_split":"paper_retrieval"}
{"document_id":"91141","document_content":"# Word Frequency Does Not Predict Grammatical Knowledge in Language Models\n## Categories\n- Computation and Language\n## Abstract\nNeural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages. In this work, we investigate whether there are systematic sources of variation in the language models' accuracy. Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models. Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun's performance on grammatical tasks. Finally, we find that a novel noun's grammatical properties can be few-shot learned from various types of training data. The results present a paradox: there should be less variation in grammatical performance than is actually observed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13870v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23484\", \"54783\"], \"outgoing_citations\": [\"127822\", \"168415\", \"195989\", \"196722\", \"219586\", \"220137\", \"220460\", \"229768\", \"237283\", \"281225\"]}","task_split":"paper_retrieval"}
{"document_id":"91154","document_content":"# VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nWe present VisualHints, a novel environment for multimodal reinforcement learning (RL) involving text-based interactions along with visual hints (obtained from the environment). Real-life problems often demand that agents interact with the environment using both natural language information and visual perception towards solving a goal. However, most traditional RL environments either solve pure vision-based tasks like Atari games or video-based robotic manipulation; or entirely use natural language as a mode of interaction, like Text-based games and dialog systems. In this work, we aim to bridge this gap and unify these two approaches in a single environment for multimodal RL. We introduce an extension of the TextWorld cooking environment with the addition of visual clues interspersed throughout the environment. The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal. We enable variations and difficulties in our environment to emulate various interactive real-world scenarios. We present a baseline multimodal agent for solving such problems using CNN-based feature extraction from visual hints and LSTMs for textual feature extraction. We believe that our proposed visual-lingual environment will facilitate novel problem settings for the RL community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13839v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"36587\"], \"outgoing_citations\": [\"103484\", \"140959\", \"145805\", \"152871\", \"168729\", \"171942\", \"192738\", \"207273\", \"218923\", \"226321\", \"226327\", \"247418\", \"256632\", \"271868\", \"285454\", \"303349\", \"310370\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"91204","document_content":"# Forethought and Hindsight in Credit Assignment\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13685v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"111065\", \"13922\", \"19780\", \"42741\", \"32526\", \"114310\", \"113050\"], \"outgoing_citations\": [\"103847\", \"111431\", \"111603\", \"113050\", \"114310\", \"120385\", \"121680\", \"139425\", \"152614\", \"155499\", \"156924\", \"167774\", \"181086\", \"192743\", \"192776\", \"197462\", \"199779\", \"201399\", \"209989\", \"214159\", \"218361\", \"219180\", \"225108\", \"228303\", \"230669\", \"237044\", \"261493\", \"273312\", \"277193\", \"315982\", \"317745\", \"350435\", \"357891\"]}","task_split":"paper_retrieval"}
{"document_id":"91301","document_content":"# On Embodied Visual Navigation in Real Environments Through Habitat\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations through reinforcement learning. Unfortunately, collecting the required experience in the real world requires the deployment of a robotic platform, which is expensive and time-consuming. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. Despite the advantages they offer, simulators present a limited realism in terms of appearance and physical dynamics, leading to navigation policies that do not generalize in the real world.   In this paper, we propose a tool based on the Habitat simulator which exploits real world images of the environment, together with sensor and actuator noise models, to produce more realistic navigation episodes. We perform a range of experiments to assess the ability of such policies to generalize using virtual and real-world images, as well as observations transformed with unsupervised domain adaptation approaches. We also assess the impact of sensor and actuation noise on the navigation performance and investigate whether it allows to learn more robust navigation policies. We show that our tool can effectively help to train and evaluate navigation policies on real-world observations without running navigation pisodes in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13439v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"40343\", \"49968\", \"63124\"], \"outgoing_citations\": [\"118301\", \"131180\", \"131814\", \"158547\", \"180898\", \"185463\", \"192738\", \"196393\", \"219658\", \"224223\", \"245731\", \"247418\", \"247749\", \"255575\", \"259869\", \"271030\", \"274102\", \"279420\", \"280570\", \"284565\", \"303155\"]}","task_split":"paper_retrieval"}
{"document_id":"91487","document_content":"# How to Make Deep RL Work in Practice\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nIn recent years, challenging control problems became solvable with deep reinforcement learning (RL). To be able to use RL for large-scale real-world applications, a certain degree of reliability in their performance is necessary. Reported results of state-of-the-art algorithms are often difficult to reproduce. One reason for this is that certain implementation details influence the performance significantly. Commonly, these details are not highlighted as important techniques to achieve state-of-the-art performance. Additionally, techniques from supervised learning are often used by default but influence the algorithms in a reinforcement learning setting in different and not well-understood ways. In this paper, we investigate the influence of certain initialization, input normalization, and adaptive learning techniques on the performance of state-of-the-art RL algorithms. We make suggestions which of those techniques to use by default and highlight areas that could benefit from a solution specifically tailored to RL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.13083v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"9804\", \"33023\", \"54181\", \"90194\"], \"outgoing_citations\": [\"190536\", \"193763\", \"226635\", \"227194\", \"240664\", \"246118\", \"248619\", \"255531\", \"258834\", \"293995\", \"336223\"]}","task_split":"paper_retrieval"}
{"document_id":"91554","document_content":"# Improved Actor Relation Graph based Group Activity Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nVideo understanding is to recognize and classify different actions or activities appearing in the video. A lot of previous work, such as video captioning, has shown promising performance in producing general video understanding. However, it is still challenging to generate a fine-grained description of human actions and their interactions using state-of-the-art video captioning techniques. The detailed description of human actions and group activities is essential information, which can be used in real-time CCTV video surveillance, health care, sports video analysis, etc. This study proposes a video understanding method that mainly focused on group activity recognition by learning the pair-wise actor appearance similarity and actor positions. We propose to use Normalized cross-correlation (NCC) and the sum of absolute differences (SAD) to calculate the pair-wise appearance similarity and build the actor relationship graph to allow the graph convolution network to learn how to classify group activities. We also propose to use MobileNet as the backbone to extract features from each video frame. A visualization model is further introduced to visualize each input video frame with predicted bounding boxes on each human object and predict individual action and collective activity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12968v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"91571","document_content":"# Disease Normalization with Graph Embeddings\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- 68T50\n- I.2.7; J.3\n## Abstract\nThe detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH's graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12925v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"68T50\", \"I.2.7; J.3\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"68T50\", \"I.2.7; J.3\"], \"incoming_citations\": [\"9763\"], \"outgoing_citations\": [\"192402\", \"243880\", \"271527\", \"297150\"]}","task_split":"paper_retrieval"}
{"document_id":"91624","document_content":"# When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models\n## Categories\n- Computation and Language\n## Abstract\nTransfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12858v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"306609\", \"4765\", \"16217\", \"16702\", \"23590\", \"26304\", \"55203\", \"13565\", \"24660\", \"38116\", \"41559\", \"44951\", \"46524\", \"60936\", \"77646\"], \"outgoing_citations\": [\"95793\", \"97546\", \"97822\", \"119541\", \"124265\", \"127811\", \"130205\", \"136604\", \"139404\", \"150286\", \"150830\", \"157123\", \"182594\", \"185852\", \"192143\", \"192418\", \"200464\", \"200678\", \"210680\", \"281121\", \"298884\"]}","task_split":"paper_retrieval"}
{"document_id":"91667","document_content":"# X-Class: Text Classification with Extremely Weak Supervision\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspective -- ideal document representations should lead to nearly the same results between clustering and the desired classification. In particular, one can classify the same corpus differently (e.g., based on topics and locations), so document representations should be adaptive to the given class names. We propose a novel framework X-Class to realize the adaptive representations. Specifically, we first estimate class representations by incrementally adding the most similar word to each class until inconsistency arises. Following a tailored mixture of class attention mechanisms, we obtain the document representation via a weighted average of contextualized word representations. With the prior of each document assigned to its nearest class, we then cluster and align the documents to classes. Finally, we pick the most confident documents from each cluster to train a text classifier. Extensive experiments demonstrate that X-Class can rival and even outperform seed-driven weakly supervised methods on 7 benchmark datasets. Our dataset and code are released at https:\/\/github.com\/ZihanWangKi\/XClass\/ .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12794v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"10979\", \"6768\", \"18663\", \"21740\", \"54719\"], \"outgoing_citations\": [\"94280\", \"133033\", \"169179\", \"169329\", \"171027\", \"202032\", \"204499\", \"219485\"]}","task_split":"paper_retrieval"}
{"document_id":"91719","document_content":"# Learning Guidance Rewards with Trajectory-space Smoothing\n## Categories\n- Machine Learning\n## Abstract\nLong-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and help with credit assignment. However, they struggle to solve tasks with delays between an action and the corresponding rewarding feedback. To make credit assignment easier, recent works have proposed algorithms to learn dense \"guidance\" rewards that could be used in place of the sparse or delayed environmental rewards. This paper is in the same vein -- starting with a surrogate RL objective that involves smoothing in the trajectory-space, we arrive at a new algorithm for learning guidance rewards. We show that the guidance rewards have an intuitive interpretation, and can be obtained without training any additional neural networks. Due to the ease of integration, we use the guidance rewards in a few popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present results in single-agent and multi-agent tasks that elucidate the benefit of our approach when the environmental rewards are sparse or delayed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12718v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7093\", \"13755\", \"17457\", \"40273\", \"59761\"], \"outgoing_citations\": [\"152614\", \"183337\", \"207506\", \"208860\", \"214159\", \"227313\", \"229802\", \"230498\", \"247158\", \"254725\", \"260583\", \"264419\", \"268060\", \"271783\", \"272222\"]}","task_split":"paper_retrieval"}
{"document_id":"91724","document_content":"# Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n- I.2.7\n## Abstract\nMachine learning techniques applied to the Natural Language Processing (NLP) component of conversational agent development show promising results for improved accuracy and quality of feedback that a conversational agent can provide. The effort required to develop an educational scenario specific conversational agent is time consuming as it requires domain experts to label and annotate noisy data sources such as classroom videos. Previous approaches to modeling annotations have relied on labeling thousands of examples and calculating inter-annotator agreement and majority votes in order to model the necessary scenarios. This method, while proven successful, ignores individual annotator strengths in labeling a data point and under-utilizes examples that do not have a majority vote for labeling. We propose using a multi-task weak supervision method combined with active learning to address these concerns. This approach requires less labeling than traditional methods and shows significant improvements in precision, efficiency, and time-requirements than the majority vote method (Ratner 2019). We demonstrate the validity of this method on the Google Jigsaw data set and then propose a scenario to apply this method using the Instructional Quality Assessment(IQA) to define the categories for labeling. We propose using probabilistic modeling of annotator labeling to generate active learning examples to further label the data. Active learning is able to iteratively improve the training performance and accuracy of the original classification model. This approach combines state-of-the art labeling techniques of weak supervision and active learning to optimize results in the educational domain and could be further used to lessen the data requirements for expanded scenarios within the education domain through transfer learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12710v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\", \"I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"118462\", \"158701\", \"164268\", \"164659\", \"188123\", \"215247\", \"229768\", \"239831\", \"281768\", \"291900\"]}","task_split":"paper_retrieval"}
{"document_id":"91796","document_content":"# Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nFeature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner workings. Here, we measure how much extremely activating images help humans to predict CNN activations. Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. (2017) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map. Given either synthetic or natural reference images, human participants choose which of two query images leads to strong positive activation. The experiments are designed to maximize participants' performance, and are the first to probe intermediate instead of final layer representations. We find that synthetic images indeed provide helpful information about feature map activations ($82\\pm4\\%$ accuracy; chance would be $50\\%$). However, natural images - originally intended as a baseline - outperform synthetic images by a wide margin ($92\\pm2\\%$). Additionally, participants are faster and more confident for natural images, whereas subjective impressions about the interpretability of the feature visualizations are mixed. The higher informativeness of natural images holds across most layers, for both expert and lay participants as well as for hand- and randomly-picked feature visualizations. Even if only a single reference image is given, synthetic images provide less information than natural images ($65\\pm5\\%$ vs. $73\\pm4\\%$). In summary, synthetic images from a popular feature visualization method are significantly less informative for assessing CNN activations than natural images. We argue that visualization methods should improve over this baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12606v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"4887\", \"40012\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"91840","document_content":"# Understanding the Extent to which Summarization Evaluation Metrics Measure the Information Quality of Summaries\n## Categories\n- Computation and Language\n## Abstract\nReference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference. Ideally, this comparison should measure the summary's information quality by calculating how much information the summaries have in common. In this work, we analyze the token alignments used by ROUGE and BERTScore to compare summaries and argue that their scores largely cannot be interpreted as measuring information overlap, but rather the extent to which they discuss the same topics. Further, we provide evidence that this result holds true for many other summarization evaluation metrics. The consequence of this result is that it means the summarization community has not yet found a reliable automatic metric that aligns with its research goal, to generate summaries with high-quality information. Then, we propose a simple and interpretable method of evaluating summaries which does directly measure information overlap and demonstrate how it can be used to gain insights into model behavior that could not be provided by other methods alone.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12495v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"55016\", \"97363\"], \"outgoing_citations\": [\"126362\", \"132176\", \"168405\", \"170657\", \"190934\", \"288699\", \"298016\"]}","task_split":"paper_retrieval"}
{"document_id":"91952","document_content":"# Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art deep neural network models have reached near perfect face recognition accuracy rates on controlled high-resolution face images. However, their performance is drastically degraded when they are tested with very low-resolution face images. This is particularly critical in surveillance systems, where a low-resolution probe image is to be matched with high-resolution gallery images. super-resolution techniques aim at producing high-resolution face images from low-resolution counterparts. While they are capable of reconstructing images that are visually appealing, the identity-related information is not preserved. Here, we propose an identity-preserving end-to-end image-to-image translation deep neural network which is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information. We achieved this by training a very deep convolutional encoder-decoder network with a symmetric contracting path between corresponding layers. This network was trained with a combination of a reconstruction and an identity-preserving loss, on multi-scale low-resolution conditions. Extensive quantitative evaluations of our proposed model demonstrated that it outperforms competing super-resolution and low-resolution face recognition methods on natural and artificial low-resolution face data sets and even unseen identities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12249v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"223755\", \"230095\", \"244877\", \"252688\", \"254116\", \"263345\", \"270376\", \"278526\", \"287611\", \"296989\"]}","task_split":"paper_retrieval"}
{"document_id":"91959","document_content":"# Algorithms for Causal Reasoning in Probability Trees\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nProbability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community. Here we present concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events. Our work expands the domain of causal reasoning to a very general class of discrete stochastic processes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.12237v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"78333\", \"31525\", \"43732\"], \"outgoing_citations\": [\"146389\", \"154589\", \"177371\", \"197466\", \"201860\", \"216257\", \"271038\", \"290722\", \"327001\", \"348963\"]}","task_split":"paper_retrieval"}
{"document_id":"92104","document_content":"# Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nUnsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and over how to extend the method to sequence labeling tasks. This method has recently gained traction for text classification. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these complex perturbation models. Furthermore, we find that applying its consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short: UDA need not be unsupervised, and does not require complex data augmentation to be effective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11966v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"18991\"], \"outgoing_citations\": [\"126170\", \"132035\", \"192608\", \"220017\", \"220611\", \"224289\", \"228459\", \"302824\"]}","task_split":"paper_retrieval"}
{"document_id":"92140","document_content":"# Error Bounds of Imitating Policies and Environments\n## Categories\n- Machine Learning\n## Abstract\nImitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding errors compared to behavioral cloning, and thus has a better sample complexity. Noticed that by considering the environment transition model as a dual agent, imitation learning can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than behavioral cloning, suggesting a novel application of adversarial imitation for model-based reinforcement learning. We hope these results could inspire future advances in imitation learning and model-based reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11876v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"61460\", \"28675\", \"66915\", \"72267\"], \"outgoing_citations\": [\"137942\", \"147984\", \"151786\", \"157804\", \"167888\", \"176360\", \"179948\", \"215836\", \"218636\", \"225108\", \"230605\", \"233167\", \"234984\", \"251102\", \"251924\", \"253953\", \"262817\", \"271358\", \"272623\", \"275998\", \"278987\", \"291301\", \"291738\", \"317874\", \"357220\"]}","task_split":"paper_retrieval"}
{"document_id":"92217","document_content":"# An Analysis of Simple Data Augmentation for Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nSimple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11683v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18991\", \"26658\", \"55305\", \"8334\", \"10513\", \"15912\", \"21526\", \"23762\", \"24425\", \"25829\", \"27451\", \"33588\", \"45164\", \"51130\", \"77353\", \"91922\"], \"outgoing_citations\": [\"97062\", \"121009\", \"129115\", \"132808\", \"181686\", \"184635\", \"185950\", \"194123\", \"200867\", \"205715\", \"218853\", \"220611\", \"225791\", \"231898\", \"234577\", \"235095\", \"235345\", \"256515\", \"267601\", \"291045\"]}","task_split":"paper_retrieval"}
{"document_id":"92343","document_content":"# Probabilistic Circuits for Variational Inference in Discrete Graphical Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nInference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO). Many sampling-based methods have been proposed for estimating these gradients, but they suffer from high bias or variance. In this paper, we propose a new approach that leverages the tractability of probabilistic circuit models, such as Sum Product Networks (SPN), to compute ELBO gradients exactly (without sampling) for a certain class of densities. In particular, we show that selective-SPNs are suitable as an expressive variational distribution, and prove that when the log-density of the target model is a polynomial the corresponding ELBO can be computed analytically. To scale to graphical models with thousands of variables, we develop an efficient and effective construction of selective-SPNs with size $O(kn)$, where $n$ is the number of variables and $k$ is an adjustable hyperparameter. We demonstrate our approach on three types of graphical models -- Ising models, Latent Dirichlet Allocation, and factor graphs from the UAI Inference Competition. Selective-SPNs give a better lower bound than mean-field and structured mean-field, and is competitive with approximations that do not provide a lower bound, such as Loopy Belief Propagation and Tree-Reweighted Belief Propagation. Our results show that probabilistic circuits are promising tools for variational inference in discrete graphical models as they combine tractability and expressivity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11446v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5868\", \"44864\", \"67899\", \"69966\"], \"outgoing_citations\": [\"162433\", \"163263\", \"180683\", \"196518\", \"208176\", \"231433\", \"242247\", \"242363\", \"251125\", \"270915\", \"280709\", \"281358\", \"285746\", \"297260\", \"297855\", \"299688\", \"335884\", \"350216\", \"357095\", \"361803\"]}","task_split":"paper_retrieval"}
{"document_id":"92464","document_content":"# Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training\n## Categories\n- Sound\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nRobustness to environmental noise is important to creating automatic speech emotion recognition systems that are deployable in the real world. Prior work on noise robustness has assumed that systems would not make use of sample-by-sample training noise conditions, or that they would have access to unlabelled testing data to generalize across noise conditions. We avoid these assumptions and introduce the resulting task as heterogeneous condition training. We show that with full knowledge of the test noise conditions, we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition, and with partial knowledge, we can use known noise conditions and domain adaptation algorithms to train systems that generalize well to unseen noise conditions. We then extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering, resulting in significant improvements over approaches that do not specialize or generalize based on noise type.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11226v1\", \"primary_category\": \"cs.SD\", \"categories\": [\"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"24759\"], \"outgoing_citations\": [\"193308\", \"224038\", \"234829\", \"249700\", \"257868\", \"275599\", \"286181\", \"320202\"]}","task_split":"paper_retrieval"}
{"document_id":"92475","document_content":"# Semantic Role Labeling as Syntactic Dependency Parsing\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11170v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"16648\"], \"outgoing_citations\": [\"127612\", \"156847\", \"175042\", \"181742\", \"183096\", \"192418\", \"195384\", \"202775\", \"204342\", \"215434\", \"219714\", \"225852\", \"228348\", \"232290\", \"233407\", \"233612\", \"234631\", \"248279\", \"257327\", \"268263\", \"271527\", \"274271\", \"281121\", \"289403\", \"291941\", \"292407\"]}","task_split":"paper_retrieval"}
{"document_id":"92478","document_content":"# Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSyntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing and syntactic generalization tests, while using fewer parameters than other models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2011.07960v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"32301\"], \"outgoing_citations\": [\"125514\", \"126370\", \"163994\", \"169687\", \"180658\", \"191740\", \"192407\", \"213362\", \"220137\", \"228740\", \"237283\", \"251565\", \"280201\", \"281225\", \"296289\", \"297648\"]}","task_split":"paper_retrieval"}
{"document_id":"92518","document_content":"# Knowledge Distillation for Improved Accuracy in Spoken Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nSpoken question answering (SQA) is a challenging task that requires the machine to fully understand the complex spoken documents. Automatic speech recognition (ASR) plays a significant role in the development of QA systems. However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task. To address the issue, we present a novel distillation framework. Specifically, we devise a training strategy to perform knowledge distillation (KD) from spoken documents and written counterparts. Our work makes a step towards distilling knowledge from the language model as a supervision signal to lead to better student accuracy by reducing the misalignment between automatic and manual transcriptions. Experiments demonstrate that our approach outperforms several state-of-the-art language models on the Spoken-SQuAD dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11067v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"2988\", \"12915\", \"29764\", \"49492\", \"10837\", \"25153\", \"79419\", \"92519\", \"108367\"], \"outgoing_citations\": [\"79419\", \"92519\", \"93464\", \"124679\", \"162299\", \"165441\", \"190231\", \"206624\", \"209863\", \"215164\", \"222054\", \"234577\", \"237064\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"92547","document_content":"# Controllable Text Simplification with Explicit Paraphrasing\n## Categories\n- Computation and Language\n## Abstract\nText Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11004v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"17436\", \"24424\", \"24637\", \"44703\"], \"outgoing_citations\": [\"99966\", \"118053\", \"127613\", \"154650\", \"162482\", \"163045\", \"173940\", \"176154\", \"179990\", \"182778\", \"191777\", \"192166\", \"212887\", \"214312\", \"214400\", \"214570\", \"214590\", \"233420\", \"248226\", \"270048\", \"280708\", \"296198\", \"298016\", \"302970\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"92661","document_content":"# Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nPurpose Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets, 2) It is non-generalizable, and 3) It lacks explainability and intuition. We have recently proposed Reinforcement Learning to address all threes. However, we applied it to images with radiologist eye tracking points, which limits the state-action space. Here we generalize the Deep-Q Learning to a gridworld-based environment, so that only the images and image masks are required.   Materials and Methods We trained a Deep Q network on 30 two-dimensional image slices from the BraTS brain tumor database. Each image contained one lesion. We then tested the trained Deep Q network on a separate set of 30 testing set images. For comparison, we also trained and tested a keypoint detection supervised deep learning network for the same set of training \/ testing images.   Results Whereas the supervised approach quickly overfit the training data, and predicably performed poorly on the testing set (11\\% accuracy), the Deep-Q learning approach showed progressive improved generalizability to the testing set over training time, reaching 70\\% accuracy.   Conclusion We have shown a proof-of-principle application of reinforcement learning to radiological images, here using 2D contrast-enhanced MRI brain images with the goal of localizing brain tumors. This represents a generalization of recent work to a gridworld setting, naturally suitable for analyzing medical images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10763v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"41234\", \"1002\", \"71406\", \"78527\"], \"outgoing_citations\": [\"64961\", \"107467\", \"153970\", \"225382\"]}","task_split":"paper_retrieval"}
{"document_id":"92787","document_content":"# Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages\n## Categories\n- Computation and Language\n## Abstract\nSpelling normalization for low resource languages is a challenging task because the patterns are hard to predict and large corpora are usually required to collect enough examples. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected, for example within a chat app. Such models are designed to be incrementally improved as feedback is given from users. In this work, we design a knowledge-base and prediction model embedded system for spelling correction in low-resource languages. Experimental results on multiple languages show that the model could become effective with a small amount of data. We perform experiments on both natural and synthetic data, as well as on data from two endangered languages (Ainu and Griko). Last, we built a prototype system that was used for a small case study on Hinglish, which further demonstrated the suitability of our approach in real world scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10472v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"184236\", \"202018\", \"212029\", \"228570\", \"287013\"]}","task_split":"paper_retrieval"}
{"document_id":"92839","document_content":"# Extracting Seasonal Gradual Patterns from Temporal Sequence Data Using Periodic Patterns Mining\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMining frequent episodes aims at recovering sequential patterns from temporal data sequences, which can then be used to predict the occurrence of related events in advance. On the other hand, gradual patterns that capture co-variation of complex attributes in the form of \" when X increases\/decreases, Y increases\/decreases\" play an important role in many real world applications where huge volumes of complex numerical data must be handled. Recently, these patterns have received attention from the data mining community exploring temporal data who proposed methods to automatically extract gradual patterns from temporal data. However, to the best of our knowledge, no method has been proposed to extract gradual patterns that regularly appear at identical time intervals in many sequences of temporal data, despite the fact that such patterns may add knowledge to certain applications, such as e-commerce. In this paper, we propose to extract co-variations of periodically repeating attributes from the sequences of temporal data that we call seasonal gradual patterns. For this purpose, we formulate the task of mining seasonal gradual patterns as the problem of mining periodic patterns in multiple sequences and then we exploit periodic pattern mining algorithms to extract seasonal gradual patterns. We discuss specific features of these patterns and propose an approach for their extraction based on mining periodic frequent patterns common to multiple sequences. We also propose a new anti-monotonous support definition associated to these seasonal gradual patterns. The illustrative results obtained from some real world data sets show that the proposed approach is efficient and that it can extract small sets of patterns by filtering numerous nonseasonal patterns to identify the seasonal ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10289v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225789\"]}","task_split":"paper_retrieval"}
{"document_id":"92861","document_content":"# Complete Multilingual Neural Machine Translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMultilingual Neural Machine Translation (MNMT) models are commonly trained on a joint set of bilingual corpora which is acutely English-centric (i.e. English either as the source or target language). While direct data between two languages that are non-English is explicitly available at times, its use is not common. In this paper, we first take a step back and look at the commonly used bilingual corpora (WMT), and resurface the existence and importance of implicit structure that existed in it: multi-way alignment across examples (the same sentence in more than two languages). We set out to study the use of multi-way aligned examples to enrich the original English-centric parallel corpora. We reintroduce this direct parallel data from multi-way aligned corpora between all source and target languages. By doing so, the English-centric graph expands into a complete graph, every language pair being connected. We call MNMT with such connectivity pattern complete Multilingual Neural Machine Translation (cMNMT) and demonstrate its utility and efficacy with a series of experiments and analysis. In combination with a novel training data sampling strategy that is conditioned on the target language only, cMNMT yields competitive translation quality for all language pairs. We further study the size effect of multi-way aligned data, its transfer learning capabilities and how it eases adding a new language in MNMT. Finally, we stress test cMNMT at scale and demonstrate that we can train a cMNMT model with up to 111*112=12,432 language pairs that provides competitive translation quality for all language pairs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10239v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"26304\", \"44334\", \"56654\", \"11082\", \"14941\", \"15355\", \"16439\", \"16594\", \"23636\", \"23827\", \"25580\", \"56556\", \"77820\"], \"outgoing_citations\": [\"129165\", \"131195\", \"166945\", \"176571\", \"182716\", \"192335\", \"194844\", \"197007\", \"228971\", \"234421\", \"243594\", \"267524\", \"277537\", \"280421\", \"280447\", \"280486\", \"282931\", \"290510\", \"300548\", \"300604\"]}","task_split":"paper_retrieval"}
{"document_id":"92929","document_content":"# Two-stage generative adversarial networks for document image binarization with color noise and background removal\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDocument image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents with severely degraded backgrounds. Convolutional neural network-based methods focus only on grayscale images and on local textual features. In this paper, we propose a two-stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. For the adversarial neural networks, we formulate loss functions between a discriminator and generators having an encoder-decoder structure. Experimental results show that the proposed method achieves better performance than many classical and state-of-the-art algorithms over the Document Image Binarization Contest (DIBCO) datasets, the LRDE Document Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan to release the shipping label dataset as well as our implementation code at github.com\/opensuh\/DocumentBinarization\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10103v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"35464\", \"91596\"], \"outgoing_citations\": [\"103980\", \"202558\", \"212915\", \"244507\", \"258854\", \"262398\", \"277021\", \"294531\"]}","task_split":"paper_retrieval"}
{"document_id":"92967","document_content":"# Convolutional-LSTM for Multi-Image to Single Output Medical Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nMedical head CT-scan imaging has been successfully combined with deep learning for medical diagnostics of head diseases and lesions[1]. State of the art classification models and algorithms for this task usually are based on 3d convolution layers for volumetric data on a supervised learning setting (1 input volume, 1 prediction per patient) or 2d convolution layers in a supervised setting (1 input image, 1 prediction per image). However a very common scenario in developing countries is to have the volume metadata lost due multiple reasons for example formatting conversion in images (for example .dicom to jpg), in this scenario the doctor analyses the collection of images and then emits a single diagnostic for the patient (with possibly an unfixed and variable number of images per patient) , this prevents it from being possible to use state of the art 3d models, but also is not possible to convert it to a supervised problem in a (1 image,1 diagnostic) setting because different angles or positions of the images for a single patient may not contain the disease or lesion. In this study we propose a solution for this scenario by combining 2d convolutional[2] models with sequence models which generate a prediction only after all images have been processed by the model for a given patient \\(i\\), this creates a multi-image to single-diagnostic setting \\(y^i=f(x_1,x_2,..,x_n)\\) where \\(n\\) may be different between patients. The experimental results demonstrate that it is possible to get a multi-image to single diagnostic model which mimics human doctor diagnostic process: evaluate the collection of patient images and then use important information in memory to decide a single diagnostic for the patient.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.10004v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"239094\"]}","task_split":"paper_retrieval"}
{"document_id":"93304","document_content":"# From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security\n## Categories\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a sub-model locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multi-party computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1002\/cpe.6002\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.DC\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"21353\", \"54391\", \"81511\"], \"outgoing_citations\": [\"107773\", \"132956\", \"157830\", \"164869\", \"169018\", \"181474\", \"198714\", \"200347\", \"201117\", \"203601\", \"206873\", \"207518\", \"208013\", \"210868\", \"217320\", \"221338\", \"226146\", \"232435\", \"232524\", \"234737\", \"234890\", \"246939\", \"247399\", \"256827\", \"280506\", \"282399\", \"283045\", \"327473\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"93345","document_content":"# Gaussian Constrained Attention Network for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduces an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https:\/\/github.com\/Pay20Y\/GCAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.09169v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"107465\", \"23130\", \"24824\", \"25116\", \"25132\", \"38059\", \"55703\"], \"outgoing_citations\": [\"123550\", \"150060\", \"158722\", \"159851\", \"161929\", \"170061\", \"172884\", \"174894\", \"192483\", \"204872\", \"205988\", \"211851\", \"217463\", \"229565\", \"232706\", \"239535\", \"247460\", \"250738\", \"251512\", \"253692\", \"256711\", \"256785\", \"269205\", \"284871\", \"294023\", \"296685\", \"296828\", \"300208\", \"309239\", \"311196\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"93370","document_content":"# Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDifferentiable rendering has paved the way to training neural networks to perform \"inverse graphics\" tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice. Recent Generative Adversarial Networks (GANs) that synthesize images, in contrast, seem to acquire 3D knowledge implicitly during training: object viewpoints can be manipulated by simply manipulating the latent codes. However, these latent codes often lack further physical interpretation and thus GANs cannot easily be inverted to perform explicit 3D reasoning. In this paper, we aim to extract and disentangle 3D knowledge learned by generative models by utilizing differentiable renderers. Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties. The entire architecture is trained iteratively using cycle consistency losses. We show that our approach significantly outperforms state-of-the-art inverse graphics networks trained on existing datasets, both quantitatively and via user studies. We further showcase the disentangled GAN as a controllable 3D \"neural renderer\", complementing traditional graphics renderers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.09125v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"1516\", \"4102\", \"4603\", \"7517\", \"12184\", \"14407\", \"14816\", \"21043\", \"43327\", \"58929\", \"11366\", \"12597\", \"13293\", \"22364\", \"26296\", \"33886\", \"40739\", \"52725\", \"55756\", \"56295\", \"56562\", \"57376\", \"57944\", \"58926\", \"59154\", \"59466\", \"75107\", \"84585\", \"84985\", \"30289\", \"89466\"], \"outgoing_citations\": [\"89466\", \"124303\", \"132835\", \"133828\", \"136895\", \"147421\", \"154346\", \"155280\", \"156709\", \"173162\", \"182539\", \"184536\", \"192020\", \"192520\", \"193589\", \"206932\", \"220030\", \"221955\", \"223503\", \"236720\", \"238344\", \"249836\", \"279030\", \"280070\", \"292385\", \"295402\", \"321461\"]}","task_split":"paper_retrieval"}
{"document_id":"93453","document_content":"# Multiple Future Prediction Leveraging Synthetic Trajectories\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nTrajectory prediction is an important task, especially in autonomous driving. The ability to forecast the position of other moving agents can yield to an effective planning, ensuring safety for the autonomous vehicle as well for the observed entities. In this work we propose a data driven approach based on Markov Chains to generate synthetic trajectories, which are useful for training a multiple future trajectory predictor. The advantages are twofold: on the one hand synthetic samples can be used to augment existing datasets and train more effective predictors; on the other hand, it allows to generate samples with multiple ground truths, corresponding to diverse equally likely outcomes of the observed trajectory. We define a trajectory prediction model and a loss that explicitly address the multimodality of the problem and we show that combining synthetic and real data leads to prediction improvements, obtaining state of the art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08948v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"120882\", \"157689\", \"158269\", \"187849\", \"193745\", \"211345\", \"217151\", \"224422\", \"229266\", \"237350\", \"268891\", \"277386\", \"287020\"]}","task_split":"paper_retrieval"}
{"document_id":"93464","document_content":"# Towards Data Distillation for End-to-end Spoken Conversational Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Audio and Speech Processing\n- Signal Processing\n## Abstract\nIn spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. In this task, our main objective is to build a QA system to deal with conversational questions both in spoken and text forms, and to explore the plausibility of providing more cues in spoken documents with systems in information gathering. To this end, instead of adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which directly fuse audio-text features to reduce the misalignment between automatic speech recognition hypotheses and the reference transcriptions. In addition, to evaluate the capacity of QA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 120k question-answer pairs. Experiments demonstrate that our proposed method achieves superior performance in spoken conversational question answering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08923v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"eess.AS\", \"eess.SP\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Audio and Speech Processing\", \"Signal Processing\"], \"incoming_citations\": [\"33114\", \"3823\", \"4248\", \"12915\", \"29764\", \"49492\", \"10837\", \"21249\", \"25153\", \"140934\", \"43919\", \"79419\", \"92519\", \"92518\", \"108367\"], \"outgoing_citations\": [\"105882\", \"124679\", \"160143\", \"171817\", \"173254\", \"190231\", \"200431\", \"206164\", \"206624\", \"215164\", \"220570\", \"220723\", \"220724\", \"233894\", \"237064\", \"241146\", \"250304\", \"278633\", \"282929\", \"286110\", \"289589\"]}","task_split":"paper_retrieval"}
{"document_id":"93489","document_content":"# Question Answering over Knowledge Base using Language Model Embeddings\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nKnowledge Base, represents facts about the world, often in some form of subsumption ontology, rather than implicitly, embedded in procedural code, the way a conventional computer program does. While there is a rapid growth in knowledge bases, it poses a challenge of retrieving information from them. Knowledge Base Question Answering is one of the promising approaches for extracting substantial knowledge from Knowledge Bases. Unlike web search, Question Answering over a knowledge base gives accurate and concise results, provided that natural language questions can be understood and mapped precisely to an answer in the knowledge base. However, some of the existing embedding-based methods for knowledge base question answering systems ignore the subtle correlation between the question and the Knowledge Base (e.g., entity types, relation paths, and context) and suffer from the Out Of Vocabulary problem. In this paper, we focused on using a pre-trained language model for the Knowledge Base Question Answering task. Firstly, we used Bert base uncased for the initial experiments. We further fine-tuned these embeddings with a two-way attention mechanism from the knowledge base to the asked question and from the asked question to the knowledge base answer aspects. Our method is based on a simple Convolutional Neural Network architecture with a Multi-Head Attention mechanism to represent the asked question dynamically in multiple aspects. Our experimental results show the effectiveness and the superiority of the Bert pre-trained language model embeddings for question answering systems on knowledge bases over other well-known embedding methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/IJCNN48605.2020.9206698\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"196257\", \"196314\", \"234577\", \"281162\", \"281163\", \"285811\", \"297226\", \"311684\", \"328352\", \"330994\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"93552","document_content":"# DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDocuments often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TPAMI.2020.3022406\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4468\", \"47109\", \"5077\", \"48483\", \"48608\", \"50103\"], \"outgoing_citations\": [\"244503\", \"244507\", \"248731\", \"258854\", \"262398\", \"269388\", \"275067\", \"279483\", \"279574\", \"295622\", \"303489\"]}","task_split":"paper_retrieval"}
{"document_id":"93574","document_content":"# A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nMultimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more context to textual data. Furthermore, the viability of training multimodal NMT models without a large parallel corpus continues to be investigated due to low availability of parallel sentences with images, particularly for English-Japanese data. However, this void can be filled with comparable sentences that contain bilingual terms and parallel phrases, which are naturally created through media such as social network posts and e-commerce product descriptions. In this paper, we propose a new multimodal English-Japanese corpus with comparable sentences that are compiled from existing image captioning datasets. In addition, we supplement our comparable sentences with a smaller parallel corpus for validation and test purposes. To test the performance of this comparable sentence translation scenario, we train several baseline NMT models with our comparable corpus and evaluate their English-Japanese translation performance. Due to low translation scores in our baseline experiments, we believe that current multimodal NMT models are not designed to effectively utilize comparable sentence data. Despite this, we hope for our corpus to be used to further research into multimodal NMT with comparable sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08725v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"154520\", \"180146\", \"182830\", \"252903\", \"267500\", \"274729\", \"275603\", \"312643\"]}","task_split":"paper_retrieval"}
{"document_id":"93586","document_content":"# Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nVisual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57\\% using fewer parameters on VQA-v2.0 test-standard split.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08708v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96335\", \"119759\", \"148700\", \"165195\", \"170603\", \"171096\", \"172138\", \"172318\", \"172370\", \"179017\", \"179219\", \"189847\", \"192531\", \"206164\", \"231300\", \"241203\", \"248620\", \"266120\", \"278894\", \"279518\"]}","task_split":"paper_retrieval"}
{"document_id":"93612","document_content":"# CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nData augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation framework dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2% while applied to the RoBERTa-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training base-lines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08670v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"36981\", \"66904\", \"18991\", \"80688\", \"14819\", \"24590\", \"24391\", \"46211\", \"46744\", \"60381\", \"61077\", \"68851\", \"89828\"], \"outgoing_citations\": [\"97943\", \"107584\", \"117118\", \"123777\", \"128975\", \"130252\", \"141037\", \"152544\", \"157349\", \"159333\", \"171642\", \"182813\", \"185155\", \"185283\", \"186386\", \"200867\", \"205715\", \"220017\", \"230755\", \"234577\", \"238702\", \"262064\", \"283114\", \"294549\", \"302824\", \"309807\", \"320317\"]}","task_split":"paper_retrieval"}
{"document_id":"93636","document_content":"# Lexicon generation for detecting fake news\n## Categories\n- Computation and Language\n## Abstract\nWith the digitization of media, an immense amount of news data has been generated by online sources, including mainstream media outlets as well as social networks. However, the ease of production and distribution resulted in circulation of fake news as well as credible, authentic news. The pervasive dissemination of fake news has extreme negative impacts on individuals and society. Therefore, fake news detection has recently become an emerging topic as an interdisciplinary research field that is attracting significant attention from many research disciplines, including social sciences and linguistics. In this study, we propose a method primarily based on lexicons including a scoring system to facilitate the detection of the fake news in Turkish. We contribute to the literature by collecting a novel, large scale, and credible dataset of Turkish news, and by constructing the first fake news detection lexicon for Turkish.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.11089v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"197826\", \"271015\", \"281788\"]}","task_split":"paper_retrieval"}
{"document_id":"93764","document_content":"# Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nObjective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning (RL) module, which is trained by electronic health records (EHRs). Experiments: We utilized two real-world EHR datasets to evaluate the performance of our model. In the disease prediction task, our model achieves 0.743 and 0.639 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the two datasets, respectively. This performance is comparable to the commonly used machine learning (ML) models in medical research. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08300v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"137147\", \"139564\", \"150640\", \"181089\", \"225410\", \"239537\", \"246001\", \"264024\", \"279891\", \"286253\"]}","task_split":"paper_retrieval"}
{"document_id":"93811","document_content":"# Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nApplying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid error accumulation due to exposure bias. Other approaches employ approximation techniques that map the text to continuous representation in order to circumvent the non-differentiable discrete process. Particularly, autoencoder-based methods effectively produce robust representations that can model complex discrete structures. In this paper, we propose a novel text GAN architecture that promotes the collaborative training of the continuous-space and discrete-space methods. Our method employs an autoencoder to learn an implicit data manifold, providing a learning objective for adversarial training in a continuous space. Furthermore, the complete textual output is directly evaluated and updated via RL in a discrete space. The collaborative interplay between the two adversarial trainings effectively regularize the text representations in different spaces. The experimental results on three standard benchmark datasets show that our model substantially outperforms state-of-the-art text GANs with respect to quality, diversity, and global consistency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08213v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"26417\"], \"outgoing_citations\": [\"184909\", \"190452\", \"228201\", \"233720\", \"236053\", \"243154\", \"255035\", \"261785\", \"263848\", \"263935\", \"264539\", \"264937\", \"273062\", \"280606\", \"284481\", \"291102\", \"311470\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"93873","document_content":"# Modeling Token-level Uncertainty to Learn Unknown Concepts in SLU via Calibrated Dirichlet Prior RNN\n## Categories\n- Artificial Intelligence\n## Abstract\nOne major task of spoken language understanding (SLU) in modern personal assistants is to extract semantic concepts from an utterance, called slot filling. Although existing slot filling models attempted to improve extracting new concepts that are not seen in training data, the performance in practice is still not satisfied. Recent research collected question and answer annotated data to learn what is unknown and should be asked, yet not practically scalable due to the heavy data collection effort. In this paper, we incorporate softmax-based slot filling neural architectures to model the sequence uncertainty without question supervision. We design a Dirichlet Prior RNN to model high-order uncertainty by degenerating as softmax layer for RNN model training. To further enhance the uncertainty modeling robustness, we propose a novel multi-task training to calibrate the Dirichlet concentration parameters. We collect unseen concepts to create two test datasets from SLU benchmark datasets Snips and ATIS. On these two and another existing Concept Learning benchmark datasets, we show that our approach significantly outperforms state-of-the-art approaches by up to 8.18%. Our method is generic and can be applied to any RNN or Transformer based slot filling models with a softmax layer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08101v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"18458\"], \"outgoing_citations\": [\"178247\", \"204763\", \"205899\", \"220829\", \"232341\", \"232362\", \"240532\", \"242269\", \"249330\", \"264232\", \"281860\", \"285317\", \"300449\", \"312009\", \"320829\"]}","task_split":"paper_retrieval"}
{"document_id":"93888","document_content":"# TextMage: The Automated Bangla Caption Generator Based On Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nNeural Networks and Deep Learning have seen an upsurge of research in the past decade due to the improved results. Generates text from the given image is a crucial task that requires the combination of both sectors which are computer vision and natural language processing in order to understand an image and represent it using a natural language. However existing works have all been done on a particular lingual domain and on the same set of data. This leads to the systems being developed to perform poorly on images that belong to specific locales' geographical context. TextMage is a system that is capable of understanding visual scenes that belong to the Bangladeshi geographical context and use its knowledge to represent what it understands in Bengali. Hence, we have trained a model on our previously developed and published dataset named BanglaLekhaImageCaptions. This dataset contains 9,154 images along with two annotations for each image. In order to access performance, the proposed model has been implemented and evaluated.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.08066v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"24321\"], \"outgoing_citations\": [\"202296\", \"219463\", \"278604\", \"284230\", \"296681\", \"300099\", \"302621\", \"312051\", \"323634\", \"336175\"]}","task_split":"paper_retrieval"}
{"document_id":"93978","document_content":"# Avoiding Side Effects By Considering Future Tasks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDesigning reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side effects that should be avoided while completing the task). To alleviate the burden on the reward designer, we propose an algorithm to automatically generate an auxiliary reward function that penalizes side effects. This auxiliary objective rewards the ability to complete possible future tasks, which decreases if the agent causes side effects during the current task. The future task reward can also give the agent an incentive to interfere with events in the environment that make future tasks less achievable, such as irreversible actions by other agents. To avoid this interference incentive, we introduce a baseline policy that represents a default course of action (such as doing nothing), and use it to filter out future tasks that are not achievable by default. We formally define interference incentives and show that the future task approach with a baseline policy avoids these incentives in the deterministic case. Using gridworld environments that test for side effects and interference, we show that our method avoids interference and is more effective for avoiding side effects than the common approach of penalizing irreversible actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07877v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"7388\", \"1961\", \"5862\", \"20461\", \"44676\", \"60332\", \"72694\", \"104316\"], \"outgoing_citations\": [\"119408\", \"197562\", \"199330\", \"229347\", \"249185\", \"250066\", \"251082\", \"261038\", \"263957\", \"265010\", \"281258\", \"289913\", \"290744\", \"311650\", \"339382\", \"358578\"]}","task_split":"paper_retrieval"}
{"document_id":"94039","document_content":"# Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods. We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3.5 BLEU on the WMT'14 French-English and WMT'16 German-English tasks and outperforming the previous state-of-the-art. Finally, we enrich the IWSLT'15 English-Vietnamese corpus with pseudo-parallel Wikipedia sentence pairs, yielding a 1.2 BLEU improvement on the low-resource MT task. We demonstrate that unsupervised bitext mining is an effective way of augmenting MT datasets and complements existing techniques like initializing with pre-trained contextual embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07761v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"38855\", \"55766\", \"17281\", \"23802\", \"55413\", \"60936\", \"63831\", \"77487\"], \"outgoing_citations\": [\"106561\", \"127390\", \"156047\", \"157109\", \"157401\", \"161914\", \"168247\", \"169335\", \"176600\", \"176793\", \"187452\", \"197584\", \"204715\", \"211711\", \"216925\", \"219455\", \"222741\", \"228171\", \"230709\", \"234377\", \"234632\", \"234807\", \"251816\", \"251974\", \"253559\"]}","task_split":"paper_retrieval"}
{"document_id":"94067","document_content":"# Diverse Keyphrase Generation with Neural Unlikelihood Training\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, demonstrated through improvements on quality metrics such as F1-score. However, the importance of diversity in keyphrase generation has been largely ignored. We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE). Our findings show that repetition of keyphrases is a major issue with MLE training. To alleviate this issue, we adopt neural unlikelihood (UL) objective for training the S2S model. Our version of UL training operates at (1) the target token level to discourage the generation of repeating tokens; (2) the copy token level to avoid copying repetitive tokens from the source text. Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well. Through extensive experiments on datasets from three different domains we demonstrate that the proposed approach attains considerably large diversity gains, while maintaining competitive output quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07665v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"61270\", \"107845\"], \"outgoing_citations\": [\"134803\", \"146975\", \"153814\", \"167979\", \"172088\", \"181558\", \"191880\", \"197057\", \"214542\", \"220295\", \"220696\", \"220788\", \"232274\", \"238321\", \"243154\", \"245154\", \"268251\", \"272080\", \"296198\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"94097","document_content":"# Object Tracking Using Spatio-Temporal Future Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOcclusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future possible locations of the target object from its past trajectory. To achieve this, we introduce a learning-based tracking method that takes into account background motion modeling and trajectory prediction. Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory. Since, in the input video, the target object's trajectory is not only affected by the object motion but also the camera motion, our background motion module estimates the camera motion. So that the object's trajectory can be made independent from it. To dynamically switch between the appearance-based tracker and the trajectory prediction, we employ a network that can assess how good a tracking prediction is, and we use the assessment scores to choose between the appearance-based tracker's prediction and the trajectory-based prediction. Comprehensive evaluations show that the proposed method sets a new state-of-the-art performance on commonly used tracking benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07605v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175291\", \"190465\", \"199454\", \"204363\", \"206223\", \"209516\", \"221569\", \"229266\", \"235103\", \"238014\", \"238433\", \"244219\", \"248822\", \"258705\", \"279356\", \"286699\", \"302914\", \"304330\", \"304441\"]}","task_split":"paper_retrieval"}
{"document_id":"94145","document_content":"# Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nNatural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual commonsense reasoning, visual-textual entailment, and visual question answering. The key challenge of accurate rationalization is comprehensive image understanding at all levels: not just their explicit content at the pixel level, but their contextual contents at the semantic and pragmatic levels. We present Rationale^VT Transformer, an integrated model that learns to generate free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. Our experiments show that the base pretrained language model benefits from visual adaptation and that free-text rationalization is a promising research direction to complement model interpretability for complex visual-textual reasoning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07526v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9230\", \"39414\", \"5918\", \"50852\", \"55765\", \"61915\", \"67313\", \"78260\", \"94168\"], \"outgoing_citations\": [\"98838\", \"115953\", \"123088\", \"127513\", \"127779\", \"128041\", \"130380\", \"131531\", \"131629\", \"131681\", \"132002\", \"132372\", \"132402\", \"165195\", \"165308\", \"166082\", \"167338\", \"168864\", \"168951\", \"169182\", \"170603\", \"171039\", \"171101\", \"171616\", \"171988\", \"180920\", \"182261\", \"183694\", \"185629\", \"192531\", \"202353\", \"203187\", \"207428\", \"208526\", \"218686\", \"222858\", \"223389\", \"227989\", \"238372\", \"239831\", \"249872\", \"262955\", \"266702\", \"273118\", \"278894\", \"287380\", \"328088\"]}","task_split":"paper_retrieval"}
{"document_id":"94161","document_content":"# MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDeveloping conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels. To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. To acquire a clear comprehension on these two medical dialogue tasks, we implement several state-of-the-art benchmarks, as well as design two dialogue models with a further consideration on the predicted entities. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset, and the response quality can be enhanced with the help of auxiliary entity information. From human evaluation, the simple retrieval model outperforms several state-of-the-art generative models, indicating that there still remains a large room for improvement on generating medically meaningful responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07497v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"158493\", \"169511\", \"179992\", \"201023\", \"305200\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"94225","document_content":"# Human-interpretable model explainability on high-dimensional data\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex. Unique challenges arise when a model's input features become high dimensional: on one hand, principled model-agnostic approaches to explainability become too computationally expensive; on the other, more efficient explainability algorithms lack natural interpretations for general users. In this work, we introduce a framework for human-interpretable explainability on high-dimensional data, consisting of two modules. First, we apply a semantically meaningful latent representation, both to reduce the raw dimensionality of the data, and to ensure its human interpretability. These latent features can be learnt, e.g. explicitly as disentangled representations or implicitly through image-to-image translation, or they can be based on any computable quantities the user chooses. Second, we adapt the Shapley paradigm for model-agnostic explainability to operate on these latent features. This leads to interpretable model explanations that are both theoretically controlled and computationally tractable. We benchmark our approach on synthetic data and demonstrate its effectiveness on several image-classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07384v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"121638\", \"126580\", \"130772\", \"135007\", \"138621\", \"143581\", \"148797\", \"158502\", \"177312\", \"189528\", \"190384\", \"223389\", \"223886\", \"226576\", \"226768\", \"227415\", \"237146\", \"241329\", \"241933\", \"242239\", \"248981\", \"264781\", \"273959\", \"303139\", \"320829\", \"337853\"]}","task_split":"paper_retrieval"}
{"document_id":"94230","document_content":"# Decoding Methods for Neural Narrative Generation\n## Categories\n- Computation and Language\n## Abstract\nNarrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation. In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters -- specifically, maximum mutual information -- analyzing results over multiple criteria with automatic and human evaluation. We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07375v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"36632\"], \"outgoing_citations\": [\"96739\", \"100503\", \"127696\", \"129680\", \"132683\", \"132799\", \"158437\", \"158493\", \"165308\", \"180695\", \"192158\", \"209388\", \"211205\", \"217640\", \"232274\", \"261692\", \"303827\", \"305200\", \"317213\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"94239","document_content":"# Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nAugmented-reality (AR) glasses that will have access to onboard sensors and an ability to display relevant information to the user present an opportunity to provide user assistance in quotidian tasks. Many such tasks can be characterized as object-rearrangement tasks. We introduce a novel framework for computing and displaying AR assistance that consists of (1) associating an optimal action sequence with the policy of an embodied agent and (2) presenting this sequence to the user as suggestions in the AR system's heads-up display. The embodied agent comprises a \"hybrid\" between the AR system and the user, with the AR system's observation space (i.e., sensors) and the user's action space (i.e., task-execution actions); its policy is learned by minimizing the task-completion time. In this initial study, we assume that the AR system's observations include the environment's map and localization of the objects and the user. These choices allow us to formalize the problem of computing AR assistance for any object-rearrangement task as a planning problem, specifically as a capacitated vehicle-routing problem. Further, we introduce a novel AR simulator that can enable web-based evaluation of AR-like assistance and associated at-scale data collection via the Habitat simulator for embodied artificial intelligence. Finally, we perform a study that evaluates user response to the proposed form of AR assistance on a specific quotidian object-rearrangement task, house cleaning, using our proposed AR simulator on mechanical turk. In particular, we study the effect of the proposed AR assistance on users' task performance and sense of agency over a range of task difficulties. Our results indicate that providing users with such assistance improves their overall performance and while users report a negative impact to their agency, they may still prefer the proposed assistance to having no assistance at all.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07358v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"158547\", \"162391\", \"165614\", \"180898\", \"192738\", \"247418\", \"279215\"]}","task_split":"paper_retrieval"}
{"document_id":"94268","document_content":"# Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator's goal is to convert the feedback into a response that answers the user's previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94% to 75.96% in ranking correct responses on the Personachat dataset, a large improvement given that the original model is already trained on 131k samples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07261v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"112289\"], \"outgoing_citations\": [\"127002\", \"158493\", \"186381\", \"202748\", \"232164\", \"232584\", \"235238\", \"244479\", \"264856\", \"265309\", \"266155\", \"305200\", \"310740\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"94357","document_content":"# Chinese Lexical Simplification\n## Categories\n- Computation and Language\n## Abstract\nLexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. In order to acquire more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that include synonym-based approach, word embedding-based approach, pretrained language model-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.07048v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"124955\", \"129858\", \"176154\", \"176673\", \"189726\", \"214400\"]}","task_split":"paper_retrieval"}
{"document_id":"94380","document_content":"# Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe application of reinforcement learning (RL) in robotic control is still limited in the environments with sparse and delayed rewards. In this paper, we propose a practical self-imitation learning method named Self-Imitation Learning with Constant Reward (SILCR). Instead of requiring hand-defined immediate rewards from environments, our method assigns the immediate rewards at each timestep with constant values according to their final episodic rewards. In this way, even if the dense rewards from environments are unavailable, every action taken by the agents would be guided properly. We demonstrate the effectiveness of our method in some challenging continuous robotics control tasks in MuJoCo simulation and the results show that our method significantly outperforms the alternative methods in tasks with sparse and delayed rewards. Even compared with alternatives with dense rewards available, our method achieves competitive performance. The ablation experiments also show the stability and reproducibility of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06962v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128799\", \"140124\", \"160003\", \"184304\", \"201681\", \"212299\", \"214302\", \"227991\", \"230498\", \"261983\", \"266419\", \"293995\"]}","task_split":"paper_retrieval"}
{"document_id":"94404","document_content":"# Development of Open Informal Dataset Affecting Autonomous Driving\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis document is a document that has written procedures and methods for collecting objects and unstructured dynamic data on the road for the development of object recognition technology for self-driving cars, and outlines the methods of collecting data, annotation data, object classifier criteria, and data processing methods. On-road object and unstructured dynamic data were collected in various environments, such as weather, time and traffic conditions, and additional reception calls for police and safety personnel were collected. Finally, 100,000 images of various objects existing on pedestrians and roads, 200,000 images of police and traffic safety personnel, 5,000 images of police and traffic safety personnel, and data sets consisting of 5,000 image data were collected and built.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06900v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"94453","document_content":"# A Self-supervised Representation Learning of Sentence Structure for Authorship Attribution\n## Categories\n- Computation and Language\n## Abstract\nSyntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different downstream tasks across many domains. Even though utilizing probing methods in several studies suggests that these learned contextual representations implicitly encode some amount of syntax, explicit syntactic information further improves the performance of deep neural models in the domain of authorship attribution. These observations have motivated us to investigate the explicit representation learning of syntactic structure of sentences. In this paper, we propose a self-supervised framework for learning structural representations of sentences. The self-supervised network contains two components; a lexical sub-network and a syntactic sub-network which take the sequence of words and their corresponding structural labels as the input, respectively. Due to the n-to-1 mapping of words to their structural labels, each word will be embedded into a vector representation which mainly carries structural information. We evaluate the learned structural representations of sentences using different probing tasks, and subsequently utilize them in the authorship attribution task. Our experimental results indicate that the structural embeddings significantly improve the classification tasks when concatenated with the existing pre-trained word embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06786v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20709\"], \"outgoing_citations\": [\"131181\", \"156306\", \"167168\", \"186130\", \"187326\", \"197674\", \"227792\", \"227983\", \"232479\", \"233400\", \"237274\", \"251565\", \"267161\", \"267460\", \"267552\", \"271934\", \"272624\", \"274586\", \"286525\", \"286594\", \"290041\", \"298159\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"94524","document_content":"# Video Game Level Repair via Mixed Integer Linear Programming\n## Categories\n- Artificial Intelligence\n## Abstract\nRecent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. However, the generated levels are often unplayable without additional editing. We propose a generate-then-repair framework for automatic generation of playable levels adhering to specific styles. The framework constructs levels using a generative adversarial network (GAN) trained with human-authored examples and repairs them using a mixed-integer linear program (MIP) with playability constraints. A key component of the framework is computing minimum cost edits between the GAN generated level and the solution of the MIP solver, which we cast as a minimum cost network flow problem. Results show that the proposed framework generates a diverse range of playable levels, that capture the spatial relationships between objects exhibited in the human-authored levels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06627v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"24178\", \"40705\"], \"outgoing_citations\": [\"163503\", \"176298\", \"183871\", \"189201\", \"217831\", \"218411\", \"228285\", \"233501\", \"234345\", \"240572\", \"265896\", \"272662\", \"274886\", \"295312\"]}","task_split":"paper_retrieval"}
{"document_id":"94644","document_content":"# F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering\n## Categories\n- Computation and Language\n## Abstract\nExplainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06283v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23095\", \"56426\", \"67313\", \"91690\"], \"outgoing_citations\": [\"140317\", \"140689\", \"154601\", \"157277\", \"157836\", \"158501\", \"161554\", \"163072\", \"182026\", \"185429\", \"185952\", \"207428\", \"216563\", \"219910\", \"234342\", \"252059\", \"281163\", \"295863\"]}","task_split":"paper_retrieval"}
{"document_id":"94662","document_content":"# Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nText summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents. They also often ignore the effect of topical information on capturing important contents. To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture inter-sentence relationships efficiently via graph-structured document representation. Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN\/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths. Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06253v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"84343\", \"14641\", \"19224\", \"66383\"], \"outgoing_citations\": [\"128886\", \"166268\", \"170657\", \"181632\", \"183462\", \"186043\", \"200414\", \"211433\", \"216554\", \"220230\", \"220695\", \"225556\", \"235463\", \"250690\", \"263243\", \"264795\", \"280557\", \"295665\", \"315546\"]}","task_split":"paper_retrieval"}
{"document_id":"94707","document_content":"# COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework\n## Categories\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nTo address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision and deep learning approaches. We discuss the evaluation of the proposed design of federated machine learning systems and discuss how differential privacy by design (dPbD) framework can enhance data privacy in federated learning systems with scalability and robustness. We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19 challenge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06177v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"40983\", \"89853\"], \"outgoing_citations\": [\"98443\", \"112927\", \"114133\", \"131141\", \"134725\", \"148978\", \"154784\", \"158597\", \"208013\", \"246939\", \"283045\", \"303573\", \"319813\"]}","task_split":"paper_retrieval"}
{"document_id":"94710","document_content":"# Causal Structure Learning: a Bayesian approach based on random graphs\n## Categories\n- Artificial Intelligence\n## Abstract\nA Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06164v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"163450\", \"225891\", \"262402\", \"276458\", \"290722\", \"295435\", \"304236\", \"320501\", \"357926\", \"358665\"]}","task_split":"paper_retrieval"}
{"document_id":"94784","document_content":"# Thinking Fast and Slow in AI\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.06002v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"163\", \"19002\", \"21685\", \"22893\", \"75057\", \"90217\"], \"outgoing_citations\": [\"126188\", \"142217\", \"206527\", \"217756\", \"254920\"]}","task_split":"paper_retrieval"}
{"document_id":"94816","document_content":"# Towards human-level performance on automatic pose estimation of infant spontaneous movements\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nAssessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.compmedimag.2021.102012\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129008\", \"154945\", \"182076\", \"197723\", \"236263\", \"259448\", \"260999\", \"292825\", \"296091\", \"315055\", \"336448\"]}","task_split":"paper_retrieval"}
{"document_id":"94846","document_content":"# Human-centric Dialog Training via Offline Reinforcement Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nHow can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL). We identify implicit conversational cues including language similarity, elicitation of laughter, sentiment, and more, which indicate positive human feedback, and embed these in multiple reward functions. A well-known challenge is that learning an RL policy in an offline setting usually fails due to the lack of ability to explore and the tendency to make over-optimistic estimates of future reward. These problems become even harder when using RL for language models, which can easily have a 20,000 action vocabulary and many possible reward functions. We solve the challenge by developing a novel class of offline RL algorithms. These algorithms use KL-control to penalize divergence from a pre-trained prior language model, and use a new strategy to make the algorithm pessimistic, instead of optimistic, in the face of uncertainty. We test the resulting dialog model with ratings from 80 users in an open-domain setting and find it achieves significant improvements over existing deep offline RL approaches. The novel offline RL method is viable for improving any existing generative dialog model using a static dataset of human feedback.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05848v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3065\", \"8426\", \"50552\", \"6736\", \"16398\", \"17658\", \"22368\", \"22978\", \"30309\", \"37807\", \"47718\", \"55479\", \"68900\"], \"outgoing_citations\": [\"110439\", \"116754\", \"145384\", \"166082\", \"166480\", \"179568\", \"182796\", \"190024\", \"197924\", \"198899\", \"201399\", \"202748\", \"205147\", \"206655\", \"206875\", \"228028\", \"233905\", \"235207\", \"236146\", \"237274\", \"242732\", \"255580\", \"256632\", \"259595\", \"262342\", \"267161\", \"272987\", \"275597\", \"279215\", \"280866\", \"290738\", \"290825\", \"291102\", \"292261\", \"303539\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"94886","document_content":"# Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models\n## Categories\n- Computation and Language\n## Abstract\nHumans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language models to reproduce this behavior in English and evaluate the effect of structural supervision on learning outcomes. First, we assess few-shot learning capabilities by developing controlled experiments that probe models' syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. Second, we assess invariance properties of learned representation: the ability of a model to transfer syntactic generalizations from a base context (e.g., a simple declarative active-voice sentence) to a transformed context (e.g., an interrogative sentence). We test four models trained on the same dataset: an n-gram baseline, an LSTM, and two LSTM-variants trained with explicit structural supervision (Dyer et al.,2016; Charniak et al., 2016). We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model. All neural models are able to leverage information learned in base contexts to drive expectations in transformed contexts, indicating that they have learned some invariance properties of syntax.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05725v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72647\", \"46118\", \"89022\"], \"outgoing_citations\": [\"126370\", \"165928\", \"167553\", \"194731\", \"196722\", \"208560\", \"219156\", \"219586\", \"219846\", \"220137\", \"220460\", \"237283\", \"259980\", \"268854\", \"280201\", \"281225\", \"297648\"]}","task_split":"paper_retrieval"}
{"document_id":"95023","document_content":"# Towards Expressive Graph Representation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Theory\n- Information Theory\n## Abstract\nGraph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborhood information in a fixed non-injective fashion, which may map different graphs or nodes to the same embedding, reducing the model expressiveness. We present a theoretical framework to design a continuous injective set function for neighborhood aggregation in GNN. Using the framework, we propose expressive GNN that aggregates the neighborhood of each node with a continuous injective set function, so that a GNN layer maps similar nodes with similar neighborhoods to similar embeddings, different nodes to different embeddings and the equivalent nodes or isomorphic graphs to the same embeddings. Moreover, the proposed expressive GNN can naturally learn expressive representations for graphs with continuous node attributes. We validate the proposed expressive GNN (ExpGNN) for graph classification on multiple benchmark datasets including simple graphs and attributed graphs. The experimental results demonstrate that our model achieves state-of-the-art performances on most of the benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05427v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Theory\", \"Information Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"193032\", \"201584\", \"204871\", \"215435\", \"217775\", \"227067\", \"229936\", \"250796\", \"283532\", \"292407\", \"303863\", \"357242\"]}","task_split":"paper_retrieval"}
{"document_id":"95139","document_content":"# Connecting the Dots Between Fact Verification and Fake News Detection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER. However, the challenging problem of fake news detection has not benefited from the improvement of fact verification models, which is closely related to fake news detection. In this paper, we propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach first employs a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection, alleviating the need of large-scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demonstrate the effectiveness of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05202v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"21011\", \"22518\", \"75518\"], \"outgoing_citations\": [\"120497\", \"143564\", \"157598\", \"175093\", \"181902\", \"191276\", \"209764\", \"211841\", \"219169\", \"219367\", \"230998\", \"238321\", \"238986\", \"249420\"]}","task_split":"paper_retrieval"}
{"document_id":"95145","document_content":"# Learning Adaptive Language Interfaces through Decomposition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nOur goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05190v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"22954\", \"73208\", \"11352\", \"17198\", \"34439\", \"37379\", \"42161\", \"44196\", \"59690\", \"64051\", \"84697\"], \"outgoing_citations\": [\"95196\", \"152871\", \"155930\", \"162157\", \"167313\", \"175344\", \"196994\", \"228916\", \"236377\", \"239137\", \"247418\", \"249906\", \"260092\", \"263104\", \"263270\", \"263927\", \"267980\", \"268224\", \"268335\", \"290231\", \"290632\", \"290891\", \"300505\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"95172","document_content":"# Mining Truck Platooning Patterns Through Massive Trajectory Data\n## Categories\n- Machine Learning\n## Abstract\nTruck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements. Properly planning platoons and evaluating the potential of truck platooning are crucial to trucking companies and transportation authorities. This study proposes a series of data mining approaches to learn spontaneous truck platooning patterns from massive trajectories. An enhanced map matching algorithm is developed to identify truck headings by using digital map data, followed by an adaptive spatial clustering algorithm to detect instantaneous co-moving truck sets. These sets are then aggregated to find the network-wide maximum platoon duration and size through frequent itemset mining for computational efficiency. We leverage real GPS data collected from truck fleeting systems in Liaoning Province, China, to evaluate platooning performance and successfully extract spatiotemporal platooning patterns. Results show that approximately 36% spontaneous truck platoons can be coordinated by speed adjustment without changing routes and schedules. The average platooning distance and duration ratios for these platooned trucks are 9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel consumption. We also distinguish the optimal platooning periods and space headways for national freeways and trunk roads, and prioritize the road segments with high possibilities of truck platooning. The derived results are reproducible, providing useful policy implications and operational strategies for large-scale truck platoon planning and roadside infrastructure construction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05142v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"95191","document_content":"# Contrastive Representation Learning: A Framework and Review\n## Categories\n- Machine Learning\n## Abstract\nContrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing. In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods. We also provide a taxonomy for each of the components of contrastive learning in order to summarise it and distinguish it from other forms of machine learning. We then discuss the inductive biases which are present in any contrastive learning system and we analyse our framework under different views from various sub-fields of Machine Learning. Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented. Finally, we discuss the challenges and some of the most promising future research directions ahead.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2020.3031549\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7571\", \"48196\", \"4974\", \"5641\", \"22738\", \"23602\", \"25688\", \"26335\", \"29695\", \"41160\", \"50898\", \"61172\", \"67913\", \"88816\", \"122995\", \"1349\", \"2485\", \"3625\", \"5537\", \"8594\", \"17520\", \"21912\", \"22979\", \"24216\", \"24212\", \"24577\", \"26411\", \"28058\", \"30807\", \"35691\", \"37931\", \"39535\", \"45179\", \"45669\", \"47017\", \"47197\", \"51519\", \"52533\", \"53003\", \"53178\", \"54810\", \"58946\", \"59462\", \"61414\", \"63677\", \"64906\", \"68138\", \"68682\", \"69609\", \"81908\", \"91006\", \"127562\", \"86070\"], \"outgoing_citations\": [\"100693\", \"103880\", \"105917\", \"106274\", \"106808\", \"107991\", \"108736\", \"109373\", \"109591\", \"111872\", \"112450\", \"113077\", \"117926\", \"118556\", \"119383\", \"119843\", \"121076\", \"123820\", \"123821\", \"124694\", \"125833\", \"128649\", \"132207\", \"136264\", \"145957\", \"152790\", \"153932\", \"160122\", \"161070\", \"167544\", \"173580\", \"173673\", \"179957\", \"180920\", \"181484\", \"182813\", \"184024\", \"185155\", \"185954\", \"190961\", \"191890\", \"193227\", \"197714\", \"209957\", \"216376\", \"219016\", \"222446\", \"239686\", \"247148\", \"247535\", \"263034\", \"265701\", \"268248\", \"279211\", \"291301\", \"298750\", \"302895\", \"313397\", \"320009\", \"330936\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"95195","document_content":"# Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a few-shot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators.   We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains. Our models achieve an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares favorably to 69% and 80% obtained for English parser trained on gold English data and a few examples from validation set. We show our approach outperforms the previous state-of-the-art methodology by more than 30% for hotels and 40% for restaurants with localized ontologies for the subset of languages tested.   Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.05106v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"11597\", \"100214\", \"95359\"], \"outgoing_citations\": [\"95359\", \"127406\", \"132812\", \"135112\", \"137436\", \"146003\", \"146930\", \"159825\", \"167138\", \"176571\", \"189821\", \"216731\", \"217957\", \"234741\", \"237054\", \"237091\", \"245042\", \"257206\", \"290632\", \"300505\"]}","task_split":"paper_retrieval"}
{"document_id":"95282","document_content":"# Cue-word Driven Neural Response Generation with a Shrinking Vocabulary\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nOpen-domain response generation is the task of generating sensible and informative re-sponses to the source sentence. However, neural models tend to generate safe and mean-ingless responses. While cue-word introducing approaches encourage responses with concrete semantics and have shown tremendous potential, they still fail to explore di-verse responses during decoding. In this paper, we propose a novel but natural approach that can produce multiple cue-words during decoding, and then uses the produced cue-words to drive decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model can explore the full space of responses and discover informative ones with efficiency. Experimental results show that our approach significantly outperforms several strong baseline models with much lower decoding complexity. Especially, our approach can converge to concrete semantics more efficiently during decoding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04927v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"128396\", \"145384\", \"154886\", \"158493\", \"158619\", \"178759\", \"180695\", \"182545\", \"199671\", \"200162\", \"205147\", \"220511\", \"245252\", \"248835\", \"256448\", \"266671\", \"275595\", \"279543\", \"283076\", \"283527\", \"286225\", \"289061\", \"289943\", \"292821\", \"295863\", \"305200\", \"316328\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"95355","document_content":"# Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDespite ample motivation from costly exploration and limited trajectory data, rapidly adapting to new environments with few-shot reinforcement learning (RL) can remain a challenging task, especially with respect to personalized settings. Here, we consider the problem of recommending optimal policies to a set of multiple entities each with potentially different characteristics, such that individual entities may parameterize distinct environments with unique transition dynamics. Inspired by existing literature in meta-learning, we extend previous work by focusing on the notion that certain environments are more similar to each other than others in personalized settings, and propose a model-free meta-learning algorithm that prioritizes past experiences by relevance during gradient-based adaptation. Our algorithm involves characterizing past policy divergence through methods in inverse reinforcement learning, and we illustrate how such metrics are able to effectively distinguish past policy parameters by the environment they were deployed in, leading to more effective fast adaptation during test time. To study personalization more effectively we introduce a navigation testbed to specifically incorporate environment diversity across training episodes, and demonstrate that our approach outperforms meta-learning alternatives with respect to few-shot reinforcement learning in personalized settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04816v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"199499\", \"205477\", \"215410\", \"228299\", \"240315\", \"255100\"]}","task_split":"paper_retrieval"}
{"document_id":"95472","document_content":"# HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs\n## Categories\n- Machine Learning\n## Abstract\nGraphs are the most ubiquitous form of structured data representation used in machine learning. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations found in many real-world datasets. To model such complex relations, hypergraphs have proven to be a natural representation. Learning the node representations in a hypergraph is more complex than in a graph as it involves information propagation at two levels: within every hyperedge and across the hyperedges. Most current approaches first transform a hypergraph structure to a graph for use in existing geometric deep learning algorithms. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperSAGE, a novel hypergraph learning framework that uses a two-level neural message passing strategy to accurately and efficiently propagate information through hypergraphs. The flexible design of HyperSAGE facilitates different ways of aggregating neighborhood information. Unlike the majority of related work which is transductive, our approach, inspired by the popular GraphSAGE method, is inductive. Thus, it can also be used on previously unseen nodes, facilitating deployment in problems such as evolving or partially observed hypergraphs. Through extensive experimentation, we show that HyperSAGE outperforms state-of-the-art hypergraph learning methods on representative benchmark datasets. We also demonstrate that the higher expressive power of HyperSAGE makes it more stable in learning node representations as compared to the alternatives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04558v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"39684\", \"125880\", \"21805\", \"43770\", \"52019\", \"75512\"], \"outgoing_citations\": [\"116933\", \"118851\", \"165845\", \"201866\", \"216618\", \"218779\", \"240758\", \"256918\", \"293199\", \"336374\"]}","task_split":"paper_retrieval"}
{"document_id":"95530","document_content":"# Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWhile reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use some form of state representations and the latest work has explored embedding techniques for actions, both with the aim of achieving better generalization and applicability. However, these approaches consider only states or actions, ignoring the interaction between them when generating embedded representations. In this work, we establish the theoretical foundations for the validity of training a reinforcement learning agent using embedded states and actions. We then propose a new approach for jointly learning embeddings for states and actions that combines aspects of model-free and model-based reinforcement learning, which can be applied in both discrete and continuous domains. Specifically, we use a model of the environment to obtain embeddings for states and actions and present a generic architecture that leverages these to learn a policy. In this way, the embedded representations obtained via our approach enable better generalization over both states and actions by capturing similarities in the embedding spaces. Evaluations of our approach on several gaming, robotic control, and recommender systems show it significantly outperforms state-of-the-art models in both discrete\/continuous domains with large state\/action spaces, thus confirming its efficacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459637.3482357\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"96500\", \"96527\", \"98069\", \"117599\", \"155106\", \"196921\", \"200321\", \"200682\", \"207273\", \"212600\", \"218114\", \"219180\", \"262449\", \"265946\", \"301005\", \"302295\", \"310370\", \"351390\", \"356588\"]}","task_split":"paper_retrieval"}
{"document_id":"95563","document_content":"# Learning 3D Face Reconstruction with a Pose Guidance Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nWe present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN). First, we unveil the bottleneck of pose estimation in prior parametric 3D face learning methods, and propose to utilize 3D face landmarks for estimating pose parameters. With our specially designed PGN, our model can learn from both faces with fully labeled 3D landmarks and unlimited unlabeled in-the-wild face images. Our network is further augmented with a self-supervised learning scheme, which exploits face geometry information embedded in multiple frames of the same person, to alleviate the ill-posed nature of regressing 3D face geometry from a single image. These three insights yield a single approach that combines the complementary strengths of parametric model learning and data-driven learning techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D, Florence and FaceWarehouse datasets, and show that our method outperforms the state-of-the-art for all metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04384v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"109032\", \"185981\", \"191463\", \"197743\", \"205494\", \"227856\", \"230031\", \"236050\", \"238254\", \"243530\", \"248005\", \"257118\", \"260916\", \"268703\", \"270042\", \"270134\", \"270795\", \"270923\", \"277948\", \"294926\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"95566","document_content":"# Token-level Adaptive Training for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nThere exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural Machine Translation (NMT). The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less low-frequency tokens compared with the golden token distribution. However, low-frequency tokens may carry critical semantic information that will affect the translation quality once they are neglected. In this paper, we explored target token-level adaptive objectives based on token frequencies to assign appropriate weights for each target token during training. We aimed that those meaningful but relatively low-frequency words could be assigned with larger weights in objectives to encourage the model to pay more attention to these tokens. Our method yields consistent improvements in translation quality on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens where we can get 1.68, 1.02, and 0.52 BLEU increases compared with baseline, respectively. Further analyses show that our method can also improve the lexical diversity of translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04380v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"383\", \"25140\", \"26460\", \"45496\", \"45519\", \"47184\", \"89772\"], \"outgoing_citations\": [\"127369\", \"167379\", \"178575\", \"182215\", \"191677\", \"193998\", \"197727\", \"211859\", \"218521\", \"218543\", \"218973\", \"241611\", \"259851\", \"275596\", \"276358\", \"295308\", \"295815\", \"302815\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"95588","document_content":"# Plug-and-Play Conversational Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThere has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model. In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not require dialogue specific datasets and does not rely on fine-tuning a large model. While effective, the decoding procedure induces considerable computational overhead, rendering the conversational model unsuitable for interactive usage. To overcome this, we introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model. We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes, while being fluent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04344v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"77878\", \"2616\", \"5301\", \"16064\", \"16426\", \"23268\", \"23521\", \"25727\", \"42790\", \"44146\", \"45745\", \"51160\", \"61352\", \"61917\", \"100711\", \"103544\"], \"outgoing_citations\": [\"99088\", \"100711\", \"104184\", \"120814\", \"127566\", \"127696\", \"128396\", \"129680\", \"129992\", \"132332\", \"139637\", \"145384\", \"152729\", \"158493\", \"163399\", \"166082\", \"166114\", \"167338\", \"168228\", \"176605\", \"179568\", \"184854\", \"197924\", \"200725\", \"201867\", \"211664\", \"212046\", \"216953\", \"216965\", \"219198\", \"219543\", \"231949\", \"244479\", \"253593\", \"261692\", \"269757\", \"274567\", \"283618\", \"284481\", \"291102\", \"292261\", \"295863\", \"296250\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"95605","document_content":"# Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning\n## Categories\n- Computation and Language\n## Abstract\nDocument-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different sizes of context. To address this problem, we propose an effective approach to select dynamic context so that the document-level translation model can utilize the more useful selected context sentences to produce better translations. Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence. Then, we propose two strategies to explicitly select a variable number of context sentences and feed them into the translation module. We train the two modules end-to-end via reinforcement learning. A novel reward is proposed to encourage the selection and utilization of dynamic context sentences. Experiments demonstrate that our approach can select adaptive context sentences for different source sentences, and significantly improves the performance of document-level translation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04314v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21968\", \"55467\", \"93450\", \"26247\", \"33741\", \"38817\", \"45828\", \"45725\", \"168075\", \"70057\", \"82324\", \"91646\", \"158725\"], \"outgoing_citations\": [\"141444\", \"163992\", \"168854\", \"169146\", \"169344\", \"176177\", \"186239\", \"186654\", \"194334\", \"210165\", \"215003\", \"219083\", \"220186\", \"220973\", \"230212\", \"230556\", \"248821\", \"249315\", \"250879\", \"251684\", \"258163\", \"263299\", \"268370\", \"268684\", \"268909\", \"278954\", \"298842\", \"299936\"]}","task_split":"paper_retrieval"}
{"document_id":"95628","document_content":"# Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to subtle choices in study design. We highlight a simple approach, trajectory inspection, to bring clinicians into an iterative design process for model-based RL studies. We identify where the model recommends unexpectedly aggressive treatments or expects surprisingly positive outcomes from its recommendations. Then, we examine clinical trajectories simulated with the learned model and policy alongside the actual hospital course. Applying this approach to recent work on RL for sepsis management, we uncover a model bias towards discharge, a preference for high vasopressor doses that may be linked to small sample sizes, and clinically implausible expectations of discharge without weaning off vasopressors. We hope that iterations of detecting and addressing the issues unearthed by our method will result in RL policies that inspire more confidence in deployment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04279v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"45923\"], \"outgoing_citations\": [\"147531\", \"147841\", \"186313\", \"196267\", \"199665\", \"206875\", \"208939\", \"229839\", \"265649\", \"268399\"]}","task_split":"paper_retrieval"}
{"document_id":"95683","document_content":"# Set Prediction without Imposing Structure as Conditional Density Estimation\n## Categories\n- Machine Learning\n## Abstract\nSet prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. Example tasks include conditional point-cloud reconstruction and predicting future states of molecules. In this paper, we propose an alternative to training via set losses by viewing learning as conditional density estimation. Our learning framework fits deep energy-based models and approximates the intractable likelihood with gradient-guided sampling. Furthermore, we propose a stochastically augmented prediction algorithm that enables multiple predictions, reflecting the possible variations in the target set. We empirically demonstrate on a variety of datasets the capability to learn multi-modal densities and produce different plausible predictions. Our approach is competitive with previous set prediction models on standard benchmarks. More importantly, it extends the family of addressable tasks beyond those that have unambiguous predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.04109v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7654\", \"18893\", \"39387\", \"45032\"], \"outgoing_citations\": [\"115824\", \"115856\", \"145013\", \"152365\", \"157608\", \"180600\", \"182087\", \"193217\", \"211226\", \"215835\", \"249769\", \"264533\", \"271301\", \"278937\", \"279410\", \"291903\", \"302934\", \"302947\", \"333529\"]}","task_split":"paper_retrieval"}
{"document_id":"95739","document_content":"# Prioritized Level Replay\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nEnvironments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy. We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level's future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels. By adapting the sampling of training levels, PLR significantly improves sample efficiency and generalization on Procgen Benchmark--matching the previous state-of-the-art in test return--and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03934v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8658\", \"11995\", \"15121\", \"41972\", \"38561\", \"18748\", \"26797\", \"33080\", \"38678\", \"41291\", \"62425\", \"68034\"], \"outgoing_citations\": [\"74451\", \"80273\", \"82943\", \"92636\", \"101550\", \"116357\", \"116687\", \"116987\", \"118051\", \"128392\", \"128664\", \"136046\", \"139653\", \"145664\", \"152918\", \"153614\", \"159298\", \"161143\", \"200264\", \"200580\", \"200920\", \"207051\", \"226544\", \"261983\", \"262325\", \"269253\", \"271375\", \"273608\", \"311595\", \"356201\"]}","task_split":"paper_retrieval"}
{"document_id":"95771","document_content":"# Dense Relational Image Captioning via Multi-task Triple-Stream Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nWe introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS; i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to learn not only to generate captions but also to understand the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. Then, we present applications of our framework to holistic image captioning, scene graph generation, and retrieval tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03855v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"14760\", \"24867\"], \"outgoing_citations\": [\"24867\", \"56499\", \"103875\", \"111318\", \"111475\", \"112826\", \"151802\", \"168553\", \"189964\", \"192948\", \"195170\", \"206733\", \"208598\", \"209956\", \"217310\", \"219014\", \"222661\", \"222670\", \"223302\", \"223689\", \"224759\", \"226318\", \"242228\", \"248734\", \"249972\", \"250108\", \"253934\", \"259205\", \"259807\", \"259865\", \"259882\", \"266825\", \"268110\", \"268586\", \"269216\", \"270154\", \"271234\", \"271958\", \"272940\", \"273783\", \"276530\", \"278604\", \"278954\", \"279914\", \"280004\", \"287454\", \"289627\", \"296681\", \"302621\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"95832","document_content":"# Multi-hop Inference for Question-driven Summarization\n## Categories\n- Computation and Language\n## Abstract\nQuestion-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for non-factoid questions. In this work, we propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG), to incorporate multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries. Specifically, we jointly model the relevance to the question and the interrelation among different sentences via a human-like multi-hop inference module, which captures important sentences for justifying the summarized answer. A gated selective pointer generator network with a multi-view coverage mechanism is designed to integrate diverse information from different perspectives. Experimental results show that the proposed method consistently outperforms state-of-the-art methods on two non-factoid QA datasets, namely WikiHow and PubMedQA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03738v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"756\", \"80634\", \"84343\", \"47485\"], \"outgoing_citations\": [\"115623\", \"154227\", \"154484\", \"154910\", \"155968\", \"167028\", \"175089\", \"180575\", \"185429\", \"203630\", \"213836\", \"231869\", \"247060\", \"247262\", \"267899\", \"279343\", \"289673\", \"290151\", \"295470\", \"351546\"]}","task_split":"paper_retrieval"}
{"document_id":"95840","document_content":"# Learning to Fuse Sentences with Transformers for Summarization\n## Categories\n- Computation and Language\n## Abstract\nThe ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer's performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03726v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19290\", \"20965\", \"45223\", \"89344\"], \"outgoing_citations\": [\"119817\", \"126346\", \"127040\", \"132176\", \"164036\", \"168405\", \"181468\", \"183462\", \"187236\", \"197275\", \"219618\", \"220230\", \"230212\", \"234631\", \"266671\"]}","task_split":"paper_retrieval"}
{"document_id":"95954","document_content":"# Learning a Cost-Effective Annotation Policy for Question Answering\n## Categories\n- Computation and Language\n## Abstract\nState-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1% of the annotation cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03476v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24719\", \"69615\"], \"outgoing_citations\": [\"118135\", \"127524\", \"155896\", \"162157\", \"167287\", \"176503\", \"181988\", \"183076\", \"215928\", \"216563\", \"216925\", \"220858\", \"221103\", \"224833\", \"227534\", \"231239\", \"238610\", \"250547\", \"257223\", \"259094\", \"266850\", \"267790\", \"270020\", \"279213\", \"279343\"]}","task_split":"paper_retrieval"}
{"document_id":"96032","document_content":"# Narrative Text Generation with a Latent Discrete Plan\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nPast work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories. However, these approaches have used heuristics or off-the-shelf models to first tag training stories with the desired type of plan, and then train generation models in a supervised fashion. In this paper, we propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. During training, our model treats the sequence of anchor words as a latent variable and attempts to induce anchoring sequences that help guide generation in an unsupervised fashion. We conduct experiments with several types of sentence decoder distributions: left-to-right and non-monotonic, with different degrees of restriction. Further, since we use amortized variational inference to train our model, we introduce two corresponding types of inference network for predicting the posterior on anchor words. We conduct human evaluations which demonstrate that the stories produced by our model are rated better in comparison with baselines which do not consider story plans, and are similar or better in quality relative to baselines which use external supervision for plans. Additionally, the proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03272v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"9320\"], \"outgoing_citations\": [\"126962\", \"152651\", \"156979\", \"167338\", \"167615\", \"169037\", \"190108\", \"200162\", \"200324\", \"210121\", \"210155\", \"220740\", \"230014\", \"231203\", \"243154\", \"250505\", \"264552\", \"265139\", \"284092\", \"295128\", \"311464\", \"335177\"]}","task_split":"paper_retrieval"}
{"document_id":"96054","document_content":"# FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRepresentation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold. Moreover, feature learning in an unsupervised setup, without class labels and a suitable learning loss function, adds to the problem complexity. Further, the learned representation and subsequent predictions should not reflect discriminatory behavior towards certain sensitive groups or attributes. The proposed feature map should preserve maximum variations present in the data and needs to be fair with respect to the sensitive variables. We propose, in the first phase of our work, an efficient encoder-decoder framework to capture the mixed-domain information. The second phase of our work focuses on de-biasing the mixed space representations by adding relevant fairness constraints. This ensures minimal information loss between the representations before and after the fairness-preserving projections. Both the information content and the fairness aspect of the final representation learned has been validated through several metrics where it shows excellent performance. Our work (FairMixRep) addresses the problem of Mixed Space Fair Representation learning from an unsupervised perspective and learns a Universal representation that is timely, unique, and a novel research contribution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.03228v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"44531\"], \"outgoing_citations\": [\"99521\", \"126678\", \"161497\", \"174083\", \"182154\", \"241812\", \"244542\", \"247815\", \"262379\", \"264914\", \"294007\", \"303062\", \"309330\"]}","task_split":"paper_retrieval"}
{"document_id":"96230","document_content":"# Support-set bottlenecks for video-text representation learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behaviour is too strict, enforcing dissimilar representations even for samples that are semantically-related -- for example, visually similar videos or ones that share the same depicted action. In this paper, we propose a novel method that alleviates this by leveraging a generative model to naturally push these related samples together: each sample's caption must be reconstructed as a weighted combination of other support samples' visual representations. This simple idea ensures that representations are not overly-specialized to individual samples, are reusable across the dataset, and results in representations that explicitly encode semantics shared between samples, unlike noise contrastive learning. Our proposed method outperforms others by a large margin on MSR-VTT, VATEX and ActivityNet, and MSVD for video-to-text and text-to-video retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02824v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"24337\", \"7637\", \"1081\", \"2212\", \"4359\", \"5478\", \"5960\", \"7218\", \"8438\", \"12103\", \"14600\", \"43252\", \"55118\", \"1284\", \"5726\", \"5927\", \"7454\", \"10348\", \"10271\", \"12778\", \"20409\", \"27865\", \"33374\", \"40597\", \"43276\", \"43644\", \"48259\", \"53510\", \"54862\", \"55460\", \"58876\", \"59465\", \"60135\", \"62053\", \"62053\", \"62260\", \"62318\", \"62806\", \"67133\", \"81390\"], \"outgoing_citations\": [\"62806\", \"87013\", \"97123\", \"107967\", \"110610\", \"116387\", \"118223\", \"119114\", \"119349\", \"125833\", \"126586\", \"128649\", \"134387\", \"138177\", \"139259\", \"139906\", \"139932\", \"142128\", \"151327\", \"152790\", \"153776\", \"156591\", \"164107\", \"164927\", \"167544\", \"170603\", \"171465\", \"172370\", \"172394\", \"173627\", \"177483\", \"180920\", \"181897\", \"188114\", \"191861\", \"192531\", \"214015\", \"217562\", \"219048\", \"219151\", \"221984\", \"224270\", \"234602\", \"236918\", \"247535\", \"249077\", \"260926\", \"261608\", \"266992\", \"267523\", \"270325\", \"313435\", \"316430\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"96273","document_content":"# Analyzing Individual Neurons in Pre-trained Language Models\n## Categories\n- Computation and Language\n## Abstract\nWhile a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02695v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"132323\", \"15773\", \"15940\", \"16353\", \"18454\", \"26824\", \"55849\", \"23858\", \"23937\", \"24964\", \"38189\", \"43032\", \"45962\", \"46212\", \"49051\", \"56011\", \"66349\", \"73062\", \"75861\", \"77914\", \"88167\", \"132251\"], \"outgoing_citations\": [\"124868\", \"127278\", \"132251\", \"132635\", \"134662\", \"144956\", \"153138\", \"158554\", \"168097\", \"181098\", \"181468\", \"186130\", \"194308\", \"195157\", \"205056\", \"205161\", \"211702\", \"220159\", \"232479\", \"233400\", \"244357\", \"269127\", \"274095\", \"286594\", \"297423\", \"311683\", \"311845\"]}","task_split":"paper_retrieval"}
{"document_id":"96274","document_content":"# SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nSlot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to X 10.77). In-depth analyses show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02693v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"24344\", \"18216\", \"29403\", \"33513\", \"33603\", \"45038\", \"47388\", \"50525\", \"56334\", \"95245\"], \"outgoing_citations\": [\"89734\", \"168563\", \"170657\", \"178308\", \"189696\", \"197154\", \"230543\", \"232290\", \"241611\", \"251203\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"96296","document_content":"# If beam search is the answer, what was the question?\n## Categories\n- Computation and Language\n## Abstract\nQuite surprisingly, exact maximum a posteriori (MAP) decoding of neural language generators frequently leads to low-quality results. Rather, most state-of-the-art results on language generation tasks are attained using beam search despite its overwhelmingly high search error rate. This implies that the MAP objective alone does not express the properties we desire in text, which merits the question: if beam search is the answer, what was the question? We frame beam search as the exact solution to a different decoding objective in order to gain insights into why high probability under a model alone may not indicate adequacy. We find that beam search enforces uniform information density in text, a property motivated by cognitive science. We suggest a set of decoding objectives that explicitly enforce this property and find that exact decoding with these objectives alleviates the problems encountered when decoding poorly calibrated language generation models. Additionally, we analyze the text produced using various decoding strategies and see that, in our neural machine translation experiments, the extent to which this property is adhered to strongly correlates with BLEU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02650v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"3028\", \"16027\", \"16293\", \"30346\", \"34300\", \"38804\", \"57298\", \"6411\", \"21450\", \"21679\", \"22231\", \"23831\", \"30599\", \"30937\", \"42273\", \"44986\", \"45336\", \"49449\", \"56761\", \"58358\", \"62234\", \"62486\", \"69858\", \"100606\"], \"outgoing_citations\": [\"113473\", \"170013\", \"176008\", \"196775\", \"219572\", \"219833\", \"219952\", \"220017\", \"229674\", \"232274\", \"240486\", \"250451\", \"260584\", \"263927\", \"284230\", \"290687\", \"291289\", \"299936\", \"301840\", \"302056\", \"305200\", \"310903\", \"311470\", \"343624\"]}","task_split":"paper_retrieval"}
{"document_id":"96305","document_content":"# Weakly Supervised Disentangled Generative Causal Representation Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally related. We show that previous methods with independent priors fail to disentangle causally related factors even under supervision. Motivated by this finding, we propose a new disentangled learning method called DEAR that enables causal controllable generation and causal representation learning. The key ingredient of this new formulation is to use a structural causal model (SCM) as the prior distribution for a bidirectional generative model. The prior is then trained jointly with a generator and an encoder using a suitable GAN algorithm incorporated with supervised information on the ground-truth factors and their underlying causal structure. We provide theoretical justification on the identifiability and asymptotic convergence of the proposed method. We conduct extensive experiments on both synthesized and real data sets to demonstrate the effectiveness of DEAR in causal controllable generation, and the benefits of the learned representations for downstream tasks in terms of sample efficiency and distributional robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02637v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"38037\", \"75323\", \"90921\", \"114351\", \"117836\", \"118768\", \"139309\", \"140938\", \"143581\", \"154589\", \"155339\", \"160471\", \"163450\", \"163690\", \"168518\", \"176649\", \"177371\", \"180811\", \"181909\", \"183000\", \"187863\", \"189408\", \"200942\", \"201860\", \"208041\", \"212169\", \"240081\", \"241933\", \"242239\", \"251597\", \"252624\", \"256720\", \"272919\", \"276018\", \"291302\", \"291440\", \"298784\", \"357230\", \"358895\"]}","task_split":"paper_retrieval"}
{"document_id":"96400","document_content":"# Semi-supervised Relation Extraction via Incremental Meta Self-Training\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nTo alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.16410v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"70193\", \"5797\", \"23762\", \"78824\"], \"outgoing_citations\": [\"92382\", \"125340\", \"132877\", \"154631\", \"181948\", \"182961\", \"198186\", \"201602\", \"223063\", \"263600\", \"284063\", \"308507\"]}","task_split":"paper_retrieval"}
{"document_id":"96445","document_content":"# Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMixup is the latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by interpolating images at the pixel level. Inspired by this line of research, in this paper, we explore i) how to apply mixup to natural language processing tasks since text data can hardly be mixed in the raw format; ii) if mixup is still effective in transformer-based learning models, e.g., BERT. To achieve the goal, we incorporate mixup to transformer-based pre-trained architecture, named \"mixup-transformer\", for a wide range of NLP tasks while keeping the whole end-to-end training system. We evaluate the proposed framework by running extensive experiments on the GLUE benchmark. Furthermore, we also examine the performance of mixup-transformer in low-resource scenarios by reducing the training data with a certain ratio. Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02394v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"9809\", \"36981\", \"677\", \"2552\", \"15754\", \"18991\", \"615\", \"23396\", \"26808\", \"37804\", \"42425\", \"49616\", \"53672\", \"55134\", \"56889\", \"62354\", \"67584\", \"77353\", \"94792\", \"77599\"], \"outgoing_citations\": [\"99373\", \"133147\", \"163280\", \"185283\", \"200867\", \"218853\", \"229768\", \"259723\"]}","task_split":"paper_retrieval"}
{"document_id":"96466","document_content":"# VisualWordGrid: Information Extraction From Scanned Documents Using A Multimodal Approach\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe introduce a novel approach for scanned document representation to perform field extraction. It allows the simultaneous encoding of the textual, visual and layout information in a 3-axis tensor used as an input to a segmentation model. We improve the recent Chargrid and Wordgrid \\cite{chargrid} models in several ways, first by taking into account the visual modality, then by boosting its robustness in regards to small datasets while keeping the inference time low. Our approach is tested on public and private document-image datasets, showing higher performances compared to the recent state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02358v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"10970\", \"9109\", \"26331\", \"47534\", \"55400\", \"84406\"], \"outgoing_citations\": [\"122763\", \"142227\", \"149004\", \"167422\", \"176090\", \"205857\", \"216765\", \"264315\", \"297147\"]}","task_split":"paper_retrieval"}
{"document_id":"96491","document_content":"# Sentiment Analysis for Reinforcement Learning\n## Categories\n- Computation and Language\n## Abstract\nWhile reinforcement learning (RL) has been successful in natural language processing (NLP) domains such as dialogue generation and text-based games, it typically faces the problem of sparse rewards that leads to slow or no convergence. Traditional methods that use text descriptions to extract only a state representation ignore the feedback inherently present in them. In text-based games, for example, descriptions like \"Good Job! You ate the food}\" indicate progress, and descriptions like \"You entered a new room\" indicate exploration. Positive and negative cues like these can be converted to rewards through sentiment analysis. This technique converts the sparse reward problem into a dense one, which is easier to solve. Furthermore, this can enable reinforcement learning without rewards, in which the agent learns entirely from these intrinsic sentiment rewards. This framework is similar to intrinsic motivation, where the environment does not necessarily provide the rewards, but the agent analyzes and realizes them by itself. We find that providing dense rewards in text-based games using sentiment analysis improves performance under some conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02316v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"196377\", \"198373\", \"207273\", \"226321\", \"261983\", \"271868\", \"291102\"]}","task_split":"paper_retrieval"}
{"document_id":"96495","document_content":"# Goal-directed Generation of Discrete Structures with Conditional Generative Models\n## Categories\n- Machine Learning\n## Abstract\nDespite recent advances, goal-directed generation of structured discrete data remains challenging. For problems such as program synthesis (generating source code) and materials design (generating molecules), finding examples which satisfy desired constraints or exhibit desired properties is difficult. In practice, expensive heuristic search or reinforcement learning algorithms are often employed. In this paper we investigate the use of conditional generative models which directly attack this inverse problem, by modeling the distribution of discrete structures given properties of interest. Unfortunately, maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties. To address this, we introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We avoid high-variance score-function estimators that would otherwise be required by sampling from an approximation to the normalized rewards, allowing simple Monte Carlo estimation of model gradients. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value. In both cases, we find improvements over maximum likelihood estimation and other baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02311v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"12851\", \"13518\"], \"outgoing_citations\": [\"181095\", \"208897\", \"209026\", \"232449\", \"233672\", \"241022\", \"254759\", \"264984\", \"272068\", \"272531\", \"285557\", \"287823\", \"290766\", \"302815\", \"303250\", \"311008\"]}","task_split":"paper_retrieval"}
{"document_id":"96512","document_content":"# Smoother Network Tuning and Interpolation for Continuous-level Image Processing\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nIn Convolutional Neural Network (CNN) based image processing, most studies propose networks that are optimized to single-level (or single-objective); thus, they underperform on other levels and must be retrained for delivery of optimal performance. Using multiple models to cover multiple levels involves very high computational costs. To solve these problems, recent approaches train networks on two different levels and propose their own interpolation methods to enable arbitrary intermediate levels. However, many of them fail to generalize or have certain side effects in practical usage. In this paper, we define these frameworks as network tuning and interpolation and propose a novel module for continuous-level learning, called Filter Transition Network (FTN). This module is a structurally smoother module than existing ones. Therefore, the frameworks with FTN generalize well across various tasks and networks and cause fewer undesirable side effects. For stable learning of FTN, we additionally propose a method to initialize non-linear neural network layers with identity mappings. Extensive results for various image processing tasks indicate that the performance of FTN is comparable in multiple continuous levels, and is significantly smoother and lighter than that of other frameworks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02270v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"190140\", \"192916\", \"208645\", \"209172\", \"217152\", \"224859\", \"228870\", \"232572\", \"248102\", \"250250\", \"253572\", \"269027\", \"269384\", \"279694\", \"307557\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"96524","document_content":"# Learning to Generalize for Sequential Decision Making\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these models do not generalize well when applied to novel task domains. However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability. We introduce a teacher-student imitation learning methodology and a means of converting a reinforcement learning model into a natural language understanding model. Together, these methodologies enable the introduction of contextualized language models into the sequential decision making problem space. We show that models can learn faster and generalize more, leveraging both the imitation learning and the reformulation. Our models exceed teacher performance on various held-out decision problems, by up to 7% on in-domain problems and 24% on out-of-domain problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02229v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"22323\", \"22124\"], \"outgoing_citations\": [\"168729\", \"169742\", \"171942\", \"184995\", \"187527\", \"201867\", \"207273\", \"218923\", \"221212\", \"226321\", \"226327\", \"231741\", \"238962\", \"254096\", \"266342\", \"271868\", \"284565\", \"293876\", \"302967\", \"310370\"]}","task_split":"paper_retrieval"}
{"document_id":"96531","document_content":"# Pareto Probing: Trading Off Accuracy for Complexity\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe question of how to probe contextual word representations for linguistic structure in a way that is both principled and useful has seen significant attention recently in the NLP literature. In our contribution to this discussion, we argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance: the Pareto hypervolume. To measure complexity, we present a number of parametric and non-parametric metrics. Our experiments using Pareto hypervolume as an evaluation metric show that probes often do not conform to our expectations---e.g., why should the non-contextual fastText representations encode more morpho-syntactic information than the contextual BERT representations? These results suggest that common, simplistic probing tasks, such as part-of-speech labeling and dependency arc labeling, are inadequate to evaluate the linguistic structure encoded in contextual word representations. This leads us to propose full dependency parsing as a probing task. In support of our suggestion that harder probing tasks are necessary, our experiments with dependency parsing reveal a wide gap in syntactic knowledge between contextual and non-contextual representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02180v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2778\", \"15940\", \"67128\", \"22428\", \"22743\", \"23858\", \"24964\", \"26685\", \"48163\", \"49471\", \"51308\", \"55359\", \"56222\", \"56526\", \"65799\", \"77789\", \"96238\"], \"outgoing_citations\": [\"100463\", \"127101\", \"132635\", \"134662\", \"163964\", \"168097\", \"182116\", \"186130\", \"186250\", \"192036\", \"201097\", \"212036\", \"230051\", \"241550\", \"244357\", \"249109\", \"263559\", \"281121\", \"283223\"]}","task_split":"paper_retrieval"}
{"document_id":"96535","document_content":"# Speakers Fill Lexical Semantic Gaps with Context\n## Categories\n- Computation and Language\n## Abstract\nLexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient. If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clear -- resulting in frequent miscommunication. For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average. To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this -- one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages. We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in WordNet (e.g. $\\rho = 0.40$ in English). We then test our main hypothesis -- that a word's lexical ambiguity should negatively correlate with its contextual uncertainty -- and find significant correlations on all 18 typologically diverse languages we analyse. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02172v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20767\", \"52824\", \"20522\"], \"outgoing_citations\": [\"126336\", \"183703\"]}","task_split":"paper_retrieval"}
{"document_id":"96560","document_content":"# Explaining The Efficacy of Counterfactually Augmented Data\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn attempts to produce ML models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable. Importantly, edits that are not necessary to flip the applicable label are prohibited. Models trained on the augmented data appear, empirically, to rely less on semantically irrelevant words and to generalize better out of domain. While this work draws loosely on causal thinking, the underlying causal model (even at an abstract level) and the principles underlying the observed out-of-domain improvements remain unclear. In this paper, we introduce a toy analog based on linear Gaussian models, observing interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals. Our analysis provides some insights that help to explain the efficacy of CAD. Moreover, we develop the hypothesis that while adding noise to causal features should degrade both in-domain and out-of-domain performance, adding noise to non-causal features should lead to relative improvements in out-of-domain performance. This idea inspires a speculative test for determining whether a feature attribution technique has identified the causal spans. If adding noise (e.g., by random word flips) to the highlighted spans degrades both in-domain and out-of-domain performance on a battery of challenge datasets, but adding noise to the complement gives improvements out-of-domain, it suggests we have identified causal spans. We present a large-scale empirical study comparing spans edited to create CAD to those selected by attention and saliency maps. Across numerous domains and models, we find that the hypothesized phenomenon is pronounced for CAD.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02114v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2372\", \"6537\", \"4404\", \"16320\", \"26223\", \"38082\", \"184206\", \"38796\", \"43997\", \"45542\", \"46027\", \"51130\", \"55196\", \"55256\"], \"outgoing_citations\": [\"112349\", \"115896\", \"128165\", \"130237\", \"157164\", \"157352\", \"164659\", \"167584\", \"169036\", \"170273\", \"171988\", \"177371\", \"181359\", \"182179\", \"193779\", \"221316\", \"222795\", \"224762\", \"227790\", \"232388\", \"233045\", \"233416\", \"235095\", \"235238\", \"235345\", \"239831\", \"248738\", \"249970\", \"260450\", \"260707\", \"265315\", \"271934\", \"272531\", \"290789\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"96578","document_content":"# Specialized federated learning using a mixture of experts\n## Categories\n- Machine Learning\n## Abstract\nIn federated learning, clients share a global model that has been trained on decentralized local client data. Although federated learning shows significant promise as a key approach when data cannot be shared or centralized, current methods show limited privacy properties and have shortcomings when applied to common real-world scenarios, especially when client data is heterogeneous. In this paper, we propose an alternative method to learn a personalized model for each client in a federated setting, with greater generalization abilities than previous methods. To achieve this personalization we propose a federated learning framework using a mixture of experts to combine the specialist nature of a locally trained model with the generalist knowledge of a global model. We evaluate our method on a variety of datasets with different levels of data heterogeneity, and our results show that the mixture of experts model is better suited as a personalized model for devices in these settings, outperforming both fine-tuned global models and local specialists.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.02056v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"96658","document_content":"# Linguistic Profiling of a Neural Language Model\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01869v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"21391\", \"20438\", \"42133\", \"46212\", \"49299\", \"53442\", \"75179\"], \"outgoing_citations\": [\"168097\", \"168415\", \"170855\", \"181468\", \"181964\", \"182508\", \"186130\", \"186250\", \"194308\", \"197416\", \"205161\", \"212036\", \"213906\", \"216455\", \"220137\", \"227792\", \"232479\", \"233400\", \"244357\", \"286594\", \"297423\", \"311683\", \"311845\"]}","task_split":"paper_retrieval"}
{"document_id":"96695","document_content":"# GenAug: Data Augmentation for Finetuning Text Generators\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language processing, and are especially challenging for low-data regimes. We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. We also examine the relationship between the amount of augmentation and the quality of the generated text. We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01794v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"36981\", \"42229\", \"5033\", \"24945\", \"25124\", \"56210\", \"8723\", \"22194\", \"29595\", \"44552\", \"51130\", \"70727\"], \"outgoing_citations\": [\"127538\", \"132683\", \"132775\", \"138517\", \"161092\", \"165308\", \"169366\", \"169372\", \"189595\", \"192782\", \"200867\", \"216792\", \"231898\", \"232334\", \"233045\", \"243154\", \"251240\", \"260450\", \"267622\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"96713","document_content":"# OLALA: Object-Level Active Learning for Efficient Document Layout Annotation\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nDocument images often have intricate layout structures, with numerous content regions (e.g. texts, figures, tables) densely arranged on each page. This makes the manual annotation of layout datasets expensive and inefficient. These characteristics also challenge existing active learning methods, as image-level scoring and selection suffer from the overexposure of common objects.Inspired by recent progresses in semi-supervised learning and self-training, we propose an Object-Level Active Learning framework for efficient document layout Annotation, OLALA. In this framework, only regions with the most ambiguous object predictions within an image are selected for annotators to label, optimizing the use of the annotation budget. For unselected predictions, the semi-automatic correction algorithm is proposed to identify certain errors based on prior knowledge of layout structures and rectifies them with minor supervision. Additionally, we carefully design a perturbation-based object scoring function for document images. It governs the object selection process via evaluating prediction ambiguities, and considers both the positions and categories of predicted layout objects. Extensive experiments show that OLALA can significantly boost model performance and improve annotation efficiency, given the same labeling budget. Code for this paper can be accessed via https:\/\/github.com\/lolipopshock\/detectron2_al.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01762v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"21570\"], \"outgoing_citations\": [\"59992\", \"130415\", \"132000\", \"154514\", \"155164\", \"156893\", \"171596\", \"172717\", \"216491\", \"237647\", \"264238\", \"266895\"]}","task_split":"paper_retrieval"}
{"document_id":"96756","document_content":"# Entity Embedding as Game Representation\n## Categories\n- Artificial Intelligence\n## Abstract\nProcedural content generation via machine learning (PCGML) has shown success at producing new video game content with machine learning. However, the majority of the work has focused on the production of static game content, including game levels and visual elements. There has been much less work on dynamic game content, such as game mechanics. One reason for this is the lack of a consistent representation for dynamic game content, which is key for a number of statistical machine learning approaches. We present an autoencoder for deriving what we call \"entity embeddings\", a consistent way to represent different dynamic entities across multiple games in the same representation. In this paper we introduce the learned representation, along with some evidence towards its quality and future utility.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01685v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"41101\", \"41052\", \"67124\"], \"outgoing_citations\": [\"123086\", \"139814\", \"177019\", \"200920\", \"216608\", \"218887\", \"261529\", \"274886\"]}","task_split":"paper_retrieval"}
{"document_id":"96766","document_content":"# Explainability via Responsibility\n## Categories\n- Artificial Intelligence\n## Abstract\nProcedural Content Generation via Machine Learning (PCGML) refers to a group of methods for creating game content (e.g. platformer levels, game maps, etc.) using machine learning models. PCGML approaches rely on black box models, which can be difficult to understand and debug by human designers who do not have expert knowledge about machine learning. This can be even more tricky in co-creative systems where human designers must interact with AI agents to generate game content. In this paper we present an approach to explainable artificial intelligence in which certain training instances are offered to human users as an explanation for the AI agent's actions during a co-creation process. We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01676v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"125366\", \"133812\", \"145664\", \"160936\", \"184390\", \"192122\", \"196744\", \"202457\", \"216607\", \"216608\", \"233501\", \"239346\", \"269165\", \"273118\", \"274886\", \"279091\", \"296187\", \"297233\", \"298512\", \"301647\", \"303951\", \"320829\"]}","task_split":"paper_retrieval"}
{"document_id":"96833","document_content":"# Reverse Operation based Data Augmentation for Solving Math Word Problems\n## Categories\n- Computation and Language\n## Abstract\nAutomatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic. We apply the augmented data on two SOTA math word problem solving models and compare our results with a strong data augmentation baseline. Experimental results show the effectiveness of our approach. We release our code and data at https:\/\/github.com\/yiyunya\/RODA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01556v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18991\", \"12817\"], \"outgoing_citations\": [\"129807\", \"141296\", \"161669\", \"183403\", \"196980\", \"200867\", \"210184\", \"211865\", \"218723\", \"232334\", \"233912\", \"234577\", \"246516\", \"266702\", \"278863\", \"284114\", \"287181\"]}","task_split":"paper_retrieval"}
{"document_id":"96875","document_content":"# Improving Lesion Detection by exploring bias on Skin Lesion dataset\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nAll datasets contain some biases, often unintentional, due to how they were acquired and annotated. These biases distort machine-learning models' performance, creating spurious correlations that the models can unfairly exploit, or, contrarily destroying clear correlations that the models could learn. With the popularity of deep learning models, automated skin lesion analysis is starting to play an essential role in the early detection of Melanoma. The ISIC Archive is one of the most used skin lesion sources to benchmark deep learning-based tools. Bissoto et al. experimented with different bounding-box based masks and showed that deep learning models could classify skin lesion images without clinically meaningful information in the input data. Their findings seem confounding since the ablated regions (random rectangular boxes) are not significant. The shape of the lesion is a crucial factor in the clinical characterization of a skin lesion. In that context, we performed a set of experiments that generate shape-preserving masks instead of rectangular bounding-box based masks. A deep learning model trained on these shape-preserving masked images does not outperform models trained on images without clinically meaningful information. That strongly suggests spurious correlations guiding the models. We propose use of general adversarial network (GAN) to mitigate the underlying bias.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01485v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189887\", \"199630\", \"241584\", \"248978\", \"272621\"]}","task_split":"paper_retrieval"}
{"document_id":"96997","document_content":"# WeMix: How to Better Utilize Data Augmentation\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Optimization and Control\n## Abstract\nData augmentation is a widely used training trick in deep learning to improve the network generalization ability. Despite many encouraging results, several recent studies did point out limitations of the conventional data augmentation scheme in certain scenarios, calling for a better theoretical understanding of data augmentation. In this work, we develop a comprehensive analysis that reveals pros and cons of data augmentation. The main limitation of data augmentation arises from the data bias, i.e. the augmented data distribution can be quite different from the original one. This data bias leads to a suboptimal performance of existing data augmentation methods. To this end, we develop two novel algorithms, termed \"AugDrop\" and \"MixLoss\", to correct the data bias in the data augmentation. Our theoretical analysis shows that both algorithms are guaranteed to improve the effect of data augmentation through the bias correction, which is further validated by our empirical studies. Finally, we propose a generic algorithm \"WeMix\" by combining AugDrop and MixLoss, whose effectiveness is observed from extensive empirical evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01267v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"117211\", \"127518\", \"140133\", \"140270\", \"142821\", \"156056\", \"169522\", \"172639\", \"174490\", \"178859\", \"185807\", \"186466\", \"187243\", \"188176\", \"189715\", \"194532\", \"201703\", \"205396\", \"206545\", \"207432\", \"209138\", \"210734\", \"210798\", \"213875\", \"215486\", \"219727\", \"238777\", \"242385\", \"249085\", \"252624\", \"280795\", \"286495\", \"340028\"]}","task_split":"paper_retrieval"}
{"document_id":"97000","document_content":"# Multilevel Text Alignment with Cross-Document Attention\n## Categories\n- Computation and Language\n## Abstract\nText alignment finds application in tasks such as citation recommendation and plagiarism detection. Existing alignment methods operate at a single, predefined level and cannot learn to align texts at, for example, sentence and document levels. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels (document-to-document and sentence-to-document). Our component is weakly supervised from document pairs and can align at multiple levels. Our evaluation on predicting document-to-document relationships and sentence-to-document relationships on the tasks of citation recommendation and plagiarism detection shows that our approach outperforms previously established hierarchical, attention encoders based on recurrent and transformer contextualization that are unaware of structural correspondence between documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01263v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9191\", \"43141\", \"45356\", \"55397\", \"75166\", \"77356\"], \"outgoing_citations\": [\"157612\", \"160103\", \"179896\", \"183462\", \"188510\", \"219083\", \"222741\", \"225130\", \"227804\", \"227981\", \"241183\", \"262139\", \"274301\", \"274817\", \"276805\", \"281092\", \"297795\", \"307223\", \"323451\", \"330941\"]}","task_split":"paper_retrieval"}
{"document_id":"97072","document_content":"# Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal. We leverage information about the past trajectory and visual context of the scene to estimate a multi-modal probability distribution over the possible goal positions, which is used to sample a potential goal during the inference. The routing is governed by a recurrent neural network that reacts to physical constraints in the nearby surroundings and generates feasible paths that route towards the sampled goal. Our extensive experimental evaluation shows that our method establishes a new state-of-the-art on several benchmarks while being able to generate a realistic and diverse set of trajectories that conform to physical constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01114v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"343094\", \"3992\", \"7156\", \"28452\", \"28894\", \"43396\", \"61695\", \"67762\", \"120882\"], \"outgoing_citations\": [\"148683\", \"166501\", \"170388\", \"174738\", \"177460\", \"179692\", \"187729\", \"187849\", \"189614\", \"214313\", \"227342\", \"229266\", \"232096\", \"237350\", \"248730\", \"249164\", \"268891\", \"294670\"]}","task_split":"paper_retrieval"}
{"document_id":"97089","document_content":"# HUMAN: Hierarchical Universal Modular ANnotator\n## Categories\n- Computation and Language\n## Abstract\nA lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes.   We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01080v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"194499\"]}","task_split":"paper_retrieval"}
{"document_id":"97095","document_content":"# Syntax Representation in Word Embeddings and Neural Networks -- A Survey\n## Categories\n- Computation and Language\n## Abstract\nNeural networks trained on natural language processing tasks capture syntax even though it is not provided as a supervision signal. This indicates that syntactic analysis is essential to the understating of language in artificial intelligence systems. This overview paper covers approaches of evaluating the amount of syntactic information included in the representations of words for different neural network architectures. We mainly summarize re-search on English monolingual data on language modeling tasks and multilingual data for neural machine translation systems and multilingual language models. We describe which pre-trained models and representations of language are best suited for transfer to syntactic tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01063v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54035\"], \"outgoing_citations\": [\"126029\", \"127993\", \"136564\", \"144956\", \"166780\", \"181098\", \"181468\", \"181964\", \"182390\", \"183179\", \"185112\", \"194308\", \"201566\", \"232479\", \"244357\", \"259706\", \"269127\", \"281121\"]}","task_split":"paper_retrieval"}
{"document_id":"97100","document_content":"# LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nEntity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Our source code and pretrained representations are available at https:\/\/github.com\/studio-ousia\/luke.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.01057v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"26103\", \"2019\", \"2681\", \"3030\", \"3492\", \"10050\", \"16132\", \"16051\", \"16224\", \"17438\", \"18863\", \"23896\", \"26567\", \"31837\", \"42156\", \"47698\", \"55762\", \"72014\", \"80770\", \"121898\", \"169211\", \"854\", \"5797\", \"5749\", \"6061\", \"6309\", \"9456\", \"10513\", \"14944\", \"15895\", \"15912\", \"19742\", \"21526\", \"22655\", \"22785\", \"22846\", \"24055\", \"24180\", \"24795\", \"25029\", \"29323\", \"32579\", \"34418\", \"36345\", \"37401\", \"38872\", \"39654\", \"45027\", \"45102\", \"45341\", \"45650\", \"46390\", \"46602\", \"48898\", \"50900\", \"55249\", \"55949\", \"56338\", \"56560\", \"59603\", \"60127\", \"61543\", \"62216\", \"77042\", \"77863\", \"95236\", \"97876\", \"111905\", \"77883\", \"87107\"], \"outgoing_citations\": [\"143992\", \"147649\", \"150253\", \"156620\", \"167753\", \"181468\", \"181948\", \"182116\", \"185889\", \"188123\", \"194633\", \"212304\", \"216419\", \"224786\", \"252059\", \"267123\", \"268728\", \"281163\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"97185","document_content":"# Autoregressive Entity Retrieval\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nEntities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. Current approaches can be understood as classifiers among atomic labels, one for each entity. Their weight vectors are dense entity representations produced by encoding entity meta information such as their descriptions. This approach has several shortcomings: (i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions; (ii) a large memory footprint is needed to store dense representations when considering large entity sets; (iii) an appropriately hard set of negative data has to be subsampled at training time. In this work, we propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion. This mitigates the aforementioned technical issues since: (i) the autoregressive formulation directly captures relations between context and entity name, effectively cross encoding both; (ii) the memory footprint is greatly reduced because the parameters of our encoder-decoder architecture scale with vocabulary size, not entity count; (iii) the softmax loss is computed without subsampling negative data. We experiment with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new state-of-the-art or very competitive results while using a tiny fraction of the memory footprint of competing systems. Finally, we demonstrate that new entities can be added by simply specifying their names. Code and pre-trained models at https:\/\/github.com\/facebookresearch\/GENRE.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00904v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2936\", \"2756\", \"2999\", \"16041\", \"17744\", \"18787\", \"26715\", \"27576\", \"49804\", \"61237\", \"63513\", \"121898\", \"169211\", \"2762\", \"12125\", \"16064\", \"21622\", \"22785\", \"24055\", \"24520\", \"24980\", \"26026\", \"26559\", \"27738\", \"29425\", \"41477\", \"45485\", \"49073\", \"50345\", \"55281\", \"55508\", \"56560\", \"58881\", \"69514\", \"94376\", \"77502\", \"92823\", \"92800\"], \"outgoing_citations\": [\"102310\", \"121381\", \"123394\", \"125976\", \"128396\", \"136774\", \"137257\", \"144895\", \"157181\", \"157308\", \"167165\", \"168589\", \"168951\", \"171884\", \"175089\", \"175482\", \"180267\", \"180333\", \"182677\", \"189532\", \"200632\", \"211664\", \"216563\", \"220553\", \"225340\", \"233941\", \"235175\", \"238986\", \"240614\", \"263874\", \"266850\", \"267881\", \"268166\", \"268728\", \"270020\", \"278948\", \"300498\", \"313713\", \"322694\"]}","task_split":"paper_retrieval"}
{"document_id":"97236","document_content":"# PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nThe ability to predict multiple possible future positions of the ego-vehicle given the surrounding context while also estimating their probabilities is key to safe autonomous driving. Most of the current state-of-the-art Deep Learning approaches are trained on trajectory data to achieve this task. However trajectory data captured by sensor systems is highly imbalanced, since by far most of the trajectories follow straight lines with an approximately constant velocity. This poses a huge challenge for the task of predicting future positions, which is inherently a regression problem. Current state-of-the-art approaches alleviate this problem only by major preprocessing of the training data, e.g. resampling, clustering into anchors etc. In this paper we propose an approach which reformulates the prediction problem as a classification task, allowing for powerful tools, e.g. focal loss, to combat the imbalance. To this end we design a generative probabilistic model consisting of a deep neural network with a Mixture of Gaussian head. A smart choice of the latent variable allows for the reformulation of the log-likelihood function as a combination of a classification problem and a much simplified regression problem. The output of our model is an estimate of the probability density function of future positions, hence allowing for prediction of multiple possible positions while also estimating their probabilities. The proposed approach can easily incorporate context information and does not require any preprocessing of the data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00802v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"153095\", \"154697\", \"157689\", \"162130\", \"227342\", \"237350\", \"268870\", \"268891\", \"278937\"]}","task_split":"paper_retrieval"}
{"document_id":"97239","document_content":"# JAKET: Joint Pre-training of Knowledge Graph and Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nKnowledge graphs (KGs) contain rich information about world knowledge, entities and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entities in text while the language module generates context-aware initial embeddings for entities and relations in the graph. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains. Experimental results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00796v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"4851\", \"16815\", \"17867\", \"77617\", \"95561\", \"15895\", \"24520\", \"24795\", \"28554\", \"40892\", \"56031\", \"56560\", \"52260\", \"62216\", \"81791\", \"91739\"], \"outgoing_citations\": [\"15892\", \"16815\", \"47726\", \"81791\", \"95561\", \"97423\", \"114729\", \"114729\", \"128180\", \"128180\", \"131012\", \"131012\", \"132687\", \"143089\", \"143089\", \"149018\", \"149018\", \"150253\", \"150253\", \"156620\", \"156620\", \"157487\", \"161513\", \"161513\", \"166443\", \"166443\", \"167753\", \"167753\", \"167880\", \"167880\", \"168870\", \"171633\", \"171633\", \"181948\", \"181948\", \"183816\", \"185889\", \"185889\", \"186432\", \"186432\", \"189600\", \"189600\", \"189726\", \"189726\", \"194308\", \"194308\", \"213179\", \"213179\", \"219306\", \"219306\", \"252936\", \"252936\", \"256171\", \"256171\"]}","task_split":"paper_retrieval"}
{"document_id":"97294","document_content":"# Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding\n## Categories\n- Computation and Language\n- Cryptography and Security\n## Abstract\nLinguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification. Recently, advances in neural language models (LMs) enable us to directly generate cover text conditioned on the secret message. In this study, we present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. We formally analyze the statistical imperceptibility of this method and empirically show it outperforms the previous state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits\/word and KL metrics, respectively. Finally, human evaluations show that 51% of generated cover texts can indeed fool eavesdroppers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00677v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Cryptography and Security\"], \"incoming_citations\": [\"54682\"], \"outgoing_citations\": [\"129596\", \"168806\", \"265004\", \"298750\", \"338025\"]}","task_split":"paper_retrieval"}
{"document_id":"97299","document_content":"# Predicting User Engagement Status for Online Evaluation of Intelligent Assistants\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nEvaluation of intelligent assistants in large-scale and online settings remains an open challenge. User behavior-based online evaluation metrics have demonstrated great effectiveness for monitoring large-scale web search and recommender systems. Therefore, we consider predicting user engagement status as the very first and critical step to online evaluation for intelligent assistants. In this work, we first proposed a novel framework for classifying user engagement status into four categories -- fulfillment, continuation, reformulation and abandonment. We then demonstrated how to design simple but indicative metrics based on the framework to quantify user engagement levels. We also aim for automating user engagement prediction with machine learning methods. We compare various models and features for predicting engagement status using four real-world datasets. We conducted detailed analyses on features and failure cases to discuss the performance of current models as well as challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00656v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"66042\"], \"outgoing_citations\": [\"257858\", \"261372\", \"291102\", \"292261\", \"292379\", \"295863\", \"298543\", \"301313\", \"305200\", \"310737\", \"310740\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"97356","document_content":"# AMUSED: An Annotation Framework of Multi-modal Social Media Data\n## Categories\n- Social and Information Networks\n- Computation and Language\n- Information Retrieval\n## Abstract\nIn this paper, we present a semi-automated framework called AMUSED for gathering multi-modal annotated data from the multiple social media platforms. The framework is designed to mitigate the issues of collecting and annotating social media data by cohesively combining machine and human in the data collection process. From a given list of the articles from professional news media or blog, AMUSED detects links to the social media posts from news articles and then downloads contents of the same post from the respective social media platform to gather details about that specific post. The framework is capable of fetching the annotated data from multiple platforms like Twitter, YouTube, Reddit. The framework aims to reduce the workload and problems behind the data annotation from the social media platforms. AMUSED can be applied in multiple application domains, as a use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media platforms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00502v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"2344\", \"21630\", \"30084\", \"43602\", \"125576\", \"29869\"], \"outgoing_citations\": [\"29869\", \"111821\", \"117359\", \"125576\", \"150688\", \"172003\", \"259202\", \"313061\", \"316713\"]}","task_split":"paper_retrieval"}
{"document_id":"97363","document_content":"# Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary\n## Categories\n- Computation and Language\n## Abstract\nA desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference. Traditional text overlap based metrics such as ROUGE fail to achieve this because they are limited to matching tokens, either lexically or via embeddings. In this work, we propose a metric to evaluate the content quality of a summary using question-answering (QA). QA-based methods directly measure a summary's information overlap with a reference, making them fundamentally different than text overlap metrics. We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval. QAEval out-performs current state-of-the-art metrics on most evaluations using benchmark datasets, while being competitive on others due to limitations of state-of-the-art models. Through a careful analysis of each component of QAEval, we identify its performance bottlenecks and estimate that its potential upper-bound performance surpasses all other automatic metrics, approaching that of the gold-standard Pyramid Method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2010.00490v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9385\", \"2684\", \"15952\", \"55016\", \"56583\", \"56864\", \"59139\", \"78031\", \"93096\", \"112910\"], \"outgoing_citations\": [\"59139\", \"91840\", \"95890\", \"109884\", \"126346\", \"132002\", \"132176\", \"168405\", \"168744\", \"183065\", \"190934\", \"218638\", \"220230\", \"241070\", \"298016\"]}","task_split":"paper_retrieval"}
{"document_id":"97520","document_content":"# The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nPredicting future behavior of other traffic participants is an essential task that needs to be solved by automated vehicles and human drivers alike to achieve safe and situationaware driving. Modern approaches to vehicles trajectory prediction typically rely on data-driven models like neural networks, in particular LSTMs (Long Short-Term Memorys), achieving promising results. However, the question of optimal composition of the underlying training data has received less attention. In this paper, we expand on previous work on vehicle trajectory prediction based on neural network models employing distributed representations to encode automotive scenes in a semantic vector substrate. We analyze the influence of variations in the training data on the performance of our prediction models. Thereby, we show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set and thereby, that the composition of training data in vehicle trajectory prediction is crucial for successful training. We conduct our analysis on challenging real-world driving data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/IJCNN48605.2020.9206627\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"9851\", \"15965\", \"21817\"], \"outgoing_citations\": [\"164133\", \"232095\", \"232096\", \"244314\"]}","task_split":"paper_retrieval"}
{"document_id":"97571","document_content":"# Attention-Aware Noisy Label Learning for Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision, such as image\/video classification. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. However, these samples often tend to contain incorrect labels (i.e. noisy labels), which will significantly degrade the network performance. In this paper, the attention-aware noisy label learning approach ($A^2NL$) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise. Specifically, a Noise-Attention model, which contains multiple noise-specific units, is designed to better capture noisy information. Each unit is expected to learn a specific noisy distribution for a subset of images so that different disturbances are more precisely modeled. Furthermore, a recursive learning process is introduced to strengthen the learning ability of the attention network by taking advantage of the learned high-level knowledge. To fully evaluate the proposed method, we conduct experiments from two aspects: manually flipped label noise on large-scale image classification datasets, including CIFAR-10, SVHN; and real-world label noise on an online crawled clothing dataset with multiple attributes. The superior results over state-of-the-art methods validate the effectiveness of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14757v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"13035\", \"34297\"], \"outgoing_citations\": [\"237153\", \"237224\", \"266873\", \"272109\", \"279862\", \"300646\", \"312090\", \"324493\", \"336408\", \"339854\"]}","task_split":"paper_retrieval"}
{"document_id":"97576","document_content":"# Multi-channel Deep 3D Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Computational Geometry\n## Abstract\nFace recognition has been of great importance in many applications as a biometric for its throughput, convenience, and non-invasiveness. Recent advancements in deep Convolutional Neural Network (CNN) architectures have boosted significantly the performance of face recognition based on two-dimensional (2D) facial texture images and outperformed the previous state of the art using conventional methods. However, the accuracy of 2D face recognition is still challenged by the change of pose, illumination, make-up, and expression. On the other hand, the geometric information contained in three-dimensional (3D) face data has the potential to overcome the fundamental limitations of 2D face data.   We propose a multi-Channel deep 3D face network for face recognition based on 3D face data. We compute the geometric information of a 3D face based on its piecewise-linear triangular mesh structure and then conformally flatten geometric information along with the color from 3D to 2D plane to leverage the state-of-the-art deep CNN architectures. We modify the input layer of the network to take images with nine channels instead of three only such that more geometric information can be explicitly fed to it. We pre-train the network using images from the VGG-Face \\cite{Parkhi2015} and then fine-tune it with the generated multi-channel face images. The face recognition accuracy of the multi-Channel deep 3D face network has achieved 98.6. The experimental results also clearly show that the network performs much better when a 9-channel image is flattened to plane based on the conformal map compared with the orthographic projection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14743v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computational Geometry\"], \"incoming_citations\": [], \"outgoing_citations\": [\"250292\", \"270106\"]}","task_split":"paper_retrieval"}
{"document_id":"97596","document_content":"# Learning Rewards from Linguistic Feedback\n## Categories\n- Artificial Intelligence\n## Abstract\nWe explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive learning from language assumes a particular form of input (e.g., commands). We propose a general framework which does not make this assumption, using aspect-based sentiment analysis to decompose feedback into sentiment about the features of a Markov decision process. We then perform an analogue of inverse reinforcement learning, regressing the sentiment on the features to infer the teacher's latent reward function. To evaluate our approach, we first collect a corpus of teaching behavior in a cooperative task where both teacher and learner are human. We implement three artificial learners: sentiment-based \"literal\" and \"pragmatic\" models, and an inference network trained end-to-end to predict latent rewards. We then repeat our initial experiment and pair them with human teachers. All three successfully learn from interactive human feedback. The sentiment models outperform the inference network, with the \"pragmatic\" model approaching human performance. Our work thus provides insight into the information structure of naturalistic linguistic feedback as well as methods to leverage it for reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14715v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"16536\", \"34439\", \"42116\", \"69518\", \"88351\"], \"outgoing_citations\": [\"105761\", \"108770\", \"142761\", \"162734\", \"175055\", \"176543\", \"176603\", \"177164\", \"181620\", \"192378\", \"196377\", \"198208\", \"208773\", \"229145\", \"232584\", \"259693\", \"263957\", \"264856\", \"268168\", \"268584\", \"275728\", \"279215\", \"290744\", \"290891\"]}","task_split":"paper_retrieval"}
{"document_id":"97694","document_content":"# Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent approaches in literature have exploited the multi-modal information in documents (text, layout, image) to serve specific downstream document tasks. However, they are limited by their - (i) inability to learn cross-modal representations across text, layout and image dimensions for documents and (ii) inability to process multi-page documents. Pre-training techniques have been shown in Natural Language Processing (NLP) domain to learn generic textual representations from large unlabelled datasets, applicable to various downstream NLP tasks. In this paper, we propose a multi-task learning-based framework that utilizes a combination of self-supervised and supervised pre-training tasks to learn a generic document representation applicable to various downstream document tasks. Specifically, we introduce Document Topic Modelling and Document Shuffle Prediction as novel pre-training tasks to learn rich image representations along with the text and layout representations for documents. We utilize the Longformer network architecture as the backbone to encode the multi-modal information from multi-page documents in an end-to-end fashion. We showcase the applicability of our pre-training framework on a variety of different real-world document tasks such as document classification, document information extraction, and document retrieval. We evaluate our framework on different standard document datasets and conduct exhaustive experiments to compare performance against various ablations of our framework and state-of-the-art baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14457v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15859\", \"31120\", \"44251\", \"47534\", \"55400\", \"45473\", \"84406\"], \"outgoing_citations\": [\"128774\", \"130460\", \"148399\", \"149004\", \"152003\", \"171596\", \"184301\", \"193222\", \"216765\", \"244011\", \"264315\", \"265587\", \"269105\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"97725","document_content":"# Multiple Word Embeddings for Increased Diversity of Representation\n## Categories\n- Computation and Language\n## Abstract\nMost state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand. The improvements afforded by these \"contextual embeddings\" come with a high computational cost. In this work, we explore a simple technique that substantially and consistently improves performance over a strong baseline with negligible increase in run time. We concatenate multiple pre-trained embeddings to strengthen our representation of words. We show that this concatenation technique works across many tasks, datasets, and model types. We analyze aspects of pre-trained embedding similarity and vocabulary coverage and find that the representational diversity between different pre-trained embeddings is the driving force of why this technique works. We provide open source implementations of our models in both TensorFlow and PyTorch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14394v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"55216\", \"95581\"], \"outgoing_citations\": [\"230543\", \"234203\", \"267692\", \"297150\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"97729","document_content":"# End-to-End Spoken Language Understanding Without Full Transcripts\n## Categories\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n- I.2.7\n## Abstract\nAn essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develop end-to-end (E2E) spoken language understanding systems that directly convert speech input to semantic entities and investigate if these E2E SLU models can be trained solely on semantic entity annotations without word-for-word transcripts. Training such models is very useful as they can drastically reduce the cost of data collection. We created two types of such speech-to-entities models, a CTC model and an attention-based encoder-decoder model, by adapting models trained originally for speech recognition. Given that our experiments involve speech input, these systems need to recognize both the entity label and words representing the entity value correctly. For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts. We also explored the scenario where the entities are in an order not necessarily related to spoken order in the utterance. With its ability to do re-ordering, the attention model did remarkably well, achieving only about 2% degradation in speech-to-bag-of-entities F1 score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14386v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\", \"I.2.7\"], \"incoming_citations\": [\"3823\", \"12476\", \"28852\", \"38881\", \"41573\", \"42707\", \"48244\", \"50569\", \"52010\", \"57429\", \"86592\"], \"outgoing_citations\": [\"89723\", \"95623\", \"146344\", \"180179\", \"190072\", \"191769\", \"216686\", \"241146\", \"285311\", \"285317\", \"291183\", \"300449\", \"310601\"]}","task_split":"paper_retrieval"}
{"document_id":"97781","document_content":"# Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nThe recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as \"put a hot piece of bread on a plate\". Currently, the best-performing models are able to complete less than 5% of these tasks successfully. In this work we focus on modeling the translation problem of converting natural language directives into detailed multi-step sequences of actions that accomplish those goals in the virtual environment. We empirically demonstrate that it is possible to generate gold multi-step plans from language directives alone without any visual input in 26% of unseen cases. When a small amount of visual information is incorporated, namely the starting location in the virtual environment, our best-performing GPT-2 model successfully generates gold command sequences in 58% of cases. Our results suggest that contextualized language models may provide strong visual semantic planning modules for grounded virtual agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.14259v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"36587\", \"39368\", \"44196\"], \"outgoing_citations\": [\"152871\", \"168951\", \"183049\", \"185524\", \"191878\", \"195772\", \"211921\", \"216731\", \"227594\", \"228916\", \"232274\", \"247418\", \"247900\", \"249906\", \"265722\"]}","task_split":"paper_retrieval"}
{"document_id":"97922","document_content":"# Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 2020\n## Categories\n- Computation and Language\n## Abstract\nThe authors of fake news often use facts from verified news sources and mix them with misinformation to create confusion and provoke unrest among the readers. The spread of fake news can thereby have serious implications on our society. They can sway political elections, push down the stock price or crush reputations of corporations or public figures. Several websites have taken on the mission of checking rumors and allegations, but are often not fast enough to check the content of all the news being disseminated. Especially social media websites have offered an easy platform for the fast propagation of information. Towards limiting fake news from being propagated among social media users, the task of this year's PAN 2020 challenge lays the focus on the fake news spreaders. The aim of the task is to determine whether it is possible to discriminate authors that have shared fake news in the past from those that have never done it. In this notebook, we describe our profiling system for the fake news detection task on Twitter. For this, we conduct different feature extraction techniques and learning experiments from a multilingual perspective, namely English and Spanish. Our final submitted systems use character n-grams as features in combination with a linear SVM for English and Logistic Regression for the Spanish language. Our submitted models achieve an overall accuracy of 73% and 79% on the English and Spanish official test set, respectively. Our experiments show that it is difficult to differentiate solidly fake news spreaders on Twitter from users who share credible information leaving room for further investigations. Our model ranked 3rd out of 72 competitors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13859v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22429\"], \"outgoing_citations\": [\"181527\", \"238986\", \"257863\", \"267552\", \"273723\"]}","task_split":"paper_retrieval"}
{"document_id":"97923","document_content":"# Neural Alignment for Face De-pixelization\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Graphics\n- Machine Learning\n- I.3.3; I.3.8; I.4.3; I.4.7; I.4.5; I.4.9; I.4.10; I.2.6; I.2.10\n## Abstract\nWe present a simple method to reconstruct a high-resolution video from a face-video, where the identity of a person is obscured by pixelization. This concealment method is popular because the viewer can still perceive a human face figure and the overall head motion. However, we show in our experiments that a fairly good approximation of the original video can be reconstructed in a way that compromises anonymity. Our system exploits the simultaneous similarity and small disparity between close-by video frames depicting a human face, and employs a spatial transformation component that learns the alignment between the pixelated frames. Each frame, supported by its aligned surrounding frames, is first encoded, then decoded to a higher resolution. Reconstruction and perceptual losses promote adherence to the ground-truth, and an adversarial loss assists in maintaining domain faithfulness. There is no need for explicit temporal coherency loss as it is maintained implicitly by the alignment of neighboring frames and reconstruction. Although simple, our framework synthesizes high-quality face reconstructions, demonstrating that given the statistical prior of a human face, multiple aligned pixelated frames contain sufficient information to reconstruct a high-quality approximation of the original signal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13856v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.GR\", \"cs.LG\", \"I.3.3; I.3.8; I.4.3; I.4.7; I.4.5; I.4.9; I.4.10; I.2.6; I.2.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Graphics\", \"Machine Learning\", \"I.3.3; I.3.8; I.4.3; I.4.7; I.4.5; I.4.9; I.4.10; I.2.6; I.2.10\"], \"incoming_citations\": [], \"outgoing_citations\": [\"109032\", \"111131\", \"137881\", \"166256\", \"182589\", \"184495\", \"187381\", \"187775\", \"194402\", \"205494\", \"209098\", \"220636\", \"220765\", \"223755\", \"230031\", \"230252\", \"245112\", \"246291\", \"248969\", \"259660\", \"269321\", \"270923\", \"280330\", \"288238\", \"294531\"]}","task_split":"paper_retrieval"}
{"document_id":"97979","document_content":"# Double Graph Based Reasoning for Document-level Relation Extraction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDocument-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entity-level graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https:\/\/github.com\/DreamInvoker\/GAIN .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13752v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"21235\", \"41725\", \"75164\", \"10438\", \"24252\", \"24888\", \"26964\", \"28348\", \"44107\", \"45204\", \"45130\", \"45088\", \"46065\", \"59299\", \"77498\", \"82764\"], \"outgoing_citations\": [\"125340\", \"131151\", \"134513\", \"164858\", \"169298\", \"180210\", \"180767\", \"181307\", \"181948\", \"185952\", \"192327\", \"200461\", \"214572\", \"216419\", \"220105\", \"220475\", \"240504\", \"258746\", \"268414\", \"284622\", \"300867\"]}","task_split":"paper_retrieval"}
{"document_id":"97992","document_content":"# Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nThe nodes of a graph existing in a cluster are more likely to connect to each other than with other nodes in the graph. Then revealing some information about some nodes, the structure of the graph (graph edges) provides this opportunity to know more information about other nodes. From this perspective, this paper revisits the node classification task in a semi-supervised scenario by graph convolutional networks (GCNs). The goal is to benefit from the flow of information that circulates around the revealed node labels. The contribution of this paper is twofold. First, this paper provides a method for extracting side information from a graph realization. Then a new GCN architecture is presented that combines the output of traditional GCN and the extracted side information. Another contribution of this paper is relevant to non-graph observations (independent side information) that exists beside a graph realization in many applications. Indeed, the extracted side information can be replaced by a sequence of side information that is independent of the graph structure. For both cases, the experiments on synthetic and real-world datasets demonstrate that the proposed model achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13734v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"24086\", \"38089\", \"40864\"], \"outgoing_citations\": [\"184499\", \"230953\", \"242145\", \"245486\", \"246497\", \"252175\", \"265022\", \"279575\", \"295676\", \"303863\"]}","task_split":"paper_retrieval"}
{"document_id":"98018","document_content":"# VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nIt is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other thanCOCO Captions, is allowed for model training. Thus, conventional Vision-Language Pre-training (VLP) methods cannot be applied. This paper presents VIsual VOcabulary pretraining (VIVO) that performs pre-training in the absence of caption annotations. By breaking the dependency of paired image-caption training data in VLP, VIVO can leverage large amounts of paired image-tag data to learn a visual vocabulary. This is done by pre-training a multi-layer Transformer model that learns to align image-level tags with their corresponding image region features. To address the unordered nature of image tags, VIVO uses a Hungarian matching loss with masked tag prediction to conduct pre-training. We validate the effectiveness of VIVO by fine-tuning the pre-trained model for image captioning. In addition, we perform an analysis of the visual-text alignment inferred by our model. The results show that our model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects. Our single model has achieved new state-of-the-art results on nocaps and surpassed the human CIDEr score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13682v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"35427\", \"6697\", \"7440\", \"24386\", \"32961\", \"46949\"], \"outgoing_citations\": [\"107473\", \"131401\", \"133695\", \"133970\", \"135255\", \"135934\", \"165195\", \"170603\", \"171096\", \"183087\", \"192659\", \"205227\", \"231619\", \"233685\", \"236042\", \"237655\", \"258345\", \"278948\", \"278954\", \"289627\", \"295639\", \"302621\", \"303209\", \"311084\", \"315108\", \"320761\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"98059","document_content":"# Anomaly Detection and Sampling Cost Control via Hierarchical GANs\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n## Abstract\nAnomaly detection incurs certain sampling and sensing costs and therefore it is of great importance to strike a balance between the detection accuracy and these costs. In this work, we study anomaly detection by considering the detection of threshold crossings in a stochastic time series without the knowledge of its statistics. To reduce the sampling cost in this detection process, we propose the use of hierarchical generative adversarial networks (GANs) to perform nonuniform sampling. In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector. In the experiments, we analyze the performance of the proposed hierarchical GAN detector considering the metrics of detection delay, miss rates, average cost of error, and sampling ratio. We identify the tradeoffs in the performance as the buffer zone sizes and the number of GAN levels in the hierarchy vary. We also compare the performance with that of a sampling policy that approximately minimizes the sum of average costs of sampling and error given the parameters of the stochastic process. We demonstrate that the proposed GAN-based detector can have significant performance improvements in terms of detection delay and average cost of error with a larger buffer zone but at the cost of increased sampling rates.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13598v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169786\", \"183273\", \"202920\", \"209409\", \"214637\", \"257625\"]}","task_split":"paper_retrieval"}
{"document_id":"98159","document_content":"# The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13290v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"51525\", \"149026\", \"33535\", \"57424\", \"63703\"], \"outgoing_citations\": [\"49673\", \"59464\", \"60171\", \"64800\", \"70243\", \"95130\", \"96970\", \"108139\", \"112193\", \"116130\", \"123715\", \"132689\", \"133676\", \"133736\", \"134101\", \"134999\", \"135047\", \"135421\", \"139829\", \"140200\", \"154273\", \"154506\", \"157301\", \"159596\", \"160037\", \"167654\", \"169121\", \"170987\", \"171403\", \"172817\", \"177337\", \"187153\", \"187442\", \"187652\", \"187749\", \"188216\", \"189549\", \"190402\", \"191293\", \"191916\", \"192174\", \"192235\", \"193007\", \"193445\", \"193533\", \"193737\", \"193843\", \"196854\", \"198338\", \"202365\", \"203840\", \"206060\", \"206202\", \"207172\", \"207771\", \"208448\", \"209385\", \"211990\", \"213155\", \"213905\", \"218745\", \"218975\", \"219131\", \"219464\", \"220959\", \"221296\", \"222085\", \"222348\", \"222824\", \"225992\", \"226432\", \"228822\", \"230415\", \"233545\", \"234861\", \"235160\", \"235188\", \"235351\", \"235618\", \"238254\", \"238286\", \"239321\", \"240261\", \"240456\", \"240929\", \"241654\", \"243176\", \"243986\", \"244877\", \"247558\", \"248500\", \"249527\", \"249920\", \"250023\", \"250081\", \"252224\", \"252688\", \"254157\", \"254268\", \"256007\", \"256279\", \"256832\", \"257586\", \"257765\", \"258129\", \"258352\", \"258667\", \"259086\", \"259852\", \"260755\", \"260916\", \"260932\", \"262491\", \"263034\", \"263648\", \"264615\", \"267612\", \"267940\", \"268241\", \"268379\", \"268405\", \"268970\", \"269388\", \"270376\", \"270923\", \"274070\", \"274550\", \"275209\", \"275470\", \"275999\", \"278071\", \"278526\", \"279419\", \"279992\", \"281201\", \"285564\", \"286311\", \"286598\", \"287010\", \"287166\", \"287611\", \"288147\", \"288159\", \"290219\", \"290682\", \"291277\", \"291992\", \"292924\", \"294284\", \"294693\", \"296001\", \"296091\", \"296384\", \"297167\", \"300606\", \"301945\", \"302157\", \"302701\", \"302868\", \"302895\", \"303481\", \"303724\", \"306222\", \"306502\", \"307292\", \"310635\", \"311792\", \"317441\", \"317768\", \"318091\", \"318394\", \"320918\", \"321122\", \"323646\", \"327014\", \"328183\", \"328797\", \"331125\"]}","task_split":"paper_retrieval"}
{"document_id":"98307","document_content":"# Learning to Improve Image Compression without Changing the Standard Decoder\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (e.g., JPEG) in personal computers and mobiles. Therefore, we propose learning to improve the encoding performance with the standard decoder. In this paper, We work on JPEG as an example. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients, aiming at facilitating the JPEG compression. Moreover, we propose learning the JPEG quantization table jointly with the pre-editing network. Most importantly, we do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this translates to better overall color retention especially when strong compression is applied. The codes are available at https:\/\/github.com\/YannickStruempler\/LearnedJPEG.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.12927v3\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"3535\", \"83013\"], \"outgoing_citations\": [\"115227\", \"144653\", \"204828\", \"218722\", \"243653\", \"245227\", \"255407\", \"259136\", \"269868\", \"270142\", \"270241\", \"272675\", \"279844\", \"281135\", \"286044\", \"286385\", \"303010\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"98455","document_content":"# Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nA fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally formulated as a reinforcement learning problem under partial observations, where an agent must estimate relevant latent variables in the world from its evidence, anticipate possible future states, and choose actions that optimize total expected reward. This problem can be solved by control theory, which allows us to find the optimal actions for a given system dynamics and objective function. However, animals often appear to behave suboptimally. Why? We hypothesize that animals have their own flawed internal model of the world, and choose actions with the highest expected subjective reward according to that flawed model. We describe this behavior as rational but not optimal. The problem of Inverse Rational Control (IRC) aims to identify which internal model would best explain an agent's actions. Our contribution here generalizes past work on Inverse Rational Control which solved this problem for discrete control in partially observable Markov decision processes. Here we accommodate continuous nonlinear dynamics and continuous actions, and impute sensory observations corrupted by unknown noise that is private to the animal. We first build an optimal Bayesian agent that learns an optimal policy generalized over the entire model space of dynamics and subjective rewards using deep reinforcement learning. Crucially, this allows us to compute a likelihood over models for experimentally observable action trajectories acquired from a suboptimal agent. We then find the model parameters that maximize the likelihood using gradient ascent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.12576v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SY\", \"eess.SY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"98550","document_content":"# A little goes a long way: Improving toxic language classification despite data scarcity\n## Categories\n- Computation and Language\n## Abstract\nDetection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation - generating new synthetic data from a labeled seed dataset - can help. The efficacy of data augmentation on toxic language classification has not been fully explored. We present the first systematic study on how data augmentation techniques impact performance across toxic language classifiers, ranging from shallow logistic regression architectures to BERT - a state-of-the-art pre-trained Transformer network. We compare the performance of eight techniques on very scarce seed datasets. We show that while BERT performed the best, shallow classifiers performed comparably when trained on data augmented with a combination of three techniques, including GPT-2-generated sentences. We discuss the interplay of performance and computational overhead, which can inform the choice of techniques under different constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.12344v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21935\", \"22451\", \"55265\", \"66559\"], \"outgoing_citations\": [\"139587\", \"147183\", \"165441\", \"167713\", \"168298\", \"168951\", \"181686\", \"191935\", \"200867\", \"207256\", \"217138\", \"220101\", \"251736\", \"254008\", \"256813\", \"264835\", \"265309\", \"267601\", \"272068\", \"272531\", \"281768\", \"283778\", \"285037\", \"308446\"]}","task_split":"paper_retrieval"}
{"document_id":"98551","document_content":"# Developing FB Chatbot Based on Deep Learning Using RASA Framework for University Enquiries\n## Categories\n- Computation and Language\n## Abstract\nSmart systems for Universities powered by Artificial Intelligence have been massively developed to help humans in various tasks. The chatbot concept is not something new in today society which is developing with recent technology. College students or candidates of college students often need actual information like asking for something to customer service, especially during this pandemic, when it is difficult to have an immediate face-to-face meeting. Chatbots are functionally helping in several things such as curriculum information, admission for new students, schedule info for any lecture courses, students grade information, and some adding features for Muslim worships schedule, also weather forecast information. This Chatbot is developed by Deep Learning models, which was adopted by an artificial intelligence model that replicates human intelligence with some specific training schemes. This kind of Deep Learning is based on RNN which has some specific memory savings scheme for the Deep Learning Model, specifically this chatbot using LSTM which already integrates by RASA framework. LSTM is also known as Long Short Term Memory which efficiently saves some required memory but will remove some memory that is not needed. This Chatbot uses the FB platform because of the FB users have already reached up to 60.8% of its entire population in Indonesia. Here's the chatbot only focuses on case studies at campus of the Magister Informatics FTI University of Islamic Indonesia. This research is a first stage development within fairly sufficient simulate data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1088\/1757-899X\/1077\/1\/012060\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"170492\", \"247467\", \"256219\"]}","task_split":"paper_retrieval"}
{"document_id":"98558","document_content":"# Empirical Study of Text Augmentation on Social Media Text in Vietnamese\n## Categories\n- Computation and Language\n## Abstract\nIn the text classification problem, the imbalance of labels in datasets affect the performance of the text-classification models. Practically, the data about user comments on social networking sites not altogether appeared - the administrators often only allow positive comments and hide negative comments. Thus, when collecting the data about user comments on the social network, the data is usually skewed about one label, which leads the dataset to become imbalanced and deteriorate the model's ability. The data augmentation techniques are applied to solve the imbalance problem between classes of the dataset, increasing the prediction model's accuracy. In this paper, we performed augmentation techniques on the VLSP2019 Hate Speech Detection on Vietnamese social texts and the UIT - VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis. The result of augmentation increases by about 1.5% in the F1-macro score on both corpora.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.12319v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"36981\"], \"outgoing_citations\": [\"112399\", \"144785\", \"200867\"]}","task_split":"paper_retrieval"}
{"document_id":"98639","document_content":"# No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes\/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May.~20,~2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.12056v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"26103\", \"2998\", \"23491\", \"45238\", \"50561\", \"55199\"], \"outgoing_citations\": [\"128027\", \"201689\", \"208172\", \"221086\", \"252059\", \"270020\", \"287982\", \"288385\"]}","task_split":"paper_retrieval"}
{"document_id":"98821","document_content":"# Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems\n## Categories\n- Computation and Language\n## Abstract\nAutomatic math word problem solving has attracted growing attention in recent years. The evaluation datasets used by previous works have serious limitations in terms of scale and diversity. In this paper, we release a new large-scale and template-rich math word problem dataset named Ape210K. It consists of 210K Chinese elementary school-level math problems, which is 9 times the size of the largest public dataset Math23K. Each problem contains both the gold answer and the equations needed to derive the answer. Ape210K is also of greater diversity with 56K templates, which is 25 times more than Math23K. Our analysis shows that solving Ape210K requires not only natural language understanding but also commonsense knowledge. We expect Ape210K to be a benchmark for math word problem solving systems. Experiments indicate that state-of-the-art models on the Math23K dataset perform poorly on Ape210K. We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3.2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset. The gap is still significant between human and our baseline model, calling for further research efforts. We make Ape210K dataset publicly available at https:\/\/github.com\/yuantiku\/ape210k","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.11506v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"32821\", \"12817\", \"13400\", \"20765\", \"29540\", \"43938\"], \"outgoing_citations\": [\"38612\", \"161669\", \"183403\", \"196990\", \"210184\", \"211865\", \"266702\", \"278863\", \"290632\", \"300505\"]}","task_split":"paper_retrieval"}
{"document_id":"98857","document_content":"# BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nElectronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top to bottom: patient journey - all the experiences of diagnoses and treatments over a period of time; individual visit - a set of medical codes in a particular visit; and medical code - a specific record in the form of medical codes. As EHRs begin to amass in millions, the potential benefits, which these data might hold for medical research and medical outcome prediction, are staggering - including, for example, predicting future admissions to hospitals, diagnosing illnesses or determining the efficacy of medical treatments. Each of these analytics tasks requires a domain knowledge extraction method to transform the hierarchical patient journey into a vector representation for further prediction procedure. The representations should embed a sequence of visits and a set of medical codes with a specific timestamp, which are crucial to any downstream prediction tasks. Hence, expressively powerful representations are appealing to boost learning performance. To this end, we propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys, based solely on the proposed attention mechanism. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset. The empirical results demonstrate the proposed BiteNet model produces higher-quality representations than state-of-the-art baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.13252v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"25266\"], \"outgoing_citations\": [\"118587\", \"166725\", \"213345\", \"229111\", \"236910\", \"244340\", \"250829\", \"255999\", \"263454\", \"264024\", \"267031\", \"271934\", \"279891\", \"286253\", \"287747\", \"291575\", \"298095\", \"307223\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"98933","document_content":"# A Comparative Study on Structural and Semantic Properties of Sentence Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSentence embeddings encode natural language sentences as low-dimensional dense vectors. A great deal of effort has been put into using sentence embeddings to improve several important natural language processing tasks. Relation extraction is such an NLP task that aims at identifying structured relations defined in a knowledge base from unstructured text. A promising and more efficient approach would be to embed both the text and structured knowledge in low-dimensional spaces and discover semantic alignments or mappings between them. Although a number of techniques have been proposed in the literature for embedding both sentences and knowledge graphs, little is known about the structural and semantic properties of these embedding spaces in terms of relation extraction. In this paper, we investigate the aforementioned properties by evaluating the extent to which sentences carrying similar senses are embedded in close proximity sub-spaces, and if we can exploit that structure to align sentences to a knowledge graph. We propose a set of experiments using a widely-used large-scale data set for relation extraction and focusing on a set of key sentence embedding methods. We additionally provide the code for reproducing these experiments at https:\/\/github.com\/akalino\/semantic-structural-sentences. These embedding methods cover a wide variety of techniques ranging from simple word embedding combination to transformer-based BERT-style model. Our experimental results show that different embedding spaces have different degrees of strength for the structural and semantic properties. These results provide useful information for developing embedding-based relation extraction methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.11226v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"137598\", \"137785\", \"168219\", \"169179\", \"204715\", \"215953\", \"217402\", \"220400\", \"239686\", \"267161\", \"274696\", \"281792\", \"310737\", \"310786\", \"316630\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"98941","document_content":"# Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nDeep Reinforcement Learning (DRL) has been successfully used to solve different challenges, e.g. complex board and computer games, recently. However, solving real-world robotics tasks with DRL seems to be a more difficult challenge. The desired approach would be to train the agent in a simulator and transfer it to the real world. Still, models trained in a simulator tend to perform poorly in real-world environments due to the differences. In this paper, we present a DRL-based algorithm that is capable of performing autonomous robot control using Deep Q-Networks (DQN). In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment. The method is evaluated in the Duckietown environment, where the agent has to follow the lane based on a monocular camera input. The trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/IJCNN48605.2020.9207497\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"1342\", \"1346\", \"78353\", \"33522\"], \"outgoing_citations\": [\"151271\", \"161295\", \"196237\", \"214419\", \"235203\", \"247012\", \"253045\", \"255302\", \"267981\", \"269382\", \"313499\"]}","task_split":"paper_retrieval"}
{"document_id":"98959","document_content":"# Hierarchical Pre-training for Sequence Labelling in Spoken Dialog\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSequence labelling tasks like Dialog Act and Emotion\/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (\\texttt{SILICONE}). \\texttt{SILICONE} is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over $2.3$ billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.11152v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5604\", \"18278\", \"22370\", \"26668\", \"27157\", \"27160\", \"51288\", \"61175\", \"134932\"], \"outgoing_citations\": [\"95622\", \"98901\", \"117770\", \"134932\", \"141098\", \"144817\", \"151647\", \"155312\", \"162284\", \"162585\", \"165441\", \"169509\", \"169630\", \"183017\", \"186043\", \"192157\", \"210078\", \"213458\", \"214692\", \"215343\", \"216019\", \"241152\", \"250398\", \"253593\", \"256124\", \"271934\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"99062","document_content":"# Leveraging Local and Global Descriptors in Parallel to Search Correspondences for Visual Localization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual localization to compute 6DoF camera pose from a given image has wide applications such as in robotics, virtual reality, augmented reality, etc. Two kinds of descriptors are important for the visual localization. One is global descriptors that extract the whole feature from each image. The other is local descriptors that extract the local feature from each image patch usually enclosing a key point. More and more methods of the visual localization have two stages: at first to perform image retrieval by global descriptors and then from the retrieval feedback to make 2D-3D point correspondences by local descriptors. The two stages are in serial for most of the methods. This simple combination has not achieved superiority of fusing local and global descriptors. The 3D points obtained from the retrieval feedback are as the nearest neighbor candidates of the 2D image points only by global descriptors. Each of the 2D image points is also called a query local feature when performing the 2D-3D point correspondences. In this paper, we propose a novel parallel search framework, which leverages advantages of both local and global descriptors to get nearest neighbor candidates of a query local feature. Specifically, besides using deep learning based global descriptors, we also utilize local descriptors to construct random tree structures for obtaining nearest neighbor candidates of the query local feature. We propose a new probabilistic model and a new deep learning based local descriptor when constructing the random trees. A weighted Hamming regularization term to keep discriminativeness after binarization is given in the loss function for the proposed local descriptor. The loss function co-trains both real and binary descriptors of which the results are integrated into the random trees.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.10891v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1837\"], \"outgoing_citations\": [\"180750\", \"187122\", \"191270\", \"199460\", \"206658\", \"224369\", \"227654\", \"235575\", \"246925\", \"259959\", \"264978\", \"268476\", \"274846\", \"312170\", \"314520\"]}","task_split":"paper_retrieval"}
{"document_id":"99195","document_content":"# Context-theoretic Semantics for Natural Language: an Algebraic Framework\n## Categories\n- Computation and Language\n## Abstract\nTechniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context.   In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts it occurs in in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings.   Together these properties form guidelines as to how to construct semantic representations within the framework. A context theory is an implementation of the framework; in an implementation strings are represented as vectors with the properties deduced from the theoretical analysis.   We show how to incorporate logical semantics into context theories; this enables us to represent statistical information about uncertainty by taking weighted sums of individual representations. We also use the framework to analyse approaches to the task of recognising textual entailment, to ontological representations of meaning and to representing syntactic structure. For the latter, we give new algebraic descriptions of link grammar.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.10542v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"349865\"]}","task_split":"paper_retrieval"}
{"document_id":"99332","document_content":"# SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nModels that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve OOD generalization. However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold. We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold. We investigate the use of SSMBA in the natural language domain, leveraging the manifold assumption to reconstruct corrupted text with masked language models. In experiments on robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently outperforms existing data augmentation methods and baseline models on both in-domain and OOD data, achieving gains of 0.8% accuracy on OOD Amazon reviews, 1.8% accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.10195v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2567\", \"18991\", \"29718\", \"38082\", \"55926\", \"615\", \"25264\", \"26301\", \"27094\", \"33281\", \"41974\", \"46466\", \"46744\", \"48848\", \"49616\", \"51130\", \"51287\", \"55134\", \"58529\", \"80747\", \"90083\"], \"outgoing_citations\": [\"129289\", \"131414\", \"138517\", \"157473\", \"158701\", \"200867\", \"201080\", \"202985\", \"205715\", \"220611\", \"231898\", \"249403\", \"250577\", \"267601\", \"272068\", \"285557\", \"290407\", \"290778\", \"320317\"]}","task_split":"paper_retrieval"}
{"document_id":"99373","document_content":"# Composed Variational Natural Language Generation for Few-shot Intents\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder. CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples. Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent. To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task. Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.10056v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8232\", \"3107\", \"23791\", \"24015\", \"26015\", \"52037\", \"52200\", \"55134\", \"53838\", \"96445\"], \"outgoing_citations\": [\"111046\", \"133147\", \"152729\", \"157509\", \"163280\", \"167338\", \"169329\", \"187236\", \"195286\", \"200867\", \"217632\", \"218853\", \"219441\", \"230543\", \"270040\", \"271625\", \"272531\", \"302948\", \"328112\"]}","task_split":"paper_retrieval"}
{"document_id":"99420","document_content":"# Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People\n## Categories\n- Quantitative Methods\n- Machine Learning\n## Abstract\nStandard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values for diabetic people. In this study, we propose the coherent mean squared glycemic error (gcMSE) loss function. It penalizes the model during its training not only of the prediction errors, but also on the predicted variation errors which is important in glucose prediction. Moreover, it makes possible to adjust the weighting of the different areas in the error space to better focus on dangerous regions. In order to use the loss function in practice, we propose an algorithm that progressively improves the clinical acceptability of the model, so that we can achieve the best tradeoff possible between accuracy and given clinical criteria. We evaluate the approaches using two diabetes datasets, one having type-1 patients and the other type-2 patients. The results show that using the gcMSE loss function, instead of a standard MSE loss function, improves the clinical acceptability of the models. In particular, the improvements are significant in the hypoglycemia region. We also show that this increased clinical acceptability comes at the cost of a decrease in the average accuracy of the model. Finally, we show that this tradeoff between accuracy and clinical acceptability can be successfully addressed with the proposed algorithm. For given clinical criteria, the algorithm can find the optimal solution that maximizes the accuracy while at the same meeting the criteria.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.10514v2\", \"primary_category\": \"q-bio.QM\", \"categories\": [\"q-bio.QM\", \"cs.LG\"], \"primary_category_human_readable\": \"Quantitative Methods\", \"categories_human_readable\": [\"Quantitative Methods\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"101758\", \"115461\", \"115463\"]}","task_split":"paper_retrieval"}
{"document_id":"99469","document_content":"# Target Conditioning for One-to-Many Generation\n## Categories\n- Machine Learning\n## Abstract\nNeural Machine Translation (NMT) models often lack diversity in their generated translations, even when paired with search algorithm, like beam search. A challenge is that the diversity in translations are caused by the variability in the target language, and cannot be inferred from the source sentence alone. In this paper, we propose to explicitly model this one-to-many mapping by conditioning the decoder of a NMT model on a latent variable that represents the domain of target sentences. The domain is a discrete variable generated by a target encoder that is jointly trained with the NMT model. The predicted domain of target sentences are given as input to the decoder during training. At inference, we can generate diverse translations by decoding with different domains. Unlike our strongest baseline (Shen et al., 2019), our method can scale to any number of domains without affecting the performance or the training time. We assess the quality and diversity of translations generated by our model with several metrics, on three different datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.09758v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"56086\", \"55960\", \"98161\"], \"outgoing_citations\": [\"198185\", \"198774\", \"221537\", \"238926\", \"239563\", \"240486\", \"249972\", \"265139\", \"269122\", \"271234\", \"273624\", \"274450\", \"279543\", \"290766\", \"291858\", \"292261\", \"302815\", \"302948\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"99493","document_content":"# Reinforcement Learning Approaches in Social Robotics\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nThis article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.09689v4\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"3907\", \"37989\", \"40343\", \"89855\"], \"outgoing_citations\": [\"169909\", \"188486\", \"210517\", \"235597\", \"239153\", \"258199\", \"266458\", \"272862\", \"273203\", \"273663\", \"287681\", \"293917\", \"309111\", \"334182\", \"334905\", \"359369\"]}","task_split":"paper_retrieval"}
{"document_id":"99608","document_content":"# Latent Representation Prediction Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nDeeply-learned planning methods are often based on learning representations that are optimized for unrelated tasks. For example, they might be trained on reconstructing the environment. These representations are then combined with predictor functions for simulating rollouts to navigate the environment. We find this principle of learning representations unsatisfying and propose to learn them such that they are directly optimized for the task at hand: to be maximally predictable for the predictor function. This results in representations that are by design optimal for the downstream task of planning, where the learned predictor function is used as a forward model.   To this end, we propose a new way of jointly learning this representation along with the prediction function, a system we dub Latent Representation Prediction Network (LARP). The prediction function is used as a forward model for search on a graph in a viewpoint-matching task and the representation learned to maximize predictability is found to outperform a pre-trained representation. Our approach is shown to be more sample-efficient than standard reinforcement learning methods and our learned representation transfers successfully to dissimilar objects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.09439v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"51080\"], \"outgoing_citations\": [\"139633\", \"147076\", \"162949\", \"164433\", \"177686\", \"182108\", \"189468\", \"207601\", \"210517\", \"220196\", \"223487\", \"225762\", \"227872\", \"229302\", \"229890\", \"236969\", \"240323\", \"242444\", \"264963\", \"265985\", \"269692\", \"281257\", \"290243\", \"290620\", \"298651\", \"308711\", \"311482\", \"313644\", \"328183\", \"332151\", \"337193\"]}","task_split":"paper_retrieval"}
{"document_id":"99717","document_content":"# BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nIn recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) bioALBERT, an effective domain-specific language model trained on large-scale biomedical corpora designed to capture biomedical context-dependent NER. We adopted a self-supervised loss used in ALBERT that focuses on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction techniques to lower memory consumption and increase the training speed in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.09223v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2566\", \"36467\", \"37082\", \"55453\", \"17477\", \"41328\", \"63318\", \"90666\"], \"outgoing_citations\": [\"136153\", \"159867\", \"180997\", \"192402\", \"197910\", \"200764\", \"200804\", \"213077\", \"213418\", \"249726\", \"292448\"]}","task_split":"paper_retrieval"}
{"document_id":"99848","document_content":"# Multi-modal Experts Network for Autonomous Driving\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nEnd-to-end learning from sensory data has shown promising results in autonomous driving. While employing many sensors enhances world perception and should lead to more robust and reliable behavior of autonomous vehicles, it is challenging to train and deploy such network and at least two problems are encountered in the considered setting. The first one is the increase of computational complexity with the number of sensing devices. The other is the phenomena of network overfitting to the simplest and most informative input. We address both challenges with a novel, carefully tailored multi-modal experts network architecture and propose a multi-stage training procedure. The network contains a gating mechanism, which selects the most relevant input at each inference time step using a mixed discrete-continuous policy. We demonstrate the plausibility of the proposed approach on our 1\/6 scale truck equipped with three cameras and one LiDAR.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICRA40945.2020.9197459\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85146\"], \"outgoing_citations\": [\"208759\", \"253953\", \"260893\", \"265091\", \"273123\", \"275599\", \"278822\", \"283956\", \"302966\", \"313499\", \"314867\", \"320127\", \"331635\", \"336507\"]}","task_split":"paper_retrieval"}
{"document_id":"99875","document_content":"# $\u03c3^2$R Loss: a Weighted Loss by Multiplicative Factors using Sigmoidal Functions\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neural networks (CNN) use the loss function as a supervisory signal to train a deep model and contribute significantly to achieving the state of the art in some fields of artificial vision. Cross-entropy and Center loss functions are commonly used to increase the discriminating power of learned functions and increase the generalization performance of the model. Center loss minimizes the class intra-class variance and at the same time penalizes the long distance between the deep features inside each class. However, the total error of the center loss will be heavily influenced by the majority of the instances and can lead to a freezing state in terms of intra-class variance. To address this, we introduce a new loss function called sigma squared reduction loss ($\\sigma^2$R loss), which is regulated by a sigmoid function to inflate\/deflate the error per instance and then continue to reduce the intra-class variance. Our loss has clear intuition and geometric interpretation, furthermore, we demonstrate by experiments the effectiveness of our proposal on several benchmark datasets showing the intra-class variance reduction and overcoming the results obtained with center loss and soft nearest neighbour functions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.08796v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"132952\", \"134205\", \"160157\", \"200093\", \"223702\", \"235160\", \"238741\", \"241796\", \"244936\", \"260426\", \"321079\"]}","task_split":"paper_retrieval"}
{"document_id":"99915","document_content":"# Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nUnderstanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge. Summarization of medical conversations is a particularly important aspect of medical conversation understanding since it addresses a very real need in medical practice: capturing the most important aspects of a medical encounter so that they can be used for medical decision making and subsequent follow ups.   In this paper we present a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient's medical history. Our approach is a variation of the pointer generator network where we introduce a penalty on the generator distribution, and we explicitly model negations. The model also captures important properties of medical conversations such as medical knowledge coming from standardized medical ontologies better than when those concepts are introduced explicitly. Through evaluation by doctors, we show that our approach is preferred on twice the number of summaries to the baseline pointer generator model and captures most or all of the information in 80% of the conversations making it a realistic alternative to costly manual summarization by medical experts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.08666v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"87999\", \"36972\", \"56032\", \"9527\", \"24856\", \"24688\", \"56583\"], \"outgoing_citations\": [\"159316\", \"163579\", \"189816\", \"212823\", \"217759\", \"218059\", \"280557\", \"296198\", \"298016\"]}","task_split":"paper_retrieval"}
{"document_id":"99927","document_content":"# Bucket of deep transfer learning features and classification models for melanoma detection\n## Categories\n- Image and Video Processing\n- I.4.10; I.5.1; I.5.4\n- Systems and Control\n- Systems and Control\n- 68T05\n## Abstract\nMalignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.08639v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"I.4.10; I.5.1; I.5.4\", \"eess.SY\", \"cs.SY\", \"68T05\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"I.4.10; I.5.1; I.5.4\", \"Systems and Control\", \"Systems and Control\", \"68T05\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"249252\", \"299339\"]}","task_split":"paper_retrieval"}
{"document_id":"99928","document_content":"# Hierarchical GPT with Congruent Transformers for Multi-Sentence Language Models\n## Categories\n- Computation and Language\n## Abstract\nWe report a GPT-based multi-sentence language model for dialogue generation and document understanding. First, we propose a hierarchical GPT which consists of three blocks, i.e., a sentence encoding block, a sentence generating block, and a sentence decoding block. The sentence encoding and decoding blocks are basically the encoder-decoder blocks of the standard Transformers, which work on each sentence independently. The sentence generating block is inserted between the encoding and decoding blocks, and generates the next sentence embedding vector from the previous sentence embedding vectors. We believe it is the way human make conversation and understand paragraphs and documents. Since each sentence may consist of fewer words, the sentence encoding and decoding Transformers can use much smaller dimensional embedding vectors. Secondly, we note the attention in the Transformers utilizes the inner-product similarity measure. Therefore, to compare the two vectors in the same space, we set the transform matrices for queries and keys to be the same. Otherwise, the similarity concept is incongruent. We report experimental results to show that these two modifications increase the language model performance for tasks with multiple sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.08636v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"132441\", \"160103\", \"183462\", \"186043\", \"241070\", \"298016\", \"317841\"]}","task_split":"paper_retrieval"}
{"document_id":"99945","document_content":"# Unsupervised Parallel Corpus Mining on Web Data\n## Categories\n- Computation and Language\n## Abstract\nWith a large amount of parallel data, neural machine translation systems are able to deliver human-level performance for sentence-level translation. However, it is costly to label a large amount of parallel data by humans. In contrast, there is a large-scale of parallel corpus created by humans on the Internet. The major difficulty to utilize them is how to filter them out from the noise website environments. Current parallel data mining methods all require labeled parallel data as the training source. In this paper, we present a pipeline to mine the parallel corpus from the Internet in an unsupervised manner. On the widely used WMT'14 English-French and WMT'16 English-German benchmarks, the machine translator trained with the data extracted by our pipeline achieves very close performance to the supervised results. On the WMT'16 English-Romanian and Romanian-English benchmarks, our system produces new state-of-the-art results, 39.81 and 38.95 BLEU scores, even compared with supervised approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.08595v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38855\", \"42553\"], \"outgoing_citations\": [\"146003\", \"187452\", \"211711\", \"220017\", \"229674\"]}","task_split":"paper_retrieval"}
{"document_id":"100049","document_content":"# Focused Clinical Query Understanding and Retrieval of Medical Snippets powered through a Healthcare Knowledge Graph\n## Categories\n- Computers and Society\n- Artificial Intelligence\n## Abstract\nClinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.09086v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"100224","document_content":"# How to marry a star: probabilistic constraints for meaning in context\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions, thus modelling the wide range of lexical shifts and ambiguities observed in utterance interpretation. We represent sentence meaning as a 'situation description system', a probabilistic model which takes utterance understanding to be the mental process of describing to oneself one or more situations that would account for an observed utterance. We show how the system can be implemented in practice, and apply it to examples containing various contextualisation phenomena.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07936v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24451\"], \"outgoing_citations\": [\"72812\", \"121020\", \"124264\", \"126614\", \"180304\", \"212840\"]}","task_split":"paper_retrieval"}
{"document_id":"100234","document_content":"# Document-level Neural Machine Translation with Document Embeddings\n## Categories\n- Computation and Language\n## Abstract\nStandard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1587\/transinf.2020EDP7244\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8714\"], \"outgoing_citations\": [\"120233\", \"157472\", \"161372\", \"163992\", \"172542\", \"215003\", \"219083\", \"228350\", \"230556\", \"233206\", \"248821\", \"249315\", \"250879\", \"258163\", \"263299\", \"268684\", \"268909\", \"286173\"]}","task_split":"paper_retrieval"}
{"document_id":"100245","document_content":"# Transfer Learning in Deep Reinforcement Learning: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07888v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"40940\", \"13854\", \"16608\", \"23028\", \"23944\", \"37164\", \"46717\", \"145459\", \"5981\", \"7912\", \"9946\", \"11406\", \"12745\", \"14671\", \"14935\", \"16345\", \"18400\", \"18533\", \"29648\", \"29694\", \"34703\", \"34787\", \"35090\", \"35899\", \"37959\", \"41025\", \"46143\", \"46902\", \"55156\", \"64645\", \"65571\", \"67161\", \"69249\", \"106495\", \"78625\", \"124768\"], \"outgoing_citations\": [\"66826\", \"70092\", \"96390\", \"133647\", \"154928\", \"156004\", \"179602\", \"197395\", \"200000\", \"200927\", \"205488\", \"214631\", \"218636\", \"219691\", \"229719\", \"233926\", \"234025\", \"239419\", \"242046\", \"242141\", \"243505\", \"243714\", \"248911\", \"251348\", \"253658\", \"253974\", \"254596\", \"255139\", \"257674\", \"258199\", \"259161\", \"259708\", \"260018\", \"260136\", \"261316\", \"263272\", \"264514\", \"268361\", \"268586\", \"269030\", \"270529\", \"271705\", \"271975\", \"274445\", \"275087\", \"277797\", \"278766\", \"280570\", \"281106\", \"283289\", \"283410\", \"284134\", \"287823\", \"290243\", \"290378\", \"290904\", \"291568\", \"296980\", \"297045\", \"300421\", \"302952\", \"302967\", \"308104\", \"310237\", \"315055\", \"317874\", \"335636\"]}","task_split":"paper_retrieval"}
{"document_id":"100258","document_content":"# Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGiven a really low-resolution input image of a face (say 16x16 or 8x8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency information is missing in the low-resolution input and needs to be hallucinated, based on prior knowledge about the image content. Rather than relying on a generic face prior, in this paper, we explore the use of a set of exemplars, i.e. other high-resolution images of the same person. These guide the neural network as we condition the output on them. Multiple exemplars work better than a single one. To combine the information from multiple exemplars effectively, we introduce a pixel-wise weight generation module. Besides standard face super-resolution, our method allows to perform subtle face editing simply by replacing the exemplars with another set with different facial features. A user study is conducted and shows the super-resolved images can hardly be distinguished from real images on the CelebA dataset. A qualitative comparison indicates our model outperforms methods proposed in the literature on the CelebA and WebFace dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07827v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\"], \"outgoing_citations\": [\"170700\", \"180372\", \"187682\", \"211273\", \"221139\", \"233623\", \"235729\", \"235779\", \"248019\", \"248969\", \"265203\", \"269149\", \"270229\", \"298700\", \"302162\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"100311","document_content":"# Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nLarge-scale dissemination of disinformation online intended to mislead or deceive the general population is a major societal problem. Rapid progression in image, video, and natural language generative models has only exacerbated this situation and intensified our need for an effective defense mechanism. While existing approaches have been proposed to defend against neural fake news, they are generally constrained to the very limited setting where articles only have text and metadata such as the title and authors. In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles as well as conduct a series of human user study experiments based on this dataset. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies, which will serve as an effective first line of defense and a useful reference for future work in defending against machine-generated disinformation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2020.emnlp-main.163\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6224\", \"47057\", \"63247\", \"3784\", \"56532\", \"75236\", \"89518\"], \"outgoing_citations\": [\"131385\", \"171342\", \"175094\", \"180878\", \"183694\", \"189628\", \"192648\", \"211893\", \"238205\", \"298750\", \"312643\"]}","task_split":"paper_retrieval"}
{"document_id":"100379","document_content":"# MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control\n## Categories\n- Robotics\n- Machine Learning\n- Human-Computer Interaction\n- Systems and Control\n- Systems and Control\n## Abstract\nReasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets (i.e., ordered sets of waypoints) that are commonly output by trajectory forecasting methods, which can hinder their integration. Towards this end, we propose Mixtures of Affine Time-varying Systems (MATS) as an output representation for trajectory forecasting that is more amenable to downstream planning and control use. Our approach leverages successful ideas from probabilistic trajectory forecasting works to learn dynamical system representations that are well-studied in the planning and control literature. We integrate our predictions with a proposed multimodal planning methodology and demonstrate significant computational efficiency improvements on a large-scale autonomous driving dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07517v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.HC\", \"eess.SY\", \"cs.SY\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\", \"Systems and Control\", \"Systems and Control\", \"Robotics\"], \"incoming_citations\": [\"16755\", \"18339\", \"53587\", \"60595\", \"31201\", \"34076\", \"36400\", \"64930\", \"83360\", \"93843\"], \"outgoing_citations\": [\"75043\", \"82376\", \"147880\", \"161127\", \"186194\", \"191115\", \"191356\", \"194321\", \"210517\", \"214313\", \"223212\", \"223882\", \"232096\", \"237350\", \"239933\", \"242322\", \"249164\", \"249901\", \"252398\", \"268891\", \"271767\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"100392","document_content":"# Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models. Meanwhile, researchers also try to answer the question that whether the obtained interpretation is faithful in explaining mechanisms behind model prediction? Specifically, (Jain and Wallace, 2019) proposes that \"attention is not explanation\" by comparing attention interpretation with gradient alternatives. However, it raises a new question that can we safely pick one interpretation method as the ground-truth? If not, on what basis can we compare different interpretation methods? In this work, we propose that it is crucial to have a concrete definition of interpretation before we could evaluate faithfulness of an interpretation. The definition will affect both the algorithm to obtain interpretation and, more importantly, the metric used in evaluation. Through both theoretical and experimental analysis, we find that although interpretation methods perform differently under a certain evaluation metric, such a difference may not result from interpretation quality or faithfulness, but rather the inherent bias of the evaluation metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07494v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"55164\", \"129323\"], \"outgoing_citations\": [\"132402\", \"145417\", \"165847\", \"171988\", \"175946\", \"180031\", \"181717\", \"197416\", \"200871\", \"213346\", \"238492\", \"252114\", \"263942\", \"269165\", \"277288\", \"279750\", \"319947\"]}","task_split":"paper_retrieval"}
{"document_id":"100475","document_content":"# Federated Dynamic GNN with Secure Aggregation\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nGiven video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications such as distributed surveillance, without storing data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from multi-user graph sequences: i) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. ii) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user's data to server. iii) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets (in four different scenes) as well as simulation demonstrate that Feddy achieves great effectiveness and security.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07351v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"12070\", \"52799\", \"39605\", \"44610\", \"47758\", \"56009\", \"70480\", \"119868\"], \"outgoing_citations\": [\"173163\", \"186164\", \"190762\", \"197412\", \"205029\", \"207518\", \"232524\", \"255737\", \"258592\", \"264424\", \"277410\", \"280506\", \"292407\", \"303573\", \"311030\", \"340973\"]}","task_split":"paper_retrieval"}
{"document_id":"100503","document_content":"# A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis work studies the widely adopted ancestral sampling algorithms for auto-regressive language models, which is not widely studied in the literature. We use the quality-diversity (Q-D) trade-off to investigate three popular sampling algorithms (top-k, nucleus and tempered sampling). We focus on the task of open-ended language generation. We first show that the existing sampling algorithms have similar performance. After carefully inspecting the transformations defined by different sampling algorithms, we identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation. To validate the importance of the identified properties, we design two sets of new sampling algorithms: one set in which each algorithm satisfies all three properties, and one set in which each algorithm violates at least one of the properties. We compare their performance with existing sampling algorithms, and find that violating the identified properties could lead to drastic performance degradation, as measured by the Q-D trade-off. On the other hand, we find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms. Our data and code are available at https:\/\/github.com\/moinnadeem\/characterizing-sampling-algorithms","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07243v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"9468\", \"30826\", \"94230\", \"184591\"], \"outgoing_citations\": [\"129680\", \"143750\", \"158437\", \"158493\", \"180695\", \"192158\", \"196340\", \"211205\", \"217640\", \"232274\", \"243154\", \"268454\", \"275597\", \"283971\", \"284481\"]}","task_split":"paper_retrieval"}
{"document_id":"100535","document_content":"# HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAccurate pedestrian trajectory prediction is of great importance for downstream tasks such as autonomous driving and mobile robot navigation. Fully investigating the social interactions within the crowd is crucial for accurate pedestrian trajectory prediction. However, most existing methods do not capture group level interactions well, focusing only on pairwise interactions and neglecting group-wise interactions. In this work, we propose a hierarchical graph convolutional network, HGCN-GJS, for trajectory prediction which well leverages group level interactions within the crowd. Furthermore, we introduce a novel joint sampling scheme for modeling the joint distribution of multiple pedestrians in the future trajectories. Based on the group information, this scheme associates the trajectory of one person with the trajectory of other people in the group, but maintains the independence of the trajectories of outsiders. We demonstrate the performance of our network on several trajectory prediction datasets, achieving state-of-the-art results on all datasets considered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.07140v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"75455\", \"127485\", \"129703\", \"133076\", \"136834\", \"165422\", \"165981\", \"177460\", \"186194\", \"189614\", \"196132\", \"214313\", \"229266\", \"233526\", \"237350\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"100596","document_content":"# Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nLossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neural network (CNN) have been developed with great success. However, these methods usually train a model based on one specific value or a small range of quality factors. Obviously, if the test image's quality factor does not match to the assumed value range, then degraded performance will be resulted. With this motivation and further consideration of practical usage, a highly robust compression artifacts removal network is proposed in this paper. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance. To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. Note that a turnkey success of our proposed network lies in the novel utilization of the quantization tables as part of the training data. Furthermore, it has two branches in parallel---i.e., the restoration branch and the global branch. The former effectively removes the local artifacts, such as ringing artifacts removal. On the other hand, the latter extracts the global features of the entire image that provides highly instrumental image quality improvement, especially effective on dealing with the global artifacts, such as blocking, color shifting. Extensive experimental results performed on color and grayscale images have clearly demonstrated the effectiveness and efficacy of our proposed single-model approach on the removal of compression artifacts from the decoded image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2020.3020389\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"1249\", \"5997\", \"21815\", \"78351\"], \"outgoing_citations\": [\"193922\", \"207551\", \"213798\", \"228748\", \"244605\", \"253572\", \"269384\", \"279844\", \"280406\", \"293446\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"100632","document_content":"# Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n## Abstract\nWe consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies. This approach learns the known abnormality by automatically interacting with an anomaly-biased simulation environment, while continuously extending the learned abnormality to novel classes of anomaly (i.e., unknown anomalies) by actively exploring possible anomalies in the unlabeled data. This is achieved by jointly optimizing the exploitation of the small labeled anomaly data and the exploration of the rare unlabeled anomalies. Extensive experiments on 48 real-world datasets show that our model significantly outperforms five state-of-the-art competing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3447548.3467417\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CR\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Cryptography and Security\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"3708\", \"30438\", \"6923\", \"32132\"], \"outgoing_citations\": [\"100432\", \"117367\", \"129707\", \"155393\", \"159044\", \"182120\", \"228256\", \"241732\", \"266419\", \"271257\", \"286018\", \"334723\"]}","task_split":"paper_retrieval"}
{"document_id":"100660","document_content":"# DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nThis paper challenges the common assumption that the weight $\\beta$, in $\\beta$-VAE, should be larger than $1$ in order to effectively disentangle latent factors. We demonstrate that $\\beta$-VAE, with $\\beta < 1$, can not only attain good disentanglement but also significantly improve reconstruction accuracy via dynamic control. The paper removes the inherent trade-off between reconstruction accuracy and disentanglement for $\\beta$-VAE. Existing methods, such as $\\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement. To mitigate this problem, a ControlVAE has recently been developed that dynamically tunes the KL-divergence weight in an attempt to control the trade-off to more a favorable point. However, ControlVAE fails to eliminate the conflict between the need for a large $\\beta$ (for disentanglement) and the need for a small $\\beta$. Instead, we propose DynamicVAE that maintains a different $\\beta$ at different stages of training, thereby decoupling disentanglement and reconstruction accuracy. In order to evolve the weight, $\\beta$, along a trajectory that enables such decoupling, DynamicVAE leverages a modified incremental PI (proportional-integral) controller, and employs a moving average as well as a hybrid annealing method to evolve the value of KL-divergence smoothly in a tightly controlled fashion. We theoretically prove the stability of the proposed approach. Evaluation results on three benchmark datasets demonstrate that DynamicVAE significantly improves the reconstruction accuracy while achieving disentanglement comparable to the best of existing methods. The results verify that our method can separate disentangled representation learning and reconstruction, removing the inherent tension between the two.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.06795v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"70206\"], \"outgoing_citations\": [\"113443\", \"131445\", \"138086\", \"143581\", \"160471\", \"169739\", \"170071\", \"180811\", \"182775\", \"183257\", \"183732\", \"187863\", \"206907\", \"207094\", \"208041\", \"218829\", \"241933\", \"242239\", \"251597\", \"253197\", \"264782\", \"264963\", \"265526\", \"280734\", \"295435\", \"300606\", \"328112\"]}","task_split":"paper_retrieval"}
{"document_id":"100696","document_content":"# Methods of the Vehicle Re-identification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make\/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer's portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set will be demonstrated. This work was partially funded under the grant.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-55180-3_38\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"186202\", \"216614\", \"258687\", \"274620\"]}","task_split":"paper_retrieval"}
{"document_id":"100950","document_content":"# Transfer learning with class-weighted and focal loss function for automatic skin cancer classification\n## Categories\n- Artificial Intelligence\n## Abstract\nSkin cancer is by far in top-3 of the world's most common cancer. Among different skin cancer types, melanoma is particularly dangerous because of its ability to metastasize. Early detection is the key to success in skin cancer treatment. However, skin cancer diagnosis is still a challenge, even for experienced dermatologists, due to strong resemblances between benign and malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed a deep learning system that can effectively and automatically classify skin lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6) Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the system. An end-to-end deep learning process, transfer learning technique, utilizing multiple pre-trained models, combining with class-weighted and focal loss were applied for the classification process. The result was that our ensemble of modified ResNet50 models can classify skin lesions into one of the seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%, respectively. This deep learning system can potentially be integrated into computer-aided diagnosis systems that support dermatologists in skin cancer diagnosis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05977v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"49774\", \"95608\"], \"outgoing_citations\": [\"177238\"]}","task_split":"paper_retrieval"}
{"document_id":"101027","document_content":"# Learning semantic Image attributes using Image recognition and knowledge graph embeddings\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nExtracting structured knowledge from texts has traditionally been used for knowledge base generation. However, other sources of information, such as images can be leveraged into this process to build more complete and richer knowledge bases. Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships between image entities. Linking known entities in knowledge graphs and learning open-world images using language models has attracted lots of interest over the years. In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized attributes of images. The proposed model premises to help us understand the semantic relationship between the entities of an image and implicitly provide a link for the extracted entities through a knowledge graph embedding model. Under the limitation of using a custom user-defined knowledge base with limited data, the proposed model presents significant accuracy and provides a new alternative to the earlier approaches. The proposed approach is a step towards bridging the gap between frameworks which learn from large amounts of data and frameworks which use a limited set of predicates to infer new knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05812v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"219150\", \"220727\", \"243422\", \"257559\", \"264567\", \"267030\", \"284154\", \"295931\"]}","task_split":"paper_retrieval"}
{"document_id":"101065","document_content":"# Syntax Role for Neural Semantic Role Labeling\n## Categories\n- Computation and Language\n## Abstract\nSemantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruning-based and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05737v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16648\", \"8714\"], \"outgoing_citations\": [\"77364\", \"78281\", \"95433\", \"157592\", \"159954\", \"162910\", \"165653\", \"168146\", \"169264\", \"169520\", \"177443\", \"181468\", \"183096\", \"195384\", \"202775\", \"213527\", \"215434\", \"230064\", \"232290\", \"233407\", \"234631\", \"235377\", \"241616\", \"248279\", \"260541\", \"271527\", \"276493\", \"281121\", \"289403\", \"291941\", \"296583\", \"316774\", \"319833\", \"334675\"]}","task_split":"paper_retrieval"}
{"document_id":"101113","document_content":"# Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks\n## Categories\n- Computation and Language\n## Abstract\nSolving arithmetic word problems is a cornerstone task in assessing language understanding and reasoning capabilities in NLP systems. Recent works use automatic extraction and ranking of candidate solution equations providing the answer to arithmetic word problems. In this work, we explore novel approaches to score such candidate solution equations using tree-structured recursive neural network (Tree-RNN) configurations. The advantage of this Tree-RNN approach over using more established sequential representations, is that it can naturally capture the structure of the equations. Our proposed method consists of transforming the mathematical expression of the equation into an expression tree. Further, we encode this tree into a Tree-RNN by using different Tree-LSTM architectures. Experimental results show that our proposed method (i) improves overall performance with more than 3% accuracy points compared to previous state-of-the-art, and with over 15% points on a subset of problems that require more complex reasoning, and (ii) outperforms sequential LSTMs by 4% accuracy points on such more complex problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.eswa.2021.114704\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"183403\", \"210184\", \"211865\", \"220655\", \"246516\", \"266702\", \"278863\", \"284357\", \"287181\", \"316774\"]}","task_split":"paper_retrieval"}
{"document_id":"101123","document_content":"# Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReal-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https:\/\/youtu.be\/FWsVJc2fvCE Code: https:\/\/git.io\/JIsqX","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05613v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"7242\", \"21656\", \"46554\", \"66831\", \"92513\", \"93496\", \"142095\", \"31888\"], \"outgoing_citations\": [\"126877\", \"142095\", \"153600\", \"186164\", \"188867\", \"212044\", \"213247\", \"241052\", \"257819\", \"276133\", \"278098\", \"281085\"]}","task_split":"paper_retrieval"}
{"document_id":"101178","document_content":"# A Comparison of LSTM and BERT for Small Corpus\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecent advancements in the NLP field showed that transfer learning helps with achieving state-of-the-art results for new tasks by tuning pre-trained models instead of starting from scratch. Transformers have made a significant improvement in creating new state-of-the-art results for many NLP tasks including but not limited to text classification, text generation, and sequence labeling. Most of these success stories were based on large datasets. In this paper we focus on a real-life scenario that scientists in academia and industry face frequently: given a small dataset, can we use a large pre-trained model like BERT and get better results than simple models? To answer this question, we use a small dataset for intent classification collected for building chatbots and compare the performance of a simple bidirectional LSTM model with a pre-trained BERT model. Our experimental results show that bidirectional LSTM models can achieve significantly higher results than a BERT model for a small dataset and these simple models get trained in much less time than tuning the pre-trained counterparts. We conclude that the performance of a model is dependent on the task and the data, and therefore before making a model choice, these factors should be taken into consideration instead of directly choosing the most popular model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05451v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"65326\", \"1754\", \"2675\", \"10868\", \"23275\", \"26792\", \"27845\", \"39002\", \"43954\", \"50063\", \"51940\", \"55615\", \"112408\", \"93476\"], \"outgoing_citations\": [\"168621\"]}","task_split":"paper_retrieval"}
{"document_id":"101231","document_content":"# Community detection in networks using graph embeddings\n## Categories\n- Physics and Society\n- Data Analysis, Statistics and Probability\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well, because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1103\/PhysRevE.103.022316\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.data-an\", \"cs.LG\", \"physics.soc-ph\", \"cs.SI\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Data Analysis, Statistics and Probability\", \"Machine Learning\", \"Physics and Society\", \"Social and Information Networks\"], \"incoming_citations\": [\"258579\", \"6219\", \"3289\"], \"outgoing_citations\": [\"267030\", \"287459\"]}","task_split":"paper_retrieval"}
{"document_id":"101281","document_content":"# FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nLarge-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-language fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. For simple tasks such as classification, translated text in the target language shares the same label as the source language. However, this shared label becomes less accurate or even unavailable for more complex tasks such as question answering, NER and POS tagging. To tackle this issue, we further propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language. Extensive experiments demonstrate that FILTER achieves new state of the art on two challenging multilingual multi-task benchmarks, XTREME and XGLUE.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.05166v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"62492\", \"78222\", \"81858\", \"25615\", \"41912\", \"43050\", \"43644\", \"44551\", \"44926\", \"51380\", \"55867\", \"62044\", \"74154\", \"90294\", \"91649\", \"92112\", \"108602\", \"91808\", \"122811\"], \"outgoing_citations\": [\"111872\", \"122811\", \"128868\", \"133328\", \"135112\", \"137436\", \"159706\", \"161377\", \"168995\", \"169205\", \"169239\", \"169332\", \"169415\", \"204715\", \"217957\", \"218392\", \"259723\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"101354","document_content":"# Dual Encoding for Video Retrieval by Text\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Multimedia\n## Abstract\nThis paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TPAMI.2021.3059295\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [\"49223\", \"5478\", \"69816\", \"4188\", \"12778\", \"13933\", \"1608\", \"25818\", \"33374\", \"45218\", \"53510\", \"57747\", \"58028\", \"61663\", \"68457\", \"72065\"], \"outgoing_citations\": [\"87013\", \"110610\", \"114022\", \"131888\", \"139259\", \"159991\", \"172394\", \"173627\", \"181437\", \"181897\", \"191861\", \"214015\", \"217562\", \"221984\", \"236468\", \"256883\", \"282943\", \"283914\", \"286978\", \"294898\", \"297471\", \"301884\", \"310738\", \"316774\", \"319169\"]}","task_split":"paper_retrieval"}
{"document_id":"101381","document_content":"# Text-independent writer identification using convolutional neural network\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThe text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by experts. However, in the last decade, deep learning methods have been successfully applied to learn features from data automatically. We propose here an end-to-end deep-learning method for text-independent writer identification that does not require prior identification of features. A Convolutional Neural Network (CNN) is trained initially to extract local features, which represent characteristics of individual handwriting in the whole character images and their sub-regions. Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features. For every training epoch, the process of randomly sampling tuples is repeated, which is equivalent to a large number of training patterns being prepared for training the CNN for text-independent writer identification. We conducted experiments on the JEITA-HP database of offline handwritten Japanese character patterns. With 200 characters, our method achieved an accuracy of 99.97% to classify 100 writers. Even when using 50 characters for 100 writers or 100 characters for 400 writers, our method achieved accuracy levels of 92.80% or 93.82%, respectively. We conducted further experiments on the Firemaker and IAM databases of offline handwritten English text. Using only one page per writer to train, our method achieved over 91.81% accuracy to classify 900 writers. Overall, we achieved a better performance than the previously published best result based on handcrafted features and clustering algorithms, which demonstrates the effectiveness of our method for handwritten English text also.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patrec.2018.07.022\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"56921\", \"5024\", \"136576\", \"145769\"], \"outgoing_citations\": [\"307811\"]}","task_split":"paper_retrieval"}
{"document_id":"101492","document_content":"# Resilient Task Allocation in Heterogeneous Multi-Robot Systems\n## Categories\n- Multiagent Systems\n- Robotics\n## Abstract\nFor a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm minimally relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.04593v2\", \"primary_category\": \"cs.MA\", \"categories\": [\"cs.RO\", \"cs.MA\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Robotics\", \"Multiagent Systems\"], \"incoming_citations\": [\"31208\", \"21125\", \"52259\"], \"outgoing_citations\": [\"138119\", \"194367\", \"195425\", \"195471\", \"215184\", \"326930\"]}","task_split":"paper_retrieval"}
{"document_id":"101535","document_content":"# Map-Adaptive Goal-Based Trajectory Prediction\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nWe present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths -- which are generated at run time and therefore dynamically adapt to the scene -- as spatial anchors, we predict a set of goal-based trajectories along with a categorical distribution over the goals. This approach allows us to directly model the goal-directed behavior of traffic actors, which unlocks the potential for more accurate long-term prediction. Our experimental results on both a large-scale internal driving dataset and on the public nuScenes dataset show that our model outperforms state-of-the-art approaches for vehicle trajectory prediction over a 6-second horizon. We also empirically demonstrate that our model is better able to generalize to road scenes from a completely new city than existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.04450v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"7145\", \"11990\", \"16755\", \"18797\", \"23743\", \"25792\", \"28894\", \"47807\", \"52676\", \"148683\"], \"outgoing_citations\": [\"74563\", \"78890\", \"113996\", \"121363\", \"126156\", \"132325\", \"154697\", \"162130\", \"164301\", \"179871\", \"217456\", \"221071\", \"229266\", \"232095\", \"237350\", \"268891\"]}","task_split":"paper_retrieval"}
{"document_id":"101629","document_content":"# One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nStructured information extraction from document images usually consists of three steps: text detection, text recognition, and text field labeling. While text detection and text recognition have been heavily studied and improved a lot in literature, text field labeling is less explored and still faces many challenges. Existing learning based methods for text labeling task usually require a large amount of labeled examples to train a specific model for each type of document. However, collecting large amounts of document images and labeling them is difficult and sometimes impossible due to privacy issues. Deploying separate models for each type of document also consumes a lot of resources. Facing these challenges, we explore one-shot learning for the text field labeling task. Existing one-shot learning methods for the task are mostly rule-based and have difficulty in labeling fields in crowded regions with few landmarks and fields consisting of multiple separate text regions. To alleviate these problems, we proposed a novel deep end-to-end trainable approach for one-shot text field labeling, which makes use of attention mechanism to transfer the layout information between document images. We further applied conditional random field on the transferred layout information for the refinement of field labeling. We collected and annotated a real-world one-shot field labeling dataset with a large variety of document types and conducted extensive experiments to examine the effectiveness of the proposed model. To stimulate research in this direction, the collected dataset and the one-shot model will be released1.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.04153v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"9109\", \"21025\", \"31120\", \"39796\"], \"outgoing_citations\": [\"149004\", \"182227\", \"188510\", \"193543\", \"205569\", \"233366\", \"264315\", \"269205\", \"296828\", \"302934\", \"309239\"]}","task_split":"paper_retrieval"}
{"document_id":"101653","document_content":"# Central Yup'ik and Machine Translation of Low-Resource Polysynthetic Languages\n## Categories\n- Computation and Language\n## Abstract\nMachine translation tools do not yet exist for the Yup'ik language, a polysynthetic language spoken by around 8,000 people who live primarily in Southwest Alaska. We compiled a parallel text corpus for Yup'ik and English and developed a morphological parser for Yup'ik based on grammar rules. We trained a seq2seq neural machine translation model with attention to translate Yup'ik input into English. We then compared the influence of different tokenization methods, namely rule-based, unsupervised (byte pair encoding), and unsupervised morphological (Morfessor) parsing, on BLEU score accuracy for Yup'ik to English translation. We find that using tokenized input increases the translation accuracy compared to that of unparsed input. Although overall Morfessor did best with a vocabulary size of 30k, our first experiments show that BPE performed best with a reduced vocabulary size.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.04087v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"101765","document_content":"# Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification\n## Categories\n- Computation and Language\n## Abstract\nNeural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.03695v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18991\", \"8723\", \"10513\", \"22194\", \"51130\", \"80865\"], \"outgoing_citations\": [\"125731\", \"138517\", \"168298\", \"168563\", \"169780\", \"194123\", \"200867\", \"212163\", \"218853\", \"225791\", \"230156\", \"230543\", \"231898\", \"232274\", \"237283\", \"267601\", \"300449\"]}","task_split":"paper_retrieval"}
{"document_id":"102019","document_content":"# Detecting Informal Organization Through Data Mining Techniques\n## Categories\n- Computers and Society\n- Machine Learning\n## Abstract\nOne of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.02895v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.LG\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"347458\"]}","task_split":"paper_retrieval"}
{"document_id":"102198","document_content":"# FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms. In the multi-party feature engineering scenario (features are stored in many different IoT devices), direct and unlimited multivariate feature transformations will quickly exhaust memory, power, and bandwidth of devices, not to mention the security of information threatened. Given this, we present a framework called FLFE to conduct privacy-preserving and communication-preserving multi-party feature transformations. The framework pre-learns the pattern of the feature to directly judge the usefulness of the transformation on a feature. Explored the new useful feature, the framework forsakes the encryption-based algorithm for the well-designed feature exchange mechanism, which largely decreases the communication overhead under the premise of confidentiality. We made experiments on datasets of both open-sourced and real-world thus validating the comparable effectiveness of FLFE to evaluation-based approaches, along with the far more superior efficacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.02557v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"154897\", \"157950\", \"167530\", \"181136\", \"205577\", \"281424\"]}","task_split":"paper_retrieval"}
{"document_id":"102229","document_content":"# Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nSuccessful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner when they teach them in an online manner using rewards and punishment. We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. To do so, we first establish a normative standard, by formulating the problem as a machine teaching optimization problem. To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. What do people assume about a learner's learning and discount rates when they teach them an idealized exploration-exploitation task? In a behavioral experiment, we find that people can teach the task to Q-learners in a relatively efficient and effective manner when the learner uses a small value for its discounting rate and a large value for its learning rate. However, they still are suboptimal. We also find that providing people with real-time updates of how possible feedback would affect the Q-learner's internal states weakly helps them teach. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.02476v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"118173\"], \"outgoing_citations\": [\"134454\", \"134575\", \"160018\", \"161948\", \"183009\", \"183993\", \"196502\", \"212543\", \"213540\", \"214272\", \"218661\", \"220152\", \"221085\", \"231160\", \"231418\", \"244798\", \"265072\", \"275728\"]}","task_split":"paper_retrieval"}
{"document_id":"102395","document_content":"# TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAdvances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video generation, especially on conditional inputs, remains a challenging and less explored area. To narrow this gap, we aim to train our model to produce a video corresponding to a given text description. We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. In the first phase, we focus on creating a high-quality single video frame while learning the relationship between the text and an image. As the steps proceed, our model is trained gradually on more number of consecutive frames.This step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions. Qualitative and quantitative experimental results on various datasets demonstrate the effectiveness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.02018v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39331\", \"19673\"], \"outgoing_citations\": [\"234607\", \"254361\", \"261089\", \"269388\", \"270105\", \"277837\", \"279997\", \"285093\", \"292385\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"102413","document_content":"# A Hybrid Deep Learning Model for Arabic Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nArabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning DL models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using 18 different Arabic font types. The objective of the testing process was to assess the model capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data. The prepared model, the custom datasets and the toolkit for generating similar datasets are made publicly available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.14569\/issn.2156-5570\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"274688\", \"321886\"]}","task_split":"paper_retrieval"}
{"document_id":"102549","document_content":"# Biomedical named entity recognition using BERT in the machine reading comprehension framework\n## Categories\n- Computation and Language\n## Abstract\nRecognition of biomedical entities from literature is a challenging research focus, which is the foundation for extracting a large amount of biomedical knowledge existing in unstructured texts into structured formats. Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method. This method, however, often cannot take full advantage of the semantic information in the dataset, and the performance is not always satisfactory. In this work, instead of treating the BioNER task as a sequence labeling problem, we formulate it as a machine reading comprehension (MRC) problem. This formulation can introduce more prior knowledge utilizing well-designed queries, and no longer need decoding processes such as conditional random fields (CRF). We conduct experiments on six BioNER datasets, and the experimental results demonstrate the effectiveness of our method. Our method achieves state-of-the-art (SOTA) performance on the BC4CHEMD, BC5CDR-Chem, BC5CDR-Disease, NCBI-Disease, BC2GM and JNLPBA datasets, achieving F1-scores of 92.92%, 94.19%, 87.83%, 90.04%, 85.48% and 78.93%, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.01560v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"55453\", \"17477\", \"49953\"], \"outgoing_citations\": [\"159862\", \"169688\", \"180997\", \"186405\", \"190434\", \"191942\", \"227323\", \"243880\", \"249726\", \"263874\", \"284528\", \"287367\", \"297147\"]}","task_split":"paper_retrieval"}
{"document_id":"102649","document_content":"# Learning to summarize from human feedback\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAs language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about -- summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN\/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.01325v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"103740\", \"2601\", \"15609\", \"15993\", \"24984\", \"31927\", \"2329\", \"5892\", \"6526\", \"9714\", \"11431\", \"16723\", \"17298\", \"19115\", \"21737\", \"22368\", \"24581\", \"30309\", \"30471\", \"36887\", \"36889\", \"37444\", \"37526\", \"40489\", \"44007\", \"44917\", \"45223\", \"51199\", \"51322\", \"55964\", \"57391\", \"57670\", \"60332\", \"63484\", \"64838\", \"71164\", \"72058\", \"75515\", \"89002\", \"109884\", \"79729\", \"80268\", \"91716\", \"95485\", \"100625\", \"102167\"], \"outgoing_citations\": [\"127538\", \"129680\", \"142153\", \"142670\", \"142761\", \"147514\", \"150522\", \"163994\", \"164743\", \"166082\", \"167137\", \"168228\", \"168908\", \"170449\", \"172088\", \"173805\", \"173940\", \"178278\", \"187236\", \"187452\", \"202748\", \"209453\", \"209926\", \"211946\", \"213652\", \"216554\", \"227323\", \"232783\", \"233342\", \"235041\", \"235393\", \"260420\", \"263957\", \"264856\", \"266671\", \"280866\", \"287823\", \"302815\", \"303997\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"102715","document_content":"# Yet Meta Learning Can Adapt Fast, It Can Also Break Easily\n## Categories\n- Machine Learning\n## Abstract\nMeta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.01672v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"13513\", \"14156\", \"67965\", \"81654\"], \"outgoing_citations\": [\"122733\", \"123835\", \"136363\", \"139118\", \"166272\", \"167064\", \"181707\", \"191732\", \"220351\", \"228280\", \"229027\", \"231222\", \"242689\", \"247127\", \"255939\", \"261577\", \"292262\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"102723","document_content":"# Face Image Quality Assessment: A Literature Survey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3507901\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"14719\", \"11794\", \"68499\", \"14608\", \"62391\", \"63703\", \"63976\", \"117456\"], \"outgoing_citations\": [\"8900\", \"8907\", \"14608\", \"14676\", \"63703\", \"63976\", \"99899\", \"117456\", \"119957\", \"120894\", \"132073\", \"134999\", \"135770\", \"138418\", \"152832\", \"170818\", \"189549\", \"192542\", \"199775\", \"200630\", \"209098\", \"210599\", \"223519\", \"230252\", \"237967\", \"252688\", \"257586\", \"262487\", \"269149\", \"272912\", \"287611\", \"302157\", \"303724\", \"304578\", \"308960\", \"321122\", \"336491\", \"346465\"]}","task_split":"paper_retrieval"}
{"document_id":"102910","document_content":"# Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline\n## Categories\n- Computation and Language\n## Abstract\nAligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.00590v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2553\"], \"outgoing_citations\": [\"92765\", \"95843\", \"157429\", \"182486\", \"183197\", \"183200\", \"219618\", \"220695\", \"230212\", \"280557\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"103057","document_content":"# Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification\n## Categories\n- Computation and Language\n- Machine Learning\n- Social and Information Networks\n- Machine Learning\n## Abstract\nThe rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history. However, the inherently unsupervised nature of social media platforms has also made it easier to spread false information and fake news. Furthermore, the high volume and velocity of information flow in such platforms make manual supervision and control of information propagation infeasible. This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media. We first introduce Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims. Furthermore, we propose a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake. Our results demonstrate that the proposed architecture trained on Sentimental LIAR can achieve an accuracy of 70%, which is an improvement of ~30% over previously reported results for the LIAR benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.01047v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"8259\", \"31480\"], \"outgoing_citations\": [\"130032\", \"211841\", \"238986\", \"267552\", \"276938\", \"302653\"]}","task_split":"paper_retrieval"}
{"document_id":"103070","document_content":"# MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nIn recent years, the proliferation of so-called \"fake news\" has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art models to auto-detect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask \"what if adversaries attempt to attack such detection models?\" and investigate related issues by (i) proposing a novel threat model against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead fake news detectors, and (ii) developing MALCOM, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average MALCOM can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, MALCOM can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare our attack model with four baselines across two real-world datasets, not only on attack performance but also on generated quality, coherency, transferability, and robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2009.01048v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7714\", \"14864\", \"157030\", \"86306\", \"23586\", \"75537\", \"85680\"], \"outgoing_citations\": [\"154915\", \"171039\", \"183694\", \"184236\", \"203890\", \"206156\", \"219169\", \"226061\", \"228585\", \"229173\", \"230998\", \"237274\", \"247127\", \"271021\", \"272531\", \"281749\", \"284481\"]}","task_split":"paper_retrieval"}
{"document_id":"103278","document_content":"# SEEC: Semantic Vector Federation across Edge Computing Environments\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nSemantic vector embedding techniques have proven useful in learning semantic representations of data across multiple domains. A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find data most similar to a given sample. State-of-the-art embedding approaches assume all data is available on a single site. However, in many business settings, data is distributed across multiple edge locations and cannot be aggregated due to a variety of constraints. Hence, the applicability of state-of-the-art embedding approaches is limited to freely shared datasets, leaving out applications with sensitive or mission-critical data. This paper addresses this gap by proposing novel unsupervised algorithms called \\emph{SEEC} for learning and applying semantic vector embedding in a variety of distributed settings. Specifically, for scenarios where multiple edge locations can engage in joint learning, we adapt the recently proposed federated learning techniques for semantic vector embedding. Where joint learning is not possible, we propose novel semantic vector translation algorithms to enable semantic query across multiple edge locations, each with its own semantic vector-space. Experimental results on natural language as well as graph datasets show that this may be a promising new direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.13298v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"119236\", \"121619\", \"147242\", \"160301\", \"164608\", \"184826\", \"235592\", \"336312\"]}","task_split":"paper_retrieval"}
{"document_id":"103301","document_content":"# Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing\n## Categories\n- Neural and Evolutionary Computing\n## Abstract\nMultiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.13253v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"2184\", \"98286\"], \"outgoing_citations\": [\"240572\", \"268227\"]}","task_split":"paper_retrieval"}
{"document_id":"103314","document_content":"# Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n- Robotics\n- I.2.6; I.2.9; I.5.2; H.1.2\n## Abstract\nRecent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.13221v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\", \"cs.RO\", \"stat.ML\", \"I.2.6; I.2.9; I.5.2; H.1.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\", \"Machine Learning\", \"I.2.6; I.2.9; I.5.2; H.1.2\"], \"incoming_citations\": [\"4773\"], \"outgoing_citations\": [\"162530\", \"171477\", \"178278\", \"180884\", \"181883\", \"189110\", \"190237\", \"191958\", \"192391\", \"199302\", \"206781\", \"209859\", \"209926\", \"212788\", \"218923\", \"219180\", \"219954\", \"222669\", \"224698\", \"230067\", \"230923\", \"236393\", \"240471\", \"243197\", \"243235\", \"253974\", \"254581\", \"254596\", \"254597\", \"258472\", \"259045\", \"260018\", \"260583\", \"260770\", \"261038\", \"261780\", \"261983\", \"262381\", \"263957\", \"264786\", \"264845\", \"266485\", \"268335\", \"268584\", \"272212\", \"272501\", \"275728\", \"280296\", \"280655\", \"280992\", \"289913\", \"291920\", \"293995\", \"297268\", \"297317\", \"309396\", \"317745\", \"352572\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"103432","document_content":"# Data augmentation using prosody and false starts to recognize non-native children's speech\n## Categories\n- Audio and Speech Processing\n- Computation and Language\n## Abstract\nThis paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Automatic Speech Recognition (ASR) for non-native children's speech. The task is to recognize non-native speech from children of various age groups given a limited amount of speech. Moreover, the speech being spontaneous has false starts transcribed as partial words, which in the test transcriptions leads to unseen partial words. To cope with these two challenges, we investigate a data augmentation-based approach. Firstly, we apply the prosody-based data augmentation to supplement the audio data. Secondly, we simulate false starts by introducing partial-word noise in the language modeling corpora creating new words. Acoustic models trained on prosody-based augmented data outperform the models using the baseline recipe or the SpecAugment-based augmentation. The partial-word noise also helps to improve the baseline language model. Our ASR system, a combination of these schemes, is placed third in the evaluation period and achieves the word error rate of 18.71%. Post-evaluation period, we observe that increasing the amounts of prosody-based augmented data leads to better performance. Furthermore, removing low-confidence-score words from hypotheses can lead to further gains. These two improvements lower the ASR error rate to 17.99%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12914v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Computation and Language\"], \"incoming_citations\": [\"41154\"], \"outgoing_citations\": [\"146052\"]}","task_split":"paper_retrieval"}
{"document_id":"103491","document_content":"# Linked Credibility Reviews for Explainable Misinformation Detection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Digital Libraries\n## Abstract\nIn recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection. The CRs serve as building blocks to compose graphs of (i) web content, (ii) existing credibility signals --fact-checked claims and reputation reviews of websites--, and (iii) automatically computed reviews. We implement this architecture on top of lightweight extensions to Schema.org and services providing generic NLP tasks for semantic similarity and stance detection. Evaluations on existing datasets of social-media posts, fake news and political speeches demonstrates several advantages over existing systems: extensibility, domain-independence, composability, explainability and transparency via provenance. Furthermore, we obtain competitive results without requiring finetuning and establish a new state of the art on the Clef'18 CheckThat! Factuality task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12742v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.DL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Digital Libraries\"], \"incoming_citations\": [\"63247\", \"56085\", \"75518\"], \"outgoing_citations\": [\"148371\", \"208471\", \"219169\", \"221930\", \"257863\", \"259723\"]}","task_split":"paper_retrieval"}
{"document_id":"103493","document_content":"# Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy\n## Categories\n- Human-Computer Interaction\n- J.4; H.1.2; I.2.0\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Additionally, many users desire the ability to have a greater level of control and fix perceived flaws in systems they rely on. However, how the ability to provide feedback to autonomous systems influences user trust is a largely unexplored area of research. Our research investigates how the act of providing feedback can affect user understanding of an intelligent system and its accuracy. We present a controlled experiment using a simulated object detection system with image data to study the effects of interactive feedback collection on user impressions. The results show that providing human-in-the-loop feedback lowered both participants' trust in the system and their perception of system accuracy, regardless of whether the system accuracy improved in response to their feedback. These results highlight the importance of considering the effects of allowing end-user feedback on user trust when designing intelligent systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12735v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"J.4; H.1.2; I.2.0\", \"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"J.4; H.1.2; I.2.0\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"31927\", \"33624\", \"52458\", \"75943\", \"102664\", \"104899\"], \"outgoing_citations\": [\"104899\", \"126862\", \"145662\", \"231048\", \"358662\"]}","task_split":"paper_retrieval"}
{"document_id":"103536","document_content":"# The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMalignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question. In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. Evaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.cmpb.2020.105725\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25943\", \"77337\", \"86927\"], \"outgoing_citations\": [\"115984\", \"172832\", \"189887\", \"199630\", \"212191\", \"222220\", \"237466\", \"263731\", \"271426\", \"271531\", \"271680\", \"271944\", \"272225\", \"272645\", \"272909\"]}","task_split":"paper_retrieval"}
{"document_id":"103566","document_content":"# An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data\n## Categories\n- Machine Learning\n## Abstract\nIn the era of big data, a large number of text data generated by the Internet has given birth to a variety of text representation methods. In natural language processing (NLP), text representation transforms text into vectors that can be processed by computer without losing the original semantic information. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in language. Therefore, a text feature representation model based on convolutional neural network (CNN) and variational autoencoder (VAE) is proposed to extract the text features and apply the obtained text feature representation on the text classification tasks. CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution. In addition, the output of the improved word2vec model is employed as the input of the proposed model to distinguish different meanings of the same word in different contexts. The experimental results show that the proposed model outperforms in k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) classification algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12522v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"280967\", \"292772\", \"298543\", \"311529\", \"316165\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"103599","document_content":"# Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\n3D face reconstruction is a fundamental task that can facilitate numerous applications such as robust facial analysis and augmented reality. It is also a challenging task due to the lack of high-quality datasets that can fuel current deep learning-based methods. However, existing datasets are limited in quantity, realisticity and diversity. To circumvent these hurdles, we introduce Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with massive annotations. Specifically, Pixel-Face contains 855 subjects aging from 18 to 80. Each subject has more than 20 samples with various expressions. Each sample is composed of high-resolution multi-view RGB images and 3D meshes with various expressions. Moreover, we collect precise landmarks annotation and 3D registration result for each data. To demonstrate the advantages of Pixel-Face, we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using the collected data. We show that the obtained Pixel-3DM is better in modeling a wide range of face shapes and expressions. We also carefully benchmark existing 3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves as an effective training source. We observe that the performance of current face reconstruction models significantly improves both on existing benchmarks and Pixel-Face after being fine-tuned using our newly collected data. Extensive experiments demonstrate the effectiveness of Pixel-3DM and the usefulness of Pixel-Face.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12444v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105206\", \"134002\", \"171293\", \"185981\", \"187153\", \"198793\", \"207278\", \"237024\", \"238254\", \"240261\", \"245917\", \"247152\", \"247495\", \"254971\", \"311317\"]}","task_split":"paper_retrieval"}
{"document_id":"103697","document_content":"# Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage features for retrieval-based localization must be invariant to dynamic objects (e.g. cars) as well as seasonal and daytime changes. Such invariances are, up to some extent, learnable with existing methods using triplet-like losses, given a large number of diverse training images. However, due to the high algorithmic training complexity, there exists insufficient comparison between different loss functions on large datasets. In this paper, we train and evaluate several localization methods on three different benchmark datasets, including Oxford RobotCar with over one million images. This large scale evaluation yields valuable insights into the generalizability and performance of retrieval-based localization. Based on our findings, we develop a novel method for learning more accurate and better generalizing localization features. It consists of two main contributions: (i) a feature volume-based loss function, and (ii) hard positive and pairwise negative mining. On the challenging Oxford RobotCar night condition, our method outperforms the well-known triplet loss by 24.4% in localization accuracy within 5m.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12165v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"135639\", \"165755\", \"167342\", \"189565\", \"191610\", \"195998\", \"206572\", \"208687\", \"216524\", \"227120\", \"236121\", \"253194\", \"259302\", \"259959\", \"264978\", \"269599\", \"277635\", \"281953\", \"287103\", \"302895\", \"303067\", \"340793\"]}","task_split":"paper_retrieval"}
{"document_id":"103709","document_content":"# Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nIntelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future conversational AI assistants promise even greater capabilities and a better user experience through a deeper understanding of the domain, the user, or the user's purposes. But what domain and what methods are best suited to researching and realizing this promise? In this article we argue for the domain of voice document editing and for the methods of model-based reinforcement learning. The primary advantages of voice document editing are that the domain is tightly scoped and that it provides something for the conversation to be about (the document) that is delimited and fully accessible to the intelligent assistant. The advantages of reinforcement learning in general are that its methods are designed to learn from interaction without explicit instruction and that it formalizes the purposes of the assistant. Model-based reinforcement learning is needed in order to genuinely understand the domain of discourse and thereby work efficiently with the user to achieve their goals. Together, voice document editing and model-based reinforcement learning comprise a promising research direction for achieving conversational AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12095v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"56667\"], \"outgoing_citations\": [\"128388\", \"130525\", \"146658\", \"148271\", \"153393\", \"155499\", \"159243\", \"166043\", \"166480\", \"168228\", \"168379\", \"171725\", \"178278\", \"186662\", \"196377\", \"196921\", \"201322\", \"205147\", \"210517\", \"216953\", \"219992\", \"224095\", \"229171\", \"229813\", \"229890\", \"232759\", \"233720\", \"234768\", \"235207\", \"242616\", \"242891\", \"245042\", \"246324\", \"247337\", \"247726\", \"248010\", \"248799\", \"248966\", \"250883\", \"255580\", \"256632\", \"261770\", \"262342\", \"263288\", \"263357\", \"263622\", \"265615\", \"268584\", \"269232\", \"271497\", \"272513\", \"274253\", \"277193\", \"277773\", \"277941\", \"279215\", \"279517\", \"284481\", \"284528\", \"285047\", \"285454\", \"289831\", \"290804\", \"290847\", \"291102\", \"291892\", \"291916\", \"292019\", \"292127\", \"292261\", \"294494\", \"302815\", \"303349\", \"310624\", \"315055\", \"317745\", \"317863\", \"346341\", \"357891\"]}","task_split":"paper_retrieval"}
{"document_id":"103868","document_content":"# Large Scale Photometric Bundle Adjustment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDirect methods have shown promise on visual odometry and SLAM, leading to greater accuracy and robustness over feature-based methods. However, offline 3-d reconstruction from internet images has not yet benefited from a joint, photometric optimization over dense geometry and camera parameters. Issues such as the lack of brightness constancy, and the sheer volume of data, make this a more challenging task. This work presents a framework for jointly optimizing millions of scene points and hundreds of camera poses and intrinsics, using a photometric cost that is invariant to local lighting changes. The improvement in metric reconstruction accuracy that it confers over feature-based bundle adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We further demonstrate qualitative reconstruction improvements on an internet photo collection, with challenging diversity in lighting and camera intrinsics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.11762v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"28883\", \"85163\", \"214791\"], \"outgoing_citations\": [\"275133\", \"287053\", \"288753\"]}","task_split":"paper_retrieval"}
{"document_id":"104114","document_content":"# Masked Face Recognition for Secure Authentication\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nWith the recent world-wide COVID-19 pandemic, using face masks have become an important part of our lives. People are encouraged to cover their faces when in public area to avoid the spread of infection. The use of these face masks has raised a serious question on the accuracy of the facial recognition system used for tracking school\/office attendance and to unlock phones. Many organizations use facial recognition as a means of authentication and have already developed the necessary datasets in-house to be able to deploy such a system. Unfortunately, masked faces make it difficult to be detected and recognized, thereby threatening to make the in-house datasets invalid and making such facial recognition systems inoperable. This paper addresses a methodology to use the current facial datasets by augmenting it with tools that enable masked faces to be recognized with low false-positive rates and high overall accuracy, without requiring the user dataset to be recreated by taking new pictures for authentication. We present an open-source tool, MaskTheFace to mask faces effectively creating a large dataset of masked faces. The dataset generated with this tool is then used towards training an effective facial recognition system with target accuracy for masked faces. We report an increase of 38% in the true positive rate for the Facenet system. We also test the accuracy of re-trained system on a custom real-world dataset MFR2 and report similar accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.11104v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"4555\", \"1917\", \"24000\", \"30486\", \"28374\", \"54662\", \"62842\", \"65825\", \"3894\", \"10826\", \"13066\", \"19137\", \"25836\", \"25020\", \"27940\", \"29333\", \"38773\", \"65748\", \"66245\", \"71144\"], \"outgoing_citations\": [\"135861\", \"277948\", \"278526\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"104205","document_content":"# Graphical Object Detection in Document Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGraphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection (GOD). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.10843v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"40295\", \"54374\", \"62999\", \"93362\", \"104211\", \"95468\", \"107516\"], \"outgoing_citations\": [\"235296\"]}","task_split":"paper_retrieval"}
{"document_id":"104264","document_content":"# Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy\n## Categories\n- Cryptography and Security\n- Artificial Intelligence\n## Abstract\nPrecision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.compbiomed.2020.104130\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.AI\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Artificial Intelligence\"], \"incoming_citations\": [\"57831\", \"43255\", \"84830\", \"146610\"], \"outgoing_citations\": [\"129061\", \"134240\", \"136622\", \"146300\", \"149407\", \"156643\", \"163085\", \"194178\", \"199282\", \"200347\", \"203601\", \"206737\", \"207326\", \"207518\", \"207602\", \"210718\", \"214295\", \"214805\", \"226146\", \"232524\", \"246939\", \"260314\", \"265073\", \"265934\", \"270807\", \"273215\", \"273221\", \"283045\", \"303573\", \"317907\"]}","task_split":"paper_retrieval"}
{"document_id":"104348","document_content":"# Lossy Image Compression with Normalizing Flows\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been established and refined over many decades. However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization. Due to that, they inherently limit the range of quality levels that can be covered. In contrast, traditional approaches in image compression allow for a larger range of quality levels. Interestingly, they employ an invertible transformation before performing the quantization step which explicitly discards information. Inspired by this, we propose a deep image compression method that is able to go from low bit-rates to near lossless quality by leveraging normalizing flows to learn a bijective mapping from the image space to a latent representation. In addition to this, we demonstrate further advantages unique to our solution, such as the ability to maintain constant quality results through re-encoding, even when performed multiple times. To the best of our knowledge, this is the first work to explore the opportunities for leveraging normalizing flows for lossy image compression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.10486v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32952\", \"85979\", \"2592\", \"22585\", \"30766\", \"34766\"], \"outgoing_citations\": [\"167493\", \"185803\", \"207812\", \"207875\", \"218722\", \"236298\", \"243653\", \"245227\", \"260017\", \"266304\", \"272675\", \"281135\", \"286385\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"104375","document_content":"# End to End Dialogue Transformer\n## Categories\n- Computation and Language\n## Abstract\nDialogue systems attempt to facilitate conversations between humans and computers, for purposes as diverse as small talk to booking a vacation. We are here inspired by the performance of the recurrent neural network-based model Sequicity, which when conducting a dialogue uses a sequence-to-sequence architecture to first produce a textual representation of what is going on in the dialogue, and in a further step use this along with database findings to produce a reply to the user. We here propose a dialogue system based on the Transformer architecture instead of Sequicity's RNN-based architecture, that works similarly in an end-to-end, sequence-to-sequence fashion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.10392v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162696\", \"176321\", \"187452\", \"290697\", \"294494\", \"296198\"]}","task_split":"paper_retrieval"}
{"document_id":"104540","document_content":"# Few-Shot Image Classification via Contrastive Self-Supervised Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nMost previous few-shot learning algorithms are based on meta-training with fake few-shot tasks as training samples, where large labeled base classes are required. The trained model is also limited by the type of tasks. In this paper we propose a new paradigm of unsupervised few-shot learning to repair the deficiencies. We solve the few-shot tasks in two phases: meta-training a transferable feature extractor via contrastive self-supervised learning and training a classifier using graph aggregation, self-distillation and manifold augmentation. Once meta-trained, the model can be used in any type of tasks with a task-dependent classifier training. Our method achieves state of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification datasets, with an 8- 28% increase compared to the available unsupervised few-shot learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.09942v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"83941\", \"6146\", \"35152\", \"1675\", \"40708\"], \"outgoing_citations\": [\"150188\", \"186261\", \"187797\", \"195439\", \"197494\", \"208207\", \"215410\", \"224464\", \"228120\", \"230604\", \"230792\"]}","task_split":"paper_retrieval"}
{"document_id":"104761","document_content":"# Method to Classify Skin Lesions using Dermoscopic images\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- I.4.6; I.5.1\n## Abstract\nSkin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less than or equal to 5 years. But, it can be cured if detected in early stages. Though there are numerous clinical procedures, the accuracy of diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has been brought into the picture. It helped in increasing the accuracy of diagnosis but could not demolish the error-prone behaviour. A quick and less error-prone solution is needed to diagnose this majorly growing skin cancer. This project deals with the usage of deep learning in skin lesion classification. In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model. Convolution neural networks are known for capturing features of an image. So, they are preferred in analyzing medical images to find the characteristics that drive the model towards success. Techniques like data augmentation for tackling class imbalance, segmentation for focusing on the region of interest and 10-fold cross-validation to make the model robust have been brought into the picture. This project also includes usage of certain preprocessing techniques like brightening the images using piece-wise linear transformation function, grayscale conversion of the image, resize the image. This project throws a set of valuable insights on how the accuracy of the model hikes with the bringing of new input strategies, preprocessing techniques. The best accuracy this model could achieve is 0.886.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.09418v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\", \"I.4.6; I.5.1\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"I.4.6; I.5.1\"], \"incoming_citations\": [\"51678\"], \"outgoing_citations\": [\"162635\", \"272621\"]}","task_split":"paper_retrieval"}
{"document_id":"104811","document_content":"# Explainable Recommender Systems via Resolving Learning Representations\n## Categories\n- Machine Learning\n## Abstract\nRecommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.09316v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"158823\", \"178176\", \"184356\", \"184568\", \"185616\", \"186848\", \"187568\", \"194689\", \"210521\", \"217310\", \"222721\", \"233732\", \"237346\", \"239537\", \"243505\", \"251010\", \"255737\", \"258416\", \"259260\", \"262955\", \"263942\", \"269165\"]}","task_split":"paper_retrieval"}
{"document_id":"104833","document_content":"# Learning to Profile: User Meta-Profile Network for Few-Shot Learning\n## Categories\n- Machine Learning\n## Abstract\nMeta-learning approaches have shown great success in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications. Although e-commerce companies have spent many efforts on learning representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile, the challenging question about how to effectively transfer the learned representation is raised simultaneously. This paper introduces the contributions that we made to address these challenges from three aspects. 1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems. Moreover, we argue that an industrial model should not only have good performance in terms of accuracy, but also have better robustness and uncertainty performance under extreme conditions. We evaluate the performance of our model with extensive control experiments in various extreme scenarios, i.e. out-of-distribution detection, data insufficiency and class imbalance scenarios. The Meta-Profile Network shows significant improvement in the model performance when compared to baseline models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12258v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"163067\", \"201258\", \"211597\", \"231496\", \"234599\"]}","task_split":"paper_retrieval"}
{"document_id":"104911","document_content":"# Monocular Expressive Body Regression through Body-Driven Attention\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nTo understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https:\/\/expose.is.tue.mpg.de .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.09062v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"48282\", \"2786\", \"5632\", \"6551\", \"85236\", \"85343\", \"101363\", \"1057\", \"18606\", \"19591\", \"25571\", \"25843\", \"27351\", \"27965\", \"29692\", \"41422\", \"50204\", \"50455\", \"52692\", \"53189\", \"55331\", \"55423\", \"55755\", \"59485\", \"59544\", \"60556\", \"63654\", \"69936\", \"73047\", \"75087\", \"64994\", \"81253\", \"105766\", \"110013\", \"132401\", \"104084\"], \"outgoing_citations\": [\"104084\", \"118476\", \"132401\", \"133118\", \"133673\", \"148360\", \"151576\", \"164278\", \"164514\", \"167666\", \"168874\", \"169032\", \"170920\", \"171293\", \"173428\", \"177876\", \"185981\", \"186553\", \"187224\", \"187760\", \"190958\", \"190986\", \"191141\", \"191569\", \"191933\", \"192187\", \"195025\", \"196774\", \"197693\", \"199586\", \"205633\", \"207276\", \"207278\", \"221041\", \"232518\", \"234241\", \"235715\", \"238935\", \"243558\", \"245917\", \"247152\", \"247750\", \"248391\", \"249625\", \"266960\", \"267376\", \"268000\", \"269985\", \"270923\", \"273235\", \"276981\", \"279700\", \"287636\", \"288864\", \"292424\", \"299248\", \"307539\"]}","task_split":"paper_retrieval"}
{"document_id":"104954","document_content":"# Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAdverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patient's unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/BigData50022.2020.9378336\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"133685\", \"201450\", \"240866\", \"274569\", \"317154\"]}","task_split":"paper_retrieval"}
{"document_id":"104973","document_content":"# Document Visual Question Answering Challenge 2020\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nThis paper presents results of Document Visual Question Answering Challenge organized as part of \"Text and Documents in the Deep Learning Era\" workshop, in CVPR 2020. The challenge introduces a new problem - Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.08899v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"10369\", \"19530\", \"53241\"], \"outgoing_citations\": [\"114913\", \"178256\", \"183268\", \"189847\", \"190154\", \"241203\"]}","task_split":"paper_retrieval"}
{"document_id":"105055","document_content":"# Text-based Localization of Moments in a Video Corpus\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPrior works on text-based video moment localization focus on temporally grounding the textual query in an untrimmed video. These works assume that the relevant video is already known and attempt to localize the moment on that relevant video only. Different from such works, we relax this assumption and address the task of localizing moments in a corpus of videos for a given sentence query. This task poses a unique challenge as the system is required to perform: (i) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, and (ii) temporal localization of moment in the relevant video based on sentence query. Towards overcoming this challenge, we propose Hierarchical Moment Alignment Network (HMAN) which learns an effective joint embedding space for moments and sentences. In addition to learning subtle differences between intra-video moments, HMAN focuses on distinguishing inter-video global semantic concepts based on sentence queries. Qualitative and quantitative results on three benchmark text-based video moment retrieval datasets - Charades-STA, DiDeMo, and ActivityNet Captions - demonstrate that our method achieves promising performance on the proposed task of temporal localization of moments in a corpus of videos.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2021.3120038\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39527\"], \"outgoing_citations\": [\"118354\", \"139259\", \"152254\", \"158795\", \"172256\", \"172394\", \"173627\", \"181437\", \"182169\", \"182192\", \"189453\", \"191966\", \"192172\", \"192280\", \"202277\", \"207818\", \"209133\", \"214015\", \"217562\", \"219151\", \"221984\", \"234836\", \"235053\", \"235648\", \"238205\", \"253115\", \"256883\", \"259318\", \"260926\", \"267223\", \"267523\", \"282943\", \"285755\", \"295117\", \"313189\", \"319169\"]}","task_split":"paper_retrieval"}
{"document_id":"105210","document_content":"# FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics\n## Categories\n- Computation and Language\n## Abstract\nCreating open-domain chatbots requires large amounts of conversational data and related benchmark tasks to evaluate them. Standardized evaluation tasks are crucial for creating automatic evaluation metrics for model development; otherwise, comparing the models would require resource-expensive human evaluation. While chatbot challenges have recently managed to provide a plethora of such resources for English, resources in other languages are not yet available. In this work, we provide a starting point for Finnish open-domain chatbot research. We describe our collection efforts to create the Finnish chat conversation corpus FinChat, which is made available publicly. FinChat includes unscripted conversations on seven topics from people of different ages. Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development. We observe that off-the-shelf chatbot models trained on conversational corpora do not perform better than chance at choosing the right answer based on automatic metrics, while humans can do the same task almost perfectly. Similarly, in a human evaluation, responses to questions from the evaluation set generated by the chatbots are predominantly marked as incoherent. Thus, FinChat provides a challenging evaluation set, meant to encourage chatbot development in Finnish.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.08315v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21559\"], \"outgoing_citations\": [\"158493\", \"197924\", \"200725\", \"201867\", \"211664\", \"211804\", \"216965\", \"219198\", \"257858\", \"266155\", \"276365\", \"295863\"]}","task_split":"paper_retrieval"}
{"document_id":"105286","document_content":"# Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nWriting is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.08165v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"202999\", \"349540\"]}","task_split":"paper_retrieval"}
{"document_id":"105383","document_content":"# Linear Disentangled Representations and Unsupervised Action Estimation\n## Categories\n- Machine Learning\n## Abstract\nDisentangled representation learning has seen a surge in interest over recent times, generally focusing on new models which optimise one of many disparate disentanglement metrics. Symmetry Based Disentangled Representation learning introduced a robust mathematical framework that defined precisely what is meant by a \"linear disentangled representation\". This framework determined that such representations would depend on a particular decomposition of the symmetry group acting on the data, showing that actions would manifest through irreducible group representations acting on independent representational subspaces. Caselles-Dupre et al [2019] subsequently proposed the first model to induce and demonstrate a linear disentangled representation in a VAE model. In this work we empirically show that linear disentangled representations are not generally present in standard VAE models and that they instead require altering the loss landscape to induce them. We proceed to show that such representations are a desirable property with regard to classical disentanglement metrics. Finally we propose a method to induce irreducible representations which forgoes the need for labelled action sequences, as was required by prior work. We explore a number of properties of this method, including the ability to learn from action sequences without knowledge of intermediate states and robustness under visual noise. We also demonstrate that it can successfully learn 4 independent symmetries directly from pixels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07922v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"87490\", \"44223\", \"68053\"], \"outgoing_citations\": [\"116650\", \"141826\", \"193061\", \"207094\", \"208041\", \"211558\", \"219397\", \"226824\", \"231307\", \"241933\", \"242142\", \"242239\", \"251597\"]}","task_split":"paper_retrieval"}
{"document_id":"105532","document_content":"# Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nTask oriented Dialogue Systems generally employ intent detection systems in order to map user queries to a set of pre-defined intents. However, user queries appearing in natural language can be easily ambiguous and hence such a direct mapping might not be straightforward harming intent detection and eventually the overall performance of a dialogue system. Moreover, acquiring domain-specific clarification questions is costly. In order to disambiguate queries which are ambiguous between two intents, we propose a novel method of generating discriminative questions using a simple rule based system which can take advantage of any question generation system without requiring annotated data of clarification questions. Our approach aims at discrimination between two intents but can be easily extended to clarification over multiple intents. Seeking clarification from the user to classify user intents not only helps understand the user intent effectively, but also reduces the roboticity of the conversation and makes the interaction considerably natural.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07559v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2750\", \"36680\", \"54261\", \"54487\", \"83014\"], \"outgoing_citations\": [\"119644\", \"129610\", \"130408\", \"132828\", \"157302\", \"176028\", \"192361\", \"237274\", \"258987\"]}","task_split":"paper_retrieval"}
{"document_id":"105562","document_content":"# A Survey on Reinforcement Learning for Combinatorial Optimization\n## Categories\n- Machine Learning\n## Abstract\nThis paper gives a detailed review of reinforcement learning in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1960s, and compares it with the reinforcement learning algorithms in recent years. We explicitly look at a famous combinatorial problem known as the Traveling Salesperson Problem (TSP). We compare the approach of the modern reinforcement learning algorithms on TSP with an approach published in 1970. Then, we discuss the similarities between these algorithms and how the approach of reinforcement learning changes due to the evolution of machine learning techniques and computing power. We also mention the deep learning approach on the TSP, which is named Deep Reinforcement Learning. We argue that deep learning is a generic approach that can be integrated with traditional reinforcement learning algorithms and optimize the outcomes of the TSP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.12248v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"43488\", \"68391\", \"88514\"], \"outgoing_citations\": [\"195945\", \"207983\", \"238077\"]}","task_split":"paper_retrieval"}
{"document_id":"105599","document_content":"# Evaluating for Diversity in Question Generation over Text\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nGenerating diverse and relevant questions over text is a task with widespread applications. We argue that commonly-used evaluation metrics such as BLEU and METEOR are not suitable for this task due to the inherent diversity of reference questions, and propose a scheme for extending conventional metrics to reflect diversity. We furthermore propose a variational encoder-decoder model for this task. We show through automatic and human evaluation that our variational model improves diversity without loss of quality, and demonstrate how our evaluation scheme reflects this improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07291v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"139463\", \"182763\", \"191641\", \"211886\", \"218765\", \"230284\", \"267694\", \"269122\", \"269583\", \"271234\", \"274513\", \"283076\", \"283732\", \"291858\", \"298784\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"105637","document_content":"# Comparison of Syntactic Parsers on Biomedical Texts\n## Categories\n- Computation and Language\n## Abstract\nSyntactic parsing is an important step in the automated text analysis which aims at information extraction. Quality of the syntactic parsing determines to a large extent the recall and precision of the text mining results. In this paper we evaluate the performance of several popular syntactic parsers in application to the biomedical text mining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07189v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"296283\"]}","task_split":"paper_retrieval"}
{"document_id":"105682","document_content":"# OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReal-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent's past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of sub-tasks required for the task, while the local variables capture the transitions between the sub-tasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07087v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162456\", \"164077\", \"186101\", \"194494\", \"206160\", \"214135\", \"231085\", \"231175\", \"236352\", \"239080\", \"240177\", \"269251\", \"280202\", \"280870\", \"281358\", \"311643\"]}","task_split":"paper_retrieval"}
{"document_id":"105761","document_content":"# Inverse Reinforcement Learning with Natural Language Goals\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHumans generally use natural language to communicate task requirements to each other. Ideally, natural language should also be usable for communicating goals to autonomous machines (e.g., robots) to minimize friction in task specification. However, understanding and mapping natural language goals to sequences of states and actions is challenging. Specifically, existing work along these lines has encountered difficulty in generalizing learned policies to new natural language goals and environments. In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function. To improve generalization of the learned policy and reward function, we use a variational goal generator to relabel trajectories and sample diverse goals during training. Our algorithm outperforms multiple baselines by a large margin on a vision-based natural language instruction following dataset (Room-2-Room), demonstrating a promising advance in enabling the use of natural language instructions in specifying agent goals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.06924v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"4773\", \"16536\", \"47401\", \"27522\", \"41627\", \"69518\", \"97596\"], \"outgoing_citations\": [\"155676\", \"168803\", \"180270\", \"180885\", \"181620\", \"191570\", \"196377\", \"198208\", \"206160\", \"208773\", \"209859\", \"213070\", \"224840\", \"228916\", \"229145\", \"240817\", \"249906\", \"251924\", \"255575\", \"261983\", \"266182\", \"275566\", \"277390\", \"290679\", \"297317\"]}","task_split":"paper_retrieval"}
{"document_id":"105795","document_content":"# SGG: Spinbot, Grammarly and GloVe based Fake News Detection\n## Categories\n- Computation and Language\n- Computers and Society\n- Multimedia\n## Abstract\nRecently, news consumption using online news portals has increased exponentially due to several reasons, such as low cost and easy accessibility. However, such online platforms inadvertently also become the cause of spreading false information across the web. They are being misused quite frequently as a medium to disseminate misinformation and hoaxes. Such malpractices call for a robust automatic fake news detection system that can keep us at bay from such misinformation and hoaxes. We propose a robust yet simple fake news detection system, leveraging the tools for paraphrasing, grammar-checking, and word-embedding. In this paper, we try to the potential of these tools in jointly unearthing the authenticity of a news article. Notably, we leverage Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for word-embedding) tools for this purpose. Using these tools, we were able to extract novel features that could yield state-of-the-art results on the Fake News AMT dataset and comparable results on Celebrity datasets when combined with some of the essential features. More importantly, the proposed method is found to be more robust empirically than the existing ones, as revealed in our cross-domain analysis and multi-domain analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.06854v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.MM\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Multimedia\"], \"incoming_citations\": [\"10531\", \"15325\", \"21011\"], \"outgoing_citations\": [\"105792\", \"111103\", \"125843\", \"163109\", \"234054\", \"257863\", \"267336\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"105871","document_content":"# Explainability in Deep Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nA large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a relatively new subfield of Explainable Artificial Intelligence, intended to be used in general public applications, with diverse audiences, requiring ethical, responsible and trustable algorithms. In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box. We evaluate mainly studies directly linking explainability to RL, and split these into two categories according to the way the explanations are generated: transparent algorithms and post-hoc explainaility. We also review the most prominent XAI works from the lenses of how they could potentially enlighten the further deployment of the latest advances in RL, in the demanding present and future of everyday problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.06693v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"214\", \"24058\", \"28573\", \"34882\", \"49427\", \"8851\", \"19211\", \"71315\", \"74803\", \"145459\", \"180525\", \"952\", \"4271\", \"10993\", \"45813\", \"71959\", \"88549\", \"90350\", \"97648\"], \"outgoing_citations\": [\"123128\", \"125378\", \"125488\", \"132495\", \"137546\", \"148433\", \"150447\", \"154292\", \"170627\", \"176551\", \"178420\", \"183535\", \"183718\", \"184390\", \"186854\", \"190646\", \"193061\", \"193091\", \"197416\", \"201197\", \"201682\", \"206349\", \"207094\", \"208041\", \"209412\", \"212214\", \"213622\", \"215081\", \"216625\", \"220865\", \"222669\", \"226635\", \"229169\", \"233926\", \"234521\", \"236631\", \"238577\", \"242478\", \"243505\", \"248225\", \"250500\", \"250973\", \"251595\", \"251785\", \"259260\", \"261391\", \"261983\", \"263306\", \"263852\", \"263957\", \"264533\", \"264549\", \"265505\", \"266419\", \"272987\", \"277472\", \"280551\", \"284470\", \"293995\", \"298281\", \"306266\", \"309396\", \"314276\", \"350497\"]}","task_split":"paper_retrieval"}
{"document_id":"105997","document_content":"# OR-Gym: A Reinforcement Learning Library for Operations Research Problems\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- 90-10\n- I.2.8\n## Abstract\nReinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.06319v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"90-10\", \"I.2.8\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"90-10\", \"I.2.8\"], \"incoming_citations\": [\"12708\", \"33866\", \"55495\", \"5324\", \"5886\", \"41605\", \"48755\", \"57869\", \"68391\", \"87491\"], \"outgoing_citations\": [\"151271\", \"154529\", \"238077\", \"247323\", \"248225\", \"258174\"]}","task_split":"paper_retrieval"}
{"document_id":"106122","document_content":"# Reinforcement Learning with Trajectory Feedback\n## Categories\n- Machine Learning\n## Abstract\nThe standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair. However, in practice, it is often the case that such frequent feedback is not available. In this work, we take a first step towards relaxing this assumption and require a weaker form of feedback, which we refer to as \\emph{trajectory feedback}. Instead of observing the reward obtained after every action, we assume we only receive a score that represents the quality of the whole trajectory observed by the agent, namely, the sum of all rewards obtained over this trajectory. We extend reinforcement learning algorithms to this setting, based on least-squares estimation of the unknown reward, for both the known and unknown transition model cases, and study the performance of these algorithms by analyzing their regret. For cases where the transition model is unknown, we offer a hybrid optimistic-Thompson Sampling approach that results in a tractable algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.06036v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7093\", \"10657\", \"15680\", \"46380\", \"61018\", \"71169\", \"72379\"], \"outgoing_citations\": [\"153052\", \"179690\", \"182051\", \"184167\", \"187062\", \"204218\", \"211058\", \"225125\", \"270826\", \"271358\", \"280021\", \"286910\", \"289229\", \"304303\", \"315055\", \"327768\", \"344075\", \"354490\"]}","task_split":"paper_retrieval"}
{"document_id":"106273","document_content":"# Imitating Unknown Policies via Exploration\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nBehavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations. Recent approaches use self-supervision of fully-observable unlabeled snapshots of the states to decode state-pairs into actions. However, the iterative learning scheme from these techniques are prone to getting stuck into bad local minima. We address these limitations incorporating a two-phase model into the original framework, which learns from unlabeled observations via exploration, substantially improving traditional behavioral cloning by exploiting (i) a sampling mechanism to prevent bad local minima, (ii) a sampling mechanism to improve exploration, and (iii) self-attention modules to capture global features. The resulting technique outperforms the previous state-of-the-art in four different environments by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.05660v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"49426\"], \"outgoing_citations\": [\"128429\", \"180258\", \"230068\", \"231307\", \"233167\", \"261522\"]}","task_split":"paper_retrieval"}
{"document_id":"106294","document_content":"# End-to-End Neural Transformer Based Spoken Language Understanding\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nSpoken language understanding (SLU) refers to the process of inferring the semantic information from audio signals. While the neural transformers consistently deliver the best performance among the state-of-the-art neural architectures in field of natural language processing (NLP), their merits in a closely related field, i.e., spoken language understanding (SLU) have not beed investigated. In this paper, we introduce an end-to-end neural transformer-based SLU model that can predict the variable-length domain, intent, and slots vectors embedded in an audio signal with no intermediate token prediction architecture. This new architecture leverages the self-attention mechanism by which the audio signal is transformed to various sub-subspaces allowing to extract the semantic context implied by an utterance. Our end-to-end transformer SLU predicts the domains, intents and slots in the Fluent Speech Commands dataset with accuracy equal to 98.1 \\%, 99.6 \\%, and 99.6 \\%, respectively and outperforms the SLU models that leverage a combination of recurrent and convolutional neural networks by 1.4 \\% while the size of our model is 25\\% smaller than that of these architectures. Additionally, due to independent sub-space projections in the self-attention layer, the model is highly parallelizable which makes it a good candidate for on-device SLU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.10984v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"86254\", \"3386\", \"20622\", \"12476\", \"28852\", \"31268\", \"38325\", \"41573\", \"54462\", \"57429\", \"57637\", \"58582\", \"70908\", \"86592\", \"88529\", \"91161\", \"87723\"], \"outgoing_citations\": [\"89723\", \"138872\", \"142276\", \"142302\", \"164326\", \"191769\", \"216686\", \"223002\", \"232847\", \"238703\", \"241146\", \"270889\", \"308468\"]}","task_split":"paper_retrieval"}
{"document_id":"106317","document_content":"# Model-Based Offline Planning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n- Systems and Control\n- Systems and Control\n## Abstract\nOffline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no direct access to the system when learning a policy. Recent work on training RL policies from offline data has shown results both with model-free policies learned directly from the data, or with planning on top of learnt models of the data. Model-free policies tend to be more performant, but are more opaque, harder to command externally, and less easy to integrate into larger systems. We propose an offline learner that generates a model that can be used to control the system directly through planning. This allows us to have easily controllable policies directly from data, without ever interacting with the system. We show the performance of our algorithm, Model-Based Offline Planning (MBOP) on a series of robotics-inspired tasks, and demonstrate its ability leverage planning to respect environmental constraints. We are able to find near-optimal polices for certain simulated systems from as little as 50 seconds of real-time system interaction, and create zero-shot goal-conditioned policies on a series of environments. An accompanying video can be found here: https:\/\/youtu.be\/nxGGHdZOFts","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.05556v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"cs.SY\", \"eess.SY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [\"1752\", \"8026\", \"15237\", \"17957\", \"18289\", \"34558\", \"49347\", \"67543\", \"90963\", \"5026\", \"7104\", \"8072\", \"19431\", \"19780\", \"22866\", \"22978\", \"33598\", \"41536\", \"41981\", \"43653\", \"44986\", \"45372\", \"56365\", \"56639\", \"63112\", \"65737\", \"68900\", \"77736\", \"79175\", \"82278\", \"104486\"], \"outgoing_citations\": [\"111316\", \"120766\", \"122699\", \"125479\", \"127099\", \"131003\", \"135201\", \"142415\", \"164957\", \"177094\", \"179805\", \"181512\", \"207601\", \"210517\", \"211425\", \"228028\", \"229890\", \"307147\"]}","task_split":"paper_retrieval"}
{"document_id":"106394","document_content":"# Text Classification based on Multi-granularity Attention Hybrid Neural Network\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNeural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.05282v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"256527\", \"272469\", \"287391\", \"291045\", \"296555\", \"314976\", \"328352\", \"331356\", \"334629\"]}","task_split":"paper_retrieval"}
{"document_id":"106441","document_content":"# Dispersed Federated Learning: Vision, Taxonomy, and Future Directions\n## Categories\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe ongoing deployment of the Internet of Things (IoT)-based smart applications is spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been a significant recent interest in the concept of federated learning. Federated learning offers on-device, privacy-preserving machine learning without the need to transfer end-devices data to a third party location. However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models. Furthermore, the federated learning process might fail due to a failure in the aggregation server (e.g., due to a malicious attack or physical defect). Other than privacy and robustness issues, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel concept of dispersed federated learning (DFL) that is based on the true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.05189v2\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.DC\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"16289\", \"48388\", \"67257\", \"54559\", \"98267\"], \"outgoing_citations\": [\"119905\", \"120557\", \"121201\", \"131330\", \"157830\", \"164869\", \"166475\", \"168486\", \"180510\", \"207518\", \"235592\", \"252956\"]}","task_split":"paper_retrieval"}
{"document_id":"106532","document_content":"# Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04991v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32549\"], \"outgoing_citations\": [\"135967\", \"136740\", \"187682\", \"200841\", \"203735\", \"204590\", \"216524\", \"223504\", \"229037\", \"230691\", \"240640\", \"248730\", \"303011\", \"322022\", \"328051\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"106567","document_content":"# Maximizing BCI Human Feedback using Active Learning\n## Categories\n- Robotics\n- Human-Computer Interaction\n## Abstract\nRecent advancements in \\textit{Learning from Human Feedback} present an effective way to train robot agents via inputs from non-expert humans, without a need for a specially designed reward function. However, this approach needs a human to be present and attentive during robot learning to provide evaluative feedback. In addition, the amount of feedback needed grows with the level of task difficulty and the quality of human feedback might decrease over time because of fatigue. To overcome these limitations and enable learning more robot tasks with higher complexities, there is a need to maximize the quality of expensive feedback received and reduce the amount of human cognitive involvement required. In this work, we present an approach that uses active learning to smartly choose queries for the human supervisor based on the uncertainty of the robot and effectively reduces the amount of feedback needed to learn a given task. We also use a novel multiple buffer system to improve robustness to feedback noise and guard against catastrophic forgetting as the robot learning evolves. This makes it possible to learn tasks with more complexity using lesser amounts of human feedback compared to previous methods. We demonstrate the utility of our proposed method on a robot arm reaching task where the robot learns to reach a location in 3D without colliding with obstacles. Our approach is able to learn this task faster, with less human feedback and cognitive involvement, compared to previous methods that do not use active learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04873v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"5616\"], \"outgoing_citations\": [\"163867\", \"219658\", \"224223\", \"227991\", \"229188\", \"254581\", \"262810\", \"263957\", \"279673\", \"290804\"]}","task_split":"paper_retrieval"}
{"document_id":"106661","document_content":"# Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Sound\n## Abstract\nIn many fields of research, labeled datasets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network engineering and classification tasks. The idea here is to reduce model over-fitting to the feature distribution of a small under-descriptive training dataset. We try to evaluate such data augmentation techniques to gather insights in the performance boost they provide for several convolutional neural networks on mel-spectrogram representations of audio data. We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice (ComParE Challenge 2020). Also we consider four varying architectures to account for augmentation robustness. Results show that most of the baselines given by ComParE are outperformed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04590v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"38360\"], \"outgoing_citations\": [\"151603\", \"202274\", \"212396\", \"233627\", \"279214\", \"283522\", \"283649\"]}","task_split":"paper_retrieval"}
{"document_id":"106681","document_content":"# Extension of JPEG XS for Two-Layer Lossless Coding\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n## Abstract\nA two-layer lossless image coding method compatible with JPEG XS is proposed. JPEG XS is a new international standard for still image coding that has the characteristics of very low latency and very low complexity. However, it does not support lossless coding, although it can achieve visual lossless coding. The proposed method has a two-layer structure similar to JPEG XT, which consists of JPEG XS coding and a lossless coding method. As a result, it enables us to losslessly restore original images, while maintaining compatibility with JPEG XS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04558v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.MM\", \"cs.CV\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Multimedia\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"187146\", \"226538\"]}","task_split":"paper_retrieval"}
{"document_id":"106682","document_content":"# Text as Neural Operator: Image Manipulation by Text Instruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community. The input to conditional image generation has evolved from image-only to multimodality. In this paper, we study a setting that allows users to edit an image with multiple objects using complex text instructions to add, remove, or change the objects. The inputs of the task are multimodal including (1) a reference image and (2) an instruction in natural language that describes desired modifications to the image. We propose a GAN-based method to tackle this problem. The key idea is to treat text as neural operators to locally modify the image feature. We show that the proposed model performs favorably against recent strong baselines on three public datasets. Specifically, it generates images of greater fidelity and semantic relevance, and when used as a image query, leads to better retrieval performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3474085.3475343\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"18117\", \"605\", \"15707\", \"24711\", \"38693\"], \"outgoing_citations\": [\"57663\", \"103261\", \"106820\", \"111567\", \"112107\", \"132564\", \"149556\", \"151411\", \"154606\", \"157902\", \"165186\", \"166632\", \"168590\", \"171211\", \"172080\", \"183623\", \"187739\", \"192697\", \"197213\", \"205306\", \"205616\", \"206919\", \"208034\", \"208868\", \"212643\", \"220605\", \"220778\", \"229037\", \"231497\", \"233661\", \"234403\", \"235769\", \"236736\", \"238566\", \"241749\", \"247393\", \"248730\", \"249031\", \"251499\", \"255134\", \"264533\", \"265397\", \"266825\", \"266974\", \"270948\", \"276597\", \"277538\", \"291119\", \"303106\"]}","task_split":"paper_retrieval"}
{"document_id":"106707","document_content":"# On Learning Language-Invariant Representations for Universal Machine Translation\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nThe goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \\emph{a small subset} of all pairs of languages. Despite impressive empirical results and an increasing interest in massively multilingual models, theoretical analysis on translation errors made by such universal machine translation models is only nascent. In this paper, we formally prove certain impossibilities of this endeavour in general, as well as prove positive results in the presence of additional (but natural) structure of data.   For the former, we derive a lower bound on the translation error in the many-to-many translation setting, which shows that any algorithm aiming to learn shared sentence representations among multiple language pairs has to make a large translation error on at least one of the translation tasks, if no assumption on the structure of the languages is made. For the latter, we show that if the paired documents in the corpus follow a natural \\emph{encoder-decoder} generative process, we can expect a natural notion of ``generalization'': a linear number of language pairs, rather than quadratic, suffices to learn a good representation. Our theory also explains what kinds of connection graphs between pairs of languages are better suited: ones with longer paths result in worse sample complexity in terms of the total number of documents per language pair needed. We believe our theoretical insights and implications contribute to the future algorithmic design of universal machine translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04510v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"38855\", \"108602\", \"79482\", \"179902\"], \"outgoing_citations\": [\"137625\", \"161497\", \"164402\", \"168995\", \"176571\", \"179902\", \"182594\", \"192418\", \"197007\", \"200263\", \"201400\", \"204715\", \"217957\", \"220330\", \"221514\", \"230709\", \"233462\", \"242125\", \"244542\", \"253559\", \"268957\", \"280447\", \"280486\", \"290510\", \"290547\", \"292736\", \"300548\", \"300604\", \"310737\", \"314240\", \"323451\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"106769","document_content":"# A Survey and Insights on Deployments of the Connected and Autonomous Vehicles in US\n## Categories\n- Computers and Society\n- Machine Learning\n## Abstract\nCV\/ITS (Connected Vehicle, Intelligent Transportation System) and AV\/ADS (Autonomous Vehicle, Automated Driving System) have been emerging for the sake of saving people lives, improving traffic efficiency and helping the environment for decades. There are separate efforts led respectively by USDOT with state DOTs for CV, and private sectors through market driven approach from start-ups and technology companies for AV. By CV\/ITS effort there are 97 deployments of V2X communications utilizing the 5.9 GHz band, 18,877 vehicles with aftermarket V2X communications devices, and 8,098 infrastructure V2X devices installed at the roadsides. However, CV\/ITS still cannot be massively deployed in US markets due to lack of regulations, dedicated wireless spectrum bands, sustainable financial & business models with mature supply chain, etc. In the other side, technology-driven AV market has been much slower than expected mainly because of immaturity of AI technology to handle different complex driving scenarios in a cost effective way. In this paper, we first present these two parallel journeys focusing on the deployments including operating models, scenarios and applications, evaluations and lessons learning. Then, come up with recommendations to a cooperative CAV approach driving a more feasible, safer, affordable and cost effective transportation, but require a great industry collaboration from Automotive, Transportation. ICT and Cloud.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.04379v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"113487\"]}","task_split":"paper_retrieval"}
{"document_id":"107102","document_content":"# Cross-modal Center Loss\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we propose an approach to jointly train the components of cross-modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities, including 2D image, 3D point cloud, and mesh data. The proposed framework significantly outperforms the state-of-the-art methods on the ModelNet40 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.03561v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"122374\", \"165826\", \"172883\", \"189860\", \"191735\", \"208324\", \"209673\", \"211527\", \"217670\", \"218786\", \"237062\", \"238741\", \"248328\", \"269499\", \"269716\", \"270389\", \"294761\", \"295093\", \"313361\"]}","task_split":"paper_retrieval"}
{"document_id":"107214","document_content":"# A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context\n## Categories\n- Image and Video Processing\n- Medical Physics\n- Computers and Society\n- Computer Vision and Pattern Recognition\n## Abstract\nPrior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 histopathologically confirmed melanomas compared with benign melanoma mimickers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.07360v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"physics.med-ph\", \"eess.IV\", \"cs.CY\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Medical Physics\", \"Image and Video Processing\", \"Computers and Society\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"146184\", \"22199\", \"35384\", \"39901\", \"41481\", \"61633\", \"85603\", \"12718\", \"16904\", \"19599\", \"28005\", \"41998\", \"51420\", \"54640\", \"54627\", \"55195\", \"55685\", \"56562\", \"64980\", \"82472\", \"88594\", \"97063\"], \"outgoing_citations\": [\"213374\"]}","task_split":"paper_retrieval"}
{"document_id":"107240","document_content":"# Learning a natural-language to LTL executable semantic parser for grounded robotics\n## Categories\n- Computation and Language\n- I.2.7\n## Abstract\nChildren acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct corrections. We take a step toward robots that can do the same by training a grounded semantic parser, which discovers latent linguistic representations that can be used for the execution of natural-language commands. In particular, we focus on the difficult domain of commands with a temporal aspect, whose semantics we capture with Linear Temporal Logic, LTL. Our parser is trained with pairs of sentences and executions as well as an executor. At training time, the parser hypothesizes a meaning representation for the input as a formula in LTL. Three competing pressures allow the parser to discover meaning from language. First, any hypothesized meaning for a sentence must be permissive enough to reflect all the annotated execution trajectories. Second, the executor -- a pretrained end-to-end LTL planner -- must find that the observe trajectories are likely executions of the meaning. Finally, a generator, which reconstructs the original input, encourages the model to find representations that conserve knowledge about the command. Together these ensure that the meaning is neither too general nor too specific. Our model generalizes well, being able to parse and execute both machine-generated and human-generated commands, with near-equal accuracy, despite the fact that the human-generated sentences are much more varied and complex with an open lexicon. The approach presented here is not specific to LTL: it can be applied to any domain where sentence meanings can be hypothesized and an executor can verify these meanings, thus opening the door to many applications for robotic agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.03277v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7\"], \"incoming_citations\": [\"69616\", \"95168\"], \"outgoing_citations\": [\"121678\", \"177296\", \"198373\", \"225551\", \"248805\", \"249906\", \"250496\", \"257501\", \"267980\", \"281106\", \"290632\", \"300505\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"107294","document_content":"# Incremental Text to Speech for Neural Sequence-to-Sequence Models using Reinforcement Learning\n## Categories\n- Audio and Speech Processing\n- Sound\n- Machine Learning\n- Machine Learning\n## Abstract\nModern approaches to text to speech require the entire input character sequence to be processed before any audio is synthesised. This latency limits the suitability of such models for time-sensitive tasks like simultaneous interpretation. Interleaving the action of reading a character with that of synthesising audio reduces this latency. However, the order of this sequence of interleaved actions varies across sentences, which raises the question of how the actions should be chosen. We propose a reinforcement learning based framework to train an agent to make this decision. We compare our performance against that of deterministic, rule-based systems. Our results demonstrate that our agent successfully balances the trade-off between the latency of audio generation and the quality of synthesised audio. More broadly, we show that neural sequence-to-sequence models can be adapted to run in an incremental manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.21437\/Interspeech.2020-1822\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.SD\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"16019\", \"77439\", \"21791\", \"38687\", \"68215\", \"78777\"], \"outgoing_citations\": [\"143720\", \"157627\", \"160284\", \"168771\", \"181097\", \"182708\", \"182898\", \"209901\", \"213698\", \"241133\", \"247430\", \"258147\", \"265720\", \"269836\", \"283451\", \"290992\", \"303296\", \"307902\", \"313427\", \"334299\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"107355","document_content":"# Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThe current main stream methods formulate their video saliency mainly from two independent venues, i.e., the spatial and temporal branches. As a complementary component, the main task for the temporal branch is to intermittently focus the spatial branch on those regions with salient movements. In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter. Thus, the key factor to improve the overall video saliency is how to further boost the performance of these branches efficiently. In this paper, we propose a novel spatiotemporal network to achieve such improvement in a full interactive fashion. We integrate a lightweight temporal model into the spatial branch to coarsely locate those spatially salient regions which are correlated with trustworthy salient movements. Meanwhile, the spatial branch itself is able to recurrently refine the temporal model in a multi-scale manner. In this way, both the spatial and temporal branches are able to interact with each other, achieving the mutual performance improvement. Our method is easy to implement yet effective, achieving high quality video saliency detection in real-time speed with 50 FPS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2021.3068644\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"14778\"], \"outgoing_citations\": [\"107372\", \"110890\", \"146522\", \"166645\", \"170677\", \"189585\", \"189915\", \"259354\", \"259523\", \"274821\", \"281482\", \"285190\", \"315233\", \"328505\"]}","task_split":"paper_retrieval"}
{"document_id":"107365","document_content":"# Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMost computer aided pathology detection systems rely on large volumes of quality annotated data to aid diagnostics and follow up procedures. However, quality assuring large volumes of annotated medical image data can be subjective and expensive. In this work we present a novel standardization framework that implements three few-shot learning (FSL) models that can be iteratively trained by atmost 5 images per 3D stack to generate multiple regional proposals (RPs) per test image. These FSL models include a novel parallel echo state network (ParESN) framework and an augmented U-net model. Additionally, we propose a novel target label selection algorithm (TLSA) that measures relative agreeability between RPs and the manually annotated target labels to detect the \"best\" quality annotation per image. Using the FSL models, our system achieves 0.28-0.64 Dice coefficient across vendor image stacks for intra-retinal cyst segmentation. Additionally, the TLSA is capable of automatically classifying high quality target labels from their noisy counterparts for 60-97% of the images while ensuring manual supervision on remaining images. Also, the proposed framework with ParESN model minimizes manual annotation checking to 12-28% of the total number of images. The TLSA metrics further provide confidence scores for the automated annotation quality assurance. Thus, the proposed framework is flexible to extensions for quality image annotation curation of other image stacks as well.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02952v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"13393\"], \"outgoing_citations\": [\"191258\", \"200291\", \"212526\", \"258090\"]}","task_split":"paper_retrieval"}
{"document_id":"107467","document_content":"# Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images\n## Categories\n- Artificial Intelligence\n## Abstract\nPurpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated data sets. 2) Non-generalizability that limits deployment to new scanners \/ institutions. And 3) Inadequate explainability and interpretability. We believe that reinforcement learning can address all three shortcomings, with robust and intuitive algorithms trainable on small datasets. To the best of our knowledge, reinforcement learning has not been directly applied to computer vision tasks for radiological images. In this proof-of-principle work, we train a deep reinforcement learning network to predict brain tumor location.   Materials and Methods: Using the BraTS brain tumor imaging database, we trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We did so in concert with image exploration, with rewards and punishments designed to localize lesions. To compare with supervised deep learning, we trained a keypoint detection convolutional neural network on the same 70 images. We applied both approaches to a separate 30 image testing set.   Results: Reinforcement learning predictions consistently improved during training, whereas those of supervised deep learning quickly diverged. Reinforcement learning predicted testing set lesion locations with 85% accuracy, compared to roughly 7% accuracy for the supervised deep network.   Conclusion: Reinforcement learning predicted lesions with high accuracy, which is unprecedented for such a small training set. We believe that reinforcement learning can propel radiology AI well past the inherent limitations of supervised deep learning, with more clinician-driven research and finally toward true clinical applicability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02708v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"41234\", \"92661\", \"1002\", \"71406\", \"78527\"], \"outgoing_citations\": [\"153970\", \"236185\", \"241835\", \"242720\", \"258199\", \"283410\", \"286821\"]}","task_split":"paper_retrieval"}
{"document_id":"107503","document_content":"# Data balancing for boosting performance of low-frequency classes in Spoken Language Understanding\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n## Abstract\nDespite the fact that data imbalance is becoming more and more common in real-world Spoken Language Understanding (SLU) applications, it has not been studied extensively in the literature. To the best of our knowledge, this paper presents the first systematic study on handling data imbalance for SLU. In particular, we discuss the application of existing data balancing techniques for SLU and propose a multi-task SLU model for intent classification and slot filling. Aiming to avoid over-fitting, in our model methods for data balancing are leveraged indirectly via an auxiliary task which makes use of a class-balanced batch generator and (possibly) synthetic data. Our results on a real-world dataset indicate that i) our proposed model can boost performance on low frequency intents significantly while avoiding a potential performance decrease on the head intents, ii) synthetic data are beneficial for bootstrapping new intents when realistic data are not available, but iii) once a certain amount of realistic data becomes available, using synthetic data in the auxiliary task only yields better performance than adding them to the primary task training data, and iv) in a joint training scenario, balancing the intent distribution individually improves not only intent classification but also slot filling performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02603v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.SD\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\", \"Computation and Language\"], \"incoming_citations\": [\"87925\"], \"outgoing_citations\": [\"151954\", \"180239\", \"192504\", \"197154\", \"230867\", \"233440\", \"247368\", \"285317\", \"299885\"]}","task_split":"paper_retrieval"}
{"document_id":"107587","document_content":"# Cross-Model Image Annotation Platform with Active Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe have seen significant leapfrog advancement in machine learning in recent decades. The central idea of machine learnability lies on constructing learning algorithms that learn from good data. The availability of more data being made publicly available also accelerates the growth of AI in recent years. In the domain of computer vision, the quality of image data arises from the accuracy of image annotation. Labeling large volume of image data is a daunting and tedious task. This work presents an End-to-End pipeline tool for object annotation and recognition aims at enabling quick image labeling. We have developed a modular image annotation platform which seamlessly incorporates assisted image annotation (annotation assistance), active learning and model training and evaluation. Our approach provides a number of advantages over current image annotation tools. Firstly, the annotation assistance utilizes reference hierarchy and reference images to locate the objects in the images, thus reducing the need for annotating the whole object. Secondly, images can be annotated using polygon points allowing for objects of any shape to be annotated. Thirdly, it is also interoperable across several image models, and the tool provides an interface for object model training and evaluation across a series of pre-trained models. We have tested the model and embeds several benchmarking deep learning models. The highest accuracy achieved is 74%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02421v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"107649","document_content":"# Can You Read Me Now? Content Aware Rectification using Angle Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe ubiquity of smartphone cameras has led to more and more documents being captured by cameras rather than scanned. Unlike flatbed scanners, photographed documents are often folded and crumpled, resulting in large local variance in text structure. The problem of document rectification is fundamental to the Optical Character Recognition (OCR) process on documents, and its ability to overcome geometric distortions significantly affects recognition accuracy. Despite the great progress in recent OCR systems, most still rely on a pre-process that ensures the text lines are straight and axis aligned. Recent works have tackled the problem of rectifying document images taken in-the-wild using various supervision signals and alignment means. However, they focused on global features that can be extracted from the document's boundaries, ignoring various signals that could be obtained from the document's content.   We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification that relies on the document's content, the location of the words and specifically their orientation, as hints to assist in the rectification process. We utilize a novel pixel-wise angle regression approach and a curvature estimation side-task for optimizing our rectification model. Our method surpasses previous approaches in terms of OCR accuracy, geometric error and visual similarity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02231v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"72134\", \"8606\", \"13051\", \"13897\"], \"outgoing_citations\": [\"118987\", \"165757\", \"192483\", \"242784\", \"245899\", \"269205\", \"272481\", \"291404\"]}","task_split":"paper_retrieval"}
{"document_id":"107653","document_content":"# BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nExisting topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of \"topic identification\" and \"text segmentation\" for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information: with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise: a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called BATS: Biclustering Approach to Topic modeling and Segmentation. BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on four datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.02218v3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"90616\", \"168110\"], \"outgoing_citations\": [\"90616\", \"105074\", \"130513\", \"184142\", \"191947\", \"214235\", \"227152\", \"237812\", \"251560\", \"253897\", \"255501\", \"259706\", \"267692\", \"272615\", \"278336\", \"283832\", \"340240\", \"356657\"]}","task_split":"paper_retrieval"}
{"document_id":"107779","document_content":"# Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network\n## Categories\n- Signal Processing\n- Systems and Control\n- Systems and Control\n- Machine Learning\n## Abstract\nTransportation networks are highly complex and the design of efficient traffic management systems is difficult due to lack of adequate measured data and accurate predictions of the traffic states. Traffic simulation models can capture the complex dynamics of transportation networks by using limited available traffic data and can help central traffic authorities in their decision-making, if appropriate input is fed into the simulator. In this paper, we design an integrated simulation-prediction system which estimates the Origin-Destination (OD) matrix of a road network using only flow rate information and predicts the behavior of the road network in different simulation scenarios. The proposed system includes an optimization-based OD matrix generation method, a Neural Network (NN) model trained to predict OD matrices via the pattern of traffic flow and a microscopic traffic simulator with a Dynamic Traffic Assignment (DTA) scheme to predict the behavior of the transportation system. We test the proposed system on the road network of the central terminal area (CTA) of the Los Angeles International Airport (LAX), which demonstrates that the integrated traffic simulation-prediction system can be used to simulate the effects of several real world scenarios such as lane closures, curbside parking and other changes. The model is an effective tool for learning the impact and possible benefits of changes in the network and for analyzing scenarios at a very low cost without disrupting the network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01902v1\", \"primary_category\": \"eess.SP\", \"categories\": [\"eess.SY\", \"eess.SP\", \"cs.SY\", \"cs.LG\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Systems and Control\", \"Signal Processing\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"107909","document_content":"# TOAD-GAN: Coherent Style Level Generation from a Single Example\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01531v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"47005\", \"18547\", \"18892\", \"33220\", \"37489\", \"41052\", \"74578\", \"95477\", \"112786\"], \"outgoing_citations\": [\"122922\", \"143545\", \"163503\", \"188010\", \"189201\", \"189837\", \"233501\", \"274886\", \"289709\", \"297233\"]}","task_split":"paper_retrieval"}
{"document_id":"107920","document_content":"# Multiple Code Hashing for Efficient Image Retrieval\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nDue to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01503v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"193986\", \"208611\", \"240410\", \"243039\", \"260176\", \"264939\", \"265613\", \"274846\", \"276451\", \"277432\", \"291400\", \"303512\", \"307882\", \"314568\", \"316473\", \"346675\"]}","task_split":"paper_retrieval"}
{"document_id":"107968","document_content":"# A Survey of Orthographic Information in Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMachine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable, but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography's influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts of cognates information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s42979-021-00723-4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"28770\", \"62397\", \"93556\", \"94036\", \"96077\"], \"outgoing_citations\": [\"122071\", \"122072\", \"162951\", \"165748\", \"167903\", \"170295\", \"174550\", \"178623\", \"178661\", \"179820\", \"180299\", \"181277\", \"182466\", \"182716\", \"183020\", \"184677\", \"189776\", \"200292\", \"209157\", \"219175\", \"219619\", \"219840\", \"220017\", \"220147\", \"221514\", \"230181\", \"233817\", \"234391\", \"251240\", \"251974\", \"251977\", \"264507\", \"267601\", \"273286\", \"280447\", \"280486\", \"282931\", \"292208\", \"302824\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"107976","document_content":"# Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n- I.2.7\n## Abstract\nSyntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01377v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\", \"I.2.7\"], \"incoming_citations\": [\"11588\", \"49386\"], \"outgoing_citations\": [\"179985\", \"194904\", \"231196\", \"312764\", \"345193\"]}","task_split":"paper_retrieval"}
{"document_id":"108052","document_content":"# Concurrent Training Improves the Performance of Behavioral Cloning from Observation\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nLearning from demonstration is widely used as an efficient way for robots to acquire new skills. However, it typically requires that demonstrations provide full access to the state and action sequences. In contrast, learning from observation offers a way to utilize unlabeled demonstrations (e.g., video) to perform imitation learning. One approach to this is behavioral cloning from observation (BCO). The original implementation of BCO proceeds by first learning an inverse dynamics model and then using that model to estimate action labels, thereby reducing the problem to behavioral cloning. However, existing approaches to BCO require a large number of initial interactions in the first step. Here, we provide a novel theoretical analysis of BCO, introduce a modification BCO*, and show that in the semi-supervised setting, BCO* can concurrently improve both its estimate for the inverse dynamics model and the expert policy. This result allows us to eliminate the dependence on initial interactions and dramatically improve the sample complexity of BCO. We evaluate the effectiveness of our algorithm through experiments on various benchmark domains. The results demonstrate that concurrent training not only improves over the performance of BCO but also results in performance that is competitive with state-of-the-art imitation learning methods such as GAIL and Value-Dice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01205v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"35842\"], \"outgoing_citations\": [\"151786\", \"151978\", \"157804\", \"162466\", \"180258\", \"218636\", \"231307\", \"233167\", \"271358\", \"271981\", \"283410\", \"287362\", \"327996\"]}","task_split":"paper_retrieval"}
{"document_id":"108055","document_content":"# Learning to Play Two-Player Perfect-Information Games without Knowledge\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper, several techniques for learning game state evaluation functions by reinforcement are proposed. The first is a generalization of tree bootstrapping (tree learning): it is adapted to the context of reinforcement learning without knowledge based on non-linear functions. With this technique, no information is lost during the reinforcement learning process. The second is a modification of minimax with unbounded depth extending the best sequences of actions to the terminal states. This modified search is intended to be used during the learning process. The third is to replace the classic gain of a game (+1 \/ -1) with a reinforcement heuristic. We study particular reinforcement heuristics such as: quick wins and slow defeats ; scoring ; mobility or presence. The four is another variant of unbounded minimax, which plays the safest action instead of playing the best action. This modified search is intended to be used after the learning process. The five is a new action selection distribution. The conducted experiments suggest that these techniques improve the level of play. Finally, we apply these different techniques to design program-players to the game of Hex (size 11 and 13) surpassing the level of Mohex 3HNN with reinforcement learning from self-play without knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01188v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"24238\", \"79489\"], \"outgoing_citations\": [\"145431\", \"186608\", \"248225\", \"265642\", \"292510\", \"293920\", \"302924\", \"320561\"]}","task_split":"paper_retrieval"}
{"document_id":"108189","document_content":"# Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characteristics depend on those of the underlying learning approach. Here, we consider a benchmark planning problem from the reinforcement learning domain, the Racetrack, to investigate the properties of agents derived from different deep (reinforcement) learning approaches. We compare the performance of deep supervised learning, in particular imitation learning, to reinforcement learning for the Racetrack model. We find that imitation learning yields agents that follow more risky paths. In contrast, the decisions of deep reinforcement learning are more foresighted, i.e., avoid states in which fatal decisions are more likely. Our evaluations show that for this sequential decision making problem, deep reinforcement learning performs best in many aspects even though for imitation learning optimal decisions are considered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.00766v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"108316","document_content":"# Structural Causal Models Are (Solvable by) Credal Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nA structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model into a credal network. Causal inferences, such as interventions and counterfactuals, can consequently be obtained by standard algorithms for the updating of credal nets. These natively return sharp values in the identifiable case, while intervals corresponding to the exact bounds are produced for unidentifiable queries. A characterization of the causal models that allow the map above to be compactly derived is given, along with a discussion about the scalability for general models. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks and hence to systematically compute causal inferences. A number of demonstrative examples is presented to clarify our methodology. Extensive experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.00463v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"17181\", \"50649\", \"89103\"], \"outgoing_citations\": [\"291854\", \"348294\", \"351382\", \"357301\", \"360748\"]}","task_split":"paper_retrieval"}
{"document_id":"108478","document_content":"# Bayesian-Assisted Inference from Visualized Data\n## Categories\n- Human-Computer Interaction\n- Methodology\n## Abstract\nA Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and subjective uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.00142v2\", \"primary_category\": \"cs.HC\", \"categories\": [\"stat.ME\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Methodology\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"58221\"], \"outgoing_citations\": [\"172968\", \"203425\"]}","task_split":"paper_retrieval"}
{"document_id":"108525","document_content":"# IntelligentPooling: Practical Thompson Sampling for mHealth\n## Categories\n- Machine Learning\n- Computers and Society\n## Abstract\nIn mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user's degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user's time since beginning treatment, thus addressing challenge three. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art. We demonstrate the promise of this approach and its ability to learn from even a small group of users in a live clinical trial.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01571v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"29694\", \"30320\", \"79151\", \"100893\"], \"outgoing_citations\": [\"64236\", \"151561\", \"165989\", \"197982\", \"205477\", \"215177\", \"216113\", \"228896\", \"238343\", \"241496\", \"261844\", \"265595\", \"269912\", \"272056\", \"280021\", \"292648\", \"299562\", \"304680\", \"305183\", \"344092\", \"350403\", \"354490\", \"357161\"]}","task_split":"paper_retrieval"}
{"document_id":"108589","document_content":"# node2coords: Graph Representation Learning with Wasserstein Barycenters\n## Categories\n- Machine Learning\n## Abstract\nIn order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn representations that can be interpreted in a straightforward way and that are robust to perturbations to the graph structure. In this work, we address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space. The patterns that span the low dimensional space reveal the graph's most important structural information. The coordinates of the nodes reveal the proximity of their local structure to the graph structural patterns. In order to measure this proximity by taking into account the underlying graph, we propose to use Wasserstein distances. We introduce an autoencoder that employs a linear layer in the encoder and a novel Wasserstein barycentric layer at the decoder. Node connectivity descriptors, that capture the local structure of the nodes, are passed through the encoder to learn the small set of graph structural patterns. In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns. The optimal weights for the barycenter representation of a node's connectivity descriptor correspond to the coordinates of that node in the low-dimensional space. Experimental results demonstrate that the representations learned with node2coords are interpretable, lead to node embeddings that are stable to perturbations of the graph structure and achieve competitive or superior results compared to state-of-the-art methods in node classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.16056v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"50629\"], \"outgoing_citations\": [\"71722\", \"115975\", \"126374\", \"158776\", \"178992\", \"182347\", \"201673\", \"231349\", \"233280\", \"255202\", \"259206\"]}","task_split":"paper_retrieval"}
{"document_id":"108663","document_content":"# Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nText simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.15823v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"88624\", \"163045\", \"167795\", \"184909\", \"219560\", \"227488\", \"270048\", \"277380\", \"289494\", \"290515\", \"303951\"]}","task_split":"paper_retrieval"}
{"document_id":"108682","document_content":"# LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nTrain machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to three issues. First, the noisy data is close to its original value with high probability, increasing the risk of information exposure. Second, a large variance is introduced to the estimated average, causing poor accuracy. Last, the privacy budget explodes due to the high dimensionality of weights in deep learning models. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It is capable of making the data more distinct from its original value and introducing lower variance. Moreover, the proposed mechanism bypasses the curse of dimensionality by splitting and shuffling model updates. A series of empirical evaluations on three commonly used datasets, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.15789v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"46353\", \"67665\", \"8794\", \"43741\", \"72802\", \"82241\", \"12938\", \"24096\", \"42456\", \"50759\", \"56494\", \"70556\", \"71073\", \"87768\", \"101146\", \"127626\", \"82378\", \"95115\"], \"outgoing_citations\": [\"51766\", \"71073\", \"82241\", \"101146\", \"120773\", \"142628\", \"207491\", \"208014\", \"210354\", \"231780\", \"246939\", \"252956\", \"254260\", \"282410\", \"290296\", \"294987\", \"326583\", \"353750\"]}","task_split":"paper_retrieval"}
{"document_id":"108770","document_content":"# PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent's exploration. However, these approaches typically operate on structured representations of the environment, and\/or assume some structure in the natural language commands. In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy learning. Our experiments on the Meta-World robot manipulation domain show that language-based rewards significantly improves the sample efficiency of policy learning, both in sparse and dense reward settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.15543v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"26076\", \"35938\", \"41041\", \"44473\", \"97596\"], \"outgoing_citations\": [\"160036\", \"160530\", \"177272\", \"181620\", \"189110\", \"194477\", \"196377\", \"198208\", \"219305\", \"229145\", \"249906\", \"251691\", \"260107\", \"268335\", \"268584\", \"310370\", \"314620\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"108829","document_content":"# The optimality of syntactic dependency distances\n## Categories\n- Computation and Language\n- Discrete Mathematics\n- Physics and Society\n## Abstract\nIt is often stated that human languages, as other biological systems, are shaped by cost-cutting pressures but, to what extent? Attempts to quantify the degree of optimality of languages by means of an optimality score have been scarce and focused mostly on English. Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies and the space is defined by the linear order of the words in the sentence. We introduce a new score to quantify the cognitive pressure to reduce the distance between linked words in a sentence. The analysis of sentences from 93 languages representing 19 linguistic families reveals that half of languages are optimized to a 70% or more. The score indicates that distances are not significantly reduced in a few languages and confirms two theoretical predictions, i.e. that longer sentences are more optimized and that distances are more likely to be longer than expected by chance in short sentences. We present a new hierarchical ranking of languages by their degree of optimization. The new score has implications for various fields of language research (dependency linguistics, typology, historical linguistics, clinical linguistics and cognitive science). Finally, the principles behind the design of the score have implications for network science.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1103\/PhysRevE.105.014308\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DM\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Discrete Mathematics\", \"Physics and Society\"], \"incoming_citations\": [\"36924\", \"171207\", \"5169\", \"5169\", \"22615\"], \"outgoing_citations\": [\"116246\", \"171207\", \"179374\", \"180911\", \"263685\", \"265212\", \"297614\", \"300194\", \"301334\", \"304127\", \"306869\", \"312835\", \"319878\", \"320716\", \"325565\", \"338464\", \"340244\", \"340551\", \"345972\", \"346043\", \"347066\"]}","task_split":"paper_retrieval"}
{"document_id":"108978","document_content":"# Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity\n## Categories\n- Computation and Language\n- Information Theory\n- Information Theory\n## Abstract\nNeural text decoding is important for generating high-quality texts using language models. To generate high-quality text, popular decoding algorithms like top-k, top-p (nucleus), and temperature-based sampling truncate or distort the unreliable low probability tail of the language model. Though these methods generate high-quality text after parameter tuning, they are ad hoc. Not much is known about the control they provide over the statistics of the output, which is important since recent reports show text quality is highest for a specific range of likelihoods. Here, first we provide a theoretical analysis of perplexity in top-k, top-p, and temperature sampling, finding that cross-entropy behaves approximately linearly as a function of p in top-p sampling whereas it is a nonlinear function of k in top-k sampling, under Zipfian statistics. We use this analysis to design a feedback-based adaptive top-k text decoding algorithm called mirostat that generates text (of any length) with a predetermined value of perplexity, and thereby high-quality text without any tuning. Experiments show that for low values of k and p in top-k and top-p sampling, perplexity drops significantly with generated text length, which is also correlated with excessive repetitions in the text (the boredom trap). On the other hand, for large values of k and p, we find that perplexity increases with generated text length, which is correlated with incoherence in the text (confusion trap). Mirostat avoids both traps: experiments show that cross-entropy has a near-linear relation with repetition in generated text. This relation is almost independent of the sampling method but slightly dependent on the model used. Hence, for a given language model, control over perplexity also gives control over repetitions. Experiments with human raters for fluency, coherence, and quality further verify our findings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14966v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Theory\", \"Information Theory\"], \"incoming_citations\": [\"27291\", \"43242\", \"63298\"], \"outgoing_citations\": [\"152729\", \"158437\", \"167338\", \"180695\", \"192158\", \"211804\", \"231949\", \"232274\"]}","task_split":"paper_retrieval"}
{"document_id":"108985","document_content":"# Learning Video Representations from Textual Web Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideos on the Internet are paired with pieces of text, such as titles and descriptions. This text typically describes the most important content in the video, such as the objects in the scene and the actions being performed. Based on this observation, we propose to use text as a method for learning video representations. To accomplish this, we propose a data collection process and use it to collect 70M video clips shared publicly on the Internet, and we then train a model to pair each video with its associated text. We evaluate the model on several down-stream action recognition tasks, including Kinetics, HMDB-51, and UCF-101. We find that this approach is an effective method of pre-training video representations. Specifically, it outperforms all existing methods for self-supervised and cross-modal video representation learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14937v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"29886\", \"76127\", \"3238\", \"25853\", \"40597\", \"61014\", \"62260\", \"66630\", \"73317\", \"119349\"], \"outgoing_citations\": [\"106968\", \"115673\", \"134177\", \"136264\", \"146888\", \"151327\", \"153776\", \"167544\", \"176053\", \"180920\", \"181897\", \"182790\", \"187996\", \"188114\", \"189824\", \"191806\", \"194508\", \"194686\", \"208341\", \"208883\", \"220529\", \"222332\", \"226232\", \"226832\", \"233440\", \"236080\", \"236252\", \"247535\", \"259415\", \"265650\", \"265701\", \"270325\", \"277126\", \"277626\", \"279987\", \"281762\", \"283807\", \"285093\", \"285985\", \"295738\", \"303174\", \"303844\", \"310322\", \"313245\", \"313644\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"109043","document_content":"# Learning Representations for Axis-Aligned Decision Forests through Input Perturbation\n## Categories\n- Machine Learning\n## Abstract\nAxis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-to-rank, decision forests deliver remarkable performance. They also possess other coveted characteristics such as interpretability. Despite their widespread use and rich history, decision forests to date fail to consume raw structured data such as text, or learn effective representations for them, a factor behind the success of deep neural networks in recent years. While there exist methods that construct smoothed decision forests to achieve representation learning, the resulting models are decision forests in name only: They are no longer axis-aligned, use stochastic decisions, or are not interpretable. Furthermore, none of the existing methods are appropriate for problems that require a Transfer Learning treatment. In this work, we present a novel but intuitive proposal to achieve representation learning for decision forests without imposing new restrictions or necessitating structural changes. Our model is simply a decision forest, possibly trained using any forest learning algorithm, atop a deep neural network. By approximating the gradients of the decision forest through input perturbation, a purely analytical procedure, the decision forest directs the neural network to learn or fine-tune representations. Our framework has the advantage that it is applicable to any arbitrary decision forest and that it allows the use of arbitrary deep neural networks for representation learning. We demonstrate the feasibility and effectiveness of our proposal through experiments on synthetic and benchmark classification datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14761v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"50840\", \"99397\"], \"outgoing_citations\": [\"126783\", \"130516\", \"133774\", \"141143\", \"154912\", \"158750\", \"160526\", \"229765\", \"229768\", \"237274\", \"259723\", \"279343\", \"308918\"]}","task_split":"paper_retrieval"}
{"document_id":"109053","document_content":"# CommuNety: A Deep Learning System for the Prediction of Cohesive Social Communities\n## Categories\n- Social and Information Networks\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nEffective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive social networks using images. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14741v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"216511\", \"276514\", \"318646\", \"332151\", \"338481\"]}","task_split":"paper_retrieval"}
{"document_id":"109101","document_content":"# Predictive Probability Path Planning Model For Dynamic Environments\n## Categories\n- Robotics\n## Abstract\nPath planning in dynamic environments is essential to high-risk applications such as unmanned aerial vehicles, self-driving cars, and autonomous underwater vehicles. In this paper, we generate collision-free trajectories for a robot within any given environment with temporal and spatial uncertainties caused due to randomly moving obstacles. We use two Poisson distributions to model the movements of obstacles across the generated trajectory of a robot in both space and time to determine the probability of collision with an obstacle. Measures are taken to avoid an obstacle by intelligently manipulating the speed of the robot at space-time intervals where a larger number of obstacles intersect the trajectory of the robot. Our method potentially reduces the use of computationally expensive collision detection libraries. Based on our experiments, there has been a significant improvement over existing methods in terms of safety, accuracy, execution time and computational cost. Our results show a high level of accuracy between the predicted and actual number of collisions with moving obstacles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14603v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"329358\", \"362319\"]}","task_split":"paper_retrieval"}
{"document_id":"109123","document_content":"# Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n- Multimedia\n## Abstract\nAutomatic video summarization is still an unsolved problem due to several challenges. We take steps towards making automatic video summarization more realistic by addressing them. Firstly, the currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking dataset VISIOCITY which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and can be used for other vision problems. Secondly, for long videos, human reference summaries are difficult to obtain. We present a novel recipe based on pareto optimality to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea. We propose a simple recipe VISIOCITY-SUM to enhance an existing model using a combination of losses and demonstrate that it beats the current state of the art techniques when tested on VISIOCITY. We also show that a single measure to evaluate a summary, as is the current typical practice, falls short. We propose a framework for better quantitative assessment of summary quality which is closer to human judgment than a single measure, say F1. We report the performance of a few representative techniques of video summarization on VISIOCITY assessed using various measures and bring out the limitation of the techniques and\/or the assessment mechanism in modeling human judgment and demonstrate the effectiveness of our evaluation framework in doing so.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14560v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\", \"Multimedia\"], \"incoming_citations\": [\"93651\"], \"outgoing_citations\": [\"144823\", \"190086\", \"191060\", \"193523\", \"196753\", \"207160\", \"216743\", \"223048\", \"224983\", \"246292\", \"257295\", \"260114\", \"261096\", \"264133\", \"267568\", \"291817\", \"296780\", \"328015\"]}","task_split":"paper_retrieval"}
{"document_id":"109165","document_content":"# Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks\n## Categories\n- Computational Engineering, Finance, and Science\n- Fluid Dynamics\n- physics.app-ph\n- Machine Learning\n## Abstract\nThe recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled data-sets (big data), which can be either obtained by experiments or first-principle simulations. Unfortunately, these labeled data-sets are expensive to obtain in AM due to the high expense of the AM experiments and prohibitive computational cost of high-fidelity simulations.   We propose a physics-informed neural network (PINN) framework that fuses both data and first physical principles, including conservation laws of momentum, mass, and energy, into the neural network to inform the learning processes. To the best knowledge of the authors, this is the first application of PINN to three dimensional AM processes modeling. Besides, we propose a hard-type approach for Dirichlet boundary conditions (BCs) based on a Heaviside function, which can not only enforce the BCs but also accelerate the learning process. The PINN framework is applied to two representative metal manufacturing problems, including the 2018 NIST AM-Benchmark test series. We carefully assess the performance of the PINN model by comparing the predictions with available experimental data and high-fidelity simulation results. The investigations show that the PINN, owed to the additional physical knowledge, can accurately predict the temperature and melt pool dynamics during metal AM processes with only a moderate amount of labeled data-sets. The foray of PINN to metal AM shows the great potential of physics-informed deep learning for broader applications to advanced manufacturing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.13547v2\", \"primary_category\": \"cs.CE\", \"categories\": [\"physics.flu-dyn\", \"cs.CE\", \"physics.app-ph\", \"cs.LG\"], \"primary_category_human_readable\": \"Computational Engineering, Finance, and Science\", \"categories_human_readable\": [\"Fluid Dynamics\", \"Computational Engineering, Finance, and Science\", \"physics.app-ph\", \"Machine Learning\"], \"incoming_citations\": [\"131164\", \"20296\", \"29317\", \"16410\", \"17711\", \"75764\"], \"outgoing_citations\": [\"174339\", \"186670\", \"203504\", \"204144\", \"208901\", \"223363\", \"249017\", \"276528\", \"282705\", \"295402\", \"316332\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"109216","document_content":"# On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks. Whilst the reported performance of these approaches is impressive, this study investigates the hitherto unapproached question of the impact of commonplace image and video compression techniques on the performance of such deep learning architectures. Focusing on the JPEG and H.264 (MPEG-4 AVC) as a representative proxy for contemporary lossy image\/video compression techniques that are in common use within network-connected image\/video devices and infrastructure, we examine the impact on performance across five discrete tasks: human pose estimation, semantic segmentation, object detection, action recognition, and monocular depth estimation. As such, within this study we include a variety of network architectures and domains spanning end-to-end convolution, encoder-decoder, region-based CNN (R-CNN), dual-stream, and generative adversarial networks (GAN). Our results show a non-linear and non-uniform relationship between network performance and the level of lossy compression applied. Notably, performance decreases significantly below a JPEG quality (quantization) level of 15% and a H.264 Constant Rate Factor (CRF) of 40. However, retraining said architectures on pre-compressed imagery conversely recovers network performance by up to 78.4% in some cases. Furthermore, there is a correlation between architectures employing an encoder-decoder pipeline and those that demonstrate resilience to lossy image compression. The characteristics of the relationship between input compression to output task performance can be used to inform design decisions within future image\/video devices and infrastructure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14314v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17537\", \"21815\", \"58477\"], \"outgoing_citations\": [\"294611\"]}","task_split":"paper_retrieval"}
{"document_id":"109228","document_content":"# Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nA division-of-focal-plane or microgrid image polarimeter enables us to acquire a set of polarization images in one shot. Since the polarimeter consists of an image sensor equipped with a monochrome or color polarization filter array (MPFA or CPFA), the demosaicking process to interpolate missing pixel values plays a crucial role in obtaining high-quality polarization images. In this paper, we propose a novel MPFA demosaicking method based on edge-aware residual interpolation (EARI) and also extend it to CPFA demosaicking. The key of EARI is a new edge detector for generating an effective guide image used to interpolate the missing pixel values. We also present a newly constructed full color-polarization image dataset captured using a 3-CCD camera and a rotating polarizer. Using the dataset, we experimentally demonstrate that our EARI-based method outperforms existing methods in MPFA and CPFA demosaicking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14292v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"13542\", \"111407\"], \"outgoing_citations\": [\"211196\", \"248152\"]}","task_split":"paper_retrieval"}
{"document_id":"109253","document_content":"# Word embedding and neural network on grammatical gender -- A case study of Swedish\n## Categories\n- Computation and Language\n## Abstract\nWe analyze the information provided by the word embeddings about the grammatical gender in Swedish. We wish that this paper may serve as one of the bridges to connect the methods of computational linguistics and general linguistics. Taking nominal classification in Swedish as a case study, we first show how the information about grammatical gender in language can be captured by word embedding models and artificial neural networks. Then, we match our results with previous linguistic hypotheses on assignment and usage of grammatical gender in Swedish and analyze the errors made by the computational model from a linguistic perspective.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14222v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"300574\"]}","task_split":"paper_retrieval"}
{"document_id":"109259","document_content":"# Lifelong Incremental Reinforcement Learning with Online Bayesian Inference\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. We develop and maintain a library that contains an infinite mixture of parameterized environment models, which is equivalent to clustering environment parameters in a latent space. The prior distribution over the mixture is formulated as a Chinese restaurant process (CRP), which incrementally instantiates new environment models without any external information to signal environmental changes in advance. During lifelong learning, we employ the expectation maximization (EM) algorithm with online Bayesian inference to update the mixture in a fully incremental manner. In EM, the E-step involves estimating the posterior expectation of environment-to-cluster assignments, while the M-step updates the environment parameters for future learning. This method allows for all environment models to be adapted as necessary, with new models instantiated for environmental changes and old models retrieved when previously seen environments are encountered again. Experiments demonstrate that LLIRL outperforms relevant existing methods, and enables effective incremental adaptation to various dynamic environments for lifelong learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2021.3055499\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"55928\", \"95451\"], \"outgoing_citations\": [\"110182\", \"176694\", \"185463\", \"197982\", \"205477\", \"208860\", \"213370\", \"214403\", \"216051\", \"222669\", \"230267\", \"237229\", \"253045\", \"253658\", \"255253\", \"271030\", \"284713\", \"289331\", \"290426\", \"293995\", \"312468\"]}","task_split":"paper_retrieval"}
{"document_id":"109282","document_content":"# Team Deep Mixture of Experts for Distributed Power Control\n## Categories\n- Information Theory\n- Machine Learning\n## Abstract\nIn the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties. In particular, it was established that DNNs can be used to derive policies that are robust with respect to the information noise statistic affecting the local information (e.g. CSI in a wireless network) used by each agent (e.g. transmitter) to make its decision. While promising, a major challenge in the implementation of such method is that information noise statistics may differ from agent to agent and, more importantly, that such statistics may not be available at the time of training or may evolve over time, making burdensome retraining necessary. This situation makes it desirable to devise a \"universal\" machine learning model, which can be trained once for all so as to allow for decentralized cooperation in any future feedback noise environment. With this goal in mind, we propose an architecture inspired from the well-known Mixture of Experts (MoE) model, which was previously used for non-linear regression and classification tasks in various contexts, such as computer vision and speech recognition. We consider the decentralized power control problem as an example to showcase the validity of the proposed model and to compare it against other power control algorithms. We show the ability of the so called Team-DMoE model to efficiently track time-varying statistical scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.14147v1\", \"primary_category\": \"cs.IT\", \"categories\": [\"math.IT\", \"cs.LG\", \"cs.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Information Theory\", \"Machine Learning\", \"Information Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148492\", \"160394\", \"183364\", \"229248\", \"267463\", \"270422\", \"275599\", \"336507\"]}","task_split":"paper_retrieval"}
{"document_id":"109410","document_content":"# Characterizing the Effect of Sentence Context on Word Meanings: Mapping Brain to Behavior\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSemantic feature models have become a popular tool for prediction and interpretation of fMRI data. In particular, prior work has shown that differences in the fMRI patterns in sentence reading can be explained by context-dependent changes in the semantic feature representations of the words. However, whether the subjects are aware of such changes and agree with them has been an open question. This paper aims to answer this question through a human-subject study. Subjects were asked to judge how the word change from their generic meaning when the words were used in specific sentences. The judgements were consistent with the model predictions well above chance. Thus, the results support the hypothesis that word meaning change systematically depending on sentence context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.13840v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"243326\", \"296028\"]}","task_split":"paper_retrieval"}
{"document_id":"109463","document_content":"# 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\n3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications, from activity recognition to creating virtual avatars. Existing deep learning methods for 3D human shape and pose estimation rely on relatively high-resolution input images; however, high-resolution visual content is not always available in several practical scenarios such as video surveillance and sports broadcasting. Low-resolution images in real scenarios can vary in a wide range of sizes, and a model trained in one resolution does not typically degrade gracefully across resolutions. Two common approaches to solve the problem of low-resolution input are applying super-resolution techniques to the input images which may result in visual artifacts, or simply training one model for each resolution, which is impractical in many realistic applications. To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed network is able to learn the 3D body shape and pose across different resolutions with a single model. The self-supervision loss encourages scale-consistency of the output, and the contrastive learning scheme enforces scale-consistency of the deep features. We show that both these new training losses provide robustness when learning 3D shape and pose in a weakly-supervised manner. Extensive experiments demonstrate that the RSC-Net can achieve consistently better results than the state-of-the-art methods for challenging low-resolution images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.13666v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"7450\", \"13645\", \"22496\", \"31602\", \"63760\", \"9408\", \"25469\", \"33820\", \"58312\", \"63613\", \"112450\", \"78503\"], \"outgoing_citations\": [\"151576\", \"164514\", \"171939\", \"177490\", \"183868\", \"186553\", \"189945\", \"191430\", \"195025\", \"195191\", \"204280\", \"207276\", \"208805\", \"232518\", \"237237\", \"239138\", \"247152\", \"263580\", \"279219\", \"287636\", \"300053\"]}","task_split":"paper_retrieval"}
{"document_id":"109524","document_content":"# Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network(LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.13435v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"163043\", \"85438\", \"4786\", \"76614\", \"92001\"], \"outgoing_citations\": [\"109517\", \"186164\", \"190866\", \"208815\", \"279575\", \"295676\"]}","task_split":"paper_retrieval"}
{"document_id":"109628","document_content":"# CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nMeta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent. We observe that (1) imposing a constraint can induce context-specific independences that render some aspects of the domain irrelevant, and (2) an agent can take advantage of this fact by imposing constraints on its own behavior. These observations lead us to propose the context-specific abstract Markov decision process (CAMP), an abstraction of a factored MDP that affords efficient planning. We then describe how to learn constraints to impose so the CAMP optimizes a trade-off between rewards and computational cost. Our experiments consider five planners across four domains, including robotic navigation among movable obstacles (NAMO), robotic task and motion planning for sequential manipulation, and classical planning. We find planning with learned CAMPs to consistently outperform baselines, including Stilman's NAMO-specific algorithm. Video: https:\/\/youtu.be\/wTXt6djcAd4 Code: https:\/\/git.io\/JTnf6","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.13202v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"21656\", \"22169\", \"3273\"], \"outgoing_citations\": [\"119929\", \"124961\", \"223312\", \"276133\", \"306018\", \"335001\", \"348963\", \"349247\", \"349518\", \"349866\"]}","task_split":"paper_retrieval"}
{"document_id":"109770","document_content":"# Interpretabilit\u00e9 des mod\u00e8les : \u00e9tat des lieux des m\u00e9thodes et application \u00e0 l'assurance\n## Categories\n- Machine Learning\n- Applications\n## Abstract\nSince May 2018, the General Data Protection Regulation (GDPR) has introduced new obligations to industries. By setting a legal framework, it notably imposes strong transparency on the use of personal data. Thus, people must be informed of the use of their data and must consent the usage of it. Data is the raw material of many models which today make it possible to increase the quality and performance of digital services. Transparency on the use of data also requires a good understanding of its use through different models. The use of models, even if efficient, must be accompanied by an understanding at all levels of the process that transform data (upstream and downstream of a model), thus making it possible to define the relationships between the individual's data and the choice that an algorithm could make based on the analysis of the latter. (For example, the recommendation of one product or one promotional offer or an insurance rate representative of the risk.) Models users must ensure that models do not discriminate against and that it is also possible to explain its result. The widening of the panel of predictive algorithms - made possible by the evolution of computing capacities -- leads scientists to be vigilant about the use of models and to consider new tools to better understand the decisions deduced from them . Recently, the community has been particularly active on model transparency with a marked intensification of publications over the past three years. The increasingly frequent use of more complex algorithms (\\textit{deep learning}, Xgboost, etc.) presenting attractive performances is undoubtedly one of the causes of this interest. This article thus presents an inventory of methods of interpreting models and their uses in an insurance context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12919v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"97373\"], \"outgoing_citations\": [\"193807\", \"227434\", \"232309\", \"236631\", \"246729\", \"251709\", \"262817\"]}","task_split":"paper_retrieval"}
{"document_id":"109779","document_content":"# Weak Human Preference Supervision For Deep Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nThe current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgement of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator via supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion -- MuJoCo games -- relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01\\% of the agent's interactions with the environment and significantly reduces the cost of human inputs by up to 30\\% compared with the existing approaches. To present the flexibility of our approach, we released a video (https:\/\/youtu.be\/jQPe1OILT0M) showing comparisons of the behaviours of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12904v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"109793","document_content":"# Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper mainly focuses on environment perception in snowy situations which forms the backbone of the autonomous driving technology. For the purpose, semantic segmentation is employed to classify the objects while the vehicle is driven autonomously. We train the Fully Convolutional Networks (FCN) on our own dataset and present the experimental results. Finally, the outcomes are analyzed to give a conclusion. It can be concluded that the database still needs to be optimized and a favorable algorithm should be proposed to get better results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12869v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268261\", \"297905\"]}","task_split":"paper_retrieval"}
{"document_id":"109829","document_content":"# Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- Robotics\n## Abstract\nHuman collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators' mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https:\/\/xfgao.github.io\/xCookingWeb\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12803v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"129980\", \"51298\", \"4271\", \"18451\", \"56708\"], \"outgoing_citations\": [\"213766\", \"231267\", \"243185\", \"253638\", \"263724\", \"272651\", \"290744\", \"302521\", \"312442\"]}","task_split":"paper_retrieval"}
{"document_id":"109884","document_content":"# SummEval: Re-evaluating Summarization Evaluation\n## Categories\n- Computation and Language\n## Abstract\nThe scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations, 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics, 3) we assemble the largest collection of summaries generated by models trained on the CNN\/DailyMail news dataset and share it in a unified format, 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics, 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN\/Daily Mail dataset annotated by both expert judges and crowd-source workers. We hope that this work will help promote a more complete evaluation protocol for text summarization as well as advance research in developing evaluation metrics that better correlate with human judgments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12626v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24337\", \"87999\", \"88725\", \"103740\", \"2684\", \"4082\", \"4498\", \"5604\", \"8747\", \"8982\", \"16485\", \"20779\", \"21729\", \"22450\", \"22977\", \"52433\", \"61976\", \"71813\", \"75094\", \"75094\", \"81985\", \"84343\", \"1461\", \"15519\", \"16503\", \"22443\", \"23776\", \"23542\", \"26822\", \"26826\", \"31613\", \"40395\", \"42802\", \"55300\", \"57244\", \"59139\", \"73139\", \"75756\", \"91642\", \"91640\", \"97363\", \"86823\", \"95890\"], \"outgoing_citations\": [\"102649\", \"112910\", \"126346\", \"126362\", \"127538\", \"132176\", \"140642\", \"150522\", \"159316\", \"166082\", \"168405\", \"168613\", \"168744\", \"168908\", \"170449\", \"170657\", \"175850\", \"182629\", \"186043\", \"187236\", \"200414\", \"212506\", \"216554\", \"218088\", \"219618\", \"220509\", \"220695\", \"225556\", \"230212\", \"230234\", \"231869\", \"233710\", \"235041\", \"235232\", \"239950\", \"241070\", \"266671\", \"295412\", \"298016\", \"307651\", \"311423\", \"311464\"]}","task_split":"paper_retrieval"}
{"document_id":"109896","document_content":"# Named entity recognition in chemical patents using ensemble of contextual language models\n## Categories\n- Computation and Language\n## Abstract\nChemical patent documents describe a broad range of applications holding key reaction and compound information, such as chemical structure, reaction formulas, and molecular properties. These informational entities should be first identified in text passages to be utilized in downstream tasks. Text mining provides means to extract relevant information from chemical patents through information extraction techniques. As part of the Information Extraction task of the Cheminformatics Elsevier Melbourne University challenge, in this work we study the effectiveness of contextualized language models to extract reaction information in chemical patents. We assess transformer architectures trained on a generic and specialised corpora to propose a new ensemble model. Our best model, based on a majority ensemble approach, achieves an exact F1-score of 92.30% and a relaxed F1-score of 96.24%. The results show that ensemble of contextualized language models can provide an effective method to extract information from chemical patents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12569v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42156\"], \"outgoing_citations\": [\"159867\", \"177444\", \"263892\"]}","task_split":"paper_retrieval"}
{"document_id":"109928","document_content":"# Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent learning-based approaches, in which models are trained by single-view images have shown promising results for monocular 3D face reconstruction, but they suffer from the ill-posed face pose and depth ambiguity issue. In contrast to previous works that only enforce 2D feature constraints, we propose a self-supervised training architecture by leveraging the multi-view geometry consistency, which provides reliable constraints on face pose and depth estimation. We first propose an occlusion-aware view synthesis method to apply multi-view geometry consistency to self-supervised learning. Then we design three novel loss functions for multi-view consistency, including the pixel consistency loss, the depth consistency loss, and the facial landmark-based epipolar loss. Our method is accurate and robust, especially under large variations of expressions, poses, and illumination conditions. Comprehensive experiments on the face alignment and 3D face reconstruction benchmarks have demonstrated superiority over state-of-the-art methods. Our code and model are released in https:\/\/github.com\/jiaxiangshang\/MGCNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12494v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5306\", \"3093\", \"5179\", \"5264\", \"7345\", \"57572\", \"12174\", \"12757\", \"15251\", \"17589\", \"41321\", \"44440\", \"39754\", \"42098\", \"54494\", \"55402\", \"57586\", \"59791\", \"82106\", \"87603\", \"89684\", \"96763\"], \"outgoing_citations\": [\"174524\", \"185981\", \"191310\", \"191463\", \"191844\", \"193656\", \"194402\", \"205494\", \"210027\", \"227856\", \"236050\", \"237222\", \"238254\", \"239395\", \"242076\", \"247495\", \"248005\", \"259476\", \"260916\", \"268703\", \"270134\", \"270235\", \"270795\", \"270923\", \"277948\", \"280394\", \"284716\", \"299047\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"110028","document_content":"# Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nWe present a randomized controlled trial for a model-in-the-loop regression task, with the goal of measuring the extent to which (1) good explanations of model predictions increase human accuracy, and (2) faulty explanations decrease human trust in the model. We study explanations based on visual saliency in an image-based age prediction task for which humans and learned models are individually capable but not highly proficient and frequently disagree. Our experimental design separates model quality from explanation quality, and makes it possible to compare treatments involving a variety of explanations of varying levels of quality. We find that presenting model predictions improves human accuracy. However, visual explanations of various kinds fail to significantly alter human accuracy or trust in the model - regardless of whether explanations characterize an accurate model, an inaccurate one, or are generated randomly and independently of the input image. These findings suggest the need for greater evaluation of explanations in downstream decision making tasks, better design-based tools for presenting explanations to users, and better approaches for generating explanations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12248v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"2180\", \"20499\", \"20706\", \"33675\", \"46056\", \"51238\", \"60179\", \"66979\", \"77839\", \"87757\", \"103932\"], \"outgoing_citations\": [\"147166\", \"148191\", \"155578\", \"166967\", \"189570\", \"197416\", \"199757\", \"209451\", \"213326\", \"215081\", \"220097\", \"227459\", \"241330\", \"243731\", \"251595\", \"251709\", \"263942\", \"268506\", \"269852\", \"290145\", \"290515\", \"295742\"]}","task_split":"paper_retrieval"}
{"document_id":"110039","document_content":"# The Representation Theory of Neural Networks\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n- Representation Theory\n- 16G20\n- F.4.1; I.2.4\n## Abstract\nIn this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Also, we show that network quivers gently adapt to common neural network concepts such as fully-connected layers, convolution operations, residual connections, batch normalization, pooling operations and even randomly wired neural networks. We show that this mathematical representation is by no means an approximation of what neural networks are as it exactly matches reality. This interpretation is algebraic and can be studied with algebraic methods. We also provide a quiver representation model to understand how a neural network creates representations from the data. We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the moduli space, which is given in terms of the underlying oriented graph of the network, i.e., its quiver. This results as a consequence of our defined objects and of understanding how the neural network computes a prediction in a combinatorial and algebraic way. Overall, representing neural networks through the quiver representation theory leads to 9 consequences and 4 inquiries for future research that we believe are of great interest to better understand what neural networks are and how they work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12213v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"math.RT\", \"stat.ML\", \"16G20\", \"F.4.1; I.2.4\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Representation Theory\", \"Machine Learning\", \"16G20\", \"F.4.1; I.2.4\"], \"incoming_citations\": [\"83322\", \"20180\", \"37249\"], \"outgoing_citations\": [\"134870\", \"138279\", \"139714\", \"174997\", \"179462\", \"180477\", \"192613\", \"196296\", \"217274\", \"242634\", \"246146\", \"246396\", \"271487\", \"304063\", \"310759\", \"311555\", \"321079\"]}","task_split":"paper_retrieval"}
{"document_id":"110102","document_content":"# The expressive power of kth-order invariant graph networks\n## Categories\n- Machine Learning\n- Combinatorics\n## Abstract\nThe expressive power of graph neural network formalisms is commonly measured by their ability to distinguish graphs. For many formalisms, the k-dimensional Weisfeiler-Leman (k-WL) graph isomorphism test is used as a yardstick. In this paper we consider the expressive power of kth-order invariant (linear) graph networks (k-IGNs). It is known that k-IGNs are expressive enough to simulate k-WL. This means that for any two graphs that can be distinguished by k-WL, one can find a k-IGN which also distinguishes those graphs. The question remains whether k-IGNs can distinguish more graphs than k-WL. This was recently shown to be false for k=2. Here, we generalise this result to arbitrary k. In other words, we show that k-IGNs are bounded in expressive power by k-WL. This implies that k-IGNs and k-WL are equally powerful in distinguishing graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12035v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.CO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Combinatorics\", \"Machine Learning\"], \"incoming_citations\": [\"18515\", \"2056\", \"19609\", \"42645\", \"46936\", \"65828\", \"192622\", \"183719\"], \"outgoing_citations\": [\"118122\", \"132809\", \"134585\", \"137772\", \"143081\", \"143456\", \"177249\", \"184286\", \"184749\", \"187289\", \"204871\", \"206402\", \"210730\", \"215435\", \"240991\", \"245763\", \"269770\", \"359939\"]}","task_split":"paper_retrieval"}
{"document_id":"110227","document_content":"# Adversarial Attacks against Face Recognition: A Comprehensive Study\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n- Image and Video Processing\n## Abstract\nFace recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC). In an advanced FR system with deep learning-based architecture, however, promoting the recognition efficiency alone is not sufficient, and the system should also withstand potential kinds of attacks designed to target its proficiency. Recent studies show that (deep) FR systems exhibit an intriguing vulnerability to imperceptible or perceptible but natural-looking adversarial input images that drive the model to incorrect output predictions. In this article, we present a comprehensive survey on adversarial attacks against FR systems and elaborate on the competence of new countermeasures against them. Further, we propose a taxonomy of existing attack and defense methods based on different criteria. We compare attack methods on the orientation and attributes and defense approaches on the category. Finally, we explore the challenges and potential research direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.11709v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"8895\", \"49673\", \"32156\"], \"outgoing_citations\": [\"107324\", \"108517\", \"131524\", \"135086\", \"143555\", \"147499\", \"152495\", \"161531\", \"165755\", \"166272\", \"170530\", \"191481\", \"208127\", \"209503\", \"212775\", \"216716\", \"222824\", \"224923\", \"234520\", \"235160\", \"238512\", \"239156\", \"241282\", \"243986\", \"245633\", \"246211\", \"247042\", \"247658\", \"252519\", \"252688\", \"253156\", \"260933\", \"269735\", \"280873\", \"281860\", \"287611\", \"293141\", \"302157\", \"303380\", \"303724\", \"321122\", \"321229\", \"328183\", \"331116\"]}","task_split":"paper_retrieval"}
{"document_id":"110233","document_content":"# Integrating Image Captioning with Rule-based Entity Masking\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nGiven an image, generating its natural language description (i.e., caption) is a well studied problem. Approaches proposed to address this problem usually rely on image features that are difficult to interpret. Particularly, these image features are subdivided into global and local features, where global features are extracted from the global representation of the image, while local features are extracted from the objects detected locally in an image. Although, local features extract rich visual information from the image, existing models generate captions in a blackbox manner and humans have difficulty interpreting which local objects the caption is aimed to represent. Hence in this paper, we propose a novel framework for the image captioning with an explicit object (e.g., knowledge graph entity) selection process while still maintaining its end-to-end training ability. The model first explicitly selects which local entities to include in the caption according to a human-interpretable mask, then generate proper captions by attending to selected entities. Experiments conducted on the MSCOCO dataset demonstrate that our method achieves good performance in terms of the caption quality and diversity with a more interpretable generating process than previous counterparts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.11690v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167615\", \"208615\", \"229734\", \"230900\", \"234545\", \"236907\", \"267476\", \"270154\", \"278604\", \"283076\"]}","task_split":"paper_retrieval"}
{"document_id":"110600","document_content":"# Fine-Grained Image Captioning with Global-Local Discriminative Objective\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nSignificant progress has been made in recent years in image captioning, an active topic in the fields of vision and language. However, existing methods tend to yield overly general captions and consist of some of the most frequent words\/phrases, resulting in inaccurate and indistinguishable descriptions (see Figure 1). This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words\/phrases while suppressing the less frequent but more concrete ones. In this work, we propose a novel global-local discriminative objective that is formulated on top of a reference model to facilitate generating fine-grained descriptive captions. Specifically, from a global perspective, we design a novel global discriminative constraint that pulls the generated sentence to better discern the corresponding image from all others in the entire dataset. From the local perspective, a local discriminative constraint is proposed to increase attention such that it emphasizes the less frequent but more concrete words\/phrases, thus facilitating the generation of captions that better describe the visual details of the given images. We evaluate the proposed method on the widely used MS-COCO dataset, where it outperforms the baseline methods by a sizable margin and achieves competitive performance over existing leading approaches. We also conduct self-retrieval experiments to demonstrate the discriminability of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.10662v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"66894\", \"89494\"], \"outgoing_citations\": [\"168542\", \"170968\", \"190585\", \"195946\", \"207034\", \"217310\", \"221440\", \"226123\", \"235954\", \"238125\", \"239242\", \"246959\", \"251088\", \"253934\", \"255208\", \"256347\", \"256883\", \"260926\", \"262564\", \"269595\", \"270154\", \"271234\", \"276436\", \"278604\", \"278894\", \"278954\", \"280246\", \"281152\", \"287504\", \"296681\", \"302815\", \"303437\", \"303833\", \"310136\", \"312261\", \"315108\", \"320003\", \"327963\"]}","task_split":"paper_retrieval"}
{"document_id":"110610","document_content":"# Multi-modal Transformer for Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe task of retrieving video content relevant to natural language queries plays a critical role in effectively handling internet-scale datasets. Most of the existing methods for this caption-to-video retrieval problem do not fully exploit cross-modal cues present in video. Furthermore, they aggregate per-frame visual features with limited or no temporal information. In this paper, we present a multi-modal transformer to jointly encode the different modalities in video, which allows each of them to attend to the others. The transformer architecture is also leveraged to encode and model the temporal information. On the natural language side, we investigate the best practices to jointly optimize the language embedding together with the multi-modal transformer. This novel framework allows us to establish state-of-the-art results for video retrieval on three datasets. More details are available at http:\/\/thoth.inrialpes.fr\/research\/MMT.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.10639v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"34555\", \"1081\", \"2212\", \"2284\", \"2622\", \"4359\", \"4348\", \"4345\", \"5478\", \"5559\", \"5960\", \"6379\", \"7889\", \"8438\", \"12103\", \"14600\", \"19948\", \"24485\", \"24775\", \"28443\", \"33472\", \"34089\", \"36637\", \"43252\", \"55118\", \"64841\", \"101354\", \"69816\", \"78802\", \"1284\", \"2838\", \"5734\", \"5726\", \"5927\", \"5982\", \"6902\", \"7454\", \"8129\", \"8845\", \"10348\", \"12199\", \"12778\", \"13933\", \"17403\", \"20039\", \"20409\", \"22104\", \"22320\", \"23047\", \"24771\", \"25818\", \"28085\", \"30524\", \"33374\", \"38120\", \"38350\", \"39527\", \"40597\", \"43276\", \"43644\", \"40616\", \"46080\", \"48259\", \"49746\", \"49800\", \"51492\", \"53510\", \"53894\", \"54088\", \"54198\", \"54595\", \"54862\", \"54820\", \"55460\", \"57216\", \"58876\", \"60135\", \"61317\", \"62053\", \"62053\", \"62260\", \"62318\", \"64249\", \"67133\", \"69945\", \"81390\", \"83575\", \"96230\", \"81067\", \"89825\", \"108197\"], \"outgoing_citations\": [\"151327\", \"171357\", \"172394\", \"173627\", \"180920\", \"181897\", \"192531\", \"214015\", \"221984\", \"236468\", \"236772\", \"238205\", \"247535\", \"267523\", \"282943\", \"283649\", \"319169\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"110754","document_content":"# A Comparison of Supervised Learning to Match Methods for Product Search\n## Categories\n- Information Retrieval\n## Abstract\nThe vocabulary gap is a core challenge in information retrieval (IR). In e-commerce applications like product search, the vocabulary gap is reported to be a bigger challenge than in more traditional application areas in IR, such as news search or web search. As recent learning to match methods have made important advances in bridging the vocabulary gap for these traditional IR areas, we investigate their potential in the context of product search. In this paper we provide insights into using recent learning to match methods for product search. We compare both effectiveness and efficiency of these methods in a product search setting and analyze their performance on two product search datasets, with 50,000 queries each. One is an open dataset made available as part of a community benchmark activity at CIKM 2016. The other is a proprietary query log obtained from a European e-commerce platform. This comparison is conducted towards a better understanding of trade-offs in choosing a preferred model for this task. We find that (1) models that have been specifically designed for short text matching, like MV-LSTM and DRMMTKS, are consistently among the top three methods in all experiments; however, taking efficiency and accuracy into account at the same time, ARC-I is the preferred model for real world use cases; and (2) the performance from a state-of-the-art BERT-based model is mediocre, which we attribute to the fact that the text BERT is pre-trained on is very different from the text we have in product search. We also provide insights into factors that can influence model behavior for different types of query, such as the length of retrieved list, and query complexity, and discuss the implications of our findings for e-commerce practitioners, with respect to choosing a well performing method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.10296v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [\"86032\"], \"outgoing_citations\": [\"150286\", \"184739\", \"189744\", \"190352\", \"190502\", \"194919\", \"196874\", \"201064\", \"201803\", \"227493\", \"235602\", \"249530\", \"258541\", \"263265\", \"267351\", \"281915\", \"285950\", \"297950\", \"316165\"]}","task_split":"paper_retrieval"}
{"document_id":"110832","document_content":"# Morphological Skip-Gram: Using morphological knowledge to improve word representation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNatural language processing models have attracted much interest in the deep learning community. This branch of study is composed of some applications such as machine translation, sentiment analysis, named entity recognition, question and answer, and others. Word embeddings are continuous word representations, they are an essential module for those applications and are generally used as input word representation to the deep learning models. Word2Vec and GloVe are two popular methods to learn word embeddings. They achieve good word representations, however, they learn representations with limited information because they ignore the morphological information of the words and consider only one representation vector for each word. This approach implies that Word2Vec and GloVe are unaware of the word inner structure. To mitigate this problem, the FastText model represents each word as a bag of characters n-grams. Hence, each n-gram has a continuous vector representation, and the final word representation is the sum of its characters n-grams vectors. Nevertheless, the use of all n-grams character of a word is a poor approach since some n-grams have no semantic relation with their words and increase the amount of potentially useless information. This approach also increases the training phase time. In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word. Thus, words with similar context and morphemes are represented by vectors close to each other. To evaluate our new approach, we performed intrinsic evaluations considering 15 different tasks, and the results show a competitive performance compared to FastText.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.10055v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"57401\"], \"outgoing_citations\": [\"177515\", \"266671\", \"269546\", \"274521\", \"276515\", \"278604\", \"325742\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"110974","document_content":"# An Overview of Natural Language State Representation for Reinforcement Learning\n## Categories\n- Computation and Language\n## Abstract\nA suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09774v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"50350\", \"41832\"], \"outgoing_citations\": [\"127451\", \"153843\", \"161143\", \"181620\", \"183368\", \"196377\", \"197395\", \"198208\", \"205161\", \"207273\", \"208773\", \"218114\", \"220186\", \"220509\", \"220569\", \"226327\", \"230212\", \"231741\", \"232525\", \"235041\", \"235232\", \"238049\", \"239749\", \"241070\", \"242478\", \"242616\", \"249109\", \"251736\", \"255580\", \"259094\", \"259693\", \"261378\", \"262292\", \"263104\", \"263270\", \"263650\", \"265139\", \"266671\", \"267779\", \"267980\", \"268584\", \"270048\", \"271085\", \"272513\", \"273400\", \"274253\", \"278954\", \"279384\", \"279517\", \"283809\", \"285454\", \"290738\", \"290819\", \"290847\", \"291102\", \"291195\", \"292127\", \"294494\", \"295884\", \"302815\", \"303349\", \"310370\", \"349042\", \"351423\"]}","task_split":"paper_retrieval"}
{"document_id":"110983","document_content":"# Hybrid Collaborative Filtering Models for Clinical Search Recommendation\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nWith increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics. While a search function can be a useful alternative to browsing through a patient's record, it is cumbersome for clinicians to search repeatedly for the same or similar information on similar patients. Under such circumstances, there is a critical need to build effective recommender systems that can generate accurate search term recommendations for clinicians. In this manuscript, we developed a hybrid collaborative filtering model using patients' encounter and search term information to recommend the next search terms for clinicians to retrieve important information fast in clinics. For each patient, the model will recommend terms that either have high co-occurrence frequencies with his\/her most recent ICD codes or are highly relevant to the most recent search terms on this patient. We have conducted comprehensive experiments to evaluate the proposed model, and the experimental results demonstrate that our model can outperform all the state-of-the-art baseline methods for top-N search term recommendation on different datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2008.01193v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"106365\", \"139803\", \"179605\", \"217192\", \"220924\", \"263938\", \"302767\"]}","task_split":"paper_retrieval"}
{"document_id":"111125","document_content":"# A Bag of Visual Words Model for Medical Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nMedical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models often fail to take the intrinsic characteristics of data into consideration, and have thus achieved limited accuracy when applied to medical images. The Bag of Visual Words (BoVW) is a technique that can be used to effectively represent intrinsic image features in vector space, so that applications like image classification and similar-image search can be optimized. In this paper, we present a MedIR approach based on the BoVW model for content-based medical image retrieval. As medical images as multi-dimensional, they exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, the BoVW features extracted for each image are used to train a supervised machine learning classifier based on positive and negative training images, for extending content based image retrieval. During experimental validation, the proposed model performed very well, achieving a Mean Average Precision of 88.89% during top-3 image retrieval experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09464v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"349865\"]}","task_split":"paper_retrieval"}
{"document_id":"111131","document_content":"# Face Super-Resolution Guided by 3D Facial Priors\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Image and Video Processing\n## Abstract\nState-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variations. In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. Our work is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are extremely efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, the Spatial Attention Module is used to better exploit this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content) for the super-resolution problem. Extensive experiments demonstrate that the proposed 3D priors achieve superior face super-resolution results over the state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09454v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"26240\", \"76110\", \"18974\", \"139332\", \"49874\", \"54361\", \"97923\"], \"outgoing_citations\": [\"109032\", \"155704\", \"170700\", \"194402\", \"211273\", \"230252\", \"236344\", \"239574\", \"239727\", \"248969\", \"258886\", \"274838\", \"288238\", \"299534\", \"303384\"]}","task_split":"paper_retrieval"}
{"document_id":"111137","document_content":"# Structure Mapping for Transferability of Causal Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHuman beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects but with the same underlying causal dynamics. We adapt continuous optimization for structure learning techniques to explicitly learn the cause and effects of the actions in an interactive environment and transfer to the target domain by categorization of the objects based on causal knowledge. We demonstrate the advantages of our approach in a gridworld setting by combining causal model-based approach with model-free approach in reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09445v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"85385\"], \"outgoing_citations\": [\"164378\", \"175541\", \"240081\", \"259045\", \"263811\", \"295736\"]}","task_split":"paper_retrieval"}
{"document_id":"111189","document_content":"# ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nWhen building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving machine learning on personal data, Article 35 of the GDPR mandates it to perform a Data Protection Impact Assessment (DPIA). In addition to the threats of illegitimate access to data through security breaches, machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. Guidances released by the Information Commissioner's Office (UK) and the National Institute of Standards and Technology (US) emphasize on the threat to data from models and recommend organizations to account for and estimate these risks to comply with data protection regulations. Hence, there is an immediate need for a tool that can quantify the privacy risk to data from models.   In this paper, we focus on this indirect leakage about training data from machine learning models. We present ML Privacy Meter, a tool that can quantify the privacy risk to data from models through state of the art membership inference attack techniques. We discuss how this tool can help practitioners in compliance with data protection regulations, when deploying machine learning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09339v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"4721\", \"8696\", \"63205\", \"71528\", \"5311\", \"34162\", \"40470\", \"75566\", \"92030\", \"99967\"], \"outgoing_citations\": [\"207518\", \"241199\", \"255133\"]}","task_split":"paper_retrieval"}
{"document_id":"111207","document_content":"# Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nWith the evolution of the cloud and customer centric culture, we inherently accumulate huge repositories of textual reviews, feedback, and support data.This has driven enterprises to seek and research engagement patterns, user network analysis, topic detections, etc.However, huge manual work is still necessary to mine data to be able to mine actionable outcomes. In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice.This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support.There are three major break-through: 1. As the advancement of deep learning technology, there have been tremendous innovations in the NLP field, yet the traditional topic modeling as one of the NLP applications lag behind the tide of deep learning. In the methodology and technical perspective, we adopt transfer learning to fine-tune a BERT-based multiclassification system to categorize the main topics and then utilize the novel PSHTI model to infer the sub-topics under the predicted main topics. 2. The traditional unsupervised learning-based topic models or clustering methods suffer from the difficulty of automatically generating a meaningful topic label, but our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling. 3. This work provides a prominent showcase by leveraging the state-of-the-art methodology in the real production to help shed light to discover user insights and drive business investment priorities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09303v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"55617\"], \"outgoing_citations\": [\"132268\", \"142328\", \"150522\", \"297803\"]}","task_split":"paper_retrieval"}
{"document_id":"111226","document_content":"# 3D Human Shape Reconstruction from a Polarization Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing surface normal of the objects of interest. Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images. A dedicated two-stage deep learning approach, SfP, is proposed: given a polarization image, stage one aims at inferring the fined-detailed body surface normal; stage two gears to reconstruct the 3D body shape of clothing details. Empirical evaluations on a synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate the qualitative and quantitative performance of our approach in estimating human poses and shapes. This indicates polarization camera is a promising alternative to the more conventional color or depth imaging for human shape estimation. Further, normal maps inferred from polarization imaging play a significant role in accurately recovering the body shapes of clothed people.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09268v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"29497\", \"56624\", \"108875\"], \"outgoing_citations\": [\"120777\", \"127884\", \"163616\", \"164082\", \"164514\", \"186553\", \"186893\", \"189108\", \"191059\", \"191962\", \"192691\", \"193877\", \"195025\", \"197500\", \"202884\", \"203160\", \"207276\", \"221041\", \"230920\", \"232517\", \"232518\", \"235715\", \"237681\", \"247152\", \"249625\", \"253057\", \"266960\", \"269400\", \"276521\", \"276551\", \"276764\", \"276981\", \"277603\", \"279406\", \"283716\", \"286847\", \"287636\", \"292424\", \"302266\", \"306596\", \"307539\", \"328588\"]}","task_split":"paper_retrieval"}
{"document_id":"111265","document_content":"# WordCraft: An Environment for Benchmarking Commonsense Agents\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nThe ability to quickly solve a wide range of real-world tasks requires a commonsense understanding of the world. Yet, how to best extract such knowledge from natural language corpora and integrate it with reinforcement learning (RL) agents remains an open challenge. This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment. To better enable research on agents making use of commonsense knowledge, we propose WordCraft, an RL environment based on Little Alchemy 2. This lightweight environment is fast to run and built upon entities and relations inspired by real-world semantics. We evaluate several representation learning methods on this new benchmark and propose a new method for integrating knowledge graphs with an RL agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09185v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"36587\", \"93074\"], \"outgoing_citations\": [\"116357\", \"124122\", \"127451\", \"128393\", \"140959\", \"151271\", \"160036\", \"160575\", \"161143\", \"162764\", \"163672\", \"168575\", \"171239\", \"181620\", \"182116\", \"212078\", \"213745\", \"218923\", \"226321\", \"227492\", \"243235\", \"248742\", \"259693\", \"261378\", \"271868\", \"316630\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"111274","document_content":"# Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?\n## Categories\n- Machine Learning\n## Abstract\nRoad accidents have a high societal cost that could be reduced through improved risk predictions using machine learning. This study investigates whether telemetric data collected on long-distance trucks can be used to predict the risk of accidents associated with a driver. We use a dataset provided by a truck transportation company containing the driving data of 1,141 drivers for 18 months.   We evaluate two different machine learning approaches to perform this task. In the first approach, features are extracted from the time series data using the FRESH algorithm and then used to estimate the risk using Random Forests. In the second approach, we use a convolutional neural network to directly estimate the risk from the time-series data. We find that neither approach is able to successfully estimate the risk of accidents on this dataset, in spite of many methodological attempts. We discuss the difficulties of using telemetric data for the estimation of the risk of accidents that could explain this negative result.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/BigData52589.2021.9671967\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167426\", \"185335\", \"212512\", \"282000\", \"311813\", \"321736\"]}","task_split":"paper_retrieval"}
{"document_id":"111325","document_content":"# Knowledge Distillation in Deep Learning and its Applications\n## Categories\n- Machine Learning\n## Abstract\nDeep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained by utilizing the information from a larger model (teacher model). In this paper, we present a survey of knowledge distillation techniques applied to deep learning models. To compare the performances of different techniques, we propose a new metric called distillation metric. Distillation metric compares different knowledge distillation algorithms based on sizes and accuracy scores. Based on the survey, some interesting conclusions are drawn and presented in this paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.7717\/peerj-cs.474\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"135\"], \"outgoing_citations\": [\"79809\", \"86909\", \"96145\", \"138248\", \"144006\", \"153402\", \"156637\", \"165805\", \"170278\", \"185555\", \"185872\", \"189752\", \"189802\", \"195527\", \"204577\", \"207211\", \"210527\", \"210999\", \"213207\", \"228310\", \"231602\", \"232302\", \"242046\", \"242885\", \"248541\", \"252610\", \"252897\", \"258631\", \"303126\", \"304173\", \"307902\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"111326","document_content":"# Sequential Explanations with Mental Model-Based Policies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nThe act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information. We apply a reinforcement learning framework which emulates this format by providing explanations based on the explainee's current mental model. We conduct novel online human experiments where explanations generated by various explanation methods are selected and presented to participants, using policies which observe participants' mental models, in order to optimize an interpretability proxy. Our results suggest that mental model-based policies (anchored in our proposed state representation) may increase interpretability over multiple sequential explanations, when compared to a random selection baseline. This work provides insight into how to select explanations which increase relevant information for users, and into conducting human-grounded experimentation to understand interpretability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.09028v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"51298\", \"93903\", \"91690\", \"107425\"], \"outgoing_citations\": [\"127043\", \"168243\", \"173149\", \"177711\", \"184390\", \"206349\", \"207559\", \"208546\", \"221505\", \"227270\", \"229719\", \"230077\", \"241330\", \"241376\", \"248571\", \"254281\", \"262066\", \"263942\", \"295302\", \"301835\"]}","task_split":"paper_retrieval"}
{"document_id":"111407","document_content":"# Polarimetric Multi-View Inverse Rendering\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nA polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) of reflected light is related to an object's surface normal. In this paper, we propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that effectively exploits geometric, photometric, and polarimetric cues extracted from input multi-view color polarization images. We first estimate camera poses and an initial 3D model by geometric reconstruction with a standard structure-from-motion and multi-view stereo pipeline. We then refine the initial model by optimizing photometric and polarimetric rendering errors using multi-view RGB and AoP images, where we propose a novel polarimetric rendering cost function that enables us to effectively constrain each estimated surface vertex's normal while considering four possible ambiguous azimuth angles revealed from the AoP measurement. Experimental results using both synthetic and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed 3D shape without assuming a specific polarized reflection depending on the material.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.08830v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1531\", \"51214\", \"55913\"], \"outgoing_citations\": [\"55913\", \"95913\", \"109228\", \"146592\", \"191653\", \"193319\", \"237468\", \"257717\", \"339203\"]}","task_split":"paper_retrieval"}
{"document_id":"111576","document_content":"# Co-generation of game levels and game-playing agents\n## Categories\n- Neural and Evolutionary Computing\n## Abstract\nOpen-endedness, primarily studied in the context of artificial life, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.08497v2\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"87353\"], \"outgoing_citations\": [\"136046\", \"151271\", \"200152\", \"200920\", \"207051\", \"226544\", \"240572\", \"257674\", \"271783\"]}","task_split":"paper_retrieval"}
{"document_id":"111733","document_content":"# Comprehensive Facial Expression Synthesis using Human-Interpretable Language\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent advances in facial expression synthesis have shown promising results using diverse expression representations including facial action units. Facial action units for an elaborate facial expression synthesis need to be intuitively represented for human comprehension, not a numeric categorization of facial action units. To address this issue, we utilize human-friendly approach: use of natural language where language helps human grasp conceptual contexts. In this paper, therefore, we propose a new facial expression synthesis model from language-based facial expression description. Our method can synthesize the facial image with detailed expressions. In addition, effectively embedding language features on facial features, our method can control individual word to handle each part of facial movement. Extensive qualitative and quantitative evaluations were conducted to verify the effectiveness of the natural language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.08154v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"160268\", \"223504\", \"238205\", \"249031\", \"257386\"]}","task_split":"paper_retrieval"}
{"document_id":"111756","document_content":"# On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nPersonal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users' personal values, and developing effective models to predict their personal values using their Facebook data. These models leverage on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations among personal values. The LIWC word categories are adapted to non-English word use in Singapore. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross-stitch layer model. Through experiments, we show that our proposed model predicts personal values with considerable improvement of accuracy over the previous works. Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.08107v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"143723\", \"294694\"]}","task_split":"paper_retrieval"}
{"document_id":"111864","document_content":"# Dialect Diversity in Text Summarization on Twitter\n## Categories\n- Computers and Society\n- Computation and Language\n## Abstract\nDiscussions on Twitter involve participation from different communities with different dialects and it is often necessary to summarize a large number of posts into a representative sample to provide a synopsis. Yet, any such representative sample should sufficiently portray the underlying dialect diversity to present the voices of different participating communities representing the dialects. Extractive summarization algorithms perform the task of constructing subsets that succinctly capture the topic of any given set of posts. However, we observe that there is dialect bias in the summaries generated by common summarization approaches, i.e., they often return summaries that under-represent certain dialects.   The vast majority of existing \"fair\" summarization approaches require socially salient attribute labels (in this case, dialect) to ensure that the generated summary is fair with respect to the socially salient attribute. Nevertheless, in many applications, these labels do not exist. Furthermore, due to the ever-evolving nature of dialects in social media, it is unreasonable to label or accurately infer the dialect of every social media post. To correct for the dialect bias, we employ a framework that takes an existing text summarization algorithm as a blackbox and, using a small set of dialect-diverse sentences, returns a summary that is relatively more dialect-diverse. Crucially, this approach does not need the posts being summarized to have dialect labels, ensuring that the diversification process is independent of dialect classification\/identification models. We show the efficacy of our approach on Twitter datasets containing posts written in dialects used by different social groups defined by race or gender; in all cases, our approach leads to improved dialect diversity compared to standard text summarization approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.07860v2\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CL\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Computation and Language\"], \"incoming_citations\": [\"35229\", \"50710\"], \"outgoing_citations\": [\"115939\", \"122414\", \"130073\", \"130347\", \"157408\", \"158087\", \"159651\", \"170449\", \"170657\", \"177125\", \"179679\", \"181902\", \"193779\", \"201134\", \"213461\", \"216554\", \"219818\", \"220680\", \"222795\", \"232388\", \"242511\", \"259870\", \"262367\", \"280557\", \"282131\", \"285628\", \"285959\", \"287941\", \"292291\", \"311423\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"111871","document_content":"# Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation\n## Categories\n- Computation and Language\n## Abstract\nSummarizing texts is not a straightforward task. Before even considering text summarization, one should determine what kind of summary is expected. How much should the information be compressed? Is it relevant to reformulate or should the summary stick to the original phrasing? State-of-the-art on automatic text summarization mostly revolves around news articles. We suggest that considering a wider variety of tasks would lead to an improvement in the field, in terms of generalization and robustness. We explore meeting summarization: generating reports from automatic transcriptions. Our work consists in segmenting and aligning transcriptions with respect to reports, to get a suitable dataset for neural summarization. Using a bootstrapping approach, we provide pre-alignments that are corrected by human annotators, making a validation set against which we evaluate automatic models. This consistently reduces annotators' efforts by providing iteratively better pre-alignment and maximizes the corpus size by using annotations from our automatic alignment models. Evaluation is conducted on \\publicmeetings, a novel corpus of aligned public meetings. We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.07841v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"89765\", \"108843\"], \"outgoing_citations\": [\"171101\", \"219843\", \"232327\", \"243803\", \"266671\", \"282749\", \"298016\", \"315764\"]}","task_split":"paper_retrieval"}
{"document_id":"111933","document_content":"# Logic Constrained Pointer Networks for Interpretable Textual Similarity\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSystematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. Finally, to guide the network with structured external knowledge, we introduce first-order logic constraints based on ConceptNet and syntactic knowledge. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task, showing large improvements over the existing solutions. Source code is available at https:\/\/github.com\/manishb89\/interpretable_sentence_similarity","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.24963\/ijcai.2020\/333\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"180712\", \"272080\", \"316774\"]}","task_split":"paper_retrieval"}
{"document_id":"111989","document_content":"# Evaluation of Neural Network Classification Systems on Document Stream\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- I.7.1; J.1\n## Abstract\nOne major drawback of state of the art Neural Networks (NN)-based approaches for document classification purposes is the large number of training samples required to obtain an efficient classification. The minimum required number is around one thousand annotated documents for each class. In many cases it is very difficult, if not impossible, to gather this number of samples in real industrial processes. In this paper, we analyse the efficiency of NN-based document classification systems in a sub-optimal training case, based on the situation of a company document stream. We evaluated three different approaches, one based on image content and two on textual content. The evaluation was divided into four parts: a reference case, to assess the performance of the system in the lab; two cases that each simulate a specific difficulty linked to document stream processing; and a realistic case that combined all of these difficulties. The realistic case highlighted the fact that there is a significant drop in the efficiency of NN-Based document classification systems. Although they remain efficient for well represented classes (with an over-fitting of the system for those classes), it is impossible for them to handle appropriately less well represented classes. NN-Based document classification systems need to be adapted to resolve these two problems before they can be considered for use in a company document stream.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.07547v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"I.7.1; J.1\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"I.7.1; J.1\"], \"incoming_citations\": [], \"outgoing_citations\": [\"241482\", \"248027\", \"257785\", \"266686\", \"271625\", \"292262\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"111991","document_content":"# RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe attention-based encoder-decoder framework has recently achieved impressive results for scene text recognition, and many variants have emerged with improvements in recognition quality. However, it performs poorly on contextless texts (e.g., random character sequences) which is unacceptable in most of real application scenarios. In this paper, we first deeply investigate the decoding process of the decoder. We empirically find that a representative character-level sequence decoder utilizes not only context information but also positional information. Contextual information, which the existing approaches heavily rely on, causes the problem of attention drift. To suppress such side-effect, we propose a novel position enhancement branch, and dynamically fuse its outputs with those of the decoder attention module for scene text recognition. Specifically, it contains a position aware module to enable the encoder to output feature vectors encoding their own spatial positions, and an attention module to estimate glimpses using the positional clue (i.e., the current decoding time step) only. The dynamic fusion is conducted for more robust feature via an element-wise gate mechanism. Theoretically, our proposed method, dubbed \\emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical. Empirically, it has achieved new state-of-the-art results on popular regular and irregular text recognition benchmarks while without much performance drop on contextless benchmarks, validating its robustness in both contextual and contextless application scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.07542v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"5857\", \"6379\", \"50917\", \"191\", \"8109\", \"9220\", \"21025\", \"21983\", \"24824\", \"28270\", \"29640\", \"31656\", \"33358\", \"33363\", \"42497\", \"48901\", \"49805\", \"50591\", \"63761\", \"64528\", \"64650\", \"122764\", \"110306\"], \"outgoing_citations\": [\"143280\", \"150060\", \"152219\", \"155455\", \"161220\", \"166353\", \"170332\", \"172884\", \"180931\", \"183268\", \"189847\", \"190056\", \"192483\", \"203406\", \"205602\", \"205988\", \"211851\", \"217463\", \"229426\", \"232706\", \"250738\", \"256108\", \"256711\", \"294023\", \"296685\", \"296828\", \"300208\", \"309239\", \"311196\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"112103","document_content":"# Using Holographically Compressed Embeddings in Question Answering\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWord vector representations are central to deep learning natural language processing models. Many forms of these vectors, known as embeddings, exist, including word2vec and GloVe. Embeddings are trained on large corpora and learn the word's usage in context, capturing the semantic relationship between words. However, the semantics from such training are at the level of distinct words (known as word types), and can be ambiguous when, for example, a word type can be either a noun or a verb. In question answering, parts-of-speech and named entity types are important, but encoding these attributes in neural models expands the size of the input. This research employs holographic compression of pre-trained embeddings, to represent a token, its part-of-speech, and named entity type, in the same dimension as representing only the token. The implementation, in a modified question answering recurrent deep learning network, shows that semantic relationships are preserved, and yields strong performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/csit.2020.100919\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"153217\", \"208172\", \"260724\", \"270020\", \"281210\"]}","task_split":"paper_retrieval"}
{"document_id":"112241","document_content":"# Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine character results with the layout information to restore document content. These two branches can be trained in parallel and are easy to train. Furthermore, we propose a re-score mechanism to minimize recognition error. Experiment results on the extended Chinese historical document MTHv2 dataset demonstrate the effectiveness of the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06890v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"28388\", \"67395\", \"81383\"], \"outgoing_citations\": [\"161220\", \"249781\", \"269205\", \"269649\", \"283025\", \"309239\"]}","task_split":"paper_retrieval"}
{"document_id":"112250","document_content":"# Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nA wide range of image captioning models has been developed, achieving significant improvement based on popular metrics, such as BLEU, CIDEr, and SPICE. However, although the generated captions can accurately describe the image, they are generic for similar images and lack distinctiveness, i.e., cannot properly describe the uniqueness of each image. In this paper, we aim to improve the distinctiveness of image captions through training with sets of similar images. First, we propose a distinctiveness metric -- between-set CIDEr (CIDErBtw) to evaluate the distinctiveness of a caption with respect to those of similar images. Our metric shows that the human annotations of each image are not equivalent based on distinctiveness. Thus we propose several new training strategies to encourage the distinctiveness of the generated caption for each image, which are based on using CIDErBtw in a weighted loss function or as a reinforcement learning reward. Finally, extensive experiments are conducted, showing that our proposed approach significantly improves both distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy (e.g., as measured by CIDEr) for a wide variety of image captioning baselines. These results are further confirmed through a user study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06877v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"18648\", \"38766\", \"11142\", \"28498\", \"39327\", \"61394\", \"80837\"], \"outgoing_citations\": [\"167838\", \"171096\", \"171902\", \"174277\", \"193328\", \"207034\", \"223302\", \"229734\", \"230900\", \"238125\", \"239242\", \"249394\", \"253934\", \"256347\", \"260926\", \"268356\", \"270154\", \"271234\", \"278954\", \"281152\", \"287504\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"112299","document_content":"# A unified survey of treatment effect heterogeneity modeling and uplift modeling\n## Categories\n- Methodology\n- Machine Learning\n- Applications\n- Machine Learning\n## Abstract\nA central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a.k.a treatment effect). In recent years, a need for estimating the heterogeneous treatment effects conditioning on the different characteristics of individuals has emerged from research fields such as personalized healthcare, social science, and online marketing. To meet the need, researchers and practitioners from different communities have developed algorithms by taking the treatment effect heterogeneity modeling approach and the uplift modeling approach, respectively. In this paper, we provide a unified survey of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We then provide a structured survey of existing methods by emphasizing on their inherent connections with a set of unified notations to make comparisons of the different methods easy. We then review the main applications of the surveyed methods in personalized marketing, personalized medicine, and social studies. Finally, we summarize the existing software packages and present discussions based on the use of methods on synthetic, semi-synthetic and real world data sets and provide some general guidelines for choosing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.12769v3\", \"primary_category\": \"stat.ME\", \"categories\": [\"stat.ME\", \"stat.ML\", \"stat.AP\", \"cs.LG\"], \"primary_category_human_readable\": \"Methodology\", \"categories_human_readable\": [\"Methodology\", \"Machine Learning\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"8527\", \"49470\", \"79787\", \"140532\"], \"outgoing_citations\": [\"118749\", \"135000\", \"145172\", \"220527\", \"241034\", \"265530\", \"290547\", \"292736\", \"350221\", \"353236\"]}","task_split":"paper_retrieval"}
{"document_id":"112307","document_content":"# Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTraditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data. Use of deformation transfer or multilinear tensor as a personalized 3DMM for blendshape interpolation does not address the fact that facial expressions result in different local and global skin deformations in different persons. Moreover, existing methods learn a single albedo per user which is not enough to capture the expression-specific skin reflectance variations. We propose an end-to-end framework that jointly learns a personalized face model per user and per-frame facial motion parameters from a large corpus of in-the-wild videos of user expressions. Specifically, we learn user-specific expression blendshapes and dynamic (expression-specific) albedo maps by predicting personalized corrections on top of a 3DMM prior. We introduce novel constraints to ensure that the corrected blendshapes retain their semantic meanings and the reconstructed geometry is disentangled from the albedo. Experimental results show that our personalization accurately captures fine-grained facial dynamics in a wide range of conditions and efficiently decouples the learned face model from facial motion, resulting in more accurate face reconstruction and facial motion retargeting compared to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06759v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26240\", \"3093\", \"57031\", \"42098\", \"57586\", \"59948\", \"79555\", \"82106\", \"87603\", \"96763\"], \"outgoing_citations\": [\"109032\", \"182641\", \"185981\", \"191310\", \"194402\", \"197211\", \"198793\", \"205494\", \"212039\", \"222599\", \"227856\", \"230031\", \"236050\", \"237222\", \"238254\", \"248005\", \"258129\", \"260916\", \"270042\", \"270134\", \"270795\", \"273729\", \"277948\", \"280394\", \"284248\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"112428","document_content":"# A Feature Analysis for Multimodal News Retrieval\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nContent-based information retrieval is based on the information contained in documents rather than using metadata such as keywords. Most information retrieval methods are either based on text or image. In this paper, we investigate the usefulness of multimodal features for cross-lingual news search in various domains: politics, health, environment, sport, and finance. To this end, we consider five feature types for image and text and compare the performance of the retrieval system using different combinations. Experimental results show that retrieval results can be improved when considering both visual and textual information. In addition, it is observed that among textual features entity overlap outperforms word embeddings, while geolocation embeddings achieve better performance among visual features in the retrieval task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06390v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"135336\", \"146087\", \"185176\", \"205616\", \"220529\", \"316430\"]}","task_split":"paper_retrieval"}
{"document_id":"112504","document_content":"# Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nDeep neural networks are highly effective when a large number of labeled samples are available but fail with few-shot classification tasks. Recently, meta-learning methods have received much attention, which train a meta-learner on massive additional tasks to gain the knowledge to instruct the few-shot classification. Usually, the training tasks are randomly sampled and performed indiscriminately, often making the meta-learner stuck into a bad local optimum. Some works in the optimization of deep neural networks have shown that a better arrangement of training data can make the classifier converge faster and perform better. Inspired by this idea, we propose an easy-to-hard expert meta-training strategy to arrange the training tasks properly, where easy tasks are preferred in the first phase, then, hard tasks are emphasized in the second phase. A task hardness aware module is designed and integrated into the training procedure to estimate the hardness of a task based on the distinguishability of its categories. In addition, we explore multiple hardness measurements including the semantic relation, the pairwise Euclidean distance, the Hausdorff distance, and the Hilbert-Schmidt independence criterion. Experimental results on the miniImageNet and tieredImageNetSketch datasets show that the meta-learners can obtain better results with our expert training strategy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06240v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"90814\", \"107465\", \"24188\", \"24824\", \"31658\", \"40821\"], \"outgoing_citations\": [\"181897\", \"183874\", \"190920\", \"207031\", \"212526\", \"224071\", \"229676\", \"232445\", \"240315\", \"245195\", \"248411\", \"248702\", \"255895\", \"259762\", \"262591\", \"264316\", \"271935\", \"279419\", \"290800\", \"292262\", \"294693\"]}","task_split":"paper_retrieval"}
{"document_id":"112656","document_content":"# Data augmentation enhanced speaker enrollment for text-dependent speaker verification\n## Categories\n- Audio and Speech Processing\n- Sound\n## Abstract\nData augmentation is commonly used for generating additional data from the available training data to achieve a robust estimation of the parameters of complex models like the one for speaker verification (SV), especially for under-resourced applications. SV involves training speaker-independent (SI) models and speaker-dependent models where speakers are represented by models derived from an SI model using the training data for the particular speaker during the enrollment phase. While data augmentation for training SI models is well studied, data augmentation for speaker enrollment is rarely explored. In this paper, we propose the use of data augmentation methods for generating extra data to empower speaker enrollment. Each data augmentation method generates a new data set. Two strategies of using the data sets are explored: the first one is to training separate systems and fuses them at the score level and the other is to conduct multi-conditional training. Furthermore, we study the effect of data augmentation under noisy conditions. Experiments are performed on RedDots challenge 2016 database, and the results validate the effectiveness of the proposed methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.08004v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.SD\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\"], \"incoming_citations\": [\"71709\"], \"outgoing_citations\": [\"181778\", \"256793\", \"286557\", \"320202\"]}","task_split":"paper_retrieval"}
{"document_id":"112729","document_content":"# Investigation of Sentiment Controllable Chatbot\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nConventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.07196v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"152729\", \"166082\", \"167338\", \"186381\", \"211937\", \"220130\", \"230014\", \"235238\", \"236477\", \"250035\", \"256194\", \"261692\", \"263848\", \"265309\", \"268265\", \"269757\", \"276160\", \"279091\", \"280606\", \"283076\", \"291102\", \"294494\", \"296198\", \"296250\", \"305200\", \"309411\", \"310903\", \"316328\", \"320032\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"112746","document_content":"# I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory\n## Categories\n- Computation and Language\n## Abstract\nTreebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to POS-tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this paper is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 7.5% in UAS and 18.8% in LAS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05772v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"55676\", \"133217\", \"304560\", \"308331\"]}","task_split":"paper_retrieval"}
{"document_id":"112756","document_content":"# Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the growing phase of artificial intelligence and autonomous learning, the self-driving car is one of the promising area of research and emerging as a center of focus for automobile industries. Behavioral cloning is the process of replicating human behavior via visuomotor policies by means of machine learning algorithms. In recent years, several deep learning-based behavioral cloning approaches have been developed in the context of self-driving cars specifically based on the concept of transfer learning. Concerning the same, the present paper proposes a transfer learning approach using VGG16 architecture, which is fine tuned by retraining the last block while keeping other blocks as non-trainable. The performance of proposed architecture is further compared with existing NVIDIA architecture and its pruned variants (pruned by 22.2% and 33.85% using 1x1 filter to decrease the total number of parameters). Experimental results show that the VGG16 with transfer learning architecture has outperformed other discussed approaches with faster convergence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-981-16-2937-2_14\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35183\"], \"outgoing_citations\": [\"127189\", \"206814\", \"233167\", \"267898\", \"267981\", \"329447\"]}","task_split":"paper_retrieval"}
{"document_id":"112786","document_content":"# Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network\n## Categories\n- Artificial Intelligence\n## Abstract\nGenerative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract interesting levels. However, human designers find latent vectors opaque and would rather explore along dimensions the designer specifies, such as number of enemies or obstacles. We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. In the benchmark domain of Super Mario Bros, we demonstrate how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics, while still maintaining stylistic similarity to human authored examples. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05674v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"2630\", \"24178\", \"37489\", \"40705\", \"48859\", \"67124\", \"72454\", \"74578\", \"81984\", \"95477\", \"89205\"], \"outgoing_citations\": [\"107909\", \"124853\", \"133342\", \"143545\", \"152647\", \"175835\", \"176932\", \"189201\", \"189206\", \"228285\", \"233501\", \"234345\", \"236005\", \"265896\", \"274886\", \"290092\", \"295312\", \"297233\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"112837","document_content":"# Vizarel: A System to Help Better Understand RL Agents\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nVisualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initial attempt at constructing a prototype of these ideas, through identifying possible features that such a system should encapsulate. Our design is motivated by envisioning the system to be a platform on which to experiment with interpretable reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05577v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"105972\"], \"outgoing_citations\": [\"251785\"]}","task_split":"paper_retrieval"}
{"document_id":"112927","document_content":"# Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JSEN.2021.3076767\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9036\", \"21162\", \"16520\", \"18587\", \"33500\", \"74725\", \"12120\", \"13561\", \"15777\", \"17947\", \"18850\", \"29448\", \"37025\", \"41332\", \"49276\", \"54218\", \"54733\", \"59584\", \"66727\", \"69843\", \"70360\", \"75552\", \"81319\", \"99226\", \"39037\", \"94707\"], \"outgoing_citations\": [\"64346\", \"116524\", \"123865\", \"126517\", \"134068\", \"135557\", \"164869\", \"174906\", \"204345\", \"235911\"]}","task_split":"paper_retrieval"}
{"document_id":"112980","document_content":"# Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sample efficiency, requiring a large amount of interaction with the environment. This is especially true in a lifelong learning setting, in which the agent needs to continually extend its capabilities. In this paper, we examine how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient. We do this by facilitating transfer learning between different related tasks. We experimentally demonstrate our approach on a set of object navigation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05196v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"134247\", \"137441\", \"155499\", \"161143\", \"162602\", \"181620\", \"229145\", \"241900\", \"259693\", \"263270\", \"284567\", \"295435\", \"309111\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"113035","document_content":"# Multi-Agent Routing Value Iteration Network\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Multiagent Systems\n- Robotics\n- Machine Learning\n- I.2.11; I.2.6\n## Abstract\nIn this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments hich contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and\/or nodes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05096v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.MA\", \"cs.RO\", \"stat.ML\", \"I.2.11; I.2.6\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Multiagent Systems\", \"Robotics\", \"Machine Learning\", \"I.2.11; I.2.6\"], \"incoming_citations\": [\"1447\", \"40588\", \"69620\", \"70359\", \"85572\", \"89573\", \"87817\", \"105537\"], \"outgoing_citations\": [\"157816\", \"177036\", \"227747\", \"231395\", \"238077\", \"246999\", \"255453\", \"264296\", \"269617\", \"291892\", \"298651\"]}","task_split":"paper_retrieval"}
{"document_id":"113040","document_content":"# Attack of the Tails: Yes, You Really Can Backdoor Federated Learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nDue to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor attacks have been introduced in the literature, but also methods to defend against them, and it is currently an open question whether FL systems can be tailored to be robust against backdoors. In this work, we provide evidence to the contrary. We first establish that, in the general case, robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in a FL model is unlikely assuming first order oracles or polynomial time. We couple our theoretical results with a new family of backdoor attacks, which we refer to as edge-case backdoors. An edge-case backdoor forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training, or test data, i.e., they live on the tail of the input distribution. We explain how these edge-case backdoors can lead to unsavory failures and may have serious repercussions on fairness, and exhibit that with careful tuning at the side of the adversary, one can insert them across a range of machine learning tasks (e.g., image classification, OCR, text prediction, sentiment analysis).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.05084v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"9036\", \"11900\", \"79556\", \"174906\", \"3618\", \"10807\", \"28009\", \"31206\", \"38928\", \"46010\", \"55141\", \"59756\", \"76669\", \"82241\", \"86356\", \"111465\", \"117293\", \"118154\", \"137962\", \"1964\", \"3990\", \"6605\", \"10768\", \"13534\", \"14491\", \"17858\", \"18017\", \"26238\", \"27798\", \"32156\", \"32473\", \"35424\", \"36065\", \"36915\", \"40759\", \"36167\", \"41895\", \"42954\", \"47655\", \"47852\", \"52174\", \"54065\", \"62820\", \"70340\", \"70414\", \"79614\", \"89126\", \"151798\", \"97049\", \"107841\", \"109147\", \"109503\"], \"outgoing_citations\": [\"109503\", \"126325\", \"136201\", \"139951\", \"142093\", \"148978\", \"151157\", \"155653\", \"156643\", \"161837\", \"162023\", \"162803\", \"164048\", \"174030\", \"181474\", \"183930\", \"198714\", \"206873\", \"208013\", \"210868\", \"211857\", \"214272\", \"225094\", \"226146\", \"228083\", \"229877\", \"230593\", \"230678\", \"237640\", \"237956\", \"240048\", \"240966\", \"244798\", \"246939\", \"247399\", \"252103\", \"252956\", \"257949\", \"262379\", \"264157\", \"264232\", \"266389\", \"274778\", \"280506\", \"289736\", \"343396\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"113070","document_content":"# Node Copying for Protection Against Graph Neural Network Topology Attacks\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Social and Information Networks\n## Abstract\nAdversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In particular, corruptions of the graph topology can degrade the performance of graph based learning algorithms severely. This is due to the fact that the prediction capability of these algorithms relies mostly on the similarity structure imposed by the graph connectivity. Therefore, detecting the location of the corruption and correcting the induced errors becomes crucial. There has been some recent work which tackles the detection problem, however these methods do not address the effect of the attack on the downstream learning task. In this work, we propose an algorithm that uses node copying to mitigate the degradation in classification that is caused by adversarial attacks. The proposed methodology is applied only after the model for the downstream task is trained and the added computation cost scales well for large graphs. Experimental results show the effectiveness of our approach for several real world datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.06704v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.SI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"195676\", \"197726\", \"197997\", \"208440\", \"229027\", \"231277\", \"243437\"]}","task_split":"paper_retrieval"}
{"document_id":"113084","document_content":"# ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Machine Learning\n- Robotics\n## Abstract\nWe introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable agents that embody AI agents; and support for human interactions with VR devices. TDW's API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that learn like a child, and attention studies in humans and neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.04954v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"2138\", \"6227\", \"7681\", \"13212\", \"15121\", \"35707\", \"39368\", \"60623\", \"64409\", \"109434\", \"4107\", \"4071\", \"6204\", \"8473\", \"10380\", \"12898\", \"16270\", \"17045\", \"18432\", \"16626\", \"23084\", \"2464\", \"30972\", \"32449\", \"41902\", \"51976\", \"54096\", \"56049\", \"57662\", \"59458\", \"63557\", \"67184\", \"82597\", \"93035\", \"80261\", \"103484\", \"110469\", \"129546\"], \"outgoing_citations\": [\"2464\", \"41902\", \"113565\", \"136058\", \"149565\", \"159745\", \"163549\", \"192738\", \"215627\", \"219658\", \"219835\", \"227274\", \"227594\", \"236252\", \"238328\", \"245731\", \"247418\", \"278195\", \"279009\", \"279026\", \"279420\", \"289719\", \"292009\", \"296366\", \"297162\", \"302659\", \"343358\", \"350010\"]}","task_split":"paper_retrieval"}
{"document_id":"113260","document_content":"# Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMulti-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.04514v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"47018\", \"93050\"], \"outgoing_citations\": [\"116950\", \"157417\", \"190298\", \"241671\", \"243165\", \"247846\", \"256227\", \"270923\", \"272912\", \"281332\"]}","task_split":"paper_retrieval"}
{"document_id":"113283","document_content":"# Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network\n## Categories\n- Machine Learning\n- Galaxy Astrophysics\n## Abstract\nThis paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example. We consider that we have a set of `one-class classification' objective-tasks with only a small set of positive examples available for each task, and a set of training tasks with full supervision (i.e. highly imbalanced classification). We propose an approach using order-equivariant networks to learn a 'meta' binary-classifier. The model will take as input an example to classify from a given task, as well as the corresponding supervised set of positive examples for this OCC task. Thus, the output of the model will be 'conditioned' on the available positive example of a given task, allowing to predict on new tasks and new examples without labeled negative examples. In this paper, we are motivated by an astronomy application. Our goal is to identify if stars belong to a specific stellar group (the 'one-class' for a given task), called \\textit{stellar streams}, where each stellar stream is a different OCC-task. We show that our method transfers well on unseen (test) synthetic streams, and outperforms the baselines even though it is not retrained and accesses a much smaller part of the data per task to predict (only positive supervision). We see however that it doesn't transfer as well on the real stream GD-1. This could come from intrinsic differences from the synthetic and real stream, highlighting the need for consistency in the 'nature' of the task for this method. However, light fine-tuning improve performances and outperform our baselines. Our experiments show encouraging results to further explore meta-learning methods for OCC tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.04459v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"astro-ph.GA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Galaxy Astrophysics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"101198\", \"113383\", \"131702\", \"138216\", \"163027\", \"215015\", \"244936\", \"290426\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"113308","document_content":"# Multilevel Graph Matching Networks for Deep Graph Similarity Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWhile the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2021.3102234\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"1240\", \"17757\", \"56889\", \"91584\"], \"outgoing_citations\": [\"91584\", \"117093\", \"127525\", \"146522\", \"154350\", \"161283\", \"171886\", \"188398\", \"188510\", \"190159\", \"196255\", \"215834\", \"218399\", \"221106\", \"227067\", \"231277\", \"239581\", \"241029\", \"242739\", \"252006\", \"257997\", \"258386\", \"264342\", \"279653\", \"287580\", \"289287\", \"303167\"]}","task_split":"paper_retrieval"}
{"document_id":"113327","document_content":"# Self-Supervised Policy Adaptation during Deployment\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nIn most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse simulation environments from DeepMind Control suite and ViZDoom, as well as real robotic manipulation tasks in continuously changing environments, taking observations from an uncalibrated camera. Our method improves generalization in 31 out of 36 environments across various tasks and outperforms domain randomization on a majority of environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.04309v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"2138\", \"8658\", \"2695\", \"16494\", \"20579\", \"39352\", \"68782\", \"13348\", \"13387\", \"18715\", \"37058\", \"37456\", \"38125\", \"41076\", \"41291\", \"43678\", \"56639\", \"57818\", \"69480\", \"76491\", \"84471\", \"86978\", \"87242\", \"87518\"], \"outgoing_citations\": [\"124846\", \"125475\", \"127845\", \"128392\", \"129794\", \"132207\", \"137263\", \"155747\", \"161304\", \"163705\", \"164362\", \"164899\", \"178485\", \"182326\", \"182497\", \"184811\", \"185155\", \"194686\", \"199022\", \"205568\", \"206160\", \"206943\", \"207499\", \"207601\", \"207758\", \"212511\", \"218435\", \"219180\", \"220228\", \"222669\", \"223713\", \"224840\", \"225752\", \"226635\", \"229604\", \"246118\", \"247268\", \"253042\", \"253045\", \"257688\", \"266419\", \"272030\", \"279211\", \"280296\", \"280570\", \"283289\", \"283410\", \"290378\", \"298331\", \"306156\", \"307383\", \"313397\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"113527","document_content":"# ISA: An Intelligent Shopping Assistant\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nDespite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people. In this paper, we present ISA, a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. ISA assists users by leveraging advanced techniques in computer vision, speech processing, and natural language processing. An in-store user only needs to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. The assistant can also guide the user through the purchase process or recommend other similar products to the user. We take a data-driven approach in building the engines of ISA's natural language processing component, and the engines achieve good performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.03805v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"88760\"], \"outgoing_citations\": [\"123836\", \"139073\", \"157013\", \"157123\", \"166960\", \"203554\", \"207466\", \"274120\"]}","task_split":"paper_retrieval"}
{"document_id":"113668","document_content":"# Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n- Robotics\n## Abstract\nRobots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely utilized in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent-IDeepRL or human-IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/app10165574\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\", \"Robotics\"], \"incoming_citations\": [\"16116\", \"77439\", \"99547\", \"41367\", \"71575\", \"114431\", \"116402\", \"101603\", \"106644\"], \"outgoing_citations\": [\"108811\", \"114431\", \"123527\", \"134219\", \"137729\", \"190578\", \"223301\", \"254597\", \"260018\", \"260508\", \"303521\"]}","task_split":"paper_retrieval"}
{"document_id":"113677","document_content":"# Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nWith great advances in vision and natural language processing, the generation of image captions becomes a need. In a recent paper, Mathews, Xie and He [1], extended a new model to generate styled captions by separating semantics and style. In continuation of this work, here a new captioning model is developed including an image encoder to extract the features, a mixture of recurrent networks to embed the set of extracted features to a set of words, and a sentence generator that combines the obtained words as a stylized sentence. The resulted system that entitled as Mixture of Recurrent Experts (MoRE), uses a new training algorithm that derives singular value decomposition (SVD) from weighting matrices of Recurrent Neural Networks (RNNs) to increase the diversity of captions. Each decomposition step depends on a distinctive factor based on the number of RNNs in MoRE. Since the used sentence generator gives a stylized language corpus without paired images, our captioning model can do the same. Besides, the styled and diverse captions are extracted without training on a densely labeled or styled dataset. To validate this captioning model, we use Microsoft COCO which is a standard factual image caption dataset. We show that the proposed captioning model can generate a diverse and stylized image captions without the necessity of extra-labeling. The results also show better descriptions in terms of content accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1002\/cpe.6866\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"61394\", \"209504\", \"213042\", \"219342\", \"225101\", \"229734\", \"231647\", \"236907\", \"243816\", \"249972\", \"259145\", \"262564\", \"269122\", \"270154\", \"271234\", \"278604\", \"278954\", \"279914\", \"281152\", \"283076\", \"283908\", \"287504\", \"291851\", \"302621\", \"305473\", \"307969\", \"312643\", \"313196\", \"315108\", \"320761\", \"321493\", \"321682\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"113756","document_content":"# Modeling and mitigating human annotation errors to design efficient stream processing systems with human-in-the-loop machine learning\n## Categories\n- Human-Computer Interaction\n## Abstract\nHigh-quality human annotations are necessary for creating effective machine learning-driven stream processing systems. We study hybrid stream processing systems based on a Human-In-The-Loop Machine Learning (HITL-ML) paradigm, in which one or many human annotators and an automatic classifier (trained at least partially by the human annotators) label an incoming stream of instances. This is typical of many near-real-time social media analytics and web applications, including annotating social media posts during emergencies by digital volunteer groups. From a practical perspective, low-quality human annotations result in wrong labels for retraining automated classifiers and indirectly contribute to the creation of inaccurate classifiers.   Considering human annotation as a psychological process allows us to address these limitations. We show that human annotation quality is dependent on the ordering of instances shown to annotators and can be improved by local changes in the instance sequence\/order provided to the annotators, yielding a more accurate annotation of the stream. We adapt a theoretically-motivated human error framework of mistakes and slips for the human annotation task to study the effect of ordering instances (i.e., an \"annotation schedule\"). Further, we propose an error-avoidance approach to the active learning paradigm for stream processing applications robust to these likely human errors (in the form of slips) when deciding a human annotation schedule. We support the human error framework using crowdsourcing experiments and evaluate the proposed algorithm against standard baselines for active learning via extensive experimentation on classification tasks of filtering relevant social media posts during natural disasters.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.ijhcs.2022.102772\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\"], \"incoming_citations\": [\"146639\"], \"outgoing_citations\": [\"165714\", \"175817\", \"216878\", \"233507\", \"338843\"]}","task_split":"paper_retrieval"}
{"document_id":"113866","document_content":"# Fast Adaptation via Policy-Dynamics Value Functions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nStandard RL algorithms assume fixed environment dynamics and require a significant amount of interaction to adapt to new environments. We introduce Policy-Dynamics Value Functions (PD-VF), a novel approach for rapidly adapting to dynamics different from those previously seen in training. PD-VF explicitly estimates the cumulative reward in a space of policies and environments. An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned. Then, a value function conditioned on both embeddings is trained. At test time, a few actions are sufficient to infer the environment embedding, enabling a policy to be selected by maximizing the learned value function (which requires no additional environment interaction). We show that our method can rapidly adapt to new dynamics on a set of MuJoCo domains. Code available at https:\/\/github.com\/rraileanu\/policy-dynamics-value-functions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02879v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"78473\", \"93176\", \"86373\"], \"outgoing_citations\": [\"139653\", \"151271\", \"160289\", \"161304\", \"171332\", \"179602\", \"181163\", \"186101\", \"194494\", \"205488\", \"218420\", \"227301\", \"228899\", \"230350\", \"230669\", \"233926\", \"235092\", \"237229\", \"238343\", \"242281\", \"248805\", \"255531\", \"261316\", \"261653\", \"263650\", \"271975\", \"272030\", \"272043\", \"277797\", \"280202\", \"281106\", \"284134\", \"290243\", \"296999\", \"302952\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"113878","document_content":"# Partially Conditioned Generative Adversarial Networks\n## Categories\n- Machine Learning\n- 68T07\n## Abstract\nGenerative models are undoubtedly a hot topic in Artificial Intelligence, among which the most common type is Generative Adversarial Networks (GANs). These architectures let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. From a practical standpoint, however, one might desire to generate data conditioned on partial information. That is, only a subset of the ancillary conditioning variables might be of interest when synthesising data. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy to deal with the ensuing problems. Experiments illustrating the value of the proposed approach in digit and face image synthesis under partial conditioning information are presented, showing that the proposed method can effectively outperform the standard approach under these circumstances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02845v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\", \"68T07\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"68T07\"], \"incoming_citations\": [], \"outgoing_citations\": [\"176457\", \"181788\", \"201305\", \"207862\", \"211010\"]}","task_split":"paper_retrieval"}
{"document_id":"113965","document_content":"# On Data Augmentation and Adversarial Risk: An Empirical Analysis\n## Categories\n- Machine Learning\n## Abstract\nData augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models. These techniques rely on different ideas such as invariance-preserving transformations (e.g, expert-defined augmentation), statistical heuristics (e.g, Mixup), and learning the data distribution (e.g, GANs). However, in the adversarial settings it remains unclear under what conditions such data augmentation methods reduce or even worsen the misclassification risk. In this paper, we therefore analyse the effect of different data augmentation techniques on the adversarial risk by three measures: (a) the well-known risk under adversarial attacks, (b) a new measure of prediction-change stress based on the Laplacian operator, and (c) the influence of training examples on prediction. The results of our empirical analysis disprove the hypothesis that an improvement in the classification performance induced by a data augmentation is always accompanied by an improvement in the risk under adversarial attack. Further, our results reveal that the augmented data has more influence than the non-augmented data, on the resulting models. Taken together, our results suggest that general-purpose data augmentations that do not take into the account the characteristics of the data and the task, must be applied with care.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02650v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"40814\", \"47117\"], \"outgoing_citations\": [\"187557\", \"212517\", \"228120\", \"228865\", \"229886\", \"245634\", \"247927\", \"250707\"]}","task_split":"paper_retrieval"}
{"document_id":"113972","document_content":"# Learning Spoken Language Representations with Neural Lattice Language Modeling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nPre-trained language models have achieved huge improvement on many NLP tasks. However, these methods are usually designed for written text, so they do not consider the properties of spoken language. Therefore, this paper aims at generalizing the idea of language model pre-training to lattices generated by recognition systems. We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency. Experiments on intent detection and dialogue act recognition datasets demonstrate that our proposed method consistently outperforms strong baselines when evaluated on spoken inputs. The code is available at https:\/\/github.com\/MiuLab\/Lattice-ELMo.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02629v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"20620\", \"54462\", \"69895\"], \"outgoing_citations\": [\"89723\", \"180966\", \"182540\", \"182662\", \"197273\", \"210262\", \"230543\", \"233136\", \"239066\"]}","task_split":"paper_retrieval"}
{"document_id":"114022","document_content":"# Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries.   To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos. Specifically, given a complex user query, we first recursively compose a latent semantic tree to structurally describe the text query. We then design a tree-augmented query encoder to derive structure-aware query representation and a temporal attentive video encoder to model the temporal characteristics of videos. Finally, both the query and videos are mapped into a joint embedding space for matching and ranking. In this approach, we have a better understanding and modeling of the complex queries, thereby achieving a better video retrieval performance. Extensive experiments on large scale video retrieval benchmark datasets demonstrate the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02503v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4453\", \"5478\", \"101354\", \"12778\", \"21149\", \"30649\", \"33374\", \"45218\", \"40616\", \"54366\", \"58028\", \"67375\", \"68457\", \"72065\", \"84992\"], \"outgoing_citations\": [\"172394\", \"179422\", \"181897\", \"182042\", \"191278\", \"208559\", \"217562\", \"221984\", \"233661\", \"236468\", \"256883\", \"259963\", \"261640\", \"263053\", \"268450\", \"273696\", \"277530\", \"279199\", \"282943\", \"283914\", \"316774\", \"319169\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"114239","document_content":"# Text Data Augmentation: Towards better detection of spear-phishing emails\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nText data augmentation, i.e., the creation of new textual data from an existing text, is challenging. Indeed, augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g., Machine Translation, Text Classification). Initially motivated by an application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic augmentation framework used as a service to augment English texts within our company. Our proposal combines different methods, utilizing BERT language model, multi-step back-translation and heuristics. We show that our augmentation framework improves performances on several text classification tasks using publicly available models and corpora as well as on a BEC detection task. We also provide a comprehensive argumentation about the limitations of our augmentation framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02033v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"18991\", \"60381\"], \"outgoing_citations\": [\"138517\", \"186386\", \"200867\", \"205715\", \"220017\", \"231898\", \"234164\", \"234646\", \"237274\", \"246146\", \"251736\", \"255894\", \"258131\", \"267161\", \"267601\", \"286557\", \"302964\"]}","task_split":"paper_retrieval"}
{"document_id":"114312","document_content":"# Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Sound\n- Computation and Language\n## Abstract\nSpoken language understanding is typically based on pipeline architectures including speech recognition and natural language understanding steps. These components are optimized independently to allow usage of available data, but the overall system suffers from error propagation. In this paper, we propose a novel training method that enables pretrained contextual embeddings to process acoustic features. In particular, we extend it with an encoder of pretrained speech recognition systems in order to construct end-to-end spoken language understanding systems. Our proposed method is based on the teacher-student framework across speech and text modalities that aligns the acoustic and the semantic latent spaces. Experimental results in three benchmarks show that our system reaches the performance comparable to the pipeline architecture without using any training data and outperforms it after fine-tuning with ten examples per class on two out of three benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.01836v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"cs.SD\", \"eess.AS\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Audio and Speech Processing\", \"Computation and Language\"], \"incoming_citations\": [\"20622\", \"31268\", \"96502\", \"69349\", \"69895\", \"80300\", \"91476\", \"91935\", \"90711\"], \"outgoing_citations\": [\"145987\", \"171946\", \"191769\", \"197091\", \"216686\", \"221967\", \"231526\", \"233817\", \"237945\", \"240264\", \"241146\", \"258300\", \"264813\", \"278443\", \"284201\"]}","task_split":"paper_retrieval"}
{"document_id":"114336","document_content":"# Eliminating Catastrophic Interference with Biased Competition\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe present here a model to take advantage of the multi-task nature of complex datasets by learning to separate tasks and subtasks in and end to end manner by biasing competitive interactions in the network. This method does not require additional labelling or reformatting of data in a dataset. We propose an alternate view to the monolithic one-task-fits-all learning of multi-task problems, and describe a model based on a theory of neuronal attention from neuroscience, proposed by Desimone. We create and exhibit a new toy dataset, based on the MNIST dataset, which we call MNIST-QA, for testing Visual Question Answering architectures in a low-dimensional environment while preserving the more difficult components of the Visual Question Answering task, and demonstrate the proposed network architecture on this new dataset, as well as on COCO-QA and DAQUAR-FULL. We then demonstrate that this model eliminates catastrophic interference between tasks on a newly created toy dataset and provides competitive results in the Visual Question Answering space. We provide further evidence that Visual Question Answering can be approached as a multi-task problem, and demonstrate that this new architecture based on the Biased Competition model is capable of learning to separate and learn the tasks in an end-to-end fashion without the need for task labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02833v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"114337\", \"246926\", \"266825\", \"278894\", \"302756\", \"303603\", \"303714\", \"303837\", \"310601\", \"313143\", \"313320\", \"323617\", \"323775\"]}","task_split":"paper_retrieval"}
{"document_id":"114390","document_content":"# HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nSynthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) exposures in dynamic scenes is challenging. There are two major problems caused by the large motions of foreground objects. One is the severe misalignment among the LDR images. The other is the missing content due to the over-\/under-saturated regions caused by the moving objects, which may not be easily compensated for by the multiple LDR exposures. Thus, it requires the HDR generation model to be able to properly fuse the LDR images and restore the missing details without introducing artifacts. To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images. To our best knowledge, this work is the first GAN-based approach for fusing multi-exposed LDR images for HDR reconstruction. By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content. In addition, we also propose a novel generator network, with a reference-based residual merging block for aligning large object motions in the feature domain, and a deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR images. Experimental results demonstrate that our model achieves state-of-the-art reconstruction performance over the prior HDR methods on diverse scenes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2021.3064433\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"9660\", \"14984\", \"37392\", \"71818\", \"19090\", \"60199\"], \"outgoing_citations\": [\"130207\", \"133418\", \"149650\", \"189323\", \"227460\", \"246985\", \"249439\", \"252829\"]}","task_split":"paper_retrieval"}
{"document_id":"114431","document_content":"# A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Multiagent Systems\n## Abstract\nA long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent's performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s12652-021-03489-y\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [\"16116\", \"13755\", \"99547\", \"71575\", \"71959\", \"113668\"], \"outgoing_citations\": [\"28573\", \"36970\", \"47007\", \"71575\", \"89522\", \"99547\", \"101603\", \"108811\", \"113668\", \"116402\", \"131357\", \"132256\", \"134219\", \"137729\", \"156004\", \"166040\", \"167687\", \"171445\", \"183993\", \"188486\", \"190578\", \"206960\", \"209859\", \"217577\", \"223301\", \"223475\", \"236923\", \"239091\", \"242141\", \"254596\", \"259960\", \"266419\", \"294612\", \"301005\", \"302952\", \"334182\"]}","task_split":"paper_retrieval"}
{"document_id":"114461","document_content":"# On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nThe goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what extend can the quality-diversity evaluation reflect the distribution-fitting goal. In this paper, we try to reveal such relation in a theoretical approach. We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution. We also show that the commonly used BLEU\/Self-BLEU metric pair fails to match any divergence metric, thus propose CR\/NRR as a substitute for quality\/diversity metric pair.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.01488v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"180695\", \"184909\", \"191641\", \"192158\", \"197057\", \"198185\", \"211205\", \"217526\", \"228201\", \"236053\", \"238906\", \"243154\", \"244368\", \"255035\", \"263935\", \"264937\", \"266791\", \"275597\", \"278954\", \"283076\", \"284481\", \"305200\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"114617","document_content":"# Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score\n## Categories\n- Machine Learning\n## Abstract\nRandomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electronic health records, a great variety of data has been collected in clinical practice, enabling the evaluation of treatments and treatment policies based on observational data. In this paper, we focus on learning individualized treatment rules (ITRs) to derive a treatment policy that is expected to generate a better outcome for an individual patient. In our framework, we cast ITRs learning as a contextual bandit problem and minimize the expected risk of the treatment policy. We conduct experiments with the proposed framework both in a simulation study and based on a real-world dataset. In the latter case, we apply our proposed method to learn the optimal ITRs for the administration of intravenous (IV) fluids and vasopressors (VP). Based on various offline evaluation methods, we could show that the policy derived in our framework demonstrates better performance compared to both the physicians and other baselines, including a simple treatment prediction approach. As a long-term goal, our derived policy might eventually lead to better clinical guidelines for the administration of IV and VP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICHI48887.2020.9374397\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"33307\"], \"outgoing_citations\": [\"215684\", \"222644\", \"226059\", \"229839\", \"249233\", \"286253\", \"289558\", \"298687\", \"303053\", \"307547\", \"317842\", \"334218\"]}","task_split":"paper_retrieval"}
{"document_id":"114637","document_content":"# NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNatural language processing has huge potential in the medical domain which recently led to a lot of research in this field. However, a prerequisite of secure processing of medical documents, e.g., patient notes and clinical trials, is the proper de-identification of privacy-sensitive information. In this paper, we describe our NLNDE system, with which we participated in the MEDDOCAN competition, the medical document anonymization task of IberLEF 2019. We address the task of detecting and classifying protected health information from Spanish data as a sequence-labeling problem and investigate different embedding methods for our neural network. Despite dealing in a non-standard language and domain setting, the NLNDE system achieves promising results in the competition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.01030v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2566\", \"22763\", \"91925\", \"91922\", \"91915\", \"121009\", \"124116\", \"138219\"], \"outgoing_citations\": [\"215625\", \"221511\", \"241616\"]}","task_split":"paper_retrieval"}
{"document_id":"114649","document_content":"# Human-centered collaborative robots with deep reinforcement learning\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent to complete the task. The framework is learned end-to-end in an unsupervised fashion addressing the perception uncertainties and decision making in an integrated manner. The framework is shown to provide more fluent coordination between human and robot partners on an example task of packaging compared to alternatives for which perception and decision-making systems are learned independently, using supervised learning. The foremost benefit of the proposed approach is that it allows for fast adaptation to new human partners and tasks since tedious annotation of motion data is avoided and the learning is performed on-line.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.01009v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"16370\", \"20668\", \"14378\", \"38799\", \"39998\"], \"outgoing_citations\": [\"125527\", \"161857\", \"166368\", \"195713\", \"195942\", \"200322\", \"272585\", \"273203\", \"274762\", \"287681\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"114666","document_content":"# Learning to search efficiently for causally near-optimal treatments\n## Categories\n- Machine Learning\n## Abstract\nFinding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The methods are evaluated on synthetic and real-world healthcare data and compared to model-free reinforcement learning. We find that our methods compare favorably to the model-free baseline while offering a more transparent trade-off between search time and treatment efficacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00973v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"81879\"], \"outgoing_citations\": [\"121753\", \"168013\", \"231042\", \"248619\", \"295281\", \"356133\"]}","task_split":"paper_retrieval"}
{"document_id":"114672","document_content":"# Iterative Bounding Box Annotation for Object Detection\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nManual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00961v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"31927\", \"48473\", \"50453\", \"74919\"], \"outgoing_citations\": [\"200930\", \"225968\", \"246830\", \"258992\", \"268417\", \"297534\"]}","task_split":"paper_retrieval"}
{"document_id":"114692","document_content":"# Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Cryptography and Security\n- 68T01\n- I.2.11\n## Abstract\nThe high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in their data silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use. Hence, we present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy. It results from the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.inffus.2020.07.009\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CR\", \"stat.ML\", \"68T01\", \"I.2.11\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Cryptography and Security\", \"Machine Learning\", \"68T01\", \"I.2.11\"], \"incoming_citations\": [\"10934\", \"108971\", \"48008\", \"73422\", \"107076\", \"151798\"], \"outgoing_citations\": [\"201117\", \"208013\", \"225769\", \"246939\", \"248982\", \"249809\", \"252956\", \"264255\", \"273215\", \"283045\", \"291778\", \"326583\", \"338290\", \"353750\"]}","task_split":"paper_retrieval"}
{"document_id":"114713","document_content":"# Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nThis paper tackles one of the greatest limitations in Machine Learning: Data Scarcity. Specifically, we explore whether high accuracy classifiers can be built from small datasets, utilizing a combination of data augmentation techniques and machine learning algorithms. In this paper, we experiment with Easy Data Augmentation (EDA) and Backtranslation, as well as with three popular learning algorithms, Logistic Regression, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory Network (Bi-LSTM). For our experimentation, we utilize the Wikipedia Toxic Comments dataset so that in the process of exploring the benefits of data augmentation, we can develop a model to detect and classify toxic speech in comments to help fight back against cyberbullying and online harassment. Ultimately, we found that data augmentation techniques can be used to significantly boost the performance of classifiers and are an excellent strategy to combat lack of data in NLP problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00875v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"18991\"], \"outgoing_citations\": [\"159153\", \"163849\", \"165193\", \"166278\", \"200867\", \"220017\", \"231898\", \"272191\", \"316486\"]}","task_split":"paper_retrieval"}
{"document_id":"114791","document_content":"# Deep learning-based holographic polarization microscopy\n## Categories\n- Optics\n- Computer Vision and Pattern Recognition\n- physics.app-ph\n- Image and Video Processing\n## Abstract\nPolarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer\/analyzer pair to an existing holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including e.g., monosodium urate (MSU) and triamcinolone acetonide (TCA) crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view, this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1021\/acsphotonics.0c01051\", \"primary_category\": \"physics.optics\", \"categories\": [\"cs.CV\", \"physics.optics\", \"physics.app-ph\", \"eess.IV\"], \"primary_category_human_readable\": \"Optics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Optics\", \"physics.app-ph\", \"Image and Video Processing\"], \"incoming_citations\": [\"69875\", \"65149\"], \"outgoing_citations\": [\"175987\", \"214186\", \"223979\", \"238180\", \"247690\", \"266833\", \"273351\"]}","task_split":"paper_retrieval"}
{"document_id":"114841","document_content":"# Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMachine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of videos remains challenging due to the significantly higher dimensionality of the input data, respectively, the significantly higher need for annotated training examples. By studying the automatic recognition of German sign language videos, we demonstrate that on the relatively scarce training data of 2.800 videos, modern deep learning architectures for video analysis (such as ResNeXt) along with transfer learning on large gesture recognition tasks, can achieve about 75% character accuracy. Considering that this leaves us with a probability of under 25% that a 5 letter word is spelled correctly, spell-correction systems are crucial for producing readable outputs. The contribution of this paper is to propose a convolutional neural network for spell-correction that expects the softmax outputs of the character recognition network (instead of a misspelled word) as an input. We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart. In contrast, training the network on several variants of the logits of the classification output i.e. scaling by a constant factor, adding of random noise, mixing of softmax and hardmax inputs or purely training on hardmax inputs, leads to better generalization while benefitting from the significant information hidden in these outputs (that have 98% top-5 accuracy), yielding a readable text despite the comparably low character accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00603v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"114889","document_content":"# Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nHumans learn all their life long. They accumulate knowledge from a sequence of learning experiences and remember the essential concepts without forgetting what they have learned previously. Artificial neural networks struggle to learn similarly. They often rely on data rigorously preprocessed to learn solutions to specific problems such as classification or regression. In particular, they forget their past learning experiences if trained on new ones. Therefore, artificial neural networks are often inept to deal with real-life settings such as an autonomous-robot that has to learn on-line to adapt to new situations and overcome new problems without forgetting its past learning-experiences. Continual learning (CL) is a branch of machine learning addressing this type of problem. Continual algorithms are designed to accumulate and improve knowledge in a curriculum of learning-experiences without forgetting. In this thesis, we propose to explore continual algorithms with replay processes. Replay processes gather together rehearsal methods and generative replay methods. Generative Replay consists of regenerating past learning experiences with a generative model to remember them. Rehearsal consists of saving a core-set of samples from past learning experiences to rehearse them later. The replay processes make possible a compromise between optimizing the current learning objective and the past ones enabling learning without forgetting in sequences of tasks settings. We show that they are very promising methods for continual learning. Notably, they enable the re-evaluation of past data with new knowledge and the confrontation of data from different learning-experiences. We demonstrate their ability to learn continually through unsupervised learning, supervised learning and reinforcement learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00487v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"161623\", \"33921\", \"43296\", \"45072\", \"69915\"], \"outgoing_citations\": [\"137119\", \"140801\", \"144508\", \"146858\", \"151271\", \"152432\", \"153104\", \"155615\", \"156519\", \"157469\", \"163028\", \"172207\", \"175604\", \"176551\", \"178420\", \"181405\", \"183426\", \"183710\", \"184430\", \"184811\", \"185559\", \"194365\", \"197666\", \"201682\", \"203834\", \"204049\", \"205108\", \"207559\", \"207676\", \"209412\", \"209773\", \"211133\", \"211707\", \"212214\", \"212582\", \"212647\", \"214194\", \"216625\", \"217663\", \"217950\", \"220865\", \"220890\", \"221473\", \"223417\", \"223428\", \"225415\", \"225539\", \"225801\", \"226506\", \"226635\", \"227119\", \"227314\", \"230245\", \"230690\", \"231361\", \"231847\", \"232302\", \"235189\", \"237015\", \"240201\", \"240506\", \"240517\", \"240519\", \"241185\", \"242202\", \"242478\", \"242751\", \"243460\", \"243820\", \"244665\", \"245957\", \"249018\", \"250354\", \"251563\", \"252086\", \"252176\", \"253953\", \"254831\", \"257688\", \"257884\", \"259143\", \"259175\", \"259336\", \"261316\", \"261983\", \"265249\", \"265568\", \"265656\", \"265959\", \"266851\", \"268563\", \"269555\", \"270105\", \"270623\", \"273802\", \"274371\", \"275087\", \"277472\", \"278207\", \"280109\", \"281468\", \"282684\", \"282841\", \"289250\", \"289719\", \"290378\", \"290915\", \"291301\", \"292458\", \"296394\", \"299985\", \"302967\", \"306156\", \"306266\", \"311413\", \"315745\", \"336181\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"115054","document_content":"# Graph Neural Networks Including Sparse Interpretability\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) are versatile, powerful machine learning methods that enable graph structure and feature representation learning, and have applications across many domains. For applications critically requiring interpretation, attention-based GNNs have been leveraged. However, these approaches either rely on specific model architectures or lack a joint consideration of graph structure and node features in their interpretation. Here we present a model-agnostic framework for interpreting important graph structure and node features, Graph neural networks Including SparSe inTerpretability (GISST). With any GNN model, GISST combines an attention mechanism and sparsity regularization to yield an important subgraph and node feature subset related to any graph-based task. Through a single self-attention layer, a GISST model learns an importance probability for each node feature and edge in the input graph. By including these importance probabilities in the model loss function, the probabilities are optimized end-to-end and tied to the task-specific performance. Furthermore, GISST sparsifies these importance probabilities with entropy and L1 regularization to reduce noise in the input graph topology and node features. Our GISST models achieve superior node feature and edge explanation precision in synthetic datasets, as compared to alternative interpretation approaches. Moreover, our GISST models are able to identify important graph structure in real-world datasets. We demonstrate in theory that edge feature importance and multiple edge types can be considered by incorporating them into the GISST edge probability computation. By jointly accounting for topology, node features, and edge features, GISST inherently provides simple and relevant interpretations for any GNN models and tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00119v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"83315\"], \"outgoing_citations\": [\"130994\", \"195767\", \"203910\", \"215081\", \"226576\", \"228992\", \"242322\", \"243529\", \"250234\", \"255737\", \"262092\", \"267030\", \"305131\", \"357936\"]}","task_split":"paper_retrieval"}
{"document_id":"115066","document_content":"# Deep Learning for Vision-based Prediction: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nVision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video prediction, action prediction, trajectory prediction, body motion prediction, and other prediction applications. For each category, we highlight the common architectures, training methods and types of data used. In addition, we discuss the common evaluation metrics and datasets used for vision-based prediction tasks. A database of all the information presented in this survey including, cross-referenced according to papers, datasets and metrics, can be found online at https:\/\/github.com\/aras62\/vision-based-prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.00095v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"86653\", \"23312\", \"32831\", \"3430\", \"38196\", \"38494\", \"55420\", \"61564\", \"66331\", \"71212\", \"122444\", \"80587\"], \"outgoing_citations\": [\"74563\", \"102898\", \"125246\", \"145698\", \"151062\", \"152530\", \"152737\", \"155688\", \"156864\", \"157689\", \"161193\", \"162130\", \"163330\", \"164133\", \"165831\", \"168054\", \"170608\", \"171709\", \"171939\", \"174738\", \"175252\", \"176524\", \"177460\", \"179692\", \"180480\", \"181535\", \"181941\", \"182459\", \"185249\", \"185315\", \"187729\", \"187849\", \"188709\", \"189466\", \"191356\", \"191556\", \"192305\", \"193745\", \"194236\", \"195463\", \"196132\", \"196428\", \"196620\", \"196757\", \"196758\", \"199454\", \"202834\", \"202881\", \"203789\", \"205045\", \"205195\", \"205479\", \"206091\", \"206321\", \"206997\", \"207276\", \"207618\", \"207664\", \"211884\", \"213313\", \"213503\", \"214011\", \"216946\", \"217456\", \"218337\", \"218597\", \"223317\", \"223371\", \"228270\", \"228466\", \"229266\", \"231717\", \"233526\", \"233978\", \"234705\", \"235738\", \"236377\", \"236413\", \"236894\", \"237190\", \"237350\", \"238455\", \"238796\", \"238864\", \"239572\", \"240531\", \"244257\", \"246400\", \"248632\", \"249037\", \"249419\", \"249515\", \"250983\", \"251550\", \"252604\", \"253435\", \"254458\", \"254931\", \"258213\", \"259489\", \"259642\", \"259728\", \"261149\", \"263418\", \"265546\", \"265650\", \"266140\", \"266174\", \"267118\", \"267134\", \"267705\", \"268497\", \"268891\", \"269088\", \"269134\", \"270325\", \"270832\", \"270890\", \"271003\", \"273208\", \"273711\", \"277390\", \"279009\", \"279542\", \"283807\", \"284827\", \"292009\", \"293491\", \"293812\", \"294226\", \"294529\", \"294875\", \"295183\", \"296086\", \"296366\", \"301083\", \"303203\", \"308617\", \"308711\", \"311119\", \"313644\", \"314670\", \"314829\", \"317379\", \"353848\"]}","task_split":"paper_retrieval"}
{"document_id":"115107","document_content":"# Learning Post-Hoc Causal Explanations for Recommendation\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nState-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models. The lack of transparency has highlighted the critical importance of improving the explainability of recommender systems. In this paper, we propose to extract causal rules from the user interaction history as post-hoc explanations for the black-box sequential recommendation mechanisms, whilst maintain the predictive accuracy of the recommendation model. Our approach firstly achieves counterfactual examples with the aid of a perturbation model, and then extracts personalized causal relationships for the recommendation model through a causal rule mining algorithm. Experiments are conducted on several state-of-the-art sequential recommendation models and real-world datasets to verify the performance of our model on generating causal explanations. Meanwhile, We evaluate the discovered causal explanations in terms of quality and fidelity, which show that compared with conventional association rules, causal rules can provide personalized and more effective explanations for the behavior of black-box recommendation models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16977v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"92753\"], \"outgoing_citations\": [\"121346\", \"178176\", \"181089\", \"195838\", \"209989\", \"210521\", \"215808\", \"217192\", \"228646\", \"232728\", \"233732\", \"250622\", \"261871\", \"262120\", \"263017\", \"283711\", \"286532\", \"304589\"]}","task_split":"paper_retrieval"}
{"document_id":"115147","document_content":"# I call BS: Fraud Detection in Crowdfunding Campaigns\n## Categories\n- Computers and Society\n- Social and Information Networks\n- Machine Learning\n- Cryptography and Security\n## Abstract\nDonations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions performed (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating.   In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16849v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.SI\", \"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Social and Information Networks\", \"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"52789\", \"86123\", \"174674\", \"177730\", \"229750\", \"236127\", \"244019\", \"282854\", \"292925\", \"329670\"]}","task_split":"paper_retrieval"}
{"document_id":"115175","document_content":"# Model-based Reinforcement Learning: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two sections, we also discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and we cover the potential benefits of model-based RL. Along the way, the survey also draws connections to several related RL fields, like hierarchical RL and transfer learning. Altogether, the survey presents a broad conceptual overview of the combination of planning and learning for MDP optimization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16712v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"9498\", \"34882\", \"48226\", \"3788\", \"4742\", \"6601\", \"9804\", \"11995\", \"15472\", \"16124\", \"19859\", \"20577\", \"22359\", \"23564\", \"24082\", \"46554\", \"62723\", \"73192\", \"115868\", \"121464\", \"1487\", \"3950\", \"8072\", \"8368\", \"8478\", \"9906\", \"10591\", \"13792\", \"13922\", \"17253\", \"17665\", \"25823\", \"29648\", \"31793\", \"32291\", \"40454\", \"44119\", \"44908\", \"44969\", \"48644\", \"48978\", \"52739\", \"54801\", \"56413\", \"58043\", \"62805\", \"66325\", \"66920\", \"85182\", \"88340\", \"120758\", \"86275\", \"92011\", \"140863\"], \"outgoing_citations\": [\"171187\", \"239841\", \"242444\", \"245182\", \"259045\", \"269012\", \"271952\", \"283391\", \"283427\", \"288338\", \"297268\", \"298718\", \"308617\", \"310171\", \"317745\", \"318666\", \"343685\"]}","task_split":"paper_retrieval"}
{"document_id":"115203","document_content":"# A Data-driven Neural Network Architecture for Sentiment Analysis\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nThe fabulous results of convolution neural networks in image-related tasks, attracted attention of text mining, sentiment analysis and other text analysis researchers. It is however difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. In this paper we present the creation steps of two big datasets of song emotions. We also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text datasets. Three variants of a simple and flexible neural network architecture are also compared. Our intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. We also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, we conducted a series of experiments with neural architectures of various configurations. Our results indicate that parallel convolutions of filter lengths up to three are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Top results we got are obtained with feature maps of lengths 6 to 18. An improvement on future neural network models for sentiment analysis, could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1108\/DTA-03-2018-0017\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"90358\", \"116558\", \"142835\"], \"outgoing_citations\": [\"138152\", \"215173\", \"279991\", \"285619\", \"288174\", \"291045\", \"305094\", \"314240\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"115235","document_content":"# Enabling Continual Learning with Differentiable Hebbian Plasticity\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nContinual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale. We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task. We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST -- a dataset that combines the challenges of class imbalance and concept drift. Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16558v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"22100\", \"128737\"], \"outgoing_citations\": [\"140345\", \"190532\", \"224871\", \"231361\", \"231847\", \"236495\", \"237584\", \"249234\", \"265568\", \"282318\", \"285165\", \"348722\"]}","task_split":"paper_retrieval"}
{"document_id":"115264","document_content":"# Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nA laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often a bottleneck in 3D reconstruction because it requires expertise on domain knowledge and intensive labor. To address this problem, various methods have been developed to recognize objects by retrieving the corresponding model in the database from an input geometry query. In recent years, the technique of converting geometric data into an image and applying view-based 3D shape retrieval has demonstrated high accuracy. Depth image which encodes depth value as intensity of pixel is frequently used for view-based 3D shape retrieval. However, geometric data collected from objects is often incomplete due to the occlusions and the limit of line of sight. Image generated by occluded point clouds lowers the performance of view-based 3D object retrieval due to loss of information. In this paper, we propose a method of viewpoint and image resolution estimation method for view-based 3D shape retrieval from point cloud query. Automatic selection of viewpoint and image resolution by calculating the data acquisition rate and density from the sampled viewpoints and image resolutions are proposed. The retrieval performance from the images generated by the proposed method is experimented and compared for various dataset. Additionally, view-based 3D shape retrieval performance with deep convolutional neural network has been experimented with the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16500v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"296237\"]}","task_split":"paper_retrieval"}
{"document_id":"115367","document_content":"# Self-Supervised MultiModal Versatile Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51, Kinetics600, AudioSet and ESC-50 when compared to previous self-supervised work. Our models are publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16228v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"27911\", \"59004\", \"1405\", \"2460\", \"3805\", \"4099\", \"4359\", \"6331\", \"7118\", \"7573\", \"12103\", \"15441\", \"16433\", \"16965\", \"22628\", \"22648\", \"32344\", \"39753\", \"43252\", \"1284\", \"4347\", \"4636\", \"5927\", \"7309\", \"7938\", \"15665\", \"16549\", \"17328\", \"19618\", \"19628\", \"19636\", \"20409\", \"22807\", \"23047\", \"10669\", \"30524\", \"31213\", \"37234\", \"38350\", \"40103\", \"40505\", \"40525\", \"41795\", \"44668\", \"48259\", \"51519\", \"51750\", \"52720\", \"53464\", \"53451\", \"53510\", \"53509\", \"54108\", \"55444\", \"57069\", \"57796\", \"58876\", \"59021\", \"59462\", \"59465\", \"59775\", \"62260\", \"62318\", \"63753\", \"65208\", \"66004\", \"66630\", \"86182\", \"106968\", \"118223\", \"137693\", \"74256\", \"81067\"], \"outgoing_citations\": [\"106968\", \"118223\", \"123821\", \"128649\", \"139906\", \"150055\", \"151327\", \"153776\", \"156436\", \"171057\", \"172394\", \"176053\", \"180920\", \"181897\", \"182813\", \"183080\", \"185155\", \"188114\", \"192531\", \"201491\", \"201585\", \"208341\", \"209299\", \"217562\", \"220529\", \"222332\", \"226232\", \"236080\", \"236468\", \"236772\", \"247148\", \"247535\", \"259415\", \"263034\", \"263620\", \"264645\", \"265701\", \"269128\", \"270325\", \"279987\", \"281762\", \"283807\", \"285985\", \"295738\", \"303011\", \"310322\", \"310452\", \"312643\", \"313189\", \"316472\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"115402","document_content":"# Human Activity Recognition based on Dynamic Spatio-Temporal Relations\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nHuman activity, which usually consists of several actions, generally covers interactions among persons and or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatio temporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.16132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"102898\", \"207400\", \"242718\", \"272469\", \"353848\"]}","task_split":"paper_retrieval"}
{"document_id":"115507","document_content":"# End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, high dynamic range (HDR) image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. These conventional networks focus on the exposure transfer task to reconstruct the multi-exposure stack. Therefore, they often fail to fuse the multi-exposure stack into a perceptually pleasant HDR image as the inversion artifacts occur. We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process. By explicitly using the loss, which compares the network's output with the ground truth HDR image, our framework enables a neural network that generates the multiple exposure stack for HDRI to train stably. In other words, our differentiable HDR synthesis layer helps the deep neural network to train to create multi-exposure stacks while reflecting the precise correlations between multi-exposure images in the HDRI process. In addition, our network uses the image decomposition and the recursive process to facilitate the exposure transfer task and to adaptively respond to recursion frequency. The experimental results show that the proposed network outperforms the state-of-the-art quantitative and qualitative results in terms of both the exposure transfer tasks and the whole HDRI process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15833v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6693\", \"14984\", \"10165\"], \"outgoing_citations\": [\"133307\", \"133418\", \"149650\", \"167645\", \"186554\", \"189044\", \"189323\", \"209333\", \"237377\", \"239855\", \"244729\", \"252829\", \"280284\"]}","task_split":"paper_retrieval"}
{"document_id":"115700","document_content":"# GPT-GNN: Generative Pre-Training of Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabeled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph. We factorize the likelihood of the graph generation into two components: 1) Attribute Generation and 2) Edge Generation. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on the billion-scale Open Academic Graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15437v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"18076\", \"33190\", \"42635\", \"49353\", \"66165\", \"67813\", \"83866\", \"118617\", \"52113\", \"3205\", \"5282\", \"5283\", \"12644\", \"15441\", \"18175\", \"23465\", \"27383\", \"36946\", \"41310\", \"66588\", \"67159\", \"80211\", \"80770\", \"82775\", \"4995\", \"8235\", \"10167\", \"91934\", \"15895\", \"18031\", \"18663\", \"19370\", \"27805\", \"28039\", \"29202\", \"29193\", \"260\", \"13637\", \"34643\", \"37202\", \"43452\", \"44064\", \"44493\", \"45197\", \"47768\", \"47862\", \"51318\", \"51716\", \"58043\", \"60189\", \"61023\", \"110709\", \"66867\", \"70439\", \"78815\", \"79232\", \"79731\", \"80836\", \"97083\", \"101265\", \"95011\", \"92075\", \"102511\", \"113751\", \"205283\"], \"outgoing_citations\": [\"138857\", \"155900\", \"163822\", \"173673\", \"183816\", \"241029\", \"253807\"]}","task_split":"paper_retrieval"}
{"document_id":"115751","document_content":"# On the Evolution of Programming Languages\n## Categories\n- Programming Languages\n- Computation and Language\n- D.3\n## Abstract\nThis paper attempts to connects the evolution of computer languages with the evolution of life, where the later has been dictated by \\emph{theory of evolution of species}, and tries to give supportive evidence that the new languages are more robust than the previous, carry-over the mixed features of older languages, such that strong features gets added into them and weak features of older languages gets removed. In addition, an analysis of most prominent programming languages is presented, emphasizing on how the features of existing languages have influenced the development of new programming languages. At the end, it suggests a set of experimental languages, which may rule the world of programming languages in the time of new multi-core architectures.   Index terms- Programming languages' evolution, classifications of languages, future languages, scripting-languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.02699v1\", \"primary_category\": \"cs.PL\", \"categories\": [\"cs.CL\", \"cs.PL\", \"D.3\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"Computation and Language\", \"Programming Languages\", \"D.3\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"115753","document_content":"# Compositional Video Synthesis with Action Graphs\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nVideos of actions are complex signals containing rich compositional structure in space and time. Current video generation methods lack the ability to condition the generation on multiple coordinated and potentially simultaneous timed actions. To address this challenge, we propose to represent the actions in a graph structure called Action Graph and present the new ``Action Graph To Video'' synthesis task. Our generative model for this task (AG2Vid) disentangles motion and appearance features, and by incorporating a scheduling mechanism for actions facilitates a timely and coordinated video generation. We train and evaluate AG2Vid on the CATER and Something-Something V2 datasets, and show that the resulting videos have better visual quality and semantic consistency compared to baselines. Finally, our model demonstrates zero-shot abilities by synthesizing novel compositions of the learned actions. For code and pretrained models, see the project page https:\/\/roeiherz.github.io\/AG2Video","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15327v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"7484\", \"15670\", \"16624\", \"85358\", \"8833\", \"8969\", \"97645\"], \"outgoing_citations\": [\"111568\", \"150149\", \"150966\", \"151123\", \"152646\", \"158006\", \"159364\", \"162372\", \"163549\", \"165842\", \"167312\", \"170608\", \"181533\", \"182589\", \"187724\", \"196563\", \"197406\", \"205195\", \"206940\", \"207400\", \"209299\", \"220636\", \"220842\", \"221244\", \"223041\", \"229179\", \"234607\", \"235749\", \"236090\", \"236736\", \"236752\", \"237190\", \"241365\", \"242092\", \"242322\", \"250108\", \"251921\", \"254361\", \"258148\", \"261089\", \"263084\", \"263838\", \"264532\", \"276530\", \"279026\", \"279997\", \"285093\", \"289605\", \"292758\", \"295117\", \"298700\", \"300739\", \"303174\", \"303203\"]}","task_split":"paper_retrieval"}
{"document_id":"115840","document_content":"# Policy-GNN: Aggregation Optimization for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often require different iterations of aggregation to fully capture the structural information, in this paper, we propose to explicitly sample diverse iterations of aggregation for different nodes to boost the performance of GNNs. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. Moreover, it is not straightforward to derive an efficient algorithm since we need to feed the sampled nodes into different number of network layers. To address the above challenges, we propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process. Specifically, Policy-GNN uses a meta-policy to adaptively determine the number of aggregations for each node. The meta-policy is trained with deep reinforcement learning (RL) by exploiting the feedback from the model. We further introduce parameter sharing and a buffer mechanism to boost the training efficiency. Experimental results on three real-world benchmark datasets suggest that Policy-GNN significantly outperforms the state-of-the-art alternatives, showing the promise in aggregation optimization for GNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15097v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"20875\", \"55646\", \"6878\", \"13291\", \"19357\", \"25973\", \"27860\", \"32758\", \"36333\", \"56240\", \"96042\", \"41260\", \"100432\", \"126737\"], \"outgoing_citations\": [\"120545\", \"150692\", \"162489\", \"168186\", \"172254\", \"179901\", \"184400\", \"189505\", \"191735\", \"217873\", \"221587\", \"226638\", \"228992\", \"230669\", \"241029\", \"243791\", \"249839\", \"255737\", \"272636\"]}","task_split":"paper_retrieval"}
{"document_id":"115867","document_content":"# Pre-training via Paraphrasing\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWe introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the dominant masked language modeling paradigm, where we self-supervise the reconstruction of target text by retrieving a set of related texts (in many languages) and conditioning on them to maximize the likelihood of generating the original. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation. We further show that fine-tuning gives strong performance on a range of discriminative and generative tasks in many languages, making MARGE the most generally applicable pre-training method to date.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15020v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2585\", \"3440\", \"15609\", \"15735\", \"16963\", \"18656\", \"21953\", \"32189\", \"46280\", \"49689\", \"2084\", \"2422\", \"20409\", \"22682\", \"24752\", \"26367\", \"27340\", \"31388\", \"35071\", \"38187\", \"42441\", \"43140\", \"43288\", \"43938\", \"45187\", \"44997\", \"45329\", \"47693\", \"50840\", \"56031\", \"56297\", \"56519\", \"56583\", \"61812\", \"61949\", \"62216\", \"63848\", \"68851\", \"72198\", \"74412\", \"77354\", \"77352\", \"77356\", \"77468\", \"79059\", \"81870\", \"83393\", \"92112\", \"92178\", \"94792\", \"97929\", \"102310\", \"104803\", \"104890\", \"107584\", \"114755\", \"77493\", \"92056\", \"95880\", \"98852\", \"100625\", \"108688\", \"124665\", \"128350\", \"153825\"], \"outgoing_citations\": [\"123394\", \"127883\", \"135112\", \"139638\", \"139854\", \"143089\", \"145957\", \"146003\", \"157109\", \"158621\", \"161377\", \"169205\", \"169415\", \"187236\", \"201070\", \"204715\", \"219083\", \"243803\", \"250505\", \"251816\", \"251974\", \"254855\", \"259706\", \"278226\", \"280486\"]}","task_split":"paper_retrieval"}
{"document_id":"115868","document_content":"# A Unifying Framework for Reinforcement Learning and Planning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nSequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide. At the end of the paper, we compare a variety of well-known planning, model-free and model-based RL algorithms along these dimensions. Altogether, the framework may help provide deeper insight in the algorithmic design space of planning and reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.15009v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"34882\", \"79803\", \"88340\", \"115175\", \"124961\", \"125475\", \"128664\", \"152531\", \"177658\", \"181084\", \"190461\", \"206951\", \"207983\", \"225762\", \"228441\", \"240282\", \"242444\", \"248938\", \"258199\", \"260583\", \"262325\", \"265133\", \"266182\", \"266419\", \"268072\", \"271783\", \"272222\", \"272290\", \"290433\", \"290825\", \"291025\", \"291460\", \"294142\", \"298281\", \"304250\", \"311595\", \"350010\", \"358783\"]}","task_split":"paper_retrieval"}
{"document_id":"115915","document_content":"# Orthogonal Deep Models As Defense Against Black-Box Attacks\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for a plethora of critical applications like disease prognostics and smart surveillance. On the other, deep learning has also been found vulnerable to adversarial attacks, which calls for new techniques to defend deep models against these attacks. Among the attack algorithms, the black-box schemes are of serious practical concern since they only need publicly available knowledge of the targeted model. We carefully analyze the inherent weakness of deep models in black-box settings where the attacker may develop the attack using a model similar to the targeted model. Based on our analysis, we introduce a novel gradient regularization scheme that encourages the internal representation of a deep model to be orthogonal to another, even if the architectures of the two models are similar. Our unique constraint allows a model to concomitantly endeavour for higher accuracy while maintaining near orthogonal alignment of gradients with respect to a reference model. Detailed empirical study verifies that controlled misalignment of gradients under our orthogonality objective significantly boosts a model's robustness against transferable black-box adversarial attacks. In comparison to regular models, the orthogonal models are significantly more robust to a range of $l_p$ norm bounded perturbations. We verify the effectiveness of our technique on a variety of large-scale models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.14856v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"75762\", \"6420\", \"23954\", \"70424\"], \"outgoing_citations\": [\"142286\", \"152898\", \"153528\", \"167393\", \"167650\", \"182040\", \"182197\", \"184154\", \"186260\", \"187557\", \"192115\", \"192406\", \"192766\", \"201251\", \"206693\", \"207929\", \"208940\", \"215081\", \"221144\", \"227266\", \"231454\", \"234520\", \"236179\", \"238512\", \"240752\", \"243277\", \"245441\", \"246146\", \"249303\", \"250297\", \"251795\", \"260963\", \"261323\", \"265949\", \"266879\", \"269735\", \"273409\", \"280873\", \"288416\", \"291899\", \"296867\", \"302937\", \"320493\"]}","task_split":"paper_retrieval"}
{"document_id":"115937","document_content":"# Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation\n## Categories\n- Artificial Intelligence\n## Abstract\nHuman explanation (e.g., in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not only the ground truth but also some form of explanation. However, this kind of human guidance was only investigated in supervised learning tasks, and it remains unclear how to best incorporate this type of human knowledge into deep reinforcement learning. In this paper, we present the first study of using human visual explanations in human-in-the-loop reinforcement learning (HRL). We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative \"good\" or \"bad\" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the model to encode task-relevant features through a context-aware data augmentation that only perturbs irrelevant features in human salient information. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate the performance and sample efficiency of this approach. We show that our method significantly outperforms methods leveraging human explanation that are adapted from supervised learning, and Human-in-the-loop RL baselines that only utilize evaluative feedback.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.14804v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"4639\", \"22157\", \"61817\"], \"outgoing_citations\": [\"22157\", \"43409\", \"93956\", \"98034\", \"109373\", \"116575\", \"116687\", \"127845\", \"128392\", \"146535\", \"147012\", \"164211\", \"165614\", \"172691\", \"194964\", \"199302\", \"205630\", \"212704\", \"229595\", \"251785\", \"254581\", \"263957\", \"271811\", \"275728\"]}","task_split":"paper_retrieval"}
{"document_id":"115984","document_content":"# Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64x64 to 768x768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.14715v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25943\", \"86927\", \"103536\", \"104055\"], \"outgoing_citations\": [\"222220\", \"271531\", \"271944\", \"272225\", \"272395\", \"272909\", \"336175\"]}","task_split":"paper_retrieval"}
{"document_id":"116150","document_content":"# LSBert: A Simple Framework for Lexical Simplification\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nLexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. In this paper, we propose a lexical simplification framework LSBert based on pretrained representation model Bert, that is capable of (1) making use of the wider context when both detecting the words in need of simplification and generating substitue candidates, and (2) taking five high-quality features into account for ranking candidates, including Bert prediction order, Bert-based language model, and the paraphrase database PPDB, in addition to the word frequency and word similarity commonly used in other LS methods. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and obtains obvious improvement compared with these baselines, outperforming the state-of-the-art by 29.8 Accuracy points on three well-known benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.14939v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179990\", \"214400\", \"284853\"]}","task_split":"paper_retrieval"}
{"document_id":"116200","document_content":"# Reinforcement Learning and its Connections with Neuroscience and Psychology\n## Categories\n- Machine Learning\n- Neurons and Cognition\n## Abstract\nReinforcement learning methods have recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker. These algorithms have outperformed humans in several tasks by learning from scratch, using only scalar rewards obtained through interaction with their environment. While there certainly has been considerable independent innovation to produce such results, many core ideas in reinforcement learning are inspired by phenomena in animal learning, psychology and neuroscience. In this paper, we comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision making in the brain. In doing so, we construct a mapping between various classes of modern RL algorithms and specific findings in both neurophysiological and behavioral literature. We then discuss the implications of this observed relationship between RL, neuroscience and psychology and its role in advancing research in both AI and brain science.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.01099v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neurons and Cognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"38914\", \"95616\", \"113549\", \"140901\", \"186313\", \"215976\", \"230702\", \"252249\", \"259693\", \"260583\", \"272222\", \"280870\", \"295435\"]}","task_split":"paper_retrieval"}
{"document_id":"116351","document_content":"# Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nMuch of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes. There is a wealth of expert-curated biomedical domain knowledge stored in knowledge bases and ontologies, but the lack of reliable methods for learning knowledge representation has limited their usefulness in machine learning applications. While text-based representation learning has significantly improved in recent years through advances in natural language processing, attempts to learn biomedical concept embeddings so far have been lacking. A recent family of models called knowledge graph embeddings have shown promising results on general domain knowledge graphs, and we explore their capabilities in the biomedical domain. We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph, provide a benchmark with comparison to existing methods and in-depth discussion on best practices, and make a case for the importance of leveraging the multi-relational nature of knowledge graphs for learning biomedical knowledge representation. The embeddings, code, and materials will be made available to the communitY.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.13774v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"49149\", \"36662\", \"41278\", \"55194\"], \"outgoing_citations\": [\"152591\", \"157837\", \"167753\", \"175331\", \"180848\", \"191942\", \"196794\", \"197408\", \"201341\", \"236766\", \"242267\", \"298095\", \"299194\", \"311834\"]}","task_split":"paper_retrieval"}
{"document_id":"116539","document_content":"# Safe Learning under Uncertain Objectives and Constraints\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Optimization and Control\n## Abstract\nIn this paper, we consider non-convex optimization problems under \\textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including robotics, manufacturing, and medical procedures, where it is infeasible to know or identify all the constraints. Therefore, the parameter space should be explored in a conservative way to ensure that none of the constraints are violated during the optimization process once we start from a safe initialization point. To this end, we develop an algorithm called Reliable Frank-Wolfe (Reliable-FW). Given a general non-convex function and an unknown polytope constraint, Reliable-FW simultaneously learns the landscape of the objective function and the boundary of the safety polytope. More precisely, by assuming that Reliable-FW has access to a (stochastic) gradient oracle of the objective function and a noisy feasibility oracle of the safety polytope, it finds an $\\epsilon$-approximate first-order stationary point with the optimal ${\\mathcal{O}}({1}\/{\\epsilon^2})$ gradient oracle complexity (resp. $\\tilde{\\mathcal{O}}({1}\/{\\epsilon^3})$ (also optimal) in the stochastic gradient setting), while ensuring the safety of all the iterates. Rather surprisingly, Reliable-FW only makes $\\tilde{\\mathcal{O}}(({d^2}\/{\\epsilon^2})\\log 1\/\\delta)$ queries to the noisy feasibility oracle (resp. $\\tilde{\\mathcal{O}}(({d^2}\/{\\epsilon^4})\\log 1\/\\delta)$ in the stochastic gradient setting) where $d$ is the dimension and $\\delta$ is the reliability parameter, tightening the existing bounds even for safe minimization of convex functions. We further specialize our results to the case that the objective function is convex. A crucial component of our analysis is to introduce and apply a technique called geometric shrinkage in the context of safe optimization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.13326v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [\"9602\", \"89610\"], \"outgoing_citations\": [\"146096\", \"150324\", \"152666\", \"184861\", \"218908\", \"289207\", \"295026\", \"298837\"]}","task_split":"paper_retrieval"}
{"document_id":"116568","document_content":"# Rescaling Egocentric Vision\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThis paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version, EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the \"test of time\" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5523\/bris.2g1n6qdydwa9u22shpxqzp0t8m\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"79061\", \"90690\", \"1960\", \"3032\", \"4110\", \"132173\", \"8710\", \"12199\"], \"outgoing_citations\": [\"38250\", \"41117\", \"119347\", \"125153\", \"127156\", \"149967\", \"159916\", \"163906\", \"164400\", \"168078\", \"170617\", \"170707\", \"171242\", \"172394\", \"173856\", \"174896\", \"176053\", \"181897\", \"183562\", \"184611\", \"187780\", \"191393\", \"203523\", \"203911\", \"205750\", \"206525\", \"207239\", \"209140\", \"209299\", \"215860\", \"226384\", \"226832\", \"231686\", \"234223\", \"234325\", \"234583\", \"237402\", \"245503\", \"246521\", \"246970\", \"247497\", \"249567\", \"252968\", \"263051\", \"263838\", \"265650\", \"266053\", \"267523\", \"268891\", \"268934\", \"270325\", \"270479\", \"271891\", \"278436\", \"280328\", \"287286\", \"287541\", \"292421\", \"293801\", \"295117\", \"295591\", \"297769\", \"297863\", \"309231\", \"312703\", \"319169\", \"320761\", \"327491\", \"351741\"]}","task_split":"paper_retrieval"}
{"document_id":"116600","document_content":"# Environment Shaping in Reinforcement Learning using State Abstraction\n## Categories\n- Machine Learning\n## Abstract\nOne of the central challenges faced by a reinforcement learning (RL) agent is to effectively learn a (near-)optimal policy in environments with large state spaces having sparse and noisy feedback signals. In real-world applications, an expert with additional domain knowledge can help in speeding up the learning process via \\emph{shaping the environment}, i.e., making the environment more learner-friendly. A popular paradigm in literature is \\emph{potential-based reward shaping}, where the environment's reward function is augmented with additional local rewards using a potential function. However, the applicability of potential-based reward shaping is limited in settings where (i) the state space is very large, and it is challenging to compute an appropriate potential function, (ii) the feedback signals are noisy, and even with shaped rewards the agent could be trapped in local optima, and (iii) changing the rewards alone is not sufficient, and effective shaping requires changing the dynamics. We address these limitations of potential-based shaping methods and propose a novel framework of \\emph{environment shaping using state abstraction}. Our key idea is to compress the environment's large state space with noisy signals to an abstracted space, and to use this abstraction in creating smoother and more effective feedback signals for the agent. We study the theoretical underpinnings of our abstraction-based environment shaping, and show that the agent's policy learnt in the shaped environment preserves near-optimal behavior in the original environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.13160v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"85542\"], \"outgoing_citations\": [\"134454\", \"134575\", \"161163\", \"161948\", \"183009\", \"183993\", \"213540\", \"230314\", \"231418\", \"240780\", \"274315\", \"276133\"]}","task_split":"paper_retrieval"}
{"document_id":"116618","document_content":"# Distilling Object Detectors with Task Adaptive Regularization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCurrent state-of-the-art object detectors are at the expense of high computational costs and are hard to deploy to low-end devices. Knowledge distillation, which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the promising solutions for model miniaturization. In this paper, we investigate each module of a typical detector in depth, and propose a general distillation framework that adaptively transfers knowledge from teacher to student according to the task specific priors. The intuition is that simply distilling all information from teacher to student is not advisable, instead we should only borrow priors from the teacher model where the student cannot perform well. Towards this goal, we propose a region proposal sharing mechanism to interflow region responses between the teacher and student models. Based on this, we adaptively transfer knowledge at three levels, \\emph{i.e.}, feature backbone, classification head, and bounding box regression head, according to which model performs more reasonably. Furthermore, considering that it would introduce optimization dilemma when minimizing distillation loss and detection loss simultaneously, we propose a distillation decay strategy to help improve model generalization via gradually reducing the distillation penalty. Experiments on widely used detection benchmarks demonstrate the effectiveness of our method. In particular, using Faster R-CNN with FPN as an instantiation, we achieve an accuracy of $39.0\\%$ with Resnet-50 on COCO dataset, which surpasses the baseline $36.3\\%$ by $2.7\\%$ points, and even better than the teacher model with $38.5\\%$ mAP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.13108v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4205\", \"7779\", \"27899\", \"67220\", \"12340\", \"13688\", \"20864\", \"29220\", \"47001\", \"60406\", \"65557\"], \"outgoing_citations\": [\"181769\", \"213776\", \"274312\", \"275577\", \"287183\", \"301197\", \"302867\", \"336175\"]}","task_split":"paper_retrieval"}
{"document_id":"116670","document_content":"# Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nUnderstanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths are found and used for adversarial training. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions and allows input of a shared vehicle feature, showing that inclusion of this module leads to improved trajectory prediction both with and without the presence of a vehicle. Through evaluation on various datasets, we demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LRA.2020.3004324\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"15379\", \"51335\", \"85367\", \"93407\"], \"outgoing_citations\": [\"147880\", \"153754\", \"164246\", \"176524\", \"177460\", \"194147\", \"194576\", \"196132\", \"200588\", \"206321\", \"211345\", \"214313\", \"223299\", \"229266\", \"237350\", \"253435\", \"268891\", \"303203\"]}","task_split":"paper_retrieval"}
{"document_id":"116769","document_content":"# Security and Privacy Preserving Deep Learning\n## Categories\n- Cryptography and Security\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nCommercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete it nor restrict the purposes for which it is used. So, data privacy has been a very important concern for governments and companies these days. It gives rise to a very interesting challenge since on the one hand, we are pushing further and further for high-quality models and accessible data, but on the other hand, we need to keep data safe from both intentional and accidental leakage. The more personal the data is it is more restricted it means some of the most important social issues cannot be addressed using machine learning because researchers do not have access to proper training data. But by learning how to machine learning that protects privacy we can make a huge difference in solving many social issues like curing disease etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, we introduce differential privacy, which ensures that different kinds of statistical analyses dont compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.12698v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210868\", \"322655\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"116853","document_content":"# Ecological Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMuch of the current work on reinforcement learning studies episodic settings, where the agent is reset between trials to an initial state distribution, often with well-shaped reward functions. Non-episodic settings, where the agent must learn through continuous interaction with the world without resets, and where the agent receives only delayed and sparse reward signals, is substantially more difficult, but arguably more realistic considering real-world environments do not present the learner with a convenient \"reset mechanism\" and easy reward shaping. In this paper, instead of studying algorithmic improvements that can address such non-episodic and sparse reward settings, we instead study the kinds of environment properties that can make learning under such conditions easier. Understanding how properties of the environment impact the performance of reinforcement learning agents can help us to structure our tasks in ways that make learning tractable. We first discuss what we term \"environment shaping\" -- modifications to the environment that provide an alternative to reward shaping, and may be easier to implement. We then discuss an even simpler property that we refer to as \"dynamism,\" which describes the degree to which the environment changes independent of the agent's actions and can be measured by environment transition entropy. Surprisingly, we find that even this property can substantially alleviate the challenges associated with non-episodic RL in sparse reward settings. We provide an empirical evaluation on a set of new tasks focused on non-episodic learning with sparse rewards. Through this study, we hope to shift the focus of the community towards analyzing how properties of the environment can affect learning and the ultimate type of behavior that is learned via RL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.12478v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2206\", \"40573\", \"9444\", \"23006\", \"32776\", \"82278\", \"111210\"], \"outgoing_citations\": [\"128799\", \"158110\", \"200920\", \"203756\", \"212299\", \"218770\", \"231351\", \"231847\", \"240506\", \"241499\", \"244294\", \"250066\", \"253149\", \"253658\", \"261017\", \"261780\", \"265568\", \"266182\", \"269253\", \"271375\", \"278195\", \"280457\", \"282696\", \"289913\", \"291460\", \"298281\", \"358578\"]}","task_split":"paper_retrieval"}
{"document_id":"117100","document_content":"# An Opportunistic Bandit Approach for User Interface Experimentation\n## Categories\n- Machine Learning\n## Abstract\nFacing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. User experience is playing an essential role in this competition, and retailers are continuously experimenting and optimizing their user interface for better user experience. The cost of experimentation is dominated by the opportunity cost of providing a suboptimal service to the customers. Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data. In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features. We achieve significant regret reduction by mitigating costly exploration and providing extra contextual information that helps to guide the testing process. Moreover, we analyze the advantages and challenges of using opportunistic bandits for online retail experimentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11873v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"213358\", \"256192\", \"333185\"]}","task_split":"paper_retrieval"}
{"document_id":"117108","document_content":"# Labeling Explicit Discourse Relations using Pre-trained Language Models\n## Categories\n- Computation and Language\n## Abstract\nLabeling explicit discourse relations is one of the most challenging sub-tasks of the shallow discourse parsing where the goal is to identify the discourse connectives and the boundaries of their arguments. The state-of-the-art models achieve slightly above 45% of F-score by using hand-crafted features. The current paper investigates the efficacy of the pre-trained language models in this task. We find that the pre-trained language models, when finetuned, are powerful enough to replace the linguistic features. We evaluate our model on PDTB 2.0 and report the state-of-the-art results in the extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11852v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"258812\"]}","task_split":"paper_retrieval"}
{"document_id":"117167","document_content":"# Match$^2$: A Matching over Matching Model for Similar Question Identification\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nCommunity Question Answering (CQA) has become a primary means for people to acquire knowledge, where people are free to ask questions or submit answers. To enhance the efficiency of the service, similar question identification becomes a core task in CQA which aims to find a similar question from the archived repository whenever a new question is asked. However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i.e., there could be different ways to ask a same question or different questions sharing similar expressions. To alleviate this problem, it is natural to involve the existing answers for the enrichment of the archived questions. Traditional methods typically take a one-side usage, which leverages the answer as some expanded representation of the corresponding question. Unfortunately, this may introduce unexpected noises into the similarity computation since answers are often long and diverse, leading to inferior performance. In this work, we propose a two-side usage, which leverages the answer as a bridge of the two questions. The key idea is based on our observation that similar questions could be addressed by similar parts of the answer while different questions may not. In other words, we can compare the matching patterns of the two questions over the same answer to measure their similarity. In this way, we propose a novel matching over matching model, namely Match$^2$, which compares the matching patterns between two question-answer pairs for similar question identification. Empirical experiments on two benchmark datasets demonstrate that our model can significantly outperform previous state-of-the-art methods on the similar question identification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11719v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [\"894\", \"64447\", \"34835\"], \"outgoing_citations\": [\"152460\", \"173474\", \"187337\", \"197416\", \"215208\", \"256098\", \"274120\", \"284357\", \"294532\", \"297950\", \"302507\"]}","task_split":"paper_retrieval"}
{"document_id":"117221","document_content":"# From Predictions to Decisions: Using Lookahead Regularization\n## Categories\n- Machine Learning\n## Abstract\nMachine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks. But when deployed, learned models also affect how users act in order to improve outcomes, whether predicted or real. The standard approach to learning is agnostic to induced user actions and provides no guarantees as to the effect of actions. We provide a framework for learning predictors that are both accurate and promote good actions. For this, we introduce look-ahead regularization which, by anticipating user actions, encourages predictive models to also induce actions that improve outcomes. This regularization carefully tailors the uncertainty estimates governing confidence in this improvement to the distribution of model-induced actions. We report the results of experiments on real and synthetic data that show the effectiveness of this approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11638v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"310699\", \"13183\", \"65796\", \"67489\", \"68791\", \"89973\"], \"outgoing_citations\": [\"89229\", \"140545\", \"157045\", \"159440\", \"160243\", \"162018\", \"163282\", \"182179\", \"185167\", \"211803\", \"220267\", \"220343\", \"224674\", \"252103\", \"252733\", \"253184\", \"290305\", \"317469\"]}","task_split":"paper_retrieval"}
{"document_id":"117350","document_content":"# Moore's Paradox and the logic of belief\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n## Abstract\nMoores Paradox is a test case for any formal theory of belief. In Knowledge and Belief, Hintikka developed a multimodal logic for statements that express sentences containing the epistemic notions of knowledge and belief. His account purports to offer an explanation of the paradox. In this paper I argue that Hintikkas interpretation of one of the doxastic operators is philosophically problematic and leads to an unnecessarily strong logical system. I offer a weaker alternative that captures in a more accurate way our logical intuitions about the notion of belief without sacrificing the possibility of providing an explanation for problematic cases such as Moores Paradox.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11363v1\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"117420","document_content":"# Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- I.2.7\n## Abstract\nChatbot is a technology that is used to mimic human behavior using natural language. There are different types of Chatbot that can be used as conversational agent in various business domains in order to increase the customer service and satisfaction. For any business domain, it requires a knowledge base to be built for that domain and design an information retrieval based system that can respond the user with a piece of documentation or generated sentences. The core component of a Chatbot is Natural Language Understanding (NLU) which has been impressively improved by deep learning methods. But we often lack such properly built NLU modules and requires more time to build it from scratch for high quality conversations. This may encourage fresh learners to build a Chatbot from scratch with simple architecture and using small dataset, although it may have reduced functionality, rather than building high quality data driven methods. This research focuses on Named Entity Recognition (NER) and Intent Classification models which can be integrated into NLU service of a Chatbot. Named entities will be inserted manually in the knowledge base and automatically detected in a given sentence. The NER model in the proposed architecture is based on artificial neural network which is trained on manually created entities and evaluated using CoNLL-2003 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2007.04248v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"266390\", \"296702\", \"297150\", \"302500\", \"310903\", \"316328\", \"328698\", \"342679\"]}","task_split":"paper_retrieval"}
{"document_id":"117426","document_content":"# Compositional Learning of Image-Text Query for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nIn this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications. For instance, a user of an E-Commerce platform is interested in buying a dress, which should look similar to her friend's dress, but the dress should be of white color with a ribbon sash. In this case, we would like the algorithm to retrieve some dresses with desired modifications in the query dress. We propose an autoencoder based model, ComposeAE, to learn the composition of image and text query for retrieving images. We adopt a deep metric learning approach and learn a metric that pushes composition of source image and text query closer to the target images. We also propose a rotational symmetry constraint on the optimization problem. Our approach is able to outperform the state-of-the-art method TIRG \\cite{TIRG} on three benchmark datasets, namely: MIT-States, Fashion200k and Fashion IQ. In order to ensure fair comparison, we introduce strong baselines by enhancing TIRG method. To ensure reproducibility of the results, we publish our code here: \\url{https:\/\/github.com\/ecom-research\/ComposeAE}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11149v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"4896\", \"9431\", \"102580\", \"43864\", \"57769\"], \"outgoing_citations\": [\"136066\", \"157175\", \"168851\", \"183623\", \"205616\", \"233661\", \"237654\", \"238205\", \"250405\", \"255134\", \"259403\", \"260926\", \"264533\", \"269977\", \"270894\", \"303011\", \"303106\", \"316785\", \"320016\"]}","task_split":"paper_retrieval"}
{"document_id":"117442","document_content":"# A general framework for defining and optimizing robustness\n## Categories\n- Machine Learning\n## Abstract\nRobustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. Therefore, in this paper, we propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our robustness concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy, and that it should be defined in purely mathematical terms without reliance on algorithmic procedures for its measurement. We develop a very general robustness framework that is applicable to any type of classification model, and that encompasses relevant robustness concepts for investigations ranging from safety against adversarial attacks to transferability of models to new domains. For two prototypical, distinct robustness objectives we then propose new learning approaches based on neural network co-training strategies for obtaining image classifiers optimized for these respective objectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11122v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129767\", \"146773\", \"149671\", \"152544\", \"173558\", \"182275\", \"186564\", \"187936\", \"188450\", \"201060\", \"207548\", \"222222\", \"229886\", \"233055\", \"238640\", \"238913\", \"243686\", \"251596\", \"265635\", \"267392\", \"267761\"]}","task_split":"paper_retrieval"}
{"document_id":"117480","document_content":"# Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nThe cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models. In this paper, we present a new training pipeline boosting the conventional active learning approach targeting label-efficient learning to resolve the mentioned problem. Existing active learning methods only focus on selecting a set of informative samples under a labeling budget. One step further, we suggest that the training efficiency can be further improved by utilizing the unlabeled samples, exceeding the labeling budget, by introducing sophisticatedly configured unsupervised loss complementing supervised loss effectively. We propose new unsupervised loss based on consistency regularization, and we configure appropriate augmentation techniques for utterances to adopt consistency regularization in the automatic speech recognition task. From the qualitative and quantitative experiments on the real-world dataset and under real-usage scenarios, we show that the proposed training pipeline can boost the efficacy of active learning approaches, thus successfully reducing a sustainable amount of human labeling cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.11021v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"15754\"], \"outgoing_citations\": [\"130105\", \"154919\", \"155439\", \"172567\", \"187087\", \"196117\", \"248246\", \"259604\", \"278324\", \"290407\", \"308468\", \"310601\", \"311660\"]}","task_split":"paper_retrieval"}
{"document_id":"117512","document_content":"# Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nExisting studies for automated melanoma diagnosis are based on single-time point images of lesions. However, melanocytic lesions de facto are progressively evolving and, moreover, benign lesions can progress into malignant melanoma. Ignoring cross-time morphological changes of lesions thus may lead to misdiagnosis in borderline cases. Based on the fact that dermatologists diagnose ambiguous skin lesions by evaluating the dermoscopic changes over time via follow-up examination, in this study, we propose an automated framework for melanoma diagnosis using sequential dermoscopic images. To capture the spatio-temporal characterization of dermoscopic evolution, we construct our model in a two-stream network architecture which capable of simultaneously learning appearance representations of individual lesions while performing temporal reasoning on both raw pixels difference and abstract features difference. We collect 184 cases of serial dermoscopic image data, which consists of histologically confirmed 92 benign lesions and 92 melanoma lesions, to evaluate the effectiveness of the proposed method. Our model achieved AUC of 74.34%, which is ~8% higher than that of only using single images and ~6% higher than the widely used sequence learning model based on LSTM.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.10950v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"247535\", \"305615\", \"315200\"]}","task_split":"paper_retrieval"}
{"document_id":"117654","document_content":"# Explainable and Discourse Topic-aware Neural Language Understanding\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMarrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semantics in language models, existing approaches incorporate latent document topic proportions and ignore topical discourse in sentences of the document. This work extends the line of research by additionally introducing an explainable topic representation in language understanding, obtained from a set of key terms correspondingly for each latent topic of the proportion. Moreover, we retain sentence-topic associations along with document-topic association by modeling topical discourse for every sentence in the document. We present a novel neural composite language model that exploits both the latent and explainable topics along with topical discourse at sentence-level in a joint learning framework of topic and language models. Experiments over a range of tasks such as language modeling, word sense disambiguation, document classification, retrieval and text generation demonstrate ability of the proposed model in improving language understanding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.10632v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"72957\", \"22097\"], \"outgoing_citations\": [\"214884\", \"217762\", \"232355\", \"246422\", \"267953\", \"281136\", \"303029\"]}","task_split":"paper_retrieval"}
{"document_id":"117810","document_content":"# Quantifying Challenges in the Application of Graph Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nGraph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current approaches including shallow embeddings and Graph Neural Networks have mostly been tested with node classification and link prediction tasks. In this work, we provide an application oriented perspective to a set of popular embedding approaches and evaluate their representational power with respect to real-world graph properties. We implement an extensive empirical data-driven framework to challenge existing norms regarding the expressive power of embedding approaches in graphs with varying patterns along with a theoretical analysis of the limitations we discovered in this process. Our results suggest that \"one-to-fit-all\" GRL approaches are hard to define in real-world scenarios and as new methods are being introduced they should be explicit about their ability to capture graph properties and their applicability in datasets with non-trivial structural differences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.10252v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"87832\"], \"outgoing_citations\": [\"138999\", \"150166\", \"175819\", \"176579\", \"184286\", \"187949\", \"194588\", \"196221\", \"215435\", \"229333\", \"239929\", \"253780\", \"265739\"]}","task_split":"paper_retrieval"}
{"document_id":"117819","document_content":"# A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- Human-Computer Interaction\n## Abstract\nIn this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.10228v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"50288\"], \"outgoing_citations\": [\"241007\"]}","task_split":"paper_retrieval"}
{"document_id":"118053","document_content":"# Iterative Edit-Based Unsupervised Sentence Simplification\n## Categories\n- Computation and Language\n## Abstract\nWe present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09639v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"3392\", \"5053\", \"10902\", \"127662\", \"19800\", \"21796\", \"26460\", \"92547\", \"113240\", \"127065\", \"167980\"], \"outgoing_citations\": [\"127065\", \"163045\", \"167980\", \"179990\", \"192166\", \"210121\", \"212887\", \"213824\", \"214312\", \"216792\", \"218912\", \"227488\", \"235238\", \"270048\", \"308756\"]}","task_split":"paper_retrieval"}
{"document_id":"118087","document_content":"# Isometric Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nMany tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph. Geometric techniques to extract such representations have poor scaling over large graph size, and recent advances in Graph Neural Network (GNN) algorithms have limited ability to reflect graph distance information beyond the first degree neighborhood. To enable this highly desired capability, we propose a technique to learn Isometric Graph Neural Networks (IGNN), which requires changing the input representation space and loss function to enable any GNN algorithm to generate representations that reflect distances between nodes. We experiment with the isometric technique on several GNN architectures for modeling multiple prediction tasks on multiple datasets. In addition to an improvement in AUC-ROC as high as $43\\%$ in these experiments, we observe a consistent and substantial improvement as high as 400% in Kendall's Tau (KT), a measure that directly reflects distance information, demonstrating that the learned embeddings do account for graph distances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09554v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"142057\", \"145997\", \"159292\", \"159311\", \"160169\", \"163822\", \"181255\", \"183719\", \"184286\", \"214434\", \"238273\", \"247270\", \"255737\", \"261201\", \"302943\", \"341746\"]}","task_split":"paper_retrieval"}
{"document_id":"118122","document_content":"# Walk Message Passing Neural Networks and Second-Order Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nThe expressive power of message passing neural networks (MPNNs) is known to match the expressive power of the 1-dimensional Weisfeiler-Leman graph (1-WL) isomorphism test. To boost the expressive power of MPNNs, a number of graph neural network architectures have recently been proposed based on higher-dimensional Weisfeiler-Leman tests. In this paper we consider the two-dimensional (2-WL) test and introduce a new type of MPNNs, referred to as $\\ell$-walk MPNNs, which aggregate features along walks of length $\\ell$ between vertices. We show that $2$-walk MPNNs match 2-WL in expressive power. More generally, $\\ell$-walk MPNNs, for any $\\ell\\geq 2$, are shown to match the expressive power of the recently introduced $\\ell$-walk refinement procedure (W[$\\ell$]). Based on a correspondence between 2-WL and W[$\\ell$], we observe that $\\ell$-walk MPNNs and $2$-walk MPNNs have the same expressive power, i.e., they can distinguish the same pairs of graphs, but $\\ell$-walk MPNNs can possibly distinguish pairs of graphs faster than $2$-walk MPNNs. When it comes to concrete learnable graph neural network (GNN) formalisms that match 2-WL or W[$\\ell$] in expressive power, we consider second-order graph neural networks that allow for non-linear layers. In particular, to match W[$\\ell$] in expressive power, we allow $\\ell-1$ matrix multiplications in each layer. We propose different versions of second-order GNNs depending on the type of features (i.e., coming from a countable set, or coming from an uncountable set) as this affects the number of dimensions needed to represent the features. Our results indicate that increasing non-linearity in layers by means of allowing multiple matrix multiplications does not increase expressive power. At the very best, it results in a faster distinction of input graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09499v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"68697\", \"110102\"], \"outgoing_citations\": [\"132809\", \"137772\", \"143081\", \"143456\", \"184286\", \"187289\", \"201584\", \"204871\", \"215435\", \"359939\"]}","task_split":"paper_retrieval"}
{"document_id":"118201","document_content":"# Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nWhile Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures. On the other hand, there is significant empirical evidence, e.g. in network science and bioinformatics, that substructures are often intimately related to downstream tasks. To this end, we propose \"Graph Substructure Networks\" (GSN), a topologically-aware message passing scheme based on substructure encoding. We theoretically analyse the expressive power of our architecture, showing that it is strictly more expressive than the WL test, and provide sufficient conditions for universality. Importantly, we do not attempt to adhere to the WL hierarchy; this allows us to retain multiple attractive properties of standard GNNs such as locality and linear network complexity, while being able to disambiguate even hard instances of graph isomorphism. We perform an extensive experimental evaluation on graph classification and regression tasks and obtain state-of-the-art results in diverse real-world settings including molecular graphs and social networks. The code is publicly available at https:\/\/github.com\/gbouritsas\/graph-substructure-networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09252v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"51256\", \"52113\", \"3222\", \"14352\", \"16180\", \"18144\", \"18515\", \"67885\", \"69149\", \"2056\", \"4995\", \"7776\", \"7914\", \"13620\", \"13814\", \"15396\", \"18304\", \"19609\", \"23232\", \"33376\", \"36248\", \"37535\", \"39957\", \"42645\", \"42699\", \"43757\", \"44601\", \"49388\", \"49956\", \"55114\", \"57782\", \"58289\", \"59866\", \"64367\", \"65206\", \"65828\", \"68697\", \"73530\", \"74440\", \"88245\", \"90641\", \"134727\"], \"outgoing_citations\": [\"43757\", \"96209\", \"101873\", \"103085\", \"111646\", \"115834\", \"116959\", \"118149\", \"127256\", \"127525\", \"131562\", \"137268\", \"137772\", \"138212\", \"138999\", \"140399\", \"142226\", \"143081\", \"143456\", \"149610\", \"150166\", \"151458\", \"156634\", \"159599\", \"160129\", \"168353\", \"168870\", \"170705\", \"177249\", \"183447\", \"183719\", \"184286\", \"184749\", \"186624\", \"187327\", \"192622\", \"196221\", \"196255\", \"201448\", \"203452\", \"204871\", \"210730\", \"210734\", \"210798\", \"215435\", \"215834\", \"227067\", \"227402\", \"228663\", \"229936\", \"239884\", \"240991\", \"242322\", \"242430\", \"243382\", \"244016\", \"244510\", \"245763\", \"249839\", \"252006\", \"264342\", \"266955\", \"269273\", \"269723\", \"269770\", \"271216\", \"277109\", \"277250\", \"277798\", \"279026\", \"279575\", \"283078\", \"292407\", \"303863\", \"305707\", \"318528\", \"323312\", \"357242\"]}","task_split":"paper_retrieval"}
{"document_id":"118223","document_content":"# AVLnet: Learning Audio-Visual Language Representations from Instructional Videos\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Multimedia\n- Sound\n- Audio and Speech Processing\n## Abstract\nCurrent methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognition (ASR) transcripts. In this work, we introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared audio-visual embedding space directly from raw video inputs. To circumvent the need for text annotation, we learn audio-visual representations from randomly segmented video clips and their raw audio waveforms. We train AVLnet on HowTo100M, a large corpus of publicly available instructional videos, and evaluate on image retrieval and video retrieval tasks, achieving state-of-the-art performance. We perform analysis of AVLnet's learned representations, showing our model utilizes speech and natural sounds to learn audio-visual concepts. Further, we propose a tri-modal model that jointly processes raw audio, video, and text captions from videos to learn a multi-modal semantic embedding space useful for text-video retrieval. Our code, data, and trained models will be released at avlnet.csail.mit.edu","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09199v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.MM\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Multimedia\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"4359\", \"43252\", \"53303\", \"151832\", \"5726\", \"5927\", \"7454\", \"12778\", \"10669\", \"16315\", \"50518\", \"53510\", \"58270\", \"58876\", \"59021\", \"60135\", \"62053\", \"62053\", \"62318\", \"67133\", \"96230\", \"81067\", \"87723\", \"115367\"], \"outgoing_citations\": [\"50518\", \"53303\", \"58270\", \"85426\", \"90581\", \"115367\", \"119557\", \"138177\", \"151327\", \"153776\", \"154976\", \"159745\", \"166011\", \"167882\", \"172394\", \"173627\", \"180920\", \"181897\", \"182790\", \"188114\", \"189824\", \"190275\", \"190511\", \"190915\", \"192531\", \"194508\", \"196110\", \"209132\", \"210764\", \"211986\", \"218780\", \"221984\", \"225313\", \"226232\", \"228175\", \"230159\", \"232070\", \"232882\", \"236080\", \"236252\", \"236468\", \"236717\", \"236772\", \"237062\", \"237765\", \"242192\", \"247148\", \"247757\", \"248328\", \"249241\", \"254061\", \"259415\", \"260141\", \"263185\", \"265701\", \"270325\", \"272075\", \"274549\", \"275409\", \"279987\", \"281762\", \"285985\", \"295738\", \"298750\", \"300855\", \"303546\", \"310322\", \"319938\"]}","task_split":"paper_retrieval"}
{"document_id":"118238","document_content":"# AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nGenerative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. The intrinsic problem complexity poses the challenge to enhance the performance of generative networks. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. To this end, we propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN. The searching process is formalized as solving a bi-level minimax optimization problem, in which the outer-level objective aims for seeking a suitable network architecture towards pure Nash Equilibrium conditioned on the generator and the discriminator network parameters optimized with a traditional GAN loss in the inner level. The entire optimization performs a first-order method by alternately minimizing the two-level objective in a fully differentiable manner, enabling architecture search to be completed in an enormous search space. Extensive experiments on CIFAR-10 and STL-10 datasets show that our algorithm can obtain high-performing architectures only with 3-GPU hours on a single GPU in the search space comprised of approximate 2 ? 1011 possible configurations. We also provide a comprehensive analysis on the behavior of the searching process and the properties of searched architectures, which would benefit further research on architectures for generative models. Pretrained models and codes are available at https:\/\/github.com\/yuesongtian\/AlphaGAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.09134v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"69071\", \"4287\", \"6458\", \"73051\", \"107932\", \"3483\", \"22580\", \"84616\", \"95318\"], \"outgoing_citations\": [\"108736\", \"111269\", \"113323\", \"117601\", \"119380\", \"134688\", \"142822\", \"152772\", \"152821\", \"159067\", \"159636\", \"165713\", \"168511\", \"172261\", \"177483\", \"179119\", \"183056\", \"190406\", \"193050\", \"193623\", \"194037\", \"195141\", \"198231\", \"200533\", \"204863\", \"210224\", \"226982\", \"231164\", \"241941\", \"242806\", \"243239\", \"244327\", \"248519\", \"248564\", \"248731\", \"249421\", \"251701\", \"258332\", \"265970\", \"272030\", \"275597\", \"280600\", \"281175\", \"290679\", \"311413\"]}","task_split":"paper_retrieval"}
{"document_id":"118362","document_content":"# Model-based Adversarial Meta-Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMeta-reinforcement learning (meta-RL) aims to learn from multiple training tasks the ability to adapt efficiently to unseen test tasks. Despite the success, existing meta-RL algorithms are known to be sensitive to the task distribution shift. When the test task distribution is different from the training task distribution, the performance may degrade significantly. To address this issue, this paper proposes Model-based Adversarial Meta-Reinforcement Learning (AdMRL), where we aim to minimize the worst-case sub-optimality gap -- the difference between the optimal return and the return that the algorithm achieves after adaptation -- across all tasks in a family of tasks, with a model-based approach. We propose a minimax objective and optimize it by alternating between learning the dynamics model on a fixed task and finding the adversarial task for the current model -- the task for which the policy induced by the model is maximally suboptimal. Assuming the family of tasks is parameterized, we derive a formula for the gradient of the suboptimality with respect to the task parameters via the implicit function theorem, and show how the gradient estimator can be efficiently implemented by the conjugate gradient method and a novel use of the REINFORCE estimator. We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy in the worst-case performance over all tasks, the generalization power to out-of-distribution tasks, and in training and test time sample efficiency, over existing state-of-the-art meta-RL algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.08875v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"22537\", \"68934\", \"78473\", \"22326\", \"46780\", \"74675\", \"76804\", \"88020\", \"89476\"], \"outgoing_citations\": [\"119122\", \"130759\", \"138836\", \"141455\", \"143630\", \"161071\", \"162581\", \"164077\", \"168858\", \"176597\", \"177658\", \"179805\", \"179948\", \"186059\", \"186101\", \"192827\", \"194494\", \"205477\", \"212299\", \"214135\", \"221473\", \"225108\", \"225762\", \"228299\", \"229890\", \"237229\", \"238343\", \"240471\", \"240495\", \"259045\", \"280202\", \"280870\", \"283289\"]}","task_split":"paper_retrieval"}
{"document_id":"118364","document_content":"# End-to-End Code Switching Language Models for Automatic Speech Recognition\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn this paper, we particularly work on the code-switched text, one of the most common occurrences in the bilingual communities across the world. Due to the discrepancies in the extraction of code-switched text from an Automated Speech Recognition(ASR) module, and thereby extracting the monolingual text from the code-switched text, we propose an approach for extracting monolingual text using Deep Bi-directional Language Models(LM) such as BERT and other Machine Translation models, and also explore different ways of extracting code-switched text from the ASR model. We also explain the robustness of the model by comparing the results of Perplexity and other different metrics like WER, to the standard bi-lingual text output without any external information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.08870v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148010\", \"259563\", \"276465\", \"282929\", \"302948\", \"307902\", \"310601\", \"320874\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"118420","document_content":"# COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nClinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.08765v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"34202\", \"47511\", \"51946\", \"70767\"], \"outgoing_citations\": [\"145697\", \"146018\", \"153949\", \"191942\", \"213345\", \"238126\", \"244209\", \"279648\", \"279891\", \"301120\", \"309148\", \"316165\"]}","task_split":"paper_retrieval"}
{"document_id":"118540","document_content":"# SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nDeep neural networks (DNNs) have been recently found popular for image captioning problems in remote sensing (RS). Existing DNN based approaches rely on the availability of a training set made up of a high number of RS images with their captions. However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from the image domain to the language domain. To overcome this limitation, in this paper, we present a novel Summarization Driven Remote Sensing Image Captioning (SD-RSIC) approach. The proposed approach consists of three main steps. The first step obtains the standard image captions by jointly exploiting convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. The second step, unlike the existing RS image captioning methods, summarizes the ground-truth captions of each training image into a single caption by exploiting sequence to sequence neural networks and eliminates the redundancy present in the training set. The third step automatically defines the adaptive weights associated to each RS image to combine the standard captions with the summarized captions based on the semantic content of the image. This is achieved by a novel adaptive weighting strategy defined in the context of LSTM networks. Experimental results obtained on the RSCID, UCM-Captions and Sydney-Captions datasets show the effectiveness of the proposed approach compared to the state-of-the-art RS image captioning approaches. The code of the proposed approach is publicly available at https:\/\/gitlab.tubit.tu-berlin.de\/rsim\/SD-RSIC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TGRS.2020.3031111\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"56885\"], \"outgoing_citations\": [\"215181\", \"246870\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"118615","document_content":"# Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention\n## Categories\n- Machine Learning\n- Cryptography and Security\n- I.2.2\n## Abstract\nPrevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adopted to predict stroke. Because of privacy concerns, patient data are stored in distributed electronic health record (EHR) databases, voluminous clinical datasets, which prevent patient data from being aggregated and restrains AI technology to boost the accuracy of stroke prediction with centralized training data. In this work, our scientists and engineers propose a privacy-preserving scheme to predict the risk of stroke and deploy our federated prediction model on cloud servers. Our system of federated prediction model asynchronously supports any number of client connections and arbitrary local gradient iterations in each communication round. It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting. With the privacy-preserving mechanism, our federated prediction model trains over all the healthcare data from hospitals in a certain city without actual data sharing among them. Therefore, it is not only secure but also more accurate than any single prediction model that trains over the data only from one single hospital. Especially for small hospitals with few confirmed stroke cases, our federated model boosts model performance by 10%~20% in several machine learning metrics. To help stroke experts comprehend the advantage of our prediction system more intuitively, we developed a mobile app that collects the key information of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.10517v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"I.2.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"I.2.2\"], \"incoming_citations\": [\"18455\", \"46150\", \"109503\"], \"outgoing_citations\": [\"128919\", \"163734\", \"167459\", \"214805\", \"283045\"]}","task_split":"paper_retrieval"}
{"document_id":"118809","document_content":"# Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction\n## Categories\n- Social and Information Networks\n## Abstract\nModeling user engagement dynamics on social media has compelling applications in user-persona detection and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large number of discussions happen on platforms that either lack any reliable social network or reveal only partially the inter-user ties (Reddit, Stackoverflow). Many approaches require observing a discussion for some considerable period before they can make useful predictions. In real-time streaming scenarios, observations incur costs. Lastly, most models do not capture complex interactions between exogenous events (such as news articles published externally) and in-network effects (such as follow-up discussions on Reddit) to determine engagement levels.   To address the three limitations noted above, we propose a novel framework, ChatterNet, which, to our knowledge, is the first that can model and predict user engagement without considering the underlying user network. Given streams of timestamped news articles and discussions, the task is to observe the streams for a short period leading up to a time horizon, then predict chatter: the volume of discussions through a specified period after the horizon. ChatterNet processes text from news and discussions using a novel time-evolving recurrent network architecture that captures both temporal properties within news and discussions, as well as the influence of news on discussions. We report on extensive experiments using a two-month-long discussion corpus of Reddit, and a contemporaneous corpus of online news articles from the Common Crawl. ChatterNet shows considerable improvements beyond recent state-of-the-art models of engagement prediction. Detailed studies controlling observation and prediction windows, over 43 different subreddits, yield further useful insights.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.07812v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\"], \"incoming_citations\": [\"42469\", \"21681\", \"39225\", \"95568\"], \"outgoing_citations\": [\"172292\", \"280305\", \"295555\", \"295560\", \"311559\", \"331356\", \"360421\", \"361037\"]}","task_split":"paper_retrieval"}
{"document_id":"118971","document_content":"# Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe present a new knowledge-base of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old's vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet. The knowledge base is available at https:\/\/allenai.org\/data\/haspartkb","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.07510v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23327\", \"6217\", \"75837\", \"79363\", \"89656\"], \"outgoing_citations\": [\"127539\", \"148999\", \"166140\", \"170992\", \"177173\", \"184371\", \"200351\"]}","task_split":"paper_retrieval"}
{"document_id":"119207","document_content":"# Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature.   In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. We collect large-scale data from 136k smartphones that can faithfully reflect heterogeneity in real-world settings. We also build a heterogeneity-aware FL platform that complies with the standard FL protocol but with heterogeneity in consideration. Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings. Results show that heterogeneity causes non-trivial performance degradation in FL, including up to 9.2% accuracy drop, 2.32x lengthened training time, and undermined fairness. Furthermore, we analyze potential impact factors and find that device failure and participant bias are two potential factors for performance degradation. Our study provides insightful implications for FL practitioners. On the one hand, our findings suggest that FL algorithm designers consider necessary heterogeneity during the evaluation. On the other hand, our findings urge system providers to design specific mechanisms to mitigate the impacts of heterogeneity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.06983v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"24894\", \"4247\", \"5867\", \"14535\", \"35424\", \"47655\"], \"outgoing_citations\": [\"75715\", \"92253\", \"109503\", \"127483\", \"140085\", \"153498\", \"157805\", \"164468\", \"164627\", \"184652\", \"187822\", \"200347\", \"200704\", \"206873\", \"207462\", \"210718\", \"210868\", \"210960\", \"214523\", \"215746\", \"221186\", \"226146\", \"232524\", \"234581\", \"248610\", \"248611\", \"252956\", \"265073\", \"266372\", \"268702\", \"283045\"]}","task_split":"paper_retrieval"}
{"document_id":"119239","document_content":"# Longitudinal Self-Supervised Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nMachine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to identify changes and consistencies across the multiple MRIs acquired of each individual over time. Specifically, we propose a new definition of disentanglement by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation. Then, factors that evolve across acquisitions of longitudinal sequences are disentangled from that mapping by self-supervised learning in such a way that changes in a single factor induce change along one direction in the representation space. We implement this model, named Longitudinal Self-Supervised Learning (LSSL), via a standard autoencoding structure with a cosine loss to disentangle brain age from the image representation. We apply LSSL to two longitudinal neuroimaging studies to highlight its strength in extracting the brain-age information from MRI and revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. Moreover, the representations learned by LSSL facilitate supervised classification by recording faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.06930v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"34868\", \"64925\", \"118617\", \"160620\", \"282154\"]}","task_split":"paper_retrieval"}
{"document_id":"119298","document_content":"# Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19\n## Categories\n- Computers and Society\n- Cryptography and Security\n- Social and Information Networks\n## Abstract\nTechnology is uniquely positioned to help us analyze large amounts of information to provide valuable insight during widespread public health concerns, like the ongoing COVID-19 pandemic. In fact, information technology companies like Apple and Google have recently launched tools for contact tracing-the ability to process location data to determine the people who have been in contact with a possible patient, in order to contain the spread of the virus. While China and Singapore have successfully led the effort, more and more countries are now implementing such surveillance systems, raising potential privacy concerns about this long term surveillance. For example, it is not clear what happens to the information post-pandemic because people are more likely to share their information during a global crisis without governments having to elaborate on their data policies. Digital Ethnography on Twitter, which has over 330 million users worldwide, with a majority in the United States where the pandemic has the worst effects provides a unique opportunity to learn about real-time opinions of the general public about current affairs in a rather naturalistic setting. Consequently, it might be useful to highlight the privacy concerns of users, should they exist, through analysis of Twitter data and information sharing policies during unprecedented public health outbreaks. This will allow governments to protect their citizens both during and after health emergencies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.13140\/RG.2.2.14162.38083\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CR\", \"cs.SI\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Cryptography and Security\", \"Social and Information Networks\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"119520","document_content":"# Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context\n## Categories\n- Computation and Language\n## Abstract\nWe present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input-output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.06259v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"125538\", \"85257\"], \"outgoing_citations\": [\"176047\", \"179900\", \"210373\", \"216520\", \"268567\"]}","task_split":"paper_retrieval"}
{"document_id":"119548","document_content":"# Exploration by Maximizing R\u00e9nyi Entropy for Reward-Free RL Framework\n## Categories\n- Machine Learning\n## Abstract\nExploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by interacting with a reward-free environment and collects a dataset of transitions by executing the policy. In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment. This framework is suitable for the meta RL setting where there are many reward functions of interest. In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. The success of using Renyi entropy as the objective results from its encouragement to explore the hard-to-reach state-actions. We further deduce a policy gradient formulation for this objective and design a practical exploration algorithm that can deal with complex environments. In the planning phase, we solve for good policies given arbitrary reward functions using a batch RL algorithm. Empirically, we show that our exploration algorithm is effective and sample efficient, and results in superior policies for arbitrary reward functions in the planning phase.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.06193v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2568\", \"32551\", \"5862\", \"6754\", \"41008\"], \"outgoing_citations\": [\"143630\", \"151749\", \"156467\", \"163467\", \"200920\", \"206875\", \"206945\", \"212299\", \"228028\", \"228299\", \"237229\", \"241900\", \"244430\", \"247163\", \"255613\", \"266419\", \"268361\", \"270826\", \"272290\", \"272422\", \"272785\", \"272987\", \"280202\", \"280870\", \"291025\", \"291460\", \"305802\", \"311595\"]}","task_split":"paper_retrieval"}
{"document_id":"119644","document_content":"# ClarQ: A large-scale and diverse dataset for Clarification Question Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nQuestion answering and conversational systems are often baffled and need help clarifying certain ambiguities. However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. The framework utilises a neural network based architecture for classifying clarification questions. It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall. We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering. The final dataset, ClarQ, consists of ~2M examples distributed across 173 domains of stackexchange. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.05986v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2750\", \"15903\", \"15246\", \"21067\", \"44917\", \"56136\", \"54487\", \"68580\", \"105480\", \"105532\"], \"outgoing_citations\": [\"176028\", \"192361\", \"202415\", \"232268\", \"232341\", \"277941\", \"301143\", \"310377\", \"311119\"]}","task_split":"paper_retrieval"}
{"document_id":"119708","document_content":"# Deep Dimension Reduction for Supervised Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nThe goal of supervised representation learning is to construct effective data representations for prediction. Among all the characteristics of an ideal nonparametric representation of high-dimensional complex data, sufficiency, low dimensionality and disentanglement are some of the most essential ones. We propose a deep dimension reduction approach to learning representations with these characteristics. The proposed approach is a nonparametric generalization of the sufficient dimension reduction method. We formulate the ideal representation learning task as that of finding a nonparametric representation that minimizes an objective function characterizing conditional independence and promoting disentanglement at the population level. We then estimate the target representation at the sample level nonparametrically using deep neural networks. We show that the estimated deep nonparametric representation is consistent in the sense that its excess risk converges to zero. Our extensive numerical experiments using simulated and real benchmark data demonstrate that the proposed methods have better performance than several existing dimension reduction methods and the standard deep learning models in the context of classification and regression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.05865v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"113674\", \"132207\", \"143597\", \"173580\", \"180984\", \"187863\", \"201739\", \"211023\", \"216470\", \"226571\", \"241933\", \"251102\", \"257973\", \"264549\", \"271981\", \"272564\", \"278987\", \"280505\", \"283223\", \"291301\", \"300683\", \"303139\", \"316314\", \"317771\", \"328112\"]}","task_split":"paper_retrieval"}
{"document_id":"119812","document_content":"# Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n## Abstract\nWith the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insights for future App design and development. While several promising neural modeling approaches were recently pioneered for accurate user engagement prediction, their black-box designs are unfortunately limited in model explainability. In this paper, we study a novel problem of explainable user engagement prediction for social network Apps. First, we propose a flexible definition of user engagement for various business scenarios, based on future metric expectations. Next, we design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement, namely friendships, user actions, and temporal dynamics to achieve explainable engagement predictions. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training\/inference time. We conduct extensive experiments on two large-scale datasets from Snapchat, where FATE outperforms state-of-the-art approaches by ${\\approx}10\\%$ error and ${\\approx}20\\%$ runtime reduction. We also evaluate explanations from FATE, showing strong quantitative and qualitative performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3394486.3403276\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"42923\", \"21375\", \"27545\", \"40261\", \"43235\", \"39225\", \"92964\", \"115602\"], \"outgoing_citations\": [\"123835\", \"154714\", \"164380\", \"170916\", \"183045\", \"183926\", \"185584\", \"188398\", \"220996\", \"222643\", \"223616\", \"229719\", \"237210\", \"240136\", \"240954\", \"269418\", \"269661\", \"283322\", \"316274\"]}","task_split":"paper_retrieval"}
{"document_id":"119926","document_content":"# Distributed Learning on Heterogeneous Resource-Constrained Devices\n## Categories\n- Machine Learning\n## Abstract\nWe consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end. These devices have different profiles, e.g., different energy budgets, or different hardware specifications, determining their capabilities on performing certain learning tasks. We propose the first approach that enables distributed learning in such a heterogeneous system. Applying our approach, each device employs a neural network (NN) with a topology that fits its capabilities; however, part of these NNs share the same topology, so that their parameters can be jointly learned. This differs from current approaches, such as federated learning, which require all devices to employ the same NN, enforcing a trade-off between achievable accuracy and computational overhead of training. We evaluate heterogeneous distributed learning for reinforcement learning (RL) and observe that it greatly improves the achievable reward on more powerful devices, compared to current approaches, while still maintaining a high reward on the weaker devices. We also explore supervised learning, observing similar gains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.05403v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2562\", \"48647\"], \"outgoing_citations\": [\"206873\", \"215746\", \"230123\", \"235592\", \"280047\", \"284141\", \"291918\", \"310485\", \"322655\"]}","task_split":"paper_retrieval"}
{"document_id":"120029","document_content":"# ConfNet2Seq: Full Length Answer Generation from Spoken Questions\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nConversational and task-oriented dialogue systems aim to interact with the user using natural responses through multi-modal interfaces, such as text or speech. These desired responses are in the form of full-length natural answers generated over facts retrieved from a knowledge source. While the task of generating natural answers to questions from an answer span has been widely studied, there has been little research on natural sentence generation over spoken content. We propose a novel system to generate full length natural language answers from spoken questions and factoid answers. The spoken sequence is compactly represented as a confusion network extracted from a pre-trained Automatic Speech Recognizer. This is the first attempt towards generating full-length natural answers from a graph input(confusion network) to the best of our knowledge. We release a large-scale dataset of 259,788 samples of spoken questions, their factoid answers and corresponding full-length textual answers. Following our proposed approach, we achieve comparable performance with best ASR hypothesis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-58323-1_56\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"144381\", \"222054\", \"231442\", \"237064\", \"260869\"]}","task_split":"paper_retrieval"}
{"document_id":"120033","document_content":"# Physically constrained short-term vehicle trajectory forecasting with naive semantic maps\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nUrban environments manifest a high level of complexity, and therefore it is of vital importance for safety systems embedded within autonomous vehicles (AVs) to be able to accurately predict the short-term future motion of nearby agents. This problem can be further understood as generating a sequence of future coordinates for a given agent based on its past motion data e.g. position, velocity, acceleration etc, and whilst current approaches demonstrate plausible results they have a propensity to neglect a scene's physical constrains. In this paper we propose the model based on a combination of the CNN and LSTM encoder-decoder architecture that learns to extract a relevant road features from semantic maps as well as general motion of agents and uses this learned representation to predict their short-term future trajectories. We train and validate the model on the publicly available dataset that provides data from urban areas, allowing us to examine it in challenging and uncertain scenarios. We show that our model is not only capable of anticipating future motion whilst taking into consideration road boundaries, but can also effectively and precisely predict trajectories for a longer time horizon than initially trained for.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.05159v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175291\", \"179871\", \"191356\", \"206321\", \"217456\", \"221071\", \"229266\", \"232095\", \"237350\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"120045","document_content":"# A Survey on Generative Adversarial Networks: Variants, Applications, and Training\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nThe Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.05132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"89089\", \"191885\", \"6719\", \"12680\", \"32349\", \"1686\", \"7559\", \"8256\", \"14286\", \"17665\", \"29030\", \"35923\", \"42085\", \"42488\", \"70577\", \"73324\", \"114758\"], \"outgoing_citations\": [\"63180\", \"143817\", \"146468\", \"148748\", \"152879\", \"170430\", \"174181\", \"174531\", \"176003\", \"176017\", \"177483\", \"178018\", \"182512\", \"183000\", \"183733\", \"184713\", \"186137\", \"188408\", \"192361\", \"192731\", \"193088\", \"193376\", \"193882\", \"193883\", \"194182\", \"198793\", \"199588\", \"200265\", \"201260\", \"201880\", \"203129\", \"204159\", \"208287\", \"208621\", \"212312\", \"212643\", \"217177\", \"217418\", \"218987\", \"219505\", \"221244\", \"221834\", \"223275\", \"225328\", \"226061\", \"226525\", \"227719\", \"227750\", \"230480\", \"231164\", \"231304\", \"232133\", \"232715\", \"234342\", \"234549\", \"234687\", \"235187\", \"235769\", \"235895\", \"236707\", \"237161\", \"237350\", \"238070\", \"238379\", \"239321\", \"240143\", \"241941\", \"242751\", \"243901\", \"243926\", \"244043\", \"244179\", \"244327\", \"245038\", \"245177\", \"245290\", \"245479\", \"245594\", \"245963\", \"247737\", \"248125\", \"248440\", \"248981\", \"249031\", \"249421\", \"250359\", \"251580\", \"252315\", \"252935\", \"254851\", \"256000\", \"257581\", \"258286\", \"259642\", \"260588\", \"261089\", \"261238\", \"261794\", \"261954\", \"262675\", \"263580\", \"264198\", \"264436\", \"264937\", \"264963\", \"264984\", \"265057\", \"265397\", \"265568\", \"265767\", \"266137\", \"266529\", \"267705\", \"268405\", \"268970\", \"269271\", \"269388\", \"269484\", \"269817\", \"270105\", \"270154\", \"270214\", \"270387\", \"270948\", \"271122\", \"271135\", \"271340\", \"271419\", \"272553\", \"272623\", \"272910\", \"272912\", \"273062\", \"273410\", \"273620\", \"274550\", \"275665\", \"275998\", \"276542\", \"276812\", \"277021\", \"277297\", \"277730\", \"278560\", \"278895\", \"279202\", \"279633\", \"280600\", \"281002\", \"281618\", \"282032\", \"282185\", \"282643\", \"284132\", \"284481\", \"284563\", \"284708\", \"284874\", \"284986\", \"285093\", \"287219\", \"287664\", \"290620\", \"294531\", \"297625\", \"299975\", \"300739\", \"310922\"]}","task_split":"paper_retrieval"}
{"document_id":"120161","document_content":"# Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWe detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure of uncertainty designed specifically for long sequences of discrete random variables -- i.e. words in the output sentence. Our new measure of uncertainty solves a major intractability in the naive application of existing approaches on long sentences. We use our new measure on a Transformer model trained with dropout approximate inference. On the task of German-English translation using WMT13 and Europarl, we show that with dropout uncertainty our measure is able to identify when Dutch source sentences, sentences which use the same word types as German, are given to the model instead of German.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.08344v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"17769\", \"48179\", \"25580\", \"40685\", \"87412\"], \"outgoing_citations\": [\"169331\", \"179973\", \"185792\", \"196775\", \"210932\", \"222360\", \"229674\", \"229890\", \"238703\", \"271988\", \"290778\"]}","task_split":"paper_retrieval"}
{"document_id":"120407","document_content":"# Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.04330v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"102194\", \"56950\"], \"outgoing_citations\": [\"111211\", \"114193\", \"143456\", \"151458\", \"155642\", \"156406\", \"163945\", \"164732\", \"174492\", \"176579\", \"177249\", \"181255\", \"183719\", \"184286\", \"184400\", \"185055\", \"186624\", \"188300\", \"188398\", \"191735\", \"196221\", \"198382\", \"204871\", \"205283\", \"206470\", \"214311\", \"215435\", \"215834\", \"228663\", \"229324\", \"240758\", \"244510\", \"252006\", \"252105\", \"264342\", \"265828\", \"269723\", \"271216\", \"279575\", \"303863\", \"305707\", \"332012\", \"336185\", \"338988\", \"352782\"]}","task_split":"paper_retrieval"}
{"document_id":"120420","document_content":"# Text Detection and Recognition in the Wild: A Review\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDetection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and\/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assesses pre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.04305v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"62258\", \"132283\", \"133417\", \"162242\", \"166370\", \"171543\", \"174894\", \"180466\", \"182255\", \"182603\", \"189663\", \"190056\", \"190403\", \"190708\", \"192471\", \"192483\", \"193382\", \"205988\", \"207071\", \"207348\", \"209083\", \"209227\", \"210634\", \"211851\", \"217463\", \"225795\", \"232706\", \"239535\", \"240965\", \"245591\", \"245899\", \"245970\", \"248133\", \"250738\", \"252159\", \"256711\", \"256785\", \"257197\", \"257954\", \"262570\", \"269108\", \"269205\", \"270668\", \"271112\", \"272390\", \"272481\", \"276664\", \"279950\", \"284605\", \"284871\", \"285788\", \"289389\", \"293979\", \"294023\", \"296685\", \"296828\", \"298359\", \"299461\", \"309239\", \"311196\", \"320148\", \"320837\", \"322955\", \"328595\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"120473","document_content":"# Learning Behaviors with Uncertain Human Feedback\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHuman feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers. For example, the reward of a sub-optimal action can be stochastic and sometimes exceeds that of the optimal action, which is common in games or real-world. Trainers are likely to provide positive feedback to sub-optimal actions, negative feedback to the optimal actions and even do not provide feedback in some confusing situations. Existing works, which utilize the Expectation Maximization (EM) algorithm and treat the feedback model as hidden parameters, do not consider uncertainties in the learning environment and human feedback. To address this challenge, we introduce a novel feedback model that considers the uncertainty of human feedback. However, this incurs intractable calculus in the EM algorithm. To this end, we propose a novel approximate EM algorithm, in which we approximate the expectation step with the Gradient Descent method. Experimental results in both synthetic scenarios and two real-world scenarios with human participants demonstrate the superior performance of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.04201v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"178146\", \"98533\"], \"outgoing_citations\": [\"213745\", \"215939\", \"254581\", \"275728\"]}","task_split":"paper_retrieval"}
{"document_id":"120568","document_content":"# Efficient Architecture Search for Continual Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nContinual learning with neural networks is an important learning framework in AI that aims to learn a sequence of tasks well. However, it is often confronted with three challenges: (1) overcome the catastrophic forgetting problem, (2) adapt the current network to new tasks, and meanwhile (3) control its model complexity. To reach these goals, we propose a novel approach named as Continual Learning with Efficient Architecture Search, or CLEAS in short. CLEAS works closely with neural architecture search (NAS) which leverages reinforcement learning techniques to search for the best neural architecture that fits a new task. In particular, we design a neuron-level NAS controller that decides which old neurons from previous tasks should be reused (knowledge transfer), and which new neurons should be added (to learn new knowledge). Such a fine-grained controller allows one to find a very concise architecture that can fit each new task well. Meanwhile, since we do not alter the weights of the reused neurons, we perfectly memorize the knowledge learned from previous tasks. We evaluate CLEAS on numerous sequential classification tasks, and the results demonstrate that CLEAS outperforms other state-of-the-art alternative methods, achieving higher classification accuracy while using simpler neural architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.04027v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"27067\", \"107932\", \"38286\", \"60826\"], \"outgoing_citations\": [\"149847\", \"183435\", \"183710\", \"192312\", \"195406\", \"208243\", \"229806\", \"241392\", \"242908\", \"243239\", \"249234\", \"252086\", \"259336\", \"261128\", \"262842\", \"279725\", \"281175\"]}","task_split":"paper_retrieval"}
{"document_id":"120682","document_content":"# The Impact of Global Structural Information in Graph Neural Networks Applications\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. A known limitation of GNNs is that, as the number of layers increases, information gets smoothed and squashed and node embeddings become indistinguishable, negatively affecting performance. Therefore, practical GNN models employ few layers and only leverage the graph structure in terms of limited, small neighbourhoods around each node. Inevitably, practical GNNs do not capture information depending on the global structure of the graph. While there have been several works studying the limitations and expressivity of GNNs, the question of whether practical applications on graph structured data require global structural knowledge or not, remains unanswered. In this work, we empirically address this question by giving access to global information to several GNN models, and observing the impact it has on downstream performance. Our results show that global information can in fact provide significant benefits for common graph-related tasks. We further identify a novel regularization strategy that leads to an average accuracy improvement of more than 5% on all considered tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/data7010010\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42239\", \"114415\", \"120001\", \"142571\", \"159315\", \"169835\", \"177249\", \"183437\", \"184749\", \"188300\", \"190159\", \"196221\", \"211646\", \"214311\", \"215435\", \"216509\", \"223897\", \"227067\", \"228663\", \"229936\", \"238422\", \"240985\", \"244510\", \"253024\", \"278305\", \"295676\", \"301488\", \"302687\", \"309186\"]}","task_split":"paper_retrieval"}
{"document_id":"120747","document_content":"# Evaluating the Disentanglement of Deep Generative Models through Manifold Topology\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nLearning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To address this, we present a method for quantifying disentanglement that only uses the generative model, by measuring the topological similarity of conditional submanifolds in the learned representation. This method showcases both unsupervised and supervised variants. To illustrate the effectiveness and applicability of our method, we empirically evaluate several state-of-the-art models across multiple datasets. We find that our method ranks models similarly to existing methods. We make ourcode publicly available at https:\/\/github.com\/stanfordmlgroup\/disentanglement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.03680v5\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9\", \"2900\", \"15093\", \"33836\", \"103627\", \"118768\", \"118821\"], \"outgoing_citations\": [\"139725\", \"146226\", \"160471\", \"180811\", \"183696\", \"198442\", \"199047\", \"202433\", \"204912\", \"207094\", \"207548\", \"208041\", \"241933\", \"242239\", \"242984\", \"249699\", \"270105\", \"277856\", \"278060\", \"278987\", \"310979\", \"311052\", \"355965\"]}","task_split":"paper_retrieval"}
{"document_id":"120808","document_content":"# Graphon Neural Networks and the Transferability of Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) rely on graph convolutions to extract local features from network data. These graph convolutions combine information from adjacent nodes using coefficients that are shared across all nodes. Since these coefficients are shared and do not depend on the graph, one can envision using the same coefficients to define a GNN on another graph. This motivates analyzing the transferability of GNNs across graphs. In this paper we introduce graphon NNs as limit objects of GNNs and prove a bound on the difference between the output of a GNN and its limit graphon-NN. This bound vanishes with growing number of nodes if the graph convolutional filters are bandlimited in the graph spectral domain. This result establishes a tradeoff between discriminability and transferability of GNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.03548v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"44078\", \"3052\", \"16467\", \"17648\", \"18213\", \"34488\", \"62968\", \"77915\", \"91450\", \"4299\", \"27805\", \"44062\", \"46443\", \"46936\", \"87797\", \"93509\", \"101265\", \"107838\", \"119464\", \"91821\", \"205283\"], \"outgoing_citations\": [\"121424\", \"130173\", \"173794\", \"181265\", \"186804\", \"193149\", \"201055\", \"233653\", \"256395\", \"259899\", \"361129\"]}","task_split":"paper_retrieval"}
{"document_id":"120925","document_content":"# DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature\n## Categories\n- Computation and Language\n- Machine Learning\n- J.3; I.2.7\n## Abstract\nWe consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches in such tasks heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.08338v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"J.3; I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"J.3; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"180997\", \"192402\", \"228348\", \"240783\", \"243880\", \"256158\", \"260613\", \"274533\", \"290680\", \"302500\", \"307547\", \"309148\"]}","task_split":"paper_retrieval"}
{"document_id":"121000","document_content":"# The Importance of Open-Endedness (for the Sake of Open-Endedness)\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nA paper in the recent Artificial Life journal special issue on open-ended evolution (OEE) presents a simple evolving computational system that, it is claimed, satisfies all proposed requirements for OEE (Hintze, 2019). Analysis and discussion of the system are used to support the further claims that complexity and diversity are the crucial features of open-endedness, and that we should concentrate on providing proper definitions for those terms rather than engaging in \"the quest for open-endedness for the sake of open-endedness\" (Hintze, 2019, p. 205). While I wholeheartedly support the pursuit of precise definitions of complexity and diversity in relation to OEE research, I emphatically reject the suggestion that OEE is not a worthy research topic in its own right. In the same issue of the journal, I presented a \"high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems\" (Taylor, 2019). In the current brief contribution I apply my framework to Hinzte's model to understand its limitations. In so doing, I demonstrate the importance of studying open-endedness for the sake of open-endedness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.03079v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [\"40575\"], \"outgoing_citations\": [\"166489\", \"229161\", \"308936\"]}","task_split":"paper_retrieval"}
{"document_id":"121206","document_content":"# Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe propose to explain the behavior of black-box prediction methods (e.g., deep neural networks trained on image pixel data) using causal graphical models. Specifically, we explore learning the structure of a causal graph where the nodes represent prediction outcomes along with a set of macro-level \"interpretable\" features, while allowing for arbitrary unmeasured confounding among these variables. The resulting graph may indicate which of the interpretable features, if any, are possible causes of the prediction outcome and which may be merely associated with prediction outcomes due to confounding. The approach is motivated by a counterfactual theory of causal explanation wherein good explanations point to factors that are \"difference-makers\" in an interventionist sense. The resulting analysis may be useful in algorithm auditing and evaluation, by identifying features which make a causal difference to the algorithm's output.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.02482v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"65457\"], \"outgoing_citations\": [\"79105\", \"142171\", \"152364\", \"159486\", \"166967\", \"174945\", \"185613\", \"190384\", \"199971\", \"226769\", \"230290\", \"230650\", \"242438\", \"262092\", \"283218\", \"291526\", \"291800\", \"297488\", \"297788\", \"320760\", \"332664\", \"352356\", \"353407\"]}","task_split":"paper_retrieval"}
{"document_id":"121282","document_content":"# Tangles: a new paradigm for clusters and types\n## Categories\n- Artificial Intelligence\n- Combinatorics\n## Abstract\nTraditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types: of behaviour, political views, texts, or viruses. If desired, tangles can also be used for direct clustering of objects. They offer a precise, quantitative paradigm suited particularly to fuzzy clusters, since they do not require any `hard' assignments of objects to the clusters they collectively form. This is a draft of the introductory chapter of a book I am preparing on the application of tangles in the empirical sciences. The purpose of posting this draft early is to give authors of tangle application papers a generic reference for the basic guiding principles underlying tangle applications outside mathematics, so that in their own papers they can concentrate on the ideas specific to their particular application rather than having to repeat the generic story each time. The text starts with three separate generic introductions to tangles in the natural sciences, in the social sciences, and in data science including machine learning. It then gives a short informal description of the abstract notion of tangles that encompasses all these potential applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.01830v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"math.CO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Combinatorics\"], \"incoming_citations\": [\"116082\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"121404","document_content":"# Quantifying the Effects of Prosody Modulation on User Engagement and Satisfaction in Conversational Systems\n## Categories\n- Human-Computer Interaction\n- Audio and Speech Processing\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAs voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3343413.3378009\", \"primary_category\": \"cs.HC\", \"categories\": [\"eess.AS\", \"cs.LG\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"121402\", \"174962\", \"202748\", \"204637\", \"210493\", \"237906\", \"245399\", \"250351\", \"278879\"]}","task_split":"paper_retrieval"}
{"document_id":"121473","document_content":"# Neural Power Units\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nConventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Units aim to overcome this difficulty, but current arithmetic units are either limited to operate on positive numbers or can only represent a subset of arithmetic operations. We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer. The NPU thus fixes the shortcomings of existing arithmetic units and extends their expressivity. We achieve this by using complex arithmetic without requiring a conversion of the network to complex numbers. A simplification of the unit to the RealNPU yields a highly transparent model. We show that the NPUs outperform their competitors in terms of accuracy and sparsity on artificial arithmetic datasets, and that the RealNPU can discover the governing equations of a dynamical system only from data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.01681v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"17384\", \"73887\", \"75620\"], \"outgoing_citations\": [\"131065\", \"136392\", \"147159\", \"147397\", \"153185\", \"163375\", \"192616\", \"211761\", \"212066\", \"216841\", \"222567\", \"251911\", \"302515\", \"302933\", \"310023\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"121505","document_content":"# Exploring Cross-sentence Contexts for Named Entity Recognition with BERT\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition (NER) is frequently addressed as a sequence classification task where each input consists of one sentence of text. It is nevertheless clear that useful information for the task can often be found outside of the scope of a single-sentence context. Recently proposed self-attention models such as BERT can both efficiently capture long-distance relationships in input as well as represent inputs consisting of several sentences, creating new opportunitites for approaches that incorporate cross-sentence information in natural language processing tasks. In this paper, we present a systematic study exploring the use of cross-sentence information for NER using BERT models in five languages. We find that adding context in the form of additional sentences to BERT input systematically increases NER performance on all of the tested languages and models. Including multiple sentences in each input also allows us to study the predictions of the same sentences in different contexts. We propose a straightforward method, Contextual Majority Voting (CMV), to combine different predictions for sentences and demonstrate this to further increase NER performance with BERT. Our approach does not require any changes to the underlying BERT architecture, rather relying on restructuring examples for training and prediction. Evaluation on established datasets, including the CoNLL'02 and CoNLL'03 NER benchmarks, demonstrates that our proposed approach can improve on the state-of-the-art NER results on English, Dutch, and Finnish, achieves the best reported BERT-based results on German, and is on par with performance reported with other BERT-based approaches in Spanish. We release all methods implemented in this work under open licenses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.01563v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2566\", \"23896\", \"6309\", \"18156\", \"27041\", \"29323\", \"32579\", \"50900\", \"53961\", \"57119\", \"59603\", \"95236\", \"87107\"], \"outgoing_citations\": [\"150286\", \"151090\", \"151647\", \"157143\", \"157803\", \"159862\", \"159867\", \"171105\", \"172107\", \"182220\", \"189792\", \"194633\", \"205022\", \"302500\", \"330797\"]}","task_split":"paper_retrieval"}
{"document_id":"121543","document_content":"# Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective\n## Categories\n- Information Retrieval\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTo maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works often study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a flexible and practical framework to explicitly tradeoff longer user browsing length and high immediate user engagement. Specifically, by considering items as actions, user's requests as states and user leaving as an absorbing state, we formulate each user's behavior as a personalized Markov decision process (MDP), and the problem of maximizing cumulative user engagement is reduced to a stochastic shortest path (SSP) problem. Meanwhile, with immediate user engagement and quit probability estimation, it is shown that the SSP problem can be efficiently solved via dynamic programming. Experiments on real-world datasets demonstrate the effectiveness of the proposed approach. Moreover, this approach is deployed at a large E-commerce platform, achieved over 7% improvement of cumulative clicks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.04520v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [\"76638\"], \"outgoing_citations\": [\"215659\", \"217192\", \"233852\"]}","task_split":"paper_retrieval"}
{"document_id":"121678","document_content":"# Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas\n## Categories\n- Robotics\n- Computation and Language\n## Abstract\nWe demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, yet the network performs zero-shot generalization to satisfy them. This is a novel form of multi-task learning for RL agents where agents learn from one diverse set of tasks and generalize to a new set of diverse tasks. The formulation of the network enables this capacity to generalize. We demonstrate this ability in two domains. In a symbolic domain, the agent finds a sequence of letters that is accepted. In a Minecraft-like environment, the agent finds a sequence of actions that conform to the formula. While prior work could learn to execute one formula reliably given examples of that formula, we demonstrate how to encode all formulas reliably. This could form the basis of new multitask agents that discover sub-tasks and execute them without any additional training, as well as the agents which follow more complex linguistic commands. The structures required for this generalization are specific to LTL formulas, which opens up an interesting theoretical question: what structures are required in neural networks for zero-shot generalization to different logics?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.01110v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computation and Language\"], \"incoming_citations\": [\"15387\", \"39395\", \"67087\", \"69616\", \"107240\", \"119188\"], \"outgoing_citations\": [\"142610\", \"248805\", \"254700\", \"257501\", \"278257\", \"281106\", \"315745\", \"316436\", \"324321\", \"330449\"]}","task_split":"paper_retrieval"}
{"document_id":"121890","document_content":"# Efficient Deployment of Conversational Natural Language Interfaces over Databases\n## Categories\n- Computation and Language\n## Abstract\nMany users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user's natural language questions for question-answering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural language-to-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.00591v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"68442\"], \"outgoing_citations\": [\"167313\", \"182297\", \"216731\", \"226991\", \"243759\", \"245042\", \"250256\", \"250689\", \"257206\", \"267790\", \"290632\", \"300505\"]}","task_split":"paper_retrieval"}
{"document_id":"121898","document_content":"# Neural Entity Linking: A Survey of Models Based on Deep Learning\n## Categories\n- Computation and Language\n## Abstract\nThis survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the \"deep learning revolution\" in natural language processing. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them. The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of entity and mention\/context embeddings to represent their meaning, this work also overviews prominent entity embedding techniques. Finally, the survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the Transformer architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3233\/SW-222986\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"45294\", \"5406\", \"7966\", \"36461\", \"42578\", \"73669\", \"77042\", \"98792\"], \"outgoing_citations\": [\"38350\", \"41806\", \"42130\", \"56532\", \"57677\", \"65208\", \"73992\", \"76611\", \"88910\", \"90752\", \"94753\", \"96431\", \"97054\", \"97100\", \"97185\", \"103097\", \"106440\", \"115509\", \"120132\", \"121381\", \"127521\", \"127709\", \"131012\", \"137257\", \"138850\", \"140120\", \"142709\", \"144530\", \"148404\", \"152591\", \"153119\", \"154917\", \"156620\", \"157168\", \"157181\", \"157255\", \"159867\", \"164387\", \"165378\", \"167165\", \"167753\", \"168589\", \"171151\", \"171884\", \"175482\", \"178230\", \"179831\", \"180267\", \"182594\", \"182677\", \"185861\", \"185889\", \"186316\", \"188690\", \"200632\", \"205022\", \"205857\", \"209228\", \"210680\", \"212304\", \"213179\", \"217090\", \"217117\", \"220553\", \"224786\", \"225253\", \"227437\", \"233941\", \"234554\", \"243397\", \"248226\", \"255202\", \"259009\", \"259758\", \"261989\", \"262673\", \"266850\", \"267030\", \"267123\", \"268728\", \"289972\", \"295323\", \"300498\", \"306949\", \"308446\", \"311464\", \"313713\", \"320035\", \"323624\", \"329586\"]}","task_split":"paper_retrieval"}
{"document_id":"121899","document_content":"# Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nDue to the increasing amount of data on the internet, finding a highly-informative, low-dimensional representation for text is one of the main challenges for efficient natural language processing tasks including text classification. This representation should capture the semantic information of the text while retaining their relevance level for document classification. This approach maps the documents with similar topics to a similar space in vector space representation. To obtain representation for large text, we propose the utilization of deep Siamese neural networks. To embed document relevance in topics in the distributed representation, we use a Siamese neural network to jointly learn document representations. Our Siamese network consists of two sub-network of multi-layer perceptron. We examine our representation for the text categorization task on BBC news dataset. The results show that the proposed representations outperform the conventional and state-of-the-art representations in the text classification task on this dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.00572v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"255016\", \"269630\", \"274591\", \"297844\", \"301397\", \"302948\", \"316774\"]}","task_split":"paper_retrieval"}
{"document_id":"122165","document_content":"# A Comparative Study of Lexical Substitution Approaches based on Neural Language Models\n## Categories\n- Computation and Language\n## Abstract\nLexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical substitution. We show that already competitive results achieved by SOTA LMs\/MLMs can be further improved if information about the target word is injected properly, and compare several target injection methods. In addition, we provide analysis of the types of semantic relations between the target and substitutes generated by different models providing insights into what kind of words are really generated or given by annotators as substitutes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.00031v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"36127\", \"43878\"], \"outgoing_citations\": [\"183703\", \"184635\", \"190642\", \"205014\", \"205715\", \"220317\", \"230543\", \"231898\"]}","task_split":"paper_retrieval"}
{"document_id":"122197","document_content":"# Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nMeasuring user satisfaction level is a challenging task, and a critical component in developing large-scale conversational agent systems serving the needs of real users. An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling. Human annotation based approaches are easier to control, but hard to scale. A novel alternative approach is to collect user's direct feedback via a feedback elicitation system embedded to the conversational agent system, and use the collected user feedback to train a machine-learned model for generalization. User feedback is the best proxy for user satisfaction, but is not available for some ineligible intents and certain situations. Thus, these two types of approaches are complementary to each other. In this work, we tackle the user satisfaction assessment problem with a hybrid approach that fuses explicit user feedback, user satisfaction predictions inferred by two machine-learned models, one trained on user feedback data and the other human annotation data. The hybrid approach is based on a waterfall policy, and the experimental results with Amazon Alexa's large-scale datasets show significant improvements in inferring user satisfaction. A detailed hybrid architecture, an in-depth analysis on user feedback data, and an algorithm that generates data sets to properly simulate the live traffic are presented in this paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.07113v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.LG\", \"stat.ML\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Machine Learning\", \"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"21067\", \"92462\"], \"outgoing_citations\": [\"155877\", \"157701\", \"171071\", \"234683\", \"234684\", \"263761\"]}","task_split":"paper_retrieval"}
{"document_id":"122444","document_content":"# Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMethods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.13934v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"38196\", \"38494\"], \"outgoing_citations\": [\"97190\", \"113579\", \"115066\", \"134643\", \"136203\", \"186194\", \"196132\", \"203762\", \"219340\", \"229266\", \"233526\", \"237350\", \"302955\"]}","task_split":"paper_retrieval"}
{"document_id":"122492","document_content":"# User Intent Inference for Web Search and Conversational Agents\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nUser intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, since the user utterances or queries can be short, ambiguous, and contextually dependent. To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain, via: 1) Developing a joint learning model to predict search queries' intents and the product categories associated with them, 2) Discovering new hidden users' intents. All the models will be evaluated on the real queries available from a major e-commerce site search engine. The results from these studies can be leveraged to improve performance of various tasks such as natural language understanding, query scoping, query suggestion, and ranking, resulting in an enriched user experience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.13808v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"4225\"], \"outgoing_citations\": [\"121402\", \"122495\", \"122498\", \"245387\", \"258109\"]}","task_split":"paper_retrieval"}
{"document_id":"122635","document_content":"# CausaLM: Causal Model Explanation Through Counterfactual Language Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nUnderstanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with existing generation technology. To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models. Our approach is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem. Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance. A byproduct of our method is a language representation model that is unaffected by the tested concept, which can be useful in mitigating unwanted bias ingrained in the data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1162\/coli_a_00404\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3345\", \"4186\", \"5174\", \"16353\", \"26223\", \"26824\", \"67128\", \"79760\", \"243111\", \"21496\", \"27889\", \"43699\", \"44454\", \"49530\", \"56330\", \"65995\", \"74878\", \"78260\", \"121728\", \"121691\", \"79805\"], \"outgoing_citations\": [\"121866\", \"127467\", \"127545\", \"128960\", \"129503\", \"130821\", \"132773\", \"156950\", \"157497\", \"159925\", \"162159\", \"164659\", \"171988\", \"174554\", \"175837\", \"183649\", \"183729\", \"186386\", \"187236\", \"190384\", \"195777\", \"197007\", \"197416\", \"201401\", \"201539\", \"205210\", \"215808\", \"216953\", \"220838\", \"228886\", \"232388\", \"233400\", \"239686\", \"243111\", \"244368\", \"249109\", \"255035\", \"264914\", \"264937\", \"264957\", \"268098\", \"268243\", \"273062\", \"274461\", \"278016\", \"285959\", \"286594\", \"290677\", \"292736\", \"310738\", \"330049\", \"354620\"]}","task_split":"paper_retrieval"}
{"document_id":"122762","document_content":"# Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems\n## Categories\n- Computation and Language\n## Abstract\nDifferent people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially in real-world industry scenarios, in which the dialogue system is unsure whether it should answer the query of user immediately or wait for further supplementary input. Motivated by such an interesting predicament, we define a novel Wait-or-Answer task for dialogue systems. We shed light on a new research topic about how the dialogue system can be more intelligent to behave in this Wait-or-Answer quandary. Further, we propose a predictive approach named Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer. The decision of decision model is made with the assistance of two ancillary prediction models: a user prediction and an agent prediction. The user prediction model tries to predict what the user would supplement and uses its prediction to persuade the decision model that the user has some information to add, so the dialogue system should wait. The agent prediction model tries to predict the answer of the dialogue system and convince the decision model that it is a superior choice to answer the query of user immediately since the input of user has come to an end. We conduct our experiments on two real-life scenarios and three public datasets. Experimental results on five datasets show our proposed PTD approach significantly outperforms the existing models in solving this Wait-or-Answer problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TASLP.2021.3110145\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"68965\", \"98195\", \"107933\", \"140423\", \"141423\", \"165107\", \"181274\", \"182669\", \"184016\", \"185347\", \"190165\", \"202748\", \"202917\", \"216019\", \"216953\", \"219180\", \"251348\", \"253593\", \"255016\", \"266411\", \"274567\", \"275597\", \"276160\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"122763","document_content":"# TRIE: End-to-End Text Reading and Information Extraction for Document Understanding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSince real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.13118v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"31775\", \"9109\", \"21025\", \"31120\", \"39796\", \"40793\", \"47534\", \"47716\", \"49809\", \"50468\", \"50580\", \"53241\", \"55400\", \"73803\", \"77979\", \"96466\", \"141395\"], \"outgoing_citations\": [\"62258\", \"130903\", \"141887\", \"149004\", \"150132\", \"154995\", \"159867\", \"162242\", \"165784\", \"167422\", \"172370\", \"193222\", \"193543\", \"205569\", \"216765\", \"225795\", \"239535\", \"245899\", \"256711\", \"257197\", \"257785\", \"261365\", \"269205\", \"271112\", \"272390\", \"279950\", \"296828\", \"297150\", \"303837\", \"309239\"]}","task_split":"paper_retrieval"}
{"document_id":"122844","document_content":"# A Survey of Languages for Formalizing Mathematics\n## Categories\n- Logic in Computer Science\n## Abstract\nIn order to work with mathematical content in computer systems, it is necessary to represent it in formal languages. Ideally, these are supported by tools that verify the correctness of the content, allow computing with it, and produce human-readable documents. These goals are challenging to combine and state-of-the-art tools typically have to make difficult compromises.   In this paper we discuss languages that have been created for this purpose, including logical languages of proof assistants and other formal systems, semi-formal languages, intermediate languages for exchanging mathematical knowledge, and language frameworks that allow building customized languages.   We evaluate their advantages based on our experience in designing and applying languages and tools for formalizing mathematics. We reach the conclusion that no existing language is truly good enough yet and derive ideas for possible future improvements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.12876v1\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\"], \"incoming_citations\": [\"73654\"], \"outgoing_citations\": [\"126886\", \"126993\", \"160061\", \"160062\", \"232558\", \"248302\", \"296194\", \"306743\", \"325436\", \"330596\"]}","task_split":"paper_retrieval"}
{"document_id":"122922","document_content":"# Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open questions how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithm such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular we augment the neighborhood of a Markov Random Fields approach to not only take local but also symmetric positional information into account. We conduct several empirical tests including a user study that show the improvements achieved by the proposed modifications, and obtain promising results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.12579v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"47005\", \"41052\", \"95477\", \"107909\"], \"outgoing_citations\": [\"133342\", \"139814\", \"147140\", \"163503\", \"189201\", \"216608\", \"225289\", \"233501\", \"274886\", \"297233\"]}","task_split":"paper_retrieval"}
{"document_id":"123196","document_content":"# Learning visual servo policies via planner cloning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nLearning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow. This paper explores planner cloning: using behavior cloning to learn policies that mimic the behavior of a full-state motion planner in simulation. We propose Penalized Q Cloning (PQC), a new behavior cloning algorithm. We show that it outperforms several baselines and ablations on some challenging problems involving visual servoing in novel environments while avoiding obstacles. Finally, we demonstrate that these policies can be transferred effectively onto a real robotic platform, achieving approximately an 87% success rate both in simulation and on a real robot.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11810v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"226635\", \"240800\", \"246921\", \"247903\", \"253042\", \"254596\", \"255139\", \"261786\", \"263733\", \"296980\", \"327996\"]}","task_split":"paper_retrieval"}
{"document_id":"123234","document_content":"# Automatic Discovery of Interpretable Planning Strategies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n- Human-Computer Interaction\n## Abstract\nWhen making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of large behavioral experiments showed that prividing the decision rules generated by AI-Interpret as flowcharts significantly improved people's planning strategies and decisions across three diferent classes of sequential decision problems. Moreover, another experiment revealed that this approach is significantly more effective than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that AI-Interpret is critical to the discovery of interpretable decision rules. We conclude that the methods and findings presented herein are an important step towards leveraging automatic strategy discovery to improve human decision-making.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10994-021-05963-2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"20217\"], \"outgoing_citations\": [\"20452\", \"72359\", \"87895\", \"149869\", \"152090\", \"176450\", \"182190\", \"187976\", \"190780\", \"209859\", \"217602\", \"217794\", \"224479\", \"226169\", \"231157\", \"236488\", \"250026\", \"251785\", \"253613\"]}","task_split":"paper_retrieval"}
{"document_id":"123257","document_content":"# MASK: A flexible framework to facilitate de-identification of clinical texts\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nMedical health records and clinical summaries contain a vast amount of important information in textual form that can help advancing research on treatments, drugs and public health. However, the majority of these information is not shared because they contain private information about patients, their families, or medical staff treating them. Regulations such as HIPPA in the US, PHIPPA in Canada and GDPR regulate the protection, processing and distribution of this information. In case this information is de-identified and personal information are replaced or redacted, they could be distributed to the research community. In this paper, we present MASK, a software package that is designed to perform the de-identification task. The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities. The user is able to select named entity recognition algorithm (currently implemented are two versions of CRF-based techniques and BiLSTM-based neural network with pre-trained GLoVe and ELMo embedding) and masking algorithm (e.g. shift dates, replace names\/locations, totally redact entity).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11687v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"263027\", \"276462\", \"290680\"]}","task_split":"paper_retrieval"}
{"document_id":"123259","document_content":"# Networks with pixels embedding: a method to improve noise resistance in images classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn the task of image classification, usually, the network is sensitive to noises. For example, an image of cat with noises might be misclassified as an ostrich. Conventionally, to overcome the problem of noises, one uses the technique of data augmentation, that is, to teach the network to distinguish noises by adding more images with noises in the training dataset. In this work, we provide a noise-resistance network in images classification by introducing a technique of pixel embedding. We test the network with pixel embedding, which is abbreviated as the network with PE, on the mnist database of handwritten digits. It shows that the network with PE outperforms the conventional network on images with noises. The technique of pixel embedding can be used in many tasks of image classification to improve noise resistance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11679v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"208434\", \"240158\", \"263162\", \"279956\", \"285059\", \"333603\"]}","task_split":"paper_retrieval"}
{"document_id":"123414","document_content":"# SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nPair-based metric learning has been widely adopted to learn sentence embedding in many NLP tasks such as semantic text similarity due to its efficiency in computation. Most existing works employed a sequence encoder model and utilized limited sentence pairs with a pair-based loss to learn discriminating sentence representation. However, it is known that the sentence representation can be biased when the sampled sentence pairs deviate from the true distribution of all sentence pairs. In this paper, our theoretical analysis shows that existing works severely suffered from a good pair sampling and instance weighting strategy. Instead of one time pair selection and learning on equal weighted pairs, we propose a unified locality weighting and learning framework to learn task-specific sentence embedding. Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair. Such weight is updated along with pair-loss optimization in each round, ensuring the model keep learning the most informative sentence pairs. Extensive experiments on four public available datasets and a self-collected place search benchmark with 1.4 million places clearly demonstrate that our model consistently outperforms existing sentence embedding methods with comparable efficiency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11347v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"190672\", \"193779\", \"214075\", \"237157\", \"237274\", \"239686\", \"267161\", \"269692\", \"274120\", \"298543\", \"302516\", \"310737\", \"320009\"]}","task_split":"paper_retrieval"}
{"document_id":"123420","document_content":"# Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)\n## Categories\n- Machine Learning\n## Abstract\nClimate change is one of the most concerning issues of this century. Emission from electric power generation is a crucial factor that drives the concern to the next level. Renewable energy sources are widespread and available globally, however, one of the major challenges is to understand their characteristics in a more informative way. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.12401v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"187125\", \"236457\"]}","task_split":"paper_retrieval"}
{"document_id":"123442","document_content":"# Evaluating Generalisation in General Video Game Playing\n## Categories\n- Artificial Intelligence\n## Abstract\nThe General Video Game Artificial Intelligence (GVGAI) competition has been running for several years with various tracks. This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for training, while 3 hidden levels are left for evaluation. This setup poses a difficult challenge for current Reinforcement Learning (RL) algorithms, as they typically require much more data. This work investigates 3 versions of the Advantage Actor-Critic (A2C) algorithm trained on a maximum of 2 levels from the available 5 from the GVGAI framework and compares their performance on all levels. The selected sub-set of games have different characteristics, like stochasticity, reward distribution and objectives. We found that stochasticity improves the generalisation, but too much can cause the algorithms to fail to learn the training levels. The quality of the training levels also matters, different sets of training levels can boost generalisation over all levels. In the GVGAI competition agents are scored based on their win rates and then their scores achieved in the games. We found that solely using the rewards provided by the game might not encourage winning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11247v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"87353\"], \"outgoing_citations\": [\"152918\", \"200264\", \"207051\", \"216076\", \"218124\", \"226544\", \"229002\", \"236067\", \"240572\", \"243235\", \"268226\", \"280296\", \"284567\", \"297667\"]}","task_split":"paper_retrieval"}
{"document_id":"123457","document_content":"# Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nBlack-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that \"simplifies\" the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.11197v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56086\", \"26460\", \"57427\"], \"outgoing_citations\": [\"179313\", \"180006\", \"212887\", \"235312\", \"251816\", \"267601\", \"270048\", \"295028\"]}","task_split":"paper_retrieval"}
{"document_id":"123531","document_content":"# Customized Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nRecently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and then use this unified model to predict labels of all the unseen graphs in the test set. However, graphs in the same dataset often have dramatically distinct structures, which indicates that a unified model may be sub-optimal given an individual graph. Therefore, in this paper, we aim to develop customized graph neural networks for graph classification. Specifically, we propose a novel customized graph neural network framework, i.e., Customized-GNN. Given a graph sample, Customized-GNN can generate a sample-specific model for this graph based on its structure. Meanwhile, the proposed framework is very general that can be applied to numerous existing graph neural network models. Comprehensive experiments on various graph classification benchmarks demonstrate the effectiveness of the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.12386v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"93054\", \"117754\", \"150166\", \"155642\", \"157487\", \"188398\", \"190165\", \"249443\", \"255134\"]}","task_split":"paper_retrieval"}
{"document_id":"123550","document_content":"# SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts. The semantic information is used both in the encoder module for supervision and in the decoder module for initializing. In particular, the state-of-the art ASTER method is integrated into the proposed framework as an exemplar. Extensive experiments demonstrate that the proposed framework is more robust for low-quality text images, and achieves state-of-the-art results on several benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.10977v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38757\", \"1039\", \"3394\", \"5857\", \"6379\", \"13927\", \"36085\", \"37618\", \"50775\", \"44535\", \"78763\", \"107465\", \"109525\", \"191\", \"236\", \"7580\", \"8109\", \"9220\", \"10204\", \"19779\", \"23130\", \"24824\", \"25116\", \"25132\", \"28270\", \"32623\", \"33358\", \"33363\", \"34290\", \"38059\", \"39759\", \"43037\", \"48822\", \"48901\", \"49805\", \"50591\", \"63761\", \"64528\", \"64650\", \"67749\", \"93345\", \"113995\", \"123348\", \"90549\", \"126431\"], \"outgoing_citations\": [\"170061\", \"173076\", \"190056\", \"205988\", \"211851\", \"212589\", \"217463\", \"227200\", \"232706\", \"250738\", \"256711\", \"264526\", \"269205\", \"279950\", \"284871\", \"294023\", \"296685\", \"296828\", \"306246\", \"309239\", \"311196\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"123556","document_content":"# Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3394231.3397922\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"68811\", \"18499\", \"64483\", \"73733\"], \"outgoing_citations\": [\"51025\", \"208663\", \"209451\", \"215230\", \"222419\", \"237983\", \"241330\"]}","task_split":"paper_retrieval"}
{"document_id":"123561","document_content":"# Automatic Discovery of Novel Intents & Domains from Text Utterances\n## Categories\n- Computation and Language\n- Information Retrieval\n- Sound\n- Audio and Speech Processing\n## Abstract\nOne of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing research formulates this as a supervised classification problem with a closed-world assumption, i.e. the domains or intents to be identified are pre-defined or known beforehand. Real-world applications however increasingly encounter dynamic, rapidly evolving environments with newly emerging intents and domains, about which no information is known during model training. We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data. We first employ an open classification model to identify all utterances potentially consisting of a novel intent. Next, we build a knowledge transfer component with a pairwise margin loss function. It learns discriminative deep features to group together utterances and discover multiple latent intent categories within them in an unsupervised manner. We finally hierarchically link mutually related intents into domains, forming an intent-domain taxonomy. ADVIN significantly outperforms baselines on three benchmark datasets, and real user utterances from a commercial voice-powered agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2006.01208v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"46822\"], \"outgoing_citations\": [\"177377\", \"183006\", \"191817\", \"204763\", \"213822\", \"217632\", \"219441\", \"226294\", \"230543\", \"244872\", \"244978\", \"254900\", \"285317\", \"293924\", \"302958\", \"302985\", \"320186\", \"357745\"]}","task_split":"paper_retrieval"}
{"document_id":"123706","document_content":"# An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges\n## Categories\n- Machine Learning\n- 68T05\n## Abstract\nIn many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.neucom.2020.04.057\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\", \"68T05\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"68T05\"], \"incoming_citations\": [\"102277\", \"10119\", \"43433\", \"107672\"], \"outgoing_citations\": [\"135007\", \"168672\", \"182154\", \"194979\", \"205704\", \"226576\", \"234240\", \"240913\", \"241812\", \"242239\", \"245929\", \"272675\", \"281135\", \"289913\", \"297788\", \"303062\", \"303139\", \"319454\", \"323681\", \"324031\"]}","task_split":"paper_retrieval"}
{"document_id":"123762","document_content":"# Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction\n## Categories\n- Signal Processing\n- Machine Learning\n- Machine Learning\n## Abstract\nThe main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 minutes in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, we propose to decompose the end-to-end recommendation task into two hierarchical models: real-time traffic prediction and plan association. We learn the connections between the two models through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. We demonstrate the effectiveness of our approach by testing this framework on the traffic network in Cranberry Township in 2019. Results show that our recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and make recommendation of an average of 22.5 minutes lead time ahead of Waze alerts. The results suggest that our framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1177\/0361198120917668\", \"primary_category\": \"eess.SP\", \"categories\": [\"eess.SP\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Signal Processing\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"97957\", \"114765\"], \"outgoing_citations\": [\"181356\", \"202316\", \"211345\", \"276106\", \"295664\", \"315231\", \"323833\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"123776","document_content":"# Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis\n## Categories\n- Audio and Speech Processing\n- Computation and Language\n- Sound\n- Machine Learning\n## Abstract\nNeural sequence-to-sequence text-to-speech synthesis (TTS) can produce high-quality speech directly from text or simple linguistic features such as phonemes. Unlike traditional pipeline TTS, the neural sequence-to-sequence TTS does not require manually annotated and complicated linguistic features such as part-of-speech tags and syntactic structures for system training. However, it must be carefully designed and well optimized so that it can implicitly extract useful linguistic features from the input features. In this paper we investigate under what conditions the neural sequence-to-sequence TTS can work well in Japanese and English along with comparisons with deep neural network (DNN) based pipeline TTS systems. Unlike past comparative studies, the pipeline systems also use autoregressive probabilistic modeling and a neural vocoder. We investigated systems from three aspects: a) model architecture, b) model parameter size, and c) language. For the model architecture aspect, we adopt modified Tacotron systems that we previously proposed and their variants using an encoder from Tacotron or Tacotron2. For the model parameter size aspect, we investigate two model parameter sizes. For the language aspect, we conduct listening tests in both Japanese and English to see if our findings can be generalized across languages. Our experiments suggest that a) a neural sequence-to-sequence TTS system should have a sufficient number of model parameters to produce high quality speech, b) it should also use a powerful encoder when it takes characters as inputs, and c) the encoder still has a room for improvement and needs to have an improved architecture to learn supra-segmental features more appropriately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.10390v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.CL\", \"cs.SD\", \"stat.ML\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"15291\", \"36210\", \"57348\", \"65052\"], \"outgoing_citations\": [\"157595\", \"160284\", \"169953\", \"176787\", \"182708\", \"182898\", \"185160\", \"185454\", \"190878\", \"191357\", \"191639\", \"192116\", \"193548\", \"209001\", \"209096\", \"209676\", \"212248\", \"212620\", \"212672\", \"217313\", \"218181\", \"222519\", \"236453\", \"237918\", \"240256\", \"252497\", \"252801\", \"260669\", \"265497\", \"270234\", \"271934\", \"277384\", \"290033\", \"291183\", \"300604\", \"307547\", \"310601\", \"313465\", \"341730\"]}","task_split":"paper_retrieval"}
{"document_id":"124122","document_content":"# Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text\n## Categories\n- Computation and Language\n## Abstract\nRecent work has described neural-network-based agents that are trained with reinforcement learning (RL) to execute language-like commands in simulated worlds, as a step towards an intelligent agent or robot that can be instructed by human users. However, the optimisation of multi-goal motor policies via deep RL from scratch requires many episodes of experience. Consequently, instruction-following with deep RL typically involves language generated from templates (by an environment simulator), which does not reflect the varied or ambiguous expressions of real users. Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions. By applying our method with a state-of-the-art pre-trained text-based language model (BERT), on tasks requiring agents to identify and position everyday objects relative to other objects in a naturalistic 3D simulated room, we demonstrate substantially-above-chance zero-shot transfer from synthetic template commands to natural instructions given by humans. Our approach is a general recipe for training any deep RL-based system to interface with human users, and bridges the gap between two research directions of notable recent success: agent-centric motor behavior and text-based representation learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.09382v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8658\", \"79803\", \"11352\", \"14861\", \"26076\", \"33224\", \"37989\", \"45609\", \"51175\", \"53305\", \"64051\", \"64499\", \"66817\", \"74771\", \"101182\", \"107630\", \"107839\", \"160608\", \"111265\", \"111340\", \"121716\"], \"outgoing_citations\": [\"155676\", \"161143\", \"180270\", \"183080\", \"184236\", \"199210\", \"201070\", \"206781\", \"208773\", \"213745\", \"219305\", \"228916\", \"229145\", \"231155\", \"243235\", \"243782\", \"245731\", \"249906\", \"263104\", \"263270\", \"263650\", \"267779\", \"290891\", \"293071\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"124140","document_content":"# Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSpatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions. To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.09343v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"220761\", \"235282\", \"251806\", \"260351\", \"264020\", \"268967\", \"276502\", \"283428\", \"291461\", \"292009\", \"302815\", \"311470\", \"317379\"]}","task_split":"paper_retrieval"}
{"document_id":"124148","document_content":"# A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing and robotics. The real-world complications of many tasks arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms. RL agents in these applications often need to react and adapt to changing operating conditions. A significant part of research on single-agent RL techniques focuses on developing algorithms when the underlying assumption of stationary environment model is relaxed. This paper provides a survey of RL methods developed for handling dynamically varying environment models. The goal of methods not limited by the stationarity assumption is to help autonomous agents adapt to varying operating conditions. This is possible either by minimizing the rewards lost during learning by RL agent or by finding a suitable policy for the RL agent which leads to efficient operation of the underlying system. A representative collection of these algorithms is discussed in detail in this work along with their categorization and their relative merits and demerits. Additionally we also review works which are tailored to application domains. Finally, we discuss future enhancements for this field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3459991\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3429\", \"9804\", \"30912\", \"67873\", \"9946\", \"44791\", \"48475\", \"48723\", \"53458\", \"131978\", \"96084\", \"115040\", \"124615\"], \"outgoing_citations\": [\"139275\", \"146974\", \"149805\", \"159108\", \"186302\", \"188486\", \"189411\", \"195088\", \"200656\", \"207983\", \"210517\", \"225538\", \"237953\", \"241392\", \"241496\", \"241499\", \"246518\", \"247323\", \"253658\", \"284206\", \"317861\"]}","task_split":"paper_retrieval"}
{"document_id":"124223","document_content":"# Retrieving and Highlighting Action with Spatiotemporal Reference\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Information Retrieval\n## Abstract\nIn this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which relate to the nouns and verbs in captions. Through experiments, we show that our model generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map. Also, our model improves retrieval recall over our baseline without alignment by 2-3% on the MSR-VTT dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.09183v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169858\", \"172394\", \"173627\", \"176053\", \"217562\", \"226619\", \"236468\", \"236696\", \"249241\", \"250183\", \"260926\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"124270","document_content":"# P-SIF: Document Embeddings Using Partition Averaging\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSimple weighted averaging of word vectors often yields effective representations for sentences which outperform sophisticated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation. This problem is less acute in single sentences and other short text fragments where the presence of a single topic is most likely. To alleviate this problem, we present P-SIF, a partitioned word averaging model to represent long documents. P-SIF retains the simplicity of simple weighted word averaging while taking a document's topical structure into account. In particular, P-SIF learns topic-specific vectors from a document and finally concatenates them all to represent the overall document. We provide theoretical justifications on the correctness of P-SIF. Through a comprehensive set of experiments, we demonstrate P-SIF's effectiveness compared to simple weighted averaging and many other baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.09069v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"46356\", \"21888\", \"60004\", \"94720\", \"140769\", \"163247\"], \"outgoing_citations\": [\"176409\", \"177166\", \"212285\", \"214235\", \"227792\", \"237274\", \"238963\", \"261768\", \"267622\", \"272080\", \"273313\", \"277554\", \"290029\", \"293087\", \"300128\", \"301644\", \"302516\", \"310455\", \"310737\", \"311384\", \"317627\"]}","task_split":"paper_retrieval"}
{"document_id":"124311","document_content":"# DHP Framework: Digital Health Passports Using Blockchain -- Use case on international tourism during the COVID-19 pandemic\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nIn order to contain the COVID-19 pandemic, several countries enforced extended social distancing measures for several weeks, effectively pausing the majority of economic activities. In an effort to resume economic activity safely, several Digital Contact Tracing applications and protocols have been introduced with success. However, DCT is a reactive method, as it aims to break existing chains of disease transmission in a population. Therefore DCT is not suitable for proactively preventing the spread of a disease; an approach that relevant to certain use cases, such as international tourism, where individuals travel across borders. In this work, we first identify the limitations characterising DCT related to privacy issues, unwillingness of the public to use DCT mobile apps due to privacy concerns, lack of interoperability among different DCT applications and protocols, and the assumption that there is limited, local mobility in the population. We then discuss the concept of a Health Passport as a means of verifying that individuals are disease risk-free and how it could be used to resume the international tourism sector. Following, we present the DHP Framework that uses a private blockchain and Proof of Authority for issuing Digital Health Passports. The framework provides a distributed infrastructure supporting the issuance of DHPs by foreign health systems and their verification by relevant stakeholders, such as airline companies and border control authorities. We discuss the attributes of the system in terms of its usability and performance, security and privacy. Finally, we conclude by identifying future extensions of our work on formal security and privacy properties that need to be rigorously guaranteed via appropriate security protocols.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1111\/j.1365-2966.2005.08922.x\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [\"22332\", \"1994\", \"75851\", \"82619\"], \"outgoing_citations\": [\"126394\", \"131172\", \"131783\", \"132239\", \"132453\", \"134952\"]}","task_split":"paper_retrieval"}
{"document_id":"124449","document_content":"# Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNumerical reasoning is often important to accurately understand the world. Recently, several format-specific datasets have been proposed, such as numerical reasoning in the settings of Natural Language Inference (NLI), Reading Comprehension (RC), and Question Answering (QA). Several format-specific models and architectures in response to those datasets have also been proposed. However, there exists a strong need for a benchmark which can evaluate the abilities of models, in performing question format independent numerical reasoning, as (i) the numerical reasoning capabilities we want to teach are not controlled by question formats, (ii) for numerical reasoning technology to have the best possible application, it must be able to process language and reason in a way that is not exclusive to a single format, task, dataset or domain. In pursuit of this goal, we introduce NUMBERGAME, a multifaceted benchmark to evaluate model performance across numerical reasoning tasks of eight diverse formats. We add four existing question types in our compilation. Two of the new types we add are about questions that require external numerical knowledge, commonsense knowledge and domain knowledge. For building a more practical numerical reasoning system, NUMBERGAME demands four capabilities beyond numerical reasoning: (i) detecting question format directly from data (ii) finding intermediate common format to which every format can be converted (iii) incorporating commonsense knowledge (iv) handling data imbalance across formats. We build several baselines, including a new model based on knowledge hunting using a cheatsheet. However, all baselines perform poorly in contrast to the human baselines, indicating the hardness of our benchmark. Our work takes forward the recent progress in generic system development, demonstrating the scope of these under-explored tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.08516v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"33114\", \"55171\", \"24594\", \"38265\", \"45468\", \"60990\", \"66890\"], \"outgoing_citations\": [\"127513\", \"149275\", \"154152\", \"161625\", \"164303\", \"165844\", \"166307\", \"169358\", \"174555\", \"183201\", \"183403\", \"192616\", \"196980\", \"203186\", \"209413\", \"227323\", \"233416\", \"238962\", \"246516\", \"266702\", \"278863\", \"287181\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"124713","document_content":"# A Text Reassembling Approach to Natural Language Generation\n## Categories\n- Computation and Language\n## Abstract\nRecent years have seen a number of proposals for performing Natural Language Generation (NLG) based in large part on statistical techniques. Despite having many attractive features, we argue that these existing approaches nonetheless have some important drawbacks, sometimes because the approach in question is not fully statistical (i.e., relies on a certain amount of handcrafting), sometimes because the approach in question lacks transparency. Focussing on some of the key NLG tasks (namely Content Selection, Lexical Choice, and Linguistic Realisation), we propose a novel approach, called the Text Reassembling approach to NLG (TRG), which approaches the ideal of a purely statistical approach very closely, and which is at the same time highly transparent. We evaluate the TRG approach and discuss how TRG may be extended to deal with other NLG tasks, such as Document Structuring, and Aggregation. We discuss the strengths and limitations of TRG, concluding that the method may hold particular promise for domain experts who want to build an NLG system despite having little expertise in linguistics and NLG.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07988v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"156600\", \"157116\", \"170864\", \"201921\", \"230014\", \"250035\", \"270292\", \"275597\", \"277060\", \"307195\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"124845","document_content":"# Face Identity Disentanglement via Latent Space Mapping\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Machine Learning\n## Abstract\nLearning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process. Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality. In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks. Our key insight is to decouple the processes of disentanglement and synthesis, by employing a leading pre-trained unconditional image generator, such as StyleGAN. By learning to map into its latent space, we leverage both its state-of-the-art quality, and its rich and expressive latent space, without the burden of training it. We demonstrate our approach on the complex and high dimensional domain of human heads. We evaluate our method qualitatively and quantitatively, and exhibit its success with de-identification operations and with temporal identity coherency in image sequences. Through extensive experimentation, we show that our method successfully disentangles identity from other facial attributes, surpassing existing methods, even though they require more training and supervision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07728v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Machine Learning\"], \"incoming_citations\": [\"298698\", \"75482\", \"172991\", \"4101\", \"4730\", \"5264\", \"7149\", \"14590\", \"33888\", \"55754\", \"19513\", \"31142\", \"32152\", \"34724\", \"35216\", \"35293\", \"39344\", \"43812\", \"50329\", \"58789\", \"59166\", \"71452\", \"89636\", \"71456\", \"119932\"], \"outgoing_citations\": [\"108127\", \"109032\", \"132073\", \"132835\", \"133828\", \"133858\", \"148973\", \"152821\", \"155482\", \"171528\", \"174537\", \"178648\", \"179226\", \"185516\", \"187702\", \"192020\", \"206218\", \"206907\", \"208041\", \"223504\", \"224353\", \"226432\", \"227030\", \"230021\", \"230031\", \"235754\", \"236123\", \"237272\", \"241933\", \"245177\", \"250081\", \"257180\", \"264963\", \"265526\", \"270245\", \"273927\", \"280070\", \"280232\", \"284874\"]}","task_split":"paper_retrieval"}
{"document_id":"124853","document_content":"# Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMethods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07677v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"28677\", \"37498\", \"41863\", \"48859\", \"67124\", \"112786\"], \"outgoing_citations\": [\"153614\", \"175872\", \"181115\", \"189206\", \"228285\", \"233501\", \"264206\", \"265896\", \"274886\", \"314276\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"124867","document_content":"# Language Conditioned Imitation Learning over Unstructured Data\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nNatural language is perhaps the most flexible and intuitive way for humans to communicate tasks to a robot. Prior work in imitation learning typically requires each task be specified with a task id or goal image -- something that is often impractical in open-world environments. On the other hand, previous approaches in instruction following allow agent behavior to be guided by language, but typically assume structure in the observations, actuators, or language that limit their applicability to complex settings like robotics. In this work, we present a method for incorporating free-form natural language conditioning into imitation learning. Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network. Unlike prior work in imitation learning, our method is able to incorporate unlabeled and unstructured demonstration data (i.e. no task or language labels). We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data. At test time, a single language conditioned visuomotor policy trained with our method can perform a wide variety of robotic manipulation skills in a 3D environment, specified only with natural language descriptions of each task (e.g. \"open the drawer...now pick up the block...now press the green button...\"). To scale up the number of instructions an agent can follow, we propose combining text conditioned policies with large pretrained neural language models. We find this allows a policy to be robust to many out-of-distribution synonym instructions, without requiring new demonstrations. See videos of a human typing live text commands to our agent at language-play.github.io","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07648v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"8658\", \"4865\", \"13710\", \"80120\", \"17658\"], \"outgoing_citations\": [\"92051\", \"140124\", \"159675\", \"160120\", \"176839\", \"180270\", \"180885\", \"181484\", \"181620\", \"190237\", \"193391\", \"196377\", \"196387\", \"204013\", \"207601\", \"208773\", \"213745\", \"224840\", \"226635\", \"243782\", \"253448\", \"254597\", \"261316\", \"261617\", \"261983\", \"263270\", \"265069\", \"267779\", \"268248\", \"290679\", \"297317\", \"308711\", \"309396\"]}","task_split":"paper_retrieval"}
{"document_id":"124961","document_content":"# Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nPlanning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07404v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"115868\", \"109628\"], \"outgoing_citations\": [\"177658\", \"229890\", \"242661\", \"265642\", \"317874\"]}","task_split":"paper_retrieval"}
{"document_id":"125015","document_content":"# DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment. In this work, we propose DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense estimation of 3D non-rigid facial flow between pairs of monocular images. Our DeepFaceFlow framework was trained and tested on two very large-scale facial video datasets, one of them of our own collection and annotation, with the aid of occlusion-aware and 3D-based loss function. We conduct comprehensive experiments probing different aspects of our approach and demonstrating its improved performance against state-of-the-art flow and 3D reconstruction methods. Furthermore, we incorporate our framework in a full-head state-of-the-art facial video synthesis method and demonstrate the ability of our method in better representing and capturing the facial dynamics, resulting in a highly-realistic facial video synthesis. Given registered pairs of images, our framework generates 3D flow maps at ~60 fps.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07298v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"51910\", \"80394\", \"117837\"], \"outgoing_citations\": [\"123562\", \"125661\", \"171363\", \"198793\", \"207172\", \"220842\", \"225235\", \"231645\", \"249748\", \"268589\", \"272729\", \"281333\", \"299047\"]}","task_split":"paper_retrieval"}
{"document_id":"125055","document_content":"# NAT: Noise-Aware Training for Robust Neural Sequence Labeling\n## Categories\n- Computation and Language\n## Abstract\nSequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs - as these systems often process user-generated text or follow an error-prone upstream component. To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation. We employ a vanilla noise model at training time. For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings. Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input. We make our code and data publicly available for the research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07162v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19290\", \"35342\", \"47433\"], \"outgoing_citations\": [\"182218\", \"182388\", \"184975\", \"200218\", \"220758\", \"231932\", \"234716\", \"245196\", \"247127\", \"250586\", \"251240\", \"255136\", \"259966\", \"268889\", \"291899\", \"294549\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"125061","document_content":"# Named Entity Recognition as Dependency Parsing\n## Categories\n- Computation and Language\n## Abstract\nNamed Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07150v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2019\", \"3492\", \"6979\", \"16354\", \"16648\", \"17300\", \"20995\", \"23896\", \"26567\", \"27492\", \"37458\", \"84089\", \"6309\", \"12251\", \"12396\", \"15191\", \"23525\", \"24940\", \"26832\", \"32579\", \"33588\", \"41559\", \"45341\", \"45650\", \"46298\", \"46602\", \"48692\", \"49595\", \"50900\", \"51180\", \"55965\", \"56776\", \"59603\", \"61663\", \"80323\", \"81520\", \"81514\", \"91925\", \"95236\", \"95234\", \"87107\", \"89178\", \"91915\", \"170213\"], \"outgoing_citations\": [\"171105\", \"178272\", \"181688\", \"191957\", \"213489\", \"215547\", \"215552\", \"216925\", \"274533\", \"281121\", \"297150\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"125254","document_content":"# Detector-SegMentor Network for Skin Lesion Localization and Segmentation\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMelanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. Although there are more cases of non-melanoma cancer than melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is crucial for the timely diagnosis of melanoma cancer and prohibit its spread to distant body parts. Segmentation of skin lesion is a crucial step in the classification of melanoma cancer from the cancerous lesions in dermoscopic images. Manual segmentation of dermoscopic skin images is very time consuming and error-prone resulting in an urgent need for an intelligent and accurate algorithm. In this study, we propose a simple yet novel network-in-network convolution neural network(CNN) based approach for segmentation of the skin lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to predict bounding boxes of the lesions in the whole image which are subsequently cropped and fed into the segmentation network to obtain the lesion mask. The segmentation network is a combination of the UNet and Hourglass networks. We trained and evaluated our models on ISIC 2018 dataset and also cross-validated on PH\\textsuperscript{2} and ISBI 2017 datasets. Our proposed method surpassed the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy 0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and Accuracy 0.971 on ISBI 2017 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.06550v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\", \"cs.LG\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"25674\"], \"outgoing_citations\": [\"199630\", \"199710\", \"201512\", \"222227\", \"237466\", \"272621\"]}","task_split":"paper_retrieval"}
{"document_id":"125340","document_content":"# Reasoning with Latent Structure Refinement for Document-Level Relation Extraction\n## Categories\n- Computation and Language\n## Abstract\nDocument-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.06312v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"101305\", \"247466\", \"5072\", \"20734\", \"21235\", \"25436\", \"26600\", \"41725\", \"55762\", \"78304\", \"10438\", \"16272\", \"22837\", \"23762\", \"24188\", \"24348\", \"24325\", \"24888\", \"28587\", \"2037\", \"41183\", \"44107\", \"45204\", \"45130\", \"45088\", \"48161\", \"53160\", \"59299\", \"63045\", \"68079\", \"70015\", \"77498\", \"79232\", \"80977\", \"92431\", \"96400\", \"81966\", \"82764\", \"90289\", \"95774\", \"97979\", \"99243\", \"100562\", \"103663\"], \"outgoing_citations\": [\"154631\", \"157009\", \"161666\", \"164858\", \"168575\", \"169298\", \"171516\", \"180210\", \"180767\", \"181307\", \"182601\", \"185805\", \"192327\", \"198422\", \"214572\", \"216419\", \"218944\", \"219849\", \"220105\", \"230374\", \"232981\", \"235408\", \"240504\", \"253068\", \"258746\", \"265431\", \"271527\", \"274817\", \"284622\", \"300593\", \"308043\", \"309429\", \"310572\"]}","task_split":"paper_retrieval"}
{"document_id":"125408","document_content":"# A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nApplying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.06148v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"38564\", \"95477\"], \"outgoing_citations\": [\"133342\", \"133812\", \"147140\", \"189201\", \"233501\", \"265896\", \"271177\", \"274886\", \"279202\", \"289709\", \"297233\"]}","task_split":"paper_retrieval"}
{"document_id":"125415","document_content":"# Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation\n## Categories\n- Computation and Language\n## Abstract\nNeural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and the teacher's insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.06128v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23922\", \"28492\", \"35402\", \"48093\", \"65915\", \"97474\", \"99759\"], \"outgoing_citations\": [\"170205\", \"171288\", \"180957\", \"182107\", \"189798\", \"211664\", \"213578\", \"216965\", \"217823\", \"220017\", \"227586\", \"247824\", \"270040\", \"274567\", \"281163\", \"283076\", \"289061\", \"289943\", \"291102\", \"292261\", \"295863\", \"305200\", \"309411\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"125425","document_content":"# Large Scale Multi-Actor Generative Dialog Modeling\n## Categories\n- Computation and Language\n## Abstract\nNon-goal oriented dialog agents (i.e. chatbots) aim to produce varying and engaging conversations with a user; however, they typically exhibit either inconsistent personality across conversations or the average personality of all users. This paper addresses these issues by controlling an agent's persona upon generation via conditioning on prior conversations of a target actor. In doing so, we are able to utilize more abstract patterns within a person's speech and better emulate them in generated responses. This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor's persona. We introduce an accompanying data collection procedure to obtain 10.3M conversations from 6 months worth of Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held out Reddit conversations. Increasing model scale yielded similar improvements in human evaluations that measure preference of model samples to the held out target distribution in terms of realism (31% increased to 37% preference), style matching (37% to 42%), grammar and content quality (29% to 42%), and conversation coherency (32% to 40%). We find that conditionally modeling past conversations improves perplexity by 0.47 in automatic evaluations. Through human trials we identify positive trends between conditional modeling and style matching and outline steps to further improve persona control.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.06114v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2633\", \"50552\", \"48802\", \"92904\", \"97214\", \"99088\", \"140784\"], \"outgoing_citations\": [\"145384\", \"158493\", \"166278\", \"174210\", \"183694\", \"188522\", \"200764\", \"201867\", \"218963\", \"244479\", \"255778\", \"263059\", \"274567\", \"280103\", \"292261\", \"296250\", \"305200\", \"309411\", \"310377\", \"310740\"]}","task_split":"paper_retrieval"}
{"document_id":"125585","document_content":"# Flight Time Prediction for Fuel Loading Decisions with a Deep Learning Approach\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nUnder increasing economic and environmental pressure, airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. However, the current practice on fuel loading, which has a significant impact on aircraft weight and fuel consumption, has yet to be thoroughly addressed by existing studies. Excess fuel is loaded by dispatchers and (or) pilots to handle fuel consumption uncertainties, primarily caused by flight time uncertainties, which cannot be predicted by current Flight Planning Systems. In this paper, we develop a novel spatial weighted recurrent neural network model to provide better flight time predictions by capturing air traffic information at a national scale based on multiple data sources, including Automatic Dependent Surveillance-Broadcast, Meteorological Aerodrome Reports, and airline records. In this model, a spatial weighted layer is designed to extract spatial dependences among network delay states. Then, a new training procedure associated with the spatial weighted layer is introduced to extract OD-specific spatial weights. Long short-term memory networks are used to extract the temporal behavior patterns of network delay states. Finally, features from delays, weather, and flight schedules are fed into a fully connected neural network to predict the flight time of a particular flight. The proposed model was evaluated using one year of historical data from an airline's real operations. Results show that our model can provide more accurate flight time predictions than baseline methods, especially for flights with extreme delays. We also show that, with the improved flight time prediction, fuel loading can be optimized and resulting in reduced fuel consumption by 0.016%-1.915% without increasing the fuel depletion risk.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.trc.2021.103179\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204375\", \"271444\"]}","task_split":"paper_retrieval"}
{"document_id":"125657","document_content":"# Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn recent years deep neural networks have been successfully applied to the domains of reinforcement learning \\cite{bengio2009learning,krizhevsky2012imagenet,hinton2006reducing}. Deep reinforcement learning \\cite{mnih2015human} is reported to have the advantage of learning effective policies directly from high-dimensional sensory inputs over traditional agents. However, within the scope of the literature, there is no fundamental change or improvement on the existing training framework. Here we propose a novel training framework that is conceptually comprehensible and potentially easy to be generalized to all feasible algorithms for reinforcement learning. We employ Monte-carlo sampling to achieve raw data inputs, and train them in batch to achieve Markov decision process sequences and synchronously update the network parameters instead of experience replay. This training framework proves to optimize the unbiased approximation of loss function whose estimation exactly matches the real probability distribution data inputs follow, and thus have overwhelming advantages of sample efficiency and convergence rate over existing deep reinforcement learning after evaluating it on both discrete action spaces and continuous control problems. Besides, we propose several algorithms embedded with our new framework to deal with typical discrete and continuous scenarios. These algorithms prove to be far more efficient than their original versions under the framework of deep reinforcement learning, and provide examples for existing and future algorithms to generalize to our new framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.07782v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"199472\", \"212832\", \"248938\", \"260770\", \"280296\", \"290904\", \"310171\"]}","task_split":"paper_retrieval"}
{"document_id":"125661","document_content":"# Real-time Facial Expression Recognition \"In The Wild'' by Disentangling 3D Expression from Identity\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nHuman emotions analysis has been the focus of many studies, especially in the field of Affective Computing, and is important for many applications, e.g. human-computer intelligent interaction, stress analysis, interactive games, animations, etc. Solutions for automatic emotion analysis have also benefited from the development of deep learning approaches and the availability of vast amount of visual facial data on the internet. This paper proposes a novel method for human emotion recognition from a single RGB image. We construct a large-scale dataset of facial videos (\\textbf{FaceVid}), rich in facial dynamics, identities, expressions, appearance and 3D pose variations. We use this dataset to train a deep Convolutional Neural Network for estimating expression parameters of a 3D Morphable Model and combine it with an effective back-end emotion classifier. Our proposed framework runs at 50 frames per second and is capable of robustly estimating parameters of 3D expression variation and accurately recognizing facial expressions from in-the-wild images. We present extensive experimental evaluation that shows that the proposed method outperforms the compared techniques in estimating the 3D expression parameters and achieves state-of-the-art performance in recognising the basic emotions from facial images, as well as recognising stress from facial videos. %compared to the current state of the art in emotion recognition from facial images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.05509v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"9397\", \"117837\", \"184492\", \"125015\"], \"outgoing_citations\": [\"198793\", \"220533\", \"221950\", \"224942\", \"232569\", \"234576\", \"243530\", \"257637\", \"274313\", \"284716\", \"302701\", \"303467\", \"343074\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"125675","document_content":"# Neural Polysynthetic Language Modelling\n## Categories\n- Computation and Language\n## Abstract\nResearch in natural language processing commonly assumes that approaches that work well for English and and other widely-used languages are \"language agnostic\". In high-resource languages, especially those that are analytic, a common approach is to treat morphologically-distinct variants of a common root as completely independent word types. This assumes, that there are limited morphological inflections per root, and that the majority will appear in a large enough corpus, so that the model can adequately learn statistics about each form. Approaches like stemming, lemmatization, or subword segmentation are often used when either of those assumptions do not hold, particularly in the case of synthetic languages like Spanish or Russian that have more inflection than English.   In the literature, languages like Finnish or Turkish are held up as extreme examples of complexity that challenge common modelling assumptions. Yet, when considering all of the world's languages, Finnish and Turkish are closer to the average case. When we consider polysynthetic languages (those at the extreme of morphological complexity), approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena, showing the need for appropriate morphological handling of words, without which it is not possible for a model to capture enough word statistics.   We examine the current state-of-the-art in language modelling, machine translation, and text prediction for four polysynthetic languages: Guaran\\'i, St. Lawrence Island Yupik, Central Alaskan Yupik, and Inuktitut. We then propose a novel framework for language modelling that combines knowledge representations from finite-state morphological analyzers with Tensor Product Representations in order to enable neural language models capable of handling the full range of typologically variant languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.05477v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1834\", \"81174\", \"25473\"], \"outgoing_citations\": [\"181279\", \"183976\", \"213642\", \"214118\", \"221514\", \"226209\", \"228426\", \"228574\", \"238143\", \"242125\", \"247368\", \"259145\", \"268344\", \"280447\", \"280486\", \"300604\", \"320202\"]}","task_split":"paper_retrieval"}
{"document_id":"125753","document_content":"# Toward Better Storylines with Sentence-Level Language Models\n## Categories\n- Computation and Language\n## Abstract\nWe propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.05255v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"64398\", \"79302\", \"157124\"], \"outgoing_citations\": [\"150446\", \"165308\", \"185754\", \"191352\", \"194308\", \"200324\", \"210155\", \"212117\", \"219792\", \"232274\", \"237274\", \"239686\", \"264552\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"125843","document_content":"# A Deep Learning Approach for Automatic Detection of Fake News\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nFake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current handcrafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (i.e. model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.04938v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"75850\", \"105795\"], \"outgoing_citations\": [\"209764\", \"221735\", \"228156\", \"238986\", \"257863\", \"267552\", \"273723\"]}","task_split":"paper_retrieval"}
{"document_id":"125969","document_content":"# BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nLearning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN). In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones. We show that existing state-of-the-art agents do not generalize well. To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially. A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps. The learning process is composed of two phases. In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps. In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions. We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk's generalization ability. Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better. The codes and the datasets are released on our project page https:\/\/github.com\/Sha-Lab\/babywalk.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.04625v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4499\", \"18764\", \"19631\", \"39368\", \"47577\", \"80120\", \"9686\", \"19911\", \"27522\", \"36974\", \"42082\", \"49578\", \"49746\", \"54802\", \"84618\", \"89855\", \"100277\", \"86813\", \"93277\", \"93950\"], \"outgoing_citations\": [\"168651\", \"172416\", \"176603\", \"183823\", \"188867\", \"191570\", \"191970\", \"196218\", \"196525\", \"203398\", \"208042\", \"208773\", \"223990\", \"228916\", \"249906\", \"255575\", \"261017\", \"263650\", \"265722\", \"281106\", \"284565\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"126244","document_content":"# On Vocabulary Reliance in Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThe pursuit of high performance on public benchmarks has been the driving force for research in scene text recognition, and notable progress has been achieved. However, a close investigation reveals a startling fact that the state-of-the-art methods perform well on images with words within vocabulary but generalize poorly to images with words outside vocabulary. We call this phenomenon \"vocabulary reliance\". In this paper, we establish an analytical framework to conduct an in-depth study on the problem of vocabulary reliance in scene text recognition. Key findings include: (1) Vocabulary reliance is ubiquitous, i.e., all existing algorithms more or less exhibit such characteristic; (2) Attention-based decoders prove weak in generalizing to words outside vocabulary and segmentation-based decoders perform well in utilizing visual features; (3) Context modeling is highly coupled with the prediction layers. These findings provide new insights and can benefit future research in scene text recognition. Furthermore, we propose a simple yet effective mutual learning strategy to allow models of two families (attention-based and segmentation-based) to learn collaboratively. This remedy alleviates the problem of vocabulary reliance and improves the overall scene text recognition performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03959v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"1039\", \"5857\", \"50775\", \"57829\", \"78763\", \"191\", \"7580\", \"33358\", \"33360\", \"33363\", \"48901\", \"49805\", \"63761\", \"90549\"], \"outgoing_citations\": [\"144173\", \"149329\", \"155242\", \"172884\", \"192483\", \"210634\", \"217463\", \"225336\", \"225795\", \"250738\", \"256108\", \"256711\", \"264787\", \"269205\", \"294023\", \"294378\", \"295089\", \"296685\", \"309239\", \"311196\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"126297","document_content":"# SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects and material properties. Such approaches have limited scalability and fail to produce realistic approximations of camera, lidar, and radar data without significant additional work.   In this paper, we present a simple yet effective approach to generate realistic scenario sensor data, based only on a limited amount of lidar and camera data collected by an autonomous vehicle. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes, preserving rich information about object 3D geometry and appearance, as well as the scene conditions. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle and moving objects in the scene. We demonstrate our approach on the Waymo Open Dataset and show that it can synthesize realistic camera data for simulated scenarios. We also create a novel dataset that contains cases in which two self-driving vehicles observe the same scene at the same time. We use this dataset to provide additional evaluation and demonstrate the usefulness of our SurfelGAN model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03844v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1577\", \"20660\", \"4109\", \"5655\", \"16742\", \"85625\", \"9370\", \"12943\", \"39349\", \"44186\", \"75107\", \"84063\", \"85572\", \"106127\"], \"outgoing_citations\": [\"78890\", \"162130\", \"177076\", \"188899\", \"201953\", \"206814\", \"219658\", \"220842\", \"233165\", \"245731\", \"248885\", \"255575\", \"279420\"]}","task_split":"paper_retrieval"}
{"document_id":"126346","document_content":"# FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization\n## Categories\n- Computation and Language\n## Abstract\nNeural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistakes effectively. We tackle the problem of evaluating faithfulness of a generated summary given its source document. We first collected human annotations of faithfulness for outputs from numerous models on two datasets. We find that current models exhibit a trade-off between abstractiveness and faithfulness: outputs with less word overlap with the source document are more likely to be unfaithful. Next, we propose an automatic question answering (QA) based metric for faithfulness, FEQA, which leverages recent advances in reading comprehension. Given question-answer pairs generated from the summary, a QA model extracts answers from the document; non-matched answers indicate unfaithful information in the summary. Among metrics based on word overlap, embedding similarity, and learned language understanding models, our QA-based metric has significantly higher correlation with human faithfulness scores, especially on highly abstractive summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2020.acl-main.454\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9385\", \"2684\", \"2641\", \"4082\", \"8747\", \"17317\", \"22450\", \"26645\", \"26779\", \"52433\", \"80634\", \"84343\", \"1055\", \"4719\", \"8354\", \"13996\", \"16064\", \"16503\", \"22443\", \"23776\", \"26826\", \"28808\", \"29612\", \"31148\", \"31578\", \"32377\", \"136011\", \"44858\", \"45198\", \"45305\", \"47416\", \"49896\", \"50478\", \"52583\", \"53256\", \"55016\", \"55367\", \"55491\", \"55781\", \"56583\", \"56864\", \"57244\", \"58194\", \"58358\", \"60148\", \"61182\", \"63848\", \"71888\", \"77640\", \"78031\", \"88960\", \"91642\", \"92956\", \"97363\", \"109884\", \"93973\", \"93975\", \"94994\", \"95174\", \"95840\", \"96411\", \"98155\", \"99749\", \"115939\", \"119817\"], \"outgoing_citations\": [\"157151\", \"164036\", \"165103\", \"168744\", \"170449\", \"170657\", \"183065\", \"183400\", \"192158\", \"200362\", \"216953\", \"218638\", \"219618\", \"220230\", \"230212\", \"233407\", \"233710\", \"238321\", \"250690\", \"257858\", \"260247\", \"260587\", \"268339\", \"280557\", \"281076\", \"284357\", \"295863\", \"298016\", \"299936\", \"305200\", \"310740\"]}","task_split":"paper_retrieval"}
{"document_id":"126370","document_content":"# A Systematic Assessment of Syntactic Generalization in Neural Language Models\n## Categories\n- Computation and Language\n## Abstract\nWhile state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge. Furthermore, existing work has not provided a clear picture about the model properties required to produce proper syntactic generalizations. We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 English-language syntactic test suites. We find substantial differences in syntactic generalization performance by model architecture, with sequential models underperforming other architectures. Factorially manipulating model architecture and training dataset size (1M--40M words), we find that variability in syntactic generalization performance is substantially greater by architecture than by dataset size for the corpora tested in our experiments. Our results also reveal a dissociation between perplexity and syntactic generalization performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03692v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20003\", \"24928\", \"25720\", \"72647\", \"16463\", \"21274\", \"21669\", \"23484\", \"24456\", \"25228\", \"32301\", \"42564\", \"42955\", \"45348\", \"46118\", \"46654\", \"49299\", \"50449\", \"50979\", \"54383\", \"54783\", \"66215\", \"73219\", \"77760\", \"83446\", \"87953\", \"92478\", \"78655\", \"89632\", \"94886\", \"95058\", \"95313\", \"95415\", \"95436\", \"101698\", \"104915\", \"121405\", \"122609\", \"160751\"], \"outgoing_citations\": [\"153289\", \"181575\", \"195989\", \"196722\", \"200362\", \"213362\", \"219156\", \"219586\", \"220137\", \"220460\", \"229768\", \"233407\", \"237283\", \"259980\", \"269127\", \"281225\", \"297648\", \"298750\"]}","task_split":"paper_retrieval"}
{"document_id":"126433","document_content":"# Bundle Recommendation with Graph Convolutional Networks\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nBundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short for \\textit{\\textBF{B}undle \\textBF{G}raph \\textBF{C}onvolutional \\textBF{N}etwork}) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item level semantics. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77\\% to 23.18\\%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03475v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"20875\", \"89083\", \"70977\", \"6878\", \"9716\", \"11274\", \"25694\", \"39216\", \"43452\", \"71083\", \"94427\", \"92362\"], \"outgoing_citations\": [\"264326\"]}","task_split":"paper_retrieval"}
{"document_id":"126587","document_content":"# Joint Multi-Dimensional Model for Global and Time-Series Annotations\n## Categories\n- Machine Learning\n## Abstract\nCrowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as valence and arousal, for each instance. Most annotation fusion schemes however ignore this aspect and model each dimension separately. In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates. The model we propose is applicable to both global and time series annotation fusion problems and treats the ground truth as a latent variable distorted by the annotators. The model parameters are estimated using the Expectation-Maximization algorithm and we evaluate its performance using synthetic data and real emotion corpora as well as on an artificial task with human annotations","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03117v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"123823\"], \"outgoing_citations\": [\"272080\", \"317214\"]}","task_split":"paper_retrieval"}
{"document_id":"126671","document_content":"# Graph-Embedding Empowered Entity Retrieval\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nIn this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-45439-5_7\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"2549\", \"50345\", \"95944\", \"121381\"], \"outgoing_citations\": [\"169848\", \"267775\", \"291855\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"126782","document_content":"# Crossing Variational Autoencoders for Answer Retrieval\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nAnswer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions\/answers is the key factor. Question-answer alignment and question\/answer semantics are two important signals for learning the representations. Existing methods learned semantic representations with dual encoders or dual variational auto-encoders. The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes. However, the alignment and semantics were too separate to capture the aligned semantics between question and answer. In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions. Experiments show that our method outperforms the state-of-the-art answer retrieval method on SQuAD.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.02557v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"35271\", \"23468\", \"76036\", \"93077\"], \"outgoing_citations\": [\"142002\", \"143117\", \"176659\", \"176839\", \"178032\", \"180895\", \"183556\", \"207226\", \"212285\", \"234814\", \"237274\", \"255322\", \"261697\", \"264747\", \"265309\", \"267161\", \"270020\", \"271934\", \"272531\", \"279343\", \"302948\", \"303029\"]}","task_split":"paper_retrieval"}
{"document_id":"126814","document_content":"# A Ladder of Causal Distances\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nCausal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie (2018) have arranged the properties of causal models in a hierarchy called the \"ladder of causation\" spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available. Finally, we highlight the usefulness of our causal distances by briefly discussing further applications beyond the evaluation of causal discovery techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.02480v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"162159\", \"230700\", \"258287\", \"271038\", \"303913\", \"335762\"]}","task_split":"paper_retrieval"}
{"document_id":"126920","document_content":"# Reproduction of Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nIn recent years, neural networks have continued to flourish, achieving high efficiency in detecting relevant objects in photos or simply recognizing (classifying) these objects - mainly using CNN networks. Current solutions, however, are far from ideal, because it often turns out that network can be effectively confused with even natural images examples. I suspect that the classification of an object is strongly influenced by the background pixels on which the object is located. In my work, I analyze the above problem using for this purpose saliency maps created by the LICNN network. They are designed to suppress the neurons surrounding the examined object and, consequently, reduce the contribution of background pixels to the classifier predictions. My experiments on the natural and adversarial images datasets show that, indeed, there is a visible correlation between the background and the wrong-classified foreground object. This behavior of the network is not supported by human experience, because, for example, we do not confuse the yellow school bus with the snow plow just because it is on the snowy background.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.02184v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.LG\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"160766\", \"175834\", \"215081\", \"296021\"]}","task_split":"paper_retrieval"}
{"document_id":"127040","document_content":"# Exploring Content Selection in Summarization of Novel Chapters\n## Categories\n- Computation and Language\n## Abstract\nWe present a new summarization task, generating summaries of novel chapters using summary\/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis. We make our data collection scripts available at https:\/\/github.com\/manestay\/novel-chapter-dataset .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.01840v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"48976\", \"56032\", \"84343\", \"21737\", \"24210\", \"24361\", \"95840\"], \"outgoing_citations\": [\"170657\", \"190934\", \"212506\", \"219618\", \"230212\", \"233710\", \"259723\", \"260587\", \"266671\", \"280557\", \"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"127099","document_content":"# Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn this tutorial article, we aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcement learning algorithms that utilize previously collected data, without additional online data collection. Offline reinforcement learning algorithms hold tremendous promise for making it possible to turn large datasets into powerful decision making engines. Effective offline reinforcement learning methods would be able to extract policies with the maximum possible utility out of the available data, thereby allowing automation of a wide range of decision-making domains, from healthcare and education to robotics. However, the limitations of current algorithms make this difficult. We will aim to provide the reader with an understanding of these challenges, particularly in the context of modern deep reinforcement learning methods, and describe some potential solutions that have been explored in recent work to mitigate these challenges, along with recent applications, and a discussion of perspectives on open problems in the field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.01643v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"1585\", \"2075\", \"8658\", \"10915\", \"35065\", \"71172\", \"343094\", \"352\", \"1235\", \"3788\", \"4258\", \"6994\", \"8454\", \"8426\", \"9804\", \"9884\", \"10698\", \"13151\", \"14284\", \"15090\", \"15237\", \"16149\", \"16951\", \"17950\", \"17889\", \"17957\", \"20577\", \"29449\", \"34558\", \"34577\", \"35680\", \"36644\", \"37832\", \"40273\", \"41147\", \"42687\", \"42781\", \"44028\", \"44890\", \"45466\", \"46554\", \"46528\", \"47175\", \"47548\", \"56916\", \"57263\", \"61460\", \"62061\", \"67543\", \"69661\", \"73192\", \"78473\", \"87566\", \"90963\", \"3273\", \"3531\", \"4506\", \"5010\", \"5856\", \"6736\", \"7811\", \"8072\", \"9044\", \"9345\", \"9906\", \"10114\", \"10591\", \"10430\", \"10424\", \"11091\", \"11506\", \"12943\", \"13297\", \"13478\", \"13449\", \"13490\", \"13631\", \"13848\", \"14120\", \"14104\", \"14068\", \"14761\", \"15383\", \"15763\", \"16398\", \"16555\", \"16665\", \"17097\", \"17651\", \"17658\", \"17932\", \"18400\", \"18812\", \"19126\", \"19431\", \"19039\", \"19780\", \"19900\", \"20029\", \"20155\", \"21756\", \"22530\", \"22866\", \"22978\", \"23340\", \"23294\", \"23880\", \"24611\", \"24970\", \"25074\", \"25638\", \"26934\", \"27991\", \"28651\", \"29923\", \"30757\", \"30961\", \"31596\", \"32449\", \"33615\", \"33598\", \"33819\", \"34461\", \"35433\", \"35657\", \"35975\", \"36421\", \"37058\", \"37807\", \"38148\", \"38349\", \"38919\", \"39119\", \"39671\", \"40220\", \"40489\", \"40737\", \"41014\", \"41463\", \"41536\", \"41610\", \"42520\", \"42559\", \"43797\", \"43961\", \"43959\", \"44208\", \"45297\", \"46516\", \"47287\", \"47373\", \"47499\", \"48183\", \"49895\", \"50487\", \"51328\", \"51441\", \"53023\", \"54518\", \"51421\", \"56365\", \"56413\", \"56639\", \"58095\", \"58683\", \"60339\", \"60605\", \"60682\", \"60708\", \"61683\", \"62034\", \"62423\", \"62516\", \"63102\", \"63112\", \"63685\", \"63851\", \"64396\", \"62792\", \"65737\", \"67095\", \"67358\", \"67567\", \"67615\", \"67803\", \"68515\", \"68473\", \"68900\", \"68878\", \"69076\", \"69225\", \"69553\", \"70092\", \"70499\", \"71864\", \"72267\", \"73917\", \"77736\", \"79012\", \"79130\", \"79175\", \"82278\", \"83952\", \"88342\", \"89321\", \"89521\", \"90883\", \"90882\", \"91231\", \"91819\", \"92122\", \"100655\", \"106120\", \"106317\", \"108091\", \"110439\", \"113545\", \"116919\", \"120766\", \"121892\", \"171948\", \"59162\", \"79836\", \"80116\", \"80924\", \"81864\", \"83285\", \"84070\", \"84270\", \"86327\", \"87027\", \"90880\", \"92011\", \"92106\", \"93467\", \"95896\", \"96714\", \"96623\", \"104486\", \"107505\", \"111316\", \"111696\", \"113870\", \"115832\", \"115825\", \"118157\", \"118689\", \"118882\", \"120201\", \"121735\", \"125479\", \"131003\", \"159565\", \"152516\", \"153997\", \"200000\"], \"outgoing_citations\": [\"118145\", \"118157\", \"120201\", \"122699\", \"125479\", \"131003\", \"135093\", \"136554\", \"137034\", \"139083\", \"140991\", \"141277\", \"142415\", \"145033\", \"148275\", \"152756\", \"154254\", \"154589\", \"159326\", \"159949\", \"160778\", \"161489\", \"164054\", \"164743\", \"167139\", \"172485\", \"176546\", \"176738\", \"177371\", \"178278\", \"179948\", \"181133\", \"181512\", \"181715\", \"182796\", \"190024\", \"193522\", \"196493\", \"196921\", \"197390\", \"200299\", \"201399\", \"206875\", \"206951\", \"207601\", \"209101\", \"210004\", \"210517\", \"212478\", \"214018\", \"214194\", \"214419\", \"220103\", \"225108\", \"225819\", \"226166\", \"226635\", \"229839\", \"229890\", \"230314\", \"233450\", \"234025\", \"237605\", \"241006\", \"242732\", \"243235\", \"244525\", \"246308\", \"246310\", \"247075\", \"248010\", \"249233\", \"250066\", \"252103\", \"253953\", \"255302\", \"255807\", \"259045\", \"261493\", \"261725\", \"264786\", \"265615\", \"268399\", \"269382\", \"272987\", \"273312\", \"274244\", \"278488\", \"278788\", \"279026\", \"280570\", \"281110\", \"281257\", \"283391\", \"283410\", \"288347\", \"289229\", \"290019\", \"291301\", \"292489\", \"295281\", \"296980\", \"297162\", \"298281\", \"298651\", \"303539\", \"304250\", \"306156\", \"306461\", \"308099\", \"308711\", \"310624\", \"315982\", \"316249\", \"328112\", \"354620\"]}","task_split":"paper_retrieval"}
{"document_id":"127100","document_content":"# Navigating the Landscape of Multiplayer Games\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nMultiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused on using well-known games to build strong agents. This progress, however, can be better informed by characterizing games and their topological landscape. Tackling this latter question can facilitate understanding of agents and help determine what game an agent should target next as part of its training. Here, we show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games, quantifying relationships between games of varying sizes and characteristics. We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another. Our results culminate in a demonstration leveraging this information to generate new and interesting games, including mixtures of empirical games synthesized from real world games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1038\/s41467-020-19244-4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"48051\", \"68876\", \"56309\"], \"outgoing_citations\": [\"130071\", \"136046\", \"138679\", \"151271\", \"153614\", \"164515\", \"165637\", \"166489\", \"170240\", \"183135\", \"184374\", \"189837\", \"191787\", \"196801\", \"198383\", \"200264\", \"201876\", \"203756\", \"204399\", \"207051\", \"212722\", \"226544\", \"230330\", \"238700\", \"240572\", \"242052\", \"242601\", \"251601\", \"255613\", \"261017\", \"269253\", \"271197\", \"274315\", \"294468\", \"321115\", \"341716\", \"344151\", \"345890\", \"347352\", \"354568\", \"358299\", \"358915\"]}","task_split":"paper_retrieval"}
{"document_id":"127101","document_content":"# A Tale of a Probe and a Parser\n## Categories\n- Computation and Language\n## Abstract\nMeasuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training \"probes\" - supervised models designed to extract linguistic structure from another model's output. One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend - the structural probe outperforms the parser. This begs the question: which metric should we prefer?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.01641v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"67128\", \"72229\", \"24964\", \"44710\", \"48163\", \"56222\", \"57416\", \"61915\", \"65799\", \"93189\", \"95106\", \"96238\", \"100467\", \"126029\", \"128087\", \"96531\"], \"outgoing_citations\": [\"132635\", \"168097\", \"175763\", \"182116\", \"194308\", \"220137\", \"233400\", \"234628\", \"249109\", \"283223\"]}","task_split":"paper_retrieval"}
{"document_id":"127232","document_content":"# Distributional Discrepancy: A Metric for Unconditional Text Generation\n## Categories\n- Computation and Language\n## Abstract\nThe purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However, when different metrics are used for comparing the methods of unconditional text generation, contradictory conclusions are drawn. The difficulty is that both the diversity and quality of the sample should be considered simultaneously when the models are evaluated. To solve this problem, a novel metric of distributional discrepancy (DD) is designed to evaluate generators based on the discrepancy between the generated and real training sentences. However, it cannot compute the DD directly because the distribution of real sentences is unavailable. Thus, we propose a method for estimating the DD by training a neural-network-based text classifier. For comparison, three existing metrics, bi-lingual evaluation understudy (BLEU) versus self-BLEU, language model score versus reverse language model score, and Fr\\'{e}chet embedding distance, along with the proposed DD, are used to evaluate two popular generative models of long short-term memory and generative pretrained transformer 2 on both syntactic and real data. Experimental results show that DD is significantly better than the three existing metrics for ranking these generative models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.01282v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"107302\"], \"outgoing_citations\": [\"183694\", \"184909\", \"192158\", \"211205\", \"228201\", \"233720\", \"234728\", \"243154\", \"244368\", \"261608\", \"263848\", \"264937\", \"264957\", \"275597\", \"283971\", \"284481\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"127561","document_content":"# Minimally Supervised Categorization of Text with Metadata\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nDocument categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1) the presence of metadata: in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2) label scarcity: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata. Specifically, we develop a generative process describing the relationships between words, documents, labels, and metadata. Guided by the generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of MetaCat over many competitive baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00624v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"10979\", \"133777\", \"67553\", \"69363\", \"91248\", \"91745\", \"94280\", \"94493\"], \"outgoing_citations\": [\"9986\", \"161517\", \"171027\", \"186386\", \"197685\", \"198995\", \"204499\", \"219485\", \"234690\", \"281576\", \"302425\", \"308627\", \"356534\"]}","task_split":"paper_retrieval"}
{"document_id":"127568","document_content":"# Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles\n## Categories\n- Statistics Theory\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWhile feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results. In this work, we show that---surprisingly---the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and $\\sigma$-faithful Structural Causal Model (SCM), FCI is sound and complete, and can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM. We extend these results to constraint-based causal discovery algorithms that exploit certain forms of background knowledge, including the causally sufficient setting (e.g., the PC algorithm) and the Joint Causal Inference setting (e.g., the FCI-JCI algorithm).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00610v2\", \"primary_category\": \"math.ST\", \"categories\": [\"math.ST\", \"cs.AI\", \"cs.LG\", \"stat.TH\"], \"primary_category_human_readable\": \"Statistics Theory\", \"categories_human_readable\": [\"Statistics Theory\", \"Artificial Intelligence\", \"Machine Learning\", \"Statistics Theory\"], \"incoming_citations\": [\"72917\", \"67026\", \"81830\", \"112119\", \"280102\", \"237987\"], \"outgoing_citations\": [\"225335\", \"233111\", \"279093\", \"280102\", \"339848\", \"346719\", \"348577\", \"348578\", \"348587\", \"348588\", \"348926\", \"356888\", \"357904\"]}","task_split":"paper_retrieval"}
{"document_id":"127603","document_content":"# SciREX: A Challenge Dataset for Document-Level Information Extraction\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nExtracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level $N$-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https:\/\/github.com\/allenai\/SciREX","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00512v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"26623\", \"69997\", \"72536\", \"21930\", \"24348\", \"24474\", \"26633\", \"29684\", \"30511\", \"40395\", \"40699\", \"45206\", \"45054\", \"45368\", \"55145\", \"56297\", \"59299\", \"73540\", \"73545\", \"91671\", \"99749\", \"106379\", \"108184\"], \"outgoing_citations\": [\"165520\", \"168008\", \"179563\", \"180767\", \"191957\", \"192327\", \"192607\", \"219946\", \"230637\", \"238321\", \"258108\", \"269283\", \"300593\"]}","task_split":"paper_retrieval"}
{"document_id":"127613","document_content":"# ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations\n## Categories\n- Computation and Language\n## Abstract\nIn order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and\/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00481v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15974\", \"127662\", \"10002\", \"15519\", \"26460\", \"28080\", \"29277\", \"32732\", \"41554\", \"55802\", \"64358\", \"71888\", \"92547\"], \"outgoing_citations\": [\"163045\", \"172006\", \"200983\", \"212887\", \"214312\", \"214590\", \"270048\", \"359810\"]}","task_split":"paper_retrieval"}
{"document_id":"127662","document_content":"# MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nProgress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity at inference time. We further present a method to mine such paraphrase data in any language from Common Crawl using semantic sentence embeddings, thus removing the need for labeled data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results, despite not using any labeled simplification data. We push the state of the art further by incorporating labeled simplification data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00352v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"12125\", \"15519\"], \"outgoing_citations\": [\"118053\", \"126870\", \"127613\", \"127956\", \"146003\", \"157109\", \"158546\", \"163045\", \"164998\", \"167980\", \"172006\", \"179990\", \"192811\", \"200983\", \"204715\", \"211711\", \"212887\", \"213824\", \"214590\", \"250360\", \"270048\", \"272822\"]}","task_split":"paper_retrieval"}
{"document_id":"127709","document_content":"# Biomedical Entity Representations with Synonym Marginalization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nBiomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their surface forms, normalization of biomedical entities is very challenging. In this paper, we focus on learning representations of biomedical entities solely based on the synonyms of entities. To learn from the incomplete synonyms, we use a model-based candidate selection and maximize the marginal likelihood of the synonyms present in top candidates. Our model-based candidates are iteratively updated to contain more difficult negative samples as our model evolves. In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates. On four biomedical entity normalization datasets having three different entity types (disease, chemical, adverse reaction), our model BioSyn consistently outperforms previous state-of-the-art models almost reaching the upper bound on each dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00239v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2937\", \"55815\", \"77478\", \"88816\", \"121898\", \"8334\", \"9763\", \"21082\", \"23405\", \"25606\", \"26026\", \"41352\", \"42199\", \"42207\", \"46359\", \"54393\", \"62785\", \"73992\", \"92185\", \"92449\", \"115509\"], \"outgoing_citations\": [\"79296\", \"172372\", \"191942\", \"192402\", \"193702\", \"209557\", \"225551\", \"243880\", \"281915\", \"289318\", \"316977\"]}","task_split":"paper_retrieval"}
{"document_id":"127748","document_content":"# Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity\n## Categories\n- Computation and Language\n## Abstract\nOpen-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user's pre-existing knowledge. Assuming a correlation between engagement and user responses such as \"liking\" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user's prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a BERT content model by 13 mean reciprocal rank points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/2020.emnlp-main.655\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"3540\", \"19499\", \"50552\"], \"outgoing_citations\": [\"68965\", \"105483\", \"122948\", \"128194\", \"134162\", \"172587\", \"177387\", \"180327\", \"182107\", \"197924\", \"199077\", \"205857\", \"211664\", \"216965\", \"217214\", \"217823\", \"220723\", \"220724\", \"238321\", \"244479\", \"255778\", \"266155\", \"266411\", \"268168\", \"270020\", \"274567\", \"279343\", \"290747\", \"294494\", \"309411\", \"309886\", \"343624\"]}","task_split":"paper_retrieval"}
{"document_id":"127749","document_content":"# Cross-lingual Entity Alignment with Incidental Supervision\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nMuch research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object. Such methods are often hindered by the insufficiency of seed alignment provided between KGs. Therefore, we propose an incidentally supervised model, JEANS , which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve entity alignment with incidental supervision signals from text. JEANS first deploys an entity grounding process to combine each KG with the monolingual text corpus. Then, two learning processes are conducted: (i) an embedding learning process to encode the KG and text of each language in one embedding space, and (ii) a selflearning based alignment learning process to iteratively induce the matching of entities and that of lexemes between embeddings. Experiments on benchmark datasets show that JEANS leads to promising improvement on entity alignment with incidental supervision, and significantly outperforms state-of-the-art methods that solely rely on internal information of KGs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00171v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"15904\", \"20997\", \"25538\", \"30129\", \"44877\", \"55150\"], \"outgoing_citations\": [\"63084\", \"96043\", \"123021\", \"129719\", \"137598\", \"155249\", \"161666\", \"165679\", \"165809\", \"167100\", \"168575\", \"169251\", \"170099\", \"170709\", \"178759\", \"182248\", \"182594\", \"184117\", \"186633\", \"189715\", \"197408\", \"200032\", \"200693\", \"211915\", \"214193\", \"217117\", \"225253\", \"227725\", \"231863\", \"249276\", \"253559\", \"258410\", \"263680\", \"265739\", \"267123\", \"280634\", \"304901\", \"323451\"]}","task_split":"paper_retrieval"}
{"document_id":"127754","document_content":"# Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification. Empirical studies on two real-world datasets confirm the superiority of the proposed model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00162v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"4175\", \"23624\", \"28591\", \"59299\"], \"outgoing_citations\": [\"154622\", \"163765\", \"167703\", \"169062\", \"180210\", \"180431\", \"181665\", \"182707\", \"200764\", \"205914\", \"207045\", \"210744\", \"216419\", \"260349\", \"264389\", \"292448\", \"300593\", \"308043\"]}","task_split":"paper_retrieval"}
{"document_id":"127760","document_content":"# Interpretable Entity Representations through Large-Scale Typing\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this paper, we present an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model's decision that the entity belongs to the corresponding type. We obtain these representations using a fine-grained entity typing model, trained either on supervised ultra-fine entity typing data (Choi et al. 2018) or distantly-supervised examples from Wikipedia. On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMo- and BERT-based embeddings in trained models. We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains. Finally, we show that these embeddings can be post-hoc modified through a small number of rules to incorporate domain knowledge and improve performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00147v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16195\", \"25862\", \"41352\", \"43879\", \"77391\", \"86291\"], \"outgoing_citations\": [\"122351\", \"131012\", \"134662\", \"136310\", \"143080\", \"143992\", \"147649\", \"150253\", \"153825\", \"165378\", \"167165\", \"167753\", \"168097\", \"169340\", \"171633\", \"180327\", \"181948\", \"185889\", \"187755\", \"213271\", \"216455\", \"224786\", \"243397\", \"249483\", \"262673\", \"268854\", \"300498\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"127787","document_content":"# Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures\n## Categories\n- Computation and Language\n- Machine Learning\n- I.2.7; I.5.4; J.5\n## Abstract\nThe use of linguistic typological resources in natural language processing has been steadily gaining more popularity. It has been observed that the use of typological information, often combined with distributed language representations, leads to significantly more powerful models. While linguistic typology representations from various resources have mostly been used for conditioning the models, there has been relatively little attention on predicting features from these resources from the input data. In this paper we investigate whether the various linguistic features from World Atlas of Language Structures (WALS) can be reliably inferred from multi-lingual text. Such a predictor can be used to infer structural features for a language never observed in training data. We frame this task as a multi-label classification involving predicting the set of non-mutually exclusive and extremely sparse multi-valued labels (WALS features). We construct a recurrent neural network predictor based on byte embeddings and convolutional layers and test its performance on 556 languages, providing analysis for various linguistic types, macro-areas, language families and individual features. We show that some features from various linguistic types can be predicted reliably.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00100v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"I.2.7; I.5.4; J.5\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"I.2.7; I.5.4; J.5\"], \"incoming_citations\": [], \"outgoing_citations\": [\"250429\", \"259030\", \"259349\", \"259840\", \"267740\", \"277444\", \"282869\", \"286860\", \"287750\", \"288697\", \"292448\", \"292691\", \"298750\", \"298944\", \"299195\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"127805","document_content":"# Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions\n## Categories\n- Machine Learning\n- Image and Video Processing\n## Abstract\nGenerative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00065v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"46822\", \"89089\", \"11934\", \"82834\", \"105409\", \"11397\", \"17665\", \"19673\", \"31969\", \"42488\", \"61882\", \"74828\", \"132740\"], \"outgoing_citations\": [\"104302\", \"117607\", \"117768\", \"118789\", \"129773\", \"141157\", \"142178\", \"147766\", \"148938\", \"154506\", \"159220\", \"163340\", \"163461\", \"175351\", \"177899\", \"184888\", \"199588\", \"211988\", \"214146\", \"216097\", \"218395\", \"222900\", \"225082\", \"227505\", \"227685\", \"228639\", \"228832\", \"235848\", \"237891\", \"237962\", \"238027\", \"238925\", \"239178\", \"240034\", \"240046\", \"241651\", \"241778\", \"241877\", \"242751\", \"243083\", \"243380\", \"245177\", \"245290\", \"246146\", \"247713\", \"248099\", \"248518\", \"249069\", \"249855\", \"250583\", \"251783\", \"254851\", \"255463\", \"255521\", \"256229\", \"256793\", \"257124\", \"258286\", \"258493\", \"259052\", \"262168\", \"263580\", \"263662\", \"263859\", \"264073\", \"264374\", \"265003\", \"265075\", \"265397\", \"265398\", \"265604\", \"265785\", \"265829\", \"265907\", \"265970\", \"265988\", \"268416\", \"268494\", \"268734\", \"268740\", \"269043\", \"269271\", \"269388\", \"269443\", \"270075\", \"270105\", \"270387\", \"270948\", \"271135\", \"271340\", \"271419\", \"272139\", \"272345\", \"272623\", \"272783\", \"272802\", \"272912\", \"272918\", \"272965\", \"273620\", \"274550\", \"274632\", \"275665\", \"275747\", \"275998\", \"276018\", \"276542\", \"277021\", \"277357\", \"277386\", \"277405\", \"277730\", \"277822\", \"277906\", \"278560\", \"278760\", \"278856\", \"279050\", \"279091\", \"280050\", \"280070\", \"280734\", \"280996\", \"281002\", \"281034\", \"281144\", \"281208\", \"282032\", \"282844\", \"283070\", \"284132\", \"284874\", \"287740\", \"289119\", \"289678\", \"290238\", \"291301\", \"291302\", \"291440\", \"296355\", \"297625\", \"297838\", \"298195\", \"299534\", \"300739\", \"303139\", \"303250\", \"303913\", \"310922\", \"312814\", \"316183\", \"317771\", \"320031\", \"328112\", \"344049\"]}","task_split":"paper_retrieval"}
{"document_id":"127814","document_content":"# Context based Text-generation using LSTM networks\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLong short-term memory(LSTM) units on sequence-based models are being used in translation, question-answering systems, classification tasks due to their capability of learning long-term dependencies. In Natural language generation, LSTM networks are providing impressive results on text generation models by learning language models with grammatically stable syntaxes. But the downside is that the network does not learn about the context. The network only learns the input-output function and generates text given a set of input words irrespective of pragmatics. As the model is trained without any such context, there is no semantic consistency among the generated sentences. The proposed model is trained to generate text for a given set of input words along with a context vector. A context vector is similar to a paragraph vector that grasps the semantic meaning(context) of the sentence. Several methods of extracting the context vectors are proposed in this work. While training a language model, in addition to the input-output sequences, context vectors are also trained along with the inputs. Due to this structure, the model learns the relation among the input words, context vector and the target word. Given a set of context terms, a well trained model will generate text around the provided context. Based on the nature of computing context vectors, the model has been tried out with two variations (word importance and word clustering). In the word clustering method, the suitable embeddings among various domains are also explored. The results are evaluated based on the semantic closeness of the generated text to the given context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00048v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"246513\", \"308857\", \"316774\"]}","task_split":"paper_retrieval"}
{"document_id":"127884","document_content":"# Polarization Human Shape and Pose Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPolarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest. Meanwhile, inspired by the recent breakthroughs in human shape estimation from a single color image, we attempt to investigate the new question of whether the geometric cues from polarization camera could be leveraged in estimating detailed human body shapes. This has led to the curation of Polarization Human Shape and Pose Dataset (PHSPD), our home-grown polarization image dataset of various human shapes and poses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14899v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"41279\", \"52255\", \"108875\", \"111226\"], \"outgoing_citations\": [\"287636\"]}","task_split":"paper_retrieval"}
{"document_id":"127889","document_content":"# Few-Shot Learning for Opinion Summarization\n## Categories\n- Machine Learning\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nOpinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practically important and has attracted a lot of attention. However, due to the high cost of summary production, datasets large enough for training supervised models are lacking. Instead, the task has been traditionally approached with extractive methods that learn to select text fragments in an unsupervised or weakly-supervised way. Recently, it has been shown that abstractive summaries, potentially more fluent and better at reflecting conflicting information, can also be produced in an unsupervised fashion. However, these models, not being exposed to actual summaries, fail to capture their essential properties. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation. We start by training a conditional Transformer language model to generate a new product review given other available reviews of the product. The model is also conditioned on review properties that are directly related to summaries; the properties are derived from reviews with no manual effort. In the second stage, we fine-tune a plug-in module that learns to predict property values on a handful of summaries. This lets us switch the generator to the summarization mode. We show on Amazon and Yelp datasets that our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14884v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"87999\", \"74961\", \"16339\", \"16191\", \"22773\", \"22770\", \"24361\", \"24752\", \"42616\", \"46928\", \"54705\", \"55285\", \"58358\"], \"outgoing_citations\": [\"127015\", \"129810\", \"157807\", \"180935\", \"183161\", \"214407\", \"220188\", \"243803\", \"248254\", \"261692\", \"266671\", \"275596\", \"286225\", \"298016\", \"298945\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"127930","document_content":"# Self-Supervised and Controlled Multi-Document Opinion Summarization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries We also provide an ablation study, which shows the importance of the control setup in controlling hallucinations and achieve high sentiment and topic alignment of the summaries with the input reviews.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14754v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16339\", \"81985\", \"14889\", \"15681\", \"25222\", \"46928\", \"58539\", \"118430\", \"127832\"], \"outgoing_citations\": [\"100711\", \"110335\", \"129810\", \"150522\", \"152729\", \"157807\", \"166540\", \"167338\", \"170449\", \"176177\", \"181185\", \"181817\", \"183462\", \"183694\", \"190086\", \"192407\", \"197924\", \"210454\", \"214407\", \"215409\", \"218912\", \"220188\", \"230014\", \"231949\", \"234742\", \"235175\", \"235463\", \"241628\", \"244102\", \"250505\", \"251816\", \"251974\", \"257721\", \"268166\", \"268454\", \"278948\", \"279002\", \"290807\", \"291102\", \"298514\", \"302815\", \"305200\", \"321329\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"127956","document_content":"# Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders\n## Categories\n- Computation and Language\n## Abstract\nText simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to leverage large amounts of unpaired corpora in TS task. We adopt the back-translation architecture in unsupervised machine translation (NMT), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation. However, it is non-trivial to generate appropriate complex-simple pair if we directly treat the set of simple and complex corpora as two different languages, since the two types of sentences are quite similar and it is hard for the model to capture the characteristics in different types of sentences. To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. When modeling simple and complex sentences with autoencoders, we introduce different types of noise into the training process. Such a method can significantly improve the simplification performance. Our model can be trained in both unsupervised and semi-supervised manner. Automatic and human evaluations show that our unsupervised model outperforms the previous systems, and with limited supervision, our model can perform competitively with multiple state-of-the-art simplification systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14693v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"127662\", \"25776\", \"44856\", \"64358\", \"82994\", \"122607\"], \"outgoing_citations\": [\"169780\", \"176753\", \"192166\", \"212887\", \"213824\", \"214312\", \"218755\", \"227488\", \"234748\", \"234807\", \"234907\", \"251816\", \"251974\", \"270048\", \"308756\"]}","task_split":"paper_retrieval"}
{"document_id":"128002","document_content":"# Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models\n## Categories\n- Computation and Language\n## Abstract\nWe propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language. We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads to the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties. Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14601v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25077\", \"55398\", \"19960\", \"24449\", \"41709\", \"47750\", \"56222\", \"63488\", \"73263\", \"75254\", \"79010\", \"90856\", \"95612\", \"127590\"], \"outgoing_citations\": [\"129558\", \"132635\", \"134662\", \"147697\", \"149018\", \"157392\", \"159706\", \"168097\", \"181468\", \"181752\", \"189792\", \"201065\", \"205056\", \"212525\", \"217957\", \"233400\", \"237283\", \"253623\", \"259145\", \"281225\", \"295028\", \"311683\"]}","task_split":"paper_retrieval"}
{"document_id":"128066","document_content":"# The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSince its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, their attention, and even intention. In this paper, we detail how this large-scale dataset was captured by 32 participants in their native kitchen environments, and densely annotated with actions and object interactions. Our videos depict nonscripted daily activities, as recording is started every time a participant entered their kitchen. Recording took place in 4 countries by participants belonging to 10 different nationalities, resulting in highly diverse kitchen habits and cooking styles. Our dataset features 55 hours of video consisting of 11.5M frames, which we densely labelled for a total of 39.6K action segments and 454.2K object bounding boxes. Our annotation is unique in that we had the participants narrate their own videos after recording, thus reflecting true intention, and we crowd-sourced ground-truths based on these. We describe our object, action and. anticipation challenges, and evaluate several baselines over two test splits, seen and unseen kitchens. We introduce new baselines that highlight the multimodal nature of the dataset and the importance of explicit temporal modelling to discriminate fine-grained actions e.g. 'closing a tap' from 'opening' it up.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.00343v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2421\", \"3517\", \"17311\", \"29536\", \"48438\", \"69360\", \"132173\", \"14882\", \"24837\", \"24946\", \"26175\", \"30277\", \"32895\", \"40206\", \"40564\", \"34758\", \"51491\", \"53500\", \"54106\", \"84482\", \"94912\", \"130800\", \"76680\", \"81067\", \"104585\", \"104641\", \"115646\", \"116367\", \"152418\"], \"outgoing_citations\": [\"143471\", \"145455\", \"145835\", \"147314\", \"170617\", \"172394\", \"176053\", \"176078\", \"181897\", \"184611\", \"188041\", \"191180\", \"191393\", \"206072\", \"227837\", \"234223\", \"245503\", \"246521\", \"248108\", \"249567\", \"261149\", \"263838\", \"270325\", \"270479\", \"274379\", \"277390\", \"279167\", \"283807\", \"295117\", \"301767\", \"313644\", \"319169\"]}","task_split":"paper_retrieval"}
{"document_id":"128086","document_content":"# Pragmatic Issue-Sensitive Image Captioning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning systems have recently improved dramatically, but they still tend to produce captions that are insensitive to the communicative goals that captions should meet. To address this, we propose Issue-Sensitive Image Captioning (ISIC). In ISIC, a captioning system is given a target image and an issue, which is a set of images partitioned in a way that specifies what information is relevant. The goal of the captioner is to produce a caption that resolves this issue. To model this task, we use an extension of the Rational Speech Acts model of pragmatic language use. Our extension is built on top of state-of-the-art pretrained neural image captioners and explicitly reasons about issues in our sense. We establish experimentally that these models generate captions that are both highly descriptive and issue-sensitive, and we show how ISIC can complement and enrich the related task of Visual Question Answering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14451v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"95572\"], \"outgoing_citations\": [\"121972\", \"197647\", \"213042\", \"235544\", \"239242\", \"250567\", \"270225\", \"276436\", \"278954\", \"292386\", \"295368\", \"295742\", \"303833\", \"304615\", \"305473\"]}","task_split":"paper_retrieval"}
{"document_id":"128115","document_content":"# Informed Sampling for Diversity in Concept-to-Text NLG\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDeep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus our experiments on concept-to-text generation where models are sensitive to the inclusion of irrelevant words due to the strict relation between input and output. Our analysis shows that previous methods for diversity underperform in this setting, while human evaluation suggests that our proposed method achieves a high level of diversity with minimal effect to the output's fluency and adequacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14364v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"139637\", \"168405\", \"197057\", \"215721\", \"216019\", \"217640\", \"232274\", \"243154\", \"260587\", \"270040\", \"305200\", \"307195\", \"308378\", \"308381\", \"317874\"]}","task_split":"paper_retrieval"}
{"document_id":"128134","document_content":"# UDapter: Language Adaptation for Truly Universal Dependency Parsing\n## Categories\n- Computation and Language\n## Abstract\nRecent advances in multilingual dependency parsing have brought the idea of a truly universal parser closer to reality. However, cross-language interference and restrained model capacity remain major obstacles. To address this, we propose a novel multilingual task adaptation approach based on contextual parameter generation and adapter modules. This approach enables to learn adapters via language embeddings while sharing model parameters across languages. It also allows for an easy but effective integration of existing linguistic typology features into the parsing network. The resulting parser, UDapter, outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach. Our in-depth analyses show that soft parameter sharing via typological features is key to this success.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14327v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2544\", \"3893\", \"16321\", \"23886\", \"28963\", \"34986\", \"55305\", \"57059\", \"12125\", \"14943\", \"24481\", \"25033\", \"38116\", \"43695\", \"43949\", \"46042\", \"55472\", \"76297\", \"77348\", \"77646\", \"77628\", \"92121\", \"105826\", \"127706\", \"43639\", \"127811\", \"130833\"], \"outgoing_citations\": [\"127811\", \"162114\", \"165830\", \"170954\", \"176571\", \"182594\", \"183673\", \"189792\", \"192418\", \"197007\", \"199847\", \"218881\", \"220126\", \"220330\", \"221514\", \"226011\", \"259840\", \"277444\", \"280447\", \"280486\", \"281121\", \"296583\", \"298944\", \"300548\", \"308757\"]}","task_split":"paper_retrieval"}
{"document_id":"128180","document_content":"# Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective which is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmark datasets, including question answering and knowledge base completion tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14224v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16792\", \"77617\", \"97239\", \"97239\", \"144530\", \"15895\", \"19742\", \"24358\", \"38265\", \"56560\", \"62216\", \"72781\", \"86883\", \"91611\", \"120762\"], \"outgoing_citations\": [\"143992\", \"158846\", \"162246\", \"166443\", \"167753\", \"167880\", \"168182\", \"168575\", \"169182\", \"171165\", \"181063\", \"182261\", \"182707\", \"185629\", \"185889\", \"189325\", \"189515\", \"189726\", \"197408\", \"201341\", \"211788\", \"212077\", \"212078\", \"233448\", \"238962\", \"243397\", \"248147\", \"250785\", \"283927\", \"300498\", \"308219\"]}","task_split":"paper_retrieval"}
{"document_id":"128185","document_content":"# Syntax-aware Data Augmentation for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nData augmentation is an effective performance enhancement in neural machine translation (NMT) by generating additional bilingual data. In this paper, we propose a novel data augmentation enhancement strategy for neural machine translation. Different from existing data augmentation methods which simply choose words with the same probability across different sentences for modification, we set sentence-specific probability for word selection by considering their roles in sentence. We use dependency parse tree of input sentence as an effective clue to determine selecting probability for every words in each sentence. Our proposed method is evaluated on WMT14 English-to-German dataset and IWSLT14 German-to-English dataset. The result of extensive experiments show our proposed syntax-aware data augmentation method may effectively boost existing sentence-independent methods for significant translation performance improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.14200v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"7836\", \"38855\", \"10937\", \"51130\", \"95549\"], \"outgoing_citations\": [\"162951\", \"181686\", \"184635\", \"210903\", \"220017\", \"251816\", \"251974\", \"252107\", \"268788\", \"270643\", \"271934\", \"272068\", \"296274\"]}","task_split":"paper_retrieval"}
{"document_id":"128249","document_content":"# Improving Target-driven Visual Navigation with Attention on 3D Spatial Relationships\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nEmbodied artificial intelligence (AI) tasks shift from tasks focusing on internet images to active settings involving embodied agents that perceive and act within 3D environments. In this paper, we investigate the target-driven visual navigation using deep reinforcement learning (DRL) in 3D indoor scenes, whose navigation task aims to train an agent that can intelligently make a series of decisions to arrive at a pre-specified target location from any possible starting positions only based on egocentric views. However, most navigation methods currently struggle against several challenging problems, such as data efficiency, automatic obstacle avoidance, and generalization. Generalization problem means that agent does not have the ability to transfer navigation skills learned from previous experience to unseen targets and scenes. To address these issues, we incorporate two designs into classic DRL framework: attention on 3D knowledge graph (KG) and target skill extension (TSE) module. On the one hand, our proposed method combines visual features and 3D spatial representations to learn navigation policy. On the other hand, TSE module is used to generate sub-targets which allow agent to learn from failures. Specifically, our 3D spatial relationships are encoded through recently popular graph convolutional network (GCN). Considering the real world settings, our work also considers open action and adds actionable targets into conventional navigation situations. Those more difficult settings are applied to test whether DRL agent really understand its task, navigating environment, and can carry out reasoning. Our experiments, performed in the AI2-THOR, show that our model outperforms the baselines in both SR and SPL metrics, and improves generalization ability across targets and scenes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.02153v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2239\", \"26678\", \"36974\", \"48662\", \"97665\", \"140039\"], \"outgoing_citations\": [\"85113\", \"167688\", \"192738\", \"207499\", \"208773\", \"214195\", \"224223\", \"226124\", \"227507\", \"228916\", \"231955\", \"234517\", \"238203\", \"238287\", \"240323\", \"243090\", \"245731\", \"247418\", \"247900\", \"248734\", \"249906\", \"255575\", \"261983\", \"263104\", \"263650\", \"265722\", \"272930\", \"274102\", \"277797\", \"277968\", \"278195\", \"279420\", \"280296\", \"280681\", \"284565\", \"290904\", \"291568\", \"292407\", \"293071\", \"293864\", \"293876\", \"302857\", \"303049\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"128252","document_content":"# Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nText summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization are generally evaluated by using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2005.03510v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"84343\", \"15519\", \"55804\", \"91769\", \"101441\"], \"outgoing_citations\": [\"132526\", \"161027\", \"166268\", \"170449\", \"170657\", \"176839\", \"177125\", \"181398\", \"200414\", \"203144\", \"209434\", \"214407\", \"216554\", \"217910\", \"218088\", \"219618\", \"220230\", \"230212\", \"230234\", \"231869\", \"235041\", \"235232\", \"235463\", \"237274\", \"239950\", \"241070\", \"243803\", \"259723\", \"266671\", \"267161\", \"268861\", \"280552\", \"307651\"]}","task_split":"paper_retrieval"}
{"document_id":"128269","document_content":"# Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization\n## Categories\n- Computation and Language\n## Abstract\nMuch progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style. In addition, there is an unmet need to generate a variety of summaries for different users. In this paper, we propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions (i.e. importance, diversity, position). These sub-aspect functions are regulated by a set of control codes to decide which sub-aspect to focus on during summary generation. We demonstrate that extracted summaries with minimal position bias is comparable with those generated by standard models that take advantage of position preference. We also show that news summaries generated with a focus on diversity can be more preferred by human raters. These results suggest that a more flexible neural summarization framework providing more control options could be desirable in tailoring to different user preferences, which is useful since it is often impractical to articulate such preferences for different applications a priori.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13983v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20781\", \"26671\"], \"outgoing_citations\": [\"164127\", \"167338\", \"169457\", \"170449\", \"170657\", \"212506\", \"219618\", \"220230\", \"221478\", \"225556\", \"241070\", \"266671\", \"280557\", \"289943\", \"353402\"]}","task_split":"paper_retrieval"}
{"document_id":"128388","document_content":"# Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHuman-computer interactive systems that rely on machine learning are becoming paramount to the lives of millions of people who use digital assistants on a daily basis. Yet, further advances are limited by the availability of data and the cost of acquiring new samples. One way to address this problem is by improving the sample efficiency of current approaches. As a solution path, we present a model-based reinforcement learning algorithm for an interactive dialogue task. We build on commonly used actor-critic methods, adding an environment model and planner that augments a learning agent to learn the model of the environment dynamics. Our results show that, on a simulation that mimics the interactive task, our algorithm requires 70 times fewer samples, compared to the baseline of commonly used model-free algorithm, and demonstrates 2~times better performance asymptotically. Moreover, we introduce a novel contribution of computing a soft planner policy and further updating a model-free policy yielding a less computationally expensive model-free agent as good as the model-based one. This model-based architecture serves as a foundation that can be extended to other human-computer interactive tasks allowing further advances in this direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13657v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"103709\"], \"outgoing_citations\": [\"179948\", \"196377\", \"196921\", \"242616\", \"268584\", \"285454\", \"290847\", \"291102\", \"303349\", \"308711\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"128396","document_content":"# Recipes for building an open-domain chatbot\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13637v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"24322\", \"38615\", \"41812\", \"2490\", \"2633\", \"2754\", \"2913\", \"3125\", \"3203\", \"3398\", \"3822\", \"5256\", \"6635\", \"8227\", \"15609\", \"15870\", \"15861\", \"16015\", \"16099\", \"16341\", \"16936\", \"18344\", \"20942\", \"22311\", \"24831\", \"24984\", \"31456\", \"32764\", \"35166\", \"35167\", \"49804\", \"52433\", \"77878\", \"89936\", \"2752\", \"5301\", \"6978\", \"10508\", \"11785\", \"12396\", \"12510\", \"16752\", \"16789\", \"17278\", \"20917\", \"21147\", \"21561\", \"22651\", \"22624\", \"23160\", \"23248\", \"23521\", \"23711\", \"23755\", \"24007\", \"24216\", \"24400\", \"24605\", \"25186\", \"25495\", \"25727\", \"25987\", \"27098\", \"27403\", \"27687\", \"31077\", \"31712\", \"125732\", \"36866\", \"41869\", \"42396\", \"42895\", \"42907\", \"43047\", \"43242\", \"43730\", \"43776\", \"44409\", \"44362\", \"44646\", \"45150\", \"44978\", \"45320\", \"45377\", \"45883\", \"91650\", \"45586\", \"24688\", \"92490\", \"91809\", \"46003\", \"46328\", \"46949\", \"48100\", \"49713\", \"51375\", \"54805\", \"55201\", \"55799\", \"62145\", \"65469\", \"66042\", \"67198\", \"73515\", \"75973\", \"76993\", \"77389\", \"78504\", \"127841\", \"80358\", \"83014\", \"83056\", \"91695\", \"91694\", \"94572\", \"97088\", \"97185\", \"97289\", \"98430\", \"103544\", \"104906\", \"107630\", \"115155\", \"128718\", \"132645\", \"77867\", \"90800\", \"48384\", \"91677\", \"91706\", \"92152\", \"92904\", \"94347\", \"95282\", \"95588\", \"95728\", \"96551\", \"97214\", \"98073\", \"98658\", \"99088\", \"100574\", \"101840\", \"103765\", \"105315\", \"115538\", \"116873\", \"122948\", \"127463\", \"128922\", \"131507\"], \"outgoing_citations\": [\"129809\", \"130525\", \"139854\", \"143589\", \"145384\", \"145923\", \"145957\", \"147374\", \"156548\", \"157151\", \"157164\", \"157211\", \"157308\", \"158493\", \"166278\", \"167338\", \"168228\", \"171356\", \"172088\", \"179568\", \"196049\", \"200725\", \"201867\", \"202748\", \"205147\", \"209785\", \"211664\", \"212046\", \"218963\", \"221363\", \"231949\", \"232274\", \"234808\", \"235913\", \"244479\", \"245042\", \"245831\", \"247726\", \"253640\", \"266155\", \"266671\", \"294144\", \"296250\"]}","task_split":"paper_retrieval"}
{"document_id":"128515","document_content":"# Heterogeneous Representation Learning: A Review\n## Categories\n- Machine Learning\n## Abstract\nThe real-world data usually exhibits heterogeneous properties such as modalities, views, or resources, which brings some unique challenges wherein the key is Heterogeneous Representation Learning (HRL) termed in this paper. This brief survey covers the topic of HRL, centered around several major learning settings and real-world applications. First of all, from the mathematical perspective, we present a unified learning framework which is able to model most existing learning settings with the heterogeneous inputs. After that, we conduct a comprehensive discussion on the HRL framework by reviewing some selected learning problems along with the mathematics perspectives, including multi-view learning, heterogeneous transfer learning, Learning using privileged information and heterogeneous multi-task learning. For each learning task, we also discuss some applications under these learning problems and instantiates the terms in the mathematical framework. Finally, we highlight the challenges that are less-touched in HRL and present future research directions. To the best of our knowledge, there is no such framework to unify these heterogeneous problems, and this survey would benefit the community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13303v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"190165\", \"226407\", \"230056\", \"260314\", \"267605\", \"272840\", \"291400\", \"292659\", \"303553\", \"345732\", \"357681\"]}","task_split":"paper_retrieval"}
{"document_id":"128699","document_content":"# Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additionally, hand-designed meta-RL architectures may not include suitable computational structures for specific learning problems. The evolution of online learning mechanisms, on the contrary, has the ability to incorporate learning strategies into an agent that can (i) evolve memory when required and (ii) optimize adaptation speed to specific online learning problems. In this paper, we exploit the highly adaptive nature of neuromodulated neural networks to evolve a controller that uses the latent space of an autoencoder in a POMDP. The analysis of the evolved networks reveals the ability of the proposed algorithm to acquire inborn knowledge in a variety of aspects such as the detection of cues that reveal implicit rewards, and the ability to evolve location neurons that help with navigation. The integration of inborn knowledge and online plasticity enabled fast adaptation and better performance in comparison to some non-evolutionary meta-reinforcement learning algorithms. The algorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3377930.3390214\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.LG\", \"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [\"12599\", \"55611\", \"113915\"], \"outgoing_citations\": [\"188651\", \"194494\", \"214135\", \"214993\", \"219180\", \"247158\", \"261391\", \"261577\", \"270171\", \"271783\", \"280202\", \"280870\", \"293995\"]}","task_split":"paper_retrieval"}
{"document_id":"128769","document_content":"# From Theory to Behaviour: Towards a General Model of Engagement\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nEngagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.12644v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"57945\", \"19376\"], \"outgoing_citations\": [\"136898\", \"176997\", \"184367\", \"202966\", \"243081\", \"288625\"]}","task_split":"paper_retrieval"}
{"document_id":"128799","document_content":"# The Ingredients of Real-World Robotic Reinforcement Learning\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nThe success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversight to enable continuous learning. In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world. We propose a particular instantiation of such a system, using dexterous manipulation as our case study. Subsequently, we investigate a number of challenges that come up when learning without instrumentation. In such settings, learning must be feasible without manually designed resets, using only on-board perception, and without hand-engineered reward functions. We propose simple and scalable solutions to these challenges, and then demonstrate the efficacy of our proposed system on a set of dexterous robotic manipulation tasks, providing an in-depth analysis of the challenges associated with this learning paradigm. We demonstrate that our complete system can learn without any human intervention, acquiring a variety of vision-based skills with a real-world three-fingered hand. Results and videos can be found at https:\/\/sites.google.com\/view\/realworld-rl\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.12570v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"209605\", \"2206\", \"14635\", \"18008\", \"64415\", \"67370\", \"78353\", \"87566\", \"13792\", \"21975\", \"22025\", \"32776\", \"33029\", \"34185\", \"35306\", \"36885\", \"37058\", \"41002\", \"42015\", \"54100\", \"59341\", \"66664\", \"70499\", \"73823\", \"82278\", \"93898\", \"94380\", \"87820\", \"90194\", \"90910\", \"96219\", \"107086\", \"116853\", \"128637\", \"182468\"], \"outgoing_citations\": [\"164957\", \"164963\", \"178094\", \"179957\", \"190237\", \"199472\", \"206160\", \"212299\", \"214302\", \"217919\", \"222669\", \"224840\", \"226635\", \"230042\", \"250066\", \"253042\", \"253045\", \"258608\", \"260018\", \"261522\", \"263957\", \"271375\", \"271952\", \"272030\", \"280296\", \"283026\", \"283391\", \"283410\", \"283427\", \"296208\", \"302720\"]}","task_split":"paper_retrieval"}
{"document_id":"128874","document_content":"# Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization\n## Categories\n- Computation and Language\n## Abstract\nRecently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the cost of decreased relevance accompanied by diversity improvements. In this paper, we propose a novel multimodal response generation framework with exemplar augmentation and curriculum optimization to enhance relevance and diversity of generated responses. First, unlike existing VAE-based models that usually approximate a simple Gaussian posterior distribution, we present a Gaussian mixture posterior distribution (i.e, multimodal) to further boost response diversity, which helps capture complex semantics of responses. Then, to ensure that relevance does not decrease while diversity increases, we fully exploit similar examples (exemplars) retrieved from the training data into posterior distribution modeling to augment response relevance. Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard. Experimental results on widely used SwitchBoard and DailyDialog datasets demonstrate that our model achieves significant improvements compared to strong baselines in terms of diversity and relevance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.12429v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"193900\", \"193998\", \"197057\", \"211859\", \"217640\", \"229813\", \"236146\", \"243133\", \"253593\", \"263848\", \"270040\", \"275597\", \"292261\", \"302948\", \"305200\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"128909","document_content":"# Detect Language of Transliterated Texts\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nInformal transliteration from other languages to English is prevalent in social media threads, instant messaging, and discussion forums. Without identifying the language of such transliterated text, users who do not speak that language cannot understand its content using translation tools. We propose a Language Identification (LID) system, with an approach for feature extraction, which can detect the language of transliterated texts reasonably well even with limited training data and computational resources. We tokenize the words into phonetic syllables and use a simple Long Short-term Memory (LSTM) network architecture to detect the language of transliterated texts. With intensive experiments, we show that the tokenization of transliterated words as phonetic syllables effectively represents their causal sound patterns. Phonetic syllable tokenization, therefore, makes it easier for even simpler model architectures to learn the characteristic patterns to identify any language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13521v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.SD\", \"eess.AS\", \"cs.CL\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Sound\", \"Audio and Speech Processing\", \"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"258458\"]}","task_split":"paper_retrieval"}
{"document_id":"129004","document_content":"# Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning\n## Categories\n- Artificial Intelligence\n## Abstract\nAs a comprehensive indicator of mathematical thinking and intelligence, the number sense (Dehaene 2011) bridges the induction of symbolic concepts and the competence of problem-solving. To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG). These visual arithmetic problems are in the form of geometric figures: each problem has a set of geometric shapes as its context and embedded number symbols. Solving such problems is not trivial; the machine not only has to recognize the number, but also to interpret the number with its contexts, shapes, and relations (e.g., symmetry) together with proper operations. We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task. Comprehensive experiments show that current neural-network-based models still struggle to understand number concepts and relational operations. We show that a simple brute-force search algorithm could work out some of the problems without context information. Crucially, taking geometric context into account by an additional perception module would provide a sharp performance gain with fewer search steps. Altogether, we call for attention in fusing the classic search-based algorithms with modern neural networks to discover the essential number concepts in future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.12193v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"7311\", \"19640\", \"60528\", \"60526\", \"65982\", \"79536\", \"130231\"], \"outgoing_citations\": [\"153518\", \"154335\", \"192616\", \"196150\", \"200715\", \"224981\", \"264533\", \"266702\", \"284114\"]}","task_split":"paper_retrieval"}
{"document_id":"129180","document_content":"# A survey on domain adaptation theory: learning bounds and theoretical guarantees\n## Categories\n- Machine Learning\n## Abstract\nAll famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This has given rise to a new machine learning framework known as transfer learning: a learning setting inspired by the capability of a human being to extrapolate knowledge across tasks to learn more efficiently. Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation. In this sub-field, the data distribution is assumed to change across the training and the test data, while the learning task remains the same. We provide a first up-to-date description of existing results related to domain adaptation problem that cover learning bounds based on different statistical learning frameworks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.11829v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"5156\", \"65490\", \"102782\", \"4079\", \"14235\", \"23063\", \"36346\", \"36367\", \"39041\", \"47499\", \"76975\", \"77073\", \"90415\"], \"outgoing_citations\": [\"142808\", \"190980\", \"195912\", \"201400\", \"218319\", \"231414\", \"262064\", \"265523\", \"278849\", \"282655\", \"310225\", \"311150\", \"315300\", \"320872\", \"330036\", \"346376\", \"356019\", \"358930\"]}","task_split":"paper_retrieval"}
{"document_id":"129191","document_content":"# A Review of Privacy-preserving Federated Learning for the Internet-of-Things\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nThe Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.11794v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"12465\", \"72424\", \"42569\", \"107501\"], \"outgoing_citations\": [\"129194\", \"149690\", \"161758\", \"189393\", \"207419\", \"208823\", \"210868\", \"216468\", \"226146\", \"234581\", \"234890\", \"235592\", \"238836\", \"246939\", \"248216\", \"250510\", \"252956\", \"273946\", \"280481\", \"300079\", \"303050\", \"303366\", \"317797\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"129246","document_content":"# YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. In this work, we present a dataset of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set [1], [2]. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object. We have also performed an initial evaluation of the cameras using our dataset on a state-of-the-art object recognition and pose estimation system [3].","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICRA40945.2020.9197426\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"31736\", \"54014\", \"22051\", \"60328\", \"67778\", \"86351\"], \"outgoing_citations\": [\"215837\", \"216243\", \"235196\", \"251767\", \"275848\", \"295264\", \"309067\", \"312525\"]}","task_split":"paper_retrieval"}
{"document_id":"129355","document_content":"# End-to-end speech-to-dialog-act recognition\n## Categories\n- Sound\n- Audio and Speech Processing\n- Computation and Language\n## Abstract\nSpoken language understanding, which extracts intents and\/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It is usually trained with oracle transcripts, but needs to deal with errors by ASR. Moreover, there are acoustic features which are related with intents but not represented with the transcripts. In this paper, we present an end-to-end model which directly converts speech into dialog acts without the deterministic transcription process. In the proposed model, the dialog act recognition network is conjunct with an acoustic-to-word ASR model at its latent layer before the softmax layer, which provides a distributed representation of word-level ASR decoding information. Then, the entire network is fine-tuned in an end-to-end manner. This allows for stable training as well as robustness against ASR errors. The model is further extended to conduct DA segmentation jointly. Evaluations with the Switchboard corpus demonstrate that the proposed method significantly improves dialog act recognition accuracy from the conventional pipeline framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.11419v2\", \"primary_category\": \"cs.SD\", \"categories\": [\"eess.AS\", \"cs.CL\", \"cs.SD\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Computation and Language\", \"Sound\"], \"incoming_citations\": [\"37395\"], \"outgoing_citations\": [\"180179\", \"191769\", \"216686\", \"241146\", \"270814\", \"295483\", \"307902\", \"308468\", \"320874\"]}","task_split":"paper_retrieval"}
{"document_id":"129448","document_content":"# Natural language technology and query expansion: issues, state-of-the-art and perspectives\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe availability of an abundance of knowledge sources has spurred a large amount of effort in the development and enhancement of Information Retrieval techniques. Users information needs are expressed in natural language and successful retrieval is very much dependent on the effective communication of the intended purpose. Natural language queries consist of multiple linguistic features which serve to represent the intended search goal. Linguistic characteristics that cause semantic ambiguity and misinterpretation of queries as well as additional factors such as the lack of familiarity with the search environment affect the users ability to accurately represent their information needs, coined by the concept intention gap. The latter directly affects the relevance of the returned search results which may not be to the users satisfaction and therefore is a major issue impacting the effectiveness of information retrieval systems. Central to our discussion is the identification of the significant constituents that characterize the query intent and their enrichment through the addition of meaningful terms, phrases or even latent representations, either manually or automatically to capture their intended meaning. Specifically, we discuss techniques to achieve the enrichment and in particular those utilizing the information gathered from statistical processing of term dependencies within a document corpus or from external knowledge sources such as ontologies. We lay down the anatomy of a generic linguistic based query expansion framework and propose its module-based decomposition, covering topical issues from query processing, information retrieval, computational linguistics and ontology engineering. For each of the modules we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10844-011-0174-3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"124564\", \"129451\", \"317355\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"129526","document_content":"# Alternating Minimization Converges Super-Linearly for Mixed Linear Regression\n## Categories\n- Machine Learning\n## Abstract\nWe address the problem of solving mixed random linear equations. We have unlabeled observations coming from multiple linear regressions, and each observation corresponds to exactly one of the regression models. The goal is to learn the linear regressors from the observations. Classically, Alternating Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used to solve this problem. AM iteratively alternates between the estimation of labels and solving the regression problems with the estimated labels. Empirically, it is observed that, for a large variety of non-convex problems including mixed linear regression, AM converges at a much faster rate compared to gradient based algorithms. However, the existing theory suggests similar rate of convergence for AM and gradient based methods, failing to capture this empirical behavior. In this paper, we close this gap between theory and practice for the special case of a mixture of $2$ linear regressions. We show that, provided initialized properly, AM enjoys a \\emph{super-linear} rate of convergence in certain parameter regimes. To the best of our knowledge, this is the first work that theoretically establishes such rate for AM. Hence, if we want to recover the unknown regressors upto an error (in $\\ell_2$ norm) of $\\epsilon$, AM only takes $\\mathcal{O}(\\log \\log (1\/\\epsilon))$ iterations. Furthermore, we compare AM with a gradient based heuristic algorithm empirically and show that AM dominates in iteration complexity as well as wall-clock time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10914v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"120533\", \"80311\", \"121165\"], \"outgoing_citations\": [\"179586\", \"183895\", \"199501\", \"241300\", \"285520\", \"286252\", \"320620\", \"325941\", \"336036\", \"343594\", \"344192\", \"351732\"]}","task_split":"paper_retrieval"}
{"document_id":"129586","document_content":"# Categories of Semantic Concepts\n## Categories\n- Computation and Language\n- Logic in Computer Science\n- Category Theory\n- Quantum Physics\n## Abstract\nModelling concept representation is a foundational problem in the study of cognition and linguistics. This work builds on the confluence of conceptual tools from G\\\"ardenfors semantic spaces, categorical compositional linguistics, and applied category theory to present a domain-independent and categorical formalism of 'concept'.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10741v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LO\", \"math.CT\", \"quant-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Logic in Computer Science\", \"Category Theory\", \"Quantum Physics\"], \"incoming_citations\": [\"125735\"], \"outgoing_citations\": [\"125735\", \"191869\", \"286695\"]}","task_split":"paper_retrieval"}
{"document_id":"129613","document_content":"# Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction\n## Categories\n- Computation and Language\n## Abstract\nMost Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10640v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"5406\"], \"outgoing_citations\": [\"159408\", \"219150\", \"227725\", \"243422\", \"279522\", \"294402\", \"323451\"]}","task_split":"paper_retrieval"}
{"document_id":"129632","document_content":"# When and Why is Unsupervised Neural Machine Translation Useless?\n## Categories\n- Computation and Language\n## Abstract\nThis paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations. We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data. Such conditions are common for low-resource language pairs, where unsupervised learning works poorly. In all of our experiments, supervised and semi-supervised baselines with 50k-sentence bilingual data outperform the best unsupervised results. Our analyses pinpoint the limits of the current unsupervised NMT and also suggest immediate research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10581v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26304\", \"38855\", \"55305\", \"15844\", \"32358\", \"34638\", \"43451\", \"55413\", \"59790\", \"60936\", \"63831\", \"63988\", \"73338\", \"93412\", \"98946\", \"106572\", \"81356\", \"95392\", \"131659\", \"133033\", \"133462\"], \"outgoing_citations\": [\"146003\", \"165748\", \"169317\", \"181277\", \"182594\", \"186425\", \"187452\", \"197007\", \"200263\", \"200292\", \"203054\", \"203812\", \"219175\", \"219455\", \"221514\", \"228868\", \"229654\", \"232631\", \"234377\", \"234807\", \"251816\", \"251974\", \"253559\", \"263927\", \"280486\", \"281472\", \"298543\", \"300548\"]}","task_split":"paper_retrieval"}
{"document_id":"129687","document_content":"# Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent's confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10439v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"48121\", \"677\", \"46115\", \"24094\", \"111496\", \"111648\", \"117991\"], \"outgoing_citations\": [\"187603\", \"206814\", \"209826\", \"213093\", \"226166\", \"228732\", \"234762\", \"238960\", \"267428\", \"269012\", \"270604\", \"282884\", \"298281\", \"303726\"]}","task_split":"paper_retrieval"}
{"document_id":"129708","document_content":"# A Framework for Evaluating Gradient Leakage Attacks in Federated Learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nFederated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server. However, recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks and intrude the client privacy regarding its training data. In this paper, we present a principled framework for evaluating and comparing different forms of client privacy leakage attacks. We first provide formal and experimental analysis to show how adversaries can reconstruct the private local training data by simply analyzing the shared parameter update from local training (e.g., local gradient or weight update vector). We then analyze how different hyperparameter configurations in federated learning and different settings of the attack algorithm may impact on both attack effectiveness and attack cost. Our framework also measures, evaluates, and analyzes the effectiveness of client privacy leakage attacks under different gradient compression ratios when using communication efficient FL protocols. Our experiments also include some preliminary mitigation strategies to highlight the importance of providing a systematic attack evaluation framework towards an in-depth understanding of the various forms of client privacy leakage threats in federated learning and developing theoretical foundations for attack mitigation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10397v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"11266\", \"30344\", \"33446\", \"39494\", \"55687\", \"81920\", \"3990\", \"5543\", \"6225\", \"8081\", \"12402\", \"13444\", \"27798\", \"35424\", \"37557\", \"42569\", \"42954\", \"63220\", \"71148\", \"72706\", \"72911\", \"75998\", \"84839\", \"94049\", \"100718\", \"86147\", \"90427\", \"93553\", \"99130\", \"106893\", \"109503\", \"117242\"], \"outgoing_citations\": [\"120773\", \"133977\", \"148031\", \"161126\", \"162883\", \"179698\", \"185996\", \"207623\", \"225299\", \"226146\", \"226452\", \"232524\", \"246939\", \"248216\", \"265829\", \"273221\", \"273717\", \"282501\", \"317637\"]}","task_split":"paper_retrieval"}
{"document_id":"129765","document_content":"# Observations on Annotations\n## Categories\n- Computation and Language\n## Abstract\nThe annotation of textual information is a fundamental activity in Linguistics and Computational Linguistics. This article presents various observations on annotations. It approaches the topic from several angles including Hypertext, Computational Linguistics and Language Technology, Artificial Intelligence and Open Science. Annotations can be examined along different dimensions. In terms of complexity, they can range from trivial to highly sophisticated, in terms of maturity from experimental to standardised. Annotations can be annotated themselves using more abstract annotations. Primary research data such as, e.g., text documents can be annotated on different layers concurrently, which are independent but can be exploited using multi-layer querying. Standards guarantee interoperability and reusability of data sets. The chapter concludes with four final observations, formulated as research questions or rather provocative remarks on the current state of annotation research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10283v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"129003\", \"130556\", \"134082\", \"134193\", \"134298\", \"134398\", \"166130\", \"200964\"]}","task_split":"paper_retrieval"}
{"document_id":"129794","document_content":"# Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nOne of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world? In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks. Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10190v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"2138\", \"20668\", \"47245\", \"12731\", \"15055\", \"64566\", \"67572\", \"84471\", \"113327\", \"86978\", \"90880\"], \"outgoing_citations\": [\"156199\", \"156707\", \"158215\", \"159949\", \"160598\", \"164957\", \"175872\", \"182535\", \"193562\", \"195713\", \"195849\", \"198915\", \"204289\", \"205477\", \"213161\", \"215102\", \"219180\", \"224142\", \"224840\", \"226635\", \"230919\", \"234025\", \"234517\", \"237229\", \"237605\", \"240506\", \"243236\", \"253045\", \"255359\", \"255939\", \"272585\", \"277564\", \"280570\", \"280681\", \"282684\", \"282696\", \"283391\", \"285677\", \"289719\", \"290378\", \"306156\", \"306266\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"129831","document_content":"# AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10087v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"74469\", \"1340\", \"28992\", \"127897\", \"10513\", \"18216\", \"26015\", \"43528\", \"45038\", \"54261\", \"65237\", \"78544\", \"82309\"], \"outgoing_citations\": [\"105764\", \"166702\", \"168563\", \"178308\", \"204763\", \"205014\", \"212163\", \"219441\", \"230543\", \"231442\", \"248279\", \"255999\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"129921","document_content":"# Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation\n## Categories\n- Computation and Language\n## Abstract\nWe present an easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models. The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We use the original (monolingual) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model. Compared to other methods for training multilingual sentence embeddings, this approach has several advantages: It is easy to extend existing models with relatively few samples to new languages, it is easier to ensure desired properties for the vector space, and the hardware requirements for training is lower. We demonstrate the effectiveness of our approach for 50+ languages from various language families. Code to extend sentence embeddings models to more than 400 languages is publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09813v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"46974\", \"1645\", \"2651\", \"10160\", \"16404\", \"19896\", \"28627\", \"35941\", \"48908\", \"55305\", \"55766\", \"56654\", \"74993\", \"78222\", \"114308\", \"1159\", \"1907\", \"2064\", \"7555\", \"8259\", \"8370\", \"11352\", \"11952\", \"14226\", \"14941\", \"17826\", \"18913\", \"20266\", \"21636\", \"22430\", \"23257\", \"23636\", \"23802\", \"24368\", \"24518\", \"24704\", \"24820\", \"26411\", \"26739\", \"27481\", \"28606\", \"28705\", \"28808\", \"31590\", \"33364\", \"33603\", \"38470\", \"42594\", \"43602\", \"44997\", \"45555\", \"46359\", \"46639\", \"54756\", \"55332\", \"56187\", \"56477\", \"56904\", \"57805\", \"58619\", \"63831\", \"72868\", \"74373\", \"78113\", \"88416\", \"91401\", \"92379\", \"104890\", \"22429\", \"76333\", \"101925\", \"110664\", \"114373\"], \"outgoing_citations\": [\"114308\", \"131679\", \"132526\", \"176600\", \"176839\", \"204715\", \"211711\", \"212322\", \"217957\", \"222741\", \"234808\", \"237274\", \"237728\", \"240090\", \"251816\", \"253559\", \"259723\", \"259758\", \"263680\", \"267161\", \"280486\", \"298543\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"129981","document_content":"# Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCausal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. They also lack the capacity to consider the complexity of treatment such as matching candidate treatments with the subject. We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching. Our proposal to decompose treatment effect functions into effectiveness factors presents a framework to model a rich space of actions using causal inference. We utilize deep learning to optimize the desired holistic metric space instead of predicting single-dimensional treatment counterfactual. This approach employs a population-wide effectiveness measure and significantly improves the overall effectiveness of the model. The performance of our algorithms is. demonstrated with experiments. When using generic continuous space treatments and matching architecture, we observe a 41% improvement upon prior art with cost-effectiveness and 68% improvement upon a similar method in the average treatment effect. The algorithms capture subtle variations in treatment space, structures the efficient optimizations techniques, and opens up the arena for many applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09703v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225548\", \"265530\", \"290547\", \"292736\", \"317842\"]}","task_split":"paper_retrieval"}
{"document_id":"129982","document_content":"# Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing\n## Categories\n- Machine Learning\n## Abstract\nUser marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desired products. Rewards incur significant cost that can be off-set by increase in future revenue. Most methodologies rely on churn predictions to prevent losing users to make marketing decisions, which cannot capture up-lift across counterfactual outcomes with business metrics. Other predictive models are capable of estimating heterogeneous treatment effects, but fail to capture the balance of cost versus benefit. We propose a treatment effect optimization methodology for user marketing. This algorithm learns from past experiments and utilizes novel optimization methods to optimize cost efficiency with respect to user selection. The method optimizes decisions using deep learning optimization models to treat and reward users, which is effective in producing cost-effective, impactful marketing campaigns. Our methodology demonstrates superior algorithmic flexibility with integration with deep learning methods and dealing with business constraints. The effectiveness of our model surpasses the quasi-oracle estimation (R-learner) model and causal forests. We also established evaluation metrics that reflect the cost-efficiency and real-world business value. Our proposed constrained and direct optimization algorithms outperform by 24.6% compared with the best performing method in prior art and baseline methods. The methodology is useful in many product scenarios such as optimal treatment allocation and it has been deployed in production world-wide.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09702v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"86431\"], \"outgoing_citations\": [\"207804\", \"264395\"]}","task_split":"paper_retrieval"}
{"document_id":"130033","document_content":"# On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction, International Journal of Social Robotics, 2019\n## Categories\n- Human-Computer Interaction\n- Robotics\n## Abstract\nIn this paper, we consider the detection of a decrease of engagement by users spontaneously interacting with a socially assistive robot in a public space. We first describe the UE-HRI dataset that collects spontaneous Human-Robot Interactions following the guidelines provided by the Affective Computing research community to collect data \"in-the-wild\". We then analyze the users' behaviors, focusing on proxemics, gaze, head motion, facial expressions and speech during interactions with the robot. Finally, we investigate the use of deep learning techniques (Recurrent and Deep Neural Networks) to detect user engagement decrease in realtime. The results of this work highlight, in particular, the relevance of taking into account the temporal dynamics of a user's behavior. Allowing 1 to 2 seconds as buffer delay improves the performance of taking a decision on user engagement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s12369-019-00591-2\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.RO\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Robotics\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"130091","document_content":"# Five Points to Check when Comparing Visual Perception in Humans and Machines\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Neurons and Cognition\n- Machine Learning\n## Abstract\nWith the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception. We demonstrate and apply these ideas through three case studies. The first case study shows how human bias can affect how we interpret results, and that several analytic tools can help to overcome this human reference point. In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks. Thereby, we show that contrary to previous suggestions, feedback mechanisms might not be necessary for the tasks in question. The third case study highlights the importance of aligning experimental conditions. We find that a previously-observed difference in object recognition does not hold when adapting the experiment to make conditions more equitable between humans and machines. In presenting a checklist for comparative studies of visual reasoning in humans and machines, we hope to highlight how to overcome potential pitfalls in design or inference.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1167\/jov.21.3.16\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"q-bio.NC\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Neurons and Cognition\", \"Machine Learning\"], \"incoming_citations\": [\"6645\", \"30810\", \"34887\", \"84231\", \"162248\", \"17780\", \"40012\", \"48258\", \"60961\", \"63989\", \"67829\"], \"outgoing_citations\": [\"115164\", \"127461\", \"134741\", \"155108\", \"173827\", \"175713\", \"175763\", \"187462\", \"194378\", \"197515\", \"199676\", \"200362\", \"212141\", \"220228\", \"224981\", \"241210\", \"255584\", \"262787\", \"262792\", \"264533\", \"287028\", \"287585\", \"291206\", \"293275\", \"294668\", \"321655\"]}","task_split":"paper_retrieval"}
{"document_id":"130150","document_content":"# A Practical Guide to Studying Emergent Communication through Grounded Language Games\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n- Multiagent Systems\n## Abstract\nThe question of how an effective and efficient communication system can emerge in a population of agents that need to solve a particular task attracts more and more attention from researchers in many fields, including artificial intelligence, linguistics and statistical physics. A common methodology for studying this question consists of carrying out multi-agent experiments in which a population of agents takes part in a series of scripted and task-oriented communicative interactions, called 'language games'. While each individual language game is typically played by two agents in the population, a large series of games allows the population to converge on a shared communication system. Setting up an experiment in which a rich system for communicating about the real world emerges is a major enterprise, as it requires a variety of software components for running multi-agent experiments, for interacting with sensors and actuators, for conceptualising and interpreting semantic structures, and for mapping between these semantic structures and linguistic utterances. The aim of this paper is twofold. On the one hand, it introduces a high-level robot interface that extends the Babel software system, presenting for the first time a toolkit that provides flexible modules for dealing with each subtask involved in running advanced grounded language game experiments. On the other hand, it provides a practical guide to using the toolkit for implementing such experiments, taking a grounded colour naming game experiment as a didactic example.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09218v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271497\", \"291892\", \"292127\"]}","task_split":"paper_retrieval"}
{"document_id":"130351","document_content":"# BanFakeNews: A Dataset for Detecting Fake News in Bangla\n## Categories\n- Computation and Language\n## Abstract\nObserving the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.08789v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"46096\", \"22430\", \"30160\", \"35460\", \"42117\", \"74523\", \"75339\", \"75520\", \"75689\", \"88560\", \"122206\"], \"outgoing_citations\": [\"241616\", \"254146\", \"254383\", \"255835\", \"256933\", \"257863\", \"267552\", \"270398\", \"281571\", \"284826\", \"307547\", \"319035\"]}","task_split":"paper_retrieval"}
{"document_id":"130373","document_content":"# Data Poisoning Attacks on Federated Machine Learning\n## Categories\n- Cryptography and Security\n- Machine Learning\n- I.2.11; I.5\n## Abstract\nFederated machine learning which enables resource constrained node devices (e.g., mobile phones and IoT devices) to learn a shared model while keeping the training data local, can provide privacy, security and economic benefits by designing an effective communication protocol. However, the communication protocol amongst different nodes could be exploited by attackers to launch data poisoning attacks, which has been demonstrated as a big threat to most machine learning models. In this paper, we attempt to explore the vulnerability of federated machine learning. More specifically, we focus on attacking a federated multi-task learning framework, which is a federated learning framework via adopting a general multi-task learning framework to handle statistical challenges. We formulate the problem of computing optimal poisoning attacks on federated multi-task learning as a bilevel program that is adaptive to arbitrary choice of target nodes and source attacking nodes. Then we propose a novel systems-aware optimization method, ATTack on Federated Learning (AT2FL), which is efficiency to derive the implicit gradients for poisoned data, and further compute optimal attack strategies in the federated machine learning. Our work is an earlier study that considers issues of data poisoning attack for federated learning. To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10020v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"cs.CR\", \"I.2.11; I.5\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"I.2.11; I.5\"], \"incoming_citations\": [\"11266\", \"79556\", \"354709\", \"28009\", \"52164\", \"82241\", \"108971\", \"1964\", \"54364\", \"59818\", \"75552\", \"106048\", \"97049\", \"87174\"], \"outgoing_citations\": [\"226146\", \"231277\", \"247042\", \"265073\", \"283045\", \"285761\", \"303573\", \"329670\", \"340486\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"130382","document_content":"# An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPixel-wise operations between polarimetric images are important for processing polarization information. For the lack of such operations, the polarization information cannot be fully utilized in convolutional neural network(CNN). In this paper, a novel end-to-end CNN framework for polarization vision tasks is proposed, which enables the networks to take full advantage of polarimetric images. The framework consists of two sub-networks: a polarization-parameter-constructing network (PPCN) and a task network. PPCN implements pixel-wise operations between images in the CNN form with 1x1 convolution kernels. It takes raw polarimetric images as input, and outputs polarization-parametric images to task network so as to complete a vison task. By training together, the PPCN can learn to provide the most suitable polarization-parametric images for the task network and the dataset. Taking faster R-CNN as task network, the experimental results show that compared with existing methods, the proposed framework achieves much higher mean-average-precision (mAP) in object detection task","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.08740v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37381\"], \"outgoing_citations\": [\"163738\", \"236443\"]}","task_split":"paper_retrieval"}
{"document_id":"130460","document_content":"# Self-Supervised Representation Learning on Document Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n- 68T05\n## Abstract\nThis work analyses the impact of self-supervised pre-training on document images in the context of document image classification. While previous approaches explore the effect of self-supervision on natural images, we show that patch-based pre-training performs poorly on document images because of their different structural properties and poor intra-sample semantic information. We propose two context-aware alternatives to improve performance on the Tobacco-3482 image classification task. We also propose a novel method for self-supervision, which makes use of the inherent multi-modality of documents (image and text), which performs better than other popular self-supervised methods, including supervised ImageNet pre-training, on document image classification scenarios with a limited amount of data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.10605v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"stat.ML\", \"68T05\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\", \"68T05\"], \"incoming_citations\": [\"44251\", \"97694\"], \"outgoing_citations\": [\"176090\", \"185155\", \"185221\", \"188119\", \"193543\", \"198711\", \"201585\", \"215410\", \"216765\", \"225732\", \"230919\", \"233596\", \"234009\", \"251500\", \"257688\", \"258857\", \"264315\", \"265587\", \"268631\", \"269105\", \"269324\", \"285677\", \"297196\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"130491","document_content":"# Effect of Text Color on Word Embeddings\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nIn natural scenes and documents, we can find the correlation between a text and its color. For instance, the word, \"hot\", is often printed in red, while \"cold\" is often in blue. This correlation can be thought of as a feature that represents the semantic difference between the words. Based on this observation, we propose the idea of using text color for word embeddings. While text-only word embeddings (e.g. word2vec) have been extremely successful, they often represent antonyms as similar since they are often interchangeable in sentences. In this paper, we try two tasks to verify the usefulness of text color in understanding the meanings of words, especially in identifying synonyms and antonyms. First, we quantify the color distribution of words from the book cover images and analyze the correlation between the color and meaning of the word. Second, we try to retrain word embeddings with the color distribution of words as a constraint. By observing the changes in the word embeddings of synonyms and antonyms before and after re-training, we aim to understand the kind of words that have positive or negative effects in their word embeddings when incorporating text color information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.08526v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"61316\", \"62344\"], \"outgoing_citations\": [\"179182\", \"197872\", \"269205\", \"272294\", \"281706\", \"302731\", \"319159\"]}","task_split":"paper_retrieval"}
{"document_id":"130549","document_content":"# Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\n``Classical'' word embeddings, such as Word2Vec, have been shown to capture the semantics of words based on their distributional properties. However, their ability to represent the different meanings that a word may have is limited. Such approaches also do not explicitly encode relations between entities, as denoted by words. Embeddings of knowledge bases (KB) capture the explicit relations between entities denoted by words, but are not able to directly capture the syntagmatic properties of these words. To our knowledge, recent research have focused on representation learning that augment the strengths of one with the other. In this work, we begin exploring another approach using contextual and KB embeddings jointly at the same level and propose two tasks -- an entity typing and a relation typing task -- that evaluate the performance of contextual and KB embeddings. We also evaluated a concatenated model of contextual and KB embeddings with these two tasks, and obtain conclusive results on the first task. We hope our work may contribute as a basis for models and datasets that develop in the direction of this approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.08371v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"127923\"], \"outgoing_citations\": [\"167753\", \"169179\", \"186130\", \"189726\", \"193259\", \"198782\", \"201297\", \"224786\", \"232527\", \"244586\", \"313947\", \"316630\"]}","task_split":"paper_retrieval"}
{"document_id":"130677","document_content":"# A Comprehensive Overview and Survey of Recent Advances in Meta-Learning\n## Categories\n- Machine Learning\n## Abstract\nThis article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics. Unlike deep learning, meta-learning can be applied to few-shot high-dimensional datasets and considers further improving model generalization to unseen tasks. Deep learning is focused upon in-sample prediction and meta-learning concerns model adaptation for out-of-sample prediction. Meta-learning can continually perform self-improvement to achieve highly autonomous AI. Meta-learning may serve as an additional generalization block complementary for original deep learning model. Meta-learning seeks adaptation of machine learning models to unseen tasks which are vastly different from trained tasks. Meta-learning with coevolution between agent and environment provides solutions for complex tasks unsolvable by training from scratch. Meta-learning methodology covers a wide range of great minds and thoughts. We briefly introduce meta-learning methodologies in the following categories: black-box meta-learning, metric-based meta-learning, layered meta-learning and Bayesian meta-learning framework. Recent applications concentrate upon the integration of meta-learning with other machine learning framework to provide feasible integrated problem solutions. We briefly present recent meta-learning advances and discuss potential future research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.11149v7\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"20588\", \"66358\", \"7674\", \"35205\", \"66222\", \"75949\", \"86075\", \"93494\"], \"outgoing_citations\": [\"122479\", \"133562\", \"136363\", \"137299\", \"138113\", \"143919\", \"148867\", \"151800\", \"162374\", \"162581\", \"167146\", \"169908\", \"178094\", \"181883\", \"184374\", \"186101\", \"191732\", \"194494\", \"196047\", \"197982\", \"198400\", \"200075\", \"201860\", \"203756\", \"205477\", \"205625\", \"208207\", \"213370\", \"214135\", \"214592\", \"215410\", \"223585\", \"224022\", \"224464\", \"228299\", \"228540\", \"228556\", \"228896\", \"230604\", \"230792\", \"231222\", \"234262\", \"237101\", \"237229\", \"240177\", \"240209\", \"240315\", \"243236\", \"244112\", \"246393\", \"255939\", \"255962\", \"259762\", \"261577\", \"261697\", \"264016\", \"264988\", \"265880\", \"269420\", \"270925\", \"271536\", \"271935\", \"280202\", \"280870\", \"281001\", \"282347\", \"286531\", \"290952\", \"295736\", \"302295\", \"317642\"]}","task_split":"paper_retrieval"}
{"document_id":"130705","document_content":"# Quantization Guided JPEG Artifact Correction\n## Categories\n- Image and Video Processing\n- Machine Learning\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nThe JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios. However, to achieve such high compression, information is lost. For aggressive quantization settings, this leads to a noticeable reduction in image quality. Artifact correction has been studied in the context of deep neural networks for some time, but the current state-of-the-art methods require a different model to be trained for each quality setting, greatly limiting their practical application. We solve this problem by creating a novel architecture which is parameterized by the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09320v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"stat.ML\", \"eess.IV\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60026\", \"10263\", \"103140\", \"12490\", \"20186\", \"21815\", \"28002\", \"59979\", \"86299\", \"78351\"], \"outgoing_citations\": [\"174787\", \"190197\", \"195126\", \"204367\", \"204828\", \"213798\", \"213816\", \"226061\", \"228748\", \"230383\", \"231629\", \"259136\", \"269384\", \"279844\", \"279958\", \"293446\", \"313846\", \"321017\"]}","task_split":"paper_retrieval"}
{"document_id":"130742","document_content":"# Divergent Search for Few-Shot Image Classification\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWhen data is unlabelled and the target task is not known a priori, divergent search offers a strategy for learning a wide range of skills. Having such a repertoire allows a system to adapt to new, unforeseen tasks. Unlabelled image data is plentiful, but it is not always known which features will be required for downstream tasks. We propose a method for divergent search in the few-shot image classification setting and evaluate with Omniglot and Mini-ImageNet. This high-dimensional behavior space includes all possible ways of partitioning the data. To manage divergent search in this space, we rely on a meta-learning framework to integrate useful features from diverse tasks into a single model. The final layer of this model is used as an index into the `archive' of all past behaviors. We search for regions in the behavior space that the current archive cannot reach. As expected, divergent search is outperformed by models with a strong bias toward the evaluation tasks. But it is able to match and sometimes exceed the performance of models that have a weak bias toward the target task or none at all. This demonstrates that divergent search is a viable approach, even in high-dimensional behavior spaces.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.07903v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"208207\", \"211503\", \"215410\", \"224022\", \"235096\", \"247161\", \"271783\", \"327091\", \"328695\"]}","task_split":"paper_retrieval"}
{"document_id":"130833","document_content":"# Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers\n## Categories\n- Computation and Language\n## Abstract\nCurrent methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, \"at treebank level\". In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer. We start from an empirical observation that different source parsers are the best choice for different Universal POS sequences in the target language. We then propose to predict the best parser at the instance level. To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences. We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and (2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V). The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13\/20 and 14\/20 test languages, respectively. Further, we show that by predicting the best parser \"at the treebank level\" (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17\/20 and 16\/20 test languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.07642v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"105826\", \"122609\", \"127555\", \"128134\", \"130205\", \"166025\", \"166780\", \"168298\", \"168812\", \"183673\", \"192418\", \"194123\", \"226011\", \"253623\", \"281121\", \"282324\", \"282869\", \"298944\"]}","task_split":"paper_retrieval"}
{"document_id":"130910","document_content":"# The Right Tool for the Job: Matching Model and Instance Complexities\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAs NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference budget, we propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) \"exit\" from neural network calculations for simple instances, and late (and accurate) exit for hard instances. To achieve this, we add classifiers to different layers of BERT and use their calibrated confidence scores to make early exit decisions. We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks. Our method presents a favorable speed\/accuracy tradeoff in almost all cases, producing models which are up to five times faster than the state of the art, while preserving their accuracy. Our method also requires almost no additional training resources (in either time or parameters) compared to the baseline BERT model. Finally, our method alleviates the need for costly retraining of multiple models at different levels of efficiency; we allow users to control the inference speed\/accuracy tradeoff using a single trained model, by setting a single variable at inference time. We publicly release our code.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.07453v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22081\", \"132323\", \"3029\", \"16569\", \"70490\", \"6852\", \"8708\", \"10734\", \"12544\", \"15036\", \"16750\", \"24825\", \"25200\", \"25682\", \"37467\", \"41308\", \"43437\", \"38333\", \"46666\", \"46630\", \"46808\", \"55151\", \"65094\", \"72007\", \"77352\", \"77999\", \"84151\", \"94365\", \"94572\", \"100541\", \"87755\", \"91331\", \"117368\", \"120497\", \"136153\"], \"outgoing_citations\": [\"132998\", \"136310\", \"141171\", \"141787\", \"160351\", \"161811\", \"164998\", \"165441\", \"167142\", \"168245\", \"168247\", \"174583\", \"184576\", \"185112\", \"193295\", \"197641\", \"206200\", \"229995\", \"230298\", \"233416\", \"234783\", \"238321\", \"239831\", \"249274\", \"251259\", \"251589\", \"256725\", \"270311\", \"273123\", \"295651\"]}","task_split":"paper_retrieval"}
{"document_id":"131013","document_content":"# Zero-Shot Compositional Policy Learning via Language Grounding\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans can adapt to new tasks quickly by leveraging prior knowledge about the world such as language descriptions. To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes. Since there are no public environments supporting this study, we introduce a new research platform BabyAI++ in which the dynamics of environments are disentangled from visual appearance. At each episode, BabyAI++ provides varied vision-dynamics combinations along with corresponding descriptive texts. To evaluate the adaption capability of learned agents, a set of vision-dynamics pairings are held-out for testing on BabyAI++. Unsurprisingly, we find that current language-guided RL\/IL techniques overfit to the training environments and suffer from a huge performance drop when facing unseen combinations. In response, we propose a multi-modal fusion method with an attention mechanism to perform visual language-grounding. Extensive experiments show strong evidence that language grounding is able to improve the generalization of agents across environments with varied dynamics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.07200v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"116023\", \"152871\", \"181037\", \"181620\", \"183049\", \"186276\", \"196377\", \"196909\", \"207051\", \"213745\", \"223487\", \"223990\", \"224464\", \"224840\", \"236969\", \"237119\", \"240602\", \"243782\", \"245731\", \"247418\", \"247900\", \"248805\", \"248885\", \"249906\", \"255134\", \"259693\", \"261316\", \"261577\", \"263104\", \"263270\", \"263650\", \"270939\", \"275003\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"131025","document_content":"# SPECTER: Document-level Representation Learning using Citation-informed Transformers\n## Categories\n- Computation and Language\n## Abstract\nRepresentation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.07180v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18076\", \"66904\", \"82136\", \"137688\", \"137688\", \"28581\", \"3368\", \"5648\", \"9191\", \"10932\", \"12087\", \"16310\", \"25921\", \"29965\", \"30931\", \"65521\", \"7361\", \"14822\", \"24430\", \"24570\", \"24576\", \"36117\", \"41907\", \"43141\", \"44479\", \"45356\", \"55245\", \"56072\", \"56297\", \"58332\", \"61095\", \"62216\", \"63354\", \"65591\", \"73035\", \"93783\", \"94601\", \"129596\"], \"outgoing_citations\": [\"164261\", \"182438\", \"185305\", \"195028\", \"206198\", \"212285\", \"219939\", \"229222\", \"230648\", \"233047\", \"238321\", \"241183\", \"250760\", \"259011\", \"267161\", \"288174\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"131223","document_content":"# A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nDue to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the user's preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3233\/FAIA200136\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"73917\"], \"outgoing_citations\": [\"204674\", \"205610\", \"210118\", \"217192\", \"220924\", \"233018\", \"240298\", \"241732\", \"246249\", \"276004\", \"301005\"]}","task_split":"paper_retrieval"}
{"document_id":"131391","document_content":"# A Divide-and-Conquer Approach to the Summarization of Long Documents\n## Categories\n- Computation and Language\n## Abstract\nWe present a novel divide-and-conquer method for the neural summarization of long documents. Our method exploits the discourse structure of the document and uses sentence similarity to split the problem into an ensemble of smaller summarization problems. In particular, we break a long document and its summary into multiple source-target pairs, which are used for training a model that learns to summarize each part of the document separately. These partial summaries are then combined in order to produce a final complete summary. With this approach we can decompose the problem of long document summarization into smaller and simpler problems, reducing computational complexity and creating more training examples, which at the same time contain less noise in the target summaries compared to the standard approach. We demonstrate that this approach paired with different summarization models, including sequence-to-sequence RNNs and Transformers, can lead to improved summarization performance. Our best models achieve results that are on par with the state-of-the-art in two two publicly available datasets of academic articles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.06190v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2919\", \"15876\", \"16045\", \"34591\", \"48179\", \"101278\", \"7431\", \"40912\", \"45863\", \"45908\", \"48100\", \"50561\", \"50834\", \"58194\", \"91705\"], \"outgoing_citations\": [\"109314\", \"147514\", \"150522\", \"168185\", \"168708\", \"170657\", \"181817\", \"185676\", \"187236\", \"187452\", \"200499\", \"214174\", \"219618\", \"230212\", \"233710\", \"233817\", \"235463\", \"235913\", \"237494\", \"241070\", \"252381\", \"263905\", \"266671\", \"268334\", \"278954\", \"280552\", \"280557\", \"298016\", \"307223\", \"334217\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"131562","document_content":"# Principal Neighbourhood Aggregation for Graph Nets\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nGraph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on isomorphism tasks and countable feature spaces. We extend this theoretical framework to include continuous features - which occur regularly in real-world input domains and within the hidden layers of GNNs - and we demonstrate the requirement for multiple aggregation functions in this context. Accordingly, we propose Principal Neighbourhood Aggregation (PNA), a novel architecture combining multiple aggregators with degree-scalers (which generalize the sum aggregator). Finally, we compare the capacity of different models to capture and exploit the graph structure via a novel benchmark containing multiple tasks taken from classical graph theory, alongside existing benchmarks from real-world domains, all of which demonstrate the strength of our model. With this work, we hope to steer some of the GNN research towards new aggregation methods which we believe are essential in the search for powerful and robust models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.05718v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"18977\", \"118201\", \"365\", \"3222\", \"4620\", \"9993\", \"10593\", \"11343\", \"16180\", \"17961\", \"18144\", \"18175\", \"18515\", \"18893\", \"20444\", \"22079\", \"25810\", \"30943\", \"38282\", \"67697\", \"97457\", \"102194\", \"119164\", \"126868\", \"132642\", \"168186\", \"4823\", \"7331\", \"10314\", \"10697\", \"13184\", \"13295\", \"13814\", \"18623\", \"19609\", \"21219\", \"21805\", \"23825\", \"25973\", \"26143\", \"27545\", \"28034\", \"29724\", \"31016\", \"34147\", \"38475\", \"39957\", \"41176\", \"41385\", \"41897\", \"42514\", \"43155\", \"43338\", \"43981\", \"44322\", \"46662\", \"49935\", \"53381\", \"58486\", \"59455\", \"59866\", \"64367\", \"64915\", \"65206\", \"67045\", \"68391\", \"68697\", \"71276\", \"80321\", \"88245\", \"88426\", \"93505\", \"95718\", \"96209\", \"96703\", \"98711\", \"106528\", \"80776\", \"85154\", \"98439\", \"109828\", \"115834\", \"123421\", \"125230\", \"192622\", \"138999\"], \"outgoing_citations\": [\"96209\", \"137772\", \"138999\", \"140442\", \"142226\", \"143081\", \"143456\", \"159680\", \"160169\", \"164732\", \"168172\", \"176579\", \"181255\", \"182323\", \"182690\", \"183437\", \"184135\", \"196221\", \"215435\", \"229343\", \"249839\", \"255737\", \"265828\", \"279575\", \"295651\", \"302934\", \"305764\"]}","task_split":"paper_retrieval"}
{"document_id":"131681","document_content":"# Unsupervised Commonsense Question Answering with Self-Talk\n## Categories\n- Computation and Language\n## Abstract\nNatural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge. We propose an unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks. Inspired by inquiry-based discovery learning (Bruner, 1961), our approach inquires language models with a number of information seeking questions such as \"$\\textit{what is the definition of ...}$\" to discover additional background knowledge. Empirical results demonstrate that the self-talk procedure substantially improves the performance of zero-shot language model baselines on four out of six commonsense benchmarks, and competes with models that obtain knowledge from external KBs. While our approach improves performance on several benchmarks, the self-talk induced knowledge even when leading to correct answers is not always seen as useful by human judges, raising interesting questions about the inner-workings of pre-trained language models for commonsense reasoning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.05483v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"657\", \"814\", \"2158\", \"2612\", \"2601\", \"2790\", \"12373\", \"15870\", \"15941\", \"49804\", \"56179\", \"58794\", \"82210\", \"4719\", \"12408\", \"14159\", \"19356\", \"20131\", \"24358\", \"25362\", \"42580\", \"43637\", \"44917\", \"46142\", \"55135\", \"55404\", \"56522\", \"56560\", \"61030\", \"61778\", \"72188\", \"49954\", \"75570\", \"75701\", \"75837\", \"80515\", \"88416\", \"157136\", \"91642\", \"92108\", \"92961\", \"94815\", \"100518\", \"146195\", \"77545\", \"89726\", \"50135\", \"90331\", \"91221\", \"94145\", \"94942\", \"117616\", \"127675\", \"158893\"], \"outgoing_citations\": [\"129112\", \"129158\", \"131629\", \"136113\", \"140689\", \"154152\", \"157386\", \"157770\", \"165975\", \"168281\", \"168575\", \"169182\", \"171970\", \"172019\", \"173889\", \"174583\", \"180327\", \"181063\", \"182261\", \"183041\", \"185903\", \"189515\", \"192361\", \"192896\", \"207755\", \"211788\", \"211915\", \"212078\", \"216391\", \"217522\", \"218444\", \"219829\", \"219975\", \"221212\", \"230234\", \"231981\", \"232987\", \"236958\", \"238610\", \"238962\", \"239831\", \"247060\", \"265786\", \"267694\", \"286416\"]}","task_split":"paper_retrieval"}
{"document_id":"131704","document_content":"# Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSocial determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text requires data-driven information extraction models trained on annotated corpora that are heterogeneous and frequently include critical SDOH. This work presents a new corpus with SDOH annotations, a novel active learning framework, and the first extraction results on the new corpus. The Social History Annotation Corpus (SHAC) includes 4,480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events. We introduce a novel active learning framework that selects samples for annotation using a surrogate text classification task as a proxy for a more complex event extraction task. The active learning framework successfully increases the frequency of health risk factors and improves automatic extraction of these events over undirected annotation. An event extraction model trained on SHAC achieves high extraction performance for substance use status (0.82-0.93 F1), employment status (0.81-0.86 F1), and living status type (0.81-0.93 F1) on data from three institutions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jbi.2020.103631\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"778\", \"4550\", \"2078\", \"7146\", \"11347\", \"30265\", \"74100\", \"83384\"], \"outgoing_citations\": [\"190651\", \"191942\", \"204342\", \"219946\", \"258108\", \"285317\", \"297147\"]}","task_split":"paper_retrieval"}
{"document_id":"131867","document_content":"# Self Punishment and Reward Backfill for Deep Q-Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nReinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, leading the agent to experience ambiguity in terms of whether those actions are effective, an issue known as the credit assignment problem. In this paper, we propose two strategies inspired by behavioural psychology to enable the agent to intrinsically estimate more informative reward values for actions with no reward. The first strategy, called self-punishment (SP), discourages the agent from making mistakes that lead to undesirable terminal states. The second strategy, called the rewards backfill (RB), backpropagates the rewards between two rewarded actions. We prove that, under certain assumptions and regardless of the reinforcement learning algorithm used, these two strategies maintain the order of policies in the space of all possible policies in terms of their total reward, and, by extension, maintain the optimal policy. Hence, our proposed strategies integrate with any reinforcement learning algorithm that learns a value or action-value function through experience. We incorporated these two strategies into three popular deep reinforcement learning approaches and evaluated the results on thirty Atari games. After parameter tuning, our results indicate that the proposed strategies improve the tested methods in over 65 percent of tested games by up to over 25 times performance improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2021.3140042\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"201452\", \"213236\", \"254596\", \"258472\", \"260018\", \"261983\", \"262381\", \"266419\", \"283410\", \"302857\", \"303049\"]}","task_split":"paper_retrieval"}
{"document_id":"131891","document_content":"# Learning to Visually Navigate in Photorealistic Environments Without any Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nLearning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04954v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"29801\", \"64409\", \"75621\", \"2239\", \"59216\", \"101182\"], \"outgoing_citations\": [\"173704\", \"181081\", \"187872\", \"192738\", \"195772\", \"196393\", \"207509\", \"214195\", \"215425\", \"219658\", \"224223\", \"237119\", \"240323\", \"240348\", \"244266\", \"246823\", \"255575\", \"255699\", \"262593\", \"272930\", \"274102\", \"280570\", \"280681\", \"284565\", \"291568\"]}","task_split":"paper_retrieval"}
{"document_id":"132034","document_content":"# CovidSens: A Vision on Reliable Social Sensing for COVID-19\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Physics and Society\n- Populations and Evolution\n## Abstract\nWith the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing based risk alert systems to spontaneously obtain and analyze social data to infer COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas. The CovidSens concept is motivated by three observations: 1) people actively share their experience of COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting on social media, and 3) online users are frequently equipped with powerful mobile devices that can perform data processing and analytics. We envision unprecedented opportunities to leverage posts generated by ordinary people to build real-time sensing and analytic system for gathering and circulating COVID-19 propagation data. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information on COVID-19 with prevailing rumors and misinformation? How to inform the general public about the state of the spread timely and effectively? How to leverage the computational power on edge devices to construct fully integrated edge-based social sensing platforms? In this vision paper, we discuss the roles of CovidSens and identify potential challenges in developing reliable social sensing based risk alert systems. We envision that approaches originating from multiple disciplines can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04565v3\", \"primary_category\": \"cs.SI\", \"categories\": [\"stat.ML\", \"cs.SI\", \"physics.soc-ph\", \"q-bio.PE\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Physics and Society\", \"Populations and Evolution\"], \"incoming_citations\": [\"58546\", \"37992\", \"108573\", \"40535\", \"108815\", \"123466\"], \"outgoing_citations\": [\"135080\", \"136527\", \"167308\", \"175664\", \"185742\", \"194734\", \"215746\", \"244055\", \"287367\"]}","task_split":"paper_retrieval"}
{"document_id":"132115","document_content":"# A category theoretical argument for causal inference\n## Categories\n- Statistics Theory\n- Algebraic Geometry\n- Category Theory\n- Artificial Intelligence\n## Abstract\nThe goal of this paper is to design a causal inference method accounting for complex interactions between causal factors. The proposed method relies on a category theoretical reformulation of the definitions of dependent variables, independent variables and latent variables in terms of products and arrows in the category of unlabeled partitions. Throughout the paper, we demonstrate how the proposed method accounts for possible hidden variables, such as environmental variables or noise, and how it can be interpreted statistically in terms of $p$-values. This interpretation, from category theory to statistics, is implemented through a collection of propositions highlighting the functorial properties of ANOVA. We use these properties in combination with our category theoretical framework to provide solutions to causal inference problems with both sound algebraic and statistical properties. As an application, we show how the proposed method can be used to design a combinatorial genome-wide association algorithm for the field of genetics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.09999v2\", \"primary_category\": \"math.ST\", \"categories\": [\"math.AG\", \"math.CT\", \"math.ST\", \"stat.TH\", \"cs.AI\"], \"primary_category_human_readable\": \"Statistics Theory\", \"categories_human_readable\": [\"Algebraic Geometry\", \"Category Theory\", \"Statistics Theory\", \"Statistics Theory\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"132121","document_content":"# Calibrating Structured Output Predictors for Natural Language Processing\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the system is to be deployed in a safety-critical domain such as healthcare. However, the output space of such structured prediction models is often too large to adapt binary or multi-class calibration methods directly. In this study, we propose a general calibration scheme for output entities of interest in neural-network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for named-entity-recognition, part-of-speech and question answering. We also improve our model's performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-of-domain test scenarios as well.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04361v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"77878\", \"23252\", \"45238\", \"83393\", \"153825\"], \"outgoing_citations\": [\"196775\", \"198436\", \"199913\", \"213077\", \"219328\", \"232362\", \"240532\", \"307776\"]}","task_split":"paper_retrieval"}
{"document_id":"132164","document_content":"# nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications\n## Categories\n- Analysis of PDEs\n- Optimization and Control\n- Machine Learning\n- 34B10, 45P05, 92B20, 35Q93, 76F10\n- Computational Physics\n## Abstract\nPhysics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integral equations with sparse, noisy, unstructured, and multi-fidelity data. PINNs incorporate all available information into a loss function, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\\delta$ goes to zero, and to the fractional Laplacian as $\\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to $\\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\\delta$ and $\\alpha$. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon: different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\\delta$. Also, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04276v1\", \"primary_category\": \"math.AP\", \"categories\": [\"math.OC\", \"math.AP\", \"cs.LG\", \"34B10, 45P05, 92B20, 35Q93, 76F10\", \"physics.comp-ph\"], \"primary_category_human_readable\": \"Analysis of PDEs\", \"categories_human_readable\": [\"Optimization and Control\", \"Analysis of PDEs\", \"Machine Learning\", \"34B10, 45P05, 92B20, 35Q93, 76F10\", \"Computational Physics\"], \"incoming_citations\": [\"13743\", \"25814\", \"29218\", \"32323\", \"35959\", \"39640\", \"58157\", \"78918\", \"95870\", \"109137\", \"13656\", \"17811\", \"31579\", \"49248\", \"72968\", \"74189\", \"75946\", \"82046\", \"104287\", \"103845\", \"103862\", \"123630\", \"124510\", \"144145\"], \"outgoing_citations\": [\"147339\", \"259573\"]}","task_split":"paper_retrieval"}
{"document_id":"132211","document_content":"# Improving Expressivity of Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nWe propose a Graph Neural Network with greater expressive power than commonly used GNNs - not constrained to only differentiate between graphs that Weisfeiler-Lehman test recognizes to be non-isomorphic. We use a graph attention network with expanding attention window that aggregates information from nodes exponentially far away. We also use partially random initial embeddings, allowing differentiation between nodes that would otherwise look the same. This could cause problem with a traditional dropout mechanism, therefore we use a \"head dropout\", randomly ignoring some attention heads rather than some dimensions of the embedding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.05994v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"151458\", \"157816\", \"179704\", \"183447\", \"183719\", \"186063\", \"186769\", \"194256\", \"195767\", \"196221\", \"225459\", \"254619\"]}","task_split":"paper_retrieval"}
{"document_id":"132214","document_content":"# Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition systems perform well on standard datasets comprising English news. But given the paucity of data, it is difficult to draw conclusions about the robustness of systems with respect to recognizing a diverse set of entities. We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities. We create entity-switched datasets, in which named entities in the original texts are replaced by plausible named entities of the same type but of different national origin. We find that state-of-the-art systems' performance vary widely even in-domain: In the same context, entities from certain origins are more reliably recognized than entities from elsewhere. Systems perform best on American and Indian entities, and worst on Vietnamese and Indonesian entities. This auditing approach can facilitate the development of more robust named entity recognition systems, and will allow research in this area to consider fairness criteria that have received heightened attention in other predictive technology work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04123v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15840\", \"77478\", \"24117\", \"24416\", \"33588\", \"72039\", \"73649\", \"132035\", \"104312\"], \"outgoing_citations\": [\"162555\", \"211771\", \"214843\", \"215230\", \"276435\", \"285628\"]}","task_split":"paper_retrieval"}
{"document_id":"132390","document_content":"# Context-Aware Group Captioning via Self-Attention and Contrastive Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile image captioning has progressed rapidly, existing works focus mainly on describing single images. In this paper, we introduce a new task, context-aware group captioning, which aims to describe a group of target images in the context of another group of related reference images. Context-aware group captioning requires not only summarizing information from both the target and reference image group but also contrasting between them. To solve this problem, we propose a framework combining self-attention mechanism with contrastive feature construction to effectively summarize common information from each image group while capturing discriminative information between them. To build the dataset for this task, we propose to group the images and generate the group captions based on single image captions using scene graphs matching. Our datasets are constructed on top of the public Conceptual Captions dataset and our new Stock Captions dataset. Experiments on the two datasets show the effectiveness of our method on this new task. Related Datasets and code are released at https:\/\/lizw14.github.io\/project\/groupcap .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.03708v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"41782\", \"93187\", \"19640\", \"28498\", \"54150\", \"90867\", \"133112\"], \"outgoing_citations\": [\"133112\", \"170868\", \"172370\", \"180149\", \"188938\", \"192531\", \"195170\", \"203562\", \"211953\", \"212232\", \"215181\", \"217310\", \"223302\", \"237852\", \"238125\", \"239242\", \"253934\", \"258818\", \"270154\", \"271234\", \"276436\", \"277060\", \"278604\", \"278954\", \"279002\", \"279914\", \"280246\", \"287427\", \"295307\", \"295368\", \"296681\", \"302621\", \"303833\", \"309886\"]}","task_split":"paper_retrieval"}
{"document_id":"132478","document_content":"# Learning from Imperfect Annotations\n## Categories\n- Machine Learning\n## Abstract\nMany machine learning systems today are trained on large amounts of human-annotated data. Data annotation tasks that require a high level of competency make data acquisition expensive, while the resulting labels are often subjective, inconsistent, and may contain a variety of human biases. To improve the data quality, practitioners often need to collect multiple annotations per example and aggregate them before training models. Such a multi-stage approach results in redundant annotations and may often produce imperfect \"ground truth\" that may limit the potential of training accurate machine learning models. We propose a new end-to-end framework that enables us to: (i) merge the aggregation step with model training, thus allowing deep learning systems to learn to predict ground truth estimates directly from the available data, and (ii) model difficulties of examples and learn representations of the annotators that allow us to estimate and take into account their competencies. Our approach is general and has many applications, including training more accurate models on crowdsourced data, ensemble learning, as well as classifier accuracy estimation from unlabeled data. We conduct an extensive experimental evaluation of our method on 5 crowdsourcing datasets of varied difficulty and show accuracy gains of up to 25% over the current state-of-the-art approaches for aggregating annotations, as well as significant reductions in the required annotation redundancy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.03473v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"11685\", \"100471\"], \"outgoing_citations\": [\"207678\", \"227728\", \"247591\", \"249126\", \"266015\", \"276578\", \"278526\", \"281005\", \"315454\", \"347510\", \"357145\"]}","task_split":"paper_retrieval"}
{"document_id":"132519","document_content":"# Windowing Models for Abstractive Summarization of Long Texts\n## Categories\n- Computation and Language\n## Abstract\nNeural summarization models suffer from the fixed-size input limitation: if text length surpasses the model's maximal number of input tokens, some document content (possibly summary-relevant) gets truncated Independently summarizing windows of maximal input size disallows for information flow between windows and leads to incoherent summaries. We propose windowing models for neural abstractive summarization of (arbitrarily) long texts. We extend the sequence-to-sequence model augmented with pointer generator network by (1) allowing the encoder to slide over different windows of the input document and (2) sharing the decoder and retaining its state across different input windows. We explore two windowing variants: Static Windowing precomputes the number of tokens the decoder should generate from each window (based on training corpus statistics); in Dynamic Windowing the decoder learns to emit a token that signals encoder's shift to the next input window. Empirical results render our models effective in their intended use-case: summarizing long texts with relevant content not bound to the very document beginning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.03324v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26671\"], \"outgoing_citations\": [\"188358\", \"213836\", \"235463\", \"237494\", \"266671\", \"267161\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"132577","document_content":"# Machine Translation with Unsupervised Length-Constraints\n## Categories\n- Computation and Language\n## Abstract\nWe have seen significant improvements in machine translation due to the usage of deep learning. While the improvements in translation quality are impressive, the encoder-decoder architecture enables many more possibilities. In this paper, we explore one of these, the generation of constraint translation. We focus on length constraints, which are essential if the translation should be displayed in a given format. In this work, we propose an end-to-end approach for this task. Compared to a traditional method that first translates and then performs sentence compression, the text compression is learned completely unsupervised. By combining the idea with zero-shot multilingual machine translation, we are also able to perform unsupervised monolingual sentence compression. In order to fulfill the length constraints, we investigated several methods to integrate the constraints into the model. Using the presented technique, we are able to significantly improve the translation quality under constraints. Furthermore, we are able to perform unsupervised monolingual sentence compression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.03176v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2597\", \"2680\", \"18097\", \"123079\"], \"outgoing_citations\": [\"158340\", \"160221\", \"188316\", \"190394\", \"200809\", \"218755\", \"249730\", \"251816\", \"251974\", \"277665\", \"280447\", \"280486\", \"283618\", \"299936\"]}","task_split":"paper_retrieval"}
{"document_id":"132611","document_content":"# Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nOpen Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recently introduced open domain question answering challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts and compose facts to correctly answer questions. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We further propose a \"knowledge fusion model\" which leverages knowledge in BERT-based language models with externally retrieved knowledge and improves the knowledge understanding of the BERT-based language models. On both OpenBookQA and QASC datasets, the knowledge fusion model with semantically re-ranked knowledge outperforms previous attempts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.03101v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2158\", \"5072\", \"18380\", \"18786\", \"25328\", \"47485\", \"91610\", \"89726\", \"97393\", \"127522\", \"127513\", \"127675\"], \"outgoing_citations\": [\"154152\", \"159864\", \"165844\", \"165968\", \"165975\", \"168247\", \"169135\", \"174555\", \"188246\", \"189515\", \"200370\", \"209413\", \"212117\", \"216563\", \"218444\", \"218698\", \"220723\", \"220724\", \"231338\", \"238610\", \"238962\", \"250722\", \"253068\", \"266850\", \"268809\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"132683","document_content":"# Evaluating the Evaluation of Diversity in Natural Language Generation\n## Categories\n- Computation and Language\n## Abstract\nDespite growing interest in natural language generation (NLG) models that produce diverse outputs, there is currently no principled method for evaluating the diversity of an NLG system. In this work, we propose a framework for evaluating diversity metrics. The framework measures the correlation between a proposed diversity metric and a diversity parameter, a single parameter that controls some aspect of diversity in generated text. For example, a diversity parameter might be a binary variable used to instruct crowdsourcing workers to generate text with either low or high content diversity. We demonstrate the utility of our framework by: (a) establishing best practices for eliciting diversity judgments from humans, (b) showing that humans substantially outperform automatic metrics in estimating content diversity, and (c) demonstrating that existing methods for controlling diversity by tuning a \"decoding parameter\" mostly affect form but not meaning. Our framework can advance the understanding of different diversity metrics, an essential step on the road towards better NLG systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.02990v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"50907\", \"61949\", \"72075\", \"94230\", \"84437\", \"94752\", \"96695\"], \"outgoing_citations\": [\"136051\", \"145384\", \"175089\", \"179568\", \"181430\", \"182271\", \"185278\", \"185605\", \"187452\", \"192158\", \"201921\", \"211205\", \"212371\", \"232274\", \"243154\", \"259723\", \"263622\", \"279543\", \"284481\", \"305200\"]}","task_split":"paper_retrieval"}
{"document_id":"132990","document_content":"# Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks\n## Categories\n- Cryptography and Security\n- Machine Learning\n- I.2.6\n- Machine Learning\n## Abstract\nThe deployment of deep learning applications has to address the growing privacy concerns when using private and sensitive data for training. A conventional deep learning model is prone to privacy attacks that can recover the sensitive information of individuals from either model parameters or accesses to the target model. Recently, differential privacy that offers provable privacy guarantees has been proposed to train neural networks in a privacy-preserving manner to protect training data. However, many approaches tend to provide the worst case privacy guarantees for model publishing, inevitably impairing the accuracy of the trained models. In this paper, we present a novel private knowledge transfer strategy, where the private teacher trained on sensitive data is not publicly accessible but teaches a student to be publicly released. In particular, a three-player (teacher-student-discriminator) learning framework is proposed to achieve trade-off between utility and privacy, where the student acquires the distilled knowledge from the teacher and is trained with the discriminator to generate similar outputs as the teacher. We then integrate a differential privacy protection mechanism into the learning procedure, which enables a rigorous privacy budget for the training. The framework eventually allows student to be trained with only unlabelled public data and very few epochs, and hence prevents the exposure of sensitive training data, while ensuring model utility with a modest privacy budget. The experiments on MNIST, SVHN and CIFAR-10 datasets show that our students obtain the accuracy losses w.r.t teachers of 0.89%, 2.29%, 5.16%, respectively with the privacy bounds of (1.93, 10^-5), (5.02, 10^-6), (8.81, 10^-6). When compared with the existing works \\cite{papernot2016semi,wang2019private}, the proposed work can achieve 5-82% accuracy loss improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.04631v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"cs.CR\", \"I.2.6\", \"stat.ML\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"I.2.6\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169861\", \"192396\", \"205882\", \"210354\", \"240981\", \"252956\", \"255133\", \"257124\", \"263935\", \"273215\", \"282410\", \"284481\", \"285037\", \"303553\"]}","task_split":"paper_retrieval"}
{"document_id":"133020","document_content":"# Reference Language based Unsupervised Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nExploiting a common language as an auxiliary for better translation has a long tradition in machine translation and lets supervised learning-based machine translation enjoy the enhancement delivered by the well-used pivot language in the absence of a source language to target language parallel corpus. The rise of unsupervised neural machine translation (UNMT) almost completely relieves the parallel corpus curse, though UNMT is still subject to unsatisfactory performance due to the vagueness of the clues available for its core back-translation training. Further enriching the idea of pivot translation by extending the use of parallel corpora beyond the source-target paradigm, we propose a new reference language-based framework for UNMT, RUNMT, in which the reference language only shares a parallel corpus with the source, but this corpus still indicates a signal clear enough to help the reconstruction training of UNMT through a proposed reference agreement mechanism. Experimental results show that our methods improve the quality of UNMT over that of a strong baseline that uses only one auxiliary language, demonstrating the usefulness of the proposed reference language-based UNMT and establishing a good start for the community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.02127v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25308\", \"38855\", \"14943\", \"21287\", \"32358\", \"34638\", \"45996\", \"48100\", \"59790\", \"98946\", \"95187\", \"143551\"], \"outgoing_citations\": [\"129799\", \"143551\", \"146003\", \"149448\", \"153039\", \"165748\", \"168549\", \"169520\", \"177443\", \"182210\", \"187452\", \"192335\", \"194844\", \"197007\", \"211259\", \"234807\", \"251816\", \"251974\", \"253559\", \"262256\", \"263927\", \"281472\", \"290778\"]}","task_split":"paper_retrieval"}
{"document_id":"133105","document_content":"# Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks\n## Categories\n- Computation and Language\n## Abstract\nWe study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation. To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation. By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum. Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.01972v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8511\", \"15861\", \"16670\", \"26247\", \"32165\", \"47718\", \"55589\", \"100825\", \"101441\", \"107933\", \"86112\"], \"outgoing_citations\": [\"176971\", \"180805\", \"182546\", \"183017\", \"187236\", \"211664\", \"240455\", \"244479\", \"253593\", \"261033\", \"269757\", \"275478\", \"289943\", \"292261\", \"296250\", \"302934\", \"307223\", \"309411\", \"310377\", \"310903\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"133200","document_content":"# Software Engineering For Automated Game Design\n## Categories\n- Software Engineering\n- Artificial Intelligence\n## Abstract\nAs we develop more assistive and automated game design systems, the question of how these systems should be integrated into game development workflows, and how much adaptation may be required, becomes increasingly important. In this paper we explore the impact of software engineering decisions on the ability of an automated game design system to understand a game's codebase, generate new game code, and evaluate its work. We argue that a new approach to software engineering may be required in order for game developers to fully benefit from automated game designers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.01770v1\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\", \"cs.AI\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"133216","document_content":"# Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nSocial media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. Given the rapidly evolving nature of news events and the limited amount of annotated data, state-of-the-art systems on fake news detection face challenges due to the lack of large numbers of annotated training instances that are hard to come by for early detection. In this work, we exploit multiple weak signals from different sources given by user and content engagements (referred to as weak social supervision), and their complementary utilities to detect fake news. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.01732v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"27086\", \"48590\", \"21541\", \"24788\", \"30489\", \"30709\", \"56085\", \"56773\", \"75518\", \"77425\", \"81800\", \"92964\", \"88313\", \"93376\", \"106017\", \"113687\", \"122551\"], \"outgoing_citations\": [\"195205\", \"215247\", \"219169\", \"237905\", \"242145\", \"246910\", \"249126\", \"269676\", \"272109\", \"273723\", \"284456\", \"320027\", \"328627\"]}","task_split":"paper_retrieval"}
{"document_id":"133342","document_content":"# CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nGenerative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. This new CPPN2GAN approach is validated in both Super Mario Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites demonstrates that CPPN2GAN can better cover the space of possible levels. The layouts of the resulting levels are also more cohesive and aesthetically consistent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3377930.3389822\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"47005\", \"33080\", \"40705\", \"67124\", \"72454\", \"74578\", \"95477\", \"112786\", \"122922\", \"125408\"], \"outgoing_citations\": [\"147140\", \"163503\", \"233501\", \"244263\", \"265896\", \"289709\", \"290842\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"133367","document_content":"# A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThe Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide practices to online customer identification based on personal identification documents, similarity and liveness checking, and proof of address. To answer the basic regulation question: are you whom you say you are? The customer needs to upload valid identification documents (ID). This task imposes some computational challenges since these documents are diverse, may present different and complex backgrounds, some occlusion, partial rotation, poor quality, or damage. Advanced text and document segmentation algorithms were used to process the ID images. In this context, we investigated a method based on U-Net to detect the document edges and text regions in ID images. Besides the promising results on image segmentation, the U-Net based approach is computationally expensive for a real application, since the image segmentation is a customer device task. We propose a model optimization based on Octave Convolutions to qualify the method to situations where storage, processing, and time resources are limited, such as in mobile and robotic applications. We conducted the evaluation experiments in two new datasets CDPhotoDataset and DTDDataset, which are composed of real ID images of Brazilian documents. Our results showed that the proposed models are efficient to document segmentation tasks and portable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/IJCNN48605.2020.9206711\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"7430\", \"41804\", \"107498\"], \"outgoing_citations\": [\"178532\", \"191257\", \"234009\", \"274806\"]}","task_split":"paper_retrieval"}
{"document_id":"133397","document_content":"# Application of Intelligent Multi Agent Based Systems For E-Healthcare Security\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nIn recent years, availability and usage of extensive systems for Electronic Healthcare Record (EHR) is increased. In medical centers such hospitals and other laboratories, more health data sets were formed during the treatment process. In order to enhance the standard of the services provided in healthcare, these records where shared and can be used by various users depends on their requirements. As a result, notable issues in the security and privacy where obtained which should be monitored and removed in order to make the use of EHR more effectively. Various researches have been done in the past literature for improving the standards of the security and privacy in E-health systems. In spite of this, it is not completely enhanced. In this paper, a comprehensive analysis is done by selecting the existing approaches and models which were proposed for the security and privacy of the E-healthcare systems. Also, a novel Intelligent-based Access Control Security Model (IBAC) based on multi agents is proposed to maintain and support the security and privacy of E-healthcare systems. This system uses agents in order to maintain security and privacy while accessing the E-health data between the users.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18576\/isl\/080204\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CR\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Cryptography and Security\"], \"incoming_citations\": [\"126174\", \"126185\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"133427","document_content":"# Sum-product networks: A survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent univariate probability distributions and non-terminal nodes represent convex combinations (weighted sums) and products of probability functions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of links in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, the main applications, a brief review of software libraries, and a comparison with related models","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.01167v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"51583\", \"55897\", \"67971\", \"86266\"], \"outgoing_citations\": [\"169130\", \"184433\", \"185413\", \"198715\", \"203232\", \"204316\", \"210733\", \"223495\", \"224614\", \"225549\", \"229152\", \"253485\", \"253685\", \"257925\", \"258035\", \"258448\", \"273931\", \"275907\", \"285746\", \"293877\", \"299688\", \"300659\", \"303207\", \"303400\", \"309996\", \"315141\", \"316721\", \"319391\", \"346695\", \"348963\", \"350216\", \"351398\", \"357314\", \"361803\"]}","task_split":"paper_retrieval"}
{"document_id":"133473","document_content":"# Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embeddings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.01019v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7097\", \"14719\", \"3475\", \"14676\", \"65891\", \"29626\", \"40125\", \"63976\", \"110825\", \"120894\", \"138418\", \"143230\"], \"outgoing_citations\": [\"135770\", \"138418\", \"143230\", \"152832\", \"154506\", \"186062\", \"192542\", \"207172\", \"218905\", \"229671\", \"279419\", \"287611\", \"346465\"]}","task_split":"paper_retrieval"}
{"document_id":"133479","document_content":"# Benchmarking End-to-End Behavioural Cloning on Video Games\n## Categories\n- Artificial Intelligence\n## Abstract\nBehavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without reinforcement learning. This also includes end-to-end approaches, where a computer plays a video game like humans do: by looking at the image displayed on the screen, and sending keystrokes to the game. As a general approach to playing video games, this has many inviting properties: no need for specialized modifications to the game, no lengthy training sessions and the ability to re-use the same tools across different games. However, related work includes game-specific engineering to achieve the results. We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data. Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.00981v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"57263\"], \"outgoing_citations\": [\"137047\", \"149771\", \"150220\", \"151271\", \"163467\", \"172062\", \"173914\", \"183941\", \"194964\", \"213994\", \"218435\", \"218770\", \"218923\", \"225886\", \"238967\", \"243235\", \"253953\", \"258472\", \"264940\", \"273716\", \"293071\", \"301005\"]}","task_split":"paper_retrieval"}
{"document_id":"133673","document_content":"# PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nRecent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.00452v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"8341\", \"52584\", \"75482\", \"172644\", \"1197\", \"1516\", \"2013\", \"1953\", \"2418\", \"3324\", \"4411\", \"5043\", \"6435\", \"6899\", \"7441\", \"7942\", \"9824\", \"14407\", \"25676\", \"29497\", \"31115\", \"32828\", \"42824\", \"43357\", \"43702\", \"44017\", \"57154\", \"65165\", \"74967\", \"113497\", \"192221\", \"4346\", \"4487\", \"5380\", \"5375\", \"5850\", \"6326\", \"6611\", \"10859\", \"11641\", \"11890\", \"12222\", \"13035\", \"14867\", \"15885\", \"16455\", \"18249\", \"19367\", \"19591\", \"19959\", \"23266\", \"23963\", \"25469\", \"26067\", \"26063\", \"27767\", \"28812\", \"28654\", \"29094\", \"30008\", \"30183\", \"31286\", \"32183\", \"32941\", \"10256\", \"39509\", \"40228\", \"40788\", \"43568\", \"42141\", \"49060\", \"49737\", \"50204\", \"51182\", \"51216\", \"51651\", \"52327\", \"54910\", \"54916\", \"55584\", \"55423\", \"55480\", \"55511\", \"55755\", \"55764\", \"56128\", \"56789\", \"57378\", \"57388\", \"57728\", \"57661\", \"57958\", \"59157\", \"59466\", \"60003\", \"59887\", \"61962\", \"62180\", \"63275\", \"63613\", \"63654\", \"63797\", \"65734\", \"67798\", \"73273\", \"75087\", \"76496\", \"77551\", \"78154\", \"78694\", \"79831\", \"79974\", \"80051\", \"81233\", \"81208\", \"81253\", \"83885\", \"84175\", \"84659\", \"84690\", \"111705\", \"146345\", \"78696\", \"141977\", \"99133\", \"104911\", \"109339\", \"110269\", \"118680\", \"78503\"], \"outgoing_citations\": [\"158372\", \"163616\", \"164082\", \"169032\", \"171045\", \"171117\", \"173428\", \"182539\", \"186553\", \"189945\", \"190958\", \"195025\", \"195191\", \"204280\", \"206909\", \"207278\", \"218340\", \"222599\", \"235715\", \"239138\", \"243558\", \"245917\", \"247152\", \"247495\", \"270235\", \"271614\", \"276521\", \"287636\"]}","task_split":"paper_retrieval"}
{"document_id":"133812","document_content":"# Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n## Abstract\nGenerative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.00151v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.AI\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Artificial Intelligence\"], \"incoming_citations\": [\"31954\", \"33213\", \"33227\", \"41462\", \"62860\", \"67124\", \"72454\", \"74578\", \"95477\", \"94739\", \"96766\", \"109729\", \"122601\", \"125408\"], \"outgoing_citations\": [\"147140\", \"153706\", \"163503\", \"189201\", \"233501\", \"244263\", \"274886\", \"279091\", \"289709\", \"297233\", \"303139\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"133864","document_content":"# Augmented Q Imitation Learning (AQIL)\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback. Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy. This paper proposes Augmented Q-Imitation-Learning, a method by which deep reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.00993v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"244669\", \"272987\"]}","task_split":"paper_retrieval"}
{"document_id":"133899","document_content":"# On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNew machine translations (MT) technologies are emerging rapidly and with them, bold claims of achieving human parity such as: (i) the results produced approach \"accuracy achieved by average bilingual human translators\" (Wu et al., 2017b) or (ii) the \"translation quality is at human parity when compared to professional human translators\" (Hassan et al., 2018) have seen the light of day (Laubli et al., 2018). Aside from the fact that many of these papers craft their own definition of human parity, these sensational claims are often not supported by a complete analysis of all aspects involved in translation. Establishing the discrepancies between the strengths of statistical approaches to MT and the way humans translate has been the starting point of our research. By looking at MT output and linguistic theory, we were able to identify some remaining issues. The problems range from simple number and gender agreement errors to more complex phenomena such as the correct translation of aspectual values and tenses. Our experiments confirm, along with other studies (Bentivogli et al., 2016), that neural MT has surpassed statistical MT in many aspects. However, some problems remain and others have emerged. We cover a series of problems related to the integration of specific linguistic features into statistical and neural MT, aiming to analyse and provide a solution to some of them. Our work focuses on addressing three main research questions that revolve around the complex relationship between linguistics and MT in general. We identify linguistic information that is lacking in order for automatic translation systems to produce more accurate translations and integrate additional features into the existing pipelines. We identify overgeneralization or 'algorithmic bias' as a potential drawback of neural MT and link it to many of the remaining linguistic issues.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.14324v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"161385\", \"167379\", \"178575\", \"179679\", \"184943\", \"195777\", \"195909\", \"198185\", \"198349\", \"203371\", \"211702\", \"213954\", \"218898\", \"219720\", \"220720\", \"222795\", \"228148\", \"233206\", \"234587\", \"235312\", \"238926\", \"240856\", \"244357\", \"248222\", \"249587\", \"259870\", \"260584\", \"263927\", \"265139\", \"267460\", \"268098\", \"268788\", \"268812\", \"269127\", \"271637\", \"271767\", \"273624\", \"274183\", \"274776\", \"280486\", \"282462\", \"282931\", \"286498\", \"290777\", \"292261\", \"292552\", \"296274\", \"297648\", \"317974\", \"320814\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"133977","document_content":"# Inverting Gradients -- How easy is it to break privacy in federated learning?\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nThe idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates (gradients) based on local data. This protocol has been designed not only to train neural networks data-efficiently, but also to provide privacy benefits for users, as their input data remains on device and only parameter gradients are shared. But how secure is sharing parameter gradients? Previous attacks have provided a false sense of security, by succeeding only in contrived settings - even for a single image. However, by exploiting a magnitude-invariant loss along with optimization strategies based on adversarial attacks, we show that is is actually possible to faithfully reconstruct images at high resolution from the knowledge of their parameter gradients, and demonstrate that such a break of privacy is possible even for trained deep networks. We analyze the effects of architecture as well as parameters on the difficulty of reconstructing an input image and prove that any input to a fully connected layer can be reconstructed analytically independent of the remaining architecture. Finally we discuss settings encountered in practice and show that even averaging gradients over several iterations or several images does not protect the user's privacy in federated learning applications in computer vision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.14053v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"11266\", \"79556\", \"174906\", \"316453\", \"393\", \"549\", \"922\", \"1828\", \"8859\", \"8843\", \"9272\", \"9564\", \"9626\", \"10701\", \"11095\", \"13368\", \"13851\", \"30344\", \"32340\", \"35654\", \"39697\", \"42681\", \"42977\", \"48513\", \"52195\", \"52799\", \"55687\", \"81561\", \"91349\", \"97728\", \"99336\", \"104208\", \"119104\", \"135\", \"1054\", \"1639\", \"1964\", \"3777\", \"4792\", \"4975\", \"5903\", \"6225\", \"10418\", \"11291\", \"12458\", \"12402\", \"13053\", \"13444\", \"15166\", \"15556\", \"18251\", \"18474\", \"18854\", \"20803\", \"27562\", \"27798\", \"30207\", \"30490\", \"31778\", \"32407\", \"34230\", \"36065\", \"36915\", \"37227\", \"37930\", \"41032\", \"41468\", \"42954\", \"43211\", \"44254\", \"45331\", \"46616\", \"47655\", \"48498\", \"51457\", \"52789\", \"52788\", \"53356\", \"53472\", \"55790\", \"60451\", \"63626\", \"66468\", \"67323\", \"67864\", \"68601\", \"70360\", \"71148\", \"81826\", \"82949\", \"84839\", \"84784\", \"86428\", \"87768\", \"92837\", \"94049\", \"96533\", \"102302\", \"110433\", \"143026\", \"81401\", \"83656\", \"86147\", \"90427\", \"94135\", \"101002\", \"110306\", \"113838\", \"117242\", \"127187\", \"129708\"], \"outgoing_citations\": [\"70336\", \"139315\", \"148031\", \"155989\", \"163964\", \"179698\", \"197850\", \"207623\", \"212593\", \"224872\", \"232524\", \"241537\", \"252956\", \"256272\", \"298700\", \"301913\", \"303573\", \"311530\"]}","task_split":"paper_retrieval"}
{"document_id":"134015","document_content":"# Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe study how to train a student deep neural network for visual recognition by distilling knowledge from a blackbox teacher model in a data-efficient manner. Progress on this problem can significantly reduce the dependence on large-scale datasets for learning high-performing visual recognition models. There are two major challenges. One is that the number of queries into the teacher model should be minimized to save computational and\/or financial costs. The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets. To tackle these challenges, we propose an approach that blends mixup and active learning. The former effectively augments the few unlabeled images by a big pool of synthetic images sampled from the convex hull of the original images, and the latter actively chooses from the pool hard examples for the student neural network and query their labels from the teacher model. We validate our approach with extensive experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.13960v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79944\", \"5361\", \"37804\", \"44264\", \"56521\", \"56732\", \"74404\", \"81732\", \"89828\", \"115089\", \"112934\"], \"outgoing_citations\": [\"159745\", \"163574\", \"170212\", \"170336\", \"171331\", \"172237\", \"173268\", \"173280\", \"174912\", \"176100\", \"185555\", \"188167\", \"189752\", \"192494\", \"192515\", \"206943\", \"218795\", \"230755\", \"234520\", \"240707\", \"242885\", \"245531\", \"247658\", \"249085\", \"249087\", \"252897\", \"258657\", \"271783\", \"271988\", \"279535\"]}","task_split":"paper_retrieval"}
{"document_id":"134076","document_content":"# AvatarMe: Realistically Renderable 3D Facial Reconstruction \"in-the-wild\"\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- I.2.10; I.3.7; I.4.1\n## Abstract\nOver the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single \"in-the-wild\" image. Nevertheless, to the best of our knowledge, there is no method which can produce high-resolution photorealistic 3D faces from \"in-the-wild\" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce AvatarMe, the first method that is able to reconstruct photorealistic 3D faces from a single \"in-the-wild\" image with an increasing level of detail. To achieve this, we capture a large dataset of facial shape and reflectance and build on a state-of-the-art 3D texture and shape reconstruction method and successively refine its results, while generating the per-pixel diffuse and specular components that are required for realistic rendering. As we demonstrate in a series of qualitative and quantitative experiments, AvatarMe outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image that, for the first time, bridges the uncanny valley.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.13845v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"I.2.10; I.3.7; I.4.1\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"I.2.10; I.3.7; I.4.1\"], \"incoming_citations\": [\"5306\", \"54087\", \"76110\", \"3093\", \"5624\", \"7345\", \"12184\", \"20006\", \"40071\", \"49287\", \"94953\", \"106867\", \"164990\", \"9232\", \"3767\", \"16773\", \"17589\", \"30261\", \"44440\", \"40450\", \"42098\", \"51399\", \"56332\", \"59948\", \"63293\", \"75585\", \"77737\", \"82106\", \"87603\", \"104420\", \"79762\", \"84680\", \"168545\"], \"outgoing_citations\": [\"155623\", \"168545\", \"191844\", \"193656\", \"195806\", \"198793\", \"213300\", \"219267\", \"219961\", \"235442\", \"248005\", \"248572\", \"249049\", \"268963\", \"270235\", \"275879\", \"275999\", \"277948\", \"278966\", \"284716\"]}","task_split":"paper_retrieval"}
{"document_id":"134297","document_content":"# MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras making them difficult to scale. In this work, we introduce MetaFuse, a pre-trained fusion model learned from a large number of cameras in the Panoptic dataset. The model can be efficiently adapted or finetuned for a new pair of cameras using a small number of labeled images. The strong adaptation power of MetaFuse is due in large part to the proposed factorization of the original fusion model into two parts (1) a generic fusion model shared by all cameras, and (2) lightweight camera-dependent transformations. Furthermore, the generic model is learned from many cameras by a meta-learning style algorithm to maximize its adaptation capability to various camera poses. We observe in experiments that MetaFuse finetuned on the public datasets outperforms the state-of-the-arts by a large margin which validates its value in practice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.13239v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"30298\", \"15362\", \"22402\", \"50832\", \"58109\", \"91373\", \"78503\"], \"outgoing_citations\": [\"168913\", \"170236\", \"186332\", \"194313\", \"203055\", \"207031\", \"207278\", \"222280\", \"225805\", \"229708\", \"235303\", \"239135\", \"244888\", \"261577\", \"266960\", \"268769\", \"280870\", \"290426\"]}","task_split":"paper_retrieval"}
{"document_id":"134314","document_content":"# Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nDocument-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence. By enforcing the NMT model to predict source context, we want the model to learn \"contextualized\" source sentence representations that capture document-level dependencies on the source side. We further propose two different methods to learn and integrate such contextualized sentence embeddings into NMT: a joint training method that jointly trains an NMT model with the source context prediction model and a pre-training & fine-tuning method that pretrains the source context prediction model on a large-scale monolingual document corpus and then fine-tunes it with the NMT model. Experiments on Chinese-English and English-German translation show that both methods can substantially improve the translation quality over a strong document-level Transformer baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.13205v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210165\", \"215003\", \"219083\", \"220720\", \"230556\", \"249315\", \"250879\", \"258163\", \"258164\", \"263299\", \"266411\", \"268909\", \"278065\", \"280884\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"134339","document_content":"# Neurals Networks for Projecting Named Entities from English to Ewondo\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNamed entity recognition is an important task in natural language processing. It is very well studied for rich language, but still under explored for low-resource languages. The main reason is that the existing techniques required a lot of annotated data to reach good performance. Recently, a new distributional representation of words has been proposed to project named entities from a rich language to a low-resource one. This representation has been coupled to a neural network in order to project named entities from English to Ewondo, a Bantu language spoken in Cameroon. Although the proposed method reached appreciable results, the size of the used neural network was too large compared to the size of the dataset. Furthermore the impact of the model parameters has not been studied. In this paper, we show experimentally that the same results can be obtained using a smaller neural network. We also emphasize the parameters that are highly correlated to the network performance. This work is a step forward to build a reliable and robust network architecture for named entity projection in low resource languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13841v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"134354","document_content":"# A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching\n## Categories\n- Databases\n- Machine Learning\n- H.2\n## Abstract\nEntity Matching (EM) is a core data cleaning task, aiming to identify different mentions of the same real-world entity. Active learning is one way to address the challenge of scarce labeled data in practice, by dynamically collecting the necessary examples to be labeled by an Oracle and refining the learned model (classifier) upon them. In this paper, we build a unified active learning benchmark framework for EM that allows users to easily combine different learning algorithms with applicable example selection algorithms. The goal of the framework is to enable concrete guidelines for practitioners as to what active learning combinations will work well for EM. Towards this, we perform comprehensive experiments on publicly available EM datasets from product and publication domains to evaluate active learning methods, using a variety of metrics including EM quality, #labels and example selection latencies. Our most surprising result finds that active learning with fewer labels can learn a classifier of comparable quality as supervised learning. In fact, for several of the datasets, we show that there is an active learning combination that beats the state-of-the-art supervised learning result. Our framework also includes novel optimizations that improve the quality of the learned model by roughly 9% in terms of F1-score and reduce example selection latencies by up to 10x without affecting the quality of the model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3318464.3380597\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.LG\", \"H.2\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Machine Learning\", \"H.2\"], \"incoming_citations\": [\"30093\", \"78681\", \"41824\", \"57377\", \"85712\", \"94107\", \"133626\"], \"outgoing_citations\": [\"180313\", \"325215\", \"355300\", \"355567\"]}","task_split":"paper_retrieval"}
{"document_id":"134373","document_content":"# Tracking the Invisible: Privacy-Preserving Contact Tracing to Control the Spread of a Virus\n## Categories\n- Cryptography and Security\n## Abstract\nToday, tracking and controlling the spread of a virus is a crucial need for almost all countries. Doing this early would save millions of lives and help countries keep a stable economy. The easiest way to control the spread of a virus is to immediately inform the individuals who recently had close contact with the diagnosed patients. However, to achieve this, a centralized authority (e.g., a health authority) needs detailed location information from both healthy individuals and diagnosed patients. Thus, such an approach, although beneficial to control the spread of a virus, results in serious privacy concerns, and hence privacy-preserving solutions are required to solve this problem. Previous works on this topic either (i) compromise privacy (especially privacy of diagnosed patients) to have better efficiency or (ii) provide unscalable solutions. In this work, we propose a technique based on private set intersection between physical contact histories of individuals (that are recorded using smart phones) and a centralized database (run by a health authority) that keeps the identities of the positive diagnosed patients for the disease. Proposed solution protects the location privacy of both healthy individuals and diagnosed patients and it guarantees that the identities of the diagnosed patients remain hidden from other individuals. Notably, proposed scheme allows individuals to receive warning messages indicating their previous contacts with a positive diagnosed patient. Such warning messages will help them realize the risk and isolate themselves from other people. We make sure that the warning messages are only observed by the corresponding individuals and not by the health authority. We also implement the proposed scheme and show its efficiency and scalability via simulations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.13073v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123865\", \"128521\", \"132453\", \"134952\", \"136032\"]}","task_split":"paper_retrieval"}
{"document_id":"134401","document_content":"# Sample Efficient Ensemble Learning with Catalyst.RL\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scale asynchronous distributed training, efficient implementations of various RL algorithms and auxiliary tricks, such as n-step returns, value distributions, hyperbolic reinforcement learning, etc. To demonstrate the effectiveness of Catalyst.RL, we applied it to a physics-based reinforcement learning challenge \"NeurIPS 2019: Learn to Move -- Walk Around\" with the objective to build a locomotion controller for a human musculoskeletal model. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. Our team took the 2nd place, capitalizing on the ability of Catalyst.RL to train high-quality and sample-efficient RL agents in only a few hours of training time. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.14210v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3721\"], \"outgoing_citations\": [\"149308\", \"198481\", \"199926\", \"205913\", \"212016\", \"236352\", \"246599\", \"248225\", \"252249\", \"255531\", \"258834\", \"260583\", \"264419\", \"270002\", \"287823\"]}","task_split":"paper_retrieval"}
{"document_id":"134499","document_content":"# Polarized Reflection Removal with Perfect Alignment in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets where the collected reflection-free images are not perfectly aligned with input mixed images due to glass refraction. Then we build a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images. Second, capitalizing on the special relationship between reflection and polarized light, we propose a polarized reflection removal model with a two-stage architecture. In addition, we design a novel perceptual NCC loss that can improve the performance of reflection removal and general image decomposition tasks. We conduct extensive experiments, and results suggest that our model outperforms state-of-the-art methods on reflection removal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.12789v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1531\", \"4615\", \"30921\", \"174146\", \"14907\", \"30889\", \"56821\", \"64716\", \"80811\"], \"outgoing_citations\": [\"173115\", \"192915\", \"195770\", \"207657\", \"228076\", \"248152\", \"258798\", \"259890\"]}","task_split":"paper_retrieval"}
{"document_id":"134561","document_content":"# Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nWe introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting. We first estimate per-view depth maps using a deep multi-view stereo network; these depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture that is trained to pool features from these coarsely aligned images and predict per-view spatially-varying diffuse albedo, surface normals, specular roughness and specular albedo. We do this by jointly optimizing the latent space of our multi-view reflectance network to minimize the photometric error between images rendered with our predictions and the input images. While previous state-of-the-art methods fail on such sparse acquisition setups, we demonstrate, via extensive experiments on synthetic and real data, that our method produces high-quality reconstructions that can be used to render photorealistic images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.12642v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"16952\", \"13293\", \"16297\", \"33273\", \"33702\", \"26629\", \"56332\", \"56935\", \"57387\", \"58868\", \"58928\", \"60069\", \"80979\", \"82123\", \"84609\", \"87011\", \"106959\", \"110914\", \"134630\"], \"outgoing_citations\": [\"178720\", \"186553\", \"188122\", \"194752\", \"208986\", \"213300\", \"219267\", \"223643\", \"233810\", \"235442\", \"236476\", \"236720\", \"236968\", \"238070\", \"238908\", \"258323\", \"259277\", \"261762\", \"269761\"]}","task_split":"paper_retrieval"}
{"document_id":"134586","document_content":"# Neural translation and automated recognition of ICD10 medical entities from natural language\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n- Machine Learning\n## Abstract\nThe recognition of medical entities from natural language is an ubiquitous problem in the medical field, with applications ranging from medical act coding to the analysis of electronic health data for public health. It is however a complex task usually requiring human expert intervention, thus making it expansive and time consuming. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems, with the notable example of neural sequence models and their powerful applications in natural language processing. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the C\\'epiDc stores an exhaustive database of death certificates at the French national scale, amounting to several millions of natural language examples provided with their associated human coded medical entities available to the machine learning practitioner. This article investigates the applications of deep neural sequence models to the medical entity recognition from natural language problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.13839v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"99260\"], \"outgoing_citations\": [\"170153\"]}","task_split":"paper_retrieval"}
{"document_id":"134658","document_content":"# Lightweight Photometric Stereo for Facial Details Recovery\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, 3D face reconstruction from a single image has achieved great success with the help of deep learning and shape prior knowledge, but they often fail to produce accurate geometry details. On the other hand, photometric stereo methods can recover reliable geometry details, but require dense inputs and need to solve a complex optimization problem. In this paper, we present a lightweight strategy that only requires sparse inputs or even a single image to recover high-fidelity face shapes with images captured under near-field lights. To this end, we construct a dataset containing 84 different subjects with 29 expressions under 3 different lights. Data augmentation is applied to enrich the data in terms of diversity in identity, lighting, expression, etc. With this constructed dataset, we propose a novel neural network specially designed for photometric stereo based 3D face reconstruction. Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with one to three facial images captured under near-field lights. Our full framework is available at https:\/\/github.com\/Juyong\/FacePSNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.12307v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"36512\", \"9232\", \"59791\", \"61913\", \"80979\", \"87603\"], \"outgoing_citations\": [\"109032\", \"185981\", \"186960\", \"191463\", \"192217\", \"193656\", \"194402\", \"194752\", \"198793\", \"219800\", \"223643\", \"238254\", \"238562\", \"240584\", \"247495\", \"248918\", \"250292\", \"259476\", \"262122\", \"270235\", \"270795\", \"273729\", \"280394\"]}","task_split":"paper_retrieval"}
{"document_id":"134739","document_content":"# Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets\n## Categories\n- Computation and Language\n- Machine Learning\n- Social and Information Networks\n- Machine Learning\n## Abstract\nSocial media, especially Twitter, is being increasingly used for research with predictive analytics. In social media studies, natural language processing (NLP) techniques are used in conjunction with expert-based, manual and qualitative analyses. However, social media data are unstructured and must undergo complex manipulation for research use. The manual annotation is the most resource and time-consuming process that multiple expert raters have to reach consensus on every item, but is essential to create gold-standard datasets for training NLP-based machine learning classifiers. To reduce the burden of the manual annotation, yet maintaining its reliability, we devised a crowdsourcing pipeline combined with active learning strategies. We demonstrated its effectiveness through a case study that identifies job loss events from individual tweets. We used Amazon Mechanical Turk platform to recruit annotators from the Internet and designed a number of quality control measures to assure annotation accuracy. We evaluated 4 different active learning strategies (i.e., least confident, entropy, vote entropy, and Kullback-Leibler divergence). The active learning strategies aim at reducing the number of tweets needed to reach a desired performance of automated classification. Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.12139v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"5119\"], \"outgoing_citations\": [\"215595\", \"263016\", \"264835\", \"276281\", \"297688\", \"357384\"]}","task_split":"paper_retrieval"}
{"document_id":"134853","document_content":"# Compact Deep Aggregation for Set Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the individual descriptors. We focus on a specific example of this general problem -- that of retrieving images containing multiple faces from a large scale dataset of images. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities, all but one, \\etc   To this end, we make the following contributions: first, we propose a CNN architecture -- {\\em SetNet} -- to achieve the objective: it learns face descriptors and their aggregation over a set to produce a compact fixed length descriptor designed for set retrieval, and the score of an image is a count of the number of identities that match the query; second, we show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that -- far exceeding a number of baselines; third, we explore the speed vs.\\ retrieval quality trade-off for set retrieval using this compact descriptor; and, finally, we collect and annotate a large dataset of images containing various number of celebrities, which we use for evaluation and is publicly released.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11794v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"55595\", \"189317\"], \"outgoing_citations\": [\"198138\", \"251473\", \"269149\", \"271295\", \"279410\", \"294983\", \"296384\", \"302934\", \"320578\", \"326914\"]}","task_split":"paper_retrieval"}
{"document_id":"134875","document_content":"# Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nImage captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of the captions are biased to only a few objects present in the scene, hence limiting their usage in daily life. In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions. We further exploit the state of the art pre-trained image captioning and object recognition networks to annotate our images and show the limitations of existing works. Furthermore, in order to evaluate the quality of the generated captions, we propose a new image captioning metric, object based Semantic Fidelity (SF). Existing image captioning metrics can evaluate a caption only in the presence of their corresponding annotations; however, SF allows evaluating captions generated for images without annotations, making it highly useful for real life generated captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11743v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"38226\", \"38766\", \"159132\"], \"outgoing_citations\": [\"137056\", \"147314\", \"148145\", \"159154\", \"159494\", \"173572\", \"182611\", \"192210\", \"199453\", \"205227\", \"208555\", \"218912\", \"234545\", \"236042\", \"237655\", \"241203\", \"253559\", \"269030\", \"270563\", \"276436\", \"278604\", \"286690\", \"289627\", \"296681\"]}","task_split":"paper_retrieval"}
{"document_id":"134965","document_content":"# PiP: Planning-informed Trajectory Prediction for Autonomous Driving\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nIt is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-58589-1_36\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"15731\", \"29020\", \"88471\", \"144528\", \"14957\", \"980\", \"23300\", \"25916\", \"40266\", \"55117\", \"64848\", \"74982\", \"75053\"], \"outgoing_citations\": [\"179336\", \"187849\", \"191356\", \"196757\", \"197609\", \"206321\", \"211345\", \"214529\", \"218432\", \"218635\", \"223882\", \"229266\", \"232095\", \"237350\", \"249164\", \"267118\", \"268891\", \"271937\"]}","task_split":"paper_retrieval"}
{"document_id":"134976","document_content":"# Preferential Batch Bayesian Optimization\n## Categories\n- Machine Learning\n## Abstract\nMost research in Bayesian optimization (BO) has focused on \\emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A\/B tests, or recommender systems, there is a need for methods that can replace direct feedback with \\emph{preferential feedback}, obtained via rankings or pairwise comparisons. In this work, we present preferential batch Bayesian optimization (PBBO), a new framework that allows finding the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11435v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"16018\"], \"outgoing_citations\": [\"83048\", \"158252\", \"290679\", \"293917\", \"295558\", \"311982\", \"318250\", \"336664\", \"350478\"]}","task_split":"paper_retrieval"}
{"document_id":"135007","document_content":"# Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Neural and Evolutionary Computing\n## Abstract\nThe last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance. Unfortunately, the black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Certain application scenarios have highlighted the importance of assessing the bounds under which Deep Learning models operate, a problem addressed by using assorted approaches aimed at audiences from different domains. However, as the focus of the application is placed more on non-expert users, it results mandatory to provide the means for him\/her to trust the model, just like a human gets familiar with a system or process: by understanding the hypothetical circumstances under which it fails. This is indeed the angular stone for this research work: to undertake an adversarial analysis of a Deep Learning model. The proposed framework constructs counterfactual examples by ensuring their plausibility, e.g. there is a reasonable probability that a human could generate them without resorting to a computer program. Therefore, this work must be regarded as valuable auditing exercise of the usable bounds a certain model is constrained within, thereby allowing for a much greater understanding of the capabilities and pitfalls of a model used in a real application. To this end, a Generative Adversarial Network (GAN) and multi-objective heuristics are used to furnish a plausible attack to the audited model, efficiently trading between the confusion of this model, the intensity and plausibility of the generated counterfactual. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11323v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"20659\", \"46119\", \"72218\", \"94225\", \"95709\", \"123706\"], \"outgoing_citations\": [\"177312\", \"196097\", \"239137\", \"248981\", \"260547\", \"262738\", \"270948\", \"297791\"]}","task_split":"paper_retrieval"}
{"document_id":"135021","document_content":"# SCATTER: Selective Context Attentional Scene Text Recognizer\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER utilizes a stacked block architecture with intermediate supervision during training, that paves the way to successfully train a deep BiLSTM encoder, thus improving the encoding of contextual dependencies. Decoding is done using a two-step 1D attention mechanism. The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer. The second attention step, similar to previous papers, treats the features as a sequence and attends to the intra-sequence relationships. Experiments show that the proposed approach surpasses SOTA performance on irregular text recognition benchmarks by 3.7\\% on average.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11288v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"1156\", \"5857\", \"50775\", \"50917\", \"78763\", \"8735\", \"9220\", \"10204\", \"21983\", \"24824\", \"29428\", \"33363\", \"33358\", \"33360\", \"40383\", \"42497\", \"43037\", \"48822\", \"48901\", \"50591\", \"55703\", \"58277\", \"64528\", \"64650\", \"67749\", \"79422\", \"113794\", \"126431\"], \"outgoing_citations\": [\"162242\", \"163092\", \"170229\", \"172884\", \"192483\", \"205988\", \"210634\", \"211851\", \"217463\", \"229426\", \"232706\", \"250738\", \"256711\", \"280289\", \"294023\", \"296685\", \"311196\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"135041","document_content":"# GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet\n## Categories\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nTraining a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (e.g., $7^{21}$). In this paper, instead of covering all paths, we ease the burden of supernet by encouraging it to focus more on evaluation of those potentially-good ones, which are identified using a surrogate portion of validation data. Concretely, during training, we propose a multi-path sampling strategy with rejection, and greedily filter the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. Moreover, we further adopt an exploration and exploitation policy by introducing an empirical candidate path pool. Our proposed method GreedyNAS is easy-to-follow, and experimental results on ImageNet dataset indicate that it can achieve better Top-1 accuracy under same search space and FLOPs or latency level, but with only $\\sim$60\\% of supernet training cost. By searching on a larger space, our GreedyNAS can also obtain new state-of-the-art architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11236v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"1781\", \"7479\", \"39480\", \"60945\", \"90664\", \"92871\", \"100440\", \"107932\", \"124900\", \"6143\", \"6589\", \"7174\", \"11162\", \"17604\", \"29276\", \"34937\", \"37299\", \"38286\", \"42593\", \"42775\", \"42727\", \"48015\", \"56781\", \"57002\", \"58903\", \"60406\", \"61495\", \"62112\", \"63436\", \"63974\", \"64033\", \"69475\", \"70339\", \"73114\", \"76029\", \"83517\", \"85497\", \"108575\", \"147231\", \"173324\", \"36888\", \"84217\", \"90392\", \"90748\", \"93808\", \"94708\", \"101002\", \"101202\", \"153461\"], \"outgoing_citations\": [\"149147\", \"166639\", \"166774\", \"171488\", \"176229\", \"177741\", \"185741\", \"190365\", \"190559\", \"192117\", \"192998\", \"193623\", \"198231\", \"203960\", \"204162\", \"206152\", \"206678\", \"222824\", \"240321\", \"262675\"]}","task_split":"paper_retrieval"}
{"document_id":"135144","document_content":"# Learning to Exploit Multiple Vision Modalities by Using Grafted Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nNovel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to using these sensors with current powerful deep neural networks is the lack of large labeled training datasets. This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames. The self-supervised training uses only synchronously-recorded intensity frames and novel sensor data to maximize feature similarity between the pretrained network and the grafted network. We show that the enhanced grafted network reaches competitive average precision (AP50) scores to the pretrained network on an object detection task using thermal and event camera datasets, with no increase in inference costs. Particularly, the grafted network driven by thermal frames showed a relative improvement of 49.11% over the use of intensity frames. The grafted front end has only 5--8% of the total parameters and can be trained in a few hours on a single GPU equivalent to 5% of the time that would be needed to train the entire object detector from labeled data. NGA allows new vision sensors to capitalize on previously pretrained powerful deep models, saving on training cost and widening a range of applications for novel sensors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.10959v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"25451\", \"45856\", \"5801\", \"20637\", \"25803\", \"7584\", \"118857\", \"81732\"], \"outgoing_citations\": [\"164899\", \"185403\", \"190036\", \"190078\", \"202067\", \"205351\", \"212426\", \"238238\", \"243802\", \"289307\", \"308474\", \"308908\"]}","task_split":"paper_retrieval"}
{"document_id":"135154","document_content":"# Learning Compact Reward for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nAdversarial learning has shown its advances in generating natural and diverse descriptions in image captioning. However, the learned reward of existing adversarial methods is vague and ill-defined due to the reward ambiguity problem. In this paper, we propose a refined Adversarial Inverse Reinforcement Learning (rAIRL) method to handle the reward ambiguity problem by disentangling reward for each word in a sentence, as well as achieve stable adversarial training by refining the loss function to shift the generator towards Nash equilibrium. In addition, we introduce a conditional term in the loss function to mitigate mode collapse and to increase the diversity of the generated descriptions. Our experiments on MS COCO and Flickr30K show that our method can learn compact reward for image captioning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.10925v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"115939\"], \"outgoing_citations\": [\"206655\", \"215836\", \"217310\", \"218912\", \"227741\", \"231619\", \"233685\", \"233720\", \"234342\", \"237655\", \"251924\", \"253934\", \"265075\", \"269030\", \"270154\", \"271234\", \"278604\", \"278954\", \"279002\", \"280655\", \"287504\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"135309","document_content":"# Neural Game Engine: Accurate learning of generalizable forward models from pixels\n## Categories\n- Artificial Intelligence\n## Abstract\nAccess to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalise to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: \\url{https:\/\/github.com\/Bam4d\/Neural-Game-Engine}","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.10520v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"39391\", \"41333\", \"85316\"], \"outgoing_citations\": [\"152912\", \"169201\", \"179594\", \"193172\", \"198217\", \"203248\", \"210517\", \"226544\", \"228796\", \"235276\", \"240572\", \"242891\", \"260770\", \"261983\", \"269458\", \"272823\", \"277472\", \"291460\", \"302515\"]}","task_split":"paper_retrieval"}
{"document_id":"135383","document_content":"# Optimising Game Tactics for Football\n## Categories\n- Artificial Intelligence\n- Computer Science and Game Theory\n- Multiagent Systems\n## Abstract\nIn this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using this formulation, we propose a method to predict the probability of game outcomes and the payoffs of team actions. Building upon this, we develop algorithms to optimise team formation and in-game tactics with different objectives. Empirical evaluation of our approach on real-world datasets from 760 matches shows that by using optimised tactics from our Bayesian and stochastic games, we can increase a team chances of winning by up to 16.1\\% and 3.4\\% respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.10294v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.GT\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Science and Game Theory\", \"Multiagent Systems\"], \"incoming_citations\": [\"5799\", \"17873\", \"45090\", \"68423\", \"69258\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"135523","document_content":"# Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAirborne LiDAR (Light Detection and Ranging) data is widely applied in building reconstruction, with studies reporting success in typical buildings. However, the reconstruction of curved buildings remains an open research problem. To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The input LiDAR point cloud are first converted into contours where individual buildings are identified. After recognizing geometric units (primitives) from building contours, we get initial models by matching basic geometric primitives to these primitives. To polish assembly models, we employ a warping field for model refinements. Specifically, an embedded deformation (ED) graph is constructed via downsampling the initial model. Then, the point-to-model displacements are minimized by adjusting node parameters in the ED graph based on our objective function. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities. The experimental results, as well as accuracy comparison, demonstrate the advantage and effectiveness of our method. {The new insight attributes to an efficient reconstruction manner.} Moreover, we prove that the primitive-based framework significantly reduces the data storage to 10-20 percent of classical mesh models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TGRS.2020.2995732\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"168285\", \"278990\", \"295810\", \"316968\"]}","task_split":"paper_retrieval"}
{"document_id":"135539","document_content":"# Ensembles of Deep Neural Networks for Action Recognition in Still Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite the fact that notable improvements have been made recently in the field of feature extraction and classification, human action recognition is still challenging, especially in images, in which, unlike videos, there is no motion. Thus, the methods proposed for recognizing human actions in videos cannot be applied to still images. A big challenge in action recognition in still images is the lack of large enough datasets, which is problematic for training deep Convolutional Neural Networks (CNNs) due to the overfitting issue. In this paper, by taking advantage of pre-trained CNNs, we employ the transfer learning technique to tackle the lack of massive labeled action recognition datasets. Furthermore, since the last layer of the CNN has class-specific information, we apply an attention mechanism on the output feature maps of the CNN to extract more discriminative and powerful features for classification of human actions. Moreover, we use eight different pre-trained CNNs in our framework and investigate their performance on Stanford 40 dataset. Finally, we propose using the Ensemble Learning technique to enhance the overall accuracy of action classification by combining the predictions of multiple models. The best setting of our method is able to achieve 93.17$\\%$ accuracy on the Stanford 40 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICCKE48569.2019.8965014\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3296\"], \"outgoing_citations\": [\"278034\", \"320693\", \"329514\"]}","task_split":"paper_retrieval"}
{"document_id":"135586","document_content":"# GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.11973v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"123254\", \"88724\"], \"outgoing_citations\": [\"139818\", \"191356\", \"199372\", \"219340\", \"232095\", \"235926\", \"244314\", \"244664\", \"268197\", \"275566\"]}","task_split":"paper_retrieval"}
{"document_id":"135639","document_content":"# Geometrically Mappable Image Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning. Features that uniquely describe the visual contents of images have a wide range of applications, including image retrieval and understanding. In this work, we propose a method that learns image features targeted for image-retrieval-based localization. Retrieval-based localization has several benefits, such as easy maintenance and quick computation. However, the state-of-the-art features only provide visual similarity scores which do not explicitly reveal the geometric distance between query and retrieved images. Knowing this distance is highly desirable for accurate localization, especially when the reference images are sparsely distributed in the scene. Therefore, we propose a novel loss function for learning image features which are both visually representative and geometrically relatable. This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional. In our experiments we show that our features not only offer significantly better localization accuracy, but also allow to estimate the trajectory of a query sequence in absence of the reference images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LRA.2020.2970616\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"103697\"], \"outgoing_citations\": [\"216524\", \"236121\", \"237488\", \"253194\", \"269599\", \"269927\", \"279689\", \"281953\", \"312170\", \"316884\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"135800","document_content":"# FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning\n## Categories\n- Computation and Language\n## Abstract\nMedical named entity recognition (NER) has wide applications in intelligent healthcare. Sufficient labeled data is critical for training accurate medical NER model. However, the labeled data in a single medical platform is usually limited. Although labeled datasets may exist in many different medical platforms, they cannot be directly shared since medical data is highly privacy-sensitive. In this paper, we propose a privacy-preserving medical NER method based on federated learning, which can leverage the labeled data in different platforms to boost the training of medical NER model and remove the need of exchanging raw data among different platforms. Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module. The private module is used to capture the characteristics of the local data in each platform, and is updated using local labeled data. The shared module is learned across different medical platform to capture the shared NER knowledge. Its local gradients from different platforms are aggregated to update the global shared module, which is further delivered to each platform to update their local shared modules. Experiments on three publicly available datasets validate the effectiveness of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.09288v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16829\", \"55141\", \"33171\", \"35792\", \"56009\", \"71148\", \"91601\", \"106508\", \"76134\", \"109503\"], \"outgoing_citations\": [\"205914\", \"234384\", \"248982\", \"260847\"]}","task_split":"paper_retrieval"}
{"document_id":"135941","document_content":"# Beheshti-NER: Persian Named Entity Recognition Using BERT\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNamed entity recognition is a natural language processing task to recognize and extract spans of text associated with named entities and classify them in semantic Categories.   Google BERT is a deep bidirectional language model, pre-trained on large corpora that can be fine-tuned to solve many NLP tasks such as question answering, named entity recognition, part of speech tagging and etc. In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian.   We also compare the results of our model with the previous state of the art results achieved on Persian NER. Our evaluation metric is CONLL 2003 score in two levels of word and phrase. This model achieved second place in NSURL-2019 task 7 competition which associated with NER for the Persian language. our results in this competition are 83.5 and 88.4 f1 CONLL score respectively in phrase and word level evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08875v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"49915\", \"99327\", \"12125\", \"58544\", \"122957\", \"81333\", \"135894\"], \"outgoing_citations\": [\"135894\", \"194633\", \"243860\"]}","task_split":"paper_retrieval"}
{"document_id":"136037","document_content":"# Lifelong Learning with Searchable Extension Units\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nLifelong learning remains an open problem. One of its main difficulties is catastrophic forgetting. Many dynamic expansion approaches have been proposed to address this problem, but they all use homogeneous models of predefined structure for all tasks. The common original model and expansion structures ignore the requirement of different model structures on different tasks, which leads to a less compact model for multiple tasks and causes the model size to increase rapidly as the number of tasks increases. Moreover, they can not perform best on all tasks. To solve those problems, in this paper, we propose a new lifelong learning framework named Searchable Extension Units (SEU) by introducing Neural Architecture Search into lifelong learning, which breaks down the need for a predefined original model and searches for specific extension units for different tasks, without compromising the performance of the model on different tasks. Our approach can obtain a much more compact model without catastrophic forgetting. The experimental results on the PMNIST, the split CIFAR10 dataset, the split CIFAR100 dataset, and the Mixture dataset empirically prove that our method can achieve higher accuracy with much smaller model, whose size is about 25-33 percentage of that of the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08559v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"185741\", \"193041\", \"207559\", \"229806\", \"243460\", \"245957\", \"249234\", \"258268\", \"259336\", \"265568\", \"275087\", \"290378\"]}","task_split":"paper_retrieval"}
{"document_id":"136039","document_content":"# Adjust Planning Strategies to Accommodate Reinforcement Learning Agents\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is a good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis to the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of reaction capability of specific agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08554v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"181081\", \"240323\", \"253600\", \"260583\", \"280296\", \"298651\"]}","task_split":"paper_retrieval"}
{"document_id":"136120","document_content":"# Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yor\u00f9b\u00e1\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to alleviate some of the negative effects of the errors in automatic annotation, noise-handling methods can be integrated. Pretrained word embeddings are another key component of most neural named entity classifiers. With the advent of more complex contextual word embeddings, an interesting trade-off between model size and performance arises. While these techniques have been shown to work well in high-resource settings, we want to study how they perform in low-resource scenarios. In this work, we perform named entity recognition for Hausa and Yor\\`ub\\'a, two languages that are widely spoken in several developing countries. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario where it can more than double a classifier's performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08370v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"7555\", \"91922\", \"96080\"], \"outgoing_citations\": [\"151719\", \"152621\", \"161842\", \"163402\", \"165834\", \"169349\", \"170164\", \"193334\", \"194633\", \"200678\", \"226054\", \"233440\", \"235452\", \"249126\", \"249957\", \"258956\", \"276713\", \"289031\", \"291900\", \"294248\"]}","task_split":"paper_retrieval"}
{"document_id":"136201","document_content":"# The Future of Digital Health with Federated Learning\n## Categories\n- Computers and Society\n- Machine Learning\n## Abstract\nData-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1038\/s41746-020-00323-1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"9036\", \"19284\", \"21096\", \"35996\", \"37270\", \"109578\", \"203074\", \"107510\", \"1828\", \"3158\", \"4079\", \"15936\", \"15946\", \"27322\", \"34352\", \"36865\", \"42977\", \"46150\", \"49221\", \"52335\", \"62912\", \"69454\", \"71429\", \"74725\", \"90200\", \"90723\", \"94099\", \"99336\", \"440\", \"490\", \"1054\", \"4271\", \"4437\", \"4571\", \"5580\", \"7634\", \"9091\", \"9906\", \"10100\", \"12918\", \"15166\", \"16284\", \"17947\", \"18256\", \"19599\", \"19695\", \"20169\", \"23249\", \"26435\", \"27798\", \"30901\", \"32812\", \"36588\", \"36880\", \"36915\", \"37388\", \"39808\", \"41753\", \"44753\", \"46616\", \"46926\", \"49756\", \"49967\", \"50294\", \"51220\", \"54543\", \"63956\", \"64980\", \"64978\", \"68872\", \"69232\", \"70379\", \"70616\", \"71257\", \"74184\", \"74828\", \"75782\", \"78786\", \"80069\", \"36031\", \"83656\", \"94135\", \"96241\", \"102450\", \"105490\", \"109503\", \"113040\", \"163290\"], \"outgoing_citations\": [\"146915\", \"156643\", \"161269\", \"163734\", \"169626\", \"179698\", \"183566\", \"185996\", \"192122\", \"193132\", \"194178\", \"197781\", \"200873\", \"207623\", \"211547\", \"214805\", \"230009\", \"241199\", \"252956\", \"264709\", \"269208\", \"273221\", \"280481\", \"283045\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"136256","document_content":"# Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAbstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or semantic structures, which is useful to generate more accurate summary. However, modeling a parsing tree for text summarization is not trivial due to its non-linear structure and it is harder to deal with a document that includes multiple sentences and their parsing trees. In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspect and generate an abstractive summary. We evaluate our approach on the CNN\/Daily Mail text summarization dataset. The experimental results show that the proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08004v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"216419\", \"219618\", \"225556\", \"227983\", \"230212\", \"231869\", \"241070\", \"266671\", \"268186\", \"268812\", \"271527\", \"280557\", \"291916\", \"296198\", \"298016\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"136331","document_content":"# Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.07813v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"103422\"], \"outgoing_citations\": [\"183066\", \"206197\"]}","task_split":"paper_retrieval"}
{"document_id":"136333","document_content":"# The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Human-Computer Interaction\n## Abstract\nIt remains unknown whether personalized recommendations increase or decrease the diversity of content people consume. We present results from a randomized field experiment on Spotify testing the effect of personalized recommendations on consumption diversity. In the experiment, both control and treatment users were given podcast recommendations, with the sole aim of increasing podcast consumption. Treatment users' recommendations were personalized based on their music listening history, whereas control users were recommended popular podcasts among users in their demographic group. We find that, on average, the treatment increased podcast streams by 28.90%. However, the treatment also decreased the average individual-level diversity of podcast streams by 11.51%, and increased the aggregate diversity of podcast streams by 5.96%, indicating that personalized recommendations have the potential to create patterns of consumption that are homogenous within and diverse across users, a pattern reflecting Balkanization. Our results provide evidence of an \"engagement-diversity trade-off\" when recommendations are optimized solely to drive consumption: while personalized recommendations increase user engagement, they also affect the diversity of consumed content. This shift in consumption diversity can affect user retention and lifetime value, and impact the optimal strategy for content producers. We also observe evidence that our treatment affected streams from sections of Spotify's app not directly affected by the experiment, suggesting that exposure to personalized recommendations can affect the content that users consume organically. We believe these findings highlight the need for academics and practitioners to continue investing in personalization methods that explicitly take into account the diversity of content recommended.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08203v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\", \"cs.HC\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"14543\", \"89614\", \"29240\", \"42702\", \"99541\"], \"outgoing_citations\": [\"109587\", \"143406\", \"170674\", \"279317\"]}","task_split":"paper_retrieval"}
{"document_id":"136352","document_content":"# Multi-modal Dense Video Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n- Image and Video Processing\n## Abstract\nDense video captioning is a task of localizing interesting events from an untrimmed video and producing textual description (captions) for each localized event. Most of the previous works in dense video captioning are solely based on visual information and completely ignore the audio track. However, audio, and speech, in particular, are vital cues for a human observer in understanding an environment. In this paper, we present a new dense video captioning approach that is able to utilize any number of modalities for event description. Specifically, we show how audio and speech modalities may improve a dense video captioning model. We apply automatic speech recognition (ASR) system to obtain a temporally aligned textual description of the speech (similar to subtitles) and treat it as a separate input alongside video frames and the corresponding audio track. We formulate the captioning task as a machine translation problem and utilize recently proposed Transformer architecture to convert multi-modal input data into textual descriptions. We demonstrate the performance of our model on ActivityNet Captions dataset. The ablation studies indicate a considerable contribution from audio and speech components suggesting that these modalities contain substantial complementary information to video frames. Furthermore, we provide an in-depth analysis of the ActivityNet Caption results by leveraging the category tags obtained from original YouTube videos. Code is publicly available: github.com\/v-iashin\/MDVC","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.07758v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\", \"Image and Video Processing\"], \"incoming_citations\": [\"340705\", \"55339\", \"2064\", \"2724\", \"8514\", \"12199\", \"25304\", \"29109\", \"38350\", \"48405\", \"62806\", \"74195\", \"75528\", \"85254\", \"83251\", \"86777\", \"124561\"], \"outgoing_citations\": [\"162966\", \"165591\", \"181897\", \"187005\", \"191680\", \"206567\", \"223293\", \"234602\", \"235531\", \"236918\", \"237150\", \"240605\", \"248855\", \"249677\", \"250211\", \"267523\", \"269646\", \"276376\", \"279337\", \"279676\", \"279740\", \"302621\", \"304481\", \"313435\", \"316803\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"136384","document_content":"# EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of reconstructing and super-resolving intensity images from LR events, when no ground truth (GT) HR images and down-sampling kernels are available. To tackle the challenges, we propose a novel end-to-end pipeline that reconstructs LR images from event streams, enhances the image qualities and upsamples the enhanced images, called EventSR. For the absence of real GT images, our method is primarily unsupervised, deploying adversarial learning. To train EventSR, we create an open dataset including both real-world and simulated scenes. The use of both datasets boosts up the network performance, and the network architectures and various loss functions in each phase help improve the image qualities. The whole pipeline is trained in three phases. While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner. Experimental results show that EventSR reconstructs high-quality SR images from events for both simulated and real-world data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.07640v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3311\", \"3629\", \"14984\", \"25451\", \"84856\", \"131467\", \"5318\", \"18686\", \"5801\", \"20637\", \"25803\", \"7584\", \"49863\", \"49930\", \"61609\", \"127359\", \"100061\"], \"outgoing_citations\": [\"148389\", \"190078\", \"190090\", \"190497\", \"205351\", \"206446\", \"208122\", \"208731\", \"209333\", \"211978\", \"219418\", \"223380\", \"226061\", \"231301\", \"236799\", \"239208\", \"243188\", \"263825\", \"271427\", \"277357\", \"281870\", \"287980\"]}","task_split":"paper_retrieval"}
{"document_id":"136872","document_content":"# A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nWeight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. However, previous pruning methods mainly focus on reducing the model size and\/or improving performance without considering the privacy of user data. To mitigate this concern, we propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset. At the algorithm level of the proposed framework, a systematic weight pruning technique based on the alternating direction method of multipliers (ADMM) is designed to iteratively solve the pattern-based pruning problem for each layer with randomly generated synthetic data. In addition, corresponding optimizations at the compiler level are leveraged for inference accelerations on devices. With the proposed framework, users could avoid the time-consuming pruning process for non-experts and directly benefit from compressed models. Experimental results show that the proposed framework outperforms three state-of-art end-to-end DNN frameworks, i.e., TensorFlow-Lite, TVM, and MNN, with speedup up to 4.2X, 2.5X, and 2.0X, respectively, with almost no accuracy loss, while preserving data privacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.06513v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"7894\", \"644\", \"29019\"], \"outgoing_citations\": [\"168372\", \"212683\", \"214530\", \"218895\", \"236201\", \"238806\", \"242026\", \"242432\", \"265846\", \"286728\", \"309161\"]}","task_split":"paper_retrieval"}
{"document_id":"136878","document_content":"# LSCP: Enhanced Large Scale Colloquial Persian Language Understanding\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n- 68T50 (Primary) 68T09, 68T07 (Secondary)\n- A.0; E.0; I.2.0; I.2.6; I.2.7; I.7.1; I.7.2\n## Abstract\nLanguage recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a \"Large Scale Colloquial Persian Dataset\" (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.06499v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\", \"68T50 (Primary) 68T09, 68T07 (Secondary)\", \"A.0; E.0; I.2.0; I.2.6; I.2.7; I.7.1; I.7.2\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\", \"68T50 (Primary) 68T09, 68T07 (Secondary)\", \"A.0; E.0; I.2.0; I.2.6; I.2.7; I.7.1; I.7.2\"], \"incoming_citations\": [\"81219\"], \"outgoing_citations\": [\"140566\", \"201065\", \"208737\", \"217957\", \"251638\"]}","task_split":"paper_retrieval"}
{"document_id":"136894","document_content":"# Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOur aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approaches in healthcare (\"AI Clinicians\"), and learn off-policy continuous dosing policy of pharmaceuticals for sepsis treatment using historical intensive care data under partially observable MDPs (POMDPs). POMPDs capture uncertainty in patient state better by taking in all historical information, yielding an efficient representation, which we investigate through ablations. We compensate for the lack of exploration in our retrospective data by evaluating each encountered state with a best-first tree search. We mitigate state distributional shift by optimizing our policy in the vicinity of the clinicians' compound policy. Crucially, we evaluate our model recommendations using not only conventional policy evaluations but a novel framework that incorporates human experts: a model-agnostic pre-clinical evaluation method to estimate the accuracy and uncertainty of clinician's decisions versus our system recommendations when confronted with the same individual patient history (\"shadow mode\").","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.06474v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23098\", \"59766\", \"85341\"], \"outgoing_citations\": [\"190024\", \"206875\", \"230891\", \"243235\", \"247075\", \"268399\", \"290547\", \"290825\", \"292736\", \"295281\"]}","task_split":"paper_retrieval"}
{"document_id":"136898","document_content":"# A Time Series Approach To Player Churn and Conversion in Videogames\n## Categories\n- Applications\n- Machine Learning\n- Machine Learning\n## Abstract\nPlayers of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3233\/IDA-194940\", \"primary_category\": \"stat.AP\", \"categories\": [\"cs.LG\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Applications\", \"categories_human_readable\": [\"Machine Learning\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"37283\", \"128769\"], \"outgoing_citations\": [\"176991\", \"176997\", \"202815\", \"208306\", \"228216\", \"243081\", \"253985\", \"258520\", \"272063\", \"314845\"]}","task_split":"paper_retrieval"}
{"document_id":"137257","document_content":"# Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nA typical architecture for end-to-end entity linking systems consists of three steps: mention detection, candidate generation and entity disambiguation. In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text-representations, i.e. BERT (Devlin et al., 2019)? (b) How much entity knowledge is already contained in pretrained BERT? (c) Does additional entity knowledge improve BERT's performance in downstream tasks? To this end, we propose an extreme simplification of the entity linking setup that works surprisingly well: simply cast it as a per token classification over the entire entity vocabulary (over 700K classes in our case). We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state-of-the-art that does mention detection and entity disambiguation jointly. Additionally, we investigate the usefulness of entity-aware token-representations in the text-understanding benchmark GLUE, as well as the question answering benchmarks SQUAD V2 and SWAG and also the EN-DE WMT14 machine translation benchmark. To our surprise, we find that most of those benchmarks do not benefit from additional entity knowledge, except for a task with very small training data, the RTE task in GLUE, which improves by 2%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/K19-1063\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"21023\", \"5798\", \"18787\", \"20997\", \"26715\", \"27470\", \"121898\", \"169211\", \"2762\", \"12125\", \"24980\", \"34173\", \"43177\", \"54510\", \"56560\", \"73669\", \"77042\", \"92416\", \"92823\", \"97185\", \"98430\", \"88667\", \"86291\", \"139638\", \"122351\", \"145242\", \"157168\", \"157255\", \"157308\"], \"outgoing_citations\": [\"185889\", \"186250\", \"195157\", \"220553\", \"221212\", \"267123\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"137300","document_content":"# How the Brain might use Division\n## Categories\n- Artificial Intelligence\n## Abstract\nOne of the most fundamental questions in Biology or Artificial Intelligence is how the human brain performs mathematical functions. How does a neural architecture that may organise itself mostly through statistics, know what to do? One possibility is to extract the problem to something more abstract. This becomes clear when thinking about how the brain handles large numbers, for example to the power of something, when simply summing to an answer is not feasible. In this paper, the author suggests that the maths question can be answered more easily if the problem is changed into one of symbol manipulation and not just number counting. If symbols can be compared and manipulated, maybe without understanding completely what they are, then the mathematical operations become relative and some of them might even be rote learned. The proposed system may also be suggested as an alternative to the traditional computer binary system. Any of the actual maths still breaks down into binary operations, while a more symbolic level above that can manipulate the numbers and reduce the problem size, thus making the binary operations simpler. An interesting result of looking at this is the possibility of a new fractal equation resulting from division, that can be used as a measure of good fit and would help the brain decide how to solve something through self-replacement and a comparison with this good fit.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05320v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"279853\", \"332473\"]}","task_split":"paper_retrieval"}
{"document_id":"137310","document_content":"# Capturing document context inside sentence-level neural machine translation models with self-training\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT) has arguably achieved human level parity when trained and evaluated at the sentence-level. Document-level neural machine translation has received less attention and lags behind its sentence-level counterpart. The majority of the proposed document-level approaches investigate ways of conditioning the model on several source or target sentences to capture document context. These approaches require training a specialized NMT model from scratch on parallel document-level corpora. We propose an approach that doesn't require training a specialized model on parallel document-level corpora and is applied to a trained sentence-level NMT model at decoding time. We process the document from left to right multiple times and self-train the sentence-level model on pairs of source sentences and generated translations. Our approach reinforces the choices made by the model, thus making it more likely that the same choices will be made in other sentences in the document. We evaluate our approach on three document-level datasets: NIST Chinese-English, WMT'19 Chinese-English and OpenSubtitles English-Russian. We demonstrate that our approach has higher BLEU score and higher human preference than the baseline. Qualitative analysis of our approach shows that choices made by model are consistent across the document.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05259v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21968\", \"55467\"], \"outgoing_citations\": [\"157631\", \"158899\", \"163902\", \"164156\", \"168854\", \"171617\", \"176177\", \"186239\", \"210165\", \"215003\", \"219083\", \"220017\", \"229654\", \"234469\", \"238703\", \"238926\", \"248821\", \"250879\", \"255253\", \"258163\", \"268684\", \"268909\", \"277537\", \"280916\", \"282318\", \"302824\", \"341730\"]}","task_split":"paper_retrieval"}
{"document_id":"137343","document_content":"# Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nUnderstanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans. Cognitive science pursues the goal of modeling human-like intelligence from a theory-driven perspective with a strong focus on explainability. Syllogistic reasoning as one of the core domains of human reasoning research has seen a surge of computational models being developed over the last years. However, recent analyses of models' predictive performances revealed a stagnation in improvement. We believe that most of the problems encountered in cognitive science are not due to the specific models that have been developed but can be traced back to the peculiarities of behavioral data instead.   Therefore, we investigate potential data-related reasons for the problems in human reasoning research by comparing model performances on human and artificially generated datasets. In particular, we apply collaborative filtering recommenders to investigate the adversarial effects of inconsistencies and noise in data and illustrate the potential for data-driven methods in a field of research predominantly concerned with gaining high-level theoretical insight into a domain.   Our work (i) provides insight into the levels of noise to be expected from human responses in reasoning data, (ii) uncovers evidence for an upper-bound of performance that is close to being reached urging for an extension of the modeling task, and (iii) introduces the tools and presents initial results to pioneer a new paradigm for investigating and modeling reasoning focusing on predicting responses for individual human reasoners.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05196v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"350178\"]}","task_split":"paper_retrieval"}
{"document_id":"137402","document_content":"# A General Arbitration Model for Robust Human-Robot Shared Control with Multi-Source Uncertainty Modeling\n## Categories\n- Robotics\n## Abstract\nShared control in teleoperation leverages both human and robot's strengths and has demonstrated great advantages of reducing the difficulties in teleoperating a robot and increasing the task performance. One fundamental question in shared control is how to effectively allocate the control power to the human and robot. Researchers have been subjectively defining the arbitrate policies following conflicting principles, which resulted in great inconsistency in the policies. We attribute this inconsistency to the inconsiderateness of the multi-resource uncertainty in the human-robot system. To fill the gap, we developed a multi-source uncertainty model that was applicable to various types of uncertainty in real world, and then a general arbitration model was developed to comprehensively fuse the uncertainty and regulate the arbitration weight assigned to the robotic agent. Beside traditional macro performance metrics, we introduced objective and quantitative metrics of robotic helpfulness and friendliness that evaluated the assistive robot's cooperation at micro and macro levels. Results from simulations and experiments showed the new arbitration model was more effective and friendly over the existing policies and was robust to coping with multi-source uncertainty. With this new arbitration model, we expect the increased adoption of human-robot shared control in practical and complex teleoperation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05097v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"243197\", \"264845\", \"275319\", \"303045\", \"315405\", \"315783\"]}","task_split":"paper_retrieval"}
{"document_id":"137441","document_content":"# Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.04960v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2138\", \"2722\", \"8658\", \"20957\", \"3479\", \"9351\", \"9649\", \"9727\", \"18344\", \"91442\", \"73516\", \"77709\", \"104784\", \"4444\", \"5981\", \"7252\", \"13540\", \"15055\", \"18748\", \"24892\", \"30101\", \"43616\", \"46143\", \"49922\", \"63182\", \"64492\", \"65571\", \"66832\", \"67700\", \"68670\", \"78650\", \"87363\", \"88647\", \"116987\", \"78005\", \"80827\", \"95611\", \"95777\", \"106665\", \"108295\", \"111907\", \"112980\", \"119151\"], \"outgoing_citations\": [\"108295\", \"166489\", \"168328\", \"180481\", \"180928\", \"200772\", \"202740\", \"206668\", \"207730\", \"217927\", \"227836\", \"229802\", \"236923\", \"240506\", \"242601\", \"246066\", \"253638\", \"261017\", \"261983\", \"262325\", \"264014\", \"266182\", \"269253\", \"270792\", \"271031\", \"271375\", \"272030\", \"275728\", \"280296\", \"288231\", \"290371\", \"293876\", \"350010\", \"352796\"]}","task_split":"paper_retrieval"}
{"document_id":"137543","document_content":"# A Survey of Adversarial Learning on Graphs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give appropriate definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and offer insights for the relevant researchers. Latest advances in graph adversarial learning are summarized in our GitHub repository https:\/\/github.com\/EdisonLeeeee\/Graph-Adversarial-Learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05730v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"40267\", \"101827\", \"2760\", \"51800\", \"80990\", \"82775\", \"95125\", \"204706\", \"26862\", \"29739\", \"37537\", \"41353\", \"52486\", \"70251\", \"91611\", \"94973\", \"81246\", \"123804\", \"205283\"], \"outgoing_citations\": [\"139651\", \"153362\", \"154562\", \"156910\", \"158776\", \"160489\", \"160564\", \"160565\", \"168835\", \"170916\", \"173122\", \"178508\", \"181525\", \"181707\", \"184355\", \"184442\", \"187085\", \"188111\", \"188763\", \"195684\", \"196521\", \"196875\", \"197099\", \"197726\", \"197997\", \"198305\", \"204706\", \"211968\", \"212305\", \"213013\", \"216926\", \"216942\", \"218691\", \"219215\", \"219533\", \"221605\", \"229027\", \"231277\", \"239929\", \"247927\", \"257414\", \"267030\", \"282492\"]}","task_split":"paper_retrieval"}
{"document_id":"137715","document_content":"# PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Robotics\n## Abstract\nTo navigate safely in urban environments, an autonomous vehicle (ego vehicle) must understand and anticipate its surroundings, in particular the behavior and intents of other road users (neighbors). Most of the times, multiple decision choices are acceptable for all road users (e.g., turn right or left, or different ways of avoiding an obstacle), leading to a highly uncertain and multi-modal decision space. We focus here on predicting multiple feasible future trajectories for both ego vehicle and neighbors through a probabilistic framework. We rely on a conditional imitation learning algorithm, conditioned by a navigation command for the ego vehicle (e.g., \"turn right\"). Our model processes ego vehicle front-facing camera images and bird-eye view grid, computed from Lidar point clouds, with detections of past and present objects, in order to generate multiple trajectories for both ego vehicle and its neighbors. Our approach is computationally efficient and relies only on on-board sensors. We evaluate our method offline on the publicly available dataset nuScenes, achieving state-of-the-art performance, investigate the impact of our architecture choices on online simulated experiments and show preliminary insights for real vehicle control","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08744v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"6518\", \"23013\", \"60595\", \"41845\", \"54563\"], \"outgoing_citations\": [\"74563\", \"78890\", \"113996\", \"120882\", \"122175\", \"147880\", \"148683\", \"151320\", \"153095\", \"153475\", \"154418\", \"154697\", \"157689\", \"158269\", \"161127\", \"162130\", \"169068\", \"179692\", \"181757\", \"186194\", \"187849\", \"206814\", \"210901\", \"211345\", \"214194\", \"214313\", \"217456\", \"218009\", \"220901\", \"224411\", \"227720\", \"229266\", \"232095\", \"232096\", \"237350\", \"241801\", \"253953\", \"265546\", \"268891\", \"270859\"]}","task_split":"paper_retrieval"}
{"document_id":"137803","document_content":"# Price-aware Recommendation with Graph Convolutional Networks\n## Categories\n- Information Retrieval\n## Abstract\nIn recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images, and so on. Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.   In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a user's intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users. Further analysis reveals that modeling the price awareness is particularly useful for predicting user preference on items of unexplored categories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03975v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [\"20875\", \"3205\", \"11274\", \"25694\", \"18024\", \"36662\", \"41493\", \"29426\", \"56240\", \"94427\"], \"outgoing_citations\": [\"210511\", \"239029\", \"249824\", \"258416\", \"261412\", \"263158\", \"264101\", \"264326\", \"276004\", \"298945\"]}","task_split":"paper_retrieval"}
{"document_id":"137902","document_content":"# Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks\n## Categories\n- Machine Learning\n- Systems and Control\n- Systems and Control\n## Abstract\nNetwork data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this work, we leverage graph signal processing to characterize the representation space of graph neural networks (GNNs). We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. These two properties offer insight about the workings of GNNs and help explain their scalability and transferability properties which, coupled with their local and distributed nature, make GNNs powerful tools for learning in physical networks. We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties. Finally, we study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MSP.2020.3016143\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SY\", \"eess.SY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [\"121125\", \"28033\", \"2091\", \"7929\", \"16361\", \"18664\", \"19884\", \"39641\", \"45929\", \"62968\", \"77915\", \"146216\", \"4299\", \"19454\", \"40878\", \"40880\", \"44062\", \"61218\", \"65820\", \"86970\", \"87797\", \"92229\", \"93509\", \"119464\", \"135388\", \"143805\", \"83542\"], \"outgoing_citations\": [\"146216\", \"186804\", \"193795\", \"233653\", \"237151\", \"265828\", \"298328\", \"303863\"]}","task_split":"paper_retrieval"}
{"document_id":"137985","document_content":"# Knowledge Graphs and Knowledge Networks: The Story in Brief\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nKnowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03623v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"34078\"], \"outgoing_citations\": [\"266319\", \"316630\"]}","task_split":"paper_retrieval"}
{"document_id":"138053","document_content":"# Scalable Approximate Inference and Some Applications\n## Categories\n- Machine Learning\n## Abstract\nApproximate inference in probability models is a fundamental task in machine learning. Approximate inference provides powerful tools to Bayesian reasoning, decision making, and Bayesian deep learning. The main goal is to estimate the expectation of interested functions w.r.t. a target distribution. When it comes to high dimensional probability models and large datasets, efficient approximate inference becomes critically important. In this thesis, we propose a new framework for approximate inference, which combines the advantages of these three frameworks and overcomes their limitations. Our proposed four algorithms are motivated by the recent computational progress of Stein's method. Our proposed algorithms are applied to continuous and discrete distributions under the setting when the gradient information of the target distribution is available or unavailable. Theoretical analysis is provided to prove the convergence of our proposed algorithms. Our adaptive IS algorithm iteratively improves the importance proposal by functionally decreasing the KL divergence between the updated proposal and the target. When the gradient of the target is unavailable, our proposed sampling algorithm leverages the gradient of a surrogate model and corrects induced bias with importance weights, which significantly outperforms other gradient-free sampling algorithms. In addition, our theoretical results enable us to perform the goodness-of-fit test on discrete distributions. At the end of the thesis, we propose an importance-weighted method to efficiently aggregate local models in distributed learning with one-shot communication. Results on simulated and real datasets indicate the statistical efficiency and wide applicability of our algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03515v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"139156\", \"159334\", \"195265\", \"205827\", \"212634\", \"215245\", \"227414\", \"228907\", \"230193\", \"250393\", \"251941\", \"269420\", \"272295\", \"272783\", \"276018\", \"279905\", \"280545\", \"281128\", \"286531\", \"289053\", \"303992\", \"311195\", \"311475\", \"335884\", \"335904\", \"339537\", \"342352\", \"354401\", \"357095\", \"358979\"]}","task_split":"paper_retrieval"}
{"document_id":"138097","document_content":"# Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Materials Science\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n## Abstract\nAdditive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.08749v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cond-mat.mtrl-sci\", \"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Materials Science\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"138118","document_content":"# Distributional semantic modeling: a revised technique to train term\/word vector space models applying the ontology-related approach\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) - term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology - SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). This transition will allow to generate more accurate semantic maps of different subject domains (also, of relations between input terms - it is useful to explore clusters and oppositions, or to test your hypotheses about them). The semantic map can be represented as a graph using Vec2graph - a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs. The Vec2graph library coupled with term embeddings will not only improve accuracy in solving standard NLP tasks, but also update the conventional concept of automated ontology development. The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.15407\/pp2020.02-03.341\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"235922\", \"237899\", \"238828\", \"241639\", \"241847\", \"242708\", \"278226\", \"313605\", \"321994\", \"324919\"]}","task_split":"paper_retrieval"}
{"document_id":"138124","document_content":"# Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning\n## Categories\n- Computational Engineering, Finance, and Science\n- Machine Learning\n## Abstract\nDuring recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This paper proposes two smart train operation algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway system and optimize multiple critical objectives without using an offline speed profile. Firstly, through learning historical data of experienced subway drivers, we extract the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality, and the safety of the subway system. Then we develop two algorithms for optimizing the energy efficiency of train operation. One is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and the other is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data from the Yizhuang Line of the Beijing Subway and illustrate that the developed smart train operation algorithm are better than expert manual driving and existing ATO algorithms in terms of energy efficiency. Moreover, STOD and STON can adapt to different trip times and different resistance conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03327v3\", \"primary_category\": \"cs.CE\", \"categories\": [\"cs.CE\", \"cs.LG\"], \"primary_category_human_readable\": \"Computational Engineering, Finance, and Science\", \"categories_human_readable\": [\"Computational Engineering, Finance, and Science\", \"Machine Learning\"], \"incoming_citations\": [\"94703\"], \"outgoing_citations\": [\"297268\", \"311851\"]}","task_split":"paper_retrieval"}
{"document_id":"138139","document_content":"# Probability Weighted Compact Feature for Domain Adaptive Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDomain adaptive image retrieval includes single-domain retrieval and cross-domain retrieval. Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar. However, in practical application, the discrepancies between retrieval databases often taken in ideal illumination\/pose\/background\/camera conditions and queries usually obtained in uncontrolled conditions are very large. In this paper, considering the practical application, we focus on challenging cross-domain retrieval. To address the problem, we propose an effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed. First, we derive our loss function through the Maximum A Posteriori Estimation (MAP): Bayesian Perspective (BP) induced focal-triplet loss, BP induced quantization loss and BP induced classification loss. Second, we propose a common manifold structure between domains to explore the potential correlation across domains. Considering the original feature representation is biased due to the inter-domain discrepancy, the manifold structure is difficult to be constructed. Therefore, we propose a new feature named Histogram Feature of Neighbors (HFON) from the sample statistics perspective. Extensive experiments on various benchmark databases validate that our method outperforms many state-of-the-art image retrieval methods for domain adaptive image retrieval. The source code is available at https:\/\/github.com\/fuxianghuang1\/PWCF","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03293v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"42097\", \"195528\", \"2838\", \"36011\", \"119839\"], \"outgoing_citations\": [\"196949\", \"263051\", \"312090\", \"316473\", \"331485\", \"358798\"]}","task_split":"paper_retrieval"}
{"document_id":"138178","document_content":"# SimLoss: Class Similarities in Cross Entropy\n## Categories\n- Machine Learning\n- I.2.6\n## Abstract\nOne common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as \"violet\" is better than as \"truck\". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https:\/\/github.com\/konstantinkobs\/SimLoss.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03182v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"I.2.6\"], \"incoming_citations\": [], \"outgoing_citations\": [\"257126\", \"272912\", \"328627\", \"336312\"]}","task_split":"paper_retrieval"}
{"document_id":"138232","document_content":"# Parsing Thai Social Data: A New Challenge for Thai NLP\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the appropriate linguistic unit as an input. The result showed that, the transition based model called, improve Elkared dependency parser outperform the others at UAS of 81.42%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/iSAI-NLP48611.2019.9045639\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"85942\"], \"outgoing_citations\": [\"257433\", \"278531\", \"281121\", \"284144\", \"296583\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"138243","document_content":"# A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n## Abstract\nA crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients' conditions and treatments from their written notes. In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note. Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of a large tertiary care hospital were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. Each Discharge Summary and Progress Note has been annotated by at least two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced\/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03044v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"4550\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"138256","document_content":"# Towards Practical Lottery Ticket Hypothesis for Adversarial Training\n## Categories\n- Machine Learning\n## Abstract\nRecent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps. While this discovery is insightful, finding proper sub-networks requires iterative training and pruning. The high cost incurred limits the applications of the lottery ticket hypothesis. We show there exists a subset of the aforementioned sub-networks that converge significantly faster during the training process and thus can mitigate the cost issue. We conduct extensive experiments to show such sub-networks consistently exist across various model structures for a restrictive setting of hyperparameters ($e.g.$, carefully selected learning rate, pruning ratio, and model capacity). As a practical application of our findings, we demonstrate that such sub-networks can help in cutting down the total time of adversarial training, a standard approach to improve robustness, by up to 49\\% on CIFAR-10 to achieve the state-of-the-art robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.05733v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"95929\", \"101185\", \"13451\", \"105906\"], \"outgoing_citations\": [\"180778\", \"188495\", \"193154\", \"196520\", \"213332\", \"214530\", \"215408\", \"216042\", \"240077\", \"280873\", \"286522\", \"291989\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"138313","document_content":"# Optimizing JPEG Quantization for Classification Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Performance\n- Image and Video Processing\n## Abstract\nDeep learning for computer vision depends on lossy image compression: it reduces the storage required for training and test data and lowers transfer costs in deployment. Mainstream datasets and imaging pipelines all rely on standard JPEG compression. In JPEG, the degree of quantization of frequency coefficients controls the lossiness: an 8 by 8 quantization table (Q-table) decides both the quality of the encoded image and the compression ratio. While a long history of work has sought better Q-tables, existing work either seeks to minimize image distortion or to optimize for models of the human visual system. This work asks whether JPEG Q-tables exist that are \"better\" for specific vision networks and can offer better quality--size trade-offs than ones designed for human perception or minimal distortion. We reconstruct an ImageNet test set with higher resolution to explore the effect of JPEG compression under novel Q-tables. We attempt several approaches to tune a Q-table for a vision task. We find that a simple sorted random sampling method can exceed the performance of the standard JPEG Q-table. We also use hyper-parameter tuning techniques including bounded random search, Bayesian optimization, and composite heuristic optimization methods. The new Q-tables we obtained can improve the compression rate by 10% to 200% when the accuracy is fixed, or improve accuracy up to $2\\%$ at the same compression rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02874v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.PF\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Performance\", \"Image and Video Processing\"], \"incoming_citations\": [\"21815\", \"39622\", \"108257\", \"124045\"], \"outgoing_citations\": [\"199022\", \"211768\", \"239049\", \"294611\"]}","task_split":"paper_retrieval"}
{"document_id":"138395","document_content":"# Adaptive Prediction Timing for Electronic Health Records\n## Categories\n- Machine Learning\n## Abstract\nIn realistic scenarios, multivariate timeseries evolve over case-by-case time-scales. This is particularly clear in medicine, where the rate of clinical events varies by ward, patient, and application. Increasingly complex models have been shown to effectively predict patient outcomes, but have failed to adapt granularity to these inherent temporal resolutions. As such, we introduce a novel, more realistic, approach to generating patient outcome predictions at an adaptive rate based on uncertainty accumulation in Bayesian recurrent models. We use a Recurrent Neural Network (RNN) and a Bayesian embedding layer with a new aggregation method to demonstrate adaptive prediction timing. Our model predicts more frequently when events are dense or the model is certain of event latent representations, and less frequently when readings are sparse or the model is uncertain. At 48 hours after patient admission, our model achieves equal performance compared to its static-windowed counterparts, while generating patient- and event-specific prediction timings that lead to improved predictive performance over the crucial first 12 hours of the patient stay.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02554v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"166936\", \"167010\", \"181651\", \"194434\", \"244340\", \"264024\", \"286253\", \"291026\", \"295651\", \"297260\", \"312534\"]}","task_split":"paper_retrieval"}
{"document_id":"138404","document_content":"# Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Sound\n- Machine Learning\n## Abstract\nRecent improvements in Generative Adversarial Neural Networks (GANs) have shown their ability to generate higher quality samples as well as to learn good representations for transfer learning. Most of the representation learning methods based on GANs learn representations ignoring their post-use scenario, which can lead to increased generalisation ability. However, the model can become redundant if it is intended for a specific task. For example, assume we have a vast unlabelled audio dataset, and we want to learn a representation from this dataset so that it can be used to improve the emotion recognition performance of a small labelled audio dataset. During the representation learning training, if the model does not know the post emotion recognition task, it can completely ignore emotion-related characteristics in the learnt representation. This is a fundamental challenge for any unsupervised representation learning model. In this paper, we aim to address this challenge by proposing a novel GAN framework: Guided Generative Neural Network (GGAN), which guides a GAN to focus on learning desired representations and generating superior quality samples for audio data leveraging fewer labelled samples. Experimental results show that using a very small amount of labelled data as guidance, a GGAN learns significantly better representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02836v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.LG\", \"cs.SD\", \"stat.ML\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"102277\", \"121768\"], \"outgoing_citations\": [\"176221\", \"177483\", \"191699\", \"195268\", \"196290\", \"197901\", \"199409\", \"201626\", \"206218\", \"208041\", \"236235\", \"242468\", \"244981\", \"245825\", \"258227\", \"264327\", \"285677\", \"291302\", \"291440\", \"302935\", \"303139\", \"338940\"]}","task_split":"paper_retrieval"}
{"document_id":"138456","document_content":"# Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nIn cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.   In this paper, we study reward design problem in cooperative MARL based on packet routing environments. Firstly, we show that the above two reward signals are prone to produce suboptimal policies. Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies. Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments. Other reward signals are also discussed in this paper. As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.03433v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"13681\", \"68930\", \"162601\"], \"outgoing_citations\": [\"153069\", \"153071\", \"197586\", \"210301\", \"254700\", \"257716\", \"257737\", \"260583\", \"263576\", \"263852\", \"264087\", \"265505\", \"272821\", \"280296\", \"291102\", \"291460\", \"302395\", \"303459\", \"324038\"]}","task_split":"paper_retrieval"}
{"document_id":"138517","document_content":"# Data Augmentation using Pre-trained Transformer Models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nLanguage model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02245v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"36981\", \"42229\", \"7637\", \"2567\", \"2552\", \"4499\", \"7112\", \"8926\", \"16498\", \"18991\", \"20586\", \"26658\", \"26981\", \"29718\", \"42896\", \"60419\", \"13866\", \"615\", \"2024\", \"3630\", \"8723\", \"9519\", \"12125\", \"13990\", \"16499\", \"17070\", \"17281\", \"22451\", \"23762\", \"25886\", \"26384\", \"26808\", \"27098\", \"28828\", \"34008\", \"39592\", \"46027\", \"47172\", \"55134\", \"55812\", \"62767\", \"128292\", \"70450\", \"72991\", \"73442\", \"91922\", \"92872\", \"86408\", \"89415\", \"89187\", \"92192\", \"95359\", \"96695\", \"96881\", \"98508\", \"99116\", \"99332\", \"99950\", \"101765\", \"109314\", \"114239\", \"129289\", \"136153\", \"139638\", \"153406\"], \"outgoing_citations\": [\"157467\", \"159333\", \"162565\", \"167338\", \"176008\", \"194198\", \"205715\", \"220017\", \"230543\", \"231898\", \"280486\"]}","task_split":"paper_retrieval"}
{"document_id":"138523","document_content":"# Contrastive estimation reveals topic posterior information to linear models\n## Categories\n- Machine Learning\n## Abstract\nContrastive learning is an approach to representation learning that utilizes naturally occurring similar and dissimilar pairs of data points to find useful embeddings of data. In the context of document classification under topic modeling assumptions, we prove that contrastive learning is capable of recovering a representation of documents that reveals their underlying topic posterior information to linear models. We apply this procedure in a semi-supervised setup and demonstrate empirically that linear classifiers with these representations perform well in document classification tasks with very few training examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02234v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"11584\", \"17467\", \"41160\", \"119540\", \"41483\", \"43651\", \"43847\", \"45977\", \"65399\", \"95883\", \"95899\", \"104452\", \"108090\", \"117585\"], \"outgoing_citations\": [\"164684\", \"197714\", \"239686\", \"313397\", \"359942\"]}","task_split":"paper_retrieval"}
{"document_id":"138541","document_content":"# Real-time Federated Evolutionary Neural Architecture Search\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication, since a large number of model parameters must be transmitted between the server and the clients. The other challenge is that training large machine learning models such as deep neural networks in federated learning requires a large amount of computational resources, which may be unrealistic for edge devices such as mobile phones. The problem becomes worse when deep neural architecture search is to be carried out in federated learning. To address the above challenges, we propose an evolutionary approach to real-time federated neural architecture search that not only optimize the model performance but also reduces the local payload. During the search, a double-sampling technique is introduced, in which for each individual, a randomly sampled sub-model of a master model is transmitted to a number of randomly sampled clients for training without reinitialization. This way, we effectively reduce computational and communication costs required for evolutionary optimization and avoid big performance fluctuations of the local models, making the proposed framework well suited for real-time federated neural architecture search.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.02793v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"104162\", \"628\", \"34937\", \"42569\", \"72911\", \"84784\", \"107076\", \"173324\", \"94677\", \"101002\", \"118108\"], \"outgoing_citations\": [\"162451\", \"177741\", \"192998\", \"194734\", \"201117\", \"205171\", \"205519\", \"205577\", \"222834\", \"226982\", \"242604\", \"242806\", \"243239\", \"247577\", \"251701\", \"258268\", \"269847\", \"272497\", \"274252\", \"281001\", \"281175\", \"282949\"]}","task_split":"paper_retrieval"}
{"document_id":"138752","document_content":"# Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nIn this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can efficiently learn to understand and generalize to different environments using high-dimensional inputs (a 2D map), while following feasible paths that avoid obstacles in obstacle-cluttered environment. To achieve this, we make use of traditional path planning algorithms, supervised learning, and reinforcement learning algorithms in a synergistic way. The key idea is to decouple the navigation problem into planning and control, the former of which is achieved by supervised learning whereas the latter is done by reinforcement learning. Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path. This allows the trained agent to achieve good generalization while learning faster. We test our proposed method in the recently proposed Safety Gym suite that allows testing of safety-constraints during training of learning agents. We compare our proposed method with existing work and show that our method consistently improves the sample efficiency and generalization capability to novel environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.01641v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"699\", \"61107\", \"75000\", \"105429\", \"90074\"], \"outgoing_citations\": [\"195243\", \"196226\", \"206814\", \"213622\", \"216405\", \"216429\", \"233165\", \"238323\", \"242478\", \"253600\", \"294142\", \"298651\"]}","task_split":"paper_retrieval"}
{"document_id":"138864","document_content":"# Convo: What does conversational programming need? An exploration of machine learning interface design\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Software Engineering\n- Artificial Intelligence\n## Abstract\nVast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study ($n$=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/VL\/HCC50065.2020.9127277\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.LG\", \"cs.SE\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Machine Learning\", \"Software Engineering\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"138947","document_content":"# Real-World Human-Robot Collaborative Reinforcement Learning\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe intuitive collaboration of humans and intelligent robots (embodied AI) in the real-world is an essential objective for many desirable applications of robotics. Whilst there is much research regarding explicit communication, we focus on how humans and robots interact implicitly, on motor adaptation level. We present a real-world setup of a human-robot collaborative maze game, designed to be non-trivial and only solvable through collaboration, by limiting the actions to rotations of two orthogonal axes, and assigning each axes to one player. This results in neither the human nor the agent being able to solve the game on their own. We use deep reinforcement learning for the control of the robotic agent, and achieve results within 30 minutes of real-world play, without any type of pre-training. We then use this setup to perform systematic experiments on human\/agent behaviour and adaptation when co-learning a policy for the collaborative game. We present results on how co-policy learning occurs over time between the human and the robotic agent resulting in each participant's agent serving as a representation of how they would play the game. This allows us to relate a person's success when playing with different agents than their own, by comparing the policy of the agent with that of their own agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.01156v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"106505\"], \"outgoing_citations\": [\"151271\", \"167405\", \"204750\", \"214440\", \"216993\", \"231867\", \"241354\", \"243197\", \"254581\", \"256103\", \"263957\", \"264845\", \"270525\"]}","task_split":"paper_retrieval"}
{"document_id":"139016","document_content":"# A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment\n## Categories\n- Robotics\n- I.2.9; I.2.6; J.2\n- Machine Learning\n## Abstract\nAn efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of such an approach has been confirmed by computational experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00946v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"I.2.9; I.2.6; J.2\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"I.2.9; I.2.6; J.2\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"141628\", \"162423\", \"189077\", \"192155\", \"216429\", \"229140\", \"235189\", \"313499\"]}","task_split":"paper_retrieval"}
{"document_id":"139179","document_content":"# Ego-based Entropy Measures for Structural Representations\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nIn complex networks, nodes that share similar structural characteristics often exhibit similar roles (e.g type of users in a social network or the hierarchical position of employees in a company). In order to leverage this relationship, a growing literature proposed latent representations that identify structurally equivalent nodes. However, most of the existing methods require high time and space complexity. In this paper, we propose VNEstruct, a simple approach for generating low-dimensional structural node embeddings, that is both time efficient and robust to perturbations of the graph structure. The proposed approach focuses on the local neighborhood of each node and employs the Von Neumann entropy, an information-theoretic tool, to extract features that capture the neighborhood's topology. Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes. Empirically, we observe that the proposed approach exhibits robustness on structural role identification tasks and state-of-the-art performance on graph classification tasks, while maintaining very high computational speed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00553v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"68287\"], \"outgoing_citations\": [\"150166\", \"186769\", \"210426\", \"230004\", \"252195\", \"269202\"]}","task_split":"paper_retrieval"}
{"document_id":"139227","document_content":"# Environment-agnostic Multitask Learning for Natural Language Grounded Navigation\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nRecent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. To close the gap between seen and unseen environments, we aim at learning a generalized navigation model from two novel perspectives: (1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks; (2) we propose to learn environment-agnostic representations for the navigation policy that are invariant among the environments seen during training, thus generalizing better on unseen environments. Extensive experiments show that environment-agnostic multitask learning significantly reduces the performance gap between seen and unseen environments, and the navigation agent trained so outperforms baselines on unseen environments by 16% (relative measure on success rate) on VLN and 120% (goal progress) on NDH. Our submission to the CVDN leaderboard establishes a new state-of-the-art for the NDH task on the holdout test set. Code is available at https:\/\/github.com\/google-research\/valan.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00443v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"33721\", \"39368\", \"47577\", \"59461\", \"93187\", \"9686\", \"13764\", \"17160\", \"35300\", \"36974\", \"48662\", \"65095\", \"84618\", \"93950\", \"99357\", \"109092\", \"127495\", \"155908\", \"169841\"], \"outgoing_citations\": [\"140318\", \"152372\", \"155831\", \"155908\", \"168528\", \"168651\", \"172416\", \"176603\", \"183378\", \"183823\", \"188629\", \"189381\", \"191570\", \"192738\", \"196218\", \"196525\", \"203398\", \"206489\", \"208042\", \"208773\", \"216221\", \"217880\", \"219142\", \"219658\", \"224223\", \"225266\", \"228916\", \"231948\", \"237119\", \"238287\", \"243235\", \"245731\", \"247418\", \"249906\", \"255575\", \"261316\", \"279518\", \"280681\", \"315839\", \"317907\"]}","task_split":"paper_retrieval"}
{"document_id":"139259","document_content":"# Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nCross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach for this problem is to learn a joint embedding space to measure cross-modal similarities. However, simple joint embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. To be specific, the model disentangles texts into hierarchical semantic graph including three levels of events, actions, entities and relationships across levels. Attention-based graph reasoning is utilized to generate hierarchical textual embeddings, which can guide the learning of diverse and hierarchical video representations. The HGR model aggregates matchings from different video-text levels to capture both global and local details. Experimental results on three video-text datasets demonstrate the advantages of our model. Such hierarchical decomposition also enables better generalization across datasets and improves the ability to distinguish fine-grained semantic differences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00392v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1081\", \"3652\", \"5478\", \"6657\", \"14600\", \"17770\", \"60281\", \"101354\", \"80211\", \"2838\", \"10348\", \"12466\", \"12778\", \"13933\", \"22104\", \"24771\", \"28085\", \"33374\", \"40597\", \"51804\", \"52925\", \"54595\", \"54820\", \"55460\", \"59458\", \"59465\", \"59840\", \"60135\", \"62318\", \"62806\", \"66734\", \"84992\", \"88202\", \"96230\", \"105055\", \"86006\", \"89825\", \"98929\", \"108197\"], \"outgoing_citations\": [\"168374\", \"172394\", \"173627\", \"182169\", \"186854\", \"191177\", \"191861\", \"193246\", \"202386\", \"206311\", \"214015\", \"221984\", \"222661\", \"223041\", \"229179\", \"238205\", \"248171\", \"250183\", \"256883\", \"260926\", \"294898\", \"302943\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"139315","document_content":"# Adaptive Federated Optimization\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n- Optimization and Control\n## Abstract\nFederated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Adam, and Yogi, and analyze their convergence in the presence of heterogeneous data for general non-convex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can significantly improve the performance of federated learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00295v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [\"1332\", \"2318\", \"2963\", \"5803\", \"9619\", \"24147\", \"24894\", \"54047\", \"174906\", \"1828\", \"2125\", \"2909\", \"3592\", \"7900\", \"8794\", \"8820\", \"8843\", \"10643\", \"11109\", \"12954\", \"13368\", \"15428\", \"16184\", \"17335\", \"17892\", \"18186\", \"18165\", \"18455\", \"20321\", \"23352\", \"24907\", \"28148\", \"28009\", \"28504\", \"28637\", \"29243\", \"29475\", \"29804\", \"31206\", \"33326\", \"38073\", \"39494\", \"39401\", \"39752\", \"41119\", \"42681\", \"44475\", \"49221\", \"52814\", \"55141\", \"69454\", \"70897\", \"71429\", \"71707\", \"78098\", \"82090\", \"91538\", \"111241\", \"117293\", \"131875\", \"148978\", \"628\", \"891\", \"1964\", \"2274\", \"104012\", \"2696\", \"4571\", \"6617\", \"6737\", \"8081\", \"8497\", \"10418\", \"10455\", \"10866\", \"12938\", \"15038\", \"15166\", \"16375\", \"17486\", \"17858\", \"18735\", \"18713\", \"18854\", \"19448\", \"19632\", \"20664\", \"23167\", \"22983\", \"24205\", \"24224\", \"25213\", \"26243\", \"26435\", \"27798\", \"28004\", \"29303\", \"29850\", \"30423\", \"25048\", \"31477\", \"34132\", \"34610\", \"35424\", \"35538\", \"40072\", \"40694\", \"42120\", \"42388\", \"42524\", \"42569\", \"43237\", \"43687\", \"44610\", \"45273\", \"47655\", \"48647\", \"48917\", \"50412\", \"56009\", \"57910\", \"64371\", \"65192\", \"66622\", \"66923\", \"69232\", \"69489\", \"69844\", \"71148\", \"71546\", \"73183\", \"75531\", \"77687\", \"77533\", \"80069\", \"82093\", \"86486\", \"90339\", \"92837\", \"94744\", \"95102\", \"108368\", \"109665\", \"114599\", \"119463\", \"125888\", \"128190\", \"151798\", \"187044\", \"80401\", \"85995\", \"24390\", \"69230\", \"97012\", \"99130\", \"100192\", \"100727\", \"100940\", \"104057\", \"104370\", \"106539\", \"107082\", \"107570\", \"109147\", \"109184\", \"109503\", \"111381\", \"112023\", \"108898\", \"114712\", \"119091\", \"118376\", \"123767\", \"133977\"], \"outgoing_citations\": [\"100727\", \"114712\", \"152392\", \"155216\", \"161758\", \"164048\", \"166970\", \"167316\", \"167532\", \"175336\", \"177601\", \"182257\", \"189715\", \"197515\", \"198396\", \"201531\", \"206368\", \"207462\", \"220594\", \"224282\", \"230678\", \"231227\", \"235592\", \"238070\", \"256871\", \"272543\", \"296248\", \"327636\", \"330696\"]}","task_split":"paper_retrieval"}
{"document_id":"139425","document_content":"# Policy-Aware Model Learning for Policy Gradient Methods\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propose that the model learning module should incorporate the way the planner is going to use the model. This is in contrast to conventional model learning approaches, such as those based on maximum likelihood estimate, that learn a predictive model of the environment without explicitly considering the interaction of the model and the planner. We focus on policy gradient type of planning algorithms and derive new loss functions for model learning that incorporate how the planner uses the model. We call this approach Policy-Aware Model Learning (PAML). We theoretically analyze a generic model-based policy gradient algorithm and provide a convergence guarantee for the optimized policy. We also empirically evaluate PAML on some benchmark problems, showing promising results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00030v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"44283\", \"68876\", \"39300\", \"65693\", \"116497\", \"119911\", \"88577\", \"91204\"], \"outgoing_citations\": [\"121680\", \"167774\", \"168339\", \"169603\", \"173489\", \"177658\", \"179057\", \"179171\", \"182468\", \"188189\", \"219180\", \"225108\", \"229579\", \"229890\", \"240495\", \"259045\", \"261493\", \"277193\", \"277696\", \"317745\", \"357891\"]}","task_split":"paper_retrieval"}
{"document_id":"139520","document_content":"# SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Multiagent Systems\n- Robotics\n## Abstract\nPredicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardless of the number of surrounding vehicles. The SCALE-Net employs the Edge-enhance Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction embedding network. Since the proposed EGCN is inherently scalable with respect to the graph node (an agent in this study), the model can be operated independently from the total number of vehicles considered. We evaluated the scalability of the SCALE-Net on the publically available NGSIM datasets by comparing variations on computation time and prediction accuracy per single driving scene with respect to the varying vehicle number. The experimental test shows that both computation time and prediction performance of the SCALE-Net consistently outperform those of previous models regardless of the level of traffic complexities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.12609v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MA\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multiagent Systems\", \"Robotics\"], \"incoming_citations\": [\"7145\", \"7156\", \"36776\", \"42382\", \"163665\", \"81665\", \"123083\"], \"outgoing_citations\": [\"179692\", \"191356\", \"196618\", \"197149\", \"214529\", \"218737\", \"221071\", \"232095\", \"232096\", \"241801\", \"265546\", \"268197\", \"268891\", \"276106\"]}","task_split":"paper_retrieval"}
{"document_id":"139585","document_content":"# Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nUsually, Neural Networks models are trained with a large dataset of images in homogeneous backgrounds. The issue is that the performance of the network models trained could be significantly degraded in a complex and heterogeneous environment. To mitigate the issue, this paper develops a framework that permits to autonomously generate a training dataset in heterogeneous cluttered backgrounds. It is clear that the learning effectiveness of the proposed framework should be improved in complex and heterogeneous environments, compared with the ones with the typical dataset. In our framework, a state-of-the-art image segmentation technique called DeepLab is used to extract objects of interest from a picture and Chroma-key technique is then used to merge the extracted objects of interest into specific heterogeneous backgrounds. The performance of the proposed framework is investigated through empirical tests and compared with that of the model trained with the COCO dataset. The results show that the proposed framework outperforms the model compared. This implies that the learning effectiveness of the framework developed is superior to the models with the typical dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/REDUAS47371.2019.8999695\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"109918\"], \"outgoing_citations\": [\"279732\", \"301506\"]}","task_split":"paper_retrieval"}
{"document_id":"139635","document_content":"# Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n## Abstract\nMobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited budget by providing incentives such as coupons, commissions to merchants. As a result, incentive optimization is the key to maximizing the commercial objective of the marketing campaign. With the analyses of online experiments, we found that the transaction network can subtly describe the similarity of merchants' responses to different incentives, which is of great use in the incentive optimization problem. In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing. With limited samples collected from online experiments, our end-to-end method first learns merchant representations based on an attributed transaction networks, then effectively models the correlations between the commercial objectives each merchant may achieve and the incentives under varying treatments. Thus we are able to model the sensitivity to incentive for each merchant, and spend the most budgets on those merchants that show strong sensitivities in the marketing campaign. Extensive offline and online experimental results at Alipay demonstrate the effectiveness of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.01515v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"581\", \"138432\"], \"outgoing_citations\": [\"243437\", \"292375\"]}","task_split":"paper_retrieval"}
{"document_id":"139807","document_content":"# Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Science and Game Theory\n- Robotics\n## Abstract\nThe integration of deep learning to reinforcement learning (RL) has enabled RL to perform efficiently in high-dimensional environments. Deep RL methods have been applied to solve many complex real-world problems in recent years. However, development of a deep RL-based system is challenging because of various issues such as the selection of a suitable deep RL algorithm, its network configuration, training time, training methods, and so on. This paper proposes a comprehensive software framework that not only plays a vital role in designing a connect-the-dots deep RL architecture but also provides a guideline to develop a realistic RL application in a short time span. We have designed and developed a deep RL-based software framework that strictly ensures flexibility, robustness, and scalability. By inheriting the proposed architecture, software managers can foresee any challenges when designing a deep RL-based system. As a result, they can expedite the design process and actively control every stage of software development, which is especially critical in agile development environments. To enforce generalization, the proposed architecture does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents. Using our framework, software developers can develop and integrate new RL algorithms or new types of agents, and can flexibly change network configuration or the number of agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.13140\/RG.2.2.16789.06883\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.GT\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Science and Game Theory\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"180897\", \"205913\", \"221473\", \"229173\", \"239666\", \"239703\", \"240233\", \"240736\", \"242457\", \"246518\", \"253974\", \"258384\", \"260284\", \"261260\", \"261983\", \"263852\", \"263957\", \"264786\", \"268361\", \"268821\", \"269382\", \"271783\", \"272422\", \"272785\", \"272930\", \"274315\", \"275087\", \"279191\", \"280296\", \"281159\", \"281257\", \"282884\", \"283001\", \"284459\", \"289913\", \"290378\", \"291892\", \"292127\", \"293995\", \"294142\", \"302001\", \"302395\", \"302952\", \"302967\", \"309111\", \"334182\", \"336181\", \"356842\", \"357986\"]}","task_split":"paper_retrieval"}
{"document_id":"139814","document_content":"# Controllable Level Blending between Games using Variational Autoencoders\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nPrevious work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolutionary search in the latent space, we evolve level segments satisfying specific constraints. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design and compare its performance with similar generative models like the GAN and the VAE-GAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.11869v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33214\", \"41462\", \"67124\", \"95477\", \"94739\", \"96756\", \"104584\", \"104574\", \"109729\", \"111464\", \"117983\", \"122922\", \"143545\"], \"outgoing_citations\": [\"173070\", \"181122\", \"189201\", \"216608\", \"218887\", \"233501\", \"265896\", \"274886\", \"289709\", \"296883\", \"297233\", \"300739\"]}","task_split":"paper_retrieval"}
{"document_id":"139825","document_content":"# Analysis of diversity-accuracy tradeoff in image captioning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWe investigate the effect of different model architectures, training objectives, hyperparameter settings and decoding procedures on the diversity of automatically generated image captions. Our results show that 1) simple decoding by naive sampling, coupled with low temperature is a competitive and fast method to produce diverse and accurate caption sets; 2) training with CIDEr-based reward using Reinforcement learning harms the diversity properties of the resulting generator, which cannot be mitigated by manipulating decoding parameters. In addition, we propose a new metric AllSPICE for evaluating both accuracy and diversity of a set of captions by a single value.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.11848v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"28498\", \"35257\", \"110219\"], \"outgoing_citations\": [\"191740\", \"193328\", \"198185\", \"206058\", \"229734\", \"231949\", \"232274\", \"249972\", \"269122\", \"270154\", \"271234\", \"276299\", \"278954\", \"283076\", \"287504\", \"297855\", \"300652\"]}","task_split":"paper_retrieval"}
{"document_id":"140337","document_content":"# Engaging Users through Social Media in Public Libraries\n## Categories\n- Other Computer Science\n- Social and Information Networks\n- Machine Learning\n- Human-Computer Interaction\n## Abstract\nThe participatory library is an emerging concept which refers to the idea that an integrated library system must allow users to take part in core functions of the library rather than engaging on the periphery. To embrace the participatory idea, libraries have employed many technologies, such as social media to help them build participatory services and engage users. To help librarians understand the impact of emerging technologies on a participatory service building, this paper takes social media as an example to explore how to use different engagement strategies that social media provides to engage more users. This paper provides three major contributions to the library system. The libraries can use the resultant engagement strategies to engage its users. Additionally, the best-fit strategy can be inferred and designed based on the preferences of users. Lastly, the preferences of users can be understood based on data analysis of social media. Three such contributions put together to fully address the proposed research question of how to use different engagement strategies on social media to build participatory library services and better engage more users visiting the library?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.04204v2\", \"primary_category\": \"cs.OH\", \"categories\": [\"cs.SI\", \"cs.OH\", \"cs.LG\", \"cs.HC\"], \"primary_category_human_readable\": \"Other Computer Science\", \"categories_human_readable\": [\"Social and Information Networks\", \"Other Computer Science\", \"Machine Learning\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"140362","document_content":"# Parsing Early Modern English for Linguistic Search\n## Categories\n- Computation and Language\n## Abstract\nWe investigate the question of whether advances in NLP over the last few years make it possible to vastly increase the size of data usable for research in historical syntax. This brings together many of the usual tools in NLP - word embeddings, tagging, and parsing - in the service of linguistic queries over automatically annotated corpora. We train a part-of-speech (POS) tagger and parser on a corpus of historical English, using ELMo embeddings trained over a billion words of similar text. The evaluation is based on the standard metrics, as well as on the accuracy of the query searches using the parsed data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.10546v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2693\"], \"outgoing_citations\": [\"192402\", \"193725\", \"204342\", \"231803\", \"233407\", \"235174\", \"266758\", \"267692\", \"296816\", \"297150\", \"310738\"]}","task_split":"paper_retrieval"}
{"document_id":"140395","document_content":"# From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s13218-020-00647-w\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"34882\"], \"outgoing_citations\": [\"149106\", \"151260\", \"151271\", \"152678\", \"166489\", \"177552\", \"188651\", \"194877\", \"198383\", \"200264\", \"200580\", \"200743\", \"200920\", \"210517\", \"222669\", \"226544\", \"229002\", \"233501\", \"234345\", \"235092\", \"247158\", \"255613\", \"257674\", \"258614\", \"274886\", \"293071\", \"322672\"]}","task_split":"paper_retrieval"}
{"document_id":"140437","document_content":"# Self-Adaptive Training: beyond Empirical Risk Minimization\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data. This problem is crucial towards robustly learning from data that are corrupted by, e.g., label noises and out-of-distribution samples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noises and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly improves generalization over ERM under various levels of noises, and mitigates the overfitting issue in both natural and adversarial training. We evaluate the error-capacity curve of self-adaptive training: the test error is monotonously decreasing w.r.t. model capacity. This is in sharp contrast to the recently-discovered double-descent phenomenon in ERM which might be a result of overfitting of noises. Experiments on CIFAR and ImageNet datasets verify the effectiveness of our approach in two applications: classification with label noise and selective classification. We release our code at https:\/\/github.com\/LayneH\/self-adaptive-training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.10319v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"48103\", \"6534\", \"23247\", \"42655\", \"45181\", \"74217\", \"79480\", \"111710\", \"3145\", \"4960\", \"10551\", \"10454\", \"10758\", \"11949\", \"16096\", \"16706\", \"16831\", \"17731\", \"28910\", \"43483\", \"46548\", \"54451\", \"54909\", \"65738\", \"71134\", \"72252\", \"93106\", \"97370\", \"84235\", \"95912\", \"118852\"], \"outgoing_citations\": [\"152688\", \"163402\", \"163626\", \"171454\", \"178410\", \"184385\", \"193433\", \"199139\", \"201516\", \"204540\", \"216632\", \"232302\", \"232944\", \"237224\", \"237905\", \"247500\", \"253651\", \"263559\", \"265022\", \"265625\", \"270545\", \"275596\", \"320027\"]}","task_split":"paper_retrieval"}
{"document_id":"140517","document_content":"# A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning\n## Categories\n- Computation and Language\n- Machine Learning\n- I.2.7\n## Abstract\nFully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD Treebank and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2022.3202947\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"220075\", \"225305\", \"296583\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"140563","document_content":"# How Transferable are the Representations Learned by Deep Q Agents?\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we consider the source of Deep Reinforcement Learning (DRL)'s sample complexity, asking how much derives from the requirement of learning useful representations of environment states and how much is due to the sample complexity of learning a policy. While for DRL agents, the distinction between representation and policy may not be clear, we seek new insight through a set of transfer learning experiments. In each experiment, we retain some fraction of layers trained on either the same game or a related game, comparing the benefits of transfer learning to learning a policy from scratch. Interestingly, we find that benefits due to transfer are highly variable in general and non-symmetric across pairs of tasks. Our experiments suggest that perhaps transfer from simpler environments can boost performance on more complex downstream tasks and that the requirements of learning a useful representation can range from negligible to the majority of the sample complexity, based on the environment. Furthermore, we find that fine-tuning generally outperforms training with the transferred layers frozen, confirming an insight first noted in the classification setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.10021v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7912\", \"18635\", \"120731\"], \"outgoing_citations\": [\"253974\", \"260583\", \"262381\"]}","task_split":"paper_retrieval"}
{"document_id":"140601","document_content":"# Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMulti-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question. Humans answer this kind of complex questions via a divide-and-conquer approach. In this paper, we investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. We adopt a neural decomposition model to generate sub-questions for a multi-hop complex question, followed by extracting the corresponding sub-answers. We show that multiple state-of-the-art multi-hop QA models fail to correctly answer a large portion of sub-questions, although their corresponding multi-hop questions are correctly answered. This indicates that these models manage to answer the multi-hop questions using some partial clues, instead of truly understanding the reasoning paths. We also propose a new model which significantly improves the performance on answering the sub-questions. Our work takes a step forward towards building a more explainable multi-hop QA system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09919v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8948\"], \"outgoing_citations\": [\"157277\", \"157836\", \"158501\", \"163072\", \"171817\", \"180353\", \"180575\", \"182026\", \"182036\", \"185429\", \"185952\", \"186432\", \"188736\", \"216563\", \"253068\", \"260450\", \"266850\", \"279213\", \"311423\"]}","task_split":"paper_retrieval"}
{"document_id":"140616","document_content":"# Unique Chinese Linguistic Phenomena\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nLinguistics holds unique characteristics of generality, stability, and nationality, which will affect the formulation of extraction strategies and should be incorporated into the relation extraction. Chinese open relation extraction is not well-established, because of the complexity of Chinese linguistics makes it harder to operate, and the methods for English are not compatible with that for Chinese. The diversities between Chinese and English linguistics are mainly reflected in morphology and syntax.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2004.00499v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"140628","document_content":"# DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labelled training data. Collecting a training set with large variations of a face identity under different poses and illumination changes, however, is very expensive, making the diversity of within-class face images a critical issue in practice. In this paper, we propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) that can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. DotFAN is structurally a conditional CycleGAN but has two additional subnetworks, namely face expert network (FEM) and face shape regressor (FSR), for latent code control. While FSR aims to extract face attributes, FEM is designed to capture a face identity. With their aid, DotFAN can learn a disentangled face representation and effectively generate face images of various facial attributes while preserving the identity of augmented faces. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity so that a better face recognition model can be learned from the augmented dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09859v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"234861\", \"240261\", \"265203\", \"268405\", \"268970\", \"275999\", \"277837\", \"287611\", \"301506\", \"302701\", \"308384\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"140633","document_content":"# Data Augmentation for Personal Knowledge Base Population\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nCold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.10943v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"81468\"], \"outgoing_citations\": [\"127899\", \"129115\", \"146069\", \"208997\", \"213589\", \"215523\", \"216292\", \"216419\", \"291175\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"140642","document_content":"# Fill in the BLANC: Human-free quality estimation of document summaries\n## Categories\n- Computation and Language\n## Abstract\nWe present BLANC, a new approach to the automatic estimation of document summary quality. Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method. Our approach achieves this by measuring the performance boost gained by a pre-trained language model with access to a document summary while carrying out its language understanding task on the document's text. We present evidence that BLANC scores have as good correlation with human evaluations as do the ROUGE family of summary quality measurements. And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully human-free summary quality estimation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09836v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2684\", \"22977\", \"61976\", \"125311\", \"1461\", \"9714\", \"21489\", \"24023\", \"132431\", \"49896\", \"56864\", \"56583\", \"78031\", \"91642\", \"109884\", \"94486\"], \"outgoing_citations\": [\"126362\", \"145589\", \"159316\", \"166082\", \"168405\", \"168744\", \"169142\", \"169914\", \"170449\", \"182629\", \"183065\", \"187236\", \"190030\", \"198397\", \"214294\", \"239950\", \"246775\", \"254401\", \"268339\", \"295087\", \"295412\", \"307651\", \"312403\"]}","task_split":"paper_retrieval"}
{"document_id":"140756","document_content":"# Markov Chain Monte-Carlo Phylogenetic Inference Construction in Computational Historical Linguistics\n## Categories\n- Computation and Language\n## Abstract\nMore and more languages in the world are under study nowadays, as a result, the traditional way of historical linguistics study is facing some challenges. For example, the linguistic comparative research among languages needs manual annotation, which becomes more and more impossible with the increasing amount of language data coming out all around the world. Although it could hardly replace linguists work, the automatic computational methods have been taken into consideration and it can help people reduce their workload. One of the most important work in historical linguistics is word comparison from different languages and find the cognate words for them, which means people try to figure out if the two languages are related to each other or not. In this paper, I am going to use computational method to cluster the languages and use Markov Chain Monte Carlo (MCMC) method to build the language typology relationship tree based on the clusters.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09637v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"235545\"]}","task_split":"paper_retrieval"}
{"document_id":"140757","document_content":"# Conceptual Game Expansion\n## Categories\n- Artificial Intelligence\n## Abstract\nAutomated game design is the problem of automatically producing games through computational processes. Traditionally, these methods have relied on the authoring of search spaces by a designer, defining the space of all possible games for the system to author. In this paper, we instead learn representations of existing games from gameplay video and use these to approximate a search space of novel games. In a human subject study we demonstrate that these novel games are indistinguishable from human games in terms of challenge, and that one of the novel games was equivalent to one of the human games in terms of fun, frustration, and likeability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TG.2021.3060005\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"18412\", \"33228\", \"41462\", \"109729\"], \"outgoing_citations\": [\"109729\", \"111464\", \"173068\", \"173070\", \"176932\", \"194378\", \"218887\", \"233501\", \"241619\", \"261387\", \"261528\", \"261529\", \"274886\", \"297233\", \"297797\"]}","task_split":"paper_retrieval"}
{"document_id":"140889","document_content":"# An Advance on Variable Elimination with Applications to Tensor-Based Computation\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe present new results on the classical algorithm of variable elimination, which underlies many algorithms including for probabilistic inference. The results relate to exploiting functional dependencies, allowing one to perform inference and learning efficiently on models that have very large treewidth. The highlight of the advance is that it works with standard (dense) factors, without the need for sparse factors or techniques based on knowledge compilation that are commonly utilized. This is significant as it permits a direct implementation of the improved variable elimination algorithm using tensors and their operations, leading to extremely efficient implementations especially when learning model parameters. Moreover, the proposed technique does not require knowledge of the specific functional dependencies, only that they exist, so can be used when learning these dependencies. We illustrate the efficacy of our proposed algorithm by compiling Bayesian network queries into tensor graphs and then learning their parameters from labeled data using a standard tool for tensor computation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09320v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"278340\"], \"outgoing_citations\": [\"172070\", \"205158\", \"257925\", \"346973\", \"349531\", \"350216\"]}","task_split":"paper_retrieval"}
{"document_id":"140976","document_content":"# Anonymizing Data for Privacy-Preserving Federated Learning\n## Categories\n- Cryptography and Security\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nFederated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09096v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3592\", \"72995\", \"92902\", \"100058\", \"112848\", \"163085\"], \"outgoing_citations\": [\"163085\", \"187044\", \"205882\", \"206770\", \"207518\", \"207623\", \"226146\", \"230382\", \"235592\", \"246939\"]}","task_split":"paper_retrieval"}
{"document_id":"141010","document_content":"# oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions\n## Categories\n- Machine Learning\n## Abstract\nExplicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \\textit{entangled} with the dynamics of the environment and therefore not portable or \\emph{robust} to changing environments. Modern adversarial methods have yielded some success in reducing reward entanglement in the IRL setting. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. We show that this method has the ability to learn \\emph{generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are comparable to those of the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.09043v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"163\"], \"outgoing_citations\": [\"216027\", \"248841\", \"251924\", \"255427\", \"261017\", \"261653\", \"265069\", \"280655\", \"284567\", \"289913\", \"293995\", \"294059\", \"297317\"]}","task_split":"paper_retrieval"}
{"document_id":"141137","document_content":"# Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAccurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2019.2955297\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"44185\", \"74059\", \"105227\", \"141111\"], \"outgoing_citations\": [\"168390\", \"169318\", \"174475\", \"177781\", \"196151\", \"202884\", \"220608\", \"253564\", \"254665\", \"254674\", \"272027\", \"272756\", \"294919\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"141163","document_content":"# A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future\n## Categories\n- Artificial Intelligence\n## Abstract\nNo comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.08627v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"114928\"], \"outgoing_citations\": [\"138377\", \"144568\", \"144588\", \"346695\", \"349825\"]}","task_split":"paper_retrieval"}
{"document_id":"141196","document_content":"# Captioning Images Taken by People Who Are Blind\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile an important problem in the vision community is to design algorithms that can automatically caption images, few publicly-available datasets for algorithm development directly address the interests of real users. Observing that people who are blind have relied on (human-based) image captioning services to learn about images they take for nearly a decade, we introduce the first image captioning dataset to represent this real use case. This new dataset, which we call VizWiz-Captions, consists of over 39,000 images originating from people who are blind that are each paired with five captions. We analyze this dataset to (1) characterize the typical captions, (2) characterize the diversity of content found in the images, and (3) compare its content to that found in eight popular vision datasets. We also analyze modern image captioning algorithms to identify what makes this new dataset challenging for the vision community. We publicly-share the dataset with captioning challenge instructions at https:\/\/vizwiz.org","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.08565v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"55418\", \"61694\", \"79138\", \"7440\", \"18933\", \"20540\", \"31502\", \"43253\", \"49033\", \"50125\", \"68626\", \"75733\", \"100023\", \"77921\", \"96085\", \"118405\", \"125904\", \"132879\", \"135255\"], \"outgoing_citations\": [\"134604\", \"171096\", \"205227\", \"207034\", \"213042\", \"215181\", \"241203\", \"260141\", \"267500\", \"287504\", \"303546\", \"315108\", \"317410\"]}","task_split":"paper_retrieval"}
{"document_id":"141253","document_content":"# SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nCertain facial parts are salient (unique) in appearance, which substantially contribute to the holistic recognition of a subject. Occlusion of these salient parts deteriorates the performance of face recognition algorithms. In this paper, we propose a generative model to reconstruct the missing parts of the face which are under occlusion. The proposed generative model (SD-GAN) reconstructs a face preserving the illumination variation and identity of the face. A novel adversarial training algorithm has been designed for a bimodal mutually exclusive Generative Adversarial Network (GAN) model, for faster convergence. A novel adversarial \"structural\" loss function is also proposed, comprising of two components: a holistic and a local loss, characterized by SSIM and patch-wise MSE. Ablation studies on real and synthetically occluded face datasets reveal that our proposed technique outperforms the competing methods by a considerable margin, even for boosting the performance of Face Recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.08448v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268494\", \"268970\"]}","task_split":"paper_retrieval"}
{"document_id":"141265","document_content":"# PrivacyFL: A simulator for privacy-preserving and secure federated learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nFederated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized. This reduces data privacy risks, however, privacy concerns still exist since it is possible to leak information about the training dataset from the trained model's weights or parameters. Setting up a federated learning environment, especially with security and privacy guarantees, is a time-consuming process with numerous configurations and parameters that can be manipulated. In order to help clients ensure that collaboration is feasible and to check that it improves their model accuracy, a real-world simulator for privacy-preserving and secure federated learning is required. In this paper, we introduce PrivacyFL, which is an extensible, easily configurable and scalable simulator for federated learning environments. Its key features include latency simulation, robustness to client departure, support for both centralized and decentralized learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation. In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages. Our paper addresses a significant real-world problem: checking the feasibility of participating in a federated learning environment under a variety of circumstances. It also has a strong practical impact because organizations such as hospitals, banks, and research institutes, which have large amounts of sensitive data and would like to collaborate, would greatly benefit from having a system that enables them to do so in a privacy-preserving and secure manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3340531.3412771\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"16829\", \"8794\", \"19374\", \"23732\", \"34833\", \"83656\", \"93553\", \"103753\"], \"outgoing_citations\": [\"187044\", \"207491\", \"207518\", \"232524\", \"246939\", \"252956\"]}","task_split":"paper_retrieval"}
{"document_id":"141295","document_content":"# Fawkes: Protecting Privacy against Unauthorized Deep Learning Models\n## Categories\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nToday's proliferation of powerful facial recognition systems poses a real threat to personal privacy. As Clearview.ai demonstrated, anyone can canvas the Internet for data and train highly accurate facial recognition models of individuals without their knowledge. We need tools to protect ourselves from potential misuses of unauthorized facial recognition systems. Unfortunately, no practical or effective solutions exist.   In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models. Fawkes achieves this by helping users add imperceptible pixel-level changes (we call them \"cloaks\") to their own photos before releasing them. When used to train facial recognition models, these \"cloaked\" images produce functional models that consistently cause normal images of the user to be misidentified. We experimentally demonstrate that Fawkes provides 95+% protection against user recognition regardless of how trackers train their models. Even when clean, uncloaked images are \"leaked\" to the tracker and used for training, Fawkes can still maintain an 80+% protection success rate. We achieve 100% success in experiments against today's state-of-the-art facial recognition services. Finally, we show that Fawkes is robust against a variety of countermeasures that try to detect or disrupt image cloaks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.08327v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CV\", \"stat.ML\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"8977\", \"35996\", \"38615\", \"84141\", \"103684\", \"183353\", \"3902\", \"12245\", \"16123\", \"16631\", \"38954\", \"49673\", \"55225\", \"65816\", \"92858\", \"2282\", \"4332\", \"6394\", \"7197\", \"8522\", \"18609\", \"21639\", \"26238\", \"27137\", \"31225\", \"34724\", \"34522\", \"36167\", \"42690\", \"54856\", \"59128\", \"63529\", \"65308\", \"67536\", \"70566\", \"74554\", \"79276\", \"79744\", \"80286\", \"84394\", \"93106\", \"116035\", \"135947\", \"136726\", \"91072\"], \"outgoing_citations\": [\"158682\", \"164304\", \"170530\", \"179581\", \"186283\", \"189775\", \"189943\", \"189990\", \"210832\", \"218665\", \"227030\", \"229886\", \"235754\", \"236933\", \"238514\", \"242884\", \"249425\", \"251795\", \"255133\", \"264157\", \"265949\", \"267612\", \"272415\", \"272670\", \"280873\", \"291989\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"141507","document_content":"# Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths\n## Categories\n- Computation and Language\n## Abstract\nAbstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand. At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in the English language show that MLS outperforms strong baselines by up to 14.70% in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.07845v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20171\", \"22770\", \"29730\", \"35672\"], \"outgoing_citations\": [\"143330\", \"146323\", \"154648\", \"154649\", \"178214\", \"197416\", \"230212\", \"230456\", \"250505\", \"266671\", \"277320\", \"283618\", \"292871\", \"298016\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"141535","document_content":"# An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- I.2.7\n## Abstract\nTree-based Long short term memory (LSTM) network has become state-of-the-art for modeling the meaning of language texts as they can effectively exploit the grammatical syntax and thereby non-linear dependencies among words of the sentence. However, most of these models cannot recognize the difference in meaning caused by a change in semantic roles of words or phrases because they do not acknowledge the type of grammatical relations, also known as typed dependencies, in sentence structure. This paper proposes an enhanced LSTM architecture, called relation gated LSTM, which can model the relationship between two inputs of a sequence using a control input. We also introduce a Tree-LSTM model called Typed Dependency Tree-LSTM that uses the sentence dependency parse structure as well as the dependency type to embed sentence meaning into a dense vector. The proposed model outperformed its type-unaware counterpart in two typical NLP tasks - Semantic Relatedness Scoring and Sentiment Analysis, in a lesser number of training epochs. The results were comparable or competitive with other state-of-the-art models. Qualitative analysis showed that changes in the voice of sentences had little effect on the model's predicted scores, while changes in nominal (noun) words had a more significant impact. The model recognized subtle semantic relationships in sentence pairs. The magnitudes of learned typed dependencies embeddings were also in agreement with human intuitions. The research findings imply the significance of grammatical relations in sentence modeling. The proposed models would serve as a base for future researches in this direction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.ipm.2020.102362\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"I.2.7\"], \"incoming_citations\": [\"26665\"], \"outgoing_citations\": [\"149419\", \"218858\", \"221004\", \"231326\", \"240157\", \"242815\", \"248015\", \"261640\", \"266699\", \"274120\", \"279384\", \"331356\"]}","task_split":"paper_retrieval"}
{"document_id":"141728","document_content":"# Federated Extra-Trees with Privacy Preserving\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nIt is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulations such as the European Union's General Data Protection Regulation (GDPR) are designed to protect the public's data privacy. However, machine learning requires a large amount of data for better performance, and the current circumstances put deploying real-life AI applications in an extremely difficult situation. To tackle these challenges, in this paper we propose a novel privacy-preserving federated machine learning model, named Federated Extra-Trees, which applies local differential privacy in the federated trees model. A secure multi-institutional machine learning system was developed to provide superior performance by processing the modeling jointly on different clients without exchanging any raw data. We have validated the accuracy of our work by conducting extensive experiments on public datasets and the efficiency and robustness were also verified by simulating the real-world scenarios. Overall, we presented an extensible, scalable and practical solution to handle the data island problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.07323v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"62742\"], \"outgoing_citations\": [\"172559\", \"201645\", \"206720\", \"207491\", \"207951\", \"208966\", \"214805\", \"241304\", \"246939\", \"248982\", \"265073\", \"326583\", \"344428\"]}","task_split":"paper_retrieval"}
{"document_id":"141775","document_content":"# Multilinear Compressive Learning with Prior Knowledge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe recently proposed Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system that takes into account the multidimensional structure of the signals when designing the sensing and feature synthesis components. The key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. Thus, the ability to find such a discriminative tensor subspace and optimize the system to project the signals onto that data manifold plays an important role in Multilinear Compressive Learning. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable? and How to optimize the sensing and feature synthesis components to transform the original signals to the data manifold found in the first question? In our proposal, the discovery of a high-quality data manifold is conducted by training a nonlinear compressive learning system on the inference task. Its knowledge of the data manifold of interest is then progressively transferred to the MCL components via multi-stage supervised training with the supervisory information encoding how the compressed measurements, the synthesized features, and the predictions should be like. The proposed knowledge transfer algorithm also comes with a semi-supervised adaption that enables compressive learning models to utilize unlabeled data effectively. Extensive experiments demonstrate that the proposed knowledge transfer method can effectively train MCL models to compressively sense and synthesize better features for the learning tasks with improved performances, especially when the complexity of the learning task increases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.07203v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"59268\", \"139160\", \"99211\"], \"outgoing_citations\": [\"179781\", \"199616\", \"214232\", \"236228\", \"259882\", \"264787\", \"281609\", \"343015\"]}","task_split":"paper_retrieval"}
{"document_id":"141780","document_content":"# A Financial Service Chatbot based on Deep Bidirectional Transformers\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWe develop a chatbot using Deep Bidirectional Transformer models (BERT) to handle client questions in financial investment customer service. The bot can recognize 381 intents, and decides when to say \"I don't know\" and escalates irrelevant\/uncertain questions to human operators. Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools, and deployed within our company's intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public dataset on Github.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.04987v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"18018\", \"45133\", \"82117\", \"79729\"], \"outgoing_citations\": [\"79729\", \"145384\", \"159455\", \"168621\", \"193295\", \"199913\", \"201516\", \"203622\", \"205147\", \"206740\", \"216953\", \"220159\", \"226220\", \"247379\", \"247467\", \"256632\", \"267692\", \"284713\", \"303997\", \"307547\", \"312534\"]}","task_split":"paper_retrieval"}
{"document_id":"141826","document_content":"# Learning Group Structure and Disentangled Representations of Dynamical Environments\n## Categories\n- Machine Learning\n## Abstract\nLearning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments. In the natural sciences, physics has found great success by describing the universe in terms of symmetry preserving transformations. Inspired by this formalism, we propose a framework, built upon the theory of group representation, for learning representations of a dynamical environment structured around the transformations that generate its evolution. Experimentally, we learn the structure of explicitly symmetric environments without supervision from observational data generated by sequential interactions. We further introduce an intuitive disentanglement regularisation to ensure the interpretability of the learnt representations. We show that our method enables accurate long-horizon predictions, and demonstrate a correlation between the quality of predictions and disentanglement in the latent space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.06991v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"87490\", \"44223\", \"57862\", \"68053\", \"68707\", \"70171\", \"84506\", \"105383\", \"116650\"], \"outgoing_citations\": [\"152573\", \"161110\", \"164155\", \"181335\", \"181921\", \"182563\", \"183732\", \"193061\", \"201682\", \"207094\", \"208041\", \"212169\", \"219397\", \"236093\", \"236895\", \"241933\", \"253197\", \"260136\", \"277856\", \"278979\", \"292185\", \"310624\", \"319797\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"141887","document_content":"# Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMany approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-trainable pipeline strategies between text detection and text recognition will lead to suboptimal performances. To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. Concretely, Text Perceptron first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies without extra parameters. It unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization. Experiments show that our method achieves competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and ICDAR 2015, and also obviously outperforms existing methods on irregular text benchmarks SCUT-CTW1500 and Total-Text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.06820v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"13927\", \"31775\", \"14976\", \"33142\", \"33498\", \"43037\", \"47089\", \"49808\", \"56713\", \"59727\", \"62678\", \"81960\", \"113025\", \"184587\", \"98220\", \"122763\", \"126431\"], \"outgoing_citations\": [\"190708\", \"194314\", \"204872\", \"205988\", \"207348\", \"209227\", \"225195\", \"225795\", \"239535\", \"245591\", \"245899\", \"250729\", \"252159\", \"256364\", \"256711\", \"261365\", \"269205\", \"272481\", \"279950\", \"294023\", \"296685\"]}","task_split":"paper_retrieval"}
{"document_id":"141994","document_content":"# Block Annotation: Better Image Annotation for Semantic Segmentation with Sub-Image Decomposition\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nImage datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are annotated. However, full-image annotations are expensive, with experts spending up to 90 minutes per image. We propose block sub-image annotation as a replacement for full-image annotation. Despite the attention cost of frequent task switching, we find that block annotations can be crowdsourced at higher quality compared to full-image annotation with equal monetary cost using existing annotation tools developed for full-image annotation. Surprisingly, we find that 50% pixels annotated with blocks allows semantic segmentation to achieve equivalent performance to 100% pixels annotated. Furthermore, as little as 12% of pixels annotated allows performance as high as 98% of the performance with dense annotation. In weakly-supervised settings, block annotation outperforms existing methods by 3-4% (absolute) given equivalent annotation time. To recover the necessary global structure for applications such as characterizing spatial context and affordance relationships, we propose an effective method to inpaint block-annotated images with high-quality labels without additional human effort. As such, fewer annotations can also be used for these applications compared to full-image annotation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.06626v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"27608\"], \"outgoing_citations\": [\"207185\", \"220288\", \"226647\", \"227420\", \"237454\", \"237693\", \"238691\", \"247419\", \"249060\", \"249405\", \"258992\", \"259076\", \"260163\", \"262302\", \"266974\", \"271779\", \"276964\", \"277452\", \"278217\", \"279420\", \"294327\", \"295969\", \"296656\", \"311660\", \"316457\", \"319887\", \"321017\"]}","task_split":"paper_retrieval"}
{"document_id":"142045","document_content":"# Facial Attribute Capsules for Noise Face Super Resolution\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nExisting face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.06518v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"13328\", \"89019\"], \"outgoing_citations\": [\"171379\", \"191293\", \"191463\", \"191481\", \"213905\", \"248969\", \"258886\", \"288238\", \"296033\"]}","task_split":"paper_retrieval"}
{"document_id":"142096","document_content":"# Undersensitivity in Neural Reading Comprehension\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nCurrent reading comprehension models generalise well to in-distribution test sets, yet perform poorly on adversarially selected inputs. Most prior work on adversarial inputs studies oversensitivity: semantically invariant text perturbations that cause a model's prediction to change when it should not. In this work we focus on the complementary problem: excessive prediction undersensitivity, where input text is meaningfully changed but the model's prediction does not, even though it should. We formulate a noisy adversarial attack which searches among semantic variations of the question for which a model erroneously predicts the same answer, and with even higher probability. Despite comprising unanswerable questions, both SQuAD2.0 and NewsQA models are vulnerable to this attack. This indicates that although accurate, models tend to rely on spurious patterns and do not fully consider the information specified in a question. We experiment with data augmentation and adversarial training as defences, and find that both substantially decrease vulnerability to attacks on held out data, as well as held out attack spaces. Addressing undersensitivity also improves results on AddSent and AddOneSent, and models furthermore generalise better when facing train\/evaluation distribution mismatch: they are less prone to overly rely on predictive cues present only in the training set, and outperform a conventional model by as much as 10.9% F1.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.04808v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"148368\", \"24728\", \"25362\", \"71336\", \"127977\", \"129432\"], \"outgoing_citations\": [\"126188\", \"164659\", \"180808\", \"181023\", \"183319\", \"211977\", \"218932\", \"220016\", \"220285\", \"221086\", \"222795\", \"229886\", \"232334\", \"234716\", \"234801\", \"235095\", \"235224\", \"235345\", \"239831\", \"247127\", \"251240\", \"251412\", \"260450\", \"270521\", \"279213\", \"281163\"]}","task_split":"paper_retrieval"}
{"document_id":"142436","document_content":"# A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild\n## Categories\n- Computers and Society\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nResearch in social psychology has shown that people's biased, subjective judgments about another's personality based solely on their appearance are not predictive of their actual personality traits. But researchers and companies often utilize computer vision models to predict similarly subjective personality attributes such as \"employability.\" We seek to determine whether state-of-the-art, black box face processing technology can learn human-like appearance biases. With features extracted with FaceNet, a widely used face recognition framework, we train a transfer learning model on human subjects' first impressions of personality traits in other faces as measured by social psychologists. We find that features extracted with FaceNet can be used to predict human appearance bias scores for deliberately manipulated faces but not for randomly generated faces scored by humans. Additionally, in contrast to work with human biases in social psychology, the model does not find a significant signal correlating politicians' vote shares with perceived competence bias. With Local Interpretable Model-Agnostic Explanations (LIME), we provide several explanations for this discrepancy. Our results suggest that some signals of appearance bias documented in social psychology are not embedded by the machine learning techniques we investigate. We shed light on the ways in which appearance bias could be embedded in face processing technology and cast further doubt on the practice of predicting subjective traits based on appearances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s43681-020-00035-y\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.LG\", \"cs.CY\", \"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"148615\", \"179609\", \"192730\", \"198048\", \"209479\", \"234693\", \"259870\", \"285959\", \"287611\"]}","task_split":"paper_retrieval"}
{"document_id":"142583","document_content":"# Learning to Generate Levels From Nothing\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMachine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to assemble suitably large datasets to bring machine learning to level design in the same way as it's been used for image generation. Here we propose Generative Playing Networks which design levels for itself to play. The algorithm is built in two parts; an agent that learns to play game levels, and a generator that learns the distribution of playable levels. As the agent learns and improves its ability, the space of playable levels, as defined by the agent, grows. The generator targets the agent's playability estimates to then update its understanding of what constitutes a playable level. We call this process of learning the distribution of data found through self-discovery with an environment, self-supervised inductive learning. Unlike previous approaches to procedural content generation, Generative Playing Networks are end-to-end differentiable and do not require human-designed examples or domain knowledge. We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.05259v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"64439\", \"82943\", \"145664\", \"207051\", \"226544\", \"229002\", \"233501\", \"235092\", \"240572\", \"257674\", \"265896\", \"274886\", \"291537\", \"320829\"]}","task_split":"paper_retrieval"}
{"document_id":"142696","document_content":"# Saliency Driven Perceptual Image Compression\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a hierarchical auto-regressive model. This paper demonstrates that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of image compression techniques as they do not align with the human perception of similarity. Alternatively, a new metric is proposed, which is learned on perceptual similarity data specific to image compression. The proposed compression model incorporates the salient regions and optimizes on the proposed perceptual similarity metric. The model not only generates images which are visually better but also gives superior performance for subsequent computer vision tasks such as object detection and segmentation when compared to existing engineered or learned compression techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04988v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"7260\", \"25721\", \"27638\", \"40317\", \"4308\"], \"outgoing_citations\": [\"172377\", \"175457\", \"217152\", \"218722\", \"236298\", \"243653\", \"245227\", \"250250\", \"259317\", \"266304\", \"269868\", \"272675\", \"277297\", \"280435\", \"281135\", \"290238\", \"311388\"]}","task_split":"paper_retrieval"}
{"document_id":"142704","document_content":"# The Space of Mathematical Software Systems -- A Survey of Paradigmatic Systems\n## Categories\n- Mathematical Software\n## Abstract\nMathematical software systems are becoming more and more important in pure and applied mathematics in order to deal with the complexity and scalability issues inherent in mathematics. In the last decades we have seen a cambric explosion of increasingly powerful but also diverging systems. To give researchers a guide to this space of systems, we devise a novel conceptualization of mathematical software that focuses on five aspects: inference covers formal logic and reasoning about mathematical statements via proofs and models, typically with strong emphasis on correctness; computation covers algorithms and software libraries for representing and manipulating mathematical objects, typically with strong emphasis on efficiency; concretization covers generating and maintaining collections of mathematical objects conforming to a certain pattern, typically with strong emphasis on complete enumeration; narration covers describing mathematical contexts and relations, typically with strong emphasis on human readability; finally, organization covers representing mathematical contexts and objects in machine-actionable formal languages, typically with strong emphasis on expressivity and system interoperability. Despite broad agreement that an ideal system would seamlessly integrate all these aspects, research has diversified into families of highly specialized systems focusing on a single aspect and possibly partially integrating others, each with their own communities, challenges, and successes. In this survey, we focus on the commonalities and differences of these systems from the perspective of a future multi-aspect system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04955v1\", \"primary_category\": \"cs.MS\", \"categories\": [\"cs.MS\"], \"primary_category_human_readable\": \"Mathematical Software\", \"categories_human_readable\": [\"Mathematical Software\"], \"incoming_citations\": [], \"outgoing_citations\": [\"312297\", \"340134\", \"346023\"]}","task_split":"paper_retrieval"}
{"document_id":"142709","document_content":"# Joint Embedding in Named Entity Linking on Sentence Level\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNamed entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document. It is difficult to link a mention when it appears multiple times in a document, since there are conflicts by the contexts around the appearances of the mention. In addition, it is difficult since the given training dataset is small due to the reason that it is done manually to link a mention to its mapping entity. In the literature, there are many reported studies among which the recent embedding methods learn vectors of entities from the training dataset at document level. To address these issues, we focus on how to link entity for mentions at a sentence level, which reduces the noises introduced by different appearances of the same mention in a document at the expense of insufficient information to be used. We propose a new unified embedding method by maximizing the relationships learned from knowledge graphs. We confirm the effectiveness of our method in our experimental studies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04936v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"121898\"], \"outgoing_citations\": [\"281792\", \"300498\", \"318072\"]}","task_split":"paper_retrieval"}
{"document_id":"142761","document_content":"# Reward-rational (implicit) choice: A unifying formalism for reward learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n- Robotics\n## Abstract\nIt is often difficult to hand-specify what the correct reward function is for a task, so researchers have instead aimed to learn reward functions from human behavior or feedback. The types of behavior interpreted as evidence of the reward function have expanded greatly in recent years. We've gone from demonstrations, to comparisons, to reading into the information leaked when the human is pushing the robot away or turning it off. And surely, there is more to come. How will a robot make sense of all these diverse types of behavior? Our key insight is that different types of behavior can be interpreted in a single unifying formalism - as a reward-rational choice that the human is making, often implicitly. The formalism offers both a unifying lens with which to view past work, as well as a recipe for interpreting new sources of information that are yet to be uncovered. We provide two examples to showcase this: interpreting a new feedback type, and reading into how the choice of feedback itself leaks information about the reward.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04833v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"88297\", \"2880\", \"10705\", \"26479\", \"38950\", \"47401\", \"12253\", \"19732\", \"32223\", \"34461\", \"37526\", \"40961\", \"59129\", \"67123\", \"87472\", \"97596\", \"171948\", \"74639\", \"80268\", \"81341\", \"86327\", \"102649\", \"117347\"], \"outgoing_citations\": [\"179705\", \"198208\", \"199330\", \"209926\", \"218582\", \"228916\", \"228980\", \"228998\", \"250567\", \"251082\", \"252304\", \"263957\", \"275728\", \"279628\", \"297317\", \"300596\", \"357182\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"142801","document_content":"# Collaborative Inference for Efficient Remote Monitoring\n## Categories\n- Machine Learning\n## Abstract\nWhile current machine learning models have impressive performance over a wide range of applications, their large size and complexity render them unsuitable for tasks such as remote monitoring on edge devices with limited storage and computational power. A naive approach to resolve this on the model level is to use simpler architectures, but this sacrifices prediction accuracy and is unsuitable for monitoring applications requiring accurate detection of the onset of adverse events. In this paper, we propose an alternative solution to this problem by decomposing the predictive model as the sum of a simple function which serves as a local monitoring tool, and a complex correction term to be evaluated on the server. A sign requirement is imposed on the latter to ensure that the local monitoring function is safe, in the sense that it can effectively serve as an early warning system. Our analysis quantifies the trade-offs between model complexity and performance, and serves as a guidance for architecture design. We validate our proposed framework on a series of monitoring experiments, where we succeed at learning monitoring models with significantly reduced complexity that minimally violate the safety requirement. More broadly, our framework is useful for learning classifiers in applications where false negatives are significantly more costly compared to false positives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04759v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227405\", \"242542\", \"261896\", \"271454\", \"277835\", \"281587\", \"297682\", \"305452\", \"320127\", \"336193\"]}","task_split":"paper_retrieval"}
{"document_id":"143064","document_content":"# Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations\n## Categories\n- Machine Learning\n## Abstract\nIdentifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04083v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1697\", \"8646\", \"44925\", \"46150\", \"73390\", \"4614\", \"4945\", \"15721\", \"22306\", \"43797\", \"46094\", \"55591\", \"67643\", \"70892\", \"71927\", \"86074\", \"128466\", \"129318\", \"181603\", \"79787\", \"79223\", \"93761\", \"138030\", \"139639\", \"200600\"], \"outgoing_citations\": [\"194202\", \"241055\", \"269298\", \"269527\", \"270120\", \"271865\", \"281634\", \"286370\", \"290547\", \"292736\", \"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"143074","document_content":"# Advances in Deep Space Exploration via Simulators & Deep Learning\n## Categories\n- Instrumentation and Methods for Astrophysics\n- Computer Vision and Pattern Recognition\n## Abstract\nThe StarLight program conceptualizes fast interstellar travel via small wafer satellites (wafersats) that are propelled by directed energy. This process is wildly different from traditional space travel and trades large and slow spacecraft for small, fast, inexpensive, and fragile ones. The main goal of these wafer satellites is to gather useful images during their deep space journey. We introduce and solve some of the main problems that accompany this concept. First, we need an object detection system that can detect planets that we have never seen before, some containing features that we may not even know exist in the universe. Second, once we have images of exoplanets, we need a way to take these images and rank them by importance. Equipment fails and data rates are slow, thus we need a method to ensure that the most important images to humankind are the ones that are prioritized for data transfer. Finally, the energy on board is minimal and must be conserved and used sparingly. No exoplanet images should be missed, but using energy erroneously would be detrimental. We introduce simulator-based methods that leverage artificial intelligence, mostly in the form of computer vision, in order to solve all three of these issues. Our results confirm that simulators provide an extremely rich training environment that surpasses that of real images, and can be used to train models on features that have yet to be observed by humans. We also show that the immersive and adaptable environment provided by the simulator, combined with deep learning, lets us navigate and save energy in an otherwise implausible way.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.newast.2020.101517\", \"primary_category\": \"astro-ph.IM\", \"categories\": [\"cs.CV\", \"astro-ph.IM\"], \"primary_category_human_readable\": \"Instrumentation and Methods for Astrophysics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Instrumentation and Methods for Astrophysics\"], \"incoming_citations\": [\"78223\"], \"outgoing_citations\": [\"215935\", \"218943\", \"219624\", \"248211\", \"291918\", \"323690\"]}","task_split":"paper_retrieval"}
{"document_id":"143081","document_content":"# Can Graph Neural Networks Count Substructures?\n## Categories\n- Machine Learning\n- Discrete Mathematics\n## Abstract\nThe ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry and biology as well as social network analysis. Inspired by this, we propose to study the expressive power of graph neural networks (GNNs) via their ability to count attributed graph substructures, extending recent works that examine their power in graph isomorphism testing and function approximation. We distinguish between two types of substructure counting: induced-subgraph-count and subgraph-count, and establish both positive and negative answers for popular GNN architectures. Specifically, we prove that Message Passing Neural Networks (MPNNs), 2-Weisfeiler-Lehman (2-WL) and 2-Invariant Graph Networks (2-IGNs) cannot perform induced-subgraph-count of substructures consisting of 3 or more nodes, while they can perform subgraph-count of star-shaped substructures. As an intermediary step, we prove that 2-WL and 2-IGNs are equivalent in distinguishing non-isomorphic graphs, partly answering an open problem raised in Maron et al. (2019). We also prove positive results for k-WL and k-IGNs as well as negative results for k-WL with a finite number of iterations. We then conduct experiments that support the theoretical results for MPNNs and 2-IGNs. Moreover, motivated by substructure counting and inspired by Murphy et al. (2019), we propose the Local Relational Pooling model and demonstrate that it is not only effective for substructure counting but also able to achieve competitive performance on molecular prediction tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04025v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DM\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Discrete Mathematics\", \"Machine Learning\"], \"incoming_citations\": [\"44078\", \"118201\", \"51256\", \"2558\", \"18144\", \"18515\", \"21372\", \"67885\", \"69149\", \"91450\", \"4995\", \"12967\", \"13814\", \"19609\", \"33376\", \"37535\", \"39957\", \"42645\", \"42699\", \"43757\", \"46936\", \"49956\", \"57782\", \"58289\", \"64367\", \"65206\", \"65828\", \"90641\", \"93505\", \"115600\", \"124325\", \"143473\", \"81711\", \"82471\", \"83801\", \"103085\", \"110102\", \"115834\", \"118122\", \"119462\", \"121424\", \"117648\", \"125230\", \"131562\", \"137772\", \"142226\", \"192622\", \"183719\"], \"outgoing_citations\": [\"137772\", \"138999\", \"142226\", \"143408\", \"149610\", \"159199\", \"171612\", \"177249\", \"181245\", \"181255\", \"183719\", \"183816\", \"184286\", \"184749\", \"186432\", \"186624\", \"195767\", \"196221\", \"201448\", \"204871\", \"207740\", \"210730\", \"215435\", \"217569\", \"226933\", \"228992\", \"240758\", \"241029\", \"242430\", \"243382\", \"263068\", \"269617\", \"269770\", \"272626\", \"354882\", \"359939\"]}","task_split":"paper_retrieval"}
{"document_id":"143083","document_content":"# A Model of Fast Concept Inference with Object-Factorized Cognitive Programs\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nThe ability of humans to quickly identify general concepts from a handful of images has proven difficult to emulate with robots. Recently, a computer architecture was developed that allows robots to mimic some aspects of this human ability by modeling concepts as cognitive programs using an instruction set of primitive cognitive functions. This allowed a robot to emulate human imagination by simulating candidate programs in a world model before generalizing to the physical world. However, this model used a naive search algorithm that required 30 minutes to discover a single concept, and became intractable for programs with more than 20 instructions. To circumvent this bottleneck, we present an algorithm that emulates the human cognitive heuristics of object factorization and sub-goaling, allowing human-level inference speed, improving accuracy, and making the output more explainable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04021v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"206918\", \"222415\", \"233924\", \"270925\"]}","task_split":"paper_retrieval"}
{"document_id":"143154","document_content":"# Vision based body gesture meta features for Affective Computing\n## Categories\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nEarly detection of psychological distress is key to effective treatment. Automatic detection of distress, such as depression, is an active area of research. Current approaches utilise vocal, facial, and bodily modalities. Of these, the bodily modality is the least investigated, partially due to the difficulty in extracting bodily representations from videos, and partially due to the lack of viable datasets. Existing body modality approaches use automatic categorization of expressions to represent body language as a series of specific expressions, much like words within natural language. In this dissertation I present a new type of feature, within the body modality, that represents meta information of gestures, such as speed, and use it to predict a non-clinical depression label. This differs to existing work by representing overall behaviour as a small set of aggregated meta features derived from a person's movement. In my method I extract pose estimation from videos, detect gestures within body parts, extract meta information from individual gestures, and finally aggregate these features to generate a small feature vector for use in prediction tasks. I introduce a new dataset of 65 video recordings of interviews with self-evaluated distress, personality, and demographic labels. This dataset enables the development of features utilising the whole body in distress detection tasks. I evaluate my newly introduced meta-features for predicting depression, anxiety, perceived stress, somatic stress, five standard personality measures, and gender. A linear regression based classifier using these features achieves a 82.70% F1 score for predicting depression within my novel dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2003.00809v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.HC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"237481\", \"258853\", \"268000\"]}","task_split":"paper_retrieval"}
{"document_id":"143317","document_content":"# Limits of Detecting Text Generated by Large-Scale Language Models\n## Categories\n- Computation and Language\n- Computers and Society\n- Machine Learning\n## Abstract\nSome consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated. We show that error exponents for particular language models are bounded in terms of their perplexity, a standard measure of language generation performance. Under the assumption that human language is stationary and ergodic, the formulation is extended from considering specific language models to considering maximum likelihood language models, among the class of k-order Markov approximations; error probabilities are characterized. Some discussion of incorporating semantic side information is also given.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.03438v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"20709\", \"127795\"], \"outgoing_citations\": [\"167338\", \"168143\", \"170345\", \"181585\", \"181884\", \"182393\", \"183694\", \"187148\", \"192158\", \"199361\", \"232172\", \"270101\", \"283971\", \"308603\"]}","task_split":"paper_retrieval"}
{"document_id":"143322","document_content":"# Graph Neural Distance Metric Learning with Graph-Bert\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nGraph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works on graph distance metric (or graph kernels) learning fail to maintain the basic properties of such metrics, e.g., non-negative, identity of indiscernibles, symmetry and triangle inequality, respectively. In this paper, we will introduce a new graph neural network based distance metric learning approaches, namely GB-DISTANCE (GRAPH-BERT based Neural Distance). Solely based on the attention mechanism, GB-DISTANCE can learn graph instance representations effectively based on a pre-trained GRAPH-BERT model. Different from the existing supervised\/unsupervised metrics, GB-DISTANCE can be learned effectively in a semi-supervised manner. In addition, GB-DISTANCE can also maintain the distance metric basic properties mentioned above. Extensive experiments have been done on several benchmark graph datasets, and the results demonstrate that GB-DISTANCE can out-perform the existing baseline methods, especially the recent graph neural network model based graph metrics, with a significant gap in computing the graph distance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.03427v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"105502\"], \"outgoing_citations\": [\"147107\", \"167195\", \"171841\", \"173523\", \"174997\", \"177817\", \"182665\", \"188510\", \"221106\", \"242430\"]}","task_split":"paper_retrieval"}
{"document_id":"143331","document_content":"# Attend to the beginning: A study on using bidirectional attention for extractive summarization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nForum discussion data differ in both structure and properties from generic form of textual data such as news. Henceforth, summarization techniques should, in turn, make use of such differences, and craft models that can benefit from the structural nature of discussion data. In this work, we propose attending to the beginning of a document, to improve the performance of extractive summarization models when applied to forum discussion data. Evaluations demonstrated that with the help of bidirectional attention mechanism, attending to the beginning of a document (initial comment\/post) in a discussion thread, can introduce a consistent boost in ROUGE scores, as well as introducing a new State Of The Art (SOTA) ROUGE scores on the forum discussions dataset. Additionally, we explored whether this hypothesis is extendable to other generic forms of textual data. We make use of the tendency of introducing important information early in the text, by attending to the first few sentences in generic textual data. Evaluations demonstrated that attending to introductory sentences using bidirectional attention, improves the performance of extractive summarization models when even applied to more generic form of textual data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.03405v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"22773\"], \"outgoing_citations\": [\"126925\", \"166268\", \"169457\", \"170657\", \"181172\", \"197273\", \"219618\", \"230458\", \"230466\", \"266671\", \"280557\", \"281163\", \"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"143441","document_content":"# Time-aware Large Kernel Convolutions\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nTo date, most state-of-the-art sequence modeling architectures use attention to build generative models for language based tasks. Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$. Alternatively, they utilize depthwise convolutions with softmax normalized kernels of size $k$ acting as a limited-window self-attention, resulting in time complexity of $O(k{\\cdot}n)$. In this paper, we introduce Time-aware Large Kernel (TaLK) Convolutions, a novel adaptive convolution operation that learns to predict the size of a summation kernel instead of using a fixed-sized kernel matrix. This method yields a time complexity of $O(n)$, effectively making the sequence encoding process linear to the number of tokens. We evaluate the proposed method on large-scale standard machine translation, abstractive summarization and language modeling datasets and show that TaLK Convolutions constitute an efficient improvement over other attention\/convolution based approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.03184v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"12479\", \"46280\", \"77674\", \"86936\", \"163696\", \"92674\", \"7860\", \"106193\", \"138837\"], \"outgoing_citations\": [\"101677\", \"178809\", \"197007\", \"201080\", \"216217\", \"220160\", \"225130\", \"229674\", \"233535\", \"234164\", \"236910\", \"237494\", \"237584\", \"238143\", \"250505\", \"251251\", \"253640\", \"266671\", \"271767\", \"277320\", \"278065\", \"281508\", \"282533\", \"283971\", \"296250\", \"299541\", \"300508\"]}","task_split":"paper_retrieval"}
{"document_id":"143456","document_content":"# Random Features Strengthen Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have been revealed. For example, GNNs cannot distinguish some non-isomorphic graphs and they cannot learn efficient graph algorithms. In this paper, we demonstrate that GNNs become powerful just by adding a random feature to each node. We prove that the random features enable GNNs to learn almost optimal polynomial-time approximation algorithms for the minimum dominating set problem and maximum matching problem in terms of approximation ratios. The main advantage of our method is that it can be combined with off-the-shelf GNN models with slight modifications. Through experiments, we show that the addition of random features enables GNNs to solve various problems that normal GNNs, including the graph convolutional networks (GCNs) and graph isomorphism networks (GINs), cannot solve.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.03155v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"118201\", \"120407\", \"52113\", \"4065\", \"7683\", \"15806\", \"18144\", \"18515\", \"22043\", \"22079\", \"37770\", \"67549\", \"102194\", \"129429\", \"2056\", \"4410\", \"5326\", \"10071\", \"13814\", \"19609\", \"41578\", \"42026\", \"42645\", \"43757\", \"46936\", \"49956\", \"50499\", \"52701\", \"68391\", \"68697\", \"88245\", \"90277\", \"90641\", \"97047\", \"110102\", \"115834\", \"118122\", \"118788\", \"125230\", \"131562\", \"137772\", \"142226\", \"192622\"], \"outgoing_citations\": [\"177249\", \"183719\", \"184286\", \"184749\", \"185616\", \"185906\", \"186624\", \"188398\", \"194689\", \"196221\", \"201448\", \"204871\", \"211417\", \"215435\", \"227067\", \"292407\", \"358918\"]}","task_split":"paper_retrieval"}
{"document_id":"143497","document_content":"# Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling\n## Categories\n- Machine Learning\n## Abstract\nLabeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model's output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints respectively. The proposed framework doesn't require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53\\% execution time without loss of any valuable labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.05520v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204200\", \"239660\", \"243408\", \"263620\", \"268000\", \"278836\", \"318535\", \"341234\"]}","task_split":"paper_retrieval"}
{"document_id":"143557","document_content":"# Student\/Teacher Advising through Reward Augmentation\n## Categories\n- Artificial Intelligence\n## Abstract\nTransfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem. This is useful when one wishes to change the architecture or learning algorithm of an agent (so that the new knowledge need not be built \"from scratch\"), when new agents are frequently introduced to the environment with no knowledge, or when an agent must adapt to similar but different problems. Great progress has been made in the agent-to-agent case using the Teacher\/Student framework proposed by (Torrey and Taylor 2013). However, that approach requires that learning from a teacher be treated differently from learning in every other reinforcement learning context. In this paper, I propose a method which allows the teacher\/student framework to be applied in a way that fits directly and naturally into the more general reinforcement learning framework by integrating the teacher feedback into the reward signal received by the learning agent. I show that this approach can significantly improve the rate of learning for an agent playing a one-player stochastic game; I give examples of potential pitfalls of the approach; and I propose further areas of research building on this framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.02938v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"143595","document_content":"# What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations\n## Categories\n- Computers and Society\n- Machine Learning\n- Human-Computer Interaction\n- Computation and Language\n- Machine Learning\n## Abstract\nRecently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A\/B test rather than a study of open questions. To answer the question \"What would a human operator like to ask the ML model?\" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds on Titanic. People can talk with dr_ant about different aspects of the model to understand the rationale behind its predictions. Having collected a corpus of 1000+ dialogues, we analyse the most common types of questions that users would like to ask. To our knowledge, it is the first study which uses a conversational system to collect the needs of human operators from the interactive and iterative dialogue explorations of a predictive model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-65965-3_30\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.LG\", \"cs.HC\", \"cs.CL\", \"cs.CY\", \"stat.ML\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\", \"Computation and Language\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"1124\", \"127611\", \"43180\"], \"outgoing_citations\": [\"162190\", \"165861\", \"168243\", \"191682\", \"193494\", \"196464\", \"200101\", \"209846\", \"227270\", \"227413\", \"248571\"]}","task_split":"paper_retrieval"}
{"document_id":"143766","document_content":"# Modeling Engagement in Long-Term, In-Home Socially Assistive Robot Interventions for Children with Autism Spectrum Disorders\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- I.2.9\n- Robotics\n## Abstract\nSocially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users; and (ii) individualized models trained on an early subset of the user's data. The models achieved approximately 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate re-engagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1126\/scirobotics.aaz3791\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.LG\", \"I.2.9\", \"cs.RO\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Machine Learning\", \"I.2.9\", \"Robotics\"], \"incoming_citations\": [\"10235\", \"73414\", \"27266\", \"35210\"], \"outgoing_citations\": [\"155633\", \"181967\", \"237905\", \"243346\"]}","task_split":"paper_retrieval"}
{"document_id":"143788","document_content":"# Context Aware Image Annotation in Active Learning\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nImage annotation for active learning is labor-intensive. Various automatic and semi-automatic labeling methods are proposed to save the labeling cost, but a reduction in the number of labeled instances does not guarantee a reduction in cost because the queries that are most valuable to the learner may be the most difficult or ambiguous cases, and therefore the most expensive for an oracle to label accurately. In this paper, we try to solve this problem by using image metadata to offer the oracle more clues about the image during annotation process. We propose a Context Aware Image Annotation Framework (CAIAF) that uses image metadata as similarity metric to cluster images into groups for annotation. We also present useful metadata information as context for each image on the annotation interface. Experiments show that it reduces that annotation cost with CAIAF compared to the conventional framework, while maintaining a high classification performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.02775v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"143435\", \"143787\"], \"outgoing_citations\": [\"222592\", \"307389\", \"312820\", \"336408\", \"356343\"]}","task_split":"paper_retrieval"}
{"document_id":"143957","document_content":"# Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention\n## Categories\n- Social and Information Networks\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nThe viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components (such as news creators and news topics) can be modeled as a heterogeneous information network (HIN for short). In this paper, we propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes. Experiments on two real-world fake news datasets show that HGAT can outperform text-based models and other network-based models. In addition, the experiment proved the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.04397v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CL\", \"stat.ML\", \"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"194773\", \"217900\", \"229275\", \"230998\", \"257863\", \"271021\", \"273723\", \"275670\", \"303300\", \"319035\"]}","task_split":"paper_retrieval"}
{"document_id":"143970","document_content":"# Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nPrior knowledge of face shape and structure plays an important role in face inpainting. However, traditional face inpainting methods mainly focus on the generated image resolution of the missing portion without consideration of the special particularities of the human face explicitly and generally produce discordant facial parts. To solve this problem, we present a domain embedded multi-model generative adversarial model for inpainting of face images with large cropped regions. We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents. Two adversarial discriminators are finally used to judge whether the generated distribution is close to the real distribution or not. It can not only synthesize novel image structures but also explicitly utilize the embedded face domain knowledge to generate better predictions with consistency on structures and appearance. Experiments on both CelebA and CelebA-HQ face datasets demonstrate that our proposed approach achieved state-of-the-art performance and generates higher quality inpainting results than existing ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.02909v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"26240\", \"96103\"], \"outgoing_citations\": [\"168994\", \"172251\", \"190170\", \"190371\", \"195652\", \"202600\", \"206757\", \"207471\", \"213592\", \"223275\", \"228631\", \"243990\", \"249537\", \"263662\", \"268494\", \"279178\", \"283732\", \"287740\", \"290092\", \"294191\", \"323646\"]}","task_split":"paper_retrieval"}
{"document_id":"143976","document_content":"# If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills\n## Categories\n- Human-Computer Interaction\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nInterview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3313831.3376131\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"57014\", \"58348\", \"75786\"], \"outgoing_citations\": [\"184544\", \"211854\", \"232268\", \"232494\", \"237274\", \"239670\", \"267694\", \"269757\", \"270040\", \"272752\", \"274253\", \"279109\", \"289943\", \"291916\", \"296214\", \"309411\", \"316630\"]}","task_split":"paper_retrieval"}
{"document_id":"143988","document_content":"# Discontinuous Constituent Parsing with Pointer Networks\n## Categories\n- Computation and Language\n- 68T50\n- I.2.7\n## Abstract\nOne of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.01824v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\", \"I.2.7\"], \"incoming_citations\": [\"14960\", \"17300\", \"99477\", \"56383\", \"92862\", \"97308\", \"134103\"], \"outgoing_citations\": [\"192928\", \"194426\", \"197834\", \"225852\", \"233392\", \"281121\", \"296583\", \"297648\", \"316788\"]}","task_split":"paper_retrieval"}
{"document_id":"144125","document_content":"# Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a method for weakly-supervised action localization based on graph convolutions. In order to find and classify video time segments that correspond to relevant action classes, a system must be able to both identify discriminative time segments in each video, and identify the full extent of each action. Achieving this with weak video level labels requires the system to use similarity and dissimilarity between moments across videos in the training data to understand both how an action appears, as well as the sub-actions that comprise the action's full extent. However, current methods do not make explicit use of similarity between video moments to inform the localization and classification predictions. We present a novel method that uses graph convolutions to explicitly model similarity between video moments. Our method utilizes similarity graphs that encode appearance and motion, and pushes the state of the art on THUMOS '14, ActivityNet 1.2, and Charades for weakly supervised action localization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.01449v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19821\", \"1677\", \"17178\", \"26800\", \"29997\", \"48098\", \"15539\", \"15755\", \"81115\", \"105151\"], \"outgoing_citations\": [\"185401\", \"206057\", \"206643\", \"207876\", \"208628\", \"209587\", \"223200\", \"229179\", \"231686\", \"236894\", \"238717\", \"247497\", \"248198\", \"259553\", \"264711\", \"268408\", \"268934\", \"270713\", \"271891\", \"283115\", \"287541\", \"292471\", \"294114\", \"295117\", \"312423\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"144194","document_content":"# ALPINE: Active Link Prediction using Network Embedding\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n## Abstract\nMany real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V-optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.01227v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [\"37537\"], \"outgoing_citations\": [\"103085\", \"140151\", \"185779\", \"186365\", \"231488\", \"240758\", \"249497\", \"266479\", \"295676\", \"349591\", \"349957\", \"350033\"]}","task_split":"paper_retrieval"}
{"document_id":"144309","document_content":"# Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections\n## Categories\n- Robotics\n- Machine Learning\n- Machine Learning\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nHuman input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives. These techniques assume that the human's desired objective already exists within the robot's hypothesis space. In reality, this assumption is often inaccurate: there will always be situations where the person might care about aspects of the task that the robot does not know about. Without this knowledge, the robot cannot infer the correct objective. Hence, when the robot's hypothesis space is misspecified, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. In this paper, we posit that the robot should reason explicitly about how well it can explain human inputs given its hypothesis space and use that situational confidence to inform how it should incorporate human input. We demonstrate our method on a 7 degree-of-freedom robot manipulator in learning from two important types of human input: demonstrations of manipulation tasks, and physical corrections during the robot's task execution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TRO.2020.2971415\", \"primary_category\": \"cs.RO\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.HC\", \"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Human-Computer Interaction\", \"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"88297\", \"20143\", \"56269\", \"59129\", \"78420\", \"83169\", \"87472\", \"116575\", \"74639\"], \"outgoing_citations\": [\"184290\", \"203662\", \"209859\", \"214560\", \"221611\", \"228998\", \"229707\", \"230042\", \"251082\", \"252304\", \"260731\", \"263957\", \"264845\", \"290744\", \"297317\", \"300596\", \"315405\", \"320135\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"144476","document_content":"# Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReal data collected from different applications that have additional topological structures and connection information are amenable to be represented as a weighted graph. Considering the node labeling problem, Graph Neural Networks (GNNs) is a powerful tool, which can mimic experts' decision on node labeling. GNNs combine node features, connection patterns, and graph structure by using a neural network to embed node information and pass it through edges in the graph. We want to identify the patterns in the input data used by the GNN model to make a decision and examine if the model works as we desire. However, due to the complex data representation and non-linear transformations, explaining decisions made by GNNs is challenging. In this work, we propose new graph features' explanation methods to identify the informative components and important node features. Besides, we propose a pipeline to identify the key factors used for node classification. We use four datasets (two synthetic and two real) to validate our methods. Our results demonstrate that our explanation approach can mimic data patterns used for node classification by human interpretation and disentangle different features in the graphs. Furthermore, our explanation methods can be used for understanding data, debugging GNN models, and examine model decisions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.00514v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"85438\", \"148583\", \"122107\", \"83007\"], \"outgoing_citations\": [\"183245\", \"206470\", \"211583\", \"218737\", \"242237\"]}","task_split":"paper_retrieval"}
{"document_id":"144508","document_content":"# Deep Reinforcement Learning for Autonomous Driving: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nWith the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2002.00444v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"1577\", \"3042\", \"48121\", \"53008\", \"62235\", \"65989\", \"162117\", \"6615\", \"13755\", \"14153\", \"14635\", \"17179\", \"19211\", \"19891\", \"20137\", \"21649\", \"21579\", \"21722\", \"23029\", \"23797\", \"39343\", \"42837\", \"50350\", \"60310\", \"62061\", \"75603\", \"76756\", \"77439\", \"127389\", \"145459\", \"6915\", \"7851\", \"8016\", \"9308\", \"9333\", \"9925\", \"14495\", \"19576\", \"20692\", \"21866\", \"23710\", \"29078\", \"30950\", \"31177\", \"31955\", \"32494\", \"32620\", \"33516\", \"35918\", \"40116\", \"41463\", \"43441\", \"45372\", \"46459\", \"20450\", \"46576\", \"46963\", \"57359\", \"61817\", \"62673\", \"63102\", \"65118\", \"62457\", \"66045\", \"67822\", \"72883\", \"74450\", \"75357\", \"87328\", \"101609\", \"106495\", \"114596\", \"135486\", \"78388\", \"80133\", \"87056\", \"88536\", \"91318\", \"94703\", \"99643\", \"101878\", \"103825\", \"113006\", \"117928\", \"114889\", \"119188\", \"137679\"], \"outgoing_citations\": [\"149829\", \"156983\", \"164133\", \"168262\", \"168803\", \"169068\", \"172432\", \"183125\", \"187778\", \"187780\", \"189615\", \"193740\", \"199588\", \"201682\", \"203834\", \"206571\", \"206814\", \"208364\", \"209238\", \"209491\", \"218770\", \"219180\", \"221473\", \"226166\", \"226862\", \"232238\", \"234762\", \"238493\", \"238740\", \"239655\", \"242357\", \"242478\", \"243597\", \"248809\", \"251413\", \"253045\", \"253658\", \"255139\", \"255531\", \"256707\", \"261749\", \"266419\", \"266485\", \"267428\", \"267981\", \"269012\", \"269382\", \"269458\", \"272987\", \"274482\", \"277822\", \"278081\", \"278225\", \"278822\", \"279054\", \"280202\", \"280870\", \"281257\", \"282884\", \"290679\", \"293839\", \"293864\", \"302857\", \"303049\", \"309111\", \"310370\", \"310624\", \"317863\", \"322636\", \"334182\"]}","task_split":"paper_retrieval"}
{"document_id":"144707","document_content":"# Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nAutomatic facial expression recognition is an important research area in the emotion recognition and computer vision. Applications can be found in several domains such as medical treatment, driver fatigue surveillance, sociable robotics, and several other human-computer interaction systems. Therefore, it is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. In recent years, deep neural networks have been used with great success in recognizing emotions. In this paper, we present a new model for continuous emotion recognition based on facial expression recognition by using an unsupervised learning approach based on transfer learning and autoencoders. The proposed approach also includes preprocessing and post-processing techniques which contribute favorably to improving the performance of predicting the concordance correlation coefficient for arousal and valence dimensions. Experimental results for predicting spontaneous and natural emotions on the RECOLA 2016 dataset have shown that the proposed approach based on visual information can achieve CCCs of 0.516 and 0.264 for valence and arousal, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.11976v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179063\", \"267821\", \"293179\", \"303467\", \"343905\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"145028","document_content":"# Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nUnderstanding the structure of complex activities in untrimmed videos is a challenging task in the area of action recognition. One problem here is that this task usually requires a large amount of hand-annotated minute- or even hour-long video data, but annotating such data is very time consuming and can not easily be automated or scaled. To address this problem, this paper proposes an approach for the unsupervised learning of actions in untrimmed video sequences based on a joint visual-temporal embedding space. To this end, we combine a visual embedding based on a predictive U-Net architecture with a temporal continuous function. The resulting representation space allows detecting relevant action clusters based on their visual as well as their temporal appearance. The proposed method is evaluated on three standard benchmark datasets, Breakfast Actions, INRIA YouTube Instructional Videos, and 50 Salads. We show that the proposed approach is able to provide a meaningful visual and temporal embedding out of the visual cues present in contiguous video frames and is suitable for the task of unsupervised temporal segmentation of actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.11122v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1960\", \"5676\", \"46493\", \"46854\", \"19925\", \"29561\", \"52435\", \"58191\", \"61811\", \"126819\"], \"outgoing_citations\": [\"138320\", \"164400\", \"182786\", \"191573\", \"198711\", \"203523\", \"210413\", \"231686\", \"237402\", \"237765\", \"248730\", \"264963\", \"270713\", \"280328\", \"287541\", \"292797\", \"295738\", \"297838\", \"298629\", \"302940\", \"303174\", \"307001\", \"310322\", \"316472\", \"317379\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"145093","document_content":"# Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations\n## Categories\n- Artificial Intelligence\n## Abstract\nIn recent years, there has been an increasing interest in studying causality-related properties in machine learning models generally, and in generative models in particular. While that is well motivated, it inherits the fundamental computational hardness of probabilistic inference, making exact reasoning intractable. Probabilistic tractable models have also recently emerged, which guarantee that conditional marginals can be computed in time linear in the size of the model, where the model is usually learned from data. Although initially limited to low tree-width models, recent tractable models such as sum product networks (SPNs) and probabilistic sentential decision diagrams (PSDDs) exploit efficient function representations and also capture high tree-width models.   In this paper, we ask the following technical question: can we use the distributions represented or learned by these models to perform causal queries, such as reasoning about interventions and counterfactuals? By appealing to some existing ideas on transforming such models to Bayesian networks, we answer mostly in the negative. We show that when transforming SPNs to a causal graph interventional reasoning reduces to computing marginal distributions; in other words, only trivial causal reasoning is possible. For PSDDs the situation is only slightly better. We first provide an algorithm for constructing a causal graph from a PSDD, which introduces augmented variables. Intervening on the original variables, once again, reduces to marginal distributions, but when intervening on the augmented variables, a deterministic but nonetheless causal-semantics can be provided for PSDDs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.10905v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"14297\", \"67971\"], \"outgoing_citations\": [\"206756\", \"208621\", \"248846\", \"256304\", \"257925\", \"263811\", \"299688\", \"319391\", \"335304\", \"350216\", \"361803\"]}","task_split":"paper_retrieval"}
{"document_id":"145384","document_content":"# Towards a Human-like Open-Domain Chatbot\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nWe present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.09977v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"24322\", \"2633\", \"2754\", \"2913\", \"3125\", \"3203\", \"3323\", \"3398\", \"5106\", \"5256\", \"6635\", \"8227\", \"15609\", \"15861\", \"15889\", \"16099\", \"16015\", \"16208\", \"16293\", \"16341\", \"16936\", \"22311\", \"24831\", \"27854\", \"29719\", \"29715\", \"31456\", \"35166\", \"35167\", \"38858\", \"42093\", \"47062\", \"47698\", \"52433\", \"55305\", \"56179\", \"70804\", \"77878\", \"2611\", \"4636\", \"5301\", \"6852\", \"10508\", \"11785\", \"12049\", \"12396\", \"14944\", \"16064\", \"16345\", \"16789\", \"16752\", \"17922\", \"18243\", \"18585\", \"20917\", \"21147\", \"21665\", \"22451\", \"22726\", \"22918\", \"23248\", \"23521\", \"24007\", \"24372\", \"24400\", \"24605\", \"27098\", \"30923\", \"31077\", \"31712\", \"125732\", \"35220\", \"36866\", \"37830\", \"39406\", \"40185\", \"42396\", \"42440\", \"43047\", \"43331\", \"43657\", \"44072\", \"44409\", \"44892\", \"44646\", \"44891\", \"44997\", \"45395\", \"45320\", \"45883\", \"132431\", \"46328\", \"46949\", \"47718\", \"48120\", \"48100\", \"51375\", \"51576\", \"55210\", \"55491\", \"55679\", \"55799\", \"56030\", \"56800\", \"56830\", \"59514\", \"62145\", \"62674\", \"63319\", \"65469\", \"66042\", \"66590\", \"67345\", \"68085\", \"70818\", \"70993\", \"72075\", \"74541\", \"75515\", \"76993\", \"78504\", \"79119\", \"111957\", \"80466\", \"80579\", \"80700\", \"83056\", \"83014\", \"91694\", \"91695\", \"92080\", \"97088\", \"98792\", \"100542\", \"103544\", \"104906\", \"107933\", \"115155\", \"115891\", \"127012\", \"128396\", \"128718\", \"129680\", \"132645\", \"132683\", \"136417\", \"141780\", \"51468\", \"77867\", \"80452\", \"80819\", \"81301\", \"82502\", \"87309\", \"88992\", \"89686\", \"89891\", \"91706\", \"91677\", \"92152\", \"92904\", \"93520\", \"94347\", \"94773\", \"94846\", \"95282\", \"95588\", \"95728\", \"95963\", \"96343\", \"96551\", \"98073\", \"98227\", \"98658\", \"99088\", \"100574\", \"101480\", \"101840\", \"103430\", \"103765\", \"105483\", \"106904\", \"107358\", \"104148\", \"109177\", \"113968\", \"115538\", \"116028\", \"116754\", \"116873\", \"116949\", \"117506\", \"119586\", \"123020\", \"123532\", \"125425\", \"127463\", \"127592\", \"128257\", \"128407\", \"129809\", \"132894\", \"134313\", \"134539\", \"137742\", \"137731\", \"139637\", \"141764\", \"142217\", \"142780\", \"142985\", \"139404\", \"186662\", \"186970\", \"163640\", \"184231\"], \"outgoing_citations\": [\"158493\", \"167338\", \"179568\", \"180695\", \"189217\", \"192158\", \"197057\", \"197924\", \"200893\", \"201867\", \"205147\", \"216953\", \"217640\", \"229813\", \"232274\", \"235913\", \"238703\", \"244479\", \"256448\", \"256632\", \"257858\", \"260587\", \"270040\", \"272497\", \"275597\", \"276365\", \"276382\", \"289943\", \"295863\", \"296250\", \"309411\", \"310740\", \"310903\", \"316328\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"145402","document_content":"# Conversations with Documents. An Exploration of Document-Centered Assistance\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Human-Computer Interaction\n- Information Retrieval\n## Abstract\nThe role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3343413.3377971\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Information Retrieval\"], \"incoming_citations\": [\"80634\", \"40064\", \"49873\", \"73917\", \"74036\", \"82199\", \"117347\"], \"outgoing_citations\": [\"181185\", \"181988\", \"190664\", \"200155\", \"216563\", \"220723\", \"247060\", \"266850\", \"268671\", \"270020\", \"279213\", \"311684\"]}","task_split":"paper_retrieval"}
{"document_id":"145493","document_content":"# Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nWith the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally improve the performance of individual task due to knowledge sharing in multi-task learning. While a few existing works have studied long-term rewards in recommendations, they mainly focus on a single recommendation task. In this paper, we propose {\\it PoDiRe}: a \\underline{po}licy \\underline{di}stilled \\underline{re}commender that can address long-term rewards of recommendations and simultaneously handle multiple recommendation tasks. This novel recommendation solution is based on a marriage of deep reinforcement learning and knowledge distillation techniques, which is able to establish knowledge sharing among different tasks and reduce the size of a learning model. The resulting model is expected to attain better performance and lower response latency for real-time recommendation services. In collaboration with Samsung Game Launcher, one of the world's largest commercial mobile game platforms, we conduct a comprehensive experimental study on large-scale real data with hundreds of millions of events and show that our solution outperforms many state-of-the-art methods in terms of several standard evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.09595v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177138\", \"200417\", \"202966\", \"207046\", \"210118\", \"210576\", \"217191\", \"217192\", \"220847\", \"225819\", \"232998\", \"233018\", \"234738\", \"241732\", \"258416\", \"259683\", \"260314\", \"260407\", \"263265\", \"282038\", \"285181\", \"302952\", \"302967\", \"324696\"]}","task_split":"paper_retrieval"}
{"document_id":"145538","document_content":"# Secondary Use of Electronic Health Record: Opportunities and Challenges\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nIn present technological era, healthcare providers generate huge amount of clinical data on daily basis. Generated clinical data is stored digitally in the form of Electronic Health Records (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients' primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues for individuals. Secondly, EHR data is also made accessible to various stake holders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiples agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of an individual could be severely affected when their sensitive private information contained in EHR is leaked or exposed to public. Data leak can cause financial losses or an individuals may encounter social boycott if their medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as GDPR, HIPAA and MHR. However, continually evolving state-of-the-art techniques in machine learning, data analytics and hacking are making it even more difficult to completely protect individual's \/ patient's privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses effect patients' privacy. Secondly, we have critically analyzed GDPR and highlighted possible areas of improvement, considering escalating use of technology and different secondary uses of EHR.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2020.3011099\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [\"19400\", \"193525\", \"31375\"], \"outgoing_citations\": [\"159867\", \"181381\", \"193525\", \"206238\", \"207294\", \"209259\", \"220641\", \"224512\", \"236687\", \"264024\", \"287367\", \"298095\", \"303053\"]}","task_split":"paper_retrieval"}
{"document_id":"145627","document_content":"# Learning Non-Markovian Reward Models in MDPs\n## Categories\n- Artificial Intelligence\n## Abstract\nThere are situations in which an agent should receive rewards only after having accomplished a series of previous tasks. In other words, the reward that the agent receives is non-Markovian. One natural and quite general way to represent history-dependent rewards is via a Mealy machine; a finite state automaton that produces output sequences (rewards in our case) from input sequences (state\/action observations in our case). In our formal setting, we consider a Markov decision process (MDP) that models the dynamic of the environment in which the agent evolves and a Mealy machine synchronised with this MDP to formalise the non-Markovian reward function. While the MDP is known by the agent, the reward function is unknown from the agent and must be learnt.   Learning non-Markov reward functions is a challenge. Our approach to overcome this challenging problem is a careful combination of the Angluin's L* active learning algorithm to learn finite automata, testing techniques for establishing conformance of finite model hypothesis and optimisation techniques for computing optimal strategies in Markovian (immediate) reward MDPs. We also show how our framework can be combined with classical heuristics such as Monte Carlo Tree Search. We illustrate our algorithms and a preliminary implementation on two typical examples for AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.09293v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"96439\", \"19828\", \"154720\", \"67335\"], \"outgoing_citations\": [\"224361\", \"234418\", \"257501\"]}","task_split":"paper_retrieval"}
{"document_id":"145664","document_content":"# PCGRL: Procedural Content Generation via Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.09212v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"18344\", \"24178\", \"133218\", \"18372\", \"21243\", \"28677\", \"33080\", \"33213\", \"33220\", \"35150\", \"37071\", \"38564\", \"39374\", \"39691\", \"41052\", \"47013\", \"51175\", \"64439\", \"81816\", \"87353\", \"95477\", \"95739\", \"107442\", \"114931\", \"142583\", \"96766\", \"47007\", \"124521\", \"143545\", \"153614\", \"193434\"], \"outgoing_citations\": [\"145053\", \"193434\", \"202457\", \"216607\", \"226544\", \"226789\", \"233501\", \"240572\", \"257674\", \"274886\", \"297233\"]}","task_split":"paper_retrieval"}
{"document_id":"145738","document_content":"# SOLAR: Second-Order Loss and Attention for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nRecent works in deep-learning have shown that second-order information is beneficial in many computer-vision tasks. Second-order information can be enforced both in the spatial context and the abstract feature dimensions. In this work, we explore two second-order components. One is focused on second-order spatial information to increase the performance of image descriptors, both local and global. It is used to re-weight feature maps, and thus emphasise salient image locations that are subsequently used for description. The second component is concerned with a second-order similarity (SOS) loss, that we extend to global descriptors for image retrieval, and is used to enhance the triplet loss with hard-negative mining. We validate our approach on two different tasks and datasets for image retrieval and image matching. The results show that our two second-order components complement each other, bringing significant performance improvements in both tasks and lead to state-of-the-art results across the public benchmarks. Code available at: http:\/\/github.com\/tonyngjichun\/SOLAR","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08972v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"458\", \"1079\", \"34971\", \"73146\", \"85376\", \"22193\", \"28643\", \"29310\", \"31118\", \"43726\", \"44154\", \"46590\", \"48708\", \"61377\", \"79969\", \"111811\", \"117835\", \"147155\"], \"outgoing_citations\": [\"169271\", \"170868\", \"180184\", \"190477\", \"191270\", \"207283\", \"237248\", \"251473\", \"264978\", \"268476\", \"294983\", \"295204\", \"295615\", \"301572\", \"303067\", \"304501\", \"314231\", \"327312\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"145761","document_content":"# MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nConversational agents such as Cortana, Alexa and Siri are continuously working on increasing their capabilities by adding new domains. The support of a new domain includes the design and development of a number of NLU components for domain classification, intents classification and slots tagging (including named entity recognition). Each component only performs well when trained on a large amount of labeled data. Second, these components are deployed on limited-memory devices which requires some model compression. Third, for some domains such as the health domain, it is hard to find a single training data set that covers all the required slot types. To overcome these mentioned problems, we present a multi-task transformer-based neural architecture for slot tagging. We consider the training of a slot tagger using multiple data sets covering different slot types as a multi-task learning problem. The experimental results on the biomedical domain have shown that the proposed approach outperforms the previous state-of-the-art systems for slot tagging on the different benchmark biomedical datasets in terms of (time and memory) efficiency and effectiveness. The output slot tagger can be used by the conversational agent to better identify entities in the input utterances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08904v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2566\", \"55453\", \"17477\", \"30029\", \"48718\", \"129792\", \"126679\"], \"outgoing_citations\": [\"122498\", \"191145\", \"191942\", \"194861\", \"200764\", \"243880\", \"249726\"]}","task_split":"paper_retrieval"}
{"document_id":"145777","document_content":"# Progressive Local Filter Pruning for Image Retrieval Acceleration\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper focuses on network pruning for image retrieval acceleration. Prevailing image retrieval works target at the discriminative feature learning, while little attention is paid to how to accelerate the model inference, which should be taken into consideration in real-world practice. The challenge of pruning image retrieval models is that the middle-level feature should be preserved as much as possible. Such different requirements of the retrieval and classification model make the traditional pruning methods not that suitable for our task. To solve the problem, we propose a new Progressive Local Filter Pruning (PLFP) method for image retrieval acceleration. Specifically, layer by layer, we analyze the local geometric properties of each filter and select the one that can be replaced by the neighbors. Then we progressively prune the filter by gradually changing the filter weights. In this way, the representation ability of the model is preserved. To verify this, we evaluate our method on two widely-used image retrieval datasets,i.e., Oxford5k and Paris6K, and one person re-identification dataset,i.e., Market-1501. The proposed method arrives with superior performance to the conventional pruning methods, suggesting the effectiveness of the proposed method for image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08878v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"191645\", \"115346\"], \"outgoing_citations\": [\"156718\", \"190464\", \"212022\", \"220760\", \"242222\", \"251473\", \"260782\", \"280047\", \"280225\", \"285659\", \"296421\", \"303067\", \"303141\", \"311552\", \"316293\", \"320127\", \"329613\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"145784","document_content":"# Exploration Based Language Learning for Text-Based Games\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents. Moreover, they provide a learning environment in which these skills can be acquired through interactions with an environment rather than using fixed corpora. One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space. Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions. In this work, we propose to use the exploration approach of Go-Explore for solving text-based games. More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories. Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment. Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08868v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"22323\", \"36587\", \"21243\", \"62373\", \"128664\", \"119028\", \"140959\", \"141309\"], \"outgoing_citations\": [\"141309\", \"171239\", \"181620\", \"184995\", \"200920\", \"206705\", \"207273\", \"207533\", \"212299\", \"218923\", \"226321\", \"226327\", \"245813\", \"247163\", \"271868\", \"272290\", \"303349\", \"309886\", \"310370\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"145848","document_content":"# Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nInteractive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers' facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem --- Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN-RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong\/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08703v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"86085\", \"98034\"], \"outgoing_citations\": [\"199302\", \"254581\", \"275728\", \"290804\"]}","task_split":"paper_retrieval"}
{"document_id":"146003","document_content":"# Multilingual Denoising Pre-training for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nThis paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.08210v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11597\", \"14960\", \"24322\", \"32774\", \"274856\", \"2585\", \"2651\", \"2988\", \"3028\", \"5083\", \"6241\", \"8136\", \"8834\", \"9618\", \"9883\", \"15904\", \"15836\", \"15848\", \"15925\", \"15935\", \"16051\", \"16144\", \"16276\", \"16513\", \"18656\", \"22237\", \"25329\", \"25308\", \"25436\", \"25863\", \"26304\", \"26658\", \"26695\", \"26981\", \"29762\", \"34878\", \"35941\", \"38433\", \"38588\", \"38855\", \"46280\", \"52464\", \"55305\", \"61237\", \"63777\", \"75094\", \"81858\", \"93450\", \"127662\", \"31574\", \"1236\", \"1776\", \"2536\", \"2943\", \"5522\", \"7177\", \"7620\", \"10734\", \"11790\", \"12125\", \"13029\", \"14226\", \"14943\", \"15355\", \"15844\", \"16406\", \"17146\", \"17281\", \"17849\", \"17897\", \"18243\", \"19018\", \"19914\", \"20273\", \"20438\", \"20703\", \"20832\", \"21287\", \"21620\", \"21636\", \"21921\", \"23177\", \"23119\", \"24048\", \"24303\", \"24568\", \"24660\", \"24892\", \"24961\", \"25273\", \"25297\", \"5068\", \"25886\", \"25870\", \"26147\", \"26460\", \"27157\", \"27657\", \"27630\", \"27836\", \"29515\", \"30029\", \"30741\", \"31586\", \"33159\", \"33414\", \"33603\", \"34638\", \"34890\", \"35580\", \"35733\", \"38116\", \"39468\", \"39510\", \"40709\", \"42049\", \"42396\", \"42513\", \"42553\", \"42931\", \"42799\", \"43140\", \"43361\", \"43377\", \"43545\", \"43451\", \"43901\", \"43938\", \"44399\", \"44583\", \"45187\", \"44926\", \"44997\", \"45259\", \"45496\", \"45837\", \"46157\", \"46127\", \"45996\", \"46764\", \"48100\", \"48458\", \"48536\", \"48738\", \"50511\", \"50756\", \"51000\", \"55172\", \"55183\", \"55223\", \"55130\", \"55493\", \"55589\", \"55413\", \"55767\", \"55895\", \"55808\", \"59110\", \"59615\", \"59790\", \"60936\", \"61113\", \"61949\", \"63634\", \"63847\", \"66349\", \"68902\", \"69343\", \"69820\", \"71888\", \"73102\", \"73442\", \"73265\", \"75038\", \"77487\", \"77611\", \"79278\", \"80136\", \"81420\", \"91644\", \"88167\", \"88960\", \"89774\", \"90294\", \"91916\", \"92112\", \"92080\", \"92489\", \"98946\", \"100606\", \"104803\", \"104826\", \"108602\", \"111872\", \"117747\", \"121147\", \"121306\", \"127879\", \"132595\", \"136417\", \"158975\", \"23311\", \"77463\", \"77667\", \"77689\", \"77651\", \"86571\", \"86682\", \"87054\", \"88410\", \"89734\", \"89663\", \"91536\", \"91763\", \"93229\", \"94764\", \"95195\", \"96126\", \"96097\", \"95923\", \"96371\", \"96300\", \"96469\", \"97253\", \"97207\", \"98227\", \"99945\", \"100144\", \"93480\", \"108342\", \"113058\", \"115867\", \"117463\", \"118104\", \"122375\", \"123181\", \"127861\", \"129632\", \"131659\", \"132587\", \"133020\", \"128024\", \"136153\", \"138930\", \"139450\", \"143551\", \"157121\", \"165224\", \"150615\"], \"outgoing_citations\": [\"141886\", \"150830\", \"157472\", \"158493\", \"158546\", \"159706\", \"161599\", \"167338\", \"170657\", \"171727\", \"176571\", \"181277\", \"182594\", \"182716\", \"187236\", \"187452\", \"192337\", \"194056\", \"197007\", \"200263\", \"217957\", \"218851\", \"219083\", \"220165\", \"234807\", \"242125\", \"249315\", \"251816\", \"251974\", \"253559\", \"258163\", \"267524\", \"268684\", \"268909\", \"280486\", \"280884\", \"290778\", \"300548\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"146082","document_content":"# Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed\n## Categories\n- Information Retrieval\n- I.2; I.2.1; I.2.7; J.4\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn the last decade, social networks became most popular medium for communication and interaction. As an example, micro-blogging service Twitter has more than 200 million registered users who exchange more than 65 million posts per day. Users express their thoughts, ideas, and even their intentions through these tweets. Most of the tweets are written informally and often in slang language, that contains misspelt and abbreviated words. This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques. It starts by presenting the method we used to construct our own dataset from extracted Twitter feeds. Following that, we present two techniques of feature selection followed by classification. In the first technique, we use Information Gain as a one-phase feature selection, followed by supervised classification algorithms. In the second technique, we use a hybrid approach based on forward feature selection algorithm in which two feature selection techniques employed followed by classification algorithms. We examine these two techniques with four classification algorithms. We evaluate them using our own dataset, and we critically review the results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.10380v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"I.2; I.2.1; I.2.7; J.4\", \"cs.AI\", \"cs.IR\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"I.2; I.2.1; I.2.7; J.4\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"258761\", \"275971\", \"349540\"]}","task_split":"paper_retrieval"}
{"document_id":"146216","document_content":"# EdgeNets:Edge Varying Graph Neural Networks\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n- Signal Processing\n## Abstract\nDriven by the outstanding performance of neural networks in the structured Euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. An interesting conclusion is the unification of GCNNs and GATs -- approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.07620v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"eess.SP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Signal Processing\", \"Machine Learning\"], \"incoming_citations\": [\"18664\", \"77915\", \"13635\", \"40878\", \"65820\", \"84537\", \"90849\", \"93509\", \"107810\", \"107838\", \"112483\", \"119464\", \"121626\", \"135388\", \"91251\", \"137902\", \"144286\", \"177249\", \"186804\"], \"outgoing_citations\": [\"112378\", \"137902\", \"144286\", \"160051\", \"173794\", \"184286\", \"186804\", \"193149\", \"196606\", \"201055\", \"203910\", \"204871\", \"206470\", \"211381\", \"215435\", \"223897\", \"229324\", \"233653\", \"252182\", \"265828\", \"269273\", \"279575\", \"279653\", \"298328\", \"303863\", \"305647\", \"328228\", \"336185\", \"343067\", \"352782\", \"352834\", \"353328\"]}","task_split":"paper_retrieval"}
{"document_id":"146437","document_content":"# Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning\n## Categories\n- Information Retrieval\n- H.3.3; I.2.6; I.2.7; I.5.4\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWhen creating text classification systems, one of the major bottlenecks is the annotation of training data. Active learning has been proposed to address this bottleneck using stopping methods to minimize the cost of data annotation. An important capability for improving the utility of stopping methods is to effectively forecast the performance of the text classification models. Forecasting can be done through the use of logarithmic models regressed on some portion of the data as learning is progressing. A critical unexplored question is what portion of the data is needed for accurate forecasting. There is a tension, where it is desirable to use less data so that the forecast can be made earlier, which is more useful, versus it being desirable to use more data, so that the forecast can be more accurate. We find that when using active learning it is even more important to generate forecasts earlier so as to make them more useful and not waste annotation effort. We investigate the difference in forecasting difficulty when using accuracy and F-measure as the text classification system performance metrics and we find that F-measure is more difficult to forecast. We conduct experiments on seven text classification datasets in different semantic domains with different characteristics and with three different base machine learning algorithms. We find that forecasting is easiest for decision tree learning, moderate for Support Vector Machines, and most difficult for neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICSC.2020.00018\", \"primary_category\": \"cs.IR\", \"categories\": [\"H.3.3; I.2.6; I.2.7; I.5.4\", \"cs.IR\", \"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"H.3.3; I.2.6; I.2.7; I.5.4\", \"Information Retrieval\", \"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"103555\"], \"outgoing_citations\": [\"201541\", \"201544\", \"244329\", \"244334\", \"273569\", \"314005\", \"324367\", \"324405\"]}","task_split":"paper_retrieval"}
{"document_id":"146476","document_content":"# A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions\n## Categories\n- Artificial Intelligence\n## Abstract\nReinforcement learning is one of the core components in designing an artificial intelligent system emphasizing real-time response. Reinforcement learning influences the system to take actions within an arbitrary environment either having previous knowledge about the environment model or not. In this paper, we present a comprehensive study on Reinforcement Learning focusing on various dimensions including challenges, the recent development of different state-of-the-art techniques, and future directions. The fundamental objective of this paper is to provide a framework for the presentation of available methods of reinforcement learning that is informative enough and simple to follow for the new researchers and academics in this domain considering the latest concerns. First, we illustrated the core techniques of reinforcement learning in an easily understandable and comparable way. Finally, we analyzed and depicted the recent developments in reinforcement learning approaches. My analysis pointed out that most of the models focused on tuning policy values rather than tuning other things in a particular state of reasoning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.06921v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"231306\", \"254596\", \"280296\"]}","task_split":"paper_retrieval"}
{"document_id":"146523","document_content":"# Discriminator Soft Actor Critic without Extrinsic Rewards\n## Categories\n- Machine Learning\n## Abstract\nIt is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and generative adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose Discriminator Soft Actor Critic (DSAC). It uses a reward function based on adversarial inverse reinforcement learning instead of constant rewards. We evaluated it on PyBullet environments with only four expert trajectories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.06808v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"105601\"], \"outgoing_citations\": [\"152120\", \"161444\", \"184304\", \"199385\", \"251924\", \"280655\"]}","task_split":"paper_retrieval"}
{"document_id":"146535","document_content":"# FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback\n## Categories\n- Artificial Intelligence\n## Abstract\nReinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including robotics and computer games, human players often find it easier to obtain higher rewards in some environments than reinforcement learning algorithms. This is especially true of high-dimensional state spaces where the reward obtained by the agent is sparse or extremely delayed. In this paper, we seek to effectively integrate feedback signals supplied by a human operator with deep reinforcement learning algorithms in high-dimensional state spaces. We call this FRESH (Feedback-based REward SHaping). During training, a human operator is presented with trajectories from a replay buffer and then provides feedback on states and actions in the trajectory. In order to generalize feedback signals provided by the human operator to previously unseen states and actions at test-time, we use a feedback neural network. We use an ensemble of neural networks with a shared network architecture to represent model uncertainty and the confidence of the neural network in its output. The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment. We evaluate our approach on the Bowling and Skiing Atari games in the arcade learning environment. Although human experts have been able to achieve high scores in these environments, state-of-the-art deep learning algorithms perform poorly. We observe that FRESH is able to achieve much higher scores than state-of-the-art deep learning algorithms in both environments. FRESH also achieves a 21.4% higher score than a human expert in Bowling and does as well as a human expert in Skiing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.06781v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"59760\", \"5616\", \"59761\", \"115937\", \"110680\", \"115266\"], \"outgoing_citations\": [\"165614\", \"175265\", \"199302\", \"212704\", \"215235\", \"243235\", \"254581\", \"263957\", \"265615\", \"275728\", \"297268\", \"298281\"]}","task_split":"paper_retrieval"}
{"document_id":"146592","document_content":"# Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.06659v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17233\", \"47568\", \"56935\", \"60277\", \"80979\", \"111407\"], \"outgoing_citations\": [\"262122\"]}","task_split":"paper_retrieval"}
{"document_id":"146594","document_content":"# Text-to-Image Generation with Attention Based Recurrent Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversarial networks. While the formers are approximated by using variational auto-encoders and rely on the intractable inference that can hamper their performance, the latter is unstable to train due to Nash equilibrium based objective function. We develop a tractable and stable caption-based image generation model. The model uses an attention-based encoder to learn word-to-pixel dependencies. A conditional autoregressive based decoder is used for learning pixel-to-pixel dependencies and generating images. Experimentations are performed on Microsoft COCO, and MNIST-with-captions datasets and performance is evaluated by using the Structural Similarity Index. Results show that the proposed model performs better than contemporary approaches and generate better quality images. Keywords: Generative image modeling, autoregressive image modeling, caption-based image generation, neural attention, recurrent neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.06658v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271821\", \"299534\", \"302162\", \"303715\", \"308711\", \"310737\", \"311388\", \"317401\"]}","task_split":"paper_retrieval"}
{"document_id":"146787","document_content":"# Knowledge Discovery from Social Media using Big Data provided Sentiment Analysis (SoMABiT)\n## Categories\n- Social and Information Networks\n- Databases\n- Machine Learning\n## Abstract\nIn todays competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries. To this aim, the use of efficient analytic algorithms ensures a better understanding of customer feedback and improves the next generation of products. Accordingly, the dramatic increase in using social media in daily life provides beneficial sources for market analytics. But how traditional analytic algorithms and methods can scale up for such disparate and multi-structured data sources is the main challenge in this regard. This paper presents and discusses the technological and scientific focus of the SoMABiT as a social media analysis platform using big data technology. Sentiment analysis has been employed in order to discover knowledge from social media. The use of MapReduce and developing a distributed algorithm towards an integrated platform that can scale for any data volume and provide a social media-driven knowledge is the main novelty of the proposed concept in comparison to the state-of-the-art technologies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05996v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.DB\", \"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Databases\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"109913\", \"222315\", \"146764\"], \"outgoing_citations\": [\"198260\"]}","task_split":"paper_retrieval"}
{"document_id":"146803","document_content":"# User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n- Machine Learning\n- Computation and Language\n## Abstract\nPeople are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for non-programming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. (2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues - intent learning and user adaptation - for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a one-shot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3377325.3377490\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"245399\", \"259723\", \"267161\", \"280646\", \"285317\", \"296250\", \"302516\", \"310737\", \"310903\", \"311384\"]}","task_split":"paper_retrieval"}
{"document_id":"146893","document_content":"# Fact-aware Sentence Split and Rephrase with Permutation Invariant Training\n## Categories\n- Computation and Language\n## Abstract\nSentence Split and Rephrase aims to break down a complex sentence into several simple sentences with its meaning preserved. Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs, which takes a complex sentence as input and sequentially generates a series of simple sentences. However, the conventional seq2seq learning has two limitations for this task: (1) it does not take into account the facts stated in the long sentence; As a result, the generated simple sentences may miss or inaccurately state the facts in the original sentence. (2) The order variance of the simple sentences to be generated may confuse the seq2seq model during training because the simple sentences derived from the long source sentence could be in any order.   To overcome the challenges, we first propose the Fact-aware Sentence Encoding, which enables the model to learn facts from the long sentence and thus improves the precision of sentence split; then we introduce Permutation Invariant Training to alleviate the effects of order variance in seq2seq learning for this task. Experiments on the WebSplit-v1.0 benchmark dataset show that our approaches can largely improve the performance over the previous seq2seq learning approaches. Moreover, an extrinsic evaluation on oie-benchmark verifies the effectiveness of our approaches by an observation that splitting long sentences with our state-of-the-art model as preprocessing is helpful for improving OpenIE performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.11383v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21684\"], \"outgoing_citations\": [\"182778\", \"233420\", \"279433\", \"289210\", \"296198\", \"297170\"]}","task_split":"paper_retrieval"}
{"document_id":"146915","document_content":"# Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results\n## Categories\n- Machine Learning\n- Image and Video Processing\n## Abstract\nDeep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05647v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"9036\", \"37270\", \"1973\", \"4079\", \"8155\", \"71429\", \"99336\", \"110259\", \"135674\", \"4119\", \"7205\", \"7979\", \"17947\", \"19599\", \"21206\", \"32407\", \"37025\", \"39808\", \"41753\", \"46926\", \"49719\", \"50294\", \"51220\", \"54218\", \"54543\", \"63371\", \"63956\", \"64978\", \"65658\", \"68872\", \"71942\", \"72995\", \"78786\", \"94289\", \"108307\", \"83656\", \"100571\", \"102450\", \"105490\", \"136201\"], \"outgoing_citations\": [\"151250\", \"157890\", \"159563\", \"163734\", \"169850\", \"173594\", \"177616\", \"179698\", \"200704\", \"201839\", \"205620\", \"212786\", \"214805\", \"229248\", \"231414\", \"234581\", \"275599\", \"288545\"]}","task_split":"paper_retrieval"}
{"document_id":"146920","document_content":"# Multimodal Story Generation on Plural Images\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nTraditionally, text generation models take in a sequence of text as input, and iteratively generate the next most probable word using pre-trained parameters. In this work, we propose the architecture to use images instead of text as the input of the text generation model, called StoryGen. In the architecture, we design a Relational Text Data Generator algorithm that relates different features from multiple images. The output samples from the model demonstrate the ability to generate meaningful paragraphs of text containing the extracted features from the input images. This is an undergraduate project report. Completed Dec. 2019 at the Cooper Union.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.10980v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"255035\", \"269654\", \"303997\", \"320003\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"146975","document_content":"# AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMany sequence-to-sequence dialogue models tend to generate safe, uninformative responses. There have been various useful efforts on trying to eliminate them. However, these approaches either improve decoding algorithms during inference, rely on hand-crafted features, or employ complex models. In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. Specifically, we start with a simple yet effective automatic metric, AvgOut, which calculates the average output probability distribution of all time steps on the decoder side during training. This metric directly estimates which tokens are more likely to be generated, thus making it a faithful evaluation of the model diversity (i.e., for diverse models, the token probabilities should be more evenly distributed rather than peaked at a few dull tokens). We then leverage this novel metric to propose three models that promote diversity without losing relevance. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch; the second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level; the third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal. Moreover, we experiment with a hybrid model by combining the loss terms of MinAvgOut and RL. All four models outperform their base LSTM-RNN model on both diversity and relevance by a large margin, and are comparable to or better than competitive baselines (also verified via human evaluation). Moreover, our approaches are orthogonal to the base model, making them applicable as an add-on to other emerging better dialogue models in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05467v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"48161\", \"94067\", \"142985\"], \"outgoing_citations\": [\"68965\", \"197057\", \"198185\", \"218932\", \"218973\", \"219198\", \"220509\", \"232783\", \"257858\", \"266671\", \"270040\", \"275595\", \"275597\", \"276365\", \"283618\", \"291102\", \"291289\", \"292261\", \"295863\", \"302815\", \"309411\", \"310377\", \"310717\", \"310740\", \"310903\", \"353790\"]}","task_split":"paper_retrieval"}
{"document_id":"147140","document_content":"# Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nGenerative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games. However, GANs have a fixed output size, so creating levels of arbitrary size for a dungeon crawling game is difficult. GANs also have trouble encoding semantic requirements that make levels interesting and playable. This paper combines a GAN approach to generating individual rooms with a graph grammar approach to combining rooms into a dungeon. The GAN captures design principles of individual rooms, but the graph grammar organizes rooms into a global layout with a sequence of obstacles determined by a designer. Room data from The Legend of Zelda is used to train the GAN. This approach is validated by a user study, showing that GAN dungeons are as enjoyable to play as a level from the original game, and levels generated with a graph grammar alone. However, GAN dungeons have rooms considered more complex, and plain graph grammar's dungeons are considered least complex and challenging. Only the GAN approach creates an extensive supply of both layouts and rooms, where rooms span across the spectrum of those seen in the training set to new creations merging design principles from multiple rooms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05065v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"47005\", \"33213\", \"41052\", \"41462\", \"72454\", \"74578\", \"95477\", \"94739\", \"109729\", \"111464\", \"122922\", \"125408\", \"133342\", \"133812\"], \"outgoing_citations\": [\"163503\", \"233501\", \"240572\", \"289709\"]}","task_split":"paper_retrieval"}
{"document_id":"147155","document_content":"# Unifying Deep Local and Global Features for Image Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been studied: global and local image features. In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction. We refer to the new model as DELG, standing for DEep Local and Global features. We leverage lessons from recent feature learning work and propose a model that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads -- requiring only image-level labels. We also introduce an autoencoder-based dimensionality reduction technique for local features, which is integrated into the model, improving training efficiency and matching performance. Comprehensive experiments show that our model achieves state-of-the-art image retrieval on the Revisited Oxford and Paris datasets, and state-of-the-art single-model instance-level recognition on the Google Landmarks dataset v2. Code and models are available at https:\/\/github.com\/tensorflow\/models\/tree\/master\/research\/delf .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05027v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"94405\", \"458\", \"5681\", \"7251\", \"17243\", \"34971\", \"70613\", \"1439\", \"7275\", \"20231\", \"21970\", \"22193\", \"25098\", \"25890\", \"27270\", \"28630\", \"31118\", \"35104\", \"36782\", \"36894\", \"37306\", \"39926\", \"40425\", \"28791\", \"47219\", \"56799\", \"61377\", \"65943\", \"74621\", \"75031\", \"76106\", \"124630\", \"87306\", \"95066\", \"96787\", \"109398\", \"109645\", \"110289\", \"112888\", \"133188\"], \"outgoing_citations\": [\"133188\", \"135052\", \"145738\", \"180184\", \"180750\", \"181568\", \"186126\", \"187122\", \"190477\", \"191270\", \"191610\", \"192851\", \"206658\", \"207283\", \"208427\", \"230706\", \"235575\", \"237248\", \"237488\", \"246925\", \"250094\", \"251473\", \"264978\", \"268379\", \"277635\", \"280378\", \"281953\", \"295204\", \"295615\", \"303067\"]}","task_split":"paper_retrieval"}
{"document_id":"147183","document_content":"# Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning\n## Categories\n- Computation and Language\n- Cryptography and Security\n- Machine Learning\n- Machine Learning\n## Abstract\nWord embeddings, i.e., low-dimensional vector representations such as GloVe and SGNS, encode word \"meaning\" in the sense that distances between words' vectors correspond to their semantic proximity. This enables transfer learning of semantics for a variety of natural language processing tasks.   Word embeddings are typically trained on large public corpora such as Wikipedia or Twitter. We demonstrate that an attacker who can modify the corpus on which the embedding is trained can control the \"meaning\" of new and existing words by changing their locations in the embedding space. We develop an explicit expression over corpus features that serves as a proxy for distance between words and establish a causative relationship between its values and embedding distances. We then show how to use this relationship for two adversarial objectives: (1) make a word a top-ranked neighbor of another word, and (2) move a word from one semantic cluster to another.   An attack on the embedding can affect diverse downstream tasks, demonstrating for the first time the power of data poisoning in transfer learning scenarios. We use this attack to manipulate query expansion in information retrieval systems such as resume search, make certain names more or less visible to named entity recognition models, and cause new words to be translated to a particular target word regardless of the language. Finally, we show how the attacker can generate linguistically likely corpus modifications, thus fooling defenses that attempt to filter implausible sentences from the corpus using a language model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.04935v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Cryptography and Security\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"74921\", \"4082\", \"80005\", \"30055\", \"91809\", \"59894\", \"108382\", \"114155\", \"116261\", \"98550\", \"110549\", \"113263\"], \"outgoing_citations\": [\"171039\", \"197656\", \"199319\", \"212305\", \"214624\", \"214994\", \"219215\", \"232850\", \"234520\", \"234716\", \"236933\", \"245196\", \"246146\", \"247127\", \"247399\", \"251240\", \"253559\", \"258657\", \"260450\", \"261635\", \"264157\", \"267955\", \"270020\", \"272415\", \"274111\", \"275005\", \"285761\", \"289654\", \"317627\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"147253","document_content":"# Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.04732v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"10128\", \"81968\", \"99456\"], \"outgoing_citations\": [\"175336\", \"183268\", \"189847\", \"191881\", \"200744\", \"219483\", \"220129\", \"239535\", \"243863\", \"245591\", \"248652\", \"258888\", \"259346\", \"266120\", \"268829\", \"269205\", \"279950\", \"285788\", \"294023\", \"296681\", \"299461\", \"320837\", \"321369\", \"343358\"]}","task_split":"paper_retrieval"}
{"document_id":"147440","document_content":"# Nmbr9 as a Constraint Programming Challenge\n## Categories\n- Artificial Intelligence\n## Abstract\nModern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, contributes new generalized variants of the game suitable for benchmarking and testing, and describes a model for the presented variants. The question of the top possible score in the standard game is an open challenge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.04238v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"147441\"]}","task_split":"paper_retrieval"}
{"document_id":"147453","document_content":"# Towards High Performance Java-based Deep Learning Frameworks\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe advent of modern cloud services along with the huge volume of data produced on a daily basis, have set the demand for fast and efficient data processing. This demand is common among numerous application domains, such as deep learning, data mining, and computer vision. Prior research has focused on employing hardware accelerators as a means to overcome this inefficiency. This trend has driven software development to target heterogeneous execution, and several modern computing systems have incorporated a mixture of diverse computing components, including GPUs and FPGAs. However, the specialization of the applications' code for heterogeneous execution is not a trivial task, as it requires developers to have hardware expertise in order to obtain high performance. The vast majority of the existing deep learning frameworks that support heterogeneous acceleration, rely on the implementation of wrapper calls from a high-level programming language to a low-level accelerator backend, such as OpenCL, CUDA or HLS.   In this paper we have employed TornadoVM, a state-of-the-art heterogeneous programming framework to transparently accelerate Deep Netts; a Java-based deep learning framework. Our initial results demonstrate up to 8x performance speedup when executing the back propagation process of the network's training on AMD GPUs against the sequential execution of the original Deep Netts framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.04206v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"147539","document_content":"# Detecting New Word Meanings: A Comparison of Word Embedding Models in Spanish\n## Categories\n- Computation and Language\n## Abstract\nSemantic neologisms (SN) are defined as words that acquire a new word meaning while maintaining their form. Given the nature of this kind of neologisms, the task of identifying these new word meanings is currently performed manually by specialists at observatories of neology. To detect SN in a semi-automatic way, we developed a system that implements a combination of the following strategies: topic modeling, keyword extraction, and word sense disambiguation. The role of topic modeling is to detect the themes that are treated in the input text. Themes within a text give clues about the particular meaning of the words that are used, for example: viral has one meaning in the context of computer science (CS) and another when talking about health. To extract keywords, we used TextRank with POS tag filtering. With this method, we can obtain relevant words that are already part of the Spanish lexicon. We use a deep learning model to determine if a given keyword could have a new meaning. Embeddings that are different from all the known meanings (or topics) indicate that a word might be a valid SN candidate. In this study, we examine the following word embedding models: Word2Vec, Sense2Vec, and FastText. The models were trained with equivalent parameters using Wikipedia in Spanish as corpora. Then we used a list of words and their concordances (obtained from our database of neologisms) to show the different embeddings that each model yields. Finally, we present a comparison of these outcomes with the concordances of each word to show how we can determine if a word could be a valid candidate for SN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05285v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"74650\"], \"outgoing_citations\": [\"298514\", \"302936\"]}","task_split":"paper_retrieval"}
{"document_id":"147593","document_content":"# Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic melanoma segmentation in dermoscopic images is essential in computer-aided diagnosis of skin cancer. Existing methods may suffer from the hole and shrink problems with limited segmentation performance. To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning. Instead of regarding the segmentation task independently, we introduce a foreground network to detect melanoma lesions and a background network to mask non-melanoma regions. Moreover, we propose adaptive atrous convolution (AAC) and knowledge aggregation module (KAM) to fill holes and alleviate the shrink problems. AAC explicitly controls the receptive field at multiple scales and KAM convolves shallow feature maps by dilated convolutions with adaptive receptive fields, which are adjusted according to deep feature maps. In addition, a novel mutual loss is proposed to utilize the dependency between the foreground and background networks, thereby enabling the reciprocally influence within these two networks. Consequently, this mutual training strategy enables the semi-supervised learning and improve the boundary-sensitivity. Training with Skin Imaging Collaboration (ISIC) 2018 skin lesion segmentation dataset, our method achieves a dice co-efficient of 86.4% and shows better performance compared with state-of-the-art melanoma segmentation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.03893v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"176840\"], \"outgoing_citations\": [\"230522\", \"254665\"]}","task_split":"paper_retrieval"}
{"document_id":"147636","document_content":"# Reward Engineering for Object Pick and Place Training\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nRobotic grasping is a crucial area of research as it can result in the acceleration of the automation of several Industries utilizing robots ranging from manufacturing to healthcare. Reinforcement learning is the field of study where an agent learns a policy to execute an action by exploring and exploiting rewards from an environment. Reinforcement learning can thus be used by the agent to learn how to execute a certain task, in our case grasping an object. We have used the Pick and Place environment provided by OpenAI's Gym to engineer rewards. Hindsight Experience Replay (HER) has shown promising results with problems having a sparse reward. In the default configuration of the OpenAI baseline and environment the reward function is calculated using the distance between the target location and the robot end-effector. By weighting the cost based on the distance of the end-effector from the goal in the x,y and z-axes we were able to almost halve the learning time compared to the baselines provided by OpenAI, an intuitive strategy that further reduced learning time. In this project, we were also able to introduce certain user desired trajectories in the learnt policies (city-block \/ Manhattan trajectories). This helps us understand that by engineering the rewards we can tune the agent to learn policies in a certain way even if it might not be the most optimal but is the desired manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.03792v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"51143\", \"69452\"], \"outgoing_citations\": [\"260018\"]}","task_split":"paper_retrieval"}
{"document_id":"147672","document_content":"# Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification. However, the ASR module might misrecognize some speeches and the first best interpretation could be erroneous and noisy. Solely relying on the first best interpretation could make the performance of downstream tasks non-optimal. To address this issue, we introduce a series of simple yet efficient models for improving the understanding of semantics of the input speeches by collectively exploiting the n-best speech interpretations from the ASR module.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05284v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"3823\", \"60797\", \"82073\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"147726","document_content":"# Are you still with me? Continuous Engagement Assessment from a Robot's Point of View\n## Categories\n- Robotics\n## Abstract\nContinuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way towards in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behaviour optimisation. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilising CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community as a tool to measure engagement in a variety of settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/frobt.2020.00116\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"8051\", \"75677\"], \"outgoing_citations\": [\"294529\"]}","task_split":"paper_retrieval"}
{"document_id":"147768","document_content":"# Inductive Document Network Embedding with Topic-Word Attention\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nDocument network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding approaches by incorporating the text content associated with the nodes in their formulations. In most cases, it is hard to interpret the learned representations. Moreover, little importance is given to the generalization to new documents that are not observed within the network. In this paper, we propose an interpretable and inductive document network embedding method. We introduce a novel mechanism, the Topic-Word Attention (TWA), that generates document representations based on the interplay between word and topic representations. We train these word and topic vectors through our general model, Inductive Document Network Embedding (IDNE), by leveraging the connections in the document network. Quantitative evaluations show that our approach achieves state-of-the-art performance on various networks and we qualitatively show that our model produces meaningful and interpretable representations of the words, topics and documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.03369v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"47502\", \"73253\", \"132430\"], \"outgoing_citations\": [\"197094\", \"197116\", \"239146\", \"261412\"]}","task_split":"paper_retrieval"}
{"document_id":"147885","document_content":"# Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification\n## Categories\n- Computers and Society\n- Computer Vision and Pattern Recognition\n## Abstract\nModern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.03071v2\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CV\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computers and Society\"], \"incoming_citations\": [\"51525\", \"116447\"], \"outgoing_citations\": [\"188069\", \"201077\", \"235160\", \"267612\", \"281201\", \"287611\", \"302157\", \"311084\", \"316430\", \"321122\", \"328120\"]}","task_split":"paper_retrieval"}
{"document_id":"148081","document_content":"# Coordination of Autonomous Vehicles: Taxonomy and Survey\n## Categories\n- Multiagent Systems\n## Abstract\nIn the near future, our streets will be populated by myriads of autonomous self-driving vehicles to serve our diverse mobility needs. This will raise the need to coordinate their movements in order to properly handle both access to shared resources (e.g., intersections and parking slots) and the execution of mobility tasks (e.g., platooning and ramp merging). In this paper, we firstly introduce the general issues associated to coordination of autonomous vehicles, by identifying and framing the key classes of coordination problems. Following, we overview the different approaches that can be adopted to manage such coordination problems, by classifying them in terms of the degree of autonomy in decision making that is left to autonomous vehicles during coordination. Finally, we overview some further peculiar challenges that research will have to address before autonomously coordinated vehicles can safely hit our streets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3431231\", \"primary_category\": \"cs.MA\", \"categories\": [\"cs.MA\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"148210","document_content":"# Backtracking Gradient Descent allowing unbounded learning rates\n## Categories\n- Optimization and Control\n- Machine Learning\n- Machine Learning\n## Abstract\nIn unconstrained optimisation on an Euclidean space, to prove convergence in Gradient Descent processes (GD) $x_{n+1}=x_n-\\delta _n \\nabla f(x_n)$ it usually is required that the learning rates $\\delta _n$'s are bounded: $\\delta _n\\leq \\delta $ for some positive $\\delta $. Under this assumption, if the sequence $x_n$ converges to a critical point $z$, then with large values of $n$ the update will be small because $||x_{n+1}-x_n||\\lesssim ||\\nabla f(x_n)||$. This may also force the sequence to converge to a bad minimum. If we can allow, at least theoretically, that the learning rates $\\delta _n$'s are not bounded, then we may have better convergence to better minima.   A previous joint paper by the author showed convergence for the usual version of Backtracking GD under very general assumptions on the cost function $f$. In this paper, we allow the learning rates $\\delta _n$ to be unbounded, in the sense that there is a function $h:(0,\\infty)\\rightarrow (0,\\infty )$ such that $\\lim _{t\\rightarrow 0}th(t)=0$ and $\\delta _n\\lesssim \\max \\{h(x_n),\\delta \\}$ satisfies Armijo's condition for all $n$, and prove convergence under the same assumptions as in the mentioned paper. It will be shown that this growth rate of $h$ is best possible if one wants convergence of the sequence $\\{x_n\\}$.   A specific way for choosing $\\delta _n$ in a discrete way connects to Two-way Backtracking GD defined in the mentioned paper. We provide some results which either improve or are implicitly contained in those in the mentioned paper and another recent paper on avoidance of saddle points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.02005v2\", \"primary_category\": \"math.OC\", \"categories\": [\"cs.LG\", \"stat.ML\", \"math.OC\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Optimization and Control\"], \"incoming_citations\": [\"104119\", \"121524\", \"146852\", \"113590\"], \"outgoing_citations\": [\"156966\"]}","task_split":"paper_retrieval"}
{"document_id":"148310","document_content":"# Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWidespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.05295v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"36993\", \"112823\", \"140301\", \"96241\"], \"outgoing_citations\": [\"207999\", \"213345\", \"214701\", \"230664\", \"244340\", \"250187\", \"279891\", \"286253\", \"287747\", \"298095\", \"298490\", \"299198\", \"303053\"]}","task_split":"paper_retrieval"}
{"document_id":"148404","document_content":"# Improving Entity Linking by Modeling Latent Entity Type Information\n## Categories\n- Computation and Language\n## Abstract\nExisting state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type. To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. In addition, we integrate a BERT-based entity similarity score into the local context model of a state-of-the-art model to better capture latent entity type information. Our model significantly outperforms the state-of-the-art entity linking models on standard benchmark (AIDA-CoNLL). Detailed experiment analysis demonstrates that our model corrects most of the type errors produced by the direct baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.01447v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"22532\", \"64775\", \"121898\", \"4514\", \"39851\", \"76611\", \"92823\", \"86291\", \"94880\", \"106440\"], \"outgoing_citations\": [\"168589\", \"175482\", \"177232\", \"178230\", \"182677\", \"185861\", \"200340\", \"224786\", \"233941\", \"239137\", \"268728\", \"300498\", \"306949\", \"312066\", \"313713\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"148504","document_content":"# Deep Face Representations for Differential Morphing Attack Detection\n## Categories\n- Cryptography and Security\n## Abstract\nThe vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection have been proposed in the scientific literature. However, the morphing attack detection algorithms proposed so far have only been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g. only created with a single morphing tool) or rather unrealistic (e.g. no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST Face Recognition Vendor Test MORPH show that the submitted MAD algorithms lack robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a large realistic database for training and testing of morphing attack detection algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different tools. Furthermore, multiple post-processings are applied on the reference images, e.g. print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential morphing attack detection algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3\\%~\\mbox{D-EER}) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.01202v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"7690\", \"13646\", \"18259\", \"27985\", \"37448\", \"39709\", \"64999\", \"60321\", \"83176\", \"89191\", \"119444\", \"120075\", \"201625\", \"81552\", \"83177\", \"148895\"], \"outgoing_citations\": [\"201625\", \"209503\", \"225647\", \"228434\", \"250081\"]}","task_split":"paper_retrieval"}
{"document_id":"148521","document_content":"# End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideo super-resolution aims at generating a high-resolution video from its low-resolution counterpart. With the rapid rise of deep learning, many recently proposed video super-resolution methods use convolutional neural networks in conjunction with explicit motion compensation to capitalize on statistical dependencies within and across low-resolution frames. Two common issues of such methods are noteworthy. Firstly, the quality of the final reconstructed HR video is often very sensitive to the accuracy of motion estimation. Secondly, the warp grid needed for motion compensation, which is specified by the two flow maps delineating pixel displacements in horizontal and vertical directions, tends to introduce additional errors and jeopardize the temporal consistency across video frames. To address these issues, we propose a novel dynamic local filter network to perform implicit motion estimation and compensation by employing, via locally connected layers, sample-specific and position-specific dynamic local filters that are tailored to the target pixels. We also propose a global refinement network based on ResBlock and autoencoder structures to exploit non-local correlations and enhance the spatial consistency of super-resolved frames. The experimental results demonstrate that the proposed method outperforms the state-of-the-art, and validate its strength in terms of local transformation handling, temporal consistency as well as edge sharpness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.01162v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60637\", \"78653\", \"2478\", \"104271\", \"100815\"], \"outgoing_citations\": [\"249406\", \"269321\", \"280330\", \"291461\", \"295622\"]}","task_split":"paper_retrieval"}
{"document_id":"148579","document_content":"# Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text\n## Categories\n- Computation and Language\n## Abstract\nNowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3341105.3374091\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"101769\"], \"outgoing_citations\": [\"153617\", \"291073\"]}","task_split":"paper_retrieval"}
{"document_id":"148612","document_content":"# Privacy in Data Service Composition\n## Categories\n- Databases\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nIn modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TSC.2019.2963309\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.CR\", \"cs.DC\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"128606\"], \"outgoing_citations\": [\"219254\"]}","task_split":"paper_retrieval"}
{"document_id":"148651","document_content":"# Making Sense of Reinforcement Learning and Probabilistic Inference\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts `RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our paper surfaces a key shortcoming in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: The exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We demonstrate that the popular `RL as inference' approximation can perform poorly in even very basic problems. However, we show that with a small modification the framework does yield algorithms that can provably perform well, and we show that the resulting algorithm is equivalent to the recently proposed K-learning, which we further connect with Thompson sampling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00805v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"11446\", \"12751\", \"45638\", \"120038\", \"165242\", \"223398\", \"59162\", \"102470\", \"125305\", \"129208\"], \"outgoing_citations\": [\"172432\", \"211715\", \"223398\", \"228028\", \"228732\", \"233450\", \"241900\", \"255807\", \"261844\", \"265133\", \"270851\", \"272987\", \"281159\", \"284713\", \"286910\", \"289229\", \"298281\", \"312534\", \"332226\", \"334172\", \"358783\"]}","task_split":"paper_retrieval"}
{"document_id":"148680","document_content":"# Automated Relational Meta-learning\n## Categories\n- Machine Learning\n## Abstract\nIn order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00745v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"37430\", \"5201\", \"5201\", \"13398\", \"20702\", \"26002\", \"57402\", \"87195\", \"131702\", \"19527\", \"24573\", \"35205\", \"109713\", \"113136\", \"79220\", \"82693\", \"92287\", \"108446\", \"113849\", \"134405\", \"136162\"], \"outgoing_citations\": [\"184854\", \"186048\", \"186514\", \"187232\", \"187682\", \"190920\", \"201727\", \"205592\", \"205915\", \"207193\", \"207480\", \"220351\", \"224464\", \"228540\", \"228896\", \"230640\", \"230792\", \"240315\", \"244112\", \"244888\", \"250796\", \"251819\", \"255134\", \"259762\", \"261577\", \"290426\"]}","task_split":"paper_retrieval"}
{"document_id":"148684","document_content":"# \"Love is as Complex as Math\": Metaphor Generation System for Social Chatbot\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nAs the wide adoption of intelligent chatbot in human daily life, user demands for such systems evolve from basic task-solving conversations to more casual and friend-like communication. To meet the user needs and build emotional bond with users, it is essential for social chatbots to incorporate more human-like and advanced linguistic features. In this paper, we investigate the usage of a commonly used rhetorical device by human -- metaphor for social chatbot. Our work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences. By embedding the framework into a chatbot system, we then enables the chatbot to communicate with users using figurative language. Human annotators validate the novelty and properness of the generated metaphors. More importantly, we evaluate the effects of employing metaphors in human-chatbot conversations. Experiments indicate that our system effectively arouses user interests in communicating with our chatbot, resulting in significantly longer human-chatbot conversations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00733v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"80466\"], \"outgoing_citations\": [\"239670\", \"245831\", \"291102\"]}","task_split":"paper_retrieval"}
{"document_id":"148728","document_content":"# A Loss-Function for Causal Machine-Learning\n## Categories\n- Machine Learning\n## Abstract\nCausal machine-learning is about predicting the net-effect (true-lift) of treatments. Given the data of a treatment group and a control group, it is similar to a standard supervised-learning problem. Unfortunately, there is no similarly well-defined loss function due to the lack of point-wise true values in the data. Many advances in modern machine-learning are not directly applicable due to the absence of such loss function.   We propose a novel method to define a loss function in this context, which is equal to mean-square-error (MSE) in a standard regression problem. Our loss function is universally applicable, thus providing a general standard to evaluate the quality of any model\/strategy that predicts the true-lift. We demonstrate that despite its novel definition, one can still perform gradient descent directly on this loss function to find the best fit. This leads to a new way to train any parameter-based model, such as deep neural networks, to solve causal machine-learning problems without going through the meta-learner strategy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00629v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"155341\"]}","task_split":"paper_retrieval"}
{"document_id":"148769","document_content":"# Deep learning for brake squeal: vibration detection, characterization and prediction\n## Categories\n- Signal Processing\n- Machine Learning\n## Abstract\nDespite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. We report here for the first time on novel strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. Machine learning-based methods to detect and characterize vibrations, to understand sensitivities and to predict brake squeal are applied with the aim to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. The detection method is rooted in recent computer vision techniques for object detection based on convolutional neural networks. It allows to overcome limitations of classical approaches that solely rely on instantaneous spectral properties of the recorded noise. Results indicate superior detection and characterization quality when compared to a state-of-the-art brake squeal detector. In the second part, a recurrent neural network is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. Given a set of multivariate loading conditions, the RNN learns to predict the noise generation of the structure. The validated RNN represents a virtual twin model for the squeal behavior of a specific brake system. It is found that this model can predict the occurrence and the onset of brake squeal with high accuracy and that it can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.01596v2\", \"primary_category\": \"eess.SP\", \"categories\": [\"eess.SP\", \"cs.LG\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Signal Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254264\", \"275783\", \"300991\", \"311979\", \"317841\", \"334092\"]}","task_split":"paper_retrieval"}
{"document_id":"148914","document_content":"# Reinforcement Learning with Goal-Distance Gradient\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning usually uses the feedback rewards of environmental to train agents. But the rewards in the actual environment are sparse, and even some environments will not rewards. Most of the current methods are difficult to get good performance in sparse reward or non-reward environments. Although using shaped rewards is effective when solving sparse reward tasks, it is limited to specific problems and learning is also susceptible to local optima. We propose a model-free method that does not rely on environmental rewards to solve the problem of sparse rewards in the general environment. Our method use the minimum number of transitions between states as the distance to replace the rewards of environmental, and proposes a goal-distance gradient to achieve policy improvement. We also introduce a bridge point planning method based on the characteristics of our method to improve exploration efficiency, thereby solving more complex tasks. Experiments show that our method performs better on sparse reward and local optimal problems in complex environments than previous work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00127v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"181081\", \"195457\", \"224840\", \"293922\"]}","task_split":"paper_retrieval"}
{"document_id":"148918","document_content":"# Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nReinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods which use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment. Source code is available at https:\/\/github.com\/sparisi\/visit-value-explore","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/a15030081\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"20805\"], \"outgoing_citations\": [\"139653\", \"181532\", \"201462\", \"212299\", \"225125\", \"228732\", \"248225\", \"261844\", \"262381\", \"264419\", \"266419\", \"267711\", \"270851\", \"272422\", \"277778\", \"280457\", \"291025\", \"291460\", \"298281\", \"304303\", \"310171\", \"334172\", \"344075\", \"350403\", \"354489\", \"358661\"]}","task_split":"paper_retrieval"}
{"document_id":"148992","document_content":"# Privacy for Rescue: A New Testimony Why Privacy is Vulnerable In Deep Models\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nThe huge computation demand of deep learning models and limited computation resources on the edge devices calls for the cooperation between edge device and cloud service by splitting the deep models into two halves. However, transferring the intermediates results from the partial models between edge device and cloud service makes the user privacy vulnerable since the attacker can intercept the intermediate results and extract privacy information from them. Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from the aspect of a single user. In this paper, we first present a formal definition of the privacy protection problem in the edge-cloud system running DNN models. Then, we analyze the-state-of-the-art methods and point out the drawbacks of their methods, especially the evaluation metrics such as the Mutual Information (MI). In addition, we perform several experiments to demonstrate that although existing methods perform well under MI, they are not effective enough to protect the privacy of a single user. To address the drawbacks of the evaluation metrics, we propose two new metrics that are more accurate to measure the effectiveness of privacy protection methods. Finally, we highlight several potential research directions to encourage future efforts addressing the privacy protection problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00493v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [\"143874\"], \"outgoing_citations\": [\"184440\", \"203771\", \"210995\", \"242848\", \"272564\", \"333598\"]}","task_split":"paper_retrieval"}
{"document_id":"149119","document_content":"# Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the development of Information technology and communication, a large part of the databases is dedicated to images and videos. Thus retrieving images related to a query image from a large database has become an important area of research in computer vision. Until now, there are various methods of image retrieval that try to define image contents by texture, color or shape properties. In this paper, a method is presented for image retrieval based on a combination of local texture information derived from two different texture descriptors. First, the color channels of the input image are separated. The texture information is extracted using two descriptors such as evaluated local binary patterns and predefined pattern units. After extracting the features, the similarity matching is done based on distance criteria. The performance of the proposed method is evaluated in terms of precision and recall on the Simplicity database. The comparative results showed that the proposed approach offers higher precision rate than many known methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12978v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"239068\", \"299079\"]}","task_split":"paper_retrieval"}
{"document_id":"149133","document_content":"# A New Approach for Explainable Multiple Organ Annotation with Few Data\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Image and Video Processing\n## Abstract\nDespite the recent successes of deep learning, such models are still far from some human abilities like learning from few examples, reasoning and explaining decisions. In this paper, we focus on organ annotation in medical images and we introduce a reasoning framework that is based on learning fuzzy relations on a small dataset for generating explanations. Given a catalogue of relations, it efficiently induces the most relevant relations and combines them for building constraints in order to both solve the organ annotation task and generate explanations. We test our approach on a publicly available dataset of medical images where several organs are already segmented. A demonstration of our model is proposed with an example of explained annotations. It was trained on a small training set containing as few as a couple of examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12932v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [\"30095\", \"31060\", \"69162\"], \"outgoing_citations\": [\"246129\", \"249204\", \"314444\"]}","task_split":"paper_retrieval"}
{"document_id":"149198","document_content":"# Learning Predictive Models From Observation and Interaction\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nLearning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes. However, learning a model that captures the dynamics of complex skills represents a major challenge: if the agent needs a good model to perform these skills, it might never be able to collect the experience on its own that is required to learn these delicate and complex behaviors. Instead, we can imagine augmenting the training set with observational data of other agents, such as humans. Such data is likely more plentiful, but represents a different embodiment. For example, videos of humans might show a robot how to use a tool, but (i) are not annotated with suitable robot actions, and (ii) contain a systematic distributional shift due to the embodiment differences between humans and robots. We address the first challenge by formulating the corresponding graphical model and treating the action as an observed variable for the interaction data and an unobserved variable for the observation data, and the second challenge by using a domain-dependent prior. In addition to interaction data, our method is able to leverage videos of passive observations in a driving dataset and a dataset of robotic manipulation videos. A robotic planning agent equipped with our method can learn to use tools in a tabletop robotic manipulation setting by observing humans without ever seeing a robotic video of tool use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12773v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"7646\", \"29891\", \"96353\", \"99251\", \"13246\", \"16270\", \"24970\", \"36692\", \"41902\", \"51243\", \"54866\", \"59341\", \"63434\", \"63809\", \"137035\", \"87299\", \"117896\", \"140833\", \"155474\"], \"outgoing_citations\": [\"154954\", \"156707\", \"159949\", \"179948\", \"183794\", \"184398\", \"184690\", \"185142\", \"186850\", \"188709\", \"191065\", \"196921\", \"204561\", \"207601\", \"210517\", \"219180\", \"220103\", \"222446\", \"224411\", \"226824\", \"228270\", \"230068\", \"231307\", \"233167\", \"236752\", \"241365\", \"243236\", \"251921\", \"252604\", \"254341\", \"259642\", \"261089\", \"261522\", \"262942\", \"263870\", \"264963\", \"268248\", \"269458\", \"270832\", \"272298\", \"274441\", \"275154\", \"277822\", \"280996\", \"283391\", \"288746\", \"289605\", \"291461\", \"292009\", \"302659\", \"302963\", \"303174\", \"308711\", \"310624\", \"313644\", \"317379\", \"320022\"]}","task_split":"paper_retrieval"}
{"document_id":"149205","document_content":"# Computational model discovery with reinforcement learning\n## Categories\n- Machine Learning\n- Fluid Dynamics\n## Abstract\nThe motivation of this study is to leverage recent breakthroughs in artificial intelligence research to unlock novel solutions to important scientific problems encountered in computational science. To address the human intelligence limitations in discovering reduced-order models, we propose to supplement human thinking with artificial intelligence. Our three-pronged strategy consists of learning (i) models expressed in analytical form, (ii) which are evaluated a posteriori, and iii) using exclusively integral quantities from the reference solution as prior knowledge. In point (i), we pursue interpretable models expressed symbolically as opposed to black-box neural networks, the latter only being used during learning to efficiently parameterize the large search space of possible models. In point (ii), learned models are dynamically evaluated a posteriori in the computational solver instead of based on a priori information from preprocessed high-fidelity data, thereby accounting for the specificity of the solver at hand such as its numerics. Finally in point (iii), the exploration of new models is solely guided by predefined integral quantities, e.g., averaged quantities of engineering interest in Reynolds-averaged or large-eddy simulations (LES). We use a coupled deep reinforcement learning framework and computational solver to concurrently achieve these objectives. The combination of reinforcement learning with objectives (i), (ii) and (iii) differentiate our work from previous modeling attempts based on machine learning. In this report, we provide a high-level description of the model discovery framework with reinforcement learning. The method is detailed for the application of discovering missing terms in differential equations. An elementary instantiation of the method is described that discovers missing terms in the Burgers' equation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00008v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"physics.flu-dyn\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Fluid Dynamics\", \"Machine Learning\"], \"incoming_citations\": [\"15986\"], \"outgoing_citations\": [\"153185\", \"164641\", \"167204\", \"207892\", \"209932\", \"221310\", \"222669\", \"249017\", \"252350\", \"255255\", \"259573\"]}","task_split":"paper_retrieval"}
{"document_id":"149260","document_content":"# Real-time Policy Distillation in Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nPolicy distillation in deep reinforcement learning provides an effective way to transfer control policies from a larger network to a smaller untrained network without a significant degradation in performance. However, policy distillation is underexplored in deep reinforcement learning, and existing approaches are computationally inefficient, resulting in a long distillation time. In addition, the effectiveness of the distillation process is still limited to the model capacity. We propose a new distillation mechanism, called real-time policy distillation, in which training the teacher model and distilling the policy to the student model occur simultaneously. Accordingly, the teacher's latest policy is transferred to the student model in real time. This reduces the distillation time to half the original time or even less and also makes it possible for extremely small student models to learn skills at the expert level. We evaluated the proposed algorithm in the Atari 2600 domain. The results show that our approach can achieve full distillation in most games, even with compression ratios up to 1.7%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12630v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"183311\", \"184979\", \"189752\", \"190971\", \"302952\", \"302967\"]}","task_split":"paper_retrieval"}
{"document_id":"149261","document_content":"# Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability\n## Categories\n- Machine Learning\n- Computation and Language\n- 68T37\n- I.2.7; I.2.3\n## Abstract\nNowadays, the use of machine learning models is becoming a utility in many applications. Companies deliver pre-trained models encapsulated as application programming interfaces (APIs) that developers combine with third party components and their own models and data to create complex data products to solve specific problems. The complexity of such products and the lack of control and knowledge of the internals of each component used cause unavoidable effects, such as lack of transparency, difficulty in auditability, and emergence of potential uncontrolled risks. They are effectively black-boxes. Accountability of such solutions is a challenge for the auditors and the machine learning community. In this work, we propose a wrapper that given a black-box model enriches its output prediction with a measure of uncertainty. By using this wrapper, we make the black-box auditable for the accuracy risk (risk derived from low quality or uncertain decisions) and at the same time we provide an actionable mechanism to mitigate that risk in the form of decision rejection; we can choose not to issue a prediction when the risk or uncertainty in that decision is significant. Based on the resulting uncertainty measure, we advocate for a rejection system that selects the more confident predictions, discarding those more uncertain, leading to an improvement in the trustability of the resulting system. We showcase the proposed technique and methodology in a practical scenario where a simulated sentiment analysis API based on natural language processing is applied to different domains. Results demonstrate the effectiveness of the uncertainty computed by the wrapper and its high correlation to bad quality predictions and misclassifications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12628v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\", \"68T37\", \"I.2.7; I.2.3\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\", \"68T37\", \"I.2.7; I.2.3\"], \"incoming_citations\": [\"87412\"], \"outgoing_citations\": [\"201516\", \"209647\", \"230142\", \"240532\", \"265625\", \"289913\", \"314672\"]}","task_split":"paper_retrieval"}
{"document_id":"149300","document_content":"# Weak Supervision for Fake News Detection via Reinforcement Learning\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Computation and Language\n## Abstract\nToday social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12520v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"31927\", \"37733\", \"11296\", \"20626\", \"30709\", \"40187\", \"50683\", \"56773\", \"69882\", \"81481\", \"87591\", \"94175\"], \"outgoing_citations\": [\"217496\", \"220475\", \"235352\", \"249184\", \"249420\", \"257863\", \"268073\", \"271021\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"149348","document_content":"# All-in-One Image-Grounded Conversational Agents\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nAs single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore. In this work, we focus on leveraging individual language and image tasks, along with resources that incorporate both vision and language towards that objective. We design an architecture that combines state-of-the-art Transformer and ResNeXt modules fed into a novel attentive multimodal module to produce a combined model trained on many tasks. We provide a thorough analysis of the components of the model, and transfer performance when training on one, some, or all of the tasks. Our final models provide a single system that obtains good results on all vision and language tasks considered, and improves the state-of-the-art in image-grounded conversational applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.12394v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"29341\", \"97088\", \"116873\", \"127495\"], \"outgoing_citations\": [\"152680\", \"157211\", \"171465\", \"181884\", \"189532\", \"189847\", \"207637\", \"211784\", \"213042\", \"218963\", \"227323\", \"233440\", \"238205\", \"266155\", \"275220\", \"279518\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"149565","document_content":"# Look, Listen, and Act: Towards Audio-Visual Embodied Navigation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n- Sound\n- Audio and Speech Processing\n## Abstract\nA crucial ability of mobile intelligent agents is to integrate the evidence from multiple sensory inputs in an environment and to make a sequence of actions to reach their goals. In this paper, we attempt to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data. To accomplish this task, the agent is required to learn from various modalities, i.e. relating the audio signal to the visual environment. Here we describe an approach to audio-visual embodied navigation that takes advantage of both visual and audio pieces of evidence. Our solution is based on three key ideas: a visual perception mapper module that constructs its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the audio-visual observations and the spatial memory of the environment to navigate toward the goal. Experimental results on a newly collected Visual-Audio-Room dataset using the simulated multi-modal environment demonstrate the effectiveness of our approach over several competitive baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.11684v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"6637\", \"25405\", \"21859\", \"22304\", \"55339\", \"60623\", \"64409\", \"77439\", \"109434\", \"2239\", \"10257\", \"10381\", \"31155\", \"40607\", \"42159\", \"43678\", \"48662\", \"49451\", \"8179\", \"58229\", \"71624\", \"77686\", \"79162\", \"104690\", \"113084\", \"104828\", \"110469\", \"110910\", \"112246\", \"112444\", \"112563\", \"113565\", \"121011\", \"118181\", \"127113\", \"136760\", \"139295\", \"149647\"], \"outgoing_citations\": [\"159745\", \"189824\", \"190915\", \"192738\", \"214195\", \"224223\", \"228916\", \"231955\", \"236080\", \"236252\", \"236717\", \"239415\", \"240323\", \"243782\", \"247148\", \"247418\", \"249760\", \"249906\", \"274102\", \"275003\", \"280570\", \"280681\", \"284565\", \"290747\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"149723","document_content":"# On Sharing Models Instead of Data using Mimic learning for Smart Health Applications\n## Categories\n- Machine Learning\n## Abstract\nElectronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model. The student model is then shared to the public and can be used to make accurate predictions. To assess the mimic learning approach, we have evaluated our scheme using different medical datasets. The results indicate that the student model mimics the teacher model performance in terms of prediction accuracy without the need to access to the patients' original data records.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.11210v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"72723\", \"62630\", \"91772\", \"145486\", \"151928\", \"156410\"], \"outgoing_citations\": [\"125684\", \"151928\", \"170833\", \"176542\", \"179587\", \"185693\", \"186737\", \"187990\", \"189531\", \"190967\", \"208863\", \"210108\", \"211403\", \"221842\", \"226192\", \"244340\", \"257865\", \"260371\", \"264024\", \"267775\", \"271122\", \"294671\"]}","task_split":"paper_retrieval"}
{"document_id":"149726","document_content":"# Learning to Combat Compounding-Error in Model-Based Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nDespite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorithm should ideally be able to trust an imperfect model over a reasonably long planning horizon, and only rely on model-free updates when the model errors get infeasibly large. In this paper, we investigate techniques for choosing the planning horizon on a state-dependent basis, where a state's planning horizon is determined by the maximum cumulative model error around that state. We demonstrate that these state-dependent model errors can be learned with Temporal Difference methods, based on a novel approach of temporally decomposing the cumulative model errors. Experimental results show that the proposed method can successfully adapt the planning horizon to account for state-dependent model accuracy, significantly improving the efficiency of policy learning compared to model-based and model-free baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.11206v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"80054\", \"99953\", \"91578\", \"93171\", \"114069\", \"114256\", \"122508\"], \"outgoing_citations\": [\"177658\", \"209253\", \"210517\", \"225762\", \"234984\", \"240471\", \"260770\", \"277696\", \"297268\"]}","task_split":"paper_retrieval"}
{"document_id":"149732","document_content":"# Improving Abstractive Text Summarization with History Aggregation\n## Categories\n- Computation and Language\n## Abstract\nRecent neural sequence to sequence models have provided feasible solutions for abstractive summarization. However, such models are still hard to tackle long text dependency in the summarization task. A high-quality summarization system usually depends on strong encoder which can refine important information from long input texts so that the decoder can generate salient summaries from the encoder's memory. In this paper, we propose an aggregation mechanism based on the Transformer model to address the challenge of long text representation. Our model can review history information to make encoder hold more memory capacity. Empirically, we apply our aggregation mechanism to the Transformer model and experiment on CNN\/DailyMail dataset to achieve higher quality summaries compared to several strong baseline models on the ROUGE metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.11046v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"34591\"], \"outgoing_citations\": [\"170657\", \"183197\", \"183462\", \"186043\", \"197339\", \"197709\", \"218088\", \"219618\", \"220695\", \"225556\", \"230212\", \"230234\", \"231869\", \"232543\", \"237494\", \"241070\", \"249319\", \"280552\", \"280557\", \"280735\", \"295815\", \"296029\", \"296198\", \"301996\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"149960","document_content":"# A Regression Framework for Predicting User's Next Location using Call Detail Records\n## Categories\n- Machine Learning\n## Abstract\nWith the growth of using cell phones and the increase in diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis of CDR is one of the favorite subjects of study among the researchers. The user next location prediction is one of the main problems in the field of human mobility analysis. In this paper, we propose a data processing framework to predict user next location. We propose domain-specific data processing strategies and design a deep neural network model which is based on recurrent neurons and perform regression tasks. Using this prediction framework, the error of the prediction decreases from 74% to 55% in comparison to the worst and best performing traditional models. Methods, strategies, the framework and the results of this paper can be helpful in many applications such as urban planning and digital marketing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10438v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"234850\", \"287075\", \"305485\", \"331700\"]}","task_split":"paper_retrieval"}
{"document_id":"149962","document_content":"# Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial image super-resolution (SR) is an important preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Recent convolutional neural network (CNN) based method has shown excellent performance by learning mapping relation using pairs of low-resolution (LR) and high-resolution (HR) facial images. However, since the HR facial image reconstruction using CNN is conventionally aimed to increase the PSNR and SSIM metrics, the reconstructed HR image might not be realistic even with high scores. An adversarial framework is proposed in this study to reconstruct the HR facial image by simultaneously generating an HR image with and without blur. First, the spatial resolution of the LR facial image is increased by eight times using a five-layer CNN. Then, the encoder extracts the features of the up-scaled image. These features are finally sent to two branches (decoders) to generate an HR facial image with and without blur. In addition, local and global discriminators are combined to focus on the reconstruction of HR facial structures. Experiment results show that the proposed algorithm generates a realistic HR facial image. Furthermore, the proposed method can generate a variety of different facial images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10427v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"180450\"], \"outgoing_citations\": [\"134378\", \"164523\", \"170700\", \"190860\", \"194014\", \"198793\", \"247495\", \"247558\", \"248969\", \"255682\", \"259660\", \"268494\", \"270923\", \"278465\", \"288238\"]}","task_split":"paper_retrieval"}
{"document_id":"149973","document_content":"# Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n- I.2.0\n## Abstract\nA growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3351095.3372829\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\", \"I.2.0\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\", \"I.2.0\"], \"incoming_citations\": [\"146184\", \"4364\", \"20686\", \"23273\", \"51968\", \"51968\", \"76039\", \"119618\", \"130708\", \"4386\", \"7019\", \"7939\", \"27822\", \"28630\", \"30294\", \"30520\", \"31121\", \"37602\", \"39586\", \"41801\", \"46606\", \"47015\", \"47101\", \"51536\", \"53483\", \"55182\", \"56519\", \"65131\", \"72073\", \"72578\", \"73104\", \"81551\", \"92021\", \"92368\", \"82669\", \"89012\", \"98838\", \"106837\", \"112060\", \"158744\", \"159651\"], \"outgoing_citations\": [\"179224\", \"206169\", \"215230\", \"237923\", \"249587\", \"271401\"]}","task_split":"paper_retrieval"}
{"document_id":"150060","document_content":"# Decoupled Attention Network for Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular\/irregular scene text recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10205v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"3120\", \"5857\", \"6379\", \"50917\", \"83847\", \"191\", \"7580\", \"8109\", \"9220\", \"21983\", \"24824\", \"27465\", \"27768\", \"28270\", \"29777\", \"33360\", \"43037\", \"48901\", \"49805\", \"50591\", \"53750\", \"55703\", \"58277\", \"60756\", \"63761\", \"63779\", \"64528\", \"64650\", \"67749\", \"81960\", \"93345\", \"120627\", \"122764\", \"150258\", \"99077\", \"111991\", \"126431\"], \"outgoing_citations\": [\"190056\", \"203406\", \"205988\", \"210634\", \"211597\", \"211851\", \"217463\", \"232706\", \"250738\", \"256711\", \"284201\", \"293655\", \"294023\", \"296685\", \"296828\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"150072","document_content":"# Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWhile recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge. In this work, we propose an effective reward shaping method through predictive coding to tackle sparse reward problems. By learning predictive representations offline and using these representations for reward shaping, we gain access to reward signals that understand the structure and dynamics of the environment. In particular, our method achieves better learning by providing reward signals that 1) understand environment dynamics 2) emphasize on features most useful for learning 3) resist noise in learned representations through reward accumulation. We demonstrate the usefulness of this approach in different domains ranging from robotic manipulation to navigation, and we show that reward signals produced through predictive coding are as effective for learning as hand-crafted rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.13414v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"44400\", \"138960\"], \"outgoing_citations\": [\"198373\", \"201452\", \"204750\", \"206705\", \"209469\", \"214902\", \"215702\", \"218936\", \"225886\", \"231175\", \"240506\", \"243235\", \"277797\"]}","task_split":"paper_retrieval"}
{"document_id":"150091","document_content":"# Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions\n## Categories\n- Computation and Language\n## Abstract\nBuilding a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms. The models should be robust enough to handle various user distractions during the interaction flow and should steer the user back into an engaging interaction for successful completion of the interaction. In this work, we have designed a goal-oriented interaction system where children can engage with agents for a series of interactions involving `Meet \\& Greet' and `Simon Says' game play. We have explored various feature extractors and models for improved intent recognition and looked at leveraging previous user and system interactions in novel ways with attention models. We have also looked at dialog adaptation methods for entrained response selection. Our bootstrapped models from limited training data perform better than many baseline approaches we have looked at for intent recognition and dialog action prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10130v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"53240\", \"163929\"], \"outgoing_citations\": [\"208235\", \"247467\", \"248966\", \"256219\", \"260475\", \"262342\", \"274253\", \"285454\", \"285972\", \"291916\", \"299981\", \"302771\"]}","task_split":"paper_retrieval"}
{"document_id":"150101","document_content":"# Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nCurrent supervised deep learning frameworks rely on annotated data for modeling the underlying data distribution of a given task. In particular for computer vision algorithms powered by deep learning, the quality of annotated data is the most critical factor in achieving the desired algorithm performance. Data annotation is, typically, a manual process where the annotator follows guidelines and operates in a best-guess manner. Labeling criteria among annotators can show discrepancies in labeling results. This may impact the algorithm inference performance. Given the popularity and widespread use of deep learning among computer vision, more and more custom datasets are needed to train neural networks to tackle different kinds of tasks. Unfortunately, there is no full understanding of the factors that affect annotated data quality, and how it translates into algorithm performance. In this paper we studied this problem for object detection and recognition.We conducted several data annotation experiments to measure inter annotator agreement and consistency, as well as how the selection of ground truth impacts the perceived algorithm performance.We propose a methodology to monitor the quality of annotations during the labeling of images and how it can be used to measure performance. We also show that neglecting to monitor the annotation process can result in significant loss in algorithm precision. Through these experiments, we observe that knowledge of the labeling process, training data, and ground truth data used for algorithm evaluation are fundamental components to accurately assess trustworthiness of an AI system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10107v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"343072\"]}","task_split":"paper_retrieval"}
{"document_id":"150121","document_content":"# End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNamed entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \\& IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.13415v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"17477\", \"129792\", \"99143\"], \"outgoing_citations\": [\"180997\", \"181098\", \"186405\", \"197910\", \"204496\", \"220758\", \"234772\", \"260349\", \"281121\", \"300593\"]}","task_split":"paper_retrieval"}
{"document_id":"150131","document_content":"# A Voice Interactive Multilingual Student Support System using IBM Watson\n## Categories\n- Human-Computer Interaction\n- Computation and Language\n- Information Retrieval\n- Artificial Intelligence\n## Abstract\nSystems powered by artificial intelligence are being developed to be more user-friendly by communicating with users in a progressively human-like conversational way. Chatbots, also known as dialogue systems, interactive conversational agents, or virtual agents are an example of such systems used in a wide variety of applications ranging from customer support in the business domain to companionship in the healthcare sector. It is becoming increasingly important to develop chatbots that can best respond to the personalized needs of their users so that they can be as helpful to the user as possible in a real human way. This paper investigates and compares three popular existing chatbots API offerings and then propose and develop a voice interactive and multilingual chatbot that can effectively respond to users mood, tone, and language using IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was evaluated using a use case that was targeted at responding to users needs regarding exam stress based on university students survey data generated using Google Forms. The results of measuring the chatbot effectiveness at analyzing responses regarding exam stress indicate that the chatbot responding appropriately to the user queries regarding how they are feeling about exams 76.5%. The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/2001.00471v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.CL\", \"cs.HC\", \"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\", \"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [\"14126\"], \"outgoing_citations\": [\"201867\", \"209884\", \"244479\", \"245831\", \"278671\", \"286852\", \"297273\", \"309411\", \"310377\", \"310740\"]}","task_split":"paper_retrieval"}
{"document_id":"150176","document_content":"# Meta-Graph: Few Shot Link Prediction via Meta Learning\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nWe consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new graph after a small amount of training. We show that current link prediction methods are generally ill-equipped to handle this task. They cannot effectively transfer learned knowledge from one graph to another and are unable to effectively learn from sparse samples of edges. To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph. Our framework leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph can learn to quickly adapt to a new graph using only a small sample of true edges, enabling not only fast adaptation but also improved results at convergence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.09867v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"20048\", \"42791\", \"19357\", \"25353\", \"37818\", \"65047\", \"66264\", \"66570\", \"92009\", \"80992\", \"95042\", \"110814\", \"116748\", \"118766\", \"119358\"], \"outgoing_citations\": [\"167398\", \"168796\", \"178533\", \"213370\", \"220131\", \"222356\", \"224464\", \"240758\", \"244112\", \"244888\", \"261201\", \"261577\", \"271935\", \"316630\", \"321600\"]}","task_split":"paper_retrieval"}
{"document_id":"150360","document_content":"# An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification\n## Categories\n- Computation and Language\n## Abstract\nThis paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete the dialogue state tracking (DST) based on the information provided by the task's schema. Different from traditional stage-wise DST, we propose an end-to-end DST system to avoid error accumulation between the dialogue turns. The DST system consists of a machine reading comprehension (MRC) model for non-categorical slots and a Wide & Deep model for categorical slots. As far as we know, this is the first time that MRC and Wide & Deep model are applied to DST problem in a fully end-to-end way. Experimental results show that our framework achieves an excellent performance on the test dataset including 50% zero-shot services with a joint goal accuracy of 0.8652 and a slot tagging F1-Score of 0.9835.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.09297v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16678\", \"14126\", \"35820\", \"49047\", \"58814\", \"71388\", \"73962\", \"75121\", \"82594\", \"101045\", \"103646\", \"144545\"], \"outgoing_citations\": [\"162766\", \"167138\", \"172890\", \"177332\", \"185347\", \"207865\", \"220017\", \"233268\", \"246324\", \"251348\", \"268809\", \"290531\", \"310667\"]}","task_split":"paper_retrieval"}
{"document_id":"150384","document_content":"# Personalized Pareto-Improving Pricing-and-Routing Schemes for Near-Optimum Freight Routing: An Alternative Approach to Congestion Pricing\n## Categories\n- Optimization and Control\n- Computer Science and Game Theory\n## Abstract\nWe design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-class model with stochastic Origin-Destination (OD) demands for the truck drivers. The main characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair and pick their individual VOT from a set of $N$ available options, and guarantees that the resulting pricing-and-routing scheme is Pareto-improving, i.e. every truck driver will be better-off compared to the User Equilibrium (UE) and that every truck driver will have an incentive to truthfully declare his\/her VOT, while leading to a revenue-neutral (budget balanced) on average mechanism. This approach enables us to design personalized (VOT-based) pricing-and-routing schemes. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To improve computational efficiency, we propose an Approximately Optimum Pricing Scheme (AOPS) and prove that it satisfies the aforementioned properties. Both pricing-and-routing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments where it is shown that OPS and AOPS achieve a much lower expected total travel time and expected total monetary cost for the users compared to the CPURR scheme, without negatively affecting the rest of the network. These results demonstrate the efficiency of personalized (VOT-based) pricing-and-routing schemes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.trc.2021.103004\", \"primary_category\": \"math.OC\", \"categories\": [\"math.OC\", \"cs.GT\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Optimization and Control\", \"Computer Science and Game Theory\"], \"incoming_citations\": [\"41199\"], \"outgoing_citations\": [\"192727\", \"237356\", \"272335\"]}","task_split":"paper_retrieval"}
{"document_id":"150450","document_content":"# Understanding Human Judgments of Causality\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDiscriminating between causality and correlation is a major problem in machine learning, and theoretical tools for determining causality are still being developed. However, people commonly make causality judgments and are often correct, even in unfamiliar domains. What are humans doing to make these judgments? This paper examines differences in human experts' and non-experts' ability to attribute causality by comparing their performances to those of machine-learning algorithms. We collected human judgments by using Amazon Mechanical Turk (MTurk) and then divided the human subjects into two groups: experts and non-experts. We also prepared expert and non-expert machine algorithms based on different training of convolutional neural network (CNN) models. The results showed that human experts' judgments were similar to those made by an \"expert\" CNN model trained on a large number of examples from the target domain. The human non-experts' judgments resembled the prediction outputs of the CNN model that was trained on only the small number of examples used during the MTurk instruction. We also analyzed the differences between the expert and non-expert machine algorithms based on their neural representations to evaluate the performances, providing insight into the human experts' and non-experts' cognitive abilities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.08998v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"280308\", \"306030\", \"320004\", \"320501\"]}","task_split":"paper_retrieval"}
{"document_id":"150533","document_content":"# MedCAT -- Medical Concept Annotation Tool\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nBiomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource documenting clinical narratives and decisions, but whilst the text can be easily understood by human doctors it is challenging to use in research and clinical applications. To uncover the potential of biomedical documents we need to extract and structure the information they contain. The task at hand is Named Entity Recognition and Linking (NER+L). The number of entities, ambiguity of words, overlapping and nesting make the biomedical area significantly more difficult than many others. To overcome these difficulties, we have developed the Medical Concept Annotation Tool (MedCAT), an open-source unsupervised approach to NER+L. MedCAT uses unsupervised machine learning to disambiguate entities. It was validated on MIMIC-III (a freely accessible critical care database) and MedMentions (Biomedical papers annotated with mentions from the Unified Medical Language System). In case of NER+L, the comparison with existing tools shows that MedCAT improves the previous best with only unsupervised learning (F1=0.848 vs 0.691 for disease detection; F1=0.710 vs. 0.222 for general concept detection). A qualitative analysis of the vector embeddings learnt by MedCAT shows that it captures latent medical knowledge available in EHRs (MIMIC-III). Unsupervised learning can improve the performance of large scale entity extraction, but it has some limitations when working with only a couple of entities and a small dataset. In that case options are supervised learning or active learning, both of which are supported in MedCAT via the MedCATtrainer extension. Our approach can detect and link millions of different biomedical concepts with state-of-the-art performance, whilst being lightweight, fast and easy to use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10166v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"25741\", \"33560\", \"47453\", \"119054\", \"126216\"], \"outgoing_citations\": [\"163617\", \"175858\", \"195734\", \"197273\", \"197659\", \"198222\"]}","task_split":"paper_retrieval"}
{"document_id":"150663","document_content":"# Uncovering Relations for Marketing Knowledge Representation\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nOnline behaviors of consumers and marketers generate massive marketing data, which ever more sophisticated models attempt to turn into insights and aid decisions by marketers. Yet, in making decisions human managers bring to bear marketing knowledge which reside outside of data and models. Thus, it behooves creation of an automated marketing knowledge base that can interact with data and models. Currently, marketing knowledge is dispersed in large corpora, but no definitive knowledge base for marketing exists. Out of the two broad aspects of marketing knowledge - representation and reasoning - this treatise focuses on the former. Specifically, we focus on creation of marketing knowledge graph from corpora, which requires identification of entities and relations. The relation identification task is particularly challenging in marketing, because of the non-factoid nature of much marketing knowledge, and the difficulty of forming rules that govern relations. Specifically, we define a set of relations to capture marketing knowledge, propose a pipeline for creating the knowledge graph from text and propose a rule-guided semi-supervised relation prediction algorithm to extract relations between marketing entities from sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.08374v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"85920\"], \"outgoing_citations\": [\"63084\", \"193907\", \"195812\", \"198186\", \"220475\", \"220544\", \"248817\", \"296214\", \"300593\"]}","task_split":"paper_retrieval"}
{"document_id":"150815","document_content":"# Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWord embeddings are rich word representations, which in combination with deep neural networks, lead to large performance gains for many NLP tasks. However, word embeddings are represented by dense, real-valued vectors and they are therefore not directly interpretable. Thus, computational operations based on them are also not well understood. In this paper, we present an approach for analyzing structures in the semantic vector space to get a better understanding of the underlying semantic encoding principles. We present a framework for decomposing word embeddings into smaller meaningful units which we call sub-vectors. The framework opens up a wide range of possibilities analyzing phenomena in vector space semantics, as well as solving concrete NLP problems: We introduce the category completion task and show that a sub-vector based approach is superior to supervised techniques; We present a sub-vector based method for solving the word analogy task, which substantially outperforms different variants of the traditional vector-offset method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10434v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"224091\", \"240090\", \"261193\", \"289600\", \"297683\", \"300128\", \"307054\", \"308276\", \"310117\", \"311696\", \"311845\"]}","task_split":"paper_retrieval"}
{"document_id":"150834","document_content":"# Deep Iterative and Adaptive Learning for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.07832v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"137613\", \"131434\", \"10856\", \"55646\", \"4840\", \"16183\", \"56703\", \"57264\", \"70439\", \"104487\", \"122288\", \"105064\", \"134148\", \"171886\", \"173570\"], \"outgoing_citations\": [\"170618\", \"171886\", \"173570\", \"181285\", \"193345\", \"208815\", \"209230\", \"217899\", \"220576\", \"227507\", \"236917\", \"242322\", \"245486\", \"253210\", \"263378\", \"291892\", \"300282\", \"327674\"]}","task_split":"paper_retrieval"}
{"document_id":"150944","document_content":"# Polynomial Rewritings from Expressive Description Logics with Closed Predicates to Variants of Datalog\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nIn many scenarios, complete and incomplete information coexist. For this reason, the knowledge representation and database communities have long shown interest in simultaneously supporting the closed- and the open-world views when reasoning about logic theories. Here we consider the setting of querying possibly incomplete data using logic theories, formalized as the evaluation of an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes called an ontology, expressing background knowledge. This can be further enriched by specifying a set of closed predicates from the theory that are to be interpreted under the closed-world assumption, while the rest are interpreted with the open-world view. In this way we can retrieve more precise answers to queries by leveraging the partial completeness of the data.   The central goal of this paper is to understand the relative expressiveness of OMQ languages in which the ontology is written in the expressive Description Logic (DL) ALCHOI and includes a set of closed predicates. We consider a restricted class of conjunctive queries. Our main result is to show that every query in this non-monotonic query language can be translated in polynomial time into Datalog with negation under the stable model semantics. To overcome the challenge that Datalog has no direct means to express the existential quantification present in ALCHOI, we define a two-player game that characterizes the satisfaction of the ontology, and design a Datalog query that can decide the existence of a winning strategy for the game. If there are no closed predicates, that is in the case of querying a plain ALCHOI knowledge base, our translation yields a positive disjunctive Datalog program of polynomial size. To the best of our knowledge, unlike previous translations for related fragments with expressive (non-Horn) DLs, these are the first polynomial time translations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.artint.2019.103220\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"333049\"]}","task_split":"paper_retrieval"}
{"document_id":"150998","document_content":"# A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThe emergence of the single-chip polarized color sensor now allows for simultaneously capturing chromatic and polarimetric information of the scene on a monochromatic image plane. However, unlike the usual camera with an embedded demosaicing method, the latest polarized color camera is not delivered with an in-built demosaicing tool. For demosaicing, the users have to down-sample the captured images or to use traditional interpolation techniques. Neither of them can perform well since the polarization and color are interdependent. Therefore, joint chromatic and polarimetric demosaicing is the key to obtaining high-quality polarized color images. In this paper, we propose a joint chromatic and polarimetric demosaicing model to address this challenging problem. Instead of mechanically demosaicing for the multi-channel polarized color image, we further present a sparse representation-based optimization strategy that utilizes chromatic information and polarimetric information to jointly optimize the model. To avoid the interaction between color and polarization during demosaicing, we separately construct the corresponding dictionaries. We also build an optical data acquisition system to collect a dataset, which contains various sources of polarization, such as illumination, reflectance and birefringence. Results of both qualitative and quantitative experiments have shown that our method is capable of faithfully recovering full RGB information of four polarization angles for each pixel from a single mosaic input image. Moreover, the proposed method can perform well not only on the synthetic data but the real captured data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2021.3069190\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"61605\", \"233177\", \"278101\"]}","task_split":"paper_retrieval"}
{"document_id":"151281","document_content":"# Action Modifiers: Learning from Adverbs in Instructional Videos\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the visual representation of the adverb is highly dependant on the action to which it applies, although the same adverb will modify multiple actions in a similar way. For instance, while 'spread quickly' and 'mix quickly' will look dissimilar, we can learn a common representation that allows us to recognize both, among other actions. We formulate this as an embedding problem, and use scaled dot-product attention to learn from weakly-supervised video narrations. We jointly learn adverbs as invertible transformations operating on the embedding space, so as to add or remove the effect of the adverb. As there is no prior work on weakly supervised learning from adverbs, we gather paired action-adverb annotations from a subset of the HowTo100M dataset for 6 adverbs: quickly\/slowly, finely\/coarsely, and partially\/completely. Our method outperforms all baselines for video-to-adverb retrieval with a performance of 0.719 mAP. We also demonstrate our model's ability to attend to the relevant video parts in order to determine the adverb for a given action.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.06617v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"97097\", \"17178\", \"62318\"], \"outgoing_citations\": [\"164525\", \"172394\", \"181897\", \"191966\", \"194508\", \"203533\", \"206072\", \"206997\", \"209520\", \"223200\", \"236377\", \"237345\", \"237654\", \"243363\", \"249253\", \"259318\", \"259867\", \"259939\", \"263185\", \"264711\", \"267523\", \"270287\", \"270325\", \"272075\", \"274379\", \"279740\", \"282943\", \"285973\", \"287541\", \"291872\", \"304596\", \"310322\", \"310452\", \"316472\", \"316803\"]}","task_split":"paper_retrieval"}
{"document_id":"151342","document_content":"# Toward an Automated Auction Framework for Wireless Federated Learning Services Market\n## Categories\n- Computer Science and Game Theory\n## Abstract\nIn traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.06370v2\", \"primary_category\": \"cs.GT\", \"categories\": [\"cs.GT\"], \"primary_category_human_readable\": \"Computer Science and Game Theory\", \"categories_human_readable\": [\"Computer Science and Game Theory\"], \"incoming_citations\": [\"8363\", \"43741\", \"71518\", \"38549\", \"39217\", \"60451\", \"99283\", \"83656\", \"99638\", \"121928\"], \"outgoing_citations\": [\"134693\", \"164869\", \"184652\", \"186014\", \"200704\", \"206896\", \"208110\", \"217320\", \"229563\", \"234581\", \"255428\", \"269617\", \"303573\"]}","task_split":"paper_retrieval"}
{"document_id":"151355","document_content":"# Spatial-Temporal Self-Attention Network for Flow Prediction\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFlow prediction (e.g., crowd flow, traffic flow) with features of spatial-temporal is increasingly investigated in AI research field. It is very challenging due to the complicated spatial dependencies between different locations and dynamic temporal dependencies among different time intervals. Although measurements of both dependencies are employed, existing methods suffer from the following two problems. First, the temporal dependencies are measured either uniformly or bias against long-term dependencies, which overlooks the distinctive impacts of short-term and long-term temporal dependencies. Second, the existing methods capture spatial and temporal dependencies independently, which wrongly assumes that the correlations between these dependencies are weak and ignores the complicated mutual influences between them. To address these issues, we propose a Spatial-Temporal Self-Attention Network (ST-SAN). As the path-length of attending long-term dependency is shorter in the self-attention mechanism, the vanishing of long-term temporal dependencies is prevented. In addition, since our model relies solely on attention mechanisms, the spatial and temporal dependencies can be simultaneously measured. Experimental results on real-world data demonstrate that, in comparison with state-of-the-art methods, our model reduces the root mean square errors by 9% in inflow prediction and 4% in outflow prediction on Taxi-NYC data, which is very significant compared to the previous improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.07663v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"48023\", \"11752\", \"108903\"], \"outgoing_citations\": [\"240136\", \"241051\", \"245764\", \"263332\", \"278848\", \"283526\"]}","task_split":"paper_retrieval"}
{"document_id":"151457","document_content":"# Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn this paper, we compare the three most popular algorithms for hyperparameter optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them for neural architecture search (NAS). We use these algorithms for building a convolutional neural network (search architecture). Experimental results on CIFAR-10 dataset further demonstrate the performance difference between compared algorithms. The comparison results are based on the execution time of the above algorithms and accuracy of the proposed models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.06059v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"2093\", \"3885\", \"104162\", \"2903\", \"15230\", \"16666\", \"3170\", \"24926\", \"115743\"], \"outgoing_citations\": [\"187446\", \"188293\", \"228939\"]}","task_split":"paper_retrieval"}
{"document_id":"151618","document_content":"# What Can Learned Intrinsic Rewards Capture?\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe objective of a reinforcement learning agent is to behave so as to maximise the sum of a suitable scalar function of state: the reward. These rewards are typically given and immutable. In this paper, we instead consider the proposition that the reward function itself can be a good locus of learned knowledge. To investigate this, we propose a scalable meta-gradient framework for learning useful intrinsic reward functions across multiple lifetimes of experience. Through several proof-of-concept experiments, we show that it is feasible to learn and capture knowledge about long-term exploration and exploitation into a reward function. Furthermore, we show that unlike policy transfer methods that capture \"how\" the agent should behave, the learned reward functions can generalise to other kinds of agents and to changes in the dynamics of the environment by capturing \"what\" the agent should strive to do.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.05500v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"7093\", \"22537\", \"23386\", \"70453\", \"78473\", \"76\", \"12253\", \"12912\", \"46858\", \"113199\", \"50608\", \"67924\", \"93557\", \"88922\", \"111431\", \"111603\"], \"outgoing_citations\": [\"162581\", \"167562\", \"180089\", \"181039\", \"200947\", \"229145\", \"229737\", \"230669\", \"235228\", \"237101\", \"237229\", \"239080\", \"255939\", \"272422\", \"280202\", \"280870\", \"291025\", \"293922\"]}","task_split":"paper_retrieval"}
{"document_id":"151719","document_content":"# Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nImage classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the labeling process or difficulty of correctly classifying data, even for the experts. Because of these practical challenges, label noise is a common problem in real-world datasets, and numerous methods to train deep neural networks with label noise are proposed in the literature. Although deep neural networks are known to be relatively robust to label noise, their tendency to overfit data makes them vulnerable to memorizing even random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its adverse effects to train deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, the literature lacks a comprehensive survey of methodologies centered explicitly around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods. Algorithms in the first group aim to estimate the noise structure and use this information to avoid the adverse effects of noisy labels. Differently, methods in the second group try to come up with inherently noise robust algorithms by using approaches like robust losses, regularizers or other learning paradigms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.knosys.2021.106771\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"4415\", \"37241\", \"38404\", \"54016\", \"61994\", \"79692\", \"118155\", \"71\", \"4960\", \"15026\", \"16745\", \"24426\", \"24577\", \"31518\", \"38726\", \"41445\", \"45821\", \"46548\", \"47894\", \"53567\", \"57325\", \"59052\", \"62565\", \"68438\", \"73769\", \"76975\", \"55025\", \"80146\", \"84874\", \"91922\", \"112715\", \"132532\", \"83058\", \"78494\", \"88163\", \"94388\", \"94838\", \"132205\", \"135327\", \"136120\", \"145212\"], \"outgoing_citations\": [\"85842\", \"112715\", \"135327\", \"136769\", \"141691\", \"158570\", \"163402\", \"171215\", \"171454\", \"172805\", \"183122\", \"183136\", \"184385\", \"184847\", \"186598\", \"187320\", \"189025\", \"190225\", \"193843\", \"194631\", \"194789\", \"197567\", \"198316\", \"199491\", \"201258\", \"201459\", \"201987\", \"203033\", \"203937\", \"206182\", \"207281\", \"208420\", \"210951\", \"211010\", \"218209\", \"219108\", \"222400\", \"228948\", \"230716\", \"231202\", \"231347\", \"235096\", \"237153\", \"237209\", \"237224\", \"237905\", \"242145\", \"242973\", \"246497\", \"247500\", \"247591\", \"248784\", \"249258\", \"249957\", \"251094\", \"251661\", \"251725\", \"256776\", \"258956\", \"263559\", \"264157\", \"264247\", \"264889\", \"265022\", \"266873\", \"267261\", \"268097\", \"270545\", \"272109\", \"275596\", \"276713\", \"279183\", \"285761\", \"290426\", \"293177\", \"293522\", \"298734\", \"301117\", \"302730\", \"302806\", \"303724\", \"303844\", \"309807\", \"311413\", \"312135\", \"313245\", \"315007\", \"320027\", \"321153\", \"328627\", \"332456\", \"339854\"]}","task_split":"paper_retrieval"}
{"document_id":"151897","document_content":"# Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nConversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs. Previous conceptual work described properties and actions a good agent should exhibit. Unlike them, we present a novel conceptual model defined in terms of conversational goals, which enables us to reason about current research practices in conversational search. Based on the literature, we elicit how existing tasks and test collections from the fields of IR, natural language processing (NLP) and dialogue systems (DS) fit into this model. We describe a set of characteristics that an ideal conversational search dataset should have. Lastly, we introduce MANtIS (the code and dataset are available at https:\/\/guzpenha.github.io\/MANtIS\/), a large-scale dataset containing multi-domain and grounded information seeking dialogues that fulfill all of our dataset desiderata. We provide baseline results for the conversation response ranking and user intent prediction tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.04639v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"33114\", \"2754\", \"19499\", \"126911\", \"50345\", \"56030\", \"70800\", \"75917\", \"80290\", \"128194\", \"87270\", \"96002\", \"108824\", \"123020\", \"150591\"], \"outgoing_citations\": [\"176028\", \"189798\", \"193627\", \"200155\", \"203266\", \"204647\", \"211664\", \"216019\", \"216953\", \"217214\", \"219975\", \"220723\", \"220724\", \"232341\", \"233646\", \"234472\", \"234768\", \"247016\", \"251348\", \"266411\", \"270018\", \"278671\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"152118","document_content":"# Transformer Based Reinforcement Learning For Games\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nRecent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic methods which are based on deep learning based models and back-propagation of gradients to train such models. An active area of research in reinforcement learning is about training agents to play complex video games, which so far has been something accomplished only by human intelligence. Some state of the art performances in video game playing using deep reinforcement learning are obtained by processing the sequence of frames from video games, passing them through a convolutional network to obtain features and then using recurrent neural networks to figure out the action leading to optimal rewards. The recurrent neural network will learn to extract the meaningful signal out of the sequence of such features. In this work, we propose a method utilizing a transformer network which have recently replaced RNNs in Natural Language Processing (NLP), and perform experiments to compare with existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03918v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"50350\", \"91729\"], \"outgoing_citations\": [\"161947\", \"207983\", \"229002\", \"230774\", \"284468\", \"299541\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"152119","document_content":"# Learning Disentangled Representations via Mutual Information Estimation\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we investigate the problem of learning disentangled representations. Given a pair of images sharing some attributes, we aim to create a low-dimensional representation which is split into two parts: a shared representation that captures the common information between the images and an exclusive representation that contains the specific information of each image. To address this issue, we propose a model based on mutual information estimation without relying on image reconstruction or image generation. Mutual information maximization is performed to capture the attributes of data in the shared and exclusive representations while we minimize the mutual information between the shared and exclusive representation to enforce representation disentanglement. We show that these representations are useful to perform downstream tasks such as image classification and image retrieval based on the shared or exclusive component. Moreover, classification results show that our model outperforms the state-of-the-art model based on VAE\/GAN approaches in representation disentanglement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03915v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1009\", \"4079\", \"65191\", \"18244\", \"30692\", \"51288\", \"52364\", \"107986\", \"111109\", \"116023\"], \"outgoing_citations\": [\"184690\", \"193391\", \"194303\", \"223252\", \"230691\", \"233977\", \"241933\", \"248730\", \"251729\", \"257220\", \"265526\", \"267394\", \"277021\", \"291440\", \"300606\", \"300739\"]}","task_split":"paper_retrieval"}
{"document_id":"152160","document_content":"# Meta-Learning without Memorization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once. For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation. This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult. Our approach substantially outperforms standard meta-learning algorithms in these settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03820v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"37430\", \"24689\", \"37164\", \"38393\", \"38758\", \"44643\", \"49567\", \"88222\", \"95517\", \"96602\", \"131702\", \"136367\", \"184092\", \"11603\", \"13641\", \"16794\", \"19527\", \"21295\", \"31975\", \"38053\", \"44332\", \"46780\", \"67803\", \"69749\", \"70997\", \"73736\", \"76452\", \"77455\", \"88552\", \"94342\", \"95935\", \"97074\", \"109713\", \"126094\", \"142465\", \"80589\", \"84221\", \"90042\", \"101356\", \"115254\", \"136162\", \"137062\", \"138792\", \"141469\", \"143394\", \"148492\", \"197936\"], \"outgoing_citations\": [\"175792\", \"184092\", \"194494\", \"214993\", \"223585\", \"225779\", \"228540\", \"228896\", \"230640\", \"231401\", \"237229\", \"244112\", \"251497\", \"264549\", \"278987\", \"290952\", \"312534\", \"316314\", \"337980\"]}","task_split":"paper_retrieval"}
{"document_id":"152318","document_content":"# Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nRecently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03440v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"130476\", \"131529\", \"74928\", \"106086\"], \"outgoing_citations\": [\"178012\", \"276502\", \"298371\", \"345732\"]}","task_split":"paper_retrieval"}
{"document_id":"152360","document_content":"# Tools for Mathematical Ludology\n## Categories\n- Artificial Intelligence\n- Computer Science and Game Theory\n## Abstract\nWe propose the study of mathematical ludology, which aims to formally interrogate questions of interest to game studies and game design in particular. The goal is to extend our mathematical understanding of complex games beyond decision-making---the typical focus of game theory and artificial intelligence efforts---to explore other aspects such as game mechanics, structure, relationships between games, and connections between game rules and user-interfaces, as well as exploring related gameplay phenomena and typical player behavior. In this paper, we build a basic foundation for this line of study by developing a hierarchy of game descriptions, mathematical formalism to compactly describe complex discrete games, and equivalence relations on the space of game systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03295v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.GT\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Science and Game Theory\"], \"incoming_citations\": [\"77570\"], \"outgoing_citations\": [\"77821\", \"186608\"]}","task_split":"paper_retrieval"}
{"document_id":"152424","document_content":"# Visual-Textual Association with Hardest and Semi-Hard Negative Pairs Mining for Person Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSearching persons in large-scale image databases with the query of natural language description is a more practical important applications in video surveillance. Intuitively, for person search, the core issue should be visual-textual association, which is still an extremely challenging task, due to the contradiction between the high abstraction of textual description and the intuitive expression of visual images. However, for this task, while positive image-text pairs are always well provided, most existing methods doesn't tackle this problem effectively by mining more reasonable negative pairs. In this paper, we proposed a novel visual-textual association approach with visual and textual attention, and cross-modality hardest and semi-hard negative pair mining. In order to evaluate the effectiveness and feasibility of the proposed approach, we conduct extensive experiments on typical person search datasdet: CUHK-PEDES, in which our approach achieves the top1 score of 55.32% as a new state-of-the-art. Besides, we also evaluate the semi-hard pair mining approach in COCO caption dataset, and validate the effectiveness and complementarity of the methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03083v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"47552\", \"33140\", \"109848\"], \"outgoing_citations\": [\"189628\", \"191892\", \"192501\", \"208749\", \"222262\", \"250405\", \"253038\", \"259197\", \"260620\", \"260926\", \"266369\", \"267040\", \"269599\", \"273696\", \"295099\", \"302943\", \"303011\", \"304419\", \"324793\"]}","task_split":"paper_retrieval"}
{"document_id":"152434","document_content":"# Feasibility-Guided Learning for Robust Control in Constrained Optimal Control Problems\n## Categories\n- Systems and Control\n- Optimization and Control\n- Machine Learning\n## Abstract\nOptimal control problems with constraints ensuring safety and convergence to desired states can be mapped onto a sequence of real time optimization problems through the use of Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in these approaches is ensuring the feasibility of the resulting quadratic programs (QPs) if the system is affine in controls. The recently proposed penalty method has the potential to improve the existence of feasible solutions to such problems. In this paper, we further improve the feasibility robustness (i.e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF) that works for arbitrary relative degree constraints; this is achieved by a proposed feasibility-guided learning approach. Specifically, we apply machine learning techniques to classify the parameter space of a HOCBF into feasible and infeasible sets, and get a differentiable classifier that is then added to the learning process. The proposed feasibility-guided learning approach is compared with the gradient-descent method on a robot control problem. The simulation results show an improved ability of the feasibility-guided learning approach over the gradient-decent method to determine the optimal parameters in the definition of a HOCBF for the feasibility robustness, as well as show the potential of the CBF method for robot safe navigation in an unknown environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.04066v1\", \"primary_category\": \"eess.SY\", \"categories\": [\"eess.SY\", \"math.OC\", \"cs.LG\", \"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\", \"Optimization and Control\", \"Machine Learning\", \"Systems and Control\"], \"incoming_citations\": [\"86589\", \"106853\", \"142873\"], \"outgoing_citations\": [\"195520\", \"348161\"]}","task_split":"paper_retrieval"}
{"document_id":"152436","document_content":"# Can AI Generate Love Advice?: Toward Neural Answer Generation for Non-Factoid Questions\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDeep learning methods that extract answers for non-factoid questions from QA sites are seen as critical since they can assist users in reaching their next decisions through conversations with AI systems. The current methods, however, have the following two problems: (1) They can not understand the ambiguous use of words in the questions as word usage can strongly depend on the context. As a result, the accuracies of their answer selections are not good enough. (2) The current methods can only select from among the answers held by QA sites and can not generate new ones. Thus, they can not answer the questions that are somewhat different with those stored in QA sites. Our solution, Neural Answer Construction Model, tackles these problems as it: (1) Incorporates the biases of semantics behind questions into word embeddings while also computing them regardless of the semantics. As a result, it can extract answers that suit the contexts of words used in the question as well as following the common usage of words across semantics. This improves the accuracy of answer selection. (2) Uses biLSTM to compute the embeddings of questions as well as those of the sentences often used to form answers. It then simultaneously learns the optimum combination of those sentences as well as the closeness between the question and those sentences. As a result, our model can construct an answer that corresponds to the situation that underlies the question; it fills the gap between answer selection and generation and is the first model to move beyond the current simple answer selection model for non-factoid QAs. Evaluations using datasets created for love advice stored in the Japanese QA site, Oshiete goo, indicate that our model achieves 20% higher accuracy in answer creation than the strong baselines. Our model is practical and has already been applied to the love advice service in Oshiete goo.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.10163v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"154484\", \"302903\", \"303469\", \"308414\", \"310903\", \"316165\", \"320868\"]}","task_split":"paper_retrieval"}
{"document_id":"152454","document_content":"# Semantic Mask for Transformer based End-to-End Speech Recognition\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nAttention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of the memorization capacity of neural networks to learn the mapping from the input sequence to the output sequence from scratch, without the assumption of prior knowledge such as the alignments. However, this model is prone to overfitting, especially when the amount of training data is limited. Inspired by SpecAugment and BERT, in this paper, we propose a semantic mask based regularization for training such kind of end-to-end (E2E) model. The idea is to mask the input features corresponding to a particular output token, e.g., a word or a word-piece, in order to encourage the model to fill the token based on the contextual information. While this approach is applicable to the encoder-decoder framework with any type of neural network architecture, we study the transformer-based model for ASR in this work. We perform experiments on Librispeech 960h and TedLium2 data sets, and achieve the state-of-the-art performance on the test set in the scope of E2E models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.03010v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"66419\", \"3399\", \"11959\", \"13523\", \"14255\", \"17931\", \"19858\", \"22777\", \"23053\", \"37540\", \"62405\", \"63881\", \"67039\", \"91988\", \"96748\", \"106217\", \"92344\", \"117525\", \"122325\", \"124034\", \"129830\", \"134542\"], \"outgoing_citations\": [\"143720\", \"151603\", \"155439\", \"160457\", \"163849\", \"166975\", \"187267\", \"188886\", \"223083\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"152527","document_content":"# Cross-Resolution Learning for Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- I.2.6, I.2.10\n- I.2.6; I.2.10\n## Abstract\nConvolutional Neural Networks have reached extremely high performances on the Face Recognition task. Largely used datasets, such as VGGFace2, focus on gender, pose and age variations trying to balance them to achieve better results. However, the fact that images have different resolutions is not usually discussed and resize to 256 pixels before cropping is used. While specific datasets for very low resolution faces have been proposed, less attention has been payed on the task of cross-resolution matching. Such scenarios are of particular interest for forensic and surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher-resolution galleries. While it is always possible to either increase the resolution of the probe image or to reduce the size of the gallery images, to the best of our knowledge an extensive experimentation of cross-resolution matching was missing in the recent deep learning based literature. In the context of low- and cross-resolution Face Recognition, the contributions of our work are: i) we proposed a training method to fine-tune a state-of-the-art model in order to make it able to extract resolution-robust deep features; ii) we tested our models on the benchmark datasets IJB-B\/C considering images at both full and low resolutions in order to show the effectiveness of the proposed training algorithm. To the best of our knowledge, this is the first work testing extensively the performance of a FR model in a cross-resolution scenario; iii) we tested our models on the low resolution and low quality datasets QMUL-SurvFace and TinyFace and showed their superior performances, even though we did not train our model on low-resolution faces only and our main focus was cross-resolution; iv) we showed that our approach can be more effective with respect to preprocessing faces with super resolution techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.imavis.2020.103927\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"I.2.6, I.2.10\", \"I.2.6; I.2.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"I.2.6, I.2.10\", \"I.2.6; I.2.10\"], \"incoming_citations\": [\"36730\", \"27406\", \"27986\", \"51376\", \"64085\", \"74016\", \"152495\"], \"outgoing_citations\": [\"208805\", \"209118\", \"211273\", \"230095\", \"234204\", \"235160\", \"244877\", \"259108\", \"261657\", \"263345\", \"287611\", \"296989\"]}","task_split":"paper_retrieval"}
{"document_id":"152542","document_content":"# Learning Human Objectives by Evaluating Hypothetical Behavior\n## Categories\n- Computers and Society\n- Machine Learning\n- Machine Learning\n## Abstract\nWe seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST). We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST significantly outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.05652v2\", \"primary_category\": \"cs.CY\", \"categories\": [\"stat.ML\", \"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [\"29957\", \"353397\", \"2405\", \"10486\", \"47401\", \"8628\", \"11431\", \"13197\", \"13824\", \"30312\", \"34461\", \"83258\", \"116575\", \"120227\", \"171948\", \"81007\", \"86323\", \"112041\", \"117347\"], \"outgoing_citations\": [\"98034\", \"115266\", \"194175\", \"196921\", \"203662\", \"209453\", \"209926\", \"210517\", \"213652\", \"214806\", \"218582\", \"219180\", \"243197\", \"244147\", \"251082\", \"254581\", \"259604\", \"261038\", \"263957\", \"271019\", \"275728\", \"289913\"]}","task_split":"paper_retrieval"}
{"document_id":"152577","document_content":"# Exploration of Neural Machine Translation in Autoformalization of Mathematics in Mizar\n## Categories\n- Logic in Computer Science\n- Machine Learning\n- Computation and Language\n## Abstract\nIn this paper we share several experiments trying to automatically translate informal mathematics into formal mathematics. In our context informal mathematics refers to human-written mathematical sentences in the LaTeX format; and formal mathematics refers to statements in the Mizar language. We conducted our experiments against three established neural network-based machine translation models that are known to deliver competitive results on translating between natural languages. To train these models we also prepared four informal-to-formal datasets. We compare and analyze our results according to whether the model is supervised or unsupervised. In order to augment the data available for auto-formalization and improve the results, we develop a custom type-elaboration mechanism and integrate it in the supervised translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3372885.3373827\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LG\", \"cs.LO\", \"cs.CL\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Machine Learning\", \"Logic in Computer Science\", \"Computation and Language\"], \"incoming_citations\": [\"69933\", \"118856\", \"61074\", \"71326\", \"73654\", \"74041\", \"75254\", \"95128\", \"120213\", \"122194\"], \"outgoing_citations\": [\"210798\", \"232558\", \"234164\", \"234807\", \"251974\", \"292053\", \"305919\", \"319238\"]}","task_split":"paper_retrieval"}
{"document_id":"152637","document_content":"# Towards countering hate speech against journalists on social media\n## Categories\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n- Social and Information Networks\n## Abstract\nThe damaging effects of hate speech on social media are evident during the last few years, and several organizations, researchers and social media platforms tried to harness them in various ways. Despite these efforts, social media users are still affected by hate speech. The problem is even more apparent to social groups that promote public discourse, such as journalists. In this work, we focus on countering hate speech that is targeted to journalistic social media accounts. To accomplish this, a group of journalists assembled a definition of hate speech, taking into account the journalistic point of view and the types of hate speech that are usually targeted against journalists. We then compile a large pool of tweets referring to journalism-related accounts in multiple languages. In order to annotate the pool of unlabeled tweets according to the definition, we follow a concise annotation strategy that involves active learning annotation stages. The outcome of this paper is a novel, publicly available collection of Twitter datasets in five different languages. Additionally, we experiment with state-of-the-art deep learning architectures for hate speech detection and use our annotated datasets to train and evaluate them. Finally, we propose an ensemble detection model that outperforms all individual models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.osnem.2020.100071\", \"primary_category\": \"cs.IR\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.SI\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Social and Information Networks\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"228455\", \"235782\", \"240638\", \"241616\", \"243569\", \"243571\", \"247591\", \"252856\", \"254583\", \"264575\", \"264835\", \"275247\", \"281768\"]}","task_split":"paper_retrieval"}
{"document_id":"152896","document_content":"# COSTRA 1.0: A Dataset of Complex Sentence Transformations\n## Categories\n- Computation and Language\n## Abstract\nWe present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting 'skeleton' in the sentence embedding space. A preliminary analysis using LASER, multi-purpose multi-lingual sentence embeddings suggests that the LASER space does not exhibit the desired properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.01673v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"152946","document_content":"# BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Multiagent Systems\n## Abstract\nIn this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication. Behavior, adaptation and learning to adapt emerges from the interactions of homogeneous experts inside a single agent. The proposed architecture should allow for generalization beyond the level seen in existing methods, in part due to the use of a single policy shared by all experts within the agent as well as the inherent modularity of 'Badger'.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.01513v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [\"21469\", \"57630\", \"77916\", \"111431\"], \"outgoing_citations\": [\"137299\", \"140345\", \"156563\", \"158109\", \"162581\", \"162995\", \"163454\", \"166489\", \"167551\", \"181037\", \"181460\", \"184374\", \"185937\", \"186101\", \"187278\", \"187845\", \"188515\", \"196162\", \"196801\", \"197711\", \"201860\", \"203756\", \"204330\", \"204914\", \"205447\", \"205643\", \"212891\", \"213166\", \"213438\", \"214440\", \"224873\", \"226660\", \"228212\", \"230669\", \"230702\", \"231351\", \"231395\", \"237101\", \"237193\", \"237229\", \"240177\", \"242097\", \"253658\", \"256103\", \"261577\", \"262591\", \"263378\", \"263620\", \"264897\", \"266200\", \"271497\", \"271536\", \"275599\", \"277491\", \"280202\", \"280870\", \"281091\", \"291892\", \"292127\", \"312758\"]}","task_split":"paper_retrieval"}
{"document_id":"152958","document_content":"# The Comparison of Methods for Individual Treatment Effect Detection\n## Categories\n- Machine Learning\n- Applications\n## Abstract\nToday, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30\\% observations of the test dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.01443v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"8527\"], \"outgoing_citations\": [\"197353\"]}","task_split":"paper_retrieval"}
{"document_id":"153133","document_content":"# Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present the first publicly available part-of-speech and morphologically tagged corpus for the Albanian language, as well as a neural morphological tagger and lemmatizer trained on it. There is currently a lack of available NLP resources for Albanian, and its complex grammar and morphology present challenges to their development. We have created an Albanian part-of-speech corpus based on the Universal Dependencies schema for morphological annotation, containing about 118,000 tokens of naturally occuring text collected from different text sources, with an addition of 67,000 tokens of artificially created simple sentences used only in training. On this corpus, we subsequently train and evaluate segmentation, morphological tagging and lemmatization models, using the Turku Neural Parser Pipeline. On the held-out evaluation set, the model achieves 92.74% accuracy on part-of-speech tagging, 85.31% on morphological tagging, and 89.95% on lemmatization. The manually annotated corpus, as well as the trained models are available under an open license.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00991v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"200377\", \"262705\"]}","task_split":"paper_retrieval"}
{"document_id":"153157","document_content":"# Lambada: Interactive Data Analytics on Cold Data using Serverless Cloud Infrastructure\n## Categories\n- Databases\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe promise of ultimate elasticity and operational simplicity of serverless computing has recently lead to an explosion of research in this area. In the context of data analytics, the concept sounds appealing, but due to the limitations of current offerings, there is no consensus yet on whether or not this approach is technically and economically viable. In this paper, we identify interactive data analytics on cold data as a use case where serverless computing excels. We design and implement Lambada, a system following a purely serverless architecture, in order to illustrate when and how serverless computing should be employed for data analytics. We propose several system components that overcome the previously known limitations inherent in the serverless paradigm as well as additional ones we identify in this work. We can show that, thanks to careful design, a serverless query processing system can be at the same time one order of magnitude faster and two orders of magnitude cheaper compared to commercial Query-as-a-Service systems, the only alternative with similar operational simplicity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3318464.3389758\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.DC\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [\"27034\", \"47574\", \"65293\", \"65293\", \"158155\", \"12715\", \"19251\", \"20619\", \"39424\", \"46107\", \"49122\", \"53030\", \"78202\", \"132472\", \"120902\", \"133134\"], \"outgoing_citations\": [\"133134\", \"154121\", \"202178\", \"206597\", \"208920\", \"219531\", \"238706\", \"274082\", \"316947\"]}","task_split":"paper_retrieval"}
{"document_id":"153162","document_content":"# Just Ask:An Interactive Learning Framework for Vision and Language Navigation\n## Categories\n- Artificial Intelligence\n## Abstract\nIn the vision and language navigation task, the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. We propose an interactive learning framework to endow the agent with the ability to ask for users' help in such situations. As part of this framework, we investigate multiple learning approaches for the agent with different levels of complexity. The simplest model-confusion-based method lets the agent ask questions based on its confusion, relying on the predefined confidence threshold of a next action prediction model. To build on this confusion-based method, the agent is expected to demonstrate more sophisticated reasoning such that it discovers the timing and locations to interact with a human. We achieve this goal using reinforcement learning (RL) with a proposed reward shaping term, which enables the agent to ask questions only when necessary. The success rate can be boosted by at least 15% with only one question asked on average during the navigation. Furthermore, we show that the RL agent is capable of adjusting dynamically to noisy human responses. Finally, we design a continual learning strategy, which can be viewed as a data augmentation method, for the agent to improve further utilizing its interaction history with a human. We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00915v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"19631\", \"39368\", \"47577\", \"80120\", \"93187\", \"14760\", \"27522\", \"34290\", \"40343\", \"175031\", \"115960\", \"127495\"], \"outgoing_citations\": [\"176603\", \"191570\", \"192738\", \"196218\", \"196525\", \"196994\", \"203398\", \"208042\", \"208773\", \"210666\", \"228916\", \"249906\", \"251816\", \"263957\"]}","task_split":"paper_retrieval"}
{"document_id":"153178","document_content":"# Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nConstructing accurate and automatic solvers of math word problems has proven to be quite challenging. Prior attempts using machine learning have been trained on corpora specific to math word problems to produce arithmetic expressions in infix notation before answer computation. We find that custom-built neural networks have struggled to generalize well. This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. In addition to training directly on domain-specific corpora, we use an approach that pre-trains on a general text corpus to provide foundational language abilities to explore if it improves performance. We compare results produced by a large number of neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by almost 10% on average when compared to the previous state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00871v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"25239\", \"45526\"], \"outgoing_citations\": [\"176321\", \"233912\", \"278863\", \"287181\"]}","task_split":"paper_retrieval"}
{"document_id":"153233","document_content":"# Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling\n## Categories\n- Computation and Language\n## Abstract\nWe describe the task of sentence expansion and enhancement, in which a sentence provided by a human is expanded in some creative way. The expansion should be understandable, believably grammatical, and optimally meaning-preserving. Sentence expansion and enhancement may serve as an authoring tool, or integrate in dynamic media, conversational agents, or variegated advertising.   We implement a neural sentence expander trained on sentence compressions generated from a corpus of modern fiction. We modify an MLE objective to support the task by focusing on new words, and decode at test time with controlled curve-like novelty sampling. We run our sentence expander on sentences provided by human subjects and have humans evaluate these expansions. We show that, although the generation methods are inferior to professional human writers, they are comparable to, and as well liked as, our subjects' original input sentences, and preferred over baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00698v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1409\", \"36632\"], \"outgoing_citations\": [\"180695\", \"218755\", \"228201\", \"232274\", \"234391\", \"234728\", \"238963\", \"240955\", \"256448\", \"258039\", \"267161\", \"302815\", \"307223\", \"335177\"]}","task_split":"paper_retrieval"}
{"document_id":"153345","document_content":"# Generalizable prediction of academic performance from short texts on social media\n## Categories\n- Social and Information Networks\n- Computation and Language\n## Abstract\nIt has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00463v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"265740\"]}","task_split":"paper_retrieval"}
{"document_id":"153401","document_content":"# Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nMachine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality. Deep learning models, in particular, require large amounts of data for model training. However, the data is often collected at different hospitals and sharing is restricted due to patient privacy concerns. In this paper, we aimed to demonstrate the potential of distributed training in achieving state-of-the-art performance while maintaining data privacy. Our results show that training the model in the federated learning framework leads to comparable performance to the traditional centralised setting. We also suggest several considerations for the success of such frameworks in future work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00354v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"27798\", \"33171\", \"156643\"], \"outgoing_citations\": [\"170944\", \"187418\", \"264024\", \"303550\", \"303573\"]}","task_split":"paper_retrieval"}
{"document_id":"153425","document_content":"# Image Based Identification of Ghanaian Timbers Using the XyloTron: Opportunities, Risks and Challenges\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nComputer vision systems for wood identification have the potential to empower both producer and consumer countries to combat illegal logging if they can be deployed effectively in the field. In this work, carried out as part of an active international partnership with the support of UNIDO, we constructed and curated a field-relevant image data set to train a classifier for wood identification of $15$ commercial Ghanaian woods using the XyloTron system. We tested model performance in the laboratory, and then collected real-world field performance data across multiple sites using multiple XyloTron devices. We present efficacies of the trained model in the laboratory and in the field, discuss practical implications and challenges of deploying machine learning wood identification models, and conclude that field testing is a necessary step - and should be considered the gold-standard - for validating computer vision wood identification systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00296v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"199022\", \"229604\", \"234583\", \"237660\", \"255047\"]}","task_split":"paper_retrieval"}
{"document_id":"153576","document_content":"# VIABLE: Fast Adaptation via Backpropagating Learned Loss\n## Categories\n- Machine Learning\n## Abstract\nIn few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem. However, given that we have few samples at test time, we argue that the loss function that we are interested in minimising is not necessarily the loss function most suitable for computing gradients in a few-shot setting. We propose VIABLE, a generic meta-learning extension that builds on existing meta-gradient-based methods by learning a differentiable loss function, replacing the pre-defined inner-loop loss function in performing task-specific updates. We show that learning a loss function capable of leveraging relational information between samples reduces underfitting, and significantly improves performance and sample efficiency on a simple regression task. Furthermore, we show VIABLE is scalable by evaluating on the Mini-Imagenet dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.13159v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167562\", \"181039\", \"185788\", \"191732\", \"212579\", \"213370\", \"214993\", \"224464\", \"228896\", \"242281\", \"255939\", \"259762\", \"261577\", \"261807\", \"262591\", \"264533\", \"280202\", \"280870\", \"287823\", \"301840\", \"302923\"]}","task_split":"paper_retrieval"}
{"document_id":"153685","document_content":"# Enhancing Passive Non-Line-of-Sight Imaging Using Polarization Cues\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a method of passive non-line-of-sight (NLOS) imaging using polarization cues. A key observation is that the oblique light has a different polarimetric signal. It turns out this effect is due to the polarization axis rotation, a phenomena which can be used to better condition the light transport matrix for non-line-of-sight imaging. Our analysis and results show that the use of a polarizer in front of the camera is not only a separate technique, but it can be seen as an enhancement technique for more advanced forms of passive NLOS imaging. For example, this paper shows that polarization can enhance passive NLOS imaging both with and without occluders. In all tested cases, despite the light attenuation from polarization optics, recovery of the occluded images is improved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.12906v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"47562\"], \"outgoing_citations\": [\"162043\", \"184123\", \"208843\", \"212719\", \"217009\", \"225523\", \"249953\"]}","task_split":"paper_retrieval"}
{"document_id":"153762","document_content":"# Cameras Viewing Cameras Geometry\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nA basic problem in computer vision is to understand the structure of a real-world scene given several images of it. Here we study several theoretical aspects of the intra multi-view geometry of calibrated cameras when all that they can reliably recognize is each other. With the proliferation of wearable cameras, autonomous vehicles and drones, the geometry of these multiple cameras is a timely and relevant problem to study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.12706v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"213372\", \"216910\"]}","task_split":"paper_retrieval"}
{"document_id":"153810","document_content":"# Multi-Agent Deep Reinforcement Learning with Adaptive Policies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nWe propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key challenges in multi-agent RL. To tackle this, we propose to train multiple policies for each agent and postpone the selection of the best policy at execution time. Specifically, we model the environment non-stationarity with a finite set of scenarios and train policies fitting each scenario. In addition to multiple policies, each agent also learns a policy predictor to determine which policy is the best with its local information. By doing so, each agent is able to adapt its policy when the environment changes and consequentially the other agents alter their policies during execution. We empirically evaluated our method on a variety of common benchmark problems proposed for multi-agent deep RL in the literature. Our experimental results show that the agents trained by our algorithm have better adaptiveness in changing environments and outperform the state-of-the-art methods in all the tested environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00949v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"181403\", \"204914\", \"240780\", \"248225\", \"253638\", \"253658\", \"254599\", \"256103\", \"259927\", \"265505\", \"272788\", \"277491\", \"284459\", \"291892\", \"292127\", \"298691\"]}","task_split":"paper_retrieval"}
{"document_id":"153822","document_content":"# DiscoTK: Using Discourse Structure for Machine Translation Evaluation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- 68T50\n- I.2.7\n## Abstract\nWe present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation with that of the human reference. We experiment with five transformations and augmentations of a base discourse tree representation based on the rhetorical structure theory, and we combine the kernel scores for each of them into a single score. Finally, we add other metrics from the ASIYA MT evaluation toolkit, and we tune the weights of the combination on actual human judgments. Experiments on the WMT12 and WMT13 metrics shared task datasets show correlation with human judgments that outperforms what the best systems that participated in these years achieved, both at the segment and at the system level.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.12547v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"68T50\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"68T50\", \"I.2.7\"], \"incoming_citations\": [\"61515\", \"308331\", \"228607\", \"254147\", \"254381\", \"254027\", \"152653\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"153843","document_content":"# Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nText-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.12511v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"22323\", \"26377\", \"36587\", \"20488\", \"22124\", \"92228\", \"96195\", \"110974\", \"119028\", \"137584\", \"139573\", \"140959\", \"141309\", \"145805\"], \"outgoing_citations\": [\"184995\", \"194494\", \"207273\", \"207533\", \"218923\", \"222345\", \"226321\", \"226327\", \"249187\", \"271868\", \"284468\", \"291025\", \"301453\", \"303349\", \"309111\", \"310370\"]}","task_split":"paper_retrieval"}
{"document_id":"153887","document_content":"# Automatic Generation of Headlines for Online Math Questions\n## Categories\n- Computation and Language\n## Abstract\nMathematical equations are an important part of dissemination and communication of scientific information. Students, however, often feel challenged in reading and understanding math content and equations. With the development of the Web, students are posting their math questions online. Nevertheless, constructing a concise math headline that gives a good description of the posted detailed math question is nontrivial. In this study, we explore a novel summarization task denoted as geNerating A concise Math hEadline from a detailed math question (NAME). Compared to conventional summarization tasks, this task has two extra and essential constraints: 1) Detailed math questions consist of text and math equations which require a unified framework to jointly model textual and mathematical information; 2) Unlike text, math equations contain semantic and structural features, and both of them should be captured together. To address these issues, we propose MathSum, a novel summarization model which utilizes a pointer mechanism combined with a multi-head attention mechanism for mathematical representation augmentation. The pointer mechanism can either copy textual tokens or math tokens from source questions in order to generate math headlines. The multi-head attention mechanism is designed to enrich the representation of math equations by modeling and integrating both its semantic and structural features. For evaluation, we collect and make available two sets of real-world detailed math questions along with human-written math headlines, namely EXEQ-300k and OFEQ-10k. Experimental results demonstrate that our model (MathSum) significantly outperforms state-of-the-art models for both the EXEQ-300k and OFEQ-10k datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.00839v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"51408\", \"52268\", \"53833\"], \"outgoing_citations\": [\"198770\", \"201977\", \"202315\", \"204334\", \"220230\", \"237880\", \"261042\", \"283758\", \"284605\"]}","task_split":"paper_retrieval"}
{"document_id":"154016","document_content":"# Recovering Facial Reflectance and Geometry from Multi-view Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nWhile the problem of estimating shapes and diffuse reflectances of human faces from images has been extensively studied, there is relatively less work done on recovering the specular albedo. This paper presents a lightweight solution for inferring photorealistic facial reflectance and geometry. Our system processes video streams from two views of a subject, and outputs two reflectance maps for diffuse and specular albedos, as well as a vector map of surface normals. A model-based optimization approach is used, consisting of the three stages of multi-view face model fitting, facial reflectance inference and facial geometry refinement. Our approach is based on a novel formulation built upon the 3D morphable model (3DMM) for representing 3D textured faces in conjunction with the Blinn-Phong reflection model. It has the advantage of requiring only a simple setup with two video streams, and is able to exploit the interaction between the diffuse and specular reflections across multiple views as well as time frames. As a result, the method is able to reliably recover high-fidelity facial reflectance and geometry, which facilitates various applications such as generating photorealistic facial images under new viewpoints or illumination conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11999v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"7345\", \"40200\"], \"outgoing_citations\": [\"109032\", \"202313\", \"257118\", \"270923\", \"278966\"]}","task_split":"paper_retrieval"}
{"document_id":"154056","document_content":"# Improving Fictitious Play Reinforcement Learning with Expanding Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nFictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fictitious play faces crucial challenges. Neural network model training employs gradient descent approaches to update all connection weights, and thus is easy to forget the old opponents after training to beat the new opponents. Existing approaches often maintain a pool of historical policy models to avoid the forgetting. However, learning to beat a pool in stochastic games, i.e., a wide distribution over policy models, is either sample-consuming or insufficient to exploit all models with limited amount of samples. In this paper, we propose a learning process with neural fictitious play to alleviate the above issues. We train a single model as our policy model, which consists of sub-models and a selector. Everytime facing a new opponent, the model is expanded by adding a new sub-model, where only the new sub-model is updated instead of the whole model. At the same time, the selector is also updated to mix up the new sub-model with the previous ones at the state-level, so that the model is maintained as a behavior strategy instead of a wide distribution over policy models. Experiments on Kuhn poker, a grid-world Treasure Hunting game, and Mini-RTS environments show that the proposed approach alleviates the forgetting problem, and consequently improves the learning efficiency and the robustness of neural fictitious play.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11928v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"170240\", \"195273\", \"200052\", \"201876\", \"212068\", \"213513\", \"218276\", \"242821\", \"251601\", \"262106\", \"297186\", \"326880\"]}","task_split":"paper_retrieval"}
{"document_id":"154142","document_content":"# Biology and Compositionality: Empirical Considerations for Emergent-Communication Protocols\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nSignificant advances have been made in artificial systems by using biological systems as a guide. However, there is often little interaction between computational models for emergent communication and biological models of the emergence of language. Many researchers in language origins and emergent communication take compositionality as their primary target for explaining how simple communication systems can become more like natural language. However, there is reason to think that compositionality is the wrong target on the biological side, and so too the wrong target on the machine-learning side. As such, the purpose of this paper is to explore this claim. This has theoretical implications for language origins research more generally, but the focus here will be the implications for research on emergent communication in computer science and machine learning---specifically regarding the types of programmes that might be expected to work and those which will not. I further suggest an alternative approach for future research which focuses on reflexivity, rather than compositionality, as a target for explaining how simple communication systems may become more like natural language. I end by providing some reference to the language origins literature that may be of some use to researchers in machine learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11668v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"207866\", \"235974\", \"236522\", \"251911\", \"262814\", \"264897\", \"265111\", \"266825\", \"268586\", \"271085\", \"277491\", \"281011\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"154192","document_content":"# Relevance-Promoting Language Model for Short-Text Conversation\n## Categories\n- Computation and Language\n## Abstract\nDespite the effectiveness of sequence-to-sequence framework on the task of Short-Text Conversation (STC), the issue of under-exploitation of training data (i.e., the supervision signals from query text is \\textit{ignored}) still remains unresolved. Also, the adopted \\textit{maximization}-based decoding strategies, inclined to generating the generic responses or responses with repetition, are unsuited to the STC task. In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation. To enhance generation performance, we design a relevance-promoting transformer language model, which performs additional supervised source attention after the self-attention to increase the importance of informative query tokens in calculating the token-level representation. The model further refines the query representation with relevance clues inferred from its multiple references during training. In testing, we adopt a \\textit{randomization-over-maximization} strategy to reduce the generation of generic responses. Experimental results on a large Chinese STC dataset demonstrate the superiority of the proposed model on relevance metrics and diversity metrics.\\footnote{Code available at https:\/\/ai.tencent.com\/ailab\/nlp\/dialogue\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11489v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"45169\", \"46304\", \"137731\", \"155361\"], \"outgoing_citations\": [\"154886\", \"158493\", \"174210\", \"176321\", \"180695\", \"182471\", \"187236\", \"210158\", \"217881\", \"221788\", \"227586\", \"232274\", \"232355\", \"259145\", \"260929\", \"270040\", \"277320\", \"279897\", \"280916\", \"284752\", \"287474\", \"289061\", \"289943\", \"291102\", \"299541\", \"309411\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"154242","document_content":"# Content-based image retrieval speedup\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nContent-based image retrieval (CBIR) is a task of retrieving images from their contents. Since retrieval process is a time-consuming task in large image databases, acceleration methods can be very useful. This paper presents a novel method to speed up CBIR systems. In the proposed method, first Zernike moments are extracted from query image and an interval is calculated for that query. Images in database which are out of the interval are ignored in retrieval process. Therefore, a database reduction occurs before retrieval which leads to speed up. It is shown that in reduced database, relevant images to query image are preserved and irrelevant images are throwed away. Therefore, the proposed method speed up retrieval process and preserve CBIR accuracy, simultaneously.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11379v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"149853\"], \"outgoing_citations\": [\"200040\", \"201442\", \"331458\"]}","task_split":"paper_retrieval"}
{"document_id":"154247","document_content":"# Representation Learning: A Statistical Perspective\n## Categories\n- Machine Learning\n## Abstract\nLearning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11374v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"60527\", \"110041\", \"130231\"], \"outgoing_citations\": [\"204582\", \"204683\", \"214434\", \"214814\", \"215686\", \"220842\", \"236989\", \"249457\", \"267998\", \"274632\", \"283651\", \"289471\", \"290683\", \"291253\", \"298585\", \"305862\", \"320099\", \"334299\"]}","task_split":"paper_retrieval"}
{"document_id":"154394","document_content":"# FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nOne of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10953v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"32564\", \"196165\", \"165673\", \"178243\", \"184957\", \"206754\", \"206755\", \"221024\", \"221025\", \"226805\", \"237874\", \"237875\", \"243184\", \"255123\", \"267465\", \"247292\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"154511","document_content":"# Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nPeople often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is challenging; there is a large variation in manipulation actions and some videos even involve multiple persons, who collaborate by sharing and exchanging objects and tools. Furthermore, the learned representations need to be general enough to be transferable to robotic systems. On the other hand, previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates actions to manipulated objects and tools. Building upon this theory of language for action, we propose a system for understanding and executing demonstrated action sequences from full-length, real-world cooking videos on the web. The system takes as input a new, previously unseen cooking video annotated with object labels and bounding boxes, and outputs a collaborative manipulation action plan for one or more robotic arms. We demonstrate performance of the system in a standardized dataset of 100 YouTube cooking videos, as well as in six full-length Youtube videos that include collaborative actions between two participants. We compare our system with a baseline system that consists of a state-of-the-art action detection baseline and show our system achieves higher action detection accuracy. We additionally propose an open-source platform for executing the learned plans in a simulation environment as well as with an actual robotic arm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10686v5\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"50632\", \"58191\", \"59341\", \"63687\", \"65262\", \"130892\", \"178006\", \"206227\", \"212935\", \"214285\", \"216061\", \"225608\", \"228679\", \"237765\", \"254396\", \"255939\", \"265650\", \"270325\", \"272651\", \"320332\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"154514","document_content":"# Image-based table recognition: data, model, and evaluation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImportant information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g., Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style. To facilitate image-based table recognition with deep learning, we develop the largest publicly available table recognition dataset PubTabNet (https:\/\/github.com\/ibm-aur-nlp\/PubTabNet), containing 568k table images with corresponding structured HTML representation. PubTabNet is automatically generated by matching the XML and PDF representations of the scientific articles in PubMed Central Open Access Subset (PMCOA). We also propose a novel attention-based encoder-dual-decoder (EDD) architecture that converts images of tables into HTML code. The model has a structure decoder which reconstructs the table structure and helps the cell decoder to recognize cell content. In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric. The experiments demonstrate that the EDD model can accurately recognize complex tables solely relying on the image representation, outperforming the state-of-the-art by 9.7% absolute TEDS score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10683v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6241\", \"7143\", \"9726\", \"10179\", \"18498\", \"19851\", \"36085\", \"96713\", \"9109\", \"15191\", \"25623\", \"43849\", \"46340\", \"49808\", \"50149\", \"51656\", \"51655\", \"52829\", \"55565\", \"64008\", \"68867\", \"75085\", \"127548\", \"127576\", \"84406\", \"95468\", \"107516\"], \"outgoing_citations\": [\"148399\", \"171596\", \"183355\", \"195964\", \"235296\", \"236501\", \"284605\"]}","task_split":"paper_retrieval"}
{"document_id":"154529","document_content":"# ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Optimization and Control\n## Abstract\nReinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of canonical online stochastic optimization problems with a range of practical applications: Bin Packing, Newsvendor, and Vehicle Routing. While there is a nascent literature that applies RL to these problems, there are no commonly accepted benchmarks which can be used to compare proposed approaches rigorously in terms of performance, scale, or generalizability. This paper aims to fill that gap. For each problem we apply both standard approaches as well as newer RL algorithms and analyze results. In each case, the performance of the trained RL policy is competitive with or superior to the corresponding baselines, while not requiring much in the way of domain knowledge. This highlights the potential of RL in real-world dynamic resource allocation problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10641v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"math.OC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Optimization and Control\"], \"incoming_citations\": [\"68178\", \"35360\", \"51973\", \"5886\", \"41605\", \"44537\", \"47037\", \"66377\", \"70391\", \"94703\", \"105997\", \"133522\"], \"outgoing_citations\": [\"238077\", \"240233\", \"242457\", \"246518\", \"258174\", \"269617\", \"279191\", \"288848\", \"293995\", \"309799\", \"322629\"]}","task_split":"paper_retrieval"}
{"document_id":"154533","document_content":"# Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nRecent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov\/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10635v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Machine Learning\"], \"incoming_citations\": [\"18792\", \"23640\", \"69088\", \"75234\", \"93959\", \"94737\", \"102165\", \"170\", \"841\", \"3665\", \"3916\", \"6615\", \"7548\", \"8786\", \"9804\", \"12166\", \"13755\", \"13681\", \"17231\", \"17411\", \"21158\", \"25861\", \"26675\", \"38642\", \"52548\", \"63056\", \"73192\", \"84123\", \"93081\", \"95653\", \"120002\", \"135053\", \"180545\", \"3004\", \"6258\", \"7325\", \"7461\", \"7817\", \"8307\", \"9888\", \"13350\", \"14489\", \"14847\", \"17687\", \"24802\", \"24878\", \"25144\", \"27374\", \"30211\", \"30866\", \"139233\", \"34038\", \"34513\", \"34703\", \"34936\", \"37352\", \"37786\", \"37932\", \"39804\", \"31209\", \"44676\", \"45022\", \"45865\", \"20450\", \"47327\", \"47984\", \"48949\", \"49002\", \"49316\", \"51425\", \"52553\", \"54894\", \"55813\", \"55720\", \"32526\", \"59392\", \"59786\", \"61652\", \"61901\", \"61919\", \"62688\", \"63579\", \"64383\", \"65204\", \"67595\", \"68574\", \"69445\", \"72332\", \"72583\", \"73186\", \"77098\", \"88012\", \"88549\", \"91858\", \"92149\", \"92606\", \"97138\", \"98052\", \"100658\", \"103322\", \"105919\", \"107501\", \"111881\", \"114191\", \"114269\", \"119406\", \"120402\", \"120805\", \"135486\", \"138645\", \"139813\", \"143035\", \"152159\", \"184410\", \"152854\", \"228064\", \"117194\", \"75971\", \"77684\", \"82456\", \"87466\", \"89746\", \"89833\", \"92207\", \"92593\", \"94034\", \"95016\", \"95100\", \"95657\", \"97549\", \"100839\", \"101567\", \"102965\", \"110000\", \"110680\", \"112031\", \"113006\", \"113074\", \"117083\", \"119372\", \"130732\", \"133455\", \"134319\", \"137679\", \"141212\", \"141799\", \"141945\", \"143087\", \"146148\", \"152504\", \"172620\", \"189160\"], \"outgoing_citations\": [\"120607\", \"134319\", \"156830\", \"158300\", \"159330\", \"160587\", \"161359\", \"162524\", \"166715\", \"168686\", \"169603\", \"169677\", \"170240\", \"170692\", \"172206\", \"172755\", \"173489\", \"174389\", \"177064\", \"177324\", \"179171\", \"179906\", \"182695\", \"183013\", \"183230\", \"183393\", \"184858\", \"186457\", \"186631\", \"189799\", \"194093\", \"194194\", \"195059\", \"195273\", \"198036\", \"198582\", \"198988\", \"199989\", \"200202\", \"200391\", \"200533\", \"204041\", \"204248\", \"204330\", \"206635\", \"206886\", \"206920\", \"209253\", \"209475\", \"210362\", \"212068\", \"212790\", \"212891\", \"213438\", \"213513\", \"213870\", \"214440\", \"214751\", \"218583\", \"221598\", \"222357\", \"225090\", \"225467\", \"227728\", \"229001\", \"229453\", \"231395\", \"231983\", \"232018\", \"235526\", \"235974\", \"237193\", \"237381\", \"237494\", \"237964\", \"238439\", \"240048\", \"241037\", \"241727\", \"242052\", \"242097\", \"243442\", \"245461\", \"248557\", \"252179\", \"253149\", \"258472\", \"259927\", \"262105\", \"263576\", \"264164\", \"264897\", \"265075\", \"265505\", \"266991\", \"267242\", \"270254\", \"271085\", \"271199\", \"271497\", \"272788\", \"274315\", \"277367\", \"278488\", \"280774\", \"282884\", \"283215\", \"284459\", \"285591\", \"289446\", \"291892\", \"292127\", \"297186\", \"302370\", \"309111\", \"310531\", \"313496\", \"315982\", \"317144\", \"321115\", \"326880\", \"335300\", \"335937\", \"338412\", \"340484\", \"347352\", \"350227\", \"351410\", \"352010\", \"353646\", \"354707\", \"356842\", \"356973\", \"358688\", \"359239\", \"360772\"]}","task_split":"paper_retrieval"}
{"document_id":"154701","document_content":"# Self-Enhanced Convolutional Network for Facial Video Hallucination\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAs a domain-specific super-resolution problem, facial image hallucination has enjoyed a series of breakthroughs thanks to the advances of deep convolutional neural networks. However, the direct migration of existing methods to video is still difficult to achieve good performance due to its lack of alignment and consistency modelling in temporal domain. Taking advantage of high inter-frame dependency in videos, we propose a self-enhanced convolutional network for facial video hallucination. It is implemented by making full usage of preceding super-resolved frames and a temporal window of adjacent low-resolution frames. Specifically, the algorithm first obtains the initial high-resolution inference of each frame by taking into consideration a sequence of consecutive low-resolution inputs through temporal consistency modelling. It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames. Quantitative and qualitative evaluations demonstrate the superiority of the proposed algorithm against state-of-the-art methods. Moreover, our algorithm also achieves excellent performance in the task of general video super-resolution in a single-shot setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2019.2955640\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"104631\"], \"outgoing_citations\": [\"187775\", \"206299\", \"226061\", \"245112\", \"248019\", \"248969\", \"259660\", \"262243\", \"267136\", \"269321\", \"270264\", \"274838\", \"279581\", \"280330\", \"296033\", \"299534\"]}","task_split":"paper_retrieval"}
{"document_id":"154778","document_content":"# Optimizing Data Usage via Differentiable Rewards\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nTo acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems. Similarly, a machine learning model could potentially be trained better with a scorer that \"adapts\" to its current learning state and estimates the importance of each training data instance. Training such an adaptive scorer efficiently is a challenging problem; in order to precisely quantify the effect of a data instance at a given time during the training, it is typically necessary to first complete the entire training process. To efficiently optimize data usage, we propose a reinforcement learning approach called Differentiable Data Selection (DDS). In DDS, we formulate a scorer network as a learnable function of the training data, which can be efficiently updated along with the main model being trained. Specifically, DDS updates the scorer with an intuitive reward signal: it should up-weigh the data that has a similar gradient with a dev set upon which we would finally like to perform well. Without significant computing overhead, DDS delivers strong and consistent improvements over several strong baselines on two very different tasks of machine translation and image classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10088v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"23273\", \"91442\", \"6136\", \"12648\", \"16594\", \"25580\", \"27582\", \"46813\", \"63578\", \"66671\", \"69143\", \"71219\", \"73521\", \"78992\", \"112938\", \"135281\", \"131195\", \"169731\"], \"outgoing_citations\": [\"182740\", \"183976\", \"185523\", \"193998\", \"197020\", \"199569\", \"201602\", \"207096\", \"209757\", \"211859\", \"221514\", \"230919\", \"235275\", \"235352\", \"237294\", \"237905\", \"242547\", \"247500\", \"259094\", \"259541\", \"269253\", \"271545\", \"272858\", \"292687\", \"295028\"]}","task_split":"paper_retrieval"}
{"document_id":"154779","document_content":"# Injecting Prior Knowledge into Image Caption Generation\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the visual and textual signals and the correlations between them. The state-of-the-art methods in image captioning struggles to approach human level performance, especially when data is limited. In this paper, we propose to improve the performance of the state-of-the-art image captioning models by incorporating two sources of prior knowledge: (i) a conditional latent topic attention, that uses a set of latent variables (topics) as an anchor to generate highly probable words and, (ii) a regularization technique that exploits the inductive biases in syntactic and semantic structure of captions and improves the generalization of image captioning models. Our experiments validate that our method produces more human interpretable captions and also leads to significant improvements on the MSCOCO dataset in both the full and low data regimes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.10082v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7085\"], \"outgoing_citations\": [\"171096\", \"201599\", \"207034\", \"217310\", \"223302\", \"234587\", \"278954\", \"281152\", \"287504\", \"290766\", \"303003\", \"303997\", \"311470\", \"321621\", \"324793\"]}","task_split":"paper_retrieval"}
{"document_id":"154784","document_content":"# Federated Learning with Bayesian Differential Privacy\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nWe consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated setting and suggest multiple improvements for more efficient privacy budgeting at different levels. Our experiments show significant advantage over the state-of-the-art differential privacy bounds for federated learning on image classification tasks, including a medical application, bringing the privacy budget below 1 at the client level, and below 0.1 at the instance level. Lower amounts of noise also benefit the model accuracy and reduce the number of communication rounds.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/BigData47090.2019.9005465\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"3592\", \"8843\", \"32340\", \"39494\", \"40783\", \"86681\", \"138994\", \"490\", \"8081\", \"8944\", \"28115\", \"37557\", \"44029\", \"53594\", \"65735\", \"68935\", \"80963\", \"87667\", \"89054\", \"105610\", \"130313\", \"133696\", \"139003\", \"87047\", \"94707\", \"102077\", \"107025\", \"109503\", \"104228\", \"132414\", \"142628\", \"110331\"], \"outgoing_citations\": [\"159322\", \"201319\", \"225769\", \"239622\", \"240981\", \"246939\", \"252956\", \"254262\", \"273215\", \"273221\", \"282410\", \"333598\", \"357986\"]}","task_split":"paper_retrieval"}
{"document_id":"154810","document_content":"# Characterizing the impact of using features extracted from pre-trained models on the quality of video captioning sequence-to-sequence models\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe task of video captioning, that is, the automatic generation of sentences describing a sequence of actions in a video, has attracted an increasing attention recently. The complex and high-dimensional representation of video data makes it difficult for a typical encoder-decoder architectures to recognize relevant features and encode them in a proper format. Video data contains different modalities that can be recognized using a mix image, scene, action and audio features. In this paper, we characterize the different features affecting video descriptions and explore the interactions among these features and how they affect the final quality of a video representation. Building on existing encoder-decoder models that utilize limited range of video information, our comparisons show how the inclusion of multi-modal video features can make a significant effect on improving the quality of generated statements. The work is of special interest to scientists and practitioners who are using sequence-to-sequence models to generate video captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-59830-3_21\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"253029\", \"276376\", \"279024\", \"279740\", \"281762\", \"303593\", \"304481\", \"313435\", \"313644\", \"315108\", \"316803\"]}","task_split":"paper_retrieval"}
{"document_id":"154860","document_content":"# Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nA relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of entities or overlapping entities among the tuples makes it more challenging. Most prior work adopted a pipeline approach where entities were identified first followed by finding the relations among them, thus missing the interaction among the relation tuples in a sentence. In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations. In the first approach, we propose a representation scheme for relation tuples which enables the decoder to generate one word at a time like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities. Next, we propose a pointer network-based decoding approach where an entire tuple is generated at every time step. Experiments on the publicly available New York Times corpus show that our proposed approaches outperform previous work and achieve significantly higher F1 scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.09886v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"76208\", \"89351\", \"2756\", \"4175\", \"20942\", \"25436\", \"83971\", \"8354\", \"17592\", \"21018\", \"23624\", \"26409\", \"28251\", \"28348\", \"28591\", \"28925\", \"36504\", \"39247\", \"40577\", \"41188\", \"45207\", \"49274\", \"55481\", \"59299\", \"75427\", \"100846\", \"90289\", \"91316\", \"100385\", \"103262\"], \"outgoing_citations\": [\"152153\", \"180210\", \"193096\", \"204496\", \"206411\", \"210744\", \"213229\", \"232454\", \"234772\", \"235065\", \"244248\", \"264389\", \"281163\", \"281792\", \"300593\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"154886","document_content":"# A Discrete CVAE for Response Generation on Short-Text Conversation\n## Categories\n- Computation and Language\n## Abstract\nNeural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled la-tent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we pro-pose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and in-formative responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.09845v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25918\", \"44197\", \"45423\", \"46304\", \"77654\", \"91095\", \"95282\", \"98620\", \"99759\", \"100130\", \"127996\", \"137731\", \"154192\"], \"outgoing_citations\": [\"209388\", \"210158\", \"217640\", \"229813\", \"234681\", \"249972\", \"265139\", \"267641\", \"270040\", \"275597\", \"279543\", \"289943\", \"291102\", \"292261\", \"295863\", \"302162\", \"308381\", \"309411\", \"310740\", \"310903\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"154905","document_content":"# Zero-Resource Cross-Lingual Named Entity Recognition\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Our model achieves this through word-level adversarial learning and augmented fine-tuning with parameter sharing and feature augmentation. Experiments on five different languages demonstrate the effectiveness of our approach, outperforming existing models by a good margin and setting a new SOTA for each language pair.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.09812v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"100771\", \"25615\", \"33985\", \"44844\", \"44951\", \"45842\", \"53276\", \"54756\", \"73229\", \"87030\", \"91925\", \"128551\", \"89474\", \"111926\", \"133395\"], \"outgoing_citations\": [\"172107\", \"219866\", \"232631\", \"234807\", \"241616\", \"253559\", \"261734\", \"267692\", \"271158\", \"274533\"]}","task_split":"paper_retrieval"}
{"document_id":"154910","document_content":"# Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering\n## Categories\n- Computation and Language\n## Abstract\nCommunity question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.09801v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"756\", \"733\", \"1442\", \"3037\", \"9999\", \"16513\", \"21898\", \"56834\", \"45485\", \"45567\", \"56426\", \"91732\", \"95832\", \"103764\", \"115623\", \"136978\"], \"outgoing_citations\": [\"153217\", \"203630\", \"207045\", \"213836\", \"216726\", \"225556\", \"231929\", \"232590\", \"234739\", \"249504\", \"253691\", \"267899\", \"280557\", \"281119\", \"296029\", \"298513\"]}","task_split":"paper_retrieval"}
{"document_id":"154931","document_content":"# An Innovative Approach to Addressing Childhood Obesity: A Knowledge-Based Infrastructure for Supporting Multi-Stakeholder Partnership Decision-Making in Quebec, Canada\n## Categories\n- Computers and Society\n- Artificial Intelligence\n## Abstract\nThe purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a portrait, which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data and can help in managing semantic interoperability between different knowledge sources.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/ijerph120201314\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [\"62636\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"155007","document_content":"# Continual Learning with Adaptive Weights (CLAW)\n## Categories\n- Machine Learning\n## Abstract\nApproaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tasks. On the one hand, separately modelling each task avoids catastrophic forgetting but it does not support transfer learning and leads to large models. On the other hand, rigidly specifying a shared component and a task-specific part enables task transfer and limits the model size, but it is vulnerable to catastrophic forgetting and restricts the form of task-transfer that can occur. Ideally, the network should adaptively identify which parts of the network to share in a data driven way. Here we introduce such an approach called Continual Learning with Adaptive Weights (CLAW), which is based on probabilistic modelling and variational inference. Experiments show that CLAW achieves state-of-the-art performance on six benchmarks in terms of overall continual learning performance, as measured by classification accuracy, and in terms of addressing catastrophic forgetting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.09514v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"27067\", \"90584\", \"8643\", \"46533\", \"50245\", \"115653\", \"14593\", \"21544\", \"46531\", \"47830\", \"58148\", \"63510\", \"66091\", \"84925\", \"115768\", \"92287\", \"96426\", \"104185\", \"164924\"], \"outgoing_citations\": [\"172207\", \"172566\", \"173452\", \"175594\", \"176132\", \"180556\", \"181107\", \"182229\", \"183710\", \"184114\", \"184149\", \"185554\", \"185559\", \"186998\", \"192027\", \"193041\", \"194365\", \"199847\", \"200429\", \"200812\", \"208243\", \"212214\", \"212647\", \"216288\", \"220865\", \"228062\", \"228111\", \"229806\", \"230303\", \"231847\", \"240600\", \"240995\", \"241499\", \"243820\", \"245957\", \"249018\", \"252086\", \"252176\", \"259175\", \"259336\", \"265568\", \"265576\", \"270623\", \"274001\", \"275087\", \"275877\", \"282436\", \"282684\", \"289250\", \"290378\", \"311571\", \"328112\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"155305","document_content":"# On Node Features for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning process. However, this prevents graph neural network from being applied into featureless graphs. In this paper, we first analyze the effects of node features on the performance of graph neural network. We show that GNNs work well if there is a strong correlation between node features and node labels. Based on these results, we propose new feature initialization methods that allows to apply graph neural network to non-attributed graphs. Our experimental results show that the artificial features are highly competitive with real features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.08795v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"5326\", \"9291\", \"19282\", \"39747\", \"93782\", \"117980\", \"94370\"], \"outgoing_citations\": [\"267766\", \"295676\"]}","task_split":"paper_retrieval"}
{"document_id":"155316","document_content":"# Joint Embedding Learning of Educational Knowledge Graphs\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAs an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.08776v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"90031\"], \"outgoing_citations\": [\"151207\", \"152591\", \"201341\", \"242267\", \"243422\", \"248817\", \"267141\", \"269294\", \"278065\", \"279522\", \"279897\", \"294402\", \"295931\", \"300508\", \"304901\", \"306399\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"155387","document_content":"# Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nFor lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent networks that \\help\" each other reconstruct same target image patches using complementary portions of spatial context that communicate via gradient signals. This dual agent system builds upon prior work that proposed the iterative refinement algorithm for recurrent neural network (RNN)based decoding which improved image reconstruction compared to standard decoding techniques. Our approach, which works with any encoder, neural or non-neural, This system progressively reduces image patch reconstruction error over a fixed number of steps. Experiment with variants of RNN memory cells, with and without future information, find that our model consistently creates lower distortion images of higher perceptual quality compared to other approaches. Specifically, on the Kodak Lossless True Color Image Suite, we observe as much as a 1:64 decibel (dB) gain over JPEG, a 1:46 dB gain over JPEG 2000, a 1:34 dB gain over the GOOG neural baseline, 0:36 over E2E (a modern competitive neural compression model), and 0:37 over a single iterative neural decoder.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.08478v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"3535\", \"133119\", \"143127\"], \"outgoing_citations\": [\"168198\", \"238842\", \"243653\", \"257927\", \"266304\", \"269868\", \"270241\", \"270523\", \"272675\", \"281135\", \"286385\", \"293570\", \"299534\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"155428","document_content":"# Towards Lingua Franca Named Entity Recognition with BERT\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nInformation extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling. Historically, research and data was produced for English text, followed in subsequent years by datasets in Arabic, Chinese (ACE\/OntoNotes), Dutch, Spanish, German (CoNLL evaluations), and many others. The natural tendency has been to treat each language as a different dataset and build optimized models for each. In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language. To improve the initial model, we study the use of regularization strategies such as multitask learning and partial gradient updates. In addition to being a single model that can tackle multiple languages (including code switch), the model could be used to make zero-shot predictions on a new language, even ones for which training data is not available, out of the box. The results show that this model not only performs competitively with monolingual models, but it also achieves state-of-the-art results on the CoNLL02 Dutch and Spanish datasets, OntoNotes Arabic and Chinese datasets. Moreover, it performs reasonably well on unseen languages, achieving state-of-the-art for zero-shot on three CoNLL languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1912.01389v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"42156\", \"28864\", \"33985\", \"38116\", \"45842\", \"73229\", \"87611\", \"91598\", \"93623\", \"100490\", \"128868\"], \"outgoing_citations\": [\"182594\", \"194633\", \"216925\", \"219866\", \"228681\", \"261734\"]}","task_split":"paper_retrieval"}
{"document_id":"155440","document_content":"# Deep Unsupervised Clustering with Clustered Generator Model\n## Categories\n- Machine Learning\n## Abstract\nThis paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both continuous and discrete latent variables. Discrete latent variables model the cluster label while the continuous ones model variations within each cluster. The learning of the model proceeds in a unified probabilistic framework and incorporates the unsupervised clustering as an inner step without the need for an extra inference model as in existing variational-based models. The latent variables learned serve as both observed data embedding or latent representation for data distribution. Our experiments show that the proposed model can achieve competitive unsupervised clustering accuracy and can learn disentangled latent representations to generate realistic samples. In addition, the model can be naturally extended to per-pixel unsupervised clustering which remains largely unexplored.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.08459v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204582\", \"224270\", \"278217\", \"280365\", \"280893\", \"289471\", \"302802\", \"303139\"]}","task_split":"paper_retrieval"}
{"document_id":"155443","document_content":"# MANGA: Method Agnostic Neural-policy Generalization and Adaptation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework that decouples the processes of policy learning and system identification. Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction. We introduce MANGA: Method Agnostic Neural-policy Generalization and Adaptation, that trains dynamics conditioned policies and efficiently learns to estimate the dynamics parameters of the environment given off-policy state-transition rollouts in the environment. Our scheme is agnostic to the type of training method used - both reinforcement learning (RL) and imitation learning (IL) strategies can be used. We demonstrate the effectiveness of our approach by experimenting with four different MuJoCo agents and comparing against previously proposed transfer baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.08444v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"123568\"], \"outgoing_citations\": [\"185747\", \"191671\", \"194653\", \"196576\", \"196671\", \"210015\", \"212299\", \"214403\", \"214631\", \"222448\", \"222669\", \"228540\", \"228896\", \"233926\", \"253045\", \"255681\", \"274445\", \"277822\", \"282841\"]}","task_split":"paper_retrieval"}
{"document_id":"155744","document_content":"# GraLSP: Graph Neural Networks with Local Structural Patterns\n## Categories\n- Machine Learning\n## Abstract\nIt is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the neighborhood of a node achieved great success. However, despite such achievements, GNNs illustrate defects in identifying some common structural patterns which, unfortunately, play significant roles in various network phenomena. In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns. The walks are then fed into the feature aggregation, where we design various mechanisms to address the impact of structural features, including adaptive receptive radius, attention and amplification. In addition, we design objectives that capture similarities between structures and are optimized jointly with node proximity objectives. With the adequate leverage of structural patterns, our model is able to outperform competitive counterparts in various prediction tasks in multiple datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.07675v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"34810\", \"8235\", \"16169\", \"19609\", \"34086\", \"57782\", \"116141\", \"117926\", \"192622\"], \"outgoing_citations\": [\"182893\", \"215435\", \"229936\", \"243437\", \"249497\", \"252195\", \"269202\", \"277109\"]}","task_split":"paper_retrieval"}
{"document_id":"155958","document_content":"# Transductive Zero-Shot Hashing for Multilabel Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nHash coding has been widely used in approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may contain undefined semantic labels, which we call unseen images, zeor-shot hashing techniques have been studied. However, existing zeor-shot hashing methods focus on the retrieval of single-label images, and cannot handle multi-label images. In this paper, for the first time, a novel transductive zero-shot hashing method is proposed for multi-label unseen image retrieval. In order to predict the labels of the unseen\/target data, a visual-semantic bridge is built via instance-concept coherence ranking on the seen\/source data. Then, pairwise similarity loss and focal quantization loss are constructed for training a hashing model using both the seen\/source and unseen\/target data. Extensive evaluations on three popular multi-label datasets demonstrate that, the proposed hashing method achieves significantly better results than the competing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2020.3043298\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"144622\", \"189033\", \"189425\", \"189924\", \"190296\", \"191832\", \"227937\", \"231521\", \"238203\", \"239660\", \"239846\", \"248330\", \"248411\", \"251069\", \"255885\", \"260176\", \"263051\", \"274566\", \"274846\", \"292884\", \"296530\", \"303392\", \"307882\", \"314568\", \"315376\", \"316473\", \"318535\", \"318839\", \"323833\", \"328183\", \"340226\", \"346675\"]}","task_split":"paper_retrieval"}
{"document_id":"156027","document_content":"# All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial expressions from one identity to another identity, known as Facial Expression Transfer (FET), 2. to transform the expression of a given image to a target expression, while preserving the identity of the image, known as Facial Expression Editing (FEE), and 3. to recognize the facial expression of a face image, known as Facial Expression Recognition (FER). In TER-GAN, we combine the capabilities of generative models to generate synthetic images, while learning important information about the input images during the reconstruction process. More specifically, two encoders are used in TER-GAN to encode identity and expression information from two input images, and a synthetic expression image is generated by the decoder part of TER-GAN. To improve the feature disentanglement and extraction process, we also introduce a novel expression consistency loss and an identity consistency loss which exploit extra expression and identity information from generated images. Experimental results show that the proposed method can be used for efficient facial expression transfer, facial expression editing and facial expression recognition. In order to evaluate the proposed technique and to compare our results with state-of-the-art methods, we have used the Oulu-CASIA dataset for our experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.07050v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"77404\", \"92731\", \"127610\"], \"outgoing_citations\": [\"109032\", \"116950\", \"162053\", \"164407\", \"223142\", \"223504\", \"227719\", \"247846\", \"250524\", \"256227\", \"272912\", \"279182\", \"282432\", \"284239\", \"320031\", \"321229\"]}","task_split":"paper_retrieval"}
{"document_id":"156039","document_content":"# On Value Discrepancy of Imitation Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nImitation learning trains a policy from expert demonstrations. Imitation learning approaches have been designed from various principles, such as behavioral cloning via supervised learning, apprenticeship learning via inverse reinforcement learning, and GAIL via generative adversarial learning. In this paper, we propose a framework to analyze the theoretical property of imitation learning approaches based on discrepancy propagation analysis. Under the infinite-horizon setting, the framework leads to the value discrepancy of behavioral cloning in an order of O((1-\\gamma)^{-2}). We also show that the framework leads to the value discrepancy of GAIL in an order of O((1-\\gamma)^{-1}). It implies that GAIL has less compounding errors than behavioral cloning, which is also verified empirically in this paper. To the best of our knowledge, we are the first one to analyze GAIL's performance theoretically. The above results indicate that the proposed framework is a general tool to analyze imitation learning approaches. We hope our theoretical results can provide insights for future improvements in imitation learning algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.07027v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"23199\", \"11802\", \"56445\"], \"outgoing_citations\": [\"183014\", \"215836\", \"233167\", \"251102\", \"251924\", \"272623\", \"291738\", \"297317\", \"303250\"]}","task_split":"paper_retrieval"}
{"document_id":"156107","document_content":"# CNN-based Dual-Chain Models for Knowledge Graph Learning\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nKnowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods on knowledge graph learning primarily focus on modeling the relations among entities as translations among the relations and entities, and many of these methods are not able to handle zero-shot problems, when new entities emerge. In this paper, we present a new convolutional neural network (CNN)-based dual-chain model. Different from translation based methods, in our model, interactions among relations and entities are directly captured via CNN over their embeddings. Moreover, a secondary chain of learning is conducted simultaneously to incorporate additional information and to enable better performance. We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions. As a result, the extended model is able to effectively handle zero-shot problems. We conducted comprehensive experiments, comparing our methods with 15 methods on 8 benchmark datasets. Extensive experimental results demonstrate that our proposed methods achieve or outperform the state-of-the-art results on knowledge graph learning, and outperform other methods on zero-shot problems. In addition, our methods applied to real-world biomedical data are able to produce results that conform to expert domain knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.06910v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227492\", \"294402\", \"304901\"]}","task_split":"paper_retrieval"}
{"document_id":"156204","document_content":"# GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion\n## Categories\n- Computer Vision and Pattern Recognition\n- Computational Geometry\n- Machine Learning\n## Abstract\nIn this paper, we present a novel deep method to reconstruct a point cloud of an object from a single still image. Prior arts in the field struggle to reconstruct an accurate and scalable 3D model due to either the inefficient and expensive 3D representations, the dependency between the output and number of model parameters or the lack of a suitable computing operation. We propose to overcome these by deforming a random point cloud to the object shape through two steps: feature blending and deformation. In the first step, the global and point-specific shape features extracted from a 2D object image are blended with the encoded feature of a randomly generated point cloud, and then this mixture is sent to the deformation step to produce the final representative point set of the object. In the deformation process, we introduce a new layer termed as GraphX that considers the inter-relationship between points like common graph convolutions but operates on unordered sets. Moreover, with a simple trick, the proposed model can generate an arbitrary-sized point cloud, which is the first deep method to do so. Extensive experiments verify that we outperform existing models and halve the state-of-the-art distance score in single image 3D reconstruction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.06600v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CG\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computational Geometry\", \"Machine Learning\"], \"incoming_citations\": [\"153937\", \"207345\"], \"outgoing_citations\": [\"213394\", \"235794\", \"236720\", \"245324\", \"263147\", \"268404\", \"279050\", \"282032\", \"295402\"]}","task_split":"paper_retrieval"}
{"document_id":"156406","document_content":"# Hierarchical Graph Pooling with Structure Learning\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations. The graph pooling (or downsampling) operations, that play an important role in learning hierarchical representations, are usually overlooked. In this paper, we propose a novel graph pooling operator, called Hierarchical Graph Pooling with Structure Learning (HGP-SL), which can be integrated into various graph neural network architectures. HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs. More specifically, the graph pooling operation adaptively selects a subset of nodes to form an induced subgraph for the subsequent layers. To preserve the integrity of graph's topological information, we further introduce a structure learning mechanism to learn a refined graph structure for the pooled graph at each layer. By combining HGP-SL operator with graph neural networks, we perform graph level representation learning with focus on graph classification task. Experimental results on six widely used benchmarks demonstrate the effectiveness of our proposed model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.05954v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"35089\", \"56502\", \"120407\", \"129270\", \"7330\", \"20281\", \"21372\", \"30460\", \"78838\", \"22486\", \"26197\", \"29397\", \"45083\", \"53195\", \"72913\", \"75522\", \"81304\", \"100975\", \"117417\", \"56175\", \"64686\", \"78173\", \"113670\", \"123312\", \"136162\"], \"outgoing_citations\": [\"184370\", \"188398\", \"190159\", \"202232\", \"215435\", \"245486\", \"250241\", \"298842\", \"302934\"]}","task_split":"paper_retrieval"}
{"document_id":"156449","document_content":"# Federated and Differentially Private Learning for Electronic Health Records\n## Categories\n- Machine Learning\n## Abstract\nThe use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.05861v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"9036\", \"36531\", \"19599\", \"54391\", \"72995\", \"73648\", \"94505\", \"83656\", \"156643\"], \"outgoing_citations\": [\"205882\", \"206737\", \"207326\", \"207602\", \"209442\", \"214295\", \"241651\", \"252956\", \"273215\"]}","task_split":"paper_retrieval"}
{"document_id":"156643","document_content":"# Federated Learning for Healthcare Informatics\n## Categories\n- Machine Learning\n## Abstract\nWith the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, \"big data\". Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges and privacy issues in federated learning, and point out the implications and potentials in healthcare.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.06270v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"9036\", \"9619\", \"21096\", \"35996\", \"1828\", \"4040\", \"6050\", \"9573\", \"17143\", \"27224\", \"31206\", \"34352\", \"46150\", \"47195\", \"52335\", \"52799\", \"56094\", \"59209\", \"69454\", \"77178\", \"80685\", \"110259\", \"440\", \"490\", \"1054\", \"15166\", \"16284\", \"17947\", \"18850\", \"19599\", \"19632\", \"19754\", \"19956\", \"20664\", \"26435\", \"27798\", \"28837\", \"30490\", \"33583\", \"33823\", \"35288\", \"36588\", \"36880\", \"38388\", \"39075\", \"39695\", \"42569\", \"44840\", \"49553\", \"51081\", \"58736\", \"66833\", \"66923\", \"74139\", \"76032\", \"80963\", \"83423\", \"85147\", \"104264\", \"81511\", \"82838\", \"83656\", \"91256\", \"96241\", \"105490\", \"113040\", \"126325\", \"136201\", \"140296\", \"110331\"], \"outgoing_citations\": [\"100058\", \"126325\", \"135986\", \"138557\", \"153401\", \"156449\", \"159558\", \"162301\", \"163085\", \"164627\", \"164869\", \"166833\", \"171823\", \"175096\", \"178024\", \"179034\", \"179589\", \"179935\", \"180560\", \"180726\", \"181136\", \"181474\", \"181774\", \"182115\", \"182492\", \"183930\", \"184652\", \"184670\", \"184990\", \"185854\", \"185996\", \"186014\", \"186794\", \"187044\", \"187313\", \"189393\", \"193748\", \"194178\", \"194734\", \"195752\", \"196107\", \"197068\", \"200635\", \"200704\", \"200873\", \"201645\", \"202443\", \"204345\", \"205519\", \"205577\", \"206604\", \"206770\", \"206873\", \"207074\", \"207462\", \"207491\", \"207518\", \"207602\", \"207623\", \"207951\", \"208013\", \"208110\", \"208413\", \"208847\", \"208924\", \"210718\", \"210777\", \"210868\", \"213659\", \"214295\", \"214903\", \"217320\", \"218321\", \"220447\", \"221338\", \"221592\", \"222321\", \"225299\", \"226146\", \"230382\", \"230526\", \"232435\", \"234581\", \"239562\", \"241304\", \"246939\", \"248803\", \"248982\", \"252956\", \"265073\", \"265459\", \"269208\", \"303573\", \"329400\", \"339350\", \"356499\", \"359693\"]}","task_split":"paper_retrieval"}
{"document_id":"156680","document_content":"# Improving Robustness of Task Oriented Dialog Systems\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nTask oriented language understanding in dialog systems is often modeled using intents (task of a query) and slots (parameters for that task). Intent detection and slot tagging are, in turn, modeled using sentence classification and word tagging techniques respectively. Similar to adversarial attack problems with computer vision models discussed in existing literature, these intent-slot tagging models are often over-sensitive to small variations in input -- predicting different and often incorrect labels when small changes are made to a query, thus reducing their accuracy and reliability. However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e.g. adding `noise') to a query sometimes changes the meaning and thus labels of a query. In this paper, we first describe how to create an adversarial test set to measure the robustness of these models. Furthermore, we introduce and adapt adversarial training methods as well as data augmentation using back-translation to mitigate these issues. Our experiments show that both techniques improve the robustness of the system substantially and can be combined to yield the best results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.05153v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"16678\", \"100214\", \"24657\", \"48161\", \"56006\", \"56334\", \"65148\", \"70413\", \"91631\", \"119809\"], \"outgoing_citations\": [\"212163\", \"214546\", \"231316\", \"234242\", \"234716\", \"235345\", \"244005\", \"244978\", \"250586\", \"271637\", \"291989\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"156881","document_content":"# Knowledge Distillation in Document Retrieval\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n## Abstract\nComplex deep learning models now achieve state of the art performance for many document retrieval tasks. The best models process the query or claim jointly with the document. However for fast scalable search it is desirable to have document embeddings which are independent of the claim. In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings. We explore this approach in document retrieval for a fact extraction and verification task. We show that by using the soft labels from a complex cross attention teacher model, the performance of claim independent student LSTM or CNN models is improved across all the ranking metrics. The student models we use are 12x faster in runtime and 20x smaller in number of parameters than the teacher","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.11065v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"119873\"], \"outgoing_citations\": [\"220570\", \"238986\", \"270020\", \"281162\", \"289589\", \"303469\", \"311174\"]}","task_split":"paper_retrieval"}
{"document_id":"157041","document_content":"# Learning to Order Sub-questions for Complex Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nAnswering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-questions and then answering those sub-questions. Existing work has followed this strategy but has not attempted to optimize the order of how those sub-questions are answered. As a result, the sub-questions are answered in an arbitrary order, leading to larger search space and a higher risk of missing an answer. In this paper, we propose a novel reinforcement learning(RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each stage of reasoning. We lever-age the expected value-variance criterion to enable the learned policy to balance between the risk and utility of answering a sub-question. Experiment results show that the RL approach can substantially improve the optimality of ordering the sub-questions, leading to improved accuracy of question answering. The proposed method for learning to order sub-questions is general and can thus be potentially combined with many existing ideas for answering complex questions to enhance their performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.04065v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"63653\"], \"outgoing_citations\": [\"182026\", \"197395\", \"219691\", \"229169\", \"238610\", \"250322\", \"260644\", \"280985\", \"281251\", \"338103\"]}","task_split":"paper_retrieval"}
{"document_id":"157117","document_content":"# HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network\n## Categories\n- Machine Learning\n- Computation and Language\n- Social and Information Networks\n## Abstract\nGraph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. However, conventional GNNs suffer from bad performance in modelling long-distance node relations due to limited-layer information propagation. Existing studies focus on building deep GNN architectures, which face the over-smoothing issue and cannot model node relations in particularly long distance. To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label. To combine our two solutions, we propose a model-agnostic training framework named HighwayGraph, which overcomes the challenge of insufficient labeled nodes by sampling node pairs from the training set and adopting the self-training method. Extensive experimental results show that our HighwayGraph achieves consistent and significant improvements over four representative GNNs on three benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03904v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"5032\"], \"outgoing_citations\": [\"164732\", \"168172\", \"174492\", \"182282\", \"182323\", \"182687\", \"185055\", \"186164\", \"191340\", \"191735\", \"193345\", \"193816\", \"197412\", \"199104\", \"201866\", \"203910\", \"208815\", \"210119\", \"215435\", \"261201\", \"263601\", \"295676\"]}","task_split":"paper_retrieval"}
{"document_id":"157122","document_content":"# A Bilingual Generative Transformer for Semantic Sentence Embedding\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSemantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences. Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific. We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. Our proposed approach differs from past work on semantic sentence encoding in two ways. First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model's posterior to predict sentence embeddings for monolingual data at test time. Second, we use high-capacity transformers as both data generating distributions and inference networks -- contrasting with most past work on sentence embeddings. In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations. Further, we demonstrate that our approach yields the largest gains on more difficult subsets of these evaluations where simple word overlap is not a good indicator of similarity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03895v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"52464\", \"18913\", \"22045\", \"55137\", \"56882\", \"67742\", \"96526\", \"94843\"], \"outgoing_citations\": [\"164127\", \"166824\", \"168448\", \"176839\", \"192755\", \"201049\", \"202724\", \"204715\", \"212322\", \"230709\", \"233400\", \"237091\", \"237157\", \"237274\", \"238963\", \"250360\", \"259723\", \"267161\", \"267622\", \"268607\", \"268957\", \"272997\", \"275596\", \"288697\", \"298543\", \"302516\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"157143","document_content":"# Knowledge Guided Named Entity Recognition for BioMedical Text\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this work, we formulate the NER task as a multi-answer knowledge guided QA task (KGQA) which helps to predict entities only by assigning B, I and O tags without associating entity types with the tags. We provide different knowledge contexts, such as, entity types, questions, definitions and examples along with the text and train on a combined dataset of 18 biomedical corpora. This formulation (a) enables systems to jointly learn NER specific features from varied NER datasets, (b) can use knowledge-text attention to identify words having higher similarity to provided knowledge, improving performance, (c) reduces system confusion by reducing the prediction classes to B, I, O only, and (d) makes detection of nested entities easier. We perform extensive experiments of this KGQA formulation on 18 biomedical NER datasets, and through experiments we note that knowledge helps in achieving better performance. Our problem formulation is able to achieve state-of-the-art results in 12 datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03869v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"121505\"], \"outgoing_citations\": [\"159862\", \"159867\", \"165103\", \"165975\", \"174555\", \"181098\", \"181468\", \"189515\", \"197910\", \"211788\", \"218698\", \"227323\", \"243880\", \"268809\", \"332277\"]}","task_split":"paper_retrieval"}
{"document_id":"157154","document_content":"# r\/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection\n## Categories\n- Computation and Language\n- Computers and Society\n- Information Retrieval\n## Abstract\nFake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03854v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Information Retrieval\"], \"incoming_citations\": [\"26778\", \"63247\", \"101350\", \"2364\", \"7737\", \"30160\", \"45840\", \"54321\", \"128184\", \"244473\"], \"outgoing_citations\": [\"169458\", \"183694\", \"191861\", \"219169\", \"235717\", \"238986\", \"267552\", \"268073\", \"302653\", \"355287\"]}","task_split":"paper_retrieval"}
{"document_id":"157166","document_content":"# In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics\n## Categories\n- Quantitative Methods\n- Computers and Society\n- Machine Learning\n- Machine Learning\n## Abstract\nTens of millions of women suffer from infertility worldwide each year. In vitro fertilization (IVF) is the best choice for many such patients. However, IVF is expensive, time-consuming, and both physically and emotionally demanding. The first question that a patient usually asks before the IVF is how likely she will conceive, given her basic medical examination information. This paper proposes three approaches to predict the cumulative pregnancy rate after multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first clustering the patients into different groups and then building a support vector machine model for each group can achieve the best overall performance. Our model could be a quick and economic approach for reliably estimating the cumulative pregnancy rate for a patient, given only her basic medical examination information, well before starting the actual IVF procedure. The predictions can help the patient make optimal decisions on whether to use her own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether to use embryo frozen, etc. They will also reduce the patient's cost and time to pregnancy, and improve her quality of life.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03839v1\", \"primary_category\": \"q-bio.QM\", \"categories\": [\"cs.CY\", \"stat.ML\", \"q-bio.QM\", \"cs.LG\"], \"primary_category_human_readable\": \"Quantitative Methods\", \"categories_human_readable\": [\"Computers and Society\", \"Machine Learning\", \"Quantitative Methods\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"157175","document_content":"# Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper explores the task of interactive image retrieval using natural language queries, where a user progressively provides input queries to refine a set of retrieval results. Moreover, our work explores this problem in the context of complex image scenes containing multiple objects. We propose Drill-down, an effective framework for encoding multiple queries with an efficient compact state representation that significantly extends current methods for single-round image retrieval. We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes. Furthermore, we find that existing image datasets with textual captions can provide a surprisingly effective form of weak supervision for this task. We compare our method with existing sequential encoding and embedding networks, demonstrating superior performance on two proposed benchmarks: automatic image retrieval on a simulated scenario that uses region captions as queries, and interactive image retrieval using real queries from human evaluators.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03826v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"12668\", \"65855\", \"117426\", \"183623\"], \"outgoing_citations\": [\"206510\", \"233661\", \"237849\", \"238205\", \"260908\", \"260926\", \"269959\", \"271085\", \"279518\", \"303011\", \"309411\", \"309886\", \"312643\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"157193","document_content":"# Human Driver Behavior Prediction based on UrbanFlow\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nHow autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in urban intersection scenarios. Meanwhile, in order to collect human drivers' driving behavior data in the urban scenario, we describe a system called UrbanFlow which includes the whole procedure from raw bird's-eye view data collection via drone to the final processed trajectories. The system is mainly intended for urban scenarios but can be extended to be used for any traffic scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03801v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"88048\", \"88070\"], \"outgoing_citations\": [\"214529\", \"232331\", \"244841\", \"278217\"]}","task_split":"paper_retrieval"}
{"document_id":"157257","document_content":"# Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent work has highlighted the advantage of jointly learning grounded sentence representations from multiple languages. However, the data used in these studies has been limited to an aligned scenario: the same images annotated with sentences in multiple languages. We focus on the more realistic disjoint scenario in which there is no overlap between the images in multilingual image--caption datasets. We confirm that training with aligned data results in better grounded sentence representations than training with disjoint data, as measured by image--sentence retrieval performance. In order to close this gap in performance, we propose a pseudopairing method to generate synthetically aligned English--German--image triplets from the disjoint sets. The method works by first training a model on the disjoint data, and then creating new triples across datasets using sentence similarity under the learned model. Experiments show that pseudopairs improve image--sentence retrieval performance compared to disjoint training, despite requiring no external data or models. However, we do find that using an external machine translation model to generate the synthetic data sets results in better performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03678v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"217125\", \"230296\", \"242857\", \"253508\", \"255628\", \"260373\", \"260746\", \"260926\", \"266669\", \"267500\", \"280498\", \"296049\", \"300096\", \"302943\", \"315108\", \"319159\", \"320039\"]}","task_split":"paper_retrieval"}
{"document_id":"157277","document_content":"# Hierarchical Graph Network for Multi-hop Question Answering\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of which are initialized with pre-trained contextual encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multi-hop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction). By weaving heterogeneous nodes into an integral unified graph, this hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art, outperforming existing multi-hop QA approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03631v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2827\", \"5072\", \"16132\", \"21898\", \"50898\", \"75347\", \"97653\", \"2185\", \"24469\", \"24428\", \"25328\", \"33485\", \"8948\", \"36938\", \"41142\", \"45556\", \"45863\", \"50633\", \"55188\", \"55804\", \"55916\", \"61378\", \"71362\", \"77656\", \"79328\", \"91785\", \"98369\", \"100407\", \"140601\", \"102856\", \"85960\", \"88767\", \"91462\", \"92389\", \"94446\", \"94644\", \"95904\", \"97393\", \"109314\", \"127468\", \"130183\", \"131193\", \"131817\", \"132614\", \"133091\", \"138091\", \"140317\", \"140689\", \"163072\"], \"outgoing_citations\": [\"100897\", \"154601\", \"158501\", \"161554\", \"163072\", \"166283\", \"166447\", \"166449\", \"167159\", \"180353\", \"180575\", \"182026\", \"182036\", \"185429\", \"185805\", \"185952\", \"188736\", \"204098\", \"216563\", \"218944\", \"219849\", \"231239\", \"235408\", \"238610\", \"247824\", \"251589\", \"253068\", \"268809\", \"270020\", \"279213\", \"281163\", \"285811\"]}","task_split":"paper_retrieval"}
{"document_id":"157308","document_content":"# How Decoding Strategies Affect the Verifiability of Generated Text\n## Categories\n- Computation and Language\n## Abstract\nRecent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03587v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2601\", \"3822\", \"16027\", \"34300\", \"55159\", \"11478\", \"17278\", \"71966\", \"77913\", \"91642\", \"97185\", \"128396\", \"132963\", \"184591\", \"89518\", \"115939\", \"119114\", \"123394\", \"125976\"], \"outgoing_citations\": [\"137257\", \"143080\", \"167338\", \"168634\", \"171533\", \"172088\", \"180327\", \"181884\", \"183694\", \"195288\", \"200162\", \"219367\", \"232274\", \"238986\", \"260587\", \"260929\", \"266671\", \"279543\", \"280916\", \"283076\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"157346","document_content":"# Community Detection for Power Systems Network Aggregation Considering Renewable Variability\n## Categories\n- Physics and Society\n- Systems and Control\n- Systems and Control\n- Machine Learning\n## Abstract\nThe increasing penetration of variable renewable energy (VRE) has brought significant challenges for power systems planning and operation. These highly variable sources are typically distributed in the grid; therefore, a detailed representation of transmission bottlenecks is fundamental to approximate the impact of the transmission network on the dispatch with VRE resources. The fine grain temporal scale of short term and day-ahead dispatch, taking into account the network constraints, also mandatory for mid-term planning studies, combined with the high variability of the VRE has brought the need to represent these uncertainties in stochastic optimization models while taking into account the transmission system. These requirements impose a computational burden to solve the planning and operation models. We propose a methodology based on community detection to aggregate the network representation, capable of preserving the locational marginal price (LMP) differences in multiple VRE scenarios, and describe a real-world operational planning study. The optimal expected cost solution considering aggregated networks is compared with the full network representation. Both representations were embedded in an operation model relying on Stochastic Dual Dynamic Programming (SDDP) to deal with the random variables in a multi-stage problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.04279v1\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"cs.SY\", \"eess.SY\", \"cs.LG\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"157360","document_content":"# Question Generation from Paragraphs: A Tale of Two Hierarchical Models\n## Categories\n- Computation and Language\n## Abstract\nAutomatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words. While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model. We conducted empirical evaluation on the widely used SQuAD and MS MARCO datasets using standard metrics. The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. Qualitatively, our hierarchical models are able to generate fluent and relevant questions","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03407v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"92389\"], \"outgoing_citations\": [\"221304\", \"231981\", \"239503\", \"239777\", \"255298\", \"267694\"]}","task_split":"paper_retrieval"}
{"document_id":"157457","document_content":"# A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation\n## Categories\n- Computation and Language\n## Abstract\nDespite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements. In this paper, we propose a general framework for adapting neural machine translation to translate simultaneously. Our framework contains two parts: prefix translation that utilizes a consecutive NMT model to translate source prefixes and a stopping criterion that determines when to stop the prefix translation. Experiments on three translation corpora and two language pairs show the efficacy of the proposed framework on balancing the quality and latency in adapting NMT to perform simultaneous translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03154v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"51632\", \"78852\"], \"outgoing_citations\": [\"152334\", \"168771\", \"181097\", \"222572\", \"228604\", \"267524\", \"283451\", \"290992\"]}","task_split":"paper_retrieval"}
{"document_id":"157503","document_content":"# Ruminating Word Representations with Random Noised Masker\n## Categories\n- Machine Learning\n## Abstract\nWe introduce a training method for both better word representation and performance, which we call GROVER (Gradual Rumination On the Vector with maskERs). The method is to gradually and iteratively add random noises to word embeddings while training a model. GROVER first starts from conventional training process, and then extracts the fine-tuned representations. Next, we gradually add random noises to the word representations and repeat the training process from scratch, but initialize with the noised word representations. Through the re-training process, we can mitigate some noises to be compensated and utilize other noises to learn better representations. As a result, we can get word representations further fine-tuned and specialized on the task. When we experiment with our method on 5 text classification datasets, our method improves model performances on most of the datasets. Moreover, we show that our method can be combined with other regularization techniques, further improving the model performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.03459v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"219280\", \"231932\", \"234646\", \"234733\", \"239971\", \"259706\", \"260565\", \"263567\", \"264791\", \"291045\", \"291899\", \"320008\"]}","task_split":"paper_retrieval"}
{"document_id":"157592","document_content":"# Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank\n## Categories\n- Computation and Language\n## Abstract\nThe latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalisms\/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this paper, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role, providing a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.02851v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"101065\", \"84032\"], \"outgoing_citations\": [\"202775\", \"232290\", \"233407\", \"234631\", \"235377\", \"271527\", \"276493\", \"281121\", \"291941\"]}","task_split":"paper_retrieval"}
{"document_id":"157598","document_content":"# Improving Grammatical Error Correction with Machine Translation Pairs\n## Categories\n- Computation and Language\n## Abstract\nWe propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models of different qualities (i.e., poor and good). The poor translation model resembles the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammatical correctness, while the good translation model generally generates fluent and grammatically correct translations. We build the poor and good translation model with phrase-based statistical machine translation model with decreased language model weight and neural machine translation model respectively. By taking the pair of their translations of the same sentences in a bridge language as error-corrected sentence pairs, we can construct unlimited pseudo parallel data. Our approach is capable of generating diverse fluency-improving patterns without being limited by the pre-defined rule set and the seed error-corrected data. Experimental results demonstrate the effectiveness of our approach and show that it can be combined with other synthetic data sources to yield further improvements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.02825v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"43467\", \"55669\", \"77352\", \"95139\", \"96037\", \"125199\", \"144803\"], \"outgoing_citations\": [\"122918\", \"144803\", \"147724\", \"164336\", \"167086\", \"169184\", \"177974\", \"191276\", \"196990\", \"220017\", \"235400\", \"261029\", \"282509\"]}","task_split":"paper_retrieval"}
{"document_id":"157600","document_content":"# Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nMulti-domain dialogue state tracking (DST) is a critical component for conversational AI systems. The domain ontology (i.e., specification of domains, slots, and values) of a conversational AI system is generally incomplete, making the capability for DST models to generalize to new slots, values, and domains during inference imperative. In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA). Within DSTQA, each turn generates a question asking for the value of a (domain, slot) pair, thus making it naturally extensible to unseen domains, slots, and values. Additionally, we use a dynamically-evolving knowledge graph to explicitly learn relationships between (domain, slot) pairs. Our model has a 5.80% and 12.21% relative improvement over the current state-of-the-art model on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, respectively. Additionally, our model consistently outperforms the state-of-the-art model in domain adaptation settings. (Code is released at https:\/\/github.com\/alexa\/dstqa )","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.06192v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"20119\", \"100214\", \"12125\", \"23248\", \"24601\", \"125732\", \"41694\", \"50621\", \"57178\", \"62145\", \"74389\", \"82284\", \"88786\", \"94939\", \"99215\", \"92491\", \"98658\", \"100338\", \"112804\", \"121509\", \"127463\", \"127406\", \"128145\", \"128221\", \"131502\", \"132503\", \"162766\"], \"outgoing_citations\": [\"167138\", \"172890\", \"175736\", \"185347\", \"207522\", \"213349\", \"216019\", \"219306\", \"224315\", \"229792\", \"231442\", \"233268\", \"234577\", \"238321\", \"246324\", \"252059\", \"270020\", \"290531\", \"290588\"]}","task_split":"paper_retrieval"}
{"document_id":"157673","document_content":"# Certain Answers to a SPARQL Query over a Knowledge Base (extended version)\n## Categories\n- Databases\n- Artificial Intelligence\n## Abstract\nOntology-Mediated Query Answering (OMQA) is a well-established framework to answer queries over an RDFS or OWL Knowledge Base (KB). OMQA was originally designed for unions of conjunctive queries (UCQs), and based on certain answers. More recently, OMQA has been extended to SPARQL queries, but to our knowledge, none of the efforts made in this direction (either in the literature, or the so-called SPARQL entailment regimes) is able to capture both certain answers for UCQs and the standard interpretation of SPARQL over a plain graph. We formalize these as requirements to be met by any semantics aiming at conciliating certain answers and SPARQL answers, and define three additional requirements, which generalize to KBs some basic properties of SPARQL answers. Then we show that a semantics can be defined that satisfies all requirements for SPARQL queries with SELECT, UNION, and OPTIONAL, and for DLs with the canonical model property. We also investigate combined complexity for query answering under such a semantics over DL-Lite R KBs. In particular, we show for different fragments of SPARQL that known upper-bounds for query answering over a plain graph are matched.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.02668v3\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.AI\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"246610\", \"335271\"]}","task_split":"paper_retrieval"}
{"document_id":"157691","document_content":"# Probabilistic Similarity Networks\n## Categories\n- Artificial Intelligence\n- Optimization and Control\n- 68T01\n- I.2.1; G.3\n## Abstract\nNormative expert systems have not become commonplace because they have been difficult to build and use. Over the past decade, however, researchers have developed the influence diagram, a graphical representation of a decision maker's beliefs, alternatives, and preferences that serves as the knowledge base of a normative expert system. Most people who have seen the representation find it intuitive and easy to use. Consequently, the influence diagram has overcome significantly the barriers to constructing normative expert systems. Nevertheless, building influence diagrams is not practical for extremely large and complex domains. In this book, I address the difficulties associated with the construction of the probabilistic portion of an influence diagram, called a knowledge map, belief network, or Bayesian network. I introduce two representations that facilitate the generation of large knowledge maps. In particular, I introduce the similarity network, a tool for building the network structure of a knowledge map, and the partition, a tool for assessing the probabilities associated with a knowledge map. I then use these representations to build Pathfinder, a large normative expert system for the diagnosis of lymph-node diseases (the domain contains over 60 diseases and over 100 disease findings). In an early version of the system, I encoded the knowledge of the expert using an erroneous assumption that all disease findings were independent, given each disease. When the expert and I attempted to build a more accurate knowledge map for the domain that would capture the dependencies among the disease findings, we failed. Using a similarity network, however, we built the knowledge-map structure for the entire domain in approximately 40 hours. Furthermore, the partition representation reduced the number of probability assessments required by the expert from 75,000 to 14,000.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.06263v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"math.OC\", \"68T01\", \"I.2.1; G.3\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Optimization and Control\", \"68T01\", \"I.2.1; G.3\"], \"incoming_citations\": [\"91024\", \"58702\", \"281787\", \"339486\", \"346968\", \"357880\", \"356831\", \"353179\", \"350170\", \"350226\", \"349211\", \"348934\", \"348963\", \"348929\", \"348937\", \"348615\", \"347547\", \"348236\", \"348244\", \"347916\", \"347893\", \"347900\", \"347909\", \"347912\", \"347922\", \"347924\", \"347535\", \"347573\", \"347556\", \"347558\", \"347570\", \"347278\", \"347276\", \"347282\", \"346929\", \"346960\", \"346989\", \"346889\"], \"outgoing_citations\": [\"346728\", \"346753\", \"346769\", \"346804\", \"346812\", \"346822\", \"346844\", \"346846\", \"346861\", \"346873\", \"346889\", \"346929\", \"346964\", \"346973\"]}","task_split":"paper_retrieval"}
{"document_id":"157807","document_content":"# Unsupervised Opinion Summarization as Copycat-Review Generation\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nOpinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce abstractive summaries. Recent progress in summarization has seen the development of supervised models which rely on large quantities of document-summary pairs. Since such training data is expensive to acquire, we instead consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the \"amount of novelty\" going into the new review or, equivalently, vary the extent to which it deviates from the input. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator (\"decoder\") has direct access to the text of input reviews through the pointer-generator mechanism. Experiments on Amazon and Yelp datasets, show that setting at test time the review's latent code to its mean, allows the model to produce fluent and coherent summaries reflecting common opinions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.02247v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"87999\", \"16339\", \"84343\", \"24752\", \"25222\", \"42007\", \"42616\", \"45586\", \"45567\", \"46928\", \"47368\", \"54705\", \"55285\", \"58539\", \"80578\", \"81917\", \"91640\", \"100630\", \"127930\", \"168499\", \"94727\", \"127015\", \"127889\", \"129810\", \"154887\"], \"outgoing_citations\": [\"166540\", \"180935\", \"191777\", \"193900\", \"214407\", \"220188\", \"243803\", \"248254\", \"266671\", \"284092\", \"286225\", \"298945\", \"307223\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"157876","document_content":"# An Alternative Probabilistic Interpretation of the Huber Loss\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nThe Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We believe the standard probabilistic interpretation that relates the Huber loss to the Huber density fails to provide adequate intuition for identifying the transition point. As a result, a hyper-parameter search is often necessary to determine an appropriate value. In this work, we propose an alternative probabilistic interpretation of the Huber loss, which relates minimizing the loss to minimizing an upper-bound on the Kullback-Leibler divergence between Laplace distributions, where one distribution represents the noise in the ground-truth and the other represents the noise in the prediction. In addition, we show that the parameters of the Laplace distributions are directly related to the transition point of the Huber loss. We demonstrate, through a toy problem, that the optimal transition point of the Huber loss is closely related to the distribution of the noise in the ground-truth data. As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.02088v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3686\", \"79692\", \"126408\", \"41264\", \"77117\", \"78946\", \"121363\", \"137081\", \"137977\", \"159904\", \"179649\"], \"outgoing_citations\": [\"192571\", \"198648\", \"238018\", \"248112\", \"294693\"]}","task_split":"paper_retrieval"}
{"document_id":"157950","document_content":"# Enhancing the Privacy of Federated Learning with Sketching\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Networking and Internet Architecture\n## Abstract\nIn response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data localized. Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties. However, current methods still share model updates, which may contain private information (e.g., one's weight and height), during the training process. Existing efforts that aim to improve the privacy of federated learning make compromises in one or more of the following key areas: performance (particularly communication cost), accuracy, or privacy. To better optimize these trade-offs, we propose that \\textit{sketching algorithms} have a unique advantage in that they can provide both privacy and performance benefits while maintaining accuracy. We evaluate the feasibility of sketching-based federated learning with a prototype on three representative learning models. Our initial findings show that it is possible to provide strong privacy guarantees for federated learning without sacrificing performance or accuracy. Our work highlights that there exists a fundamental connection between privacy and communication in distributed settings, and suggests important open problems surrounding the theoretical understanding, methodology, and system design of practical, private federated learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.01812v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.NI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Networking and Internet Architecture\", \"Machine Learning\"], \"incoming_citations\": [\"3592\", \"30490\", \"134736\", \"109665\", \"178962\", \"102198\", \"106539\", \"158280\"], \"outgoing_citations\": [\"158280\", \"207462\", \"207491\", \"210868\", \"230382\", \"241199\", \"246939\", \"252956\", \"273803\", \"307636\", \"335904\"]}","task_split":"paper_retrieval"}
{"document_id":"158020","document_content":"# Sparse Lifting of Dense Vectors: Unifying Word and Sentence Representations\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAs the first step in automated natural language processing, representing words and sentences is of central importance and has attracted significant research attention. Different approaches, from the early one-hot and bag-of-words representation to more recent distributional dense and sparse representations, were proposed. Despite the successful results that have been achieved, such vectors tend to consist of uninterpretable components and face nontrivial challenge in both memory and computational requirement in practical applications. In this paper, we designed a novel representation model that projects dense word vectors into a higher dimensional space and favors a highly sparse and binary representation of word vectors with potentially interpretable components, while trying to maintain pairwise inner products between original vectors as much as possible. Computationally, our model is relaxed as a symmetric non-negative matrix factorization problem which admits a fast yet effective solution. In a series of empirical evaluations, the proposed model exhibited consistent improvement and high potential in practical applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.01625v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"206456\", \"249483\", \"269110\", \"311696\", \"328648\", \"336326\", \"349865\"]}","task_split":"paper_retrieval"}
{"document_id":"158033","document_content":"# LIDA: Lightweight Interactive Dialogue Annotator\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning models as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing annotations for a dataset. LIDA is fully open source, documented and publicly available [ https:\/\/github.com\/Wluper\/lida ]","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.01599v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"186970\"], \"outgoing_citations\": [\"204637\", \"216019\"]}","task_split":"paper_retrieval"}
{"document_id":"158215","document_content":"# Learning One-Shot Imitation from Humans without Humans\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nHumans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.01103v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"102138\", \"353397\", \"5280\", \"39969\", \"15055\", \"30956\", \"55486\", \"59341\", \"64984\", \"76394\", \"85829\", \"87299\", \"91106\", \"129794\"], \"outgoing_citations\": [\"178857\", \"205568\", \"215102\", \"227314\", \"243236\", \"245255\", \"253448\", \"255139\", \"255939\", \"261697\", \"261786\", \"263733\", \"272498\", \"280570\", \"284833\", \"297104\", \"297317\", \"343159\"]}","task_split":"paper_retrieval"}
{"document_id":"158242","document_content":"# Scene Text Recognition with Temporal Convolutional Encoder\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTexts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequence models by an encoder to have the visual representations and then a decoder to translate the features into the label sequence. In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage. We demonstrate that the proposed Temporal Convolutional Encoder with increased sequential extents improves the accuracy of text recognition. We also study the impact of different attention modules in convolutional blocks for learning accurate text representations. We conduct comparisons on seven datasets and the experiments demonstrate the effectiveness of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.01051v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"177597\", \"192483\", \"205988\", \"210634\", \"211851\", \"232706\", \"250738\", \"256711\", \"285755\", \"294023\", \"296828\", \"311196\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"158298","document_content":"# Machine Translation in Pronunciation Space\n## Categories\n- Computation and Language\n## Abstract\nThe research in machine translation community focus on translation in text space. However, humans are in fact also good at direct translation in pronunciation space. Some existing translation systems, such as simultaneous machine translation, are inherently more natural and thus potentially more robust by directly translating in pronunciation space. In this paper, we conduct large scale experiments on a self-built dataset with about $20$M En-Zh pairs of text sentences and corresponding pronunciation sentences. We proposed three new categories of translations: $1)$ translating a pronunciation sentence in source language into a pronunciation sentence in target language (P2P-Tran), $2)$ translating a text sentence in source language into a pronunciation sentence in target language (T2P-Tran), and $3)$ translating a pronunciation sentence in source language into a text sentence in target language (P2T-Tran), and compare them with traditional text translation (T2T-Tran). Our experiments clearly show that all $4$ categories of translations have comparable performances, with small and sometimes ignorable differences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00932v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189099\", \"213698\", \"214156\", \"251240\", \"273119\", \"282256\"]}","task_split":"paper_retrieval"}
{"document_id":"158332","document_content":"# Scene Graph based Image Retrieval -- A case study on the CLEVR Dataset\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community. However most of the state of the art techniques model this problem in a purely neural way, which makes it difficult to incorporate pragmatic strategies in searching a large scale catalog especially when the search requirements are insufficient and the model needs to resort to an interactive retrieval process through multiple iterations of question-answering. Motivated by this, we propose a neural-symbolic approach for a one-shot retrieval of images from a large scale catalog, given the caption description. To facilitate this, we represent the catalog and caption as scene-graphs and model the retrieval task as a learnable graph matching problem, trained end-to-end with a REINFORCE algorithm. Further, we briefly describe an extension of this pipeline to an iterative retrieval framework, based on interactive questioning and answering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00850v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"62580\", \"37170\", \"102580\", \"102789\"], \"outgoing_citations\": [\"196027\", \"205616\", \"287504\"]}","task_split":"paper_retrieval"}
{"document_id":"158439","document_content":"# Credibility-based Fake News Detection\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nFake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00643v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"14416\", \"8211\", \"19514\", \"207715\", \"119855\", \"188875\"], \"outgoing_citations\": [\"181527\", \"197381\", \"215771\", \"219169\", \"230998\", \"257863\", \"270399\", \"273723\", \"304737\", \"319035\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"158515","document_content":"# Positive-Unlabeled Reward Learning\n## Categories\n- Machine Learning\n## Abstract\nLearning reward functions from data is a promising path towards achieving scalable Reinforcement Learning (RL) for robotics. However, a major challenge in training agents from learned reward models is that the agent can learn to exploit errors in the reward model to achieve high reward behaviors that do not correspond to the intended task. These reward delusions can lead to unintended and even dangerous behaviors. On the other hand, adversarial imitation learning frameworks tend to suffer the opposite problem, where the discriminator learns to trivially distinguish agent and expert behavior, resulting in reward models that produce low reward signal regardless of the input state. In this paper, we connect these two classes of reward learning methods to positive-unlabeled (PU) learning, and we show that by applying a large-scale PU learning algorithm to the reward learning problem, we can address both the reward under- and over-estimation problems simultaneously. Our approach drastically improves both GAIL and supervised reward learning, without any additional assumptions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00459v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"36644\", \"63862\", \"115575\", \"7381\", \"45629\", \"55721\", \"61195\", \"144521\", \"81007\", \"80924\", \"84270\", \"96635\"], \"outgoing_citations\": [\"163689\", \"164743\", \"191888\", \"209926\", \"215643\", \"215828\", \"215836\", \"218636\", \"218939\", \"229145\", \"234575\", \"240233\", \"246118\", \"251924\", \"260583\", \"263957\", \"270529\", \"272298\", \"272618\", \"281170\", \"289913\", \"292832\", \"296820\", \"298734\"]}","task_split":"paper_retrieval"}
{"document_id":"158642","document_content":"# A Narration-based Reward Shaping Approach using Grounded Natural Language Commands\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nWhile deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of reward sparsity. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strategy game where reward is only given at the end of a game which is usually very long. While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge. Inspired by the vision of enabling reward shaping through the more-accessible paradigm of natural-language narration, we develop a technique that can provide the benefits of reward shaping using natural language commands. Our narration-guided RL agent projects sequences of natural-language commands into the same high-dimensional representation space as corresponding goal states. We show that we can get improved performance with our method compared to traditional reward-shaping approaches. Additionally, we demonstrate the ability of our method to generalize to unseen natural-language commands.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00497v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"14597\", \"17680\", \"64051\"], \"outgoing_citations\": [\"189110\", \"198208\", \"216837\", \"219305\", \"229726\", \"231925\", \"233924\", \"254581\", \"258472\", \"268335\", \"268584\", \"303349\", \"311272\", \"314620\"]}","task_split":"paper_retrieval"}
{"document_id":"158667","document_content":"# DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAutomatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for end-to-end analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space and leverages past knowledge gained from previously-analyzed datasets to make the problem more tractable. Additionally, we propose a novel hierarchical-actions algorithm that serves as a plugin, mediating the environment-agent interaction in deep reinforcement learning problems. The plugin significantly speeds up the training process of our model. Evaluation on 56 datasets shows that DeepLine outperforms state-of-the-art approaches both in accuracy and in computational cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.00061v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"30438\", \"39284\", \"66033\", \"111235\", \"81535\", \"113398\"], \"outgoing_citations\": [\"11622\", \"183144\", \"184714\", \"200682\", \"218923\", \"240309\", \"248225\", \"255255\", \"265642\", \"301005\", \"355279\"]}","task_split":"paper_retrieval"}
{"document_id":"158725","document_content":"# Document-level Neural Machine Translation with Associated Memory Network\n## Categories\n- Computation and Language\n## Abstract\nStandard neural machine translation (NMT) is on the assumption that the document-level context is independent. Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network. The capacity of the memory network that detecting the most relevant part of the current sentence from memory renders a natural solution to model the rich document-level context. In this work, the proposed document-aware memory network is implemented to enhance the Transformer NMT baseline. Experiments on several tasks show that the proposed method significantly improves the NMT performance over strong Transformer baselines and other related studies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.14528v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"95605\", \"120233\", \"161372\", \"173016\", \"215003\", \"219083\", \"228350\", \"230556\", \"233206\", \"248821\", \"249315\", \"250879\", \"258163\", \"263299\", \"268344\", \"268684\", \"268909\", \"286173\"]}","task_split":"paper_retrieval"}
{"document_id":"158830","document_content":"# Extending Causal Models from Machines into Humans\n## Categories\n- Artificial Intelligence\n## Abstract\nCausal Models are increasingly suggested as a means to reason about the behavior of cyber-physical systems in socio-technical contexts. They allow us to analyze courses of events and reason about possible alternatives. Until now, however, such reasoning is confined to the technical domain and limited to single systems or at most groups of systems. The humans that are an integral part of any such socio-technical system are usually ignored or dealt with by \"expert judgment\". We show how a technical causal model can be extended with models of human behavior to cover the complexity and interplay between humans and technical systems. This integrated socio-technical causal model can then be used to reason not only about actions and decisions taken by the machine, but also about those taken by humans interacting with the system. In this paper we demonstrate the feasibility of merging causal models about machines with causal models about humans and illustrate the usefulness of this approach with a highly automated vehicle example.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.308.2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"74465\", \"120870\", \"126504\"], \"outgoing_citations\": [\"123844\", \"123858\", \"213305\", \"313520\", \"346612\", \"350497\"]}","task_split":"paper_retrieval"}
{"document_id":"158894","document_content":"# Building an Application Independent Natural Language Interface\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n- Information Retrieval\n## Abstract\nTraditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application. However, it is still challenging for a semantic parser to correctly parse natural language. For a different domain, the parser may need to be retrained or tuned, and a new translator also needs to be written to convert the logical forms to executable actions. In this work, we propose a novel and application independent approach to building NL interfaces that does not need a semantic parser or a translator. It is based on natural language to natural language matching and learning, where the representation of each action and each user command are both in natural language. To perform a user intended action, the system only needs to match the user command with the correct action representation, and then execute the corresponding action. The system also interactively learns new (paraphrased) commands for actions to expand the action representations over time. Our experimental results show the effectiveness of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.14084v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\", \"Information Retrieval\"], \"incoming_citations\": [\"66657\", \"99128\"], \"outgoing_citations\": [\"169740\", \"200747\", \"207395\", \"220091\", \"237033\", \"239909\", \"240951\", \"250567\", \"257206\", \"261378\", \"267980\", \"279428\", \"290891\", \"296150\", \"307569\", \"308610\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"158899","document_content":"# Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMost neural machine translation systems still translate sentences in isolation. To make further progress, a promising line of research additionally considers the surrounding context in order to provide the model potentially missing source-side information, as well as to maintain a coherent output. One difficulty in training such larger-context (i.e. document-level) machine translation systems is that context may be missing from many parallel examples. To circumvent this issue, two-stage approaches, in which sentence-level translations are post-edited in context, have recently been proposed. In this paper, we instead consider the viability of filling in the missing context. In particular, we consider three distinct approaches to generate the missing context: using random contexts, applying a copy heuristic or generating it with a language model. In particular, the copy heuristic significantly helps with lexical coherence, while using completely random contexts hurts performance on many long-distance linguistic phenomena. We also validate the usefulness of tagged back-translation. In addition to improving BLEU scores as expected, using back-translated data helps larger-context machine translation systems to better capture long-range phenomena.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.14075v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21968\", \"137310\", \"99771\"], \"outgoing_citations\": [\"168854\", \"176177\", \"180654\", \"183627\", \"186239\", \"198037\", \"201546\", \"215003\", \"219083\", \"220017\", \"220720\", \"230556\", \"238926\", \"249315\", \"250879\", \"251684\", \"258163\", \"268684\", \"268909\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"158915","document_content":"# Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- I.2.10; I.2.6\n## Abstract\nAuto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degradation of the trained model. To deal with this problem we propose filtering the auto-labeled data using a trained model that predicts the quality of the annotation from the degree of consensus between ensemble models. Using semantic segmentation as an example, we show the advantage of the proposed auto-annotation filtering over training on data contaminated with inaccurate labels.   Moreover, our experimental results show that in the case of semantic segmentation, the performance of a state-of-the-art model can be achieved by training it with only a fraction (30$\\%$) of the original manually labeled data set, and replacing the rest with the auto-annotated, quality filtered labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.13988v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"I.2.10; I.2.6\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"I.2.10; I.2.6\"], \"incoming_citations\": [\"121320\"], \"outgoing_citations\": [\"247616\", \"248529\", \"253740\", \"268261\", \"270002\", \"295969\"]}","task_split":"paper_retrieval"}
{"document_id":"159173","document_content":"# Knowledge Tracing with Sequential Key-Value Memory Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nCan machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence. In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN). This model unifies the strengths of recurrent modelling capacity and memory capacity of the existing deep learning KT models for modelling student learning. We have extensively evaluated our proposed model on five benchmark datasets. The experimental results show that (1) SKVMN outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can better discover the correlation between latent concepts and questions, and (3) SKVMN can trace the knowledge state of students dynamics, and a leverage sequential dependencies in an exercise sequence for improved predication accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3331184.3331195\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"9751\", \"109659\", \"2738\", \"28961\", \"199\", \"16141\", \"45697\", \"51307\", \"86983\", \"100945\", \"81666\", \"128428\", \"146237\", \"152429\"], \"outgoing_citations\": [\"203607\", \"257927\", \"268252\", \"279648\", \"280111\", \"287830\", \"296633\", \"310890\", \"357416\"]}","task_split":"paper_retrieval"}
{"document_id":"159191","document_content":"# Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nEvaluating, explaining, and visualizing high-level concepts in generative models, such as variational autoencoders (VAEs), is challenging in part due to a lack of known prediction classes that are required to generate saliency maps in supervised learning. While saliency maps may help identify relevant features (e.g., pixels) in the input for classification tasks of deep neural networks, similar frameworks are understudied in unsupervised learning. Therefore, we introduce a new method of obtaining saliency maps for latent representations of known or novel high-level concepts, often called concept vectors in generative models. Concept scores, analogous to class scores in classification tasks, are defined as dot products between concept vectors and encoded input data, which can be readily used to compute the gradients. The resulting concept saliency maps are shown to highlight input features deemed important for high-level concepts. Our method is applied to the VAE's latent space of CelebA dataset in which known attributes such as \"smiles\" and \"hats\" are used to elucidate relevant facial features. Furthermore, our application to spatial transcriptomic (ST) data of a mouse olfactory bulb demonstrates the potential of latent representations of morphological layers and molecular features in advancing our understanding of complex biological systems. By extending the popular method of saliency maps to generative models, the proposed concept saliency maps help improve interpretability of latent variable models in deep learning.   Codes to reproduce and to implement concept saliency maps: https:\/\/github.com\/lenbrocki\/concept-saliency-maps","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.13140v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"87928\", \"110288\"], \"outgoing_citations\": [\"199083\", \"231642\", \"276018\", \"284708\", \"300739\", \"306238\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"159316","document_content":"# Evaluating the Factual Consistency of Abstractive Text Summarization\n## Categories\n- Computation and Language\n## Abstract\nCurrently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary. Training data is generated by applying a series of rule-based transformations to the sentences of source documents. The factual consistency model is then trained jointly for three tasks: 1) identify whether sentences remain factually consistent after transformation, 2) extract a span in the source documents to support the consistency prediction, 3) extract a span in the summary sentence that is inconsistent if one exists. Transferring this model to summaries generated by several state-of-the art models reveals that this highly scalable approach substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.12840v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2684\", \"2584\", \"3028\", \"8747\", \"15952\", \"17317\", \"22450\", \"22977\", \"26645\", \"26779\", \"45853\", \"48179\", \"52433\", \"55214\", \"61976\", \"71438\", \"84343\", \"1461\", \"1890\", \"8753\", \"12408\", \"13996\", \"15895\", \"16503\", \"17278\", \"20781\", \"21684\", \"22443\", \"23052\", \"23776\", \"26826\", \"28080\", \"29580\", \"29730\", \"31148\", \"31578\", \"32081\", \"36870\", \"38416\", \"40395\", \"40468\", \"41371\", \"136011\", \"43047\", \"44858\", \"45198\", \"45223\", \"24688\", \"47416\", \"50478\", \"52583\", \"53256\", \"53191\", \"55016\", \"55196\", \"55199\", \"55285\", \"55367\", \"55327\", \"55491\", \"55781\", \"55782\", \"55775\", \"56864\", \"57244\", \"57199\", \"57604\", \"58194\", \"58358\", \"61182\", \"61496\", \"62920\", \"68336\", \"68562\", \"68780\", \"72160\", \"78031\", \"88960\", \"91642\", \"91640\", \"92956\", \"93920\", \"109884\", \"177966\", \"49608\", \"127602\", \"91716\", \"92612\", \"93583\", \"93973\", \"93975\", \"94175\", \"94486\", \"95174\", \"96129\", \"96741\", \"96411\", \"98155\", \"99789\", \"99915\", \"115939\", \"121677\", \"123228\", \"127411\", \"127538\", \"127883\", \"132176\", \"132284\", \"128387\", \"129702\", \"140414\", \"140642\", \"157706\"], \"outgoing_citations\": [\"127538\", \"170449\", \"170657\", \"181713\", \"183400\", \"192782\", \"193848\", \"197709\", \"205715\", \"216554\", \"218088\", \"219485\", \"219618\", \"220017\", \"220230\", \"220509\", \"230212\", \"230234\", \"231869\", \"233710\", \"235041\", \"235232\", \"235345\", \"235463\", \"238986\", \"243803\", \"250690\", \"259706\", \"266671\", \"280557\", \"307223\"]}","task_split":"paper_retrieval"}
{"document_id":"159423","document_content":"# Fine-Grained Object Detection over Scientific Document Images with Region Embeddings\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n## Abstract\nWe study the problem of object detection over scanned images of scientific documents. We consider images that contain objects of varying aspect ratios and sizes and range from coarse elements such as tables and figures to fine elements such as equations and section headers. We find that current object detectors fail to produce properly localized region proposals over such page objects. We revisit the original R-CNN model and present a method for generating fine-grained proposals over document elements. We also present a region embedding model that uses the convolutional maps of a proposal's neighbors as context to produce an embedding for each proposal. This region embedding is able to capture the semantic relationships between a target region and its surrounding context. Our end-to-end model produces an embedding for each proposal, then classifies each proposal by using a multi-head attention model that attends to the most important neighbors of a proposal. To evaluate our model, we collect and annotate a dataset of publications from heterogeneous journals. We show that our model, referred to as Attentive-RCNN, yields a 17% mAP improvement compared to standard object detection models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.12462v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"233047\", \"264315\", \"319781\"]}","task_split":"paper_retrieval"}
{"document_id":"159538","document_content":"# Pre-train and Learn: Preserve Global Information for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have shown great power in learning on attributed graphs. However, it is still a challenge for GNNs to utilize information faraway from the source node. Moreover, general GNNs require graph attributes as input, so they cannot be appled to plain graphs. In the paper, we propose new models named G-GNNs (Global information for GNNs) to address the above limitations. First, the global structure and attribute features for each node are obtained via unsupervised pre-training, which preserve the global information associated to the node. Then, using the global features and the raw network attributes, we propose a parallel framework of GNNs to learn different aspects from these features. The proposed learning methods can be applied to both plain graphs and attributed graphs. Extensive experiments have shown that G-GNNs can outperform other state-of-the-art models on three standard evaluation graphs. Specially, our methods establish new benchmark records on Cora (84.31\\%) and Pubmed (80.95\\%) when learning on attributed graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11390-020-0142-x\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"24963\", \"14967\"], \"outgoing_citations\": [\"114415\", \"181255\", \"188300\", \"189505\", \"214311\", \"228663\", \"240985\", \"241165\", \"243791\", \"261412\", \"295676\"]}","task_split":"paper_retrieval"}
{"document_id":"159570","document_content":"# Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThis study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs; and using more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed extremely well under good illumination and weather conditions. Its drawbacks are that it sometimes failed to detect object under bad weather conditions like snow and failed to distinguish speed limits signs with different limit values.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1911.05626v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"190576\", \"292192\"]}","task_split":"paper_retrieval"}
{"document_id":"159618","document_content":"# A Preliminary Study on Optimal Placement of Cameras\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThis paper primarily focuses on figuring out the best array of cameras, or visual sensors, so that such a placement enables the maximum utilization of these visual sensors. Maximizing the utilization of these cameras can convert to another problem that is simpler for the formulation, that is, maximizing the total coverage with these cameras. To solve the problem, the coverage problem is first defined subject to the capabilities and limits of cameras. Then, poses of cameras are analyzed for the best arrangement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.12053v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"159742","document_content":"# Exploring Multilingual Syntactic Sentence Representations\n## Categories\n- Computation and Language\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nWe study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus augmented by Universal Parts-of-Speech tags. We evaluate the quality of the learned embeddings by examining sentence-level nearest neighbours and functional dissimilarity in the embedding space. We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning. Our results show that syntactic sentence-embeddings can be learned while using less training data, fewer model parameters, and resulting in better evaluation metrics than state-of-the-art language models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11768v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"94843\", \"98919\"], \"outgoing_citations\": [\"192755\", \"201065\", \"204715\", \"206456\", \"219895\", \"220137\", \"229768\", \"230709\", \"238963\", \"253559\", \"281225\", \"289031\"]}","task_split":"paper_retrieval"}
{"document_id":"159812","document_content":"# Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning\n## Categories\n- Cryptography and Security\n- Machine Learning\n## Abstract\nMachine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distributed framework for privacy-preserving, traceable and collaborative Machine Learning. Substra gathers data providers and algorithm designers into a network of nodes that can train models on demand but under advanced permission regimes. To guarantee data privacy, Substra implements distributed learning: the data never leave their nodes; only algorithms, predictive models and non-sensitive metadata are exchanged on the network. The computations are orchestrated by a Distributed Ledger Technology which guarantees traceability and authenticity of information without needing to trust a third party. Although originally developed for Healthcare applications, Substra is not data, algorithm or programming language specific. It supports many types of computation plans including parallel computation plan commonly used in Federated Learning. With appropriate guidelines, it can be deployed for numerous Machine Learning use-cases with data or algorithm providers where trust is limited.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11567v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [\"103684\", \"146045\"], \"outgoing_citations\": [\"174763\", \"210718\", \"256827\", \"280659\", \"285037\"]}","task_split":"paper_retrieval"}
{"document_id":"159862","document_content":"# A Unified MRC Framework for Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nThe task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately developed for the two tasks, since sequence labeling models, the most widely used backbone for flat NER, are only able to assign a single label to a particular token, which is unsuitable for nested NER where a token may be assigned several labels.   In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks. Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task. For example, extracting entities with the \\textsc{per} label is formalized as extracting answer spans to the question \"{\\it which person is mentioned in the text?}\". This formulation naturally tackles the entity overlapping issue in nested NER: the extraction of two overlapping entities for different categories requires answering two independent questions. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances for not only nested NER, but flat NER.   We conduct experiments on both {\\em nested} and {\\em flat} NER datasets. Experimental results demonstrate the effectiveness of the proposed formulation. We are able to achieve vast amount of performance boost over current SOTA models on nested NER datasets, i.e., +1.28, +2.55, +5.44, +6.37, respectively on ACE04, ACE05, GENIA and KBP17, along with SOTA results on flat NER datasets, i.e.,+0.24, +1.95, +0.21, +1.49 respectively on English CoNLL 2003, English OntoNotes 5.0, Chinese MSRA, Chinese OntoNotes 4.0.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11476v7\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"32774\", \"2019\", \"2533\", \"16354\", \"16358\", \"18787\", \"20995\", \"21108\", \"23643\", \"23896\", \"24691\", \"25863\", \"26567\", \"27393\", \"27492\", \"30690\", \"37458\", \"49190\", \"55815\", \"84089\", \"144530\", \"205022\", \"6061\", \"6698\", \"8714\", \"12251\", \"12125\", \"13052\", \"14944\", \"24256\", \"26832\", \"29684\", \"29902\", \"32579\", \"33985\", \"35820\", \"33588\", \"38481\", \"39138\", \"42874\", \"44422\", \"45341\", \"45650\", \"45706\", \"46012\", \"48692\", \"49295\", \"49595\", \"49953\", \"53443\", \"56776\", \"57190\", \"59603\", \"62068\", \"63290\", \"73223\", \"75427\", \"76611\", \"77348\", \"77812\", \"79082\", \"80323\", \"80466\", \"81514\", \"81520\", \"87611\", \"102549\", \"211624\", \"169651\", \"79775\", \"81966\", \"83050\", \"84962\", \"87226\", \"87151\", \"90592\", \"92420\", \"93812\", \"96765\", \"99542\", \"100483\", \"104312\", \"121505\", \"122252\", \"125364\", \"127705\", \"128398\", \"129041\", \"143508\", \"157178\", \"157587\", \"157143\", \"157965\"], \"outgoing_citations\": [\"168525\", \"171105\", \"178272\", \"181688\", \"186405\", \"191957\", \"201190\", \"213489\", \"215547\", \"216925\", \"227323\", \"233136\", \"234577\", \"251798\", \"263874\", \"270020\", \"274533\", \"278104\", \"281162\", \"281163\", \"284528\", \"285811\", \"302500\", \"312608\"]}","task_split":"paper_retrieval"}
{"document_id":"159891","document_content":"# Comparison of Quality Indicators in User-generated Content Using Social Media and Scholarly Text\n## Categories\n- Computation and Language\n## Abstract\nPredicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (meta) features that are not directly available from the text, and show how these features inform prediction of document quality in different ways. Moreover, we also compare our methods on both social user-generated data such as tweets, and scholarly user-generated data such as academic articles, showing how the same features differently influence prediction of quality across these disparate domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11399v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"159927","document_content":"# Emotion recognition with 4kresolution database\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nClassifying the human emotion through facial expressions is a big topic in both the Computer Vision and Deep learning fields. Human emotion can be classified as one of the basic emotion types like being angry, happy or dimensional emotion with valence and arousal values. There are a lot of related challenges in this topic, one of the most famous challenges is called the 'Affect-in-the-wild Challenge'(Aff-Wild Challenge). It is the first challenge on the estimation of valence and arousal in-the-wild. This project is an extension of the Aff-wild Challenge. Aff-wild database was created using images with a mean resolution of 607*359, I and Dimitrios sought to find out the performance of the model that is trained on a database that contains4K resolution in-the-wild images. Since there is no existing database to satisfy the requirement, I built this database from scratch with help from Dimitrios and trained neural network models with different hyperparameters on this database. I used network models likeVGG16, AlexNet, ResNet and also some pre-trained models like Ima-geNet VGG. I compared the results of the different network models alongside the results from the Aff-wild database to exploit the optimal model for my database.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11276v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210562\", \"210607\", \"218152\", \"233290\", \"267284\", \"268873\"]}","task_split":"paper_retrieval"}
{"document_id":"159954","document_content":"# Syntax-Enhanced Self-Attention-Based Semantic Role Labeling\n## Categories\n- Computation and Language\n## Abstract\nAs a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of encoding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we conduct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11204v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"43056\", \"55257\", \"72660\", \"77364\", \"101065\", \"100582\"], \"outgoing_citations\": [\"175042\", \"221642\", \"232290\", \"234631\", \"248279\", \"271527\", \"276493\", \"281121\", \"291941\"]}","task_split":"paper_retrieval"}
{"document_id":"160037","document_content":"# Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10896v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"98159\", \"143391\", \"173646\"], \"outgoing_citations\": [\"188069\", \"209539\", \"219131\", \"238809\", \"244877\", \"267347\", \"278526\", \"279419\", \"302157\", \"302985\", \"321122\", \"328183\"]}","task_split":"paper_retrieval"}
{"document_id":"160120","document_content":"# Contextual Imagined Goals for Self-Supervised Robotic Learning\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of behaviors. Instead of learning a large collection of skills individually, can we instead enable a robot to propose and practice its own behaviors automatically, learning about the affordances and behaviors that it can perform in its environment, such that it can then repurpose this knowledge once a new task is commanded by the user? In this paper, we study this question in the context of self-supervised goal-conditioned reinforcement learning. A central challenge in this learning regime is the problem of goal setting: in order to practice useful skills, the robot must be able to autonomously set goals that are feasible but diverse. When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand. Previous work only studies self-supervised goal-conditioned RL in a single-environment setting, where goal proposals come from the robot's past experience or a generative model are sufficient. In more diverse settings, this frequently leads to impossible goals and, as we show experimentally, prevents effective learning. We propose a conditional goal-setting model that aims to propose goals that are feasible from the robot's current state. We demonstrate that this enables self-supervised goal-conditioned off-policy learning with raw image observations in the real world, enabling a robot to manipulate a variety of objects and generalize to new objects that were not seen during training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11670v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"47401\", \"56916\", \"79803\", \"138348\", \"13872\", \"24821\", \"30979\", \"31874\", \"37462\", \"45630\", \"53903\", \"56552\", \"63319\", \"70182\", \"77736\", \"84068\", \"93791\", \"124867\", \"137339\", \"119120\", \"87198\", \"112129\", \"119169\", \"125315\", \"140901\"], \"outgoing_citations\": [\"185607\", \"195849\", \"204013\", \"207601\", \"208351\", \"215168\", \"220103\", \"224840\", \"226635\", \"231175\", \"236752\", \"253291\", \"254494\", \"272030\", \"283026\", \"283391\", \"289719\", \"296980\", \"297268\", \"306156\", \"310624\", \"343050\"]}","task_split":"paper_retrieval"}
{"document_id":"160131","document_content":"# Feature Selection and Extraction for Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nGraph Neural Networks (GNNs) have been a latest hot research topic in data science, due to the fact that they use the ubiquitous data structure graphs as the underlying elements for constructing and training neural networks. In a GNN, each node has numerous features associated with it. The entire task (for example, classification, or clustering) utilizes the features of the nodes to make decisions, at node level or graph level. In this paper, (1) we extend the feature selection algorithm presented in via Gumbel Softmax to GNNs. We conduct a series of experiments on our feature selection algorithms, using various benchmark datasets: Cora, Citeseer and Pubmed. (2) We implement a mechanism to rank the extracted features. We demonstrate the effectiveness of our algorithms, for both feature selection and ranking. For the Cora dataset, (1) we use the algorithm to select 225 features out of 1433 features. Our experimental results demonstrate their effectiveness for the same classification problem. (2) We extract features such that they are linear combinations of the original features, where the coefficients for each extracted features are non-negative and sum up to one. We propose an algorithm to rank the extracted features in the sense that when using them for the same classification problem, the accuracy goes down gradually for the extracted features within the rank 1 - 50, 51 - 100, 100 - 150, and 151 - 200.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10682v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"19141\", \"91700\", \"67802\", \"126846\"], \"outgoing_citations\": [\"201446\", \"221587\", \"229333\", \"252105\", \"263488\", \"269617\", \"279026\"]}","task_split":"paper_retrieval"}
{"document_id":"160191","document_content":"# Semantic Segmentation of Skin Lesions using a Small Data Set\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n- 68T01\n## Abstract\nEarly detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is \"How to segment skin lesion images using a neural network with low available data?\". This question is divided into three sub questions regarding best performing network structure, training data and training method. First theory associated with these questions is discussed. Literature states that U-net CNN structures have excellent performances on the segmentation task, more training data increases network performance and utilizing transfer learning enables networks to generalize to new data better.   To validate these findings in the literature two experiments are conducted. The first experiment trains a network on data sets of different size. The second experiment proposes twelve network structures and trains them on the same data set. The experimental results support the findings in the literature. The FCN16 and FCN32 networks perform best in the accuracy, intersection over union and mean BF1 Score metric. Concluding from these results the skin lesion segmentation network is a fully convolutional structure with a skip architecture and an encoder depth of either one or two. Weights of this network should be initialized using transfer learning from the pre trained VGG16 network. Training data should be cropped to reduce complexity and augmented during training to reduce the likelihood of overfitting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10534v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\", \"68T01\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"68T01\"], \"incoming_citations\": [], \"outgoing_citations\": [\"246396\", \"281232\", \"284947\", \"296021\", \"316486\"]}","task_split":"paper_retrieval"}
{"document_id":"160285","document_content":"# RNN based Incremental Online Spoken Language Understanding\n## Categories\n- Computation and Language\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nSpoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion, i.e., the NLU often has to wait for the ASR to finish processing on an utterance basis, potentially leading to high latencies that render the spoken interaction less natural. In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems. A lexical End-of-Sentence (EOS) detector is proposed for segmenting the stream of transcript into sentences for intent classification. Intent detection experiments are conducted on benchmark ATIS, Snips and Facebook's multilingual task oriented dialog datasets modified to emulate a continuous incremental stream of words with no utterance demarcation. We also analyze the prospects of early intent detection, before EOS, with our proposed system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10287v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"178308\", \"191817\", \"212163\", \"226345\", \"230543\", \"244978\", \"285311\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"160335","document_content":"# Mask Combination of Multi-layer Graphs for Global Structure Inference\n## Categories\n- Machine Learning\n- Signal Processing\n## Abstract\nStructure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a data space whose inner relationships are encoded by multi-layer graphs. We aim at properly exploiting the information originating from each layer to infer the global structure underlying the signals. We thus present a novel method for combining the multiple graphs into a global graph using mask matrices, which are estimated through an optimization problem that accommodates the multi-layer graph information and a signal representation model. The proposed mask combination method also estimates the contribution of each graph layer in the structure of signals. The experiments conducted both on synthetic and real-world data suggest that integrating the multi-layer graph representation of the data in the structure inference framework enhances the learning procedure considerably by adapting to the quality and the quantity of the input data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10114v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.SP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Signal Processing\", \"Machine Learning\"], \"incoming_citations\": [\"87811\", \"55263\"], \"outgoing_citations\": [\"158926\", \"200860\", \"201702\", \"211381\", \"213217\", \"229462\", \"236122\", \"253210\", \"286867\", \"288569\", \"292949\", \"300281\", \"314148\", \"327561\", \"327674\", \"347232\", \"347727\"]}","task_split":"paper_retrieval"}
{"document_id":"160356","document_content":"# Predictive Coding Networks Meet Action Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAction recognition is a key problem in computer vision that labels videos with a set of predefined actions. Capturing both, semantic content and motion, along the video frames is key to achieve high accuracy performance on this task. Most of the state-of-the-art methods rely on RGB frames for extracting the semantics and pre-computed optical flow fields as a motion cue. Then, both are combined using deep neural networks. Yet, it has been argued that such models are not able to leverage the motion information extracted from the optical flow, but instead the optical flow allows for better recognition of people and objects in the video. This urges the need to explore different cues or models that can extract motion in a more informative fashion. To tackle this issue, we propose to explore the predictive coding network, so called PredNet, a recurrent neural network that propagates predictive coding errors across layers and time steps. We analyze whether PredNet can better capture motions in videos by estimating over time the representations extracted from pre-trained networks for action recognition. In this way, the model only relies on the video frames, and does not need pre-processed optical flows as input. We report the effectiveness of our proposed model on UCF101 and HMDB51 datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.10056v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"167544\", \"245503\", \"246736\", \"248411\", \"250762\", \"280136\", \"301592\", \"303465\", \"315200\", \"319165\", \"329514\"]}","task_split":"paper_retrieval"}
{"document_id":"160384","document_content":"# Learning Resilient Behaviors for Navigation Under Uncertainty\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDeep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertainty-aware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09998v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"13581\", \"57727\", \"97665\"], \"outgoing_citations\": [\"175919\", \"196770\", \"213622\", \"215984\", \"216429\", \"216750\", \"226166\", \"233165\", \"253045\", \"253600\", \"254599\", \"257046\", \"270525\", \"272668\", \"274762\", \"283956\", \"296980\", \"298281\"]}","task_split":"paper_retrieval"}
{"document_id":"160387","document_content":"# Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer\n## Categories\n- Sound\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nWe propose a sequence-to-sequence singing synthesizer, which avoids the need for training data with pre-aligned phonetic and acoustic features. Rather than the more common approach of a content-based attention mechanism combined with an autoregressive decoder, we use a different mechanism suitable for feed-forward synthesis. Given that phonetic timings in singing are highly constrained by the musical score, we derive an approximate initial alignment with the help of a simple duration model. Then, using a decoder based on a feed-forward variant of the Transformer model, a series of self-attention and convolutional layers refines the result of the initial alignment to reach the target acoustic features. Advantages of this approach include faster inference and avoiding the exposure bias issues that affect autoregressive models trained by teacher forcing. We evaluate the effectiveness of this model compared to an autoregressive baseline, the importance of self-attention, and the importance of the accuracy of the duration model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09989v2\", \"primary_category\": \"cs.SD\", \"categories\": [\"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Sound\", \"categories_human_readable\": [\"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"15700\", \"16364\", \"16784\", \"18006\", \"51392\", \"17461\", \"31187\", \"38812\", \"42077\", \"88856\", \"92074\", \"129480\", \"87187\", \"102477\", \"107330\", \"107430\", \"117773\", \"119519\"], \"outgoing_citations\": [\"172904\", \"179678\", \"185160\", \"190581\", \"193700\", \"196641\", \"212248\", \"212631\", \"217313\", \"237754\", \"252497\", \"252801\", \"277320\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"160391","document_content":"# Exchangeable deep neural networks for set-to-set matching and learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nMatching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of sets, as well as the items in each set, should be exchangeable. In this study, we propose a novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching. We evaluate the methods through experiments based on two industrial applications: fashion set recommendation and group re-identification. In these experiments, we show that the proposed method provides significant improvements and results compared with the state-of-the-art methods, thereby validating our architecture for the heterogeneous set matching problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-58520-4_37\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"61316\", \"26892\"], \"outgoing_citations\": [\"145482\", \"163150\", \"173173\", \"185899\", \"188510\", \"196401\", \"197416\", \"199504\", \"201584\", \"204871\", \"213215\", \"215835\", \"221106\", \"222949\", \"226313\", \"229770\", \"234036\", \"237616\", \"237849\", \"242311\", \"248034\", \"259930\", \"260908\", \"264978\", \"265604\", \"269149\", \"280210\", \"286865\", \"295547\", \"302934\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"160421","document_content":"# Digital Twin approach to Clinical DSS with Explainable AI\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe propose a digital twin approach to improve healthcare decision support systems with a combination of domain knowledge and data. Domain knowledge helps build decision thresholds that doctors can use to determine a risk or recommend a treatment or test based on the specific patient condition. However, these assessments tend to be highly subjective and differ from doctor to doctor and from patient to patient. We propose a system where we collate this subjective risk by compiling data from different doctors treating different patients and build a machine learning model that learns from this knowledge. Then using state-of-the-art explainability concepts we derive explanations from this model. These explanations give us a summary of different doctor domain knowledge applied in different cases to give a more generic perspective. Also these explanations are specific to a particular patient and are customized for their condition. This is a form of a digital twin for the patient that can now be used to enhance decision boundaries for earlier defined decision tables that help in diagnosis. We will show an example of running this analysis for a liver disease risk diagnosis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.13520v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"160471","document_content":"# Weakly Supervised Disentanglement with Guarantees\n## Categories\n- Machine Learning\n## Abstract\nLearning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision. However, there is currently no formalism that identifies when and how weak supervision will guarantee disentanglement. To address this issue, we provide a theoretical framework to assist in analyzing the disentanglement guarantees (or lack thereof) conferred by weak supervision when coupled with learning algorithms based on distribution matching. We empirically verify the guarantees and limitations of several weak supervision methods (restricted labeling, match-pairing, and rank-pairing), demonstrating the predictive power and usefulness of our theoretical framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09772v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"315\", \"1785\", \"3852\", \"5274\", \"12721\", \"34887\", \"45970\", \"65191\", \"72458\", \"13493\", \"14234\", \"18189\", \"20747\", \"24977\", \"151109\", \"36764\", \"38455\", \"43651\", \"45942\", \"63434\", \"65007\", \"65861\", \"89349\", \"90921\", \"98752\", \"103627\", \"110493\", \"118768\", \"120747\", \"93244\", \"87061\", \"91004\", \"96305\", \"100660\", \"100782\", \"108991\", \"110924\", \"118821\", \"119003\", \"120772\", \"132719\", \"139925\", \"143581\", \"152148\", \"154401\"], \"outgoing_citations\": [\"178648\", \"182775\", \"186021\", \"205889\", \"207094\", \"208041\", \"216342\", \"229719\", \"236595\", \"241933\", \"242055\", \"242142\", \"242239\", \"251597\", \"264782\", \"265526\", \"316183\", \"328112\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"160502","document_content":"# Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n- 68T01\n## Abstract\nLegg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.2478\/jagi-2019-0003\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\", \"68T01\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\", \"68T01\"], \"incoming_citations\": [\"16639\", \"3004\", \"18744\", \"73715\", \"142084\"], \"outgoing_citations\": [\"304902\"]}","task_split":"paper_retrieval"}
{"document_id":"160526","document_content":"# Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nHow to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval (IR) research. However, this topic has not been well-studied in the learning-to-rank setting and most of the existing work still treats each document independently while scoring. The recent development of deep learning shows strength in modeling complex relationships across sequences and sets. It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework. In this paper, we formally define the permutation-equivariance requirement for a scoring function that captures cross-document interactions. We then propose a self-attention based document interaction network and show that it satisfies the permutation-equivariant requirement, and can generate scores for document sets of varying sizes. Our proposed methods can automatically learn to capture document interactions without any auxiliary information, and can scale across large document sets. We conduct experiments on three ranking datasets: the benchmark Web30k, a Gmail search, and a Google Drive Quick Access dataset. Experimental results show that our proposed methods are both more effective and efficient than baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09676v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"2556\", \"30206\", \"55333\", \"154912\", \"78191\", \"109043\", \"120534\", \"128415\", \"151490\"], \"outgoing_citations\": [\"190609\", \"194919\", \"207827\", \"210570\", \"215499\", \"235402\", \"242311\", \"253211\", \"267775\", \"281915\", \"286532\", \"297950\"]}","task_split":"paper_retrieval"}
{"document_id":"160608","document_content":"# HIGhER : Improving instruction following with Hindsight Generation for Experience Replay\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nLanguage creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may foster instructing, conditioning or structuring interactive agent behavior, it remains an open-problem to correctly relate language understanding and reinforcement learning in even simple instruction following scenarios. This joint learning problem is alleviated through expert demonstrations, auxiliary losses, or neural inductive biases. In this paper, we propose an orthogonal approach called Hindsight Generation for Experience Replay (HIGhER) that extends the Hindsight Experience Replay (HER) approach to the language-conditioned policy setting. Whenever the agent does not fulfill its instruction, HIGhER learns to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, HIGhER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We show the efficiency of our approach in the BabyAI environment, and demonstrate how it complements other instruction following methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09451v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16536\", \"79803\", \"41627\", \"55605\", \"69518\"], \"outgoing_citations\": [\"124122\", \"180270\", \"181620\", \"196909\", \"198208\", \"199210\", \"200574\", \"207866\", \"208773\", \"209452\", \"216774\", \"228916\", \"229145\", \"239633\", \"248885\", \"249906\", \"255134\", \"259693\", \"261983\", \"263104\", \"263270\", \"282065\", \"291025\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"160668","document_content":"# Localization of Fake News Detection via Multitask Transfer Learning\n## Categories\n- Computation and Language\n## Abstract\nThe use of the internet as a fast medium of spreading fake news reinforces the need for computational tools that combat it. Techniques that train fake news classifiers exist, but they all assume an abundance of resources including large labeled datasets and expert-curated corpora, which low-resource languages may not have. In this work, we make two main contributions: First, we alleviate resource scarcity by constructing the first expertly-curated benchmark dataset for fake news detection in Filipino, which we call \"Fake News Filipino.\" Second, we benchmark Transfer Learning (TL) techniques and show that they can be used to train robust fake news classifiers from little data, achieving 91% accuracy on our fake news dataset, reducing the error by 14% compared to established few-shot baselines. Furthermore, lifting ideas from multitask learning, we show that augmenting transformer-based transfer techniques with auxiliary language modeling losses improves their performance by adapting to writing style. Using this, we improve TL performance by 4-6%, achieving an accuracy of 96% on our best model. Lastly, we show that our method generalizes well to different types of news articles, including political news, entertainment news, and opinion articles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.13140\/RG.2.2.23028.40322\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"7674\", \"30160\", \"42046\", \"92269\", \"94579\", \"126945\"], \"outgoing_citations\": [\"178300\", \"197273\", \"200764\", \"219840\", \"257863\", \"259145\", \"267552\", \"273723\", \"283971\"]}","task_split":"paper_retrieval"}
{"document_id":"160673","document_content":"# Dealing with Sparse Rewards in Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSuccessfully navigating a complex environment to obtain a desired outcome is a difficult task, that up to recently was believed to be capable only by humans. This perception has been broken down over time, especially with the introduction of deep reinforcement learning, which has greatly increased the difficulty of tasks that can be automated. However, for traditional reinforcement learning agents this requires an environment to be able to provide frequent extrinsic rewards, which are not known or accessible for many real-world environments. This project aims to explore and contrast existing reinforcement learning solutions that circumnavigate the difficulties of an environment that provide sparse rewards. Different reinforcement solutions will be implemented over a several video game environments with varying difficulty and varying frequency of rewards, as to properly investigate the applicability of these solutions. This project introduces a novel reinforcement learning solution by combining aspects of two existing state of the art sparse reward solutions, curiosity driven exploration and unsupervised auxiliary tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09281v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"14318\", \"11876\", \"44418\", \"62673\", \"78402\", \"142989\", \"119188\"], \"outgoing_citations\": [\"212299\", \"221473\", \"240617\", \"243235\", \"258384\", \"258472\", \"280296\", \"291025\"]}","task_split":"paper_retrieval"}
{"document_id":"160739","document_content":"# Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nThroughout the course of my Ph.D., I have been designing the user experience (UX) of various machine learning (ML) systems. In this workshop, I share two projects as case studies in which people engage with ML in much more complicated and nuanced ways than the technical HCML work might assume. The first case study describes how cardiology teams in three hospitals used a clinical decision-support system that helps them decide whether and when to implant an artificial heart to a heart failure patient. I demonstrate that physicians cannot draw on their decision-making experience by seeing only patient data on paper. They are also confused by some fundamental premises upon which ML operates. For example, physicians asked: Are ML predictions made based on clinicians' best efforts? Is it ethical to make decisions based on previous patients' collective outcomes? In the second case study, my collaborators and I designed an intelligent text editor, with the goal of improving authors' writing experience with NLP (Natural Language Processing) technologies. We prototyped a number of generative functionalities where the system provides phrase-or-sentence-level writing suggestions upon user request. When writing with the prototype, however, authors shared that they need to \"see where the sentence is going two paragraphs later\" in order to decide whether the suggestion aligns with their writing; Some even considered adopting machine suggestions as plagiarism, therefore \"is simply wrong\".   By sharing these unexpected and intriguing responses from these real-world ML users, I hope to start a discussion about such previously-unknown complexities and nuances of -- as the workshop proposal states -- \"putting ML at the service of people in a way that is accessible, useful, and trustworthy to all\".","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.09137v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189570\"]}","task_split":"paper_retrieval"}
{"document_id":"160745","document_content":"# Enforcing Reasoning in Visual Commonsense Reasoning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nThe task of Visual Commonsense Reasoning is extremely challenging in the sense that the model has to not only be able to answer a question given an image, but also be able to learn to reason. The baselines introduced in this task are quite limiting because two networks are trained for predicting answers and rationales separately. Question and image is used as input to train answer prediction network while question, image and correct answer are used as input in the rationale prediction network. As rationale is conditioned on the correct answer, it is based on the assumption that we can solve Visual Question Answering task without any error - which is over ambitious. Moreover, such an approach makes both answer and rationale prediction two completely independent VQA tasks rendering cognition task meaningless. In this paper, we seek to address these issues by proposing an end-to-end trainable model which considers both answers and their reasons jointly. Specifically, we first predict the answer for the question and then use the chosen answer to predict the rationale. However, a trivial design of such a model becomes non-differentiable which makes it difficult to train. We solve this issue by proposing four approaches - softmax, gumbel-softmax, reinforcement learning based sampling and direct cross entropy against all pairs of answers and rationales. We demonstrate through experiments that our model performs competitively against current state-of-the-art. We conclude with an analysis of presented approaches and discuss avenues for further work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11124v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"91630\"], \"outgoing_citations\": [\"202067\", \"208526\", \"231300\", \"233440\", \"266120\", \"280606\", \"289503\", \"311008\"]}","task_split":"paper_retrieval"}
{"document_id":"160755","document_content":"# Learning to Recommend from Sparse Data via Generative User Feedback\n## Categories\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nTraditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions\/ratings are highly scarce. To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback. The proposed framework consists of a \"recommender\" and a \"virtual user\". The \"recommender\" is formulated as a CF model, recommending items according to observed user preference. The \"virtual user\" estimates rewards from the recommended items and generates a \\emph{feedback} in addition to the observed user preference. The \"recommender\" connected with the \"virtual user\" constructs a closed loop, that recommends users with items and imitates the \\emph{unobserved} feedback of the users to the recommended items. The synthetic feedback is used to augment the observed user preference and improve recommendation results. Theoretically, such model design can be interpreted as inverse reinforcement learning, which can be learned effectively via rollout (simulation). Experimental results show that the proposed framework is able to enrich the learning of user preference and boost the performance of existing collaborative filtering methods on multiple datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.12735v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"188081\", \"217780\", \"221604\", \"232822\", \"242002\", \"246280\", \"280470\", \"296554\", \"302085\", \"317770\"]}","task_split":"paper_retrieval"}
{"document_id":"160907","document_content":"# Facial Emotion Recognition Using Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nWe aim to construct a system that captures real-world facial images through the front camera on a laptop. The system is capable of processing\/recognizing the captured image and predict a result in real-time. In this system, we exploit the power of deep learning technique to learn a facial emotion recognition (FER) model based on a set of labeled facial images. Finally, experiments are conducted to evaluate our model using largely used public database.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.11113v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"302137\"]}","task_split":"paper_retrieval"}
{"document_id":"160936","document_content":"# Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Science and Game Theory\n## Abstract\nMotivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energy storage device given variable tariff structures. In most of the studies, the RL agent is considered as a black box, and how the agent has learned is often ignored. We explain the learning progression of the RL agent, and the strategies it follows based on the capacity of the storage device.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08719v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.GT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Science and Game Theory\", \"Machine Learning\"], \"incoming_citations\": [\"69408\", \"96766\"], \"outgoing_citations\": [\"248225\"]}","task_split":"paper_retrieval"}
{"document_id":"161026","document_content":"# Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data\n## Categories\n- Machine Learning\n## Abstract\nIn clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federated environment while maintaining privacy. However, it is quite challenging to develop a federated learning algorithm that can address both privacy-preserving and class imbalanced issues. In this study, we introduce a federated generative model learning platform for generating samples in a data-distributed environment while preserving privacy. We specifically propose approximate Bayesian computation-based Gaussian Mixture Model called 'Federated ABC-GMM', which can oversample data in a minor class by estimating the posterior distribution of model parameters across institutions in a privacy-preserving manner. PhysioNet2012, a dataset for prediction of mortality of patients in an Intensive Care Unit (ICU), was used to verify the performance of the proposed method. Experimental results show that our method boosts classification performance in terms of F1 score up to nearly an ideal situation. It is believed that the proposed method can be a novel alternative to solving class imbalance problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08489v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"194178\", \"213120\", \"247378\", \"259543\"]}","task_split":"paper_retrieval"}
{"document_id":"161027","document_content":"# Concept Pointer Network for Abstractive Summarization\n## Categories\n- Computation and Language\n- 68U15, 68T50\n## Abstract\nA quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08486v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68U15, 68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68U15, 68T50\"], \"incoming_citations\": [\"17850\", \"57604\", \"61496\", \"65094\", \"68961\", \"86983\", \"129200\", \"92291\", \"95033\", \"50181\", \"128252\", \"131340\"], \"outgoing_citations\": [\"220509\", \"220754\", \"225558\", \"230212\", \"230234\", \"230456\", \"231869\", \"232543\", \"237494\", \"238496\", \"266671\", \"268186\", \"296198\", \"311754\"]}","task_split":"paper_retrieval"}
{"document_id":"161034","document_content":"# Towards Learning Cross-Modal Perception-Trace Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nRepresentation learning is a key element of state-of-the-art deep learning approaches. It enables to transform raw data into structured vector space embeddings. Such embeddings are able to capture the distributional semantics of their context, e.g. by word windows on natural language sentences, graph walks on knowledge graphs or convolutions on images. So far, this context is manually defined, resulting in heuristics which are solely optimized for computational performance on certain tasks like link-prediction. However, such heuristic models of context are fundamentally different to how humans capture information. For instance, when reading a multi-modal webpage (i) humans do not perceive all parts of a document equally: Some words and parts of images are skipped, others are revisited several times which makes the perception trace highly non-sequential; (ii) humans construct meaning from a document's content by shifting their attention between text and image, among other things, guided by layout and design elements. In this paper we empirically investigate the difference between human perception and context heuristics of basic embedding models. We conduct eye tracking experiments to capture the underlying characteristics of human perception of media documents containing a mixture of text and images. Based on that, we devise a prototypical computational perception-trace model, called CMPM. We evaluate empirically how CMPM can improve a basic skip-gram embedding approach. Our results suggest, that even with a basic human-inspired computational perception model, there is a huge potential for improving embeddings since such a model does inherently capture multiple modalities, as well as layout and design elements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08549v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"262903\", \"269128\", \"300283\", \"316430\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"161143","document_content":"# RTFM: Generalising to Novel Environment Dynamics via Reading\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nObtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2$\\pi$, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2$\\pi$ generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2$\\pi$ produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08210v6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"22954\", \"14861\", \"16824\", \"26365\", \"35181\", \"69948\", \"70783\", \"74771\", \"89875\", \"95739\", \"100277\", \"116987\", \"110974\", \"111265\", \"112980\", \"119169\", \"116357\", \"124122\", \"127494\", \"137584\", \"155986\"], \"outgoing_citations\": [\"162764\", \"180270\", \"181620\", \"182870\", \"209452\", \"228916\", \"243235\", \"249906\", \"251691\", \"255134\", \"260541\", \"263270\", \"290891\", \"291102\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"161168","document_content":"# Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIt is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents' learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particular regions in a contingency-aware observation as a per step reward. Based on such decomposed rewards, we further learn a dynamics model and use Model Predictive Control (MPC) to obtain a policy. Since the rewards are decomposed to finer-granularity observations, they are naturally generalizable to new environments that are composed of similar basic elements. We demonstrate our method on a wide range of environments, including a classic video game -- Super Mario Bros, as well as a robotic continuous control task. Please refer to the project page for more visualized results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.08143v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"171702\", \"186669\", \"209786\", \"210234\", \"211558\", \"212299\", \"212511\", \"223990\", \"227313\", \"230557\", \"240617\", \"240817\", \"258608\", \"260136\", \"261983\", \"263650\", \"266419\", \"280457\", \"281110\", \"311595\"]}","task_split":"paper_retrieval"}
{"document_id":"161220","document_content":"# Convolutional Character Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: https:\/\/github.com\/MalongTech\/research-charnet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.07954v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2918\", \"13927\", \"14003\", \"50917\", \"52248\", \"101165\", \"180823\", \"8109\", \"14976\", \"18970\", \"33498\", \"35782\", \"43153\", \"47089\", \"56713\", \"59727\", \"43191\", \"81960\", \"113025\", \"83582\", \"102479\", \"111035\", \"111116\", \"111991\", \"112241\", \"126431\", \"140006\", \"140478\", \"156029\", \"210634\", \"153142\", \"167606\"], \"outgoing_citations\": [\"204872\", \"207348\", \"232706\", \"239535\", \"245899\", \"252159\", \"256711\", \"257197\", \"257954\", \"261365\", \"262570\", \"269205\", \"271112\", \"272481\", \"279687\", \"279950\", \"284871\", \"287166\", \"294023\", \"311196\"]}","task_split":"paper_retrieval"}
{"document_id":"161304","document_content":"# Single Episode Policy Transfer in Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nTransfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation. To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy. This modular approach enables integration of state-of-the-art algorithms for variational inference or RL. Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot. In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.07719v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"36403\", \"41291\", \"43678\", \"46143\", \"46780\", \"57818\", \"81039\", \"84471\", \"113327\", \"122847\", \"176979\", \"113866\", \"114800\", \"119473\"], \"outgoing_citations\": [\"194494\", \"206699\", \"214993\", \"218420\", \"224464\", \"228899\", \"230350\", \"233926\", \"239080\", \"244798\", \"263272\", \"274445\", \"283289\", \"341343\"]}","task_split":"paper_retrieval"}
{"document_id":"161412","document_content":"# Evolution of transfer learning in natural language processing\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit among others. Classically, tasks in natural language processing have been performed through rule-based and statistical methodologies. However, owing to the vast nature of natural languages these methods do not generalise well and failed to learn the nuances of language. Thus machine learning algorithms such as Naive Bayes and decision trees coupled with traditional models such as Bag-of-Words and N-grams were used to usurp this problem. Eventually, with the advent of advanced recurrent neural network architectures such as the LSTM, we were able to achieve state-of-the-art performance in several natural language processing tasks such as text classification and machine translation. We talk about how Transfer Learning has brought about the well-known ImageNet moment for NLP. Several advanced architectures such as the Transformer and its variants have allowed practitioners to leverage knowledge gained from unrelated task to drastically fasten convergence and provide better performance on the target task. This survey represents an effort at providing a succinct yet complete understanding of the recent advances in natural language processing using deep learning in with a special focus on detailing transfer learning and its potential advantages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.07370v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18601\", \"25894\", \"121872\"], \"outgoing_citations\": [\"259145\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"161444","document_content":"# Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nMastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrated that using sparse rewards, i.e. rewarding the agent only when the task has been successfully completed, can lead to better policies. However, state-action space exploration is more difficult in this case. Recent RL approaches to learning with sparse rewards have leveraged high-quality human demonstrations for the task, but these can be costly, time consuming or even impossible to obtain. In this paper, we propose a novel and effective approach that does not require human demonstrations. We observe that every robotic manipulation task could be seen as involving a locomotion task from the perspective of the object being manipulated, i.e. the object could learn how to reach a target state on its own. In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator. This policy is then used to generate auxiliary rewards, called simulated locomotion demonstration rewards (SLDRs), which enable us to learn the robot manipulation policy. The proposed approach has been evaluated on 13 tasks of increasing complexity, and can achieve higher success rate and faster learning rates compared to alternative algorithms. SLDRs are especially beneficial for tasks like multi-object stacking and non-rigid object manipulation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10994-021-06116-1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"19003\", \"54532\", \"107705\", \"121762\", \"141880\", \"146523\"], \"outgoing_citations\": [\"177272\", \"190766\", \"212299\", \"214302\", \"215425\", \"215627\", \"220103\", \"221473\", \"222669\", \"224840\", \"229890\", \"240506\", \"240817\", \"242141\", \"254596\", \"260018\", \"261653\", \"261983\", \"266182\", \"266419\", \"269235\", \"270925\", \"271375\", \"278822\", \"283410\", \"290679\", \"291460\", \"297317\", \"306153\", \"315055\"]}","task_split":"paper_retrieval"}
{"document_id":"161594","document_content":"# Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution. We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a \"best-first\" heuristic for exploration, using existing upper confidence bound methods. Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions. We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06862v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8849\", \"43737\", \"114915\", \"94958\"], \"outgoing_citations\": [\"177174\", \"203753\", \"212175\", \"214024\", \"249347\", \"258121\", \"263035\", \"268361\", \"297855\", \"311008\", \"311424\", \"334299\", \"335304\", \"350634\"]}","task_split":"paper_retrieval"}
{"document_id":"161648","document_content":"# A Method to Generate Synthetically Warped Document Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06621v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"136710\"], \"outgoing_citations\": [\"298700\"]}","task_split":"paper_retrieval"}
{"document_id":"161669","document_content":"# Text2Math: End-to-end Parsing Text into Math Expressions\n## Categories\n- Computation and Language\n## Abstract\nWe propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06571v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"32821\", \"96833\", \"24666\", \"43967\", \"53764\", \"66890\", \"80713\", \"98821\", \"127526\", \"132440\", \"143461\"], \"outgoing_citations\": [\"169320\", \"169321\", \"210184\", \"228046\", \"232288\", \"234579\", \"238788\", \"246516\", \"266702\", \"278863\", \"283758\", \"287181\", \"290632\", \"300505\", \"302107\", \"337956\"]}","task_split":"paper_retrieval"}
{"document_id":"161692","document_content":"# MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n## Abstract\nRecently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech recognition. However, high word error rates (WERs) still prevent these systems from being used in practical applications. On the other hand, the spatial information in multi-channel signals has proven helpful in far-field speech recognition tasks. In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-Speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition. MIMO-Speech is a fully neural end-to-end framework, which is optimized only via an ASR criterion. It is comprised of: 1) a monaural masking network, 2) a multi-source neural beamformer, and 3) a multi-output speech recognition model. With this processing, the input overlapped speech is directly mapped to text sequences. We further adopted a curriculum learning strategy, making the best use of the training set to improve the performance. The experiments on the spatialized wsj1-2mix corpus show that our model can achieve more than 60% WER reduction compared to the single-channel system with high quality enhanced signals (SI-SDR = 23.1 dB) obtained by the above separation function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06522v2\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.SD\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\", \"Computation and Language\"], \"incoming_citations\": [\"4743\", \"8105\", \"11959\", \"17150\", \"17523\", \"60889\", \"69085\", \"21604\", \"26915\", \"30063\", \"37124\", \"50861\", \"65536\", \"67526\", \"67473\", \"70908\", \"78667\", \"83823\", \"86524\", \"88493\", \"90106\", \"106689\", \"117525\", \"123724\", \"123861\", \"124425\", \"129570\", \"134542\", \"143122\", \"154885\", \"160477\", \"155543\", \"158932\"], \"outgoing_citations\": [\"187145\", \"189807\", \"195370\", \"210397\", \"211222\", \"211380\", \"214985\", \"221967\", \"232007\", \"237174\", \"260843\", \"270860\", \"271544\", \"284201\", \"288850\", \"289210\", \"307918\"]}","task_split":"paper_retrieval"}
{"document_id":"161707","document_content":"# Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nPredicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a rich source for learning patient representations, that can facilitate effective risk assessment. However, a large portion of clinical notes are unstructured and also contain domain specific terminologies, from which we need to extract structured information. In this paper, we introduce an embedding framework to learn semantically-plausible distributed representations of clinical notes that exploits the semantic correspondence between the unstructured texts and their corresponding structured knowledge, known as semantic frame, in a hierarchical fashion. Our approach integrates text modeling and semantic correspondence learning into a single model that comprises 1) an unstructured embedding module that makes use of self-similarity matrix representations in order to inject structural regularities of different segments inherent in clinical texts to promote local coherence, 2) a structured embedding module to embed the semantic frames (e.g., UMLS semantic types) with deep ConvNet and 3) a hierarchical semantic correspondence module that embeds by enhancing the interactions between text-semantic frame embedding pairs at multiple levels (i.e., words, sentence, note). Evaluations on multiple embedding benchmarks on post discharge intensive care patient mortality prediction tasks demonstrate its effectiveness compared to approaches that do not exploit the semantic interactions between structured and unstructured information present in clinical notes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06492v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"205014\", \"213077\", \"219051\", \"225853\", \"234577\", \"258494\", \"266725\", \"278980\", \"312760\"]}","task_split":"paper_retrieval"}
{"document_id":"161715","document_content":"# Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning is a research hotspot where encoder-decoder models combining convolutional neural network (CNN) and long short-term memory (LSTM) achieve promising results. Despite significant progress, these models generate sentences differently from human cognitive styles. Existing models often generate a complete sentence from the first word to the end, without considering the influence of the following words on the whole sentence generation. In this paper, we explore the utilization of a human-like cognitive style, i.e., building overall cognition for the image to be described and the sentence to be constructed, for enhancing computer image understanding. This paper first proposes a Mutual-aid network structure with Bidirectional LSTMs (MaBi-LSTMs) for acquiring overall contextual information. In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner. In the captioning process, the LSTM implicitly utilizes the subsequent semantic information contained in its hidden states. In fact, MaBi-LSTMs can generate two sentences in forward and backward directions. To bridge the gap between cross-domain models and generate a sentence with higher quality, we further develop a cross-modal attention mechanism to retouch the two sentences by fusing their salient parts as well as the salient areas of the image. Experimental results on the Microsoft COCO dataset show that the proposed model improves the performance of encoder-decoder models and achieves state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06475v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"93187\", \"15902\", \"43903\", \"74508\", \"144758\"], \"outgoing_citations\": [\"223314\", \"226123\", \"237199\", \"237655\", \"249394\", \"249641\", \"251088\", \"268096\", \"268218\", \"274183\", \"278604\", \"278846\", \"279676\", \"281152\", \"291851\", \"295307\", \"296681\", \"311824\", \"321621\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"161774","document_content":"# Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nTraining models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuous effort and crucial for business operations. In such cases there is often an abundance of unlabeled data, while labeled data is scarce or unavailable. Pre-trained language models trained to extract contextual features from text were shown to improve many natural language processing (NLP) tasks, including scarcely labeled tasks, by leveraging transfer learning. However, such models impose a heavy memory and computational burden, making it a challenge to train and deploy such models for inference use. In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples. Preliminary evaluations show that the compact models can achieve competitive accuracy with 36x compression rate when compared with a state-of-the-art pre-trained language model, and run significantly faster in inference, allowing deployment of such models in production environments or on edge devices.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06294v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"131584\"], \"outgoing_citations\": [\"169364\", \"274533\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"161855","document_content":"# Improving Question Generation With to the Point Context\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nQuestion generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.06036v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"25694\", \"38344\", \"44098\", \"68754\", \"71422\", \"81248\", \"93096\", \"132550\", \"40920\", \"96088\", \"96671\", \"130408\", \"158044\"], \"outgoing_citations\": [\"218708\", \"218826\", \"225181\", \"250690\", \"267246\", \"267694\", \"269583\", \"295815\", \"296122\", \"296198\", \"296279\", \"315108\"]}","task_split":"paper_retrieval"}
{"document_id":"162038","document_content":"# A Research Platform for Multi-Robot Dialogue with Humans\n## Categories\n- Robotics\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nThis paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which users can verbally issue search, navigate, and follow instructions to two robotic teammates: a simulated ground robot and an aerial robot. This flexible language and robotic platform takes advantage of existing tools for speech recognition and dialogue management that are compatible with new domains, and implements an inter-agent communication protocol (tactical behavior specification), where verbal instructions are encoded for tasks assigned to the appropriate robot.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.05624v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"36974\", \"87706\", \"91773\", \"115538\", \"176603\"], \"outgoing_citations\": [\"223869\", \"253078\"]}","task_split":"paper_retrieval"}
{"document_id":"162053","document_content":"# Facial Expression Recognition Using Human to Animated-Character Expression Translation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial expression recognition is a challenging task due to two major problems: the presence of inter-subject variations in facial expression recognition dataset and impure expressions posed by human subjects. In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to overcome these two problems by using many (human faces) to one (animated face) mapping. Specifically, for any given input human expression image, our HA-GAN transfers the expression information from the input image to a fixed animated identity. Stylized animated characters from the Facial Expression Research Group-Database (FERGDB) are used for the generation of fixed identity. By learning this many-to-one identity mapping function using our proposed HA-GAN, the effect of inter-subject variations can be reduced in Facial Expression Recognition(FER). We also argue that the expressions in the generated animated images are pure expressions and since FER is performed on these generated images, the performance of facial expression recognition is improved. Our initial experimental results on the state-of-the-art datasets show that facial expression recognition carried out on the generated animated images using our HA-GAN framework outperforms the baseline deep neural network and produces comparable or even better results than the state-of-the-art methods for facial expression recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.05595v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"153460\", \"156027\"], \"outgoing_citations\": [\"164407\", \"194547\", \"253762\", \"284239\", \"287844\"]}","task_split":"paper_retrieval"}
{"document_id":"162060","document_content":"# How to Not Measure Disentanglement\n## Categories\n- Machine Learning\n## Abstract\nTo evaluate disentangled representations several metrics have been proposed. However, theoretical guarantees for conventional metrics of disentanglement are missing. Moreover, conventional metrics do not have a consistent correlation with the outcomes of qualitative studies. In this paper we analyze metrics of disentanglement and their properties. We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition. In addition, we propose a new metric of disentanglement and prove that it satisfies both of the properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.05587v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"154101\", \"181388\", \"194901\", \"207094\", \"208041\", \"241933\", \"242239\", \"249874\", \"251597\", \"264963\", \"277631\"]}","task_split":"paper_retrieval"}
{"document_id":"162159","document_content":"# The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nCausal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.05387v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"99412\", \"9875\", \"16143\", \"29505\", \"70159\", \"96147\", \"122635\", \"126814\", \"94478\", \"98033\", \"112143\", \"114351\", \"127545\", \"137051\"], \"outgoing_citations\": [\"242204\", \"263725\", \"320501\", \"348612\"]}","task_split":"paper_retrieval"}
{"document_id":"162227","document_content":"# Cross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage-text retrieval of natural scenes has been a popular research topic. Since image and text are heterogeneous cross-modal data, one of the key challenges is how to learn comprehensive yet unified representations to express the multi-modal data. A natural scene image mainly involves two kinds of visual concepts, objects and their relationships, which are equally essential to image-text retrieval. Therefore, a good representation should account for both of them. In the light of recent success of scene graph in many CV and NLP tasks for describing complex natural scenes, we propose to represent image and text with two kinds of scene graphs: visual scene graph (VSG) and textual scene graph (TSG), each of which is exploited to jointly characterize objects and relationships in the corresponding modality. The image-text retrieval task is then naturally formulated as cross-modal scene graph matching. Specifically, we design two particular scene graph encoders in our model for VSG and TSG, which can refine the representation of each node on the graph by aggregating neighborhood information. As a result, both object-level and relationship-level cross-modal features can be obtained, which favorably enables us to evaluate the similarity of image and text in the two levels in a more plausible way. We achieve state-of-the-art results on Flickr30k and MSCOCO, which verifies the advantages of our graph matching based approach for image-text retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.05134v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2123\", \"62580\", \"11273\", \"24165\", \"31846\", \"96143\", \"17157\", \"28264\", \"33170\", \"42740\", \"44781\", \"50613\", \"58057\", \"65937\", \"70460\", \"76974\", \"81968\", \"85822\", \"92282\", \"161696\", \"102789\", \"125228\"], \"outgoing_citations\": [\"181437\", \"206311\", \"207034\", \"217310\", \"236736\", \"237852\", \"238205\", \"248171\", \"250108\", \"250183\", \"259807\", \"260926\", \"276530\", \"280249\", \"284449\", \"287504\", \"303011\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"162285","document_content":"# Learning Invariant Representations of Social Media Users\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n- Computation and Language\n## Abstract\nThe evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, na\\\"ive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users' invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04979v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.SI\", \"cs.CL\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Social and Information Networks\", \"Computation and Language\"], \"incoming_citations\": [\"14965\", \"22629\", \"49389\", \"54528\", \"58827\", \"81225\", \"122606\"], \"outgoing_citations\": [\"155567\", \"194499\", \"233817\", \"239078\", \"265022\", \"265194\", \"271361\", \"277853\", \"283923\", \"287750\", \"292772\", \"298159\", \"302157\", \"302806\", \"330936\", \"338638\"]}","task_split":"paper_retrieval"}
{"document_id":"162384","document_content":"# NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification\n## Categories\n- Machine Learning\n## Abstract\nDeep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network. When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training. Encouragingly, the agent learns, without explicit instruction, a search policy in agreement with standardized radiological hallmarks of Alzheimer's disease, suggesting a route to automated biomarker discovery for more poorly understood disorders.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04721v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"84177\"], \"outgoing_citations\": [\"187985\", \"199022\", \"215917\", \"253407\", \"255281\", \"275582\", \"289151\", \"317804\", \"319781\"]}","task_split":"paper_retrieval"}
{"document_id":"162482","document_content":"# Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints\n## Categories\n- Computation and Language\n## Abstract\nSentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with sequence-to-sequence models which have been developed assuming homogeneous target audiences. In this paper we argue that different users have different simplification needs (e.g. dyslexics vs. non-native speakers), and propose CROSS, ContROllable Sentence Simplification model, which allows to control both the level of simplicity and the type of the simplification. We achieve this by enriching a Transformer-based architecture with syntactic and lexical constraints (which can be set or learned from data). Empirical results on two benchmark datasets show that constraints are key to successful simplification, offering flexible generation output.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04387v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"47725\", \"92547\", \"78896\", \"96200\", \"108688\"], \"outgoing_citations\": [\"212887\", \"227488\", \"234907\", \"235175\", \"235345\", \"250505\", \"250615\", \"261692\", \"270048\", \"283618\", \"288415\"]}","task_split":"paper_retrieval"}
{"document_id":"162527","document_content":"# Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSequence labeling is a fundamental framework for various natural language processing problems. Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios, and obtaining ground truth labels is often costly. In many cases, ground truth labels do not exist, but noisy annotations or annotations from different domains are accessible. In this paper, we propose a novel framework Consensus Network (ConNet) that can be trained on annotations from multiple sources (e.g., crowd annotation, cross-domain data...). It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module. Finally, it leads to a model reflecting the agreement (consensus) among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. We also demonstrate that the method can apply to various tasks and cope with different encoders.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04289v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"7555\", \"21541\", \"24680\", \"41585\", \"47060\", \"48390\", \"62920\", \"115664\", \"130889\", \"130886\"], \"outgoing_citations\": [\"130886\", \"130889\", \"168832\", \"200678\", \"214221\", \"216925\", \"234242\", \"234783\", \"242038\", \"243880\", \"256776\", \"262328\", \"270545\", \"272917\", \"288545\", \"291900\", \"321690\"]}","task_split":"paper_retrieval"}
{"document_id":"162602","document_content":"# Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nOver its lifetime, a reinforcement learning agent is often tasked with different tasks. How to efficiently adapt a previously learned control policy from one task to another, remains an open research question. In this paper, we investigate how instructions formulated in natural language can enable faster and more effective task adaptation. This can serve as the basis for developing language instructed skills, which can be used in a lifelong learning setting. Our method is capable of assessing, given a set of developed base control policies, which policy will adapt best to a new unseen task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04040v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"209605\", \"112980\"], \"outgoing_citations\": [\"180270\", \"181620\", \"196377\", \"218124\", \"259693\", \"263270\", \"263650\", \"267779\", \"281106\"]}","task_split":"paper_retrieval"}
{"document_id":"162762","document_content":"# Knowledge-based Biomedical Data Science 2019\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- I.2.0; I.2.1; I.2.4; I.2.7; I.2.m; I.5.0; I.7.0; J.3\n## Abstract\nKnowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1146\/annurev-biodatasci-010820-091627\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"I.2.0; I.2.1; I.2.4; I.2.7; I.2.m; I.5.0; I.7.0; J.3\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"I.2.0; I.2.1; I.2.4; I.2.7; I.2.m; I.5.0; I.7.0; J.3\"], \"incoming_citations\": [], \"outgoing_citations\": [\"169330\", \"173127\", \"177903\", \"181169\", \"196605\", \"216902\", \"233047\", \"233826\", \"242922\", \"278098\", \"362644\"]}","task_split":"paper_retrieval"}
{"document_id":"162893","document_content":"# Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nTraditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem. Though there is a hierarchy in the way humans use language by forming paragraphs from sentences and sentences from words, hierarchical models have usually not worked that much better than their traditional seq2seq counterparts. This effect is mainly because either the hierarchical attention mechanisms are too sparse using hard attention or noisy using soft attention. In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences. In a typical text summarization dataset consisting of documents that are 800 tokens in length (average), capturing long-term dependencies is very important, e.g., the last sentence can be grouped with the first sentence of a document to form a summary. LSTMs (Long Short-Term Memory) proved useful for machine translation. However, they often fail to capture long-term dependencies while modeling long sequences. To address these issues, we have adapted Neural Semantic Encoders (NSE) to text summarization, a class of memory-augmented neural networks by improving its functionalities and proposed a novel hierarchical NSE that outperforms similar previous models significantly. The quality of summarization was improved by augmenting linguistic factors, namely lemma, and Part-of-Speech (PoS) tags, to each word in the dataset for improved vocabulary coverage and generalization. The hierarchical NSE model on factored dataset outperformed the state-of-the-art by nearly 4 ROUGE points. We further designed and used the first GPU-based self-critical Reinforcement Learning model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.03177v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"230212\", \"237494\", \"241070\", \"266671\", \"278954\", \"280557\", \"288415\", \"298842\"]}","task_split":"paper_retrieval"}
{"document_id":"162996","document_content":"# Learning Navigation by Visual Localization and Trajectory Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nWhen driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current self-driving models improve their performances when using additional GPS information. Here we aim to push forward self-driving research and perform route planning even in the absence of GPS. Our system learns to predict in real-time vehicle's current location and future trajectory, as a function of time, on a known map, given only the raw video stream and the intended destination. The GPS signal is available only at training time, with training data annotation being fully automatic. Different from other published models, we predict the vehicle's trajectory for up to seven seconds ahead, from which complete steering, speed and acceleration information can be derived for the entire time span. Trajectories capture navigational information on multiple levels, from instant steering commands that depend on present traffic and obstacles ahead, to longer-term navigation decisions, towards a specific destination. We collect our dataset with a regular car and a smartphone that records video and GPS streams. The GPS data is used to derive ground-truth supervision labels and create an analytical representation of the traversed map. In tests, our system outperforms published methods on visual localization and steering and gives accurate navigation assistance between any two known locations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02818v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"149568\", \"161295\", \"193618\", \"202990\", \"204255\", \"208759\", \"236793\", \"237119\", \"237546\", \"259959\", \"268589\", \"269927\", \"270054\", \"277405\", \"278822\", \"287219\", \"312170\", \"318935\"]}","task_split":"paper_retrieval"}
{"document_id":"163085","document_content":"# Differential Privacy-enabled Federated Learning for Sensitive Health Data\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nLeveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02578v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\"], \"incoming_citations\": [\"9036\", \"174906\", \"5929\", \"34352\", \"39494\", \"42977\", \"51684\", \"99336\", \"110259\", \"118805\", \"18850\", \"19599\", \"37557\", \"65735\", \"73648\", \"75782\", \"104264\", \"146300\", \"83656\", \"94015\", \"97063\", \"107025\", \"140976\", \"142628\", \"156643\", \"110331\"], \"outgoing_citations\": [\"140976\", \"226146\", \"235592\", \"246939\", \"256327\", \"319813\"]}","task_split":"paper_retrieval"}
{"document_id":"163091","document_content":"# Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n- Image and Video Processing\n## Abstract\nA defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a solution for predicting how the future will unfold visually, and thus, many models have been proposed that are capable of predicting future frames based on a history of observed frames~(and sometimes robot actions). However, a comprehensive method for determining the fitness of different video prediction models at guiding the selection of actions is yet to be developed. Current metrics assess video prediction models based on human perception of frame quality. In contrast, we argue that if these systems are to be used to guide action, necessarily, the actions the robot performs should be encoded in the predicted frames. In this paper, we are proposing a new metric to compare different video prediction models based on this argument. More specifically, we propose an action inference system and quantitatively rank different models based on how well we can infer the robot actions from the predicted frames. Our extensive experiments show that models with high perceptual scores can perform poorly in the proposed action inference tests and thus, may not be suitable options to be used in robot planning systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02564v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\", \"Image and Video Processing\"], \"incoming_citations\": [\"64770\", \"72808\"], \"outgoing_citations\": [\"159949\", \"188709\", \"196563\", \"207601\", \"207618\", \"219180\", \"236752\", \"241365\", \"251921\", \"253291\", \"262942\", \"270832\", \"283391\", \"288746\", \"291461\", \"292009\", \"303174\", \"308711\", \"311470\", \"317379\"]}","task_split":"paper_retrieval"}
{"document_id":"163092","document_content":"# MASTER: Multi-Aspect Non-local Network for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAttention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture. However, such methods suffer from attention-drift problem because high similarity among encoded features leads to attention confusion under the RNN-based local attention mechanism. Moreover, RNN-based methods have low efficiency due to poor parallelization. To overcome these problems, we propose the MASTER, a self-attention based scene text recognizer that (1) not only encodes the input-output attention but also learns self-attention which encodes feature-feature and target-target relationships inside the encoder and decoder and (2) learns a more powerful and robust intermediate representation to spatial distortion, and (3) owns a great training efficiency because of high training parallelization and a high-speed inference because of an efficient memory-cache mechanism. Extensive experiments on various benchmarks demonstrate the superior performance of our MASTER on both regular and irregular scene text. Pytorch code can be found at https:\/\/github.com\/wenwenyu\/MASTER-pytorch, and Tensorflow code can be found at https:\/\/github.com\/jiangxiluning\/MASTER-TF.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patcog.2021.107980\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"36229\", \"122798\", \"191\", \"4188\", \"7620\", \"9220\", \"42497\", \"43849\", \"46340\", \"51656\", \"51655\", \"55703\", \"67749\", \"113794\", \"99077\", \"135021\"], \"outgoing_citations\": [\"162477\", \"172884\", \"188938\", \"192483\", \"192671\", \"203406\", \"205988\", \"208801\", \"210634\", \"211851\", \"217463\", \"229426\", \"232706\", \"248734\", \"250738\", \"256711\", \"266955\", \"290130\", \"294023\", \"294931\", \"296685\", \"296828\", \"300208\", \"309239\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"163195","document_content":"# Towards Deployment of Robust AI Agents for Human-Machine Partnerships\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where the parameters correspond to a user's type and characterize her behavior. In the test phase, the AI agent has to interact with a user of unknown type. Our approach to designing a robust AI agent relies on observing the user's actions to make inferences about the user's type and adapting its policy to facilitate efficient cooperation. We show that without being adaptive, an AI agent can end up performing arbitrarily bad in the test phase. We develop two algorithms for computing policies that automatically adapt to the user in the test phase. We demonstrate the effectiveness of our approach in solving a two-agent collaborative task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02330v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"10705\", \"7674\", \"11237\", \"117113\", \"99698\", \"183009\"], \"outgoing_citations\": [\"183009\", \"183993\", \"201891\", \"213540\", \"218124\", \"227728\", \"238343\", \"240780\", \"256103\", \"261706\", \"263716\", \"272030\", \"274315\", \"290744\"]}","task_split":"paper_retrieval"}
{"document_id":"163237","document_content":"# A Machine Learning Analysis of the Features in Deceptive and Credible News\n## Categories\n- Computation and Language\n- J.5.5\n## Abstract\nFake news is a type of pervasive propaganda that spreads misinformation online, taking advantage of social media's extensive reach to manipulate public perception. Over the past three years, fake news has become a focal discussion point in the media due to its impact on the 2016 U.S. presidential election. Fake news can have severe real-world implications: in 2016, a man walked into a pizzeria carrying a rifle because he read that Hillary Clinton was harboring children as sex slaves. This project presents a high accuracy (87%) machine learning classifier that determines the validity of news based on the word distributions and specific linguistic and stylistic differences in the first few sentences of an article. This can help readers identify the validity of an article by looking for specific features in the opening lines aiding them in making informed decisions. Using a dataset of 2,107 articles from 30 different websites, this project establishes an understanding of the variations between fake and credible news by examining the model, dataset, and features. This classifier appears to use the differences in word distribution, levels of tone authenticity, and frequency of adverbs, adjectives, and nouns. The differentiation in the features of these articles can be used to improve future classifiers. This classifier can also be further applied directly to browsers as a Google Chrome extension or as a filter for social media outlets or news websites to reduce the spread of misinformation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02223v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"J.5.5\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"J.5.5\"], \"incoming_citations\": [], \"outgoing_citations\": [\"257863\", \"267552\"]}","task_split":"paper_retrieval"}
{"document_id":"163253","document_content":"# Learning from Fact-checkers: Analysis and Generation of Fact-checking Language\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nIn fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named \\emph{fact-checkers}, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30\\% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02202v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"23929\", \"1907\", \"11296\", \"71484\", \"82909\", \"96086\", \"115939\", \"125124\", \"148166\"], \"outgoing_citations\": [\"209874\", \"217496\", \"223318\", \"227425\", \"238986\", \"239614\", \"249184\", \"257600\", \"257829\", \"267552\", \"270399\", \"274781\", \"275597\", \"292261\", \"295863\", \"297117\", \"307547\", \"309411\", \"310903\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"163298","document_content":"# Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computers and Society\n## Abstract\nHealth information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more degrees confederated machine learning. We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02109v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computers and Society\"], \"incoming_citations\": [\"9036\", \"207951\"], \"outgoing_citations\": [\"207602\", \"246939\", \"247194\", \"248982\", \"280506\", \"295633\", \"303573\"]}","task_split":"paper_retrieval"}
{"document_id":"163450","document_content":"# Causal Induction from Visual Observations for Goal Directed Tasks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nCausal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world. In this work, we propose to endow an artificial agent with the capability of causal reasoning for completing goal-directed tasks. We develop learning-based approaches to inducing causal knowledge in the form of directed acyclic graphs, which can be used to contextualize a learned goal-conditional policy to perform tasks in novel environments with latent causal structures. We leverage attention mechanisms in our causal induction model and goal-conditional policy, enabling us to incrementally generate the causal graph from the agent's visual observations and to selectively use the induced graph for determining actions. Our experiments show that our method effectively generalizes towards completing new tasks in novel environments with previously unseen causal structures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01751v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"65457\", \"34142\", \"17667\", \"32561\", \"38037\", \"39620\", \"39701\", \"40927\", \"45824\", \"51475\", \"78981\", \"121684\", \"138335\", \"86777\", \"94710\", \"96305\", \"96956\", \"99854\", \"104822\", \"110678\", \"122822\", \"138449\", \"139684\", \"139764\", \"154592\"], \"outgoing_citations\": [\"174232\", \"182321\", \"189408\", \"200942\", \"201860\", \"209989\", \"223487\", \"238343\", \"253045\", \"256720\", \"274445\", \"320760\", \"347356\"]}","task_split":"paper_retrieval"}
{"document_id":"163467","document_content":"# Benchmarking Batch Deep Reinforcement Learning Algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWidely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following this result, there have been several papers showing reasonable performances under a variety of environments and batch settings. In this paper, we benchmark the performance of recent off-policy and batch reinforcement learning algorithms under unified settings on the Atari domain, with data generated by a single partially-trained behavioral policy. We find that under these conditions, many of these algorithms underperform DQN trained online with the same amount of data, as well as the partially-trained behavioral policy. To introduce a strong baseline, we adapt the Batch-Constrained Q-learning algorithm to a discrete-action setting, and show it outperforms all existing algorithms at this task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01708v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"10698\", \"10858\", \"17950\", \"36644\", \"37832\", \"61460\", \"78387\", \"9044\", \"11091\", \"13478\", \"13490\", \"16555\", \"22534\", \"22866\", \"23294\", \"25823\", \"26934\", \"33819\", \"41610\", \"42559\", \"42563\", \"40969\", \"44208\", \"46828\", \"50130\", \"54518\", \"62516\", \"63102\", \"70761\", \"72267\", \"90883\", \"93481\", \"94620\", \"100292\", \"106323\", \"108091\", \"117217\", \"119548\", \"120766\", \"80116\", \"84070\", \"85341\", \"93943\", \"96623\", \"112604\", \"115825\", \"116312\", \"116584\", \"119068\", \"121175\", \"119408\", \"121735\", \"133479\", \"140106\", \"143509\", \"159565\", \"176738\", \"204248\", \"126464\"], \"outgoing_citations\": [\"167341\", \"178278\", \"182796\", \"205913\", \"206875\", \"212016\", \"212299\", \"247075\", \"252249\", \"255613\", \"260583\", \"280457\", \"280866\", \"291025\", \"295281\"]}","task_split":"paper_retrieval"}
{"document_id":"163503","document_content":"# Bootstrapping Conditional GANs for Video Game Level Generation\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nGenerative Adversarial Networks (GANs) have shown im-pressive results for image generation. However, GANs facechallenges in generating contents with certain types of con-straints, such as game levels. Specifically, it is difficult togenerate levels that have aesthetic appeal and are playable atthe same time. Additionally, because training data usually islimited, it is challenging to generate unique levels with cur-rent GANs. In this paper, we propose a new GAN architec-ture namedConditional Embedding Self-Attention Genera-tive Adversarial Network(CESAGAN) and a new bootstrap-ping training procedure. The CESAGAN is a modification ofthe self-attention GAN that incorporates an embedding fea-ture vector input to condition the training of the discriminatorand generator. This allows the network to model non-localdependency between game objects, and to count objects. Ad-ditionally, to reduce the number of levels necessary to trainthe GAN, we propose a bootstrapping mechanism in whichplayable generated levels are added to the training set. Theresults demonstrate that the new approach does not only gen-erate a larger number of levels that are playable but also gen-erates fewer duplicate levels compared to a standard GAN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01603v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.LG\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"47005\", \"33220\", \"33228\", \"37071\", \"40705\", \"48859\", \"49106\", \"56433\", \"57919\", \"72454\", \"74578\", \"95477\", \"107442\", \"94739\", \"94524\", \"105513\", \"107909\", \"109632\", \"122922\", \"133342\", \"133812\", \"147140\", \"153614\"], \"outgoing_citations\": [\"181122\", \"224955\", \"226794\", \"233501\", \"234345\", \"240572\", \"241619\", \"265896\", \"265970\", \"267006\", \"274886\", \"294007\", \"297233\", \"299541\"]}","task_split":"paper_retrieval"}
{"document_id":"163504","document_content":"# SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge\n## Categories\n- Networking and Internet Architecture\n- Machine Learning\n- Signal Processing\n## Abstract\nIn IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01601v1\", \"primary_category\": \"cs.NI\", \"categories\": [\"cs.LG\", \"cs.NI\", \"eess.SP\"], \"primary_category_human_readable\": \"Networking and Internet Architecture\", \"categories_human_readable\": [\"Machine Learning\", \"Networking and Internet Architecture\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"249590\", \"256801\", \"256809\", \"262065\", \"301197\"]}","task_split":"paper_retrieval"}
{"document_id":"163508","document_content":"# Graph Analysis and Graph Pooling in the Spatial Domain\n## Categories\n- Machine Learning\n## Abstract\nThe spatial convolution layer which is widely used in the Graph Neural Networks (GNNs) aggregates the feature vector of each node with the feature vectors of its neighboring nodes. The GNN is not aware of the locations of the nodes in the global structure of the graph and when the local structures corresponding to different nodes are similar to each other, the convolution layer maps all those nodes to similar or same feature vectors in the continuous feature space. Therefore, the GNN cannot distinguish two graphs if their difference is not in their local structures. In addition, when the nodes are not labeled\/attributed the convolution layers can fail to distinguish even different local structures. In this paper, we propose an effective solution to address this problem of the GNNs. The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph. The spatial representation which is equivalent to a point-cloud representation of the graph is obtained by a graph embedding method. Using the proposed approach, the local feature extractor of the GNN distinguishes similar local structures in different locations of the graph and the GNN infers the topological structure of the graph from the spatial distribution of the locally extracted feature vectors. Moreover, the spatial representation is utilized to simplify the graph down-sampling problem. A new graph pooling method is proposed and it is shown that the proposed pooling method achieves competitive or better results in comparison with the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01589v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"29397\"], \"outgoing_citations\": [\"190159\", \"231240\", \"242322\", \"249443\", \"259862\", \"265828\", \"266844\", \"269273\", \"303863\", \"311030\", \"330940\"]}","task_split":"paper_retrieval"}
{"document_id":"163528","document_content":"# A Stereo Algorithm for Thin Obstacles and Reflective Objects\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nStereo cameras are a popular choice for obstacle avoidance for outdoor lighweight, low-cost robotics applications. However, they are unable to sense thin and reflective objects well. Currently, many algorithms are tuned to perform well on indoor scenes like the Middlebury dataset. When navigating outdoors, reflective objects, like windows and glass, and thin obstacles, like wires, are not well handled by most stereo disparity algorithms. Reflections, repeating patterns and objects parallel to the cameras' baseline causes mismatches between image pairs which leads to bad disparity estimates. Thin obstacles are difficult for many sliding window based disparity methods to detect because they do not take up large portions of the pixels in the sliding window. We use a trinocular camera setup and micropolarizer camera capable of detecting reflective objects to overcome these issues. We present a hierarchical disparity algorithm that reduces noise, separately identify wires using semantic object triangulation in three images, and use information about the polarization of light to estimate the disparity of reflective objects. We evaluate our approach on outdoor data that we collected. Our method contained an average of 9.27% of bad pixels compared to a typical stereo algorithm's 18.4% of bad pixels in scenes containing reflective objects. Our trinocular and semantic wire disparity methods detected 53% of wire pixels, whereas a typical two camera stereo algorithm detected 5%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.04874v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"111979\"], \"outgoing_citations\": [\"258654\"]}","task_split":"paper_retrieval"}
{"document_id":"163549","document_content":"# CLEVRER: CoLlision Events for Video REpresentation and Reasoning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nThe ability to reason about temporal and causal events from videos lies at the core of human intelligence. Most video reasoning benchmarks, however, focus on pattern recognition from complex visual and language input, instead of on causal structure. We study the complementary problem, exploring the temporal and causal structures behind videos of objects with simple visual appearance. To this end, we introduce the CoLlision Events for Video REpresentation and Reasoning (CLEVRER), a diagnostic video dataset for systematic evaluation of computational models on a wide range of reasoning tasks. Motivated by the theory of human casual judgment, CLEVRER includes four types of questions: descriptive (e.g., \"what color\"), explanatory (\"what is responsible for\"), predictive (\"what will happen next\"), and counterfactual (\"what if\"). We evaluate various state-of-the-art models for visual reasoning on our benchmark. While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations. We also study an oracle model that explicitly combines these components via symbolic representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01442v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16574\", \"26642\", \"65457\", \"101741\", \"7311\", \"7484\", \"15084\", \"15272\", \"29678\", \"38128\", \"39639\", \"51371\", \"60623\", \"76023\", \"81981\", \"4107\", \"9040\", \"12898\", \"16774\", \"16942\", \"16867\", \"17103\", \"16626\", \"22556\", \"23031\", \"23855\", \"24946\", \"25164\", \"29772\", \"36306\", \"36874\", \"38037\", \"39599\", \"41282\", \"41609\", \"41627\", \"46468\", \"48946\", \"50373\", \"56858\", \"57662\", \"59720\", \"59458\", \"59895\", \"60528\", \"60526\", \"62117\", \"65743\", \"66374\", \"67184\", \"75057\", \"75085\", \"76934\", \"77485\", \"80307\", \"83364\", \"84568\", \"107635\", \"113084\", \"115753\", \"120414\", \"126474\", \"132041\", \"79159\", \"79655\", \"85758\", \"91048\", \"96580\", \"97962\", \"99293\", \"99753\", \"111340\", \"114455\", \"114831\", \"115856\", \"115980\", \"117602\", \"116746\", \"127494\", \"128386\", \"107363\", \"134923\", \"135328\", \"162372\", \"164814\", \"144119\"], \"outgoing_citations\": [\"162372\", \"164814\", \"188867\", \"191515\", \"197653\", \"202065\", \"208526\", \"215409\", \"216112\", \"219048\", \"219835\", \"237145\", \"237226\", \"237960\", \"238773\", \"239000\", \"239633\", \"247849\", \"253291\", \"255134\", \"258511\", \"259176\", \"259318\", \"264532\", \"264533\", \"266825\", \"267223\", \"268586\", \"268871\", \"278655\", \"279009\", \"279026\", \"279676\", \"289719\", \"292009\", \"296366\", \"297162\", \"301767\", \"302659\", \"303603\", \"303837\", \"313435\"]}","task_split":"paper_retrieval"}
{"document_id":"163633","document_content":"# AI Assisted Annotator using Reinforcement Learning\n## Categories\n- Signal Processing\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHealthcare data suffers from both noise and lack of ground truth. The cost of data increases as it is cleaned and annotated in healthcare. Unlike other data sets, medical data annotation, which is critical to accurate ground truth, requires medical domain expertise for a better patient outcome. In this work, we report on the use of reinforcement learning to mimic the decision making process of annotators for medical events, to automate annotation and labelling. The reinforcement agent learns to annotate alarm data based on annotations done by an expert. Our method shows promising results on medical alarm data sets. We trained DQN and A2C agents using the data from monitoring devices annotated by an expert. Initial results from these RL agents learning the expert annotation behavior are promising. The A2C agent performs better in terms of learning the sparse events in a given state, thereby choosing more right actions compared to DQN agent. To the best of our knowledge, this is the first reinforcement learning application for the automation of medical events annotation, which has far-reaching practical use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s42979-020-00356-z\", \"primary_category\": \"eess.SP\", \"categories\": [\"cs.AI\", \"cs.LG\", \"eess.SP\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Signal Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"229623\", \"242211\", \"268399\", \"306647\"]}","task_split":"paper_retrieval"}
{"document_id":"163671","document_content":"# Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nThe blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/D19-5004\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"44215\", \"94503\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"163715","document_content":"# SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWe propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which we collected human summaries. In this task, five-sentence stories (paragraphs) are summarized with one sentence, using human summaries only for evaluation. We show results for extractive and human baselines to demonstrate a large abstractive gap in performance. Our model, SummAE, consists of a denoising auto-encoder that embeds sentences and paragraphs in a common space, from which either can be decoded. Summaries for paragraphs are generated by decoding a sentence from the paragraph representations. We find that traditional sequence-to-sequence auto-encoders fail to produce good summaries and describe how specific architectural choices and pre-training techniques can significantly improve performance, outperforming extractive baselines. The data, training, evaluation code, and best model weights are open-sourced.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00998v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"16590\", \"149602\", \"44858\", \"80738\", \"168499\", \"122375\"], \"outgoing_citations\": [\"170449\", \"180935\", \"182486\", \"185889\", \"187452\", \"191777\", \"214407\", \"215243\", \"218755\", \"220130\", \"220230\", \"234164\", \"234807\", \"251816\", \"251974\", \"265309\", \"272531\", \"280884\", \"286225\", \"295128\", \"298750\"]}","task_split":"paper_retrieval"}
{"document_id":"163734","document_content":"# Privacy-preserving Federated Brain Tumour Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDue to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00962v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9036\", \"24894\", \"37270\", \"174906\", \"1973\", \"4079\", \"30344\", \"36865\", \"39697\", \"49221\", \"60494\", \"71429\", \"90200\", \"99336\", \"110259\", \"111117\", \"2051\", \"4119\", \"4571\", \"6737\", \"7205\", \"7979\", \"8081\", \"10455\", \"19599\", \"21197\", \"23249\", \"27798\", \"28782\", \"29660\", \"34937\", \"37025\", \"37227\", \"41753\", \"42524\", \"46926\", \"47655\", \"49219\", \"49559\", \"50294\", \"51220\", \"53537\", \"54391\", \"54543\", \"63956\", \"64978\", \"64980\", \"65192\", \"65658\", \"68872\", \"71073\", \"72995\", \"73648\", \"74184\", \"78786\", \"80069\", \"85147\", \"94744\", \"102991\", \"105375\", \"108307\", \"124067\", \"83656\", \"86926\", \"98209\", \"100058\", \"102450\", \"102793\", \"105490\", \"109503\", \"110306\", \"118615\", \"129815\", \"131944\", \"136201\", \"146915\", \"178321\"], \"outgoing_citations\": [\"187061\", \"211547\", \"212743\", \"214805\", \"273221\", \"297077\"]}","task_split":"paper_retrieval"}
{"document_id":"163770","document_content":"# Language is Power: Representing States Using Natural Language in Reinforcement Learning\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning methods tend to struggle. To overcome this, we explore methods for representing the semantic information embedded in the state. While previous methods focused on information in its raw form (e.g., raw visual input), we propose to represent the state using natural language. Language can represent complex scenarios and concepts, making it a favorable candidate for representation. Empirical evidence, within the domain of ViZDoom, suggests that natural language based agents are more robust, converge faster and perform better than vision based agents, showing the benefit of using natural language representations for reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.02789v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"95814\"], \"outgoing_citations\": [\"161163\", \"182870\", \"184995\", \"200321\", \"215181\", \"226321\", \"256948\", \"257563\", \"264549\", \"291019\", \"293071\", \"310639\"]}","task_split":"paper_retrieval"}
{"document_id":"163851","document_content":"# Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction\n## Categories\n- Robotics\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nTo interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additional sources of information (i.e., context) over time, gaze, movement and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e., deterministic) criterion. The main contributions of this paper are four-fold: (i) We propose a novel and effective decision-making criterion that can be used to anticipate actions even in situations of high ambiguity; (ii) we propose a deep architecture that outperforms previous results in the action anticipation task when using the Acticipate collaborative dataset; (iii) we show that contextual information is important to disambiguate the interpretation of similar actions; and (iv) we also provide a formal description of three existing performance metrics that can be easily used to evaluate action anticipation models.Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00714v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9520\", \"75719\"], \"outgoing_citations\": [\"141043\", \"155688\", \"180615\", \"188407\", \"199712\", \"203188\", \"222678\", \"223501\", \"226421\", \"227837\", \"240531\", \"242940\", \"271003\", \"278909\", \"306486\", \"311571\", \"312534\", \"314670\", \"330318\"]}","task_split":"paper_retrieval"}
{"document_id":"163867","document_content":"# Accelerated Robot Learning via Human Brain Signals\n## Categories\n- Robotics\n## Abstract\nIn reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning signal is mostly zeros. In contrast, humans are good at assessing and predicting the future consequences of actions and can serve as good reward\/policy shapers to accelerate the robot learning process. Previous works have shown that the human brain generates an error-related signal, measurable using electroencephelography (EEG), when the human perceives the task being done erroneously. In this work, we propose a method that uses evaluative feedback obtained from human brain signals measured via scalp EEG to accelerate RL for robotic agents in sparse reward settings. As the robot learns the task, the EEG of a human observer watching the robot attempts is recorded and decoded into noisy error feedback signal. From this feedback, we use supervised learning to obtain a policy that subsequently augments the behavior policy and guides exploration in the early stages of RL. This bootstraps the RL learning process to enable learning from sparse reward. Using a robotic navigation task as a test bed, we show that our method achieves a stable obstacle-avoidance policy with high success rate, outperforming learning from sparse rewards only that struggles to achieve obstacle avoidance behavior or fails to advance to the goal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00682v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"43510\", \"5616\", \"35842\", \"106567\"], \"outgoing_citations\": [\"219658\", \"224223\", \"254581\", \"254596\", \"259348\", \"260018\", \"263957\", \"290804\"]}","task_split":"paper_retrieval"}
{"document_id":"163902","document_content":"# Better Document-Level Machine Translation with Bayes' Rule\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the \"reverse translation probability\" of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model's independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00553v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"21968\", \"93450\", \"44069\", \"46157\", \"62145\", \"71469\", \"73265\", \"91646\", \"95128\", \"137310\", \"87054\", \"106686\", \"152729\", \"150615\"], \"outgoing_citations\": [\"157631\", \"168854\", \"169255\", \"171101\", \"171617\", \"176008\", \"176177\", \"183694\", \"186239\", \"187236\", \"187452\", \"194056\", \"194334\", \"197273\", \"197709\", \"198185\", \"200292\", \"210165\", \"215003\", \"219083\", \"220017\", \"230556\", \"234807\", \"238143\", \"249315\", \"250879\", \"251684\", \"251816\", \"251974\", \"258163\", \"259145\", \"259706\", \"268684\", \"268909\", \"280916\", \"283905\", \"289589\", \"290402\", \"316102\", \"349514\"]}","task_split":"paper_retrieval"}
{"document_id":"164032","document_content":"# Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Systems and Control\n- Systems and Control\n## Abstract\nThis paper describes a purely data-driven solution to a class of sequential decision-making problems with a large number of concurrent online decisions, with applications to computing systems and operations research. We assume that while the micro-level behaviour of the system can be broadly captured by analytical expressions or simulation, the macro-level or emergent behaviour is complicated by non-linearity, constraints, and stochasticity. If we represent the set of concurrent decisions to be computed as a vector, each element of the vector is assumed to be a continuous variable, and the number of such elements is arbitrarily large and variable from one problem instance to another. We first formulate the decision-making problem as a canonical reinforcement learning (RL) problem, which can be solved using purely data-driven techniques. We modify a standard approach known as advantage actor critic (A2C) to ensure its suitability to the problem at hand, and compare its performance to that of baseline approaches on the specific instance of a multi-product inventory management task. The key modifications include a parallelised formulation of the decision-making task, and a training procedure that explicitly recognises the quantitative relationship between different decisions. We also present experimental results probing the learned policies, and their robustness to variations in the data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.00211v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"200682\", \"238967\", \"249438\", \"254596\", \"258834\", \"270925\", \"282884\"]}","task_split":"paper_retrieval"}
{"document_id":"164141","document_content":"# RLCache: Automated Cache Management Using Reinforcement Learning\n## Categories\n- Machine Learning\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThis study investigates the use of reinforcement learning to guide a general purpose cache manager decisions. Cache managers directly impact the overall performance of computer systems. They govern decisions about which objects should be cached, the duration they should be cached for, and decides on which objects to evict from the cache if it is full. These three decisions impact both the cache hit rate and size of the storage that is needed to achieve that cache hit rate. An optimal cache manager will avoid unnecessary operations, maximise the cache hit rate which results in fewer round trips to a slower backend storage system, and minimise the size of storage needed to achieve a high hit-rate.   This project investigates using reinforcement learning in cache management by designing three separate agents for each of the cache manager tasks. Furthermore, the project investigates two advanced reinforcement learning architectures for multi-decision problems: a single multi-task agent and a multi-agent. We also introduce a framework to simplify the modelling of computer systems problems as a reinforcement learning task. The framework abstracts delayed experiences observations and reward assignment in computer systems while providing a flexible way to scale to multiple agents.   Simulation results based on an established database benchmark system show that reinforcement learning agents can achieve a higher cache hit rate over heuristic driven algorithms while minimising the needed space. They are also able to adapt to a changing workload and dynamically adjust their caching strategy accordingly. The proposed cache manager model is generic and applicable to other types of caches, such as file system caches. This project is the first, to our knowledge, to model cache manager decisions as a multi-task control problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13839v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.DC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"22448\"], \"outgoing_citations\": [\"201063\", \"213511\", \"218124\", \"220513\", \"240208\", \"244859\", \"248359\", \"281549\", \"297268\"]}","task_split":"paper_retrieval"}
{"document_id":"164205","document_content":"# Towards Robust Direct Perception Networks for Automated Driving\n## Categories\n- Machine Learning\n- Software Engineering\n## Abstract\nWe consider the problem of engineering robust direct perception neural networks with output being regression. Such networks take high dimensional input image data, and they produce affordances such as the curvature of the upcoming road segment or the distance to the front vehicle. Our proposal starts by allowing a neural network prediction to deviate from the label with tolerance $\\Delta$. The source of tolerance can be either contractual or from limiting factors where two entities may label the same data with slightly different numerical values. The tolerance motivates the use of a non-standard loss function where the loss is set to $0$ so long as the prediction-to-label distance is less than $\\Delta$. We further extend the loss function and define a new provably robust criterion that is parametric to the allowed output tolerance $\\Delta$, the layer index $\\tilde{l}$ where perturbation is considered, and the maximum perturbation amount $\\kappa$. During training, the robust loss is computed by first propagating symbolic errors from the $\\tilde{l}$-th layer (with quantity bounded by $\\kappa$) to the output layer, followed by computing the overflow between the error bounds and the allowed tolerance. The overall concept is experimented in engineering a direct perception neural network for understanding the central position of the ego-lane in pixel coordinates.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13600v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Software Engineering\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179056\", \"181746\", \"197501\", \"227720\", \"229749\", \"232434\", \"233055\", \"233680\", \"233865\", \"234202\", \"238640\", \"239405\", \"242506\", \"244005\", \"251596\", \"251696\", \"252103\", \"255440\", \"263089\", \"266128\", \"267392\", \"267747\", \"274778\", \"282181\", \"313499\"]}","task_split":"paper_retrieval"}
{"document_id":"164216","document_content":"# Meta-learning algorithms for Few-Shot Computer Vision\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nFew-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we can teach these algorithms to solve new, unseen tasks. This document reports my work on meta-learning algorithms for Few-Shot Computer Vision. This work was done during my internship at Sicara, a French company building image recognition solutions for businesses. It contains: 1. an extensive review of the state-of-the-art in few-shot computer vision; 2. a benchmark of meta-learning algorithms for few-shot image classification; 3. the introduction to a novel meta-learning algorithm for few-shot object detection, which is still in development.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13579v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"194150\", \"207193\", \"231222\", \"240039\", \"240519\", \"244924\", \"250707\", \"250796\", \"259762\", \"261577\", \"273803\", \"290800\"]}","task_split":"paper_retrieval"}
{"document_id":"164256","document_content":"# Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nIn recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from overfitting when the amount of training data is limited. This is a serious issue for low-resource language pairs and many specialized translation domains that are inherently limited in the amount of available supervised data. For this reason, in this paper we propose regressing word (ReWE) and sentence (ReSE) embeddings at training time as a way to regularize NMT models and improve their generalization. During training, our models are trained to jointly predict categorical (words in the vocabulary) and continuous (word and sentence embeddings) outputs. An extensive set of experiments over four language pairs of variable training set size has showed that ReWE and ReSE can outperform strong state-of-the-art baseline models, with an improvement that is larger for smaller training sets (e.g., up to +5:15 BLEU points in Basque-English translation). Visualizations of the decoder's output space show that the proposed regularizers improve the clustering of unique words, facilitating correct predictions. In a final experiment on unsupervised NMT, we show that ReWE and ReSE are also able to improve the quality of machine translation when no parallel data are available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13466v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"192272\", \"192328\", \"194056\", \"204715\", \"206516\", \"216925\", \"221557\", \"222945\", \"232192\", \"232260\", \"233817\", \"234377\", \"234807\", \"251974\", \"262535\", \"263764\", \"263927\", \"267601\", \"287823\", \"302815\", \"310737\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"164301","document_content":"# Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nAccurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work treats the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving history to form a spatio-temporal graph, and uses methods from Graph Neural Networks to solve the problem. Not only is our approach a pioneering attempt in using non-Euclidean methods to process static environmental features around a predicted object, our model also outperforms other state-of-the-art models in several metrics. The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13377v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"7062\", \"7145\", \"14957\", \"52254\", \"59041\", \"61618\", \"101535\", \"112768\", \"143953\"], \"outgoing_citations\": [\"157689\", \"196132\", \"211345\", \"221569\", \"229333\", \"232095\", \"237350\", \"241801\", \"244314\", \"248734\", \"253435\", \"263488\", \"265371\", \"279026\", \"303203\"]}","task_split":"paper_retrieval"}
{"document_id":"164306","document_content":"# Unfolding the Structure of a Document using Deep Learning\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nUnderstanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports, poses challenges not present in short documents. Such large documents may be multi-themed, complex, noisy and cover diverse topics. We describe a framework that can analyze large documents and help people and computer systems locate desired information in them. We aim to automatically identify and classify different sections of documents and understand their purpose within the document. A key contribution of our research is modeling and extracting the logical and semantic structure of electronic documents using deep learning techniques. We evaluate the effectiveness and robustness of our framework through extensive experiments on two collections: more than one million scholarly articles from arXiv and a collection of requests for proposal documents from government sources.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.03678v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"223592\", \"257043\", \"290092\", \"297955\", \"301996\", \"317966\", \"339810\"]}","task_split":"paper_retrieval"}
{"document_id":"164402","document_content":"# The Source-Target Domain Mismatch Problem in Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nWhile we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that particularly in low resource settings this causes the domains of the source and target language to greatly mismatch, as the two languages are often spoken in further apart regions of the world with more distinctive cultural traits and unrelated local events. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence corroborating our intuition that organic text produced by people speaking very different languages exhibits the most dramatic differences. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems for low resource language pairs. In particular, we find that it severely affects back-translation, but the degradation can be alleviated by combining back-translation with self-training and by increasing the relative amount of target side monolingual data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13151v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18194\", \"44273\", \"92489\", \"86571\", \"106707\", \"157177\", \"157719\", \"161599\"], \"outgoing_citations\": [\"161599\", \"164156\", \"179900\", \"180006\", \"180654\", \"193488\", \"219439\", \"220017\", \"221514\", \"228431\", \"232631\", \"234469\", \"234807\", \"240486\", \"259541\", \"269960\", \"277537\", \"302824\", \"358962\"]}","task_split":"paper_retrieval"}
{"document_id":"164412","document_content":"# Feature Level Fusion from Facial Attributes for Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce a deep convolutional neural networks (CNN) architecture to classify facial attributes and recognize face images simultaneously via a shared learning paradigm to improve the accuracy for facial attribute prediction and face recognition performance. In this method, we use facial attributes as an auxiliary source of information to assist CNN features extracted from the face images to improve the face recognition performance. Specifically, we use a shared CNN architecture that jointly predicts facial attributes and recognize face images simultaneously via a shared learning parameters, and then we use facial attribute features an an auxiliary source of information concatenated by face features to increase the discrimination of the CNN for face recognition. This process assists the CNN classifier to better recognize face images. The experimental results show that our model increases both the face recognition and facial attribute prediction performance, especially for the identity attributes such as gender and race. We evaluated our method on several standard datasets labeled by identities and face attributes and the results show that the proposed method outperforms state-of-the-art face recognition models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13126v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"104688\", \"172947\", \"191383\", \"198664\", \"199348\", \"214505\", \"234787\", \"286574\", \"287888\", \"298448\", \"310635\", \"337553\"]}","task_split":"paper_retrieval"}
{"document_id":"164781","document_content":"# Improving Fine-grained Entity Typing with Entity Linking\n## Categories\n- Computation and Language\n## Abstract\nFine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5\\% absolute strict accuracy improvement over the state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.12079v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16195\", \"24038\", \"26940\", \"43879\", \"130332\", \"132948\"], \"outgoing_citations\": [\"177232\", \"190739\", \"224723\", \"224786\", \"239564\", \"268728\", \"273421\", \"291175\", \"296825\", \"298157\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"164896","document_content":"# SIM: A Slot-Independent Neural Model for Dialogue State Tracking\n## Categories\n- Computation and Language\n## Abstract\nDialogue state tracking is an important component in task-oriented dialogue systems to identify users' goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model size of previous models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.11833v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"35820\", \"78006\"], \"outgoing_citations\": [\"224315\", \"230137\", \"235207\", \"246324\", \"290588\", \"294494\", \"309654\", \"311571\"]}","task_split":"paper_retrieval"}
{"document_id":"165103","document_content":"# Question Answering is a Format; When is it Useful?\n## Categories\n- Computation and Language\n## Abstract\nRecent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to image and video understanding. With this expansion, there are many differing views on the utility and definition of \"question answering\" itself. Some argue that its scope should be narrow, or broad, or that it is overused in datasets today. In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself. We discuss when a task is correctly described as question answering, and when a task is usefully posed as question answering, instead of using some other format.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.11291v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"9230\", \"15847\", \"37451\", \"41924\", \"49190\", \"46390\", \"56563\", \"92420\", \"125364\", \"126346\", \"127678\", \"61111\", \"130744\", \"137436\", \"137554\", \"143508\", \"149018\", \"158744\", \"157143\"], \"outgoing_citations\": [\"178032\", \"178327\", \"182036\", \"183076\", \"183403\", \"184848\", \"186405\", \"189515\", \"196980\", \"197653\", \"200804\", \"208526\", \"211788\", \"214421\", \"218698\", \"227323\", \"232123\", \"234628\", \"250313\", \"260450\", \"263874\", \"266850\", \"267790\", \"268671\", \"286757\", \"310639\"]}","task_split":"paper_retrieval"}
{"document_id":"165201","document_content":"# Assessing the Lexico-Semantic Relational Knowledge Captured by Word and Concept Embeddings\n## Categories\n- Computation and Language\n## Abstract\nDeep learning currently dominates the benchmarks for various NLP tasks and, at the basis of such systems, words are frequently represented as embeddings --vectors in a low dimensional space-- learned from large text corpora and various algorithms have been proposed to learn both word and concept embeddings. One of the claimed benefits of such embeddings is that they capture knowledge about semantic relations. Such embeddings are most often evaluated through tasks such as predicting human-rated similarity and analogy which only test a few, often ill-defined, relations. In this paper, we propose a method for (i) reliably generating word and concept pair datasets for a wide number of relations by using a knowledge graph and (ii) evaluating to what extent pre-trained embeddings capture those relations. We evaluate the approach against a proprietary and a public knowledge graph and analyze the results, showing which lexico-semantic relational knowledge is captured by current embedding learning approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.11042v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"236681\", \"280288\", \"286416\", \"291019\", \"298805\", \"311845\", \"316630\", \"334307\"]}","task_split":"paper_retrieval"}
{"document_id":"165268","document_content":"# Deep Text Mining of Instagram Data Without Strong Supervision\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- 68T50\n## Abstract\nWith the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text.   With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/WI.2018.00-94\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"68T50\"], \"incoming_citations\": [\"85426\"], \"outgoing_citations\": [\"249126\", \"264409\", \"280161\", \"291085\", \"291900\", \"309271\", \"311529\", \"321035\", \"349865\"]}","task_split":"paper_retrieval"}
{"document_id":"165350","document_content":"# Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy by appealing to a number of intuitive benefits, including learning over temporally extended transitions, exploring over temporally extended periods, and training and exploring in a more semantically meaningful action space, among others. However, in fully observed, Markovian settings, it is not immediately clear why hierarchical RL should provide benefits over standard \"shallow\" RL architectures. In this work, we isolate and evaluate the claimed benefits of hierarchical RL on a suite of tasks encompassing locomotion, navigation, and manipulation. Surprisingly, we find that most of the observed benefits of hierarchy can be attributed to improved exploration, as opposed to easier policy learning or imposed hierarchical structures. Given this insight, we present exploration techniques inspired by hierarchy that achieve performance competitive with hierarchical RL while at the same time being much simpler to use and implement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.10618v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"12555\", \"4134\", \"11364\", \"46165\", \"2483\", \"14806\", \"15368\", \"26464\", \"27991\", \"68878\", \"69685\", \"82278\", \"91231\", \"93649\", \"94561\", \"105429\", \"80731\", \"90966\", \"109485\", \"116671\", \"117297\", \"171112\"], \"outgoing_citations\": [\"171962\", \"215702\", \"231175\", \"248423\", \"251957\", \"252334\", \"261780\", \"262381\", \"264419\", \"269251\", \"271358\", \"272464\", \"272530\", \"284567\", \"294142\", \"298281\", \"334172\"]}","task_split":"paper_retrieval"}
{"document_id":"165396","document_content":"# Improving Generative Visual Dialog by Answering Diverse Questions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nPrior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach can lead to improved performance at the downstream dialog-conditioned image-guessing task. However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. We find that this is due in part to repeated interactions between Qbot and Abot during self-talk, which are not informative with respect to the image. To improve this, we devise a simple auxiliary objective that incentivizes Qbot to ask diverse questions, thus reducing repetitions and in turn enabling Abot to explore a larger state space during RL ie. be exposed to more visual concepts to talk about, and varied questions to answer. We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, ie. dialog that is more diverse (ie. less repetitive), consistent (ie. has fewer conflicting exchanges), fluent (ie. more human-like),and detailed, while still being comparably image-relevant as prior work and ablations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.10470v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"24280\", \"25574\", \"36066\", \"61872\", \"69973\", \"137555\", \"61874\", \"88937\", \"152659\", \"278894\"], \"outgoing_citations\": [\"197057\", \"217640\", \"229734\", \"242597\", \"262814\", \"263622\", \"271085\", \"279518\", \"279678\", \"291102\", \"296122\", \"302815\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"165425","document_content":"# GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries\n## Categories\n- Computation and Language\n## Abstract\nMonitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neural networks and pre-trained language models. We also explore the effect of augmenting word representations with semantic features for clinical named entity recognition. Our feature-augmented BiLSTM-CRF model performed with F1-score of 92.67% and ranked 4th for entity extraction sub-task among submitted systems to n2c2 challenge. The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%. The augmentation of word vectors with semantic features extracted using available clinical NLP toolkits can further improve the performance. Word embeddings that are pre-trained on a large unannotated corpus of relevant documents and further fine-tuned to the task perform rather well. However, the augmentation of word embeddings with semantic features can help improve the performance (primarily by boosting precision) of drug-related named entity recognition from electronic health records.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.10390v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"184059\", \"185889\", \"281590\", \"287367\", \"289319\"]}","task_split":"paper_retrieval"}
{"document_id":"165708","document_content":"# Controlling Assistive Robots with Learned Latent Actions\n## Categories\n- Robotics\n## Abstract\nAssistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms useful also makes them challenging to teleoperate: the robot has more degrees-of-freedom than the human can directly coordinate with a handheld joystick. Our insight is that we can make assistive robots easier for humans to control by leveraging latent actions. Latent actions provide a low-dimensional embedding of high-dimensional robot behavior: for example, one latent dimension might guide the assistive arm along a pouring motion. In this paper, we design a teleoperation algorithm for assistive robots that learns latent actions from task demonstrations. We formulate the controllability, consistency, and scaling properties that user-friendly latent actions should have, and evaluate how different low-dimensional embeddings capture these properties. Finally, we conduct two user studies on a robotic arm to compare our latent action approach to both state-of-the-art shared autonomy baselines and a teleoperation strategy currently used by assistive arms. Participants completed assistive eating and cooking tasks more efficiently when leveraging our latent actions, and also subjectively reported that latent actions made the task easier to perform. The video accompanying this paper can be found at: https:\/\/youtu.be\/wjnhrzugBj4.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09674v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"7954\", \"20775\", \"37110\", \"73900\", \"11352\", \"17668\", \"19732\", \"37671\", \"52173\", \"56269\", \"59809\", \"64007\", \"81984\", \"87472\", \"110257\", \"119110\", \"126543\", \"126773\", \"147399\", \"163661\"], \"outgoing_citations\": [\"196387\", \"200682\", \"220420\", \"222967\", \"228899\", \"231307\", \"243197\", \"264845\", \"290092\", \"310624\", \"315783\"]}","task_split":"paper_retrieval"}
{"document_id":"165745","document_content":"# Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser\n## Categories\n- Computation and Language\n## Abstract\nIn this work we explore a deep learning-based dialogue system that generates sarcastic and humorous responses from a conversation design perspective. We trained a seq2seq model on a carefully curated dataset of 3000 question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We show that end-to-end systems learn patterns very quickly from small datasets and thus, are able to transfer simple linguistic structures representing abstract concepts to unseen settings. We also deploy our LSTM-based encoder-decoder model in the browser, where users can directly interact with the chatbot. Human raters evaluated linguistic quality, creativity and human-like traits, revealing the system's strengths, limitations and potential for future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09531v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"264189\", \"269757\", \"295863\", \"296250\", \"301313\", \"309411\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"165769","document_content":"# Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n## Abstract\nOne of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce good representations in combination with aggregation methods, appropriate fine tuning for the task of image retrieval has shown to significantly boost retrieval performance. In this paper, we present a simple yet effective supervised aggregation method built on top of existing regional pooling approaches. In addition to the maximum activation of a given region, we calculate regional average activations of extracted feature maps. Subsequently, weights for each of the pooled feature vectors are learned to perform a weighted aggregation to a single feature vector. Furthermore, we apply our newly proposed NRA loss function for deep metric learning to fine tune the backbone neural network and to learn the aggregation weights. Our method achieves state-of-the-art results for the INRIA Holidays data set and competitive results for the Oxford Buildings and Paris data sets while reducing the training time significantly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09420v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"7251\"], \"outgoing_citations\": [\"165767\", \"227120\", \"251473\", \"266027\", \"277635\", \"278784\", \"281953\", \"303067\", \"304501\"]}","task_split":"paper_retrieval"}
{"document_id":"165835","document_content":"# Fine-grained Action Segmentation using the Semi-Supervised Action GAN\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream. The challenge for this task lies in the need to represent the hierarchical nature of the actions and to detect the transitions between actions, allowing us to localise the actions within the video effectively. We propose a novel recurrent semi-supervised Generative Adversarial Network (GAN) model for continuous fine-grained human action segmentation. Temporal context information is captured via a novel Gated Context Extractor (GCE) module, composed of gated attention units, that directs the queued context information through the generator model, for enhanced action segmentation. The GAN is made to learn features in a semi-supervised manner, enabling the model to perform action classification jointly with the standard, unsupervised, GAN learning procedure. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed network architecture, and show that it is capable of outperforming current state-of-the-art on three challenging datasets: 50 Salads, MERL Shopping and Georgia Tech Egocentric Activities dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09269v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"90\", \"82834\", \"15831\", \"107335\", \"87954\", \"158427\", \"87753\", \"126544\"], \"outgoing_citations\": [\"165828\", \"205612\", \"209564\", \"239572\", \"244483\", \"265273\", \"265791\", \"270529\", \"271912\", \"274548\", \"280050\", \"280328\", \"283007\", \"285861\", \"292922\", \"298629\", \"303174\", \"314271\"]}","task_split":"paper_retrieval"}
{"document_id":"165839","document_content":"# Towards Multimodal Understanding of Passenger-Vehicle Interactions in Autonomous Vehicles: Intent\/Slot Recognition Utilizing Audio-Visual Data\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nUnderstanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV). In this study, we continued exploring AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling certain multimodal passenger-vehicle interactions. When the passengers give instructions to AMIE, the agent should parse such commands properly considering available three modalities (language\/text, audio, video) and trigger the appropriate functionality of the AV system. We had collected a multimodal in-cabin dataset with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme via realistic scavenger hunt game. In our previous explorations, we experimented with various RNN-based models to detect utterance-level intents (set destination, change route, go faster, go slower, stop, park, pull over, drop off, open door, and others) along with intent keywords and relevant slots (location, position\/direction, object, gesture\/gaze, time-guidance, person) associated with the action to be performed in our AV scenarios. In this recent work, we propose to discuss the benefits of multimodal understanding of in-cabin utterances by incorporating verbal\/language input (text and speech embeddings) together with the non-verbal\/acoustic and visual input from inside and outside the vehicle (i.e., passenger gestures and gaze from in-cabin video stream, referred objects outside of the vehicle from the road view camera stream). Our experimental results outperformed text-only baselines and with multimodality, we achieved improved performances for utterance-level intent detection and slot filling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.13714v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.MM\", \"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Multimedia\", \"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189265\", \"206026\", \"237945\"]}","task_split":"paper_retrieval"}
{"document_id":"165873","document_content":"# Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nAlthough many methods have been proposed to deal with nature image super-resolution (SR) and get impressive performance, the text images SR is not good due to their ignorance of document images. In this paper, we propose a matting-based dual generative adversarial network (mdGAN) for document image SR. Firstly, the input image is decomposed into document text, foreground and background layers using deep image matting. Then two parallel branches are constructed to recover text boundary information and color information respectively. Furthermore, in order to improve the restoration accuracy of characters in output image, we use the input image's corresponding ground truth text label as extra supervise information to refine the two-branch networks during training. Experiments on real text images demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.12909v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"236344\", \"271779\", \"275067\", \"311645\"]}","task_split":"paper_retrieval"}
{"document_id":"165902","document_content":"# Responsible Facial Recognition and Beyond\n## Categories\n- Computer Vision and Pattern Recognition\n- Computers and Society\n## Abstract\nFacial recognition is changing the way we live in and interact with our society. Here we discuss the two sides of facial recognition, summarizing potential risks and current concerns. We introduce current policies and regulations in different countries. Very importantly, we point out that the risks and concerns are not only from facial recognition, but also realistically very similar to other biometric recognition technology, including but not limited to gait recognition, iris recognition, fingerprint recognition, voice recognition, etc. To create a responsible future, we discuss possible technological moves and efforts that should be made to keep facial recognition (and biometric recognition in general) developing for social good.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.12935v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CY\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computers and Society\"], \"incoming_citations\": [\"147177\"], \"outgoing_citations\": [\"205223\"]}","task_split":"paper_retrieval"}
{"document_id":"165972","document_content":"# Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nData augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a few intensive data augmentation techniques, which indeed improved accuracy, yet we notice that these methods augment data have also caused a considerable gap between clean and augmented data. In this paper, we revisit this problem from an analytical perspective, for which we estimate the upper-bound of expected risk using two terms, namely, empirical risk and generalization error, respectively. We develop an understanding of data augmentation as regularization, which highlights the major features. As a result, data augmentation significantly reduces the generalization error, but meanwhile leads to a slightly higher empirical risk. On the assumption that data augmentation helps models converge to a better region, the model can benefit from a lower empirical risk achieved by a simple method, i.e., using less-augmented data to refine the model trained on fully-augmented data. Our approach achieves consistent accuracy gain on a few standard image classification benchmarks, and the gain transfers to object detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09148v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"2532\", \"43871\", \"90519\", \"11056\", \"39461\", \"41974\", \"47747\", \"57257\", \"63911\", \"75865\", \"75926\", \"76426\", \"90513\", \"94013\", \"120684\", \"134997\", \"139728\", \"141037\", \"217374\"], \"outgoing_citations\": [\"164734\", \"183417\", \"186466\", \"187243\", \"188176\", \"188525\", \"194378\", \"207432\", \"218795\", \"228120\", \"238777\", \"246926\", \"249085\", \"249196\", \"249463\", \"252107\", \"256813\", \"265872\", \"270643\", \"307136\", \"312178\", \"312931\"]}","task_split":"paper_retrieval"}
{"document_id":"165981","document_content":"# Social and Scene-Aware Trajectory Prediction in Crowded Spaces\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars. Autonomous systems may gain advantage on anticipating human motion to avoid collisions or to naturally behave alongside people. To foresee plausible trajectories, we construct an LSTM (long short-term memory)-based model considering three fundamental factors: people interactions, past observations in terms of previously crossed areas and semantics of surrounding space. Our model encompasses several pooling mechanisms to join the above elements defining multiple tensors, namely social, navigation and semantic tensors. The network is tested in unstructured environments where complex paths emerge according to both internal (intentions) and external (other people, not accessible areas) motivations. As demonstrated, modeling paths unaware of social interactions or context information, is insufficient to correctly predict future positions. Experimental results corroborate the effectiveness of the proposed framework in comparison to LSTM-based models for human path prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08840v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"10133\", \"39639\", \"66786\", \"100535\", \"113579\", \"144528\", \"28894\", \"32241\", \"38000\", \"139151\", \"59257\", \"88879\", \"124545\", \"153627\", \"87955\", \"94863\", \"94978\", \"114292\", \"120882\", \"127485\", \"144247\"], \"outgoing_citations\": [\"229266\", \"233526\", \"238796\", \"249164\", \"253435\", \"273754\", \"296086\", \"311683\"]}","task_split":"paper_retrieval"}
{"document_id":"165983","document_content":"# How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing\n## Categories\n- Computation and Language\n## Abstract\nUnder special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08837v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"733\", \"84343\", \"14774\", \"23052\", \"31148\", \"42365\", \"80697\", \"86983\", \"177966\", \"88937\", \"95033\", \"125951\", \"137534\"], \"outgoing_citations\": [\"183270\", \"206114\", \"211838\", \"216384\", \"217881\", \"220230\", \"225558\", \"227586\", \"231869\", \"231949\", \"232261\", \"233710\", \"241070\", \"254855\", \"259563\", \"266671\", \"280557\", \"296198\"]}","task_split":"paper_retrieval"}
{"document_id":"165999","document_content":"# Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTo learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real\/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimising the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08797v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"248981\", \"261512\", \"264911\", \"265203\", \"268405\", \"268494\", \"268734\", \"268970\", \"270105\", \"272345\", \"274313\", \"274550\", \"275350\", \"277059\", \"277386\", \"277837\", \"277856\", \"281453\", \"284986\", \"294284\", \"299975\", \"303724\", \"310922\", \"314758\", \"316183\", \"318091\", \"320918\", \"321117\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"166041","document_content":"# Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nPersonal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expensive and time consuming to collect. The ability to incorporate unsupervised, weakly supervised, or distantly supervised data holds significant promise in overcoming this bottleneck. In this paper, we describe a framework that leverages user engagement signals (user behaviors that demonstrate a positive or negative response to content) to automatically create granular entity labels for training data augmentation. Strategies such as multi-task learning and validation using an external knowledge base are employed to incorporate the engagement annotated data and to boost the model's accuracy on a sequence labeling task. Our results show that learning from data automatically labeled by user engagement signals achieves significant accuracy gains in a production deep learning system, when measured on both the sequence labeling task as well as on user facing results produced by the system end-to-end. We believe this is the first use of user engagement signals to help generate training data for a sequence labeling task on a large scale, and can be applied in practical settings to speed up new feature deployment when little human annotated data is available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.09143v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"20718\", \"91950\", \"119635\", \"202381\"], \"outgoing_citations\": [\"197767\", \"285047\"]}","task_split":"paper_retrieval"}
{"document_id":"166043","document_content":"# CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNatural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - intent classification (IC) and slot labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context management to DM. However, contextual information is critical to the correct prediction of intents and slots in a conversation. Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals, such as previous intents, slots, dialog acts and utterances over a variable context window, in addition to the current user utterance. CASA-NLU outperforms a recurrent contextual NLU baseline on two conversational datasets, yielding a gain of up to 7% on the IC task for one of the datasets. Moreover, a non-contextual variant of CASA-NLU achieves state-of-the-art performance for IC task on standard public datasets - Snips and ATIS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08705v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"74469\", \"3386\", \"26015\", \"42875\", \"103709\", \"136051\"], \"outgoing_citations\": [\"205014\", \"213349\", \"230543\", \"231442\", \"254576\", \"255999\", \"285311\", \"285317\", \"291916\", \"294494\"]}","task_split":"paper_retrieval"}
{"document_id":"166082","document_content":"# Fine-Tuning Language Models from Human Preferences\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nReward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks. In this paper, we build on advances in generative pretraining of language models to apply reward learning to four natural language tasks: continuing text with positive sentiment or physically descriptive language, and summarization tasks on the TL;DR and CNN\/Daily Mail datasets. For stylistic continuation we achieve good results with only 5,000 comparisons evaluated by humans. For summarization, models trained with 60,000 comparisons copy whole sentences from the input but skip irrelevant preamble; this leads to reasonable ROUGE scores and very good performance according to our human labelers, but may be exploiting the fact that labelers rely on simple heuristics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08593v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"41812\", \"2601\", \"4498\", \"5467\", \"5923\", \"6697\", \"31927\", \"61976\", \"89936\", \"2650\", \"3319\", \"9714\", \"15943\", \"17298\", \"18102\", \"21191\", \"21737\", \"22231\", \"23521\", \"24581\", \"25727\", \"26086\", \"26417\", \"30094\", \"30826\", \"30911\", \"31590\", \"32981\", \"37086\", \"43457\", \"44007\", \"45297\", \"47753\", \"56830\", \"56832\", \"61157\", \"61917\", \"63484\", \"72937\", \"73767\", \"73757\", \"78031\", \"79155\", \"89002\", \"100711\", \"109884\", \"120814\", \"120964\", \"77716\", \"84960\", \"86969\", \"145834\", \"92280\", \"94145\", \"94451\", \"94486\", \"94846\", \"94898\", \"95588\", \"98642\", \"98838\", \"102649\", \"104184\", \"108688\", \"110240\", \"112729\", \"115152\", \"120280\", \"122375\", \"125091\", \"140642\", \"152270\", \"152729\", \"178278\"], \"outgoing_citations\": [\"167137\", \"168908\", \"170449\", \"173805\", \"176100\", \"178278\", \"181620\", \"181743\", \"182023\", \"185326\", \"202748\", \"209453\", \"211946\", \"213652\", \"219618\", \"229145\", \"230362\", \"235041\", \"260420\", \"263957\", \"266671\", \"269654\", \"278324\", \"279215\", \"280866\", \"302815\", \"303997\", \"311119\"]}","task_split":"paper_retrieval"}
{"document_id":"166142","document_content":"# A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWith a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic. We discuss the stylistically expressive elements within each of these levels and use existing methods to quantify the linguistic intuitions related to some of these elements. We show that such a multi-level analysis is useful for developing a well-knit understanding of style - which is independent of the natural language task at hand, and also demonstrate its value in solving three downstream tasks: authors' style analysis, authorship attribution, and emotion prediction. We conduct experiments on a variety of datasets, comprising texts from social networking sites, user reviews, legal documents, literary books, and newswire. The results on the aforementioned tasks and datasets illustrate that such a multi-level understanding of style, which has been largely ignored in recent works, models style-related subjectivity in text and can be leveraged to improve performance on multiple downstream tasks both qualitatively and quantitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08349v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"21191\", \"72937\", \"92280\", \"96343\", \"165585\"], \"outgoing_citations\": [\"186381\", \"221525\", \"222353\", \"231706\", \"232783\", \"234391\", \"235238\", \"250035\", \"258758\", \"262088\", \"265309\", \"338893\", \"340958\", \"340964\"]}","task_split":"paper_retrieval"}
{"document_id":"166184","document_content":"# Reasoning in Highly Reactive Environments\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nThe aim of my Ph.D. thesis concerns Reasoning in Highly Reactive Environments. As reasoning in highly reactive environments, we identify the setting in which a knowledge-based agent, with given goals, is deployed in an environment subject to repeated, sudden and possibly unknown changes. This is for instance the typical setting in which, e.g., artificial agents for video-games (the so called \"bots\"), cleaning robots, bomb clearing robots, and so on are deployed. In all these settings one can follow the classical approach in which the operations of the agent are distinguished in \"sensing\" the environment with proper interface devices, \"thinking\", and then behaving accordingly using proper actuators. In order to operate in an highly reactive environment, an artificial agent needs to be: 1. Responsive -> The agent must be able to react repeatedly and in a reasonable amount of time; 2. Elastic -> The agent must stay reactive also under varying workload; 3. Resilient -> The agent must stay responsive also in case of internal failure or failure of one of the programmed actions in the environment. Nowadays, thanks to new technologies in the field of Artificial Intelligence, it is already technically possible to create AI agents that are able to operate in reactive environments. Nevertheless, several issues stay unsolved, and are subject of ongoing research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.306.57\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"175078\", \"240572\", \"257674\", \"261014\", \"265261\", \"284905\"]}","task_split":"paper_retrieval"}
{"document_id":"166254","document_content":"# AdaCompress: Adaptive Compression for Online Computer Vision Services\n## Categories\n- Multimedia\n- Image and Video Processing\n## Abstract\nWith the growth of computer vision based applications and services, an explosive amount of images have been uploaded to cloud servers which host such computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the {\\em de facto} compression and encapsulation method before one uploads the images, due to its wide adaptation. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model, e.g., the standard quality level of JPEG leads to 50\\% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models including InceptionNet, ResNet, etc. Knowing this, designing a better JPEG configuration for online computer vision services is still extremely challenging: 1) Cloud-based computer vision models are usually a black box to end-users; thus it is difficult to design JPEG configuration without knowing their model structures. 2) JPEG configuration has to change when different users use it. In this paper, we propose a reinforcement learning based JPEG configuration framework. In particular, we design an agent that adaptively chooses the compression level according to the input image's features and backend deep learning models. Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size. In our real-world evaluation on Amazon Rekognition, Face++ and Baidu Vision, our approach can reduce the size of images by 1\/2 -- 1\/3 while the overall classification accuracy only decreases slightly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3343031.3350874\", \"primary_category\": \"cs.MM\", \"categories\": [\"eess.IV\", \"cs.MM\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Image and Video Processing\", \"Multimedia\"], \"incoming_citations\": [\"15469\", \"158506\"], \"outgoing_citations\": [\"204822\", \"238760\", \"239049\", \"259984\", \"266304\", \"272675\", \"272839\", \"286385\", \"294611\", \"300845\", \"303010\", \"311257\", \"320127\"]}","task_split":"paper_retrieval"}
{"document_id":"166256","document_content":"# Identity-Aware Deep Face Hallucination via Adversarial Face Verification\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this paper, we address the problem of face hallucination by proposing a novel multi-scale generative adversarial network (GAN) architecture optimized for face verification. First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions. The intermediate outputs have the growing goal of synthesizing small to large images. Second, we incorporate a face verifier with the original GAN discriminator and propose a novel discriminator which learns to discriminate different identities while distinguishing fake generated HR face images from their ground truth images. In particular, the learned generator cares for not only the visual quality of hallucinated face images but also preserving the discriminative features in the hallucination process. In addition, to capture perceptually relevant differences we employ a perceptual similarity loss, instead of similarity in pixel space. We perform a quantitative and qualitative evaluation of our framework on the LFW and CelebA datasets. The experimental results show the advantages of our proposed method against the state-of-the-art methods on the 8x downsampled testing dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08130v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"76110\", \"36730\", \"97923\", \"127354\", \"82752\"], \"outgoing_citations\": [\"199348\", \"208150\", \"210191\", \"223755\", \"234787\", \"248019\", \"252448\", \"258886\", \"259118\", \"259660\", \"274838\", \"279655\", \"287888\", \"296033\", \"312178\"]}","task_split":"paper_retrieval"}
{"document_id":"166353","document_content":"# Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Multimedia\n## Abstract\nMost existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data. To address this issue, we introduce a new large-scale text reading benchmark dataset named Chinese Street View Text (C-SVT) with 430,000 street view images, which is at least 14 times as large as the existing Chinese text reading benchmarks. To recognize Chinese text in the wild while keeping large-scale datasets labeling cost-effective, we propose to annotate one part of the CSVT dataset (30,000 images) in locations and text labels as full annotations and add 400,000 more images, where only the corresponding text-of-interest in the regions is given as weak annotations. To exploit the rich information from the weakly annotated data, we design a text reading network in a partially supervised learning framework, which enables to localize and recognize text, learn from fully and weakly annotated data simultaneously. To localize the best matched text proposals from weakly labeled images, we propose an online proposal matching module incorporated in the whole model, spotting the keyword regions by sharing parameters for end-to-end training. Compared with fully supervised training algorithms, this model can improve the end-to-end recognition performance remarkably by 4.03% in F-score at the same labeling cost. The proposed model can also achieve state-of-the-art results on the ICDAR 2017-RCTW dataset, which demonstrates the effectiveness of the proposed partially supervised learning framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07808v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Multimedia\"], \"incoming_citations\": [\"84505\", \"9220\", \"25235\", \"63761\", \"64650\", \"113794\", \"83582\", \"99423\", \"111991\", \"126431\", \"134664\", \"140469\", \"210634\"], \"outgoing_citations\": [\"204872\", \"225195\", \"233440\", \"239002\", \"239535\", \"240477\", \"245591\", \"245899\", \"247460\", \"248133\", \"249060\", \"252159\", \"253398\", \"256711\", \"257286\", \"257954\", \"260012\", \"261365\", \"269205\", \"271112\", \"294023\", \"296685\", \"296828\", \"299461\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"166370","document_content":"# ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Multimedia\n## Abstract\nRobust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 50, 000 and 400, 000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing the gap between research benchmarks and real applications. During the competition period, a total of 41 teams participated in the two proposed tasks with 132 valid submissions, i.e., text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2019-LSVT challenge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07741v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Multimedia\"], \"incoming_citations\": [\"2918\", \"31775\", \"50917\", \"68564\", \"121818\", \"191\", \"36052\", \"50125\", \"59727\", \"64650\", \"81622\", \"150258\", \"111035\", \"120420\", \"126431\"], \"outgoing_citations\": [\"193382\", \"239535\", \"240477\", \"245591\", \"245970\", \"248133\", \"252159\", \"257286\", \"270668\", \"271112\", \"272390\", \"279950\", \"294023\", \"299461\"]}","task_split":"paper_retrieval"}
{"document_id":"166446","document_content":"# Learning to Generate Questions with Adaptive Copying Neural Networks\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nAutomatic question generation is an important problem in natural language processing. In this paper we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08187v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"267694\", \"269583\", \"274513\", \"281162\", \"281472\", \"290789\", \"296122\", \"296279\"]}","task_split":"paper_retrieval"}
{"document_id":"166461","document_content":"# Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Digital Libraries\n## Abstract\nThe trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.08191v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.DL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Digital Libraries\"], \"incoming_citations\": [\"37818\", \"115331\"], \"outgoing_citations\": [\"115331\", \"193499\", \"223711\", \"224202\", \"227492\", \"242267\"]}","task_split":"paper_retrieval"}
{"document_id":"166480","document_content":"# Hierarchical Reinforcement Learning for Open-Domain Dialog\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOpen-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text. Reinforcement Learning (RL) is a powerful framework that could potentially address these issues, for example by allowing a dialog model to optimize for reducing toxicity and repetitiveness. However, previous approaches which apply RL to open-domain dialog generation do so at the word level, making it difficult for the model to learn proper credit assignment for long-term conversational rewards. In this paper, we propose a novel approach to hierarchical reinforcement learning, VHRL, which uses policy gradients to tune the utterance-level embedding of a variational sequence model. This hierarchical approach provides greater flexibility for learning long-term, conversational rewards. We use self-play and RL to optimize for a set of human-centered conversation metrics, and show that our approach provides significant improvements -- in terms of both human evaluation and automatic metrics -- over state-of-the-art dialog models, including Transformers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07547v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"23614\", \"77439\", \"6978\", \"22124\", \"22368\", \"27403\", \"119299\", \"91095\", \"94846\", \"103709\", \"108688\", \"120468\", \"123235\", \"179568\"], \"outgoing_citations\": [\"171039\", \"177387\", \"178278\", \"179568\", \"182546\", \"191851\", \"191923\", \"192157\", \"197924\", \"202748\", \"205147\", \"206655\", \"218253\", \"224095\", \"231175\", \"232759\", \"234768\", \"235207\", \"237274\", \"239503\", \"243133\", \"249416\", \"255580\", \"256632\", \"257858\", \"259595\", \"262342\", \"263357\", \"266155\", \"269232\", \"270040\", \"272464\", \"275597\", \"284567\", \"287823\", \"291102\", \"292261\", \"293876\", \"294142\", \"295863\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"166494","document_content":"# Probing Natural Language Inference Models through Semantic Fragments\n## Categories\n- Computation and Language\n## Abstract\nDo state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena are involved in natural language inference (NLI) and go beyond basic linguistic understanding, it is unclear the extent to which they are captured in existing NLI benchmarks and effectively learned by models. To investigate this, we propose the use of semantic fragments---systematically generated datasets that each target a different semantic phenomenon---for probing, and efficiently improving, such capabilities of linguistic models. This approach to creating challenge datasets allows direct control over the semantic diversity and complexity of the targeted linguistic phenomena, and results in a more precise characterization of a model's linguistic behavior. Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task. (b) On the other hand, with only a few minutes of additional fine-tuning---with a carefully selected learning rate and a novel variation of \"inoculation\"---a BERT-based model can master all of these logic and monotonicity fragments while retaining its performance on established NLI benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07521v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2436\", \"2778\", \"4879\", \"6214\", \"32080\", \"91712\", \"5253\", \"13213\", \"18310\", \"18779\", \"20920\", \"22218\", \"23755\", \"42578\", \"43938\", \"45420\", \"45353\", \"45293\", \"49062\", \"54805\", \"57416\", \"58255\", \"61030\", \"66167\", \"73361\", \"77302\", \"100518\", \"127472\", \"79774\", \"81778\", \"88284\", \"88336\", \"92805\", \"95013\", \"95106\", \"96142\", \"96227\", \"97669\", \"100071\", \"117723\", \"119381\", \"122176\", \"124723\", \"126130\", \"127991\", \"131446\", \"132633\", \"127907\", \"139638\", \"125976\", \"142346\", \"148999\", \"149018\", \"160911\"], \"outgoing_citations\": [\"158350\", \"160911\", \"168415\", \"180652\", \"186418\", \"186873\", \"188708\", \"192195\", \"200362\", \"212270\", \"229524\", \"233045\", \"233416\", \"234628\", \"238321\", \"239831\", \"251911\", \"267161\", \"284357\", \"291019\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"166499","document_content":"# Short-Text Classification Using Unsupervised Keyword Expansion\n## Categories\n- Computation and Language\n## Abstract\nShort-text classification, like all data science, struggles to achieve high performance using limited data. As a solution, a short sentence may be expanded with new and relevant feature words to form an artificially enlarged dataset, and add new features to testing data. This paper applies a novel approach to text expansion by generating new words directly for each input sentence, thus requiring no additional datasets or previous training. In this unsupervised approach, new keywords are formed within the hidden states of a pre-trained language model and then used to create extended pseudo documents. The word generation process was assessed by examining how well the predicted words matched to topics of the input sentence. It was found that this method could produce 3-10 relevant new words for each target topic, while generating just 1 word related to each non-target topic. Generated words were then added to short news headlines to create extended pseudo headlines. Experimental results have shown that models trained using the pseudo headlines can improve classification accuracy when limiting the number of training examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07512v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"166518","document_content":"# Document classification methods\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nInformation on different fields which are collected by users requires appropriate management and organization to be structured in a standard way and retrieved fast and more easily. Document classification is a conventional method to separate text based on their subjects among scientific text, web pages and digital library. Different methods and techniques are proposed for document classifications that have advantages and deficiencies. In this paper, several unsupervised and supervised document classification methods are studied and compared.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07368v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"25516\", \"27401\", \"28479\", \"49891\", \"64244\", \"76324\", \"82174\", \"82788\", \"103928\", \"42292\", \"265\", \"428\", \"321\", \"1089\", \"2919\", \"3011\", \"3556\", \"6841\", \"9371\", \"10979\", \"13419\", \"16805\", \"18720\", \"18863\", \"5759\", \"26623\", \"27450\", \"27850\", \"32678\", \"38282\", \"39197\", \"39682\", \"40004\", \"41091\", \"42055\", \"48590\", \"51878\", \"55874\", \"58027\", \"59368\", \"68495\", \"70000\", \"70491\", \"73947\", \"81322\", \"85733\", \"88537\", \"93564\", \"97230\", \"99044\", \"99260\", \"99577\", \"108725\", \"113230\", \"122212\", \"125115\", \"129123\", \"131501\", \"152195\", \"212503\", \"93187\", \"312\", \"684\", \"1040\", \"1335\", \"1452\", \"3289\", \"3565\", \"3790\", \"5792\", \"86692\", \"8067\", \"8141\", \"9135\", \"9282\", \"9542\", \"11114\", \"12329\", \"16435\", \"16785\", \"17627\", \"19181\", \"19476\", \"19676\", \"20753\", \"20762\", \"22767\", \"22826\", \"22849\", \"23981\", \"24537\", \"24535\", \"24508\", \"25212\", \"25445\", \"26012\", \"26572\", \"27743\", \"28356\", \"28492\", \"29315\", \"29512\", \"23586\", \"30489\", \"30681\", \"30746\", \"32137\", \"33182\", \"33423\", \"33508\", \"34074\", \"35462\", \"35857\", \"36093\", \"38055\", \"38796\", \"39676\", \"39987\", \"40327\", \"41611\", \"42629\", \"42874\", \"43574\", \"44220\", \"44384\", \"44708\", \"44663\", \"45475\", \"45299\", \"45356\", \"45450\", \"45441\", \"45412\", \"45863\", \"45832\", \"44301\", \"46245\", \"46768\", \"46926\", \"48659\", \"47748\", \"49614\", \"50858\", \"51299\", \"118856\", \"53340\", \"53435\", \"54500\", \"54719\", \"55376\", \"55808\", \"55866\", \"55963\", \"56085\", \"56742\", \"56773\", \"58321\", \"59753\", \"60807\"], \"outgoing_citations\": [\"302425\", \"308310\", \"311849\", \"319833\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"166702","document_content":"# CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nSpoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize co-occurrence relations between slots and intents, which restricts their potential performance. To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block. The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations. Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other. We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS). Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS. Additionally, we make the CAIS dataset publicly available for the research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.06937v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"74469\", \"27450\", \"50552\", \"127897\", \"27362\", \"29403\", \"33513\", \"45038\", \"47388\", \"63968\", \"65237\", \"77774\", \"80865\", \"81520\", \"95751\", \"95759\", \"129831\", \"139378\"], \"outgoing_citations\": [\"194488\", \"197154\", \"204763\", \"205014\", \"210262\", \"230543\", \"232980\", \"259192\", \"285317\", \"290996\", \"291617\", \"300449\"]}","task_split":"paper_retrieval"}
{"document_id":"166762","document_content":"# State Representation Learning from Demonstration\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nRobots could learn their own state and world representation from perception and experience without supervision. This desirable goal is the main focus of our field of interest, state representation learning (SRL). Indeed, a compact representation of such a state is beneficial to help robots grasp onto their environment for interacting. The properties of this representation have a strong impact on the adaptive capability of the agent. In this article we present an approach based on imitation learning. The idea is to train several policies that share the same representation to reproduce various demonstrations. To do so, we use a multi-head neural network with a shared state representation feeding a task-specific agent. If the demonstrations are diverse, the trained representation will eventually contain the information necessary for all tasks, while discarding irrelevant information. As such, it will potentially become a compact state representation useful for new tasks. We call this approach SRLfD (State Representation Learning from Demonstration). Our experiments confirm that when a controller takes SRLfD-based representations as input, it can achieve better performance than with other representation strategies and promote more efficient reinforcement learning (RL) than with an end-to-end RL strategy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-64580-9_26\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"20579\"], \"outgoing_citations\": [\"69108\", \"138758\", \"181460\", \"216625\", \"242478\", \"246118\", \"254596\", \"255939\", \"260018\", \"265263\", \"268248\", \"277472\", \"283724\", \"284134\", \"296021\", \"302967\", \"305367\", \"306266\", \"310624\", \"313480\"]}","task_split":"paper_retrieval"}
{"document_id":"166891","document_content":"# Machine learning in healthcare -- a system's perspective\n## Categories\n- Computers and Society\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nA consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS.   Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07370v2\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"166904","document_content":"# Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nFor automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then translate with machine translation (MT). A major cause of the performance gap is that, while existing AST corpora are small, massive datasets exist for both the ASR and MT subsystems. In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets. Simple data augmentation by translating ASR transcripts proves most effective on the English--French augmented LibriSpeech dataset, closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strong cascade that could directly utilize copious ASR and MT data. The same end-to-end approach plus fine-tuning closes the gap on the English--Romanian MuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we present practical recommendations for augmentation and pretraining approaches. Finally, we decrease the performance gap to 0.01 BLEU using a Transformer-based architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.06515v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"23299\", \"24716\", \"45438\", \"99488\", \"50006\", \"50511\", \"51120\", \"56207\", \"71859\", \"91508\", \"92484\", \"99473\", \"93678\", \"101439\", \"121204\", \"131318\", \"139673\", \"144162\", \"153733\"], \"outgoing_citations\": [\"188886\", \"190470\", \"211387\", \"219122\", \"223071\", \"233583\", \"237174\", \"237918\", \"242472\", \"242853\", \"270599\", \"278633\"]}","task_split":"paper_retrieval"}
{"document_id":"167008","document_content":"# Distributed representation of patients and its use for medical cost prediction\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nEfficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction. Many existing methods, such as diagnostic Cost Groups (DCG), rely on expert knowledge to build patient representation from medical data, which is resource consuming and non-scalable. Unsupervised machine learning algorithms are a good choice for automating the representation learning process. However, there is very little research focusing on onpatient-level representation learning directly from medical claims. In this paper, weproposed a novel patient vector learning architecture that learns high quality,fixed-length patient representation from claims data. We conducted several experiments to test the quality of our learned representation, and the empirical results show that our learned patient vectors are superior to vectors learned through other methods including a popular commercial model. Lastly, we provide potential clinical interpretation for using our representation on predictive tasks, as interpretability is vital in the healthcare domain","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.07157v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"263454\", \"286253\", \"298095\", \"303053\"]}","task_split":"paper_retrieval"}
{"document_id":"167359","document_content":"# Dependency-Aware Named Entity Recognition with Relative and Global Attentions\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition is one of the core tasks in NLP. Although many improvements have been made on this task during the last years, the state-of-the-art systems do not explicitly take into account the recursive nature of language. Instead of only treating the text as a plain sequence of words, we incorporate a linguistically-inspired way to recognize entities based on syntax and tree structures. Our model exploits syntactic relationships among words using a Tree-LSTM guided by dependency trees. Then, we enhance these features by applying relative and global attention mechanisms. On the one hand, the relative attention detects the most informative words in the sentence with respect to the word being evaluated. On the other hand, the global attention spots the most relevant words in the sequence. Lastly, we linearly project the weighted vectors into the tagging space so that a conditional random field classifier predicts the entity labels. Our findings show that the model detects words that disclose the entity types based on their syntactic roles in a sentence (e.g., verbs such as speak and write are attended when the entity type is PERSON, whereas meet and travel strongly relate to LOCATION). We confirm our findings and establish a new state of the art on two datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.05166v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56776\"], \"outgoing_citations\": [\"181546\", \"213687\", \"228348\", \"256158\", \"297261\", \"302500\"]}","task_split":"paper_retrieval"}
{"document_id":"167379","document_content":"# Getting Gender Right in Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nSpeakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such difference is related to the way gender is expressed in a language. Saying \"I am happy\" in English, does not encode any additional knowledge of the speaker that uttered the sentence. However, many other languages do have grammatical gender systems and so such knowledge would be encoded. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained\/recovered. The same sentence would become either \"Je suis heureux\", for a male speaker or \"Je suis heureuse\" for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker et al., 2003). We integrate gender information into NMT systems. Our contribution is two-fold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/D18-1334\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"35996\", \"22927\", \"55858\", \"56086\", \"516\", \"21227\", \"23872\", \"26423\", \"30381\", \"31151\", \"35765\", \"45880\", \"46671\", \"50710\", \"55748\", \"56479\", \"57330\", \"61132\", \"70057\", \"72477\", \"72879\", \"73891\", \"118569\", \"81228\", \"81708\", \"84437\", \"85024\", \"94688\", \"95079\", \"95566\", \"89276\", \"118358\", \"119746\", \"122414\", \"122738\", \"131801\", \"132058\", \"125066\", \"128233\", \"129890\", \"133899\", \"136197\", \"158941\", \"222795\", \"158340\", \"163932\", \"170525\", \"178575\", \"179679\", \"180146\", \"191947\", \"197854\", \"203371\"], \"outgoing_citations\": [\"282462\"]}","task_split":"paper_retrieval"}
{"document_id":"167398","document_content":"# Learning to Propagate for Graph Meta-Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nMeta-learning extracts common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data, e.g., few shot learning. In most meta-learning methods, tasks are implicitly related by sharing parameters or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can significantly improve few shot learning. The graph's structure is usually free or cheap to obtain but has rarely been explored in previous works. We develop a novel meta-learner of this type for prototype-based classification, in which a prototype is generated for each class, such that the nearest neighbor search among the prototypes produces an accurate classification. The meta-learner, called \"Gated Propagation Network (GPN)\", learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class benefits from the data of other related classes. In GPN, an attention mechanism aggregates messages from neighboring classes of each class, with a gate choosing between the aggregated message and the message from the class itself. We train GPN on a sequence of tasks from many-shot to few shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, under different training-test discrepancy and test task generation settings, GPN outperforms recent meta-learning methods on two benchmark datasets. The code of GPN and dataset generation is available at https:\/\/github.com\/liulu112601\/Gated-Propagation-Net.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.05024v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"38319\", \"73107\", \"73199\", \"115681\", \"2370\", \"41793\", \"49630\", \"66264\", \"69491\", \"71717\", \"75181\", \"73827\", \"95428\", \"107285\", \"112069\", \"117183\", \"80992\", \"88729\", \"109828\", \"111473\", \"113849\", \"116748\", \"118587\", \"118766\", \"119358\", \"121352\", \"98722\", \"136162\", \"154068\", \"150176\"], \"outgoing_citations\": [\"186981\", \"199208\", \"230792\", \"234262\", \"240315\", \"250796\", \"271935\", \"311030\"]}","task_split":"paper_retrieval"}
{"document_id":"167453","document_content":"# Dynamic Fusion: Attentional Language Model for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nNeural Machine Translation (NMT) can be used to generate fluent output. As such, language models have been investigated for incorporation with NMT. In prior investigations, two models have been used: a translation model and a language model. The translation model's predictions are weighted by the language model with a hand-crafted ratio in advance. However, these approaches fail to adopt the language model weighting with regard to the translation history. In another line of approach, language model prediction is incorporated into the translation model by jointly considering source and target information. However, this line of approach is limited because it largely ignores the adequacy of the translation output.   Accordingly, this work employs two mechanisms, the translation model and the language model, with an attentive architecture to the language model as an auxiliary element of the translation model. Compared with previous work in English--Japanese machine translation using a language model, the experimental results obtained with the proposed Dynamic Fusion mechanism improve BLEU and Rank-based Intuitive Bilingual Evaluation Scores (RIBES) scores. Additionally, in the analyses of the attention and predictivity of the language model, the Dynamic Fusion mechanism allows predictive language modeling that conforms to the appropriate grammatical structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04879v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"192914\", \"219543\", \"235275\", \"258039\", \"280884\", \"290777\", \"299936\", \"316102\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"167462","document_content":"# Deep Elastic Networks with Model Selection for Multi-Task Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this work, we consider the problem of instance-wise dynamic network model selection for multi-task learning. To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks. The proposed method consists of an estimator and a selector. The estimator is based on a backbone architecture and structured hierarchically. It can produce multiple different network models of different configurations in a hierarchical structure. The selector chooses a model dynamically from a pool of candidate models given an input instance. The selector is a relatively small-size network consisting of a few layers, which estimates a probability distribution over the candidate models when an input instance of a task is given. Both estimator and selector are jointly trained in a unified learning framework in conjunction with a sampling-based learning strategy, without additional computation steps. We demonstrate the proposed approach for several image classification tasks compared to existing approaches performing model selection or learning multiple tasks. Experimental results show that our approach gives not only outstanding performance compared to other competitors but also the versatility to perform instance-wise model selection for multiple tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04860v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"33680\", \"6759\", \"13843\", \"17479\", \"89807\", \"101301\", \"130456\", \"153883\"], \"outgoing_citations\": [\"191434\", \"223081\", \"230118\", \"230672\", \"247770\", \"248753\", \"249590\", \"250354\", \"251499\", \"251797\", \"255606\", \"294694\"]}","task_split":"paper_retrieval"}
{"document_id":"167557","document_content":"# Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nDriven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be regarded as visual-based retrieval approaches which search and return a set of similar images from a database to a given query image. Although retrieval methods have achieved great success, there is still a question that needs to be responded to: Can we obtain the accurate semantic labels of the returned similar images to further help analyzing and processing imagery? Inspired by the above question, in this paper, we redefine the image retrieval problem as visual and semantic retrieval of images. Specifically, we propose a novel deep hashing convolutional neural network (DHCNN) to simultaneously retrieve the similar images and classify their semantic labels in a unified framework. In more detail, a convolutional neural network (CNN) is used to extract high-dimensional deep features. Then, a hash layer is perfectly inserted into the network to transfer the deep features into compact hash codes. In addition, a fully connected layer with a softmax function is performed on hash layer to generate class distribution. Finally, a loss function is elaborately designed to simultaneously consider the label loss of each image and similarity loss of pairs of images. Experimental results on two remote sensing datasets demonstrate that the proposed method achieves the state-of-art retrieval and classification performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04614v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"133251\"], \"outgoing_citations\": [\"159620\", \"191832\", \"260176\", \"260452\", \"282928\", \"286380\", \"299079\", \"303512\", \"307882\", \"329670\", \"358798\"]}","task_split":"paper_retrieval"}
{"document_id":"167564","document_content":"# Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nAutomated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly carried out over private datasets, have tackled the problem by using artificial neural network architectures that cannot deal with low-cardinality datasets, or by means of non-generalizable inference approaches. We introduce a Deep Learning architecture whose design results from an intensive experimental process. The final architecture is based on two parallel Minimal Gated Recurrent Unit networks working in bi-directional manner, which was extensively tested with the open-access Mimic-III dataset. Our results demonstrate significant improvements in automated medical prognosis, as measured with Recall@k. We summarize our experience as a set of relevant insights for the design of Deep Learning architectures. Our work improves the performance of computer-aided medicine and can serve as a guide in designing artificial neural networks used in prediction tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04605v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"103465\", \"109430\"], \"outgoing_citations\": [\"290033\", \"295566\", \"303053\", \"334129\", \"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"167642","document_content":"# Compositional Generalization in Image Captioning\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image--sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/K19-1009\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"30857\", \"53028\", \"72906\", \"88636\", \"105893\", \"110241\", \"115127\", \"121303\", \"127469\", \"154316\"], \"outgoing_citations\": [\"201065\", \"205219\", \"205227\", \"218291\", \"232274\", \"237655\", \"242449\", \"249972\", \"251209\", \"251911\", \"260926\", \"270154\", \"271234\", \"273718\", \"277419\", \"278948\", \"284230\", \"285882\", \"287504\", \"296681\", \"300099\", \"302943\", \"303209\", \"313196\", \"313934\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"167688","document_content":"# Bayesian Relational Memory for Semantic Visual Navigation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nWe introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over semantic entities (e.g., room types), which allows (1) capturing the layout prior from training environments, i.e., prior knowledge, (2) estimating posterior layout at test time, i.e., memory update, and (3) efficient planning for navigation, altogether. We develop a BRM agent consisting of a BRM module for producing sub-goals and a goal-conditioned locomotion module for control. When testing in unseen environments, the BRM agent outperforms baselines that do not explicitly utilize the probabilistic relational memory structure","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04306v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"15877\", \"17155\", \"31846\", \"75621\", \"128249\", \"2239\", \"6704\", \"15383\", \"25684\", \"27522\", \"36974\", \"39279\", \"49451\", \"48479\", \"54696\", \"65095\", \"66007\", \"71624\", \"82091\", \"92592\", \"93649\", \"101182\", \"152072\", \"82126\", \"90871\", \"110466\", \"110941\", \"113074\", \"117896\", \"123031\", \"131180\", \"140039\", \"149647\", \"164699\"], \"outgoing_citations\": [\"195772\", \"196898\", \"198161\", \"200941\", \"210374\", \"212893\", \"214195\", \"215425\", \"223487\", \"224223\", \"228916\", \"234517\", \"237119\", \"237267\", \"238203\", \"238287\", \"240323\", \"240348\", \"240506\", \"245731\", \"246823\", \"247418\", \"247749\", \"248742\", \"249906\", \"255575\", \"258592\", \"261577\", \"263104\", \"267779\", \"272930\", \"272940\", \"274102\", \"277968\", \"278822\", \"279177\", \"280296\", \"280681\", \"280870\", \"283391\", \"284565\", \"292009\", \"294142\", \"298651\", \"302756\", \"308711\", \"313499\", \"317466\", \"357896\"]}","task_split":"paper_retrieval"}
{"document_id":"167753","document_content":"# Knowledge Enhanced Contextual Word Representations\n## Categories\n- Computation and Language\n## Abstract\nContextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance their representations with structured, human-curated knowledge. For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. In contrast to previous approaches, the entity linkers and self-supervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. KnowBert's runtime is comparable to BERT's and it scales to large KBs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04164v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"39728\", \"47276\", \"193208\", \"2681\", \"5798\", \"8136\", \"15904\", \"16224\", \"16051\", \"16963\", \"23896\", \"25467\", \"26715\", \"27470\", \"29573\", \"46629\", \"47208\", \"47698\", \"48623\", \"55762\", \"63777\", \"72014\", \"77617\", \"78304\", \"78802\", \"97239\", \"97239\", \"121898\", \"854\", \"4523\", \"13441\", \"14522\", \"15912\", \"15895\", \"16195\", \"18842\", \"19742\", \"21872\", \"21967\", \"22520\", \"22518\", \"24180\", \"24571\", \"24469\", \"24520\", \"24795\", \"24980\", \"25393\", \"26295\", \"26924\", \"28587\", \"28554\", \"30029\", \"31656\", \"32315\", \"34252\", \"34418\", \"37401\", \"37455\", \"40307\", \"40892\", \"41278\", \"42028\", \"42396\", \"43288\", \"45225\", \"45171\", \"46818\", \"48100\", \"48898\", \"50345\", \"50614\", \"51435\", \"54258\", \"54471\", \"55406\", \"55481\", \"56081\", \"56031\", \"56560\", \"57269\", \"52260\", \"103917\", \"62216\", \"66795\", \"68898\", \"69510\", \"70993\", \"71008\", \"71639\", \"71705\", \"72160\", \"72781\", \"72874\", \"73669\", \"75336\", \"77713\", \"77863\", \"78210\", \"79624\", \"80977\", \"83586\", \"88416\", \"91656\", \"91653\", \"91652\", \"91629\", \"91739\", \"92823\", \"97876\", \"97929\", \"98430\", \"99146\", \"103476\", \"104921\", \"143992\", \"144552\", \"156620\", \"59243\", \"81789\", \"82276\", \"83604\", \"88174\", \"86291\", \"91820\", \"92103\", \"94483\", \"94883\", \"95827\", \"95877\", \"96631\", \"97015\", \"97100\", \"97423\", \"98293\", \"98815\", \"98890\", \"99663\", \"102041\", \"104221\", \"105152\", \"107547\", \"115512\", \"115936\", \"116351\", \"120283\", \"123204\", \"127467\", \"127539\", \"124544\", \"127481\", \"127675\", \"128180\", \"129023\", \"129112\", \"130549\", \"130667\", \"130625\", \"131012\", \"131151\", \"127899\", \"127760\", \"135303\", \"136153\", \"139638\", \"122351\", \"143089\", \"148999\", \"150253\", \"153825\", \"157255\", \"157726\", \"158400\", \"168495\", \"171633\", \"205857\", \"151278\", \"152550\", \"158893\", \"171165\"], \"outgoing_citations\": [\"154631\", \"180327\", \"181948\", \"181969\", \"185889\", \"191177\", \"197767\", \"200351\", \"201341\", \"216419\", \"217522\", \"218444\", \"219306\", \"220098\", \"220553\", \"224786\", \"231338\", \"237157\", \"250722\", \"259525\", \"259595\", \"259706\", \"260541\", \"268728\", \"281158\", \"287413\", \"301467\"]}","task_split":"paper_retrieval"}
{"document_id":"167766","document_content":"# Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?\n## Categories\n- Computation and Language\n## Abstract\nNatural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of them. The typical recipe involves: (i) training a deep, possibly bidirectional, neural network with an objective related to language modeling, for which training data is plentiful; and (ii) using the trained network to derive contextual representations that are far richer than standard linear word embeddings such as word2vec, and thus result in important gains. In this work, we wonder whether the opposite perspective is also true: can contextual representations trained for different NLP tasks improve language modeling itself? Since language models (LMs) are predominantly locally optimized, other NLP tasks may help them make better predictions based on the entire semantic fabric of a document. We test the performance of several types of pre-trained embeddings in neural LMs, and we investigate whether it is possible to make the LM more aware of global semantic information through embeddings pre-trained with a domain classification model. Initial experiments suggest that as long as the proper objective criterion is used during training, pre-trained embeddings are likely to be beneficial for neural language modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04130v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"253914\", \"266848\", \"268157\", \"281136\", \"283971\"]}","task_split":"paper_retrieval"}
{"document_id":"167774","document_content":"# Gradient-Aware Model-based Policy Search\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTraditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1609\/aaai.v34i04.5791\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"44283\", \"18590\", \"91204\", \"112129\", \"128143\", \"139425\"], \"outgoing_citations\": [\"179948\", \"196921\", \"212130\", \"217595\", \"219180\", \"225108\", \"225762\", \"229579\", \"229890\", \"240471\", \"240495\", \"261493\", \"270429\", \"271601\", \"277193\", \"304250\", \"338782\", \"342370\", \"361126\"]}","task_split":"paper_retrieval"}
{"document_id":"167838","document_content":"# Hierarchy Parsing for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nIt is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image. Nevertheless, there has not been evidence in support of the idea on describing an image with a natural-language utterance. In this paper, we introduce a new design to model a hierarchy from instance level (segmentation), region level (detection) to the whole image to delve into a thorough image understanding for captioning. Specifically, we present a HIerarchy Parsing (HIP) architecture that novelly integrates hierarchical structure into image encoder. Technically, an image decomposes into a set of regions and some of the regions are resolved into finer ones. Each region then regresses to an instance, i.e., foreground of the region. Such process naturally builds a hierarchal tree. A tree-structured Long Short-Term Memory (Tree-LSTM) network is then employed to interpret the hierarchal structure and enhance all the instance-level, region-level and image-level features. Our HIP is appealing in view that it is pluggable to any neural captioning models. Extensive experiments on COCO image captioning dataset demonstrate the superiority of HIP. More remarkably, HIP plus a top-down attention-based LSTM decoder increases CIDEr-D performance from 120.1% to 127.2% on COCO Karpathy test split. When further endowing instance-level and region-level features from HIP with semantic relation learnt through Graph Convolutional Networks (GCN), CIDEr-D is boosted up to 130.6%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.03918v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"23109\", \"35427\", \"4080\", \"8053\", \"67993\", \"89494\", \"76299\", \"114091\", \"148583\", \"93187\", \"6500\", \"3079\", \"17157\", \"20379\", \"27871\", \"28908\", \"31372\", \"40652\", \"73032\", \"75147\", \"81451\", \"107388\", \"111075\", \"137828\", \"192417\", \"228640\", \"80061\", \"89629\", \"89648\", \"111062\", \"111456\", \"112250\", \"116404\", \"128175\", \"133970\", \"149491\", \"133973\"], \"outgoing_citations\": [\"173493\", \"189018\", \"217310\", \"223302\", \"249060\", \"258345\", \"278604\", \"281152\", \"287504\", \"296681\", \"311824\"]}","task_split":"paper_retrieval"}
{"document_id":"167906","document_content":"# Reasoning Over Semantic-Level Graph for Fact Checking\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances. Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph. We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy. Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.03745v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"96167\", \"23894\", \"27370\", \"27576\", \"55862\", \"66512\", \"78437\", \"21122\", \"23728\", \"23915\", \"34209\", \"45354\", \"45490\", \"46200\", \"47485\", \"54565\", \"62534\", \"80136\", \"89911\", \"101392\", \"83513\", \"84108\", \"88762\", \"93011\", \"120523\", \"123394\", \"160459\", \"163052\", \"205283\"], \"outgoing_citations\": [\"160459\", \"171777\", \"175093\", \"183400\", \"191177\", \"195288\", \"208471\", \"209764\", \"219367\", \"219939\", \"221735\", \"238986\", \"267552\", \"284357\", \"288699\"]}","task_split":"paper_retrieval"}
{"document_id":"167986","document_content":"# Unified Underwater Structure-from-Motion\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstruction techniques for a variety of scenarios such as single static camera and moving refractive interface, single moving camera and static refractive interface, and single moving camera and moving refractive interface. In our basic setup, we assume that the refractive interface is planar, and simultaneously estimate the unknown transformations of the planar interface and the camera, and the unknown target shape using bundle adjustment. We also extend it to relax the planarity assumption, which enables us to use waves of the refractive interface for the reconstruction task. Experiments with real data show the superiority of our method to existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.03583v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25662\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"168038","document_content":"# Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks\n## Categories\n- Computation and Language\n## Abstract\nDue to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.03477v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8221\", \"18083\", \"27470\", \"31382\", \"80211\", \"127897\", \"13722\", \"15812\", \"20147\", \"25521\", \"51357\", \"56951\", \"63345\", \"140730\", \"94147\", \"96163\", \"133116\", \"160056\", \"91326\", \"91704\", \"128596\", \"96272\", \"96889\", \"96861\", \"96907\", \"97701\", \"84959\", \"100216\", \"104063\", \"128898\", \"133091\", \"163765\"], \"outgoing_citations\": [\"200687\", \"216419\", \"231638\", \"233393\", \"235195\", \"257007\", \"271527\", \"291617\", \"302088\", \"314946\"]}","task_split":"paper_retrieval"}
{"document_id":"168085","document_content":"# Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article. In this paper, we show that recent neural systems excessively exploit this trend, which although powerful for many inputs, is also detrimental when summarizing documents where important content should be extracted from later parts of the article. We propose two techniques to make systems sensitive to the importance of content in different parts of the article. The first technique employs 'unbiased' data; i.e., randomly shuffled sentences of the source document, to pretrain the model. The second technique uses an auxiliary ROUGE-based loss that encourages the model to distribute importance scores throughout a document by mimicking sentence-level ROUGE scores on the training data. We show that these techniques significantly improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.04028v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"84343\", \"16191\", \"19290\", \"21820\", \"24361\", \"24449\", \"149602\", \"46445\", \"53640\", \"72649\", \"127602\", \"90986\"], \"outgoing_citations\": [\"187871\", \"193848\", \"212506\", \"216554\", \"220230\", \"224726\", \"225556\", \"234421\", \"235041\", \"241070\", \"266671\", \"279333\", \"280557\", \"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"168219","document_content":"# Efficient Sentence Embedding using Discrete Cosine Transform\n## Categories\n- Computation and Language\n## Abstract\nVector averaging remains one of the most popular sentence embedding methods in spite of its obvious disregard for syntactic structure. While more complex sequential or convolutional networks potentially yield superior classification performance, the improvements in classification accuracy are typically mediocre compared to the simple vector averaging. As an efficient alternative, we propose the use of discrete cosine transform (DCT) to compress word sequences in an order-preserving manner. The lower order DCT coefficients represent the overall feature patterns in sentences, which results in suitable embeddings for tasks that could benefit from syntactic features. Our results in semantic probing tasks demonstrate that DCT embeddings indeed preserve more syntactic information compared with vector averaging. With practically equivalent complexity, the model yields better overall performance in downstream classification tasks that correlate with syntactic features, which illustrates the capacity of DCT to preserve word order information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.03104v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19536\", \"45499\", \"70974\", \"140769\", \"91201\", \"157114\", \"98933\", \"137785\", \"141983\"], \"outgoing_citations\": [\"228286\", \"233400\", \"238963\", \"246513\", \"259723\", \"298543\", \"302516\", \"310737\", \"325323\"]}","task_split":"paper_retrieval"}
{"document_id":"168238","document_content":"# Annotating Student Talk in Text-based Classroom Discussions\n## Categories\n- Computation and Language\n## Abstract\nClassroom discussions in English Language Arts have a positive effect on students' reading, writing and reasoning skills. Although prior work has largely focused on teacher talk and student-teacher interactions, we focus on three theoretically-motivated aspects of high-quality student talk: argumentation, specificity, and knowledge domain. We introduce an annotation scheme, then show that the scheme can be used to produce reliable annotations and that the annotations are predictive of discussion quality. We also highlight opportunities provided by our scheme for education and natural language processing research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/W18-0511\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"64589\", \"68059\", \"123415\", \"168239\"], \"outgoing_citations\": [\"168825\", \"268257\", \"293849\"]}","task_split":"paper_retrieval"}
{"document_id":"168353","document_content":"# NEAR: Neighborhood Edge AggregatoR for Graph Classification\n## Categories\n- Machine Learning\n## Abstract\nLearning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis and data mining. Recent GNN algorithms are based on neural message passing, which enables GNNs to integrate local structures and node features recursively. However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships. In this paper, we propose Neighborhood Edge AggregatoR (NEAR), a framework that aggregates relations between the nodes in the neighborhood via edges. NEAR, which can be orthogonally combined with Graph Isomorphism Network (GIN), gives integrated information that describes which nodes in the neighborhood are connected. Therefore, NEAR can reflect additional information of a local structure of each node beyond the nodes themselves in 1-hop neighborhood. Experimental results on multiple graph classification tasks show that our algorithm makes a good improvement over other existing 1-hop based GNN-based algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.02746v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"118201\"], \"outgoing_citations\": [\"155642\", \"173127\", \"183447\", \"187338\", \"190159\", \"194689\", \"215435\", \"218754\", \"227067\", \"228663\", \"228992\", \"229275\", \"229927\", \"229936\", \"238422\", \"242267\", \"252363\", \"272626\", \"279575\", \"292407\"]}","task_split":"paper_retrieval"}
{"document_id":"168493","document_content":"# An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n## Abstract\nAutomated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a novel active learning framework for cost-effective skin lesion analysis. The goal is to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance. Our sample selection criteria complementarily consider both informativeness and representativeness, derived from decoupled aspects of measuring model certainty and covering sample diversity. To make wise use of the selected samples, we further design a simple yet effective strategy to aggregate intra-class images in pixel space, as a new form of data augmentation. We validate our proposed method on data of ISIC 2017 Skin Lesion Classification Challenge for two tasks. Using only up to 50% of samples, our approach can achieve state-of-the-art performances on both tasks, which are comparable or exceeding the accuracies with full-data training, and outperform other well-known active learning methods by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.02344v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"73680\", \"87658\", \"115879\", \"124972\", \"129987\", \"129907\"], \"outgoing_citations\": [\"201987\", \"228043\", \"263706\", \"271680\"]}","task_split":"paper_retrieval"}
{"document_id":"168495","document_content":"# Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity\n## Categories\n- Computation and Language\n## Abstract\nUnsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT's masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our \"Lexically Informed\" BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind \"vanilla\" BERT on several language understanding tasks. Concretely, LIBERT outperforms BERT in 9 out of 10 tasks of the GLUE benchmark and is on a par with BERT in the remaining one. Moreover, we show consistent gains on 3 benchmarks for lexical simplification, a task where knowledge about word-level semantic similarity is paramount.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.02339v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"77617\", \"15895\", \"19742\", \"22045\", \"25393\", \"30029\", \"46359\", \"54258\", \"56081\", \"72649\", \"77713\", \"97876\", \"143992\", \"82276\", \"95336\", \"109939\", \"111890\", \"123204\", \"125756\", \"136153\", \"137472\"], \"outgoing_citations\": [\"96350\", \"123204\", \"123890\", \"127598\", \"127811\", \"143992\", \"166443\", \"167753\", \"171633\", \"176154\", \"185889\", \"189726\", \"213349\", \"218235\", \"229768\", \"232765\", \"234733\", \"253087\", \"259703\", \"259723\", \"260467\", \"264791\", \"297242\", \"307168\", \"311384\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"168527","document_content":"# Generative Machine Learning for Robust Free-Space Communication\n## Categories\n- Signal Processing\n- Optics\n- Machine Learning\n- Quantum Physics\n## Abstract\nRealistic free-space optical communications systems suffer from turbulent propagation of light through the atmosphere and detector noise at the receiver, which can significantly degrade the optical mode quality of the received state, increase cross-talk between modes, and correspondingly increase the symbol error ratio (SER) of the system. In order to overcome these obstacles, we develop a state-of-the-art generative machine learning (GML) and convolutional neural network (CNN) system in combination, and demonstrate its efficacy in a free-space optical (FSO) communications setting. The system corrects for the distortion effects due to turbulence and reduces detector noise, resulting in significantly lowered SERs and cross-talk at the output of the receiver, while requiring no feedback. This scheme is straightforward to scale, and may provide a concrete and cost effective technique to establishing long range classical and quantum communication links in the near future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1038\/s42005-020-00444-9\", \"primary_category\": \"eess.SP\", \"categories\": [\"physics.optics\", \"cs.LG\", \"eess.SP\", \"quant-ph\"], \"primary_category_human_readable\": \"Signal Processing\", \"categories_human_readable\": [\"Optics\", \"Machine Learning\", \"Signal Processing\", \"Quantum Physics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"138094\", \"168429\", \"250359\", \"265204\", \"286488\"]}","task_split":"paper_retrieval"}
{"document_id":"168655","document_content":"# Privacy with Surgical Robotics: Challenges in Applying Contextual Privacy Theory\n## Categories\n- Cryptography and Security\n- 68M99\n- Robotics\n## Abstract\nThe use of connected surgical robotics to automate medical procedures presents new privacy challenges. We argue that conventional patient consent protocols no longer work. Indeed robots that replace human surgeons take on an extraordinary level of responsibility. Surgeons undergo years of training and peer review in a strongly regulated environment, and derive trust via a patient's faith in the hospital system. Robots on the other hand derive trust differently, via the integrity of the software that governs their operation. From a privacy perspective, there are two fundamental shifts. First, the threat model has shifted from one where the humans involved were untrusted to one where the robotic software is untrusted. Second, the basic unit of privacy control is no longer a medical record, but is replaced by four new basic units: the subject on which the robot is taking action; the tools used by the robot; the sensors (i.e data) the robot can access; and, finally access to monitoring and calibration services which afford correct operation of the robot. We suggest that contextual privacy provides useful theoretical tools to solve the privacy problems posed by surgical robots. However, it also poses some challenges: not least that the complexity of the contextual-privacy policies, if rigorously specified to achieve verification and enforceability, will be exceedingly high to directly expose to humans that review contextual privacy policies. A medical robot works with both information and physical material. While informational norms allow for judgements about contextual integrity and the transmission principle governs the constraints applied on information transfer, nothing is said about material property. Certainly, contextual privacy provides an anchor for useful notions of privacy in this scenario and thus should be considered to be extended to cover both information and material flows.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.01862v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"68M99\", \"cs.RO\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"68M99\", \"Robotics\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"273221\"]}","task_split":"paper_retrieval"}
{"document_id":"168762","document_content":"# Big Data Intelligence Using Distributed Deep Neural Networks\n## Categories\n- Distributed, Parallel, and Cluster Computing\n- 68Txx\n## Abstract\nLarge amount of data is often required to train and deploy useful machine learning models in industry. Smaller enterprises do not have the luxury of accessing enough data for machine learning, For privacy sensitive fields such as banking, insurance and healthcare, aggregating data to a data warehouse poses a challenge of data security and limited computational resources. These challenges are critical when developing machine learning algorithms in industry. Several attempts have been made to address the above challenges by using distributed learning techniques such as federated learning over disparate data stores in order to circumvent the need for centralised data aggregation. This paper proposes an improved algorithm to securely train deep neural networks over several data sources in a distributed way, in order to eliminate the need to centrally aggregate the data and the need to share the data thus preserving privacy. The proposed method allows training of deep neural networks using data from multiple de-linked nodes in a distributed environment and to secure the representation shared during training. Only a representation of the trained models (network architecture and weights) are shared. The algorithm was evaluated on existing healthcare patients data and the performance of this implementation was compared to that of a regular deep neural network trained on a single centralised architecture. This algorithm will pave a way for distributed training of neural networks on privacy sensitive applications where raw data may not be shared directly or centrally aggregating this data in a data warehouse is not feasible.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.02873v1\", \"primary_category\": \"cs.DC\", \"categories\": [\"cs.DC\", \"68Txx\"], \"primary_category_human_readable\": \"Distributed, Parallel, and Cluster Computing\", \"categories_human_readable\": [\"Distributed, Parallel, and Cluster Computing\", \"68Txx\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214295\", \"273803\"]}","task_split":"paper_retrieval"}
{"document_id":"168789","document_content":"# Social Influence and Radicalization: A Social Data Analytics Study\n## Categories\n- Computers and Society\n- Machine Learning\n## Abstract\nThe confluence of technological and societal advances is changing the nature of global terrorism. For example, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals and influence extremist and criminal behaviors such as Radicalization. In this context, social data analytics (i.e., the discovery, interpretation, and communication of meaningful patterns in social data) and influence maximization (i.e., the problem of finding a small subset of nodes in a social network which can maximize the propagation of influence) has the potential to become a vital asset to explore the factors involved in influencing people to participate in extremist activities.   To address this challenge, we study and analyze the recent work done in influence maximization and social data analytics from effectiveness, efficiency and scalability viewpoints. We introduce a social data analytics pipeline, namely iRadical, to enable analysts engage with social data to explore the potential for online radicalization. In iRadical, we present algorithms to analyse the social data as well as the user activity patterns to learn how influence flows in social networks. We implement iRadical as an extensible architecture that is publicly available on GitHub and present the evaluation results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1910.01212v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.LG\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"278307\", \"289411\", \"304368\", \"310808\", \"316315\", \"334405\", \"337882\", \"351689\", \"359556\"]}","task_split":"paper_retrieval"}
{"document_id":"168861","document_content":"# Generalization in Transfer Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nAgents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments. We investigate transferring the learning acquired in one task to a set of previously unseen tasks. Generalization and overfitting in deep reinforcement learning are not commonly addressed in current transfer learning research. Conducting a comparative analysis without an intermediate regularization step results in underperforming benchmarks and inaccurate algorithm comparisons due to rudimentary assessments. In this study, we propose regularization techniques in deep reinforcement learning for continuous control through the application of sample elimination, early stopping and maximum entropy regularized adversarial learning. First, the importance of the inclusion of training iteration number to the hyperparameters in deep transfer reinforcement learning will be discussed. Because source task performance is not indicative of the generalization capacity of the algorithm, we start by acknowledging the training iteration number as a hyperparameter. In line with this, we introduce an additional step of resorting to earlier snapshots of policy parameters to prevent overfitting to the source task. Then, to generate robust policies, we discard the samples that lead to overfitting via a method we call strict clipping. Furthermore, we increase the generalization capacity in widely used transfer learning benchmarks by using maximum entropy regularization, different critic methods, and curriculum learning in an adversarial setup. Subsequently, we propose maximum entropy adversarial reinforcement learning to increase the domain randomization. Finally, we evaluate the robustness of these methods on simulated robots in target environments where the morphology of the robot, gravity, and tangential friction coefficient of the environment are altered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.01331v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"51256\", \"7391\"], \"outgoing_citations\": [\"9359\", \"10411\", \"14588\", \"16867\", \"18653\", \"34887\", \"36367\", \"38128\", \"38582\", \"42012\", \"43665\", \"44101\", \"45211\", \"47736\", \"48018\", \"49046\", \"49092\", \"49233\", \"51004\", \"51767\", \"59015\", \"59590\", \"60361\", \"67296\", \"67998\", \"69759\", \"80696\", \"87412\", \"88624\", \"93106\", \"94275\", \"98272\", \"102997\", \"111560\", \"114487\", \"114823\", \"115360\", \"115856\", \"116421\", \"138734\", \"139100\", \"141948\", \"143581\", \"143773\", \"143855\", \"146773\", \"147734\", \"149671\", \"152544\", \"156893\", \"160357\", \"163906\", \"164174\", \"165238\", \"175713\", \"175834\", \"182179\", \"182326\", \"183257\", \"183874\", \"192575\", \"193267\", \"199022\", \"199404\", \"199913\", \"205714\", \"207239\", \"207432\", \"208041\", \"215230\", \"223269\", \"224762\", \"229879\", \"230755\", \"243562\", \"248017\", \"248417\", \"253776\", \"261323\", \"263744\", \"283080\", \"286352\"]}","task_split":"paper_retrieval"}
{"document_id":"168892","document_content":"# Neural Attentive Bag-of-Entities Model for Text Classification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThis study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https:\/\/github.com\/wikipedia2vec\/wikipedia2vec.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.01259v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16224\", \"30358\", \"37401\", \"39855\", \"47144\", \"132906\", \"205857\"], \"outgoing_citations\": [\"178230\", \"205857\", \"209228\", \"220553\", \"228842\", \"230664\", \"238024\", \"253019\", \"262673\", \"267123\", \"268728\", \"290102\", \"300498\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"168952","document_content":"# Modeling Named Entity Embedding Distribution into Hypersphere\n## Categories\n- Computation and Language\n## Abstract\nThis work models named entity distribution from a way of visualizing topological structure of embedding space, so that we make an assumption that most, if not all, named entities (NEs) for a language tend to aggregate together to be accommodated by a specific hypersphere in embedding space. Thus we present a novel open definition for NE which alleviates the obvious drawback in previous closed NE definition with a limited NE dictionary. Then, we show two applications with introducing the proposed named entity hypersphere model. First, using a generative adversarial neural network to learn a transformation matrix of two embedding spaces, which results in a convenient determination of named entity distribution in the target language, indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces. Second, the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-the-art results. Only assuming that embeddings are available, we show a prior knowledge free approach on effective named entity distribution depiction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.01065v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"79525\"], \"outgoing_citations\": [\"218407\", \"231863\", \"282324\", \"330797\", \"336720\", \"339424\"]}","task_split":"paper_retrieval"}
{"document_id":"168956","document_content":"# Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering\n## Categories\n- Computation and Language\n## Abstract\nOver the past few years, question answering and information retrieval systems have become widely used. These systems attempt to find the answer of the asked questions from raw text sources. A component of these systems is Answer Selection which selects the most relevant from candidate answers. Syntactic similarities were mostly used to compute the similarity, but in recent works, deep neural networks have been used, making a significant improvement in this field. In this research, a model is proposed to select the most relevant answers to the factoid question from the candidate answers. The proposed model ranks the candidate answers in terms of semantic and syntactic similarity to the question, using convolutional neural networks. In this research, Attention mechanism and Sparse feature vector use the context-sensitive interactions between questions and answer sentence. Wide convolution increases the importance of the interrogative word. Pairwise ranking is used to learn differentiable representations to distinguish positive and negative answers. Our model indicates strong performance on the TrecQA Raw beating previous state-of-the-art systems by 1.4% in MAP and 1.1% in MRR while using the benefits of no additional syntactic parsers and external tools. The results show that using context-sensitive interactions between question and answer sentences can help to find the correct answer more accurately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.01059v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"158062\"], \"outgoing_citations\": [\"245908\", \"260297\", \"260321\", \"260724\", \"308414\", \"320868\"]}","task_split":"paper_retrieval"}
{"document_id":"169079","document_content":"# Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nExisting approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking. This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots. In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list. Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure. In this way, the computational complexity of our model will be a constant regardless of the number of pre-defined slots. Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00754v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"20119\", \"100214\", \"12766\", \"125732\", \"33508\", \"50944\", \"73962\", \"86043\", \"94939\", \"99215\", \"129606\", \"140759\", \"86113\", \"91125\", \"91687\", \"92491\", \"96864\", \"98658\", \"99219\", \"100338\", \"106272\", \"121509\", \"124982\", \"126262\", \"128221\", \"128145\", \"132503\", \"157115\", \"162766\"], \"outgoing_citations\": [\"172890\", \"178119\", \"185347\", \"207522\", \"213349\", \"224315\", \"228250\", \"233268\", \"246324\", \"264533\", \"290531\", \"290588\", \"294494\"]}","task_split":"paper_retrieval"}
{"document_id":"169128","document_content":"# Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nWith the advents of deep learning, improved image classification with complex discriminative models has been made possible. However, such deep models with increased complexity require a huge set of labeled samples to generalize the training. Such classification models can easily overfit when applied for medical images because of limited training data, which is a common problem in the field of medical image analysis. This paper proposes and investigates a reinforced classifier for improving the generalization under a few available training data. Partially following the idea of reinforcement learning, the proposed classifier uses a generalization-feedback from a subset of the training data to update its parameter instead of only using the conventional cross-entropy loss about the training data. We evaluate the improvement of the proposed classifier by applying it on three different classification problems against the standard deep classifiers equipped with existing overfitting-prevention techniques. Besides an overall improvement in classification performance, the proposed classifier showed remarkable characteristics of generalized learning, which can have great potential in medical classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.05630v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"240319\", \"245673\", \"293522\"]}","task_split":"paper_retrieval"}
{"document_id":"169263","document_content":"# Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research\n## Categories\n- Machine Learning\n## Abstract\nCancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power. Consequently, there is a growing need for the development of data-driven and, in particular, deep learning methods for various tasks such as cancer diagnosis, detection, prognosis, and prediction. Despite recent successes, however, designing high-performing deep learning models for nonimage and nontext cancer data is a time-consuming, trial-and-error, manual task that requires both cancer domain and deep learning expertise. To that end, we develop a reinforcement-learning-based neural architecture search to automate deep-learning-based predictive model development for a class of representative cancer data. We develop custom building blocks that allow domain experts to incorporate the cancer-data-specific characteristics. We show that our approach discovers deep neural network architectures that have significantly fewer trainable parameters, shorter training time, and accuracy similar to or higher than those of manually designed architectures. We study and demonstrate the scalability of our approach on up to 1,024 Intel Knights Landing nodes of the Theta supercomputer at the Argonne Leadership Computing Facility.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3295500.3356202\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"107932\", \"65998\", \"90161\", \"85502\", \"86957\", \"88558\", \"103841\", \"109883\", \"159118\"], \"outgoing_citations\": [\"166044\", \"198072\", \"198231\", \"198498\", \"202136\", \"204859\", \"205973\", \"206152\", \"208875\", \"222660\", \"224177\", \"226949\", \"239230\", \"239450\", \"240372\", \"240876\", \"242604\", \"245924\", \"247323\", \"255255\", \"255606\", \"260017\", \"267780\", \"269847\", \"281001\", \"296236\"]}","task_split":"paper_retrieval"}
{"document_id":"169290","document_content":"# Entity Projection via Machine Translation for Cross-Lingual NER\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nAlthough over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition. We propose a system that improves over prior entity-projection methods by: (a) leveraging machine translation systems twice: first for translating sentences and subsequently for translating entities; (b) matching entities based on orthographic and phonetic similarity; and (c) identifying matches based on distributional statistics derived from the dataset. Our approach improves upon current state-of-the-art methods for cross-lingual named entity recognition on 5 diverse languages by an average of 4.1 points. Further, our method achieves state-of-the-art F_1 scores for Armenian, outperforming even a monolingual model trained on Armenian source data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.05356v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"55305\", \"24757\", \"44583\", \"44844\", \"51380\", \"73229\", \"73442\", \"144897\", \"97253\", \"111926\", \"91882\", \"128121\"], \"outgoing_citations\": [\"213623\", \"219866\", \"219979\", \"253559\", \"253840\", \"259758\", \"261734\", \"323243\", \"335264\"]}","task_split":"paper_retrieval"}
{"document_id":"169324","document_content":"# Open Named Entity Modeling from Embedding Distribution\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nIn this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule update. Our initial visualization of monolingual word embeddings indicates named entities tend to gather together despite of named entity types and language difference, which enable us to model all named entities using a specific geometric structure inside embedding space, namely, the named entity hypersphere. For monolingual cases, the proposed named entity model gives an open description of diverse named entity types and different languages. For cross-lingual cases, mapping the proposed named entity model provides a novel way to build a named entity dataset for resource-poor languages. At last, the proposed named entity model may be shown as a handy clue to enhance state-of-the-art named entity recognition systems generally.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00170v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"127635\", \"168549\", \"169327\", \"171817\", \"176377\", \"218407\", \"226959\", \"227993\", \"228681\", \"231863\", \"253559\", \"256158\", \"260847\", \"261734\", \"261742\", \"268382\", \"282324\", \"297147\", \"297150\", \"302500\", \"308333\", \"330797\", \"336720\", \"339424\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"169327","document_content":"# Named Entity Recognition Only from Word Embeddings\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nDeep neural network models have helped named entity (NE) recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made to replace human annotations with external knowledge (e.g., NE dictionary, part-of-speech tags), while it is another challenge to obtain such effective resources. In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings. We first apply Gaussian Hidden Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selector based on reinforcement learning to distinguish positive sentences from noisy sentences and refine these coarse-grained annotations through neural networks. Extensive experiments on CoNLL benchmark datasets demonstrate that our proposed light NE recognition model achieves remarkable performance without using any annotated lexicon or corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00164v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [\"169324\", \"113865\", \"158797\", \"169520\"], \"outgoing_citations\": [\"127635\", \"157803\", \"168549\", \"177443\", \"182662\", \"191471\", \"202775\", \"210594\", \"218407\", \"220102\", \"220475\", \"227993\", \"228348\", \"256158\", \"260847\", \"268382\", \"301596\", \"315542\"]}","task_split":"paper_retrieval"}
{"document_id":"169340","document_content":"# EntEval: A Holistic Evaluation Benchmark for Entity Representations\n## Categories\n- Computation and Language\n## Abstract\nRich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models and show that they improve strong baselines on multiple EntEval tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00137v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"41352\", \"43288\", \"54510\", \"66795\", \"77391\", \"86291\", \"97015\", \"127760\", \"169337\"], \"outgoing_citations\": [\"169337\", \"175482\", \"180267\", \"180327\", \"181804\", \"182261\", \"182677\", \"185629\", \"185889\", \"187755\", \"189515\", \"189726\", \"194308\", \"211788\", \"213271\", \"214554\", \"218698\", \"220159\", \"221212\", \"221735\", \"224786\", \"225253\", \"228894\", \"233400\", \"233941\", \"238963\", \"238986\", \"244357\", \"250346\", \"259525\", \"259706\", \"260541\", \"267123\", \"267552\", \"268014\", \"268168\", \"268728\", \"286594\", \"289601\", \"294814\", \"295323\", \"300498\", \"313713\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"169369","document_content":"# Automatically Inferring Gender Associations from Language\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings - one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00091v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56060\", \"121865\", \"206046\", \"93669\", \"157164\"], \"outgoing_citations\": [\"191947\", \"192610\", \"195777\", \"219818\", \"249587\", \"288476\", \"295679\", \"299923\", \"353350\"]}","task_split":"paper_retrieval"}
{"document_id":"169393","document_content":"# Handling Syntactic Divergence in Low-resource Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nDespite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs. Data augmentation methods such as back-translation make it possible to use monolingual data to help alleviate these issues, but back-translation itself fails in extreme low-resource scenarios, especially for syntactically divergent languages. In this paper, we propose a simple yet effective solution, whereby target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. Experiments with simulated low-resource Japanese-to-English, and real low-resource Uyghur-to-English scenarios find significant improvements over other semi-supervised alternatives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.00040v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72647\", \"49285\", \"58439\", \"77820\", \"89663\", \"126439\", \"126471\", \"128053\"], \"outgoing_citations\": [\"192331\", \"200263\", \"219175\", \"219866\", \"220218\", \"221514\", \"241616\", \"251974\", \"263927\", \"281472\", \"290402\", \"317334\"]}","task_split":"paper_retrieval"}
{"document_id":"169403","document_content":"# Systematic Analysis of Image Generation using GANs\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nGenerative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy, these networks aim to estimate the potential distribution from the real data and then use this as input to generate the synthetic data. Based on this fundamental principle, several frameworks can be generated that are paragon implementations in several real-life applications such as art synthesis, generation of high resolution outputs and synthesis of images from human drawn sketches, to name a few. While theoretically GANs present better results and prove to be an improvement over conventional methods in many factors, the implementation of these frameworks for dedicated applications remains a challenge. This study explores and presents a taxonomy of these frameworks and their use in various image to image synthesis and text to image synthesis applications. The basic GANs, as well as a variety of different niche frameworks, are critically analyzed. The advantages of GANs for image generation over conventional methods as well their disadvantages amongst other frameworks are presented. The future applications of GANs in industries such as healthcare, art and entertainment are also discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11863v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"245594\", \"249031\", \"260588\", \"261794\", \"264198\", \"270075\", \"274222\", \"274550\", \"278895\", \"283070\", \"285093\", \"294289\", \"310922\"]}","task_split":"paper_retrieval"}
{"document_id":"169457","document_content":"# Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization\n## Categories\n- Computation and Language\n## Abstract\nDespite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11723v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2767\", \"9999\", \"26671\", \"149602\", \"40468\", \"46445\", \"55327\", \"55782\", \"75756\", \"80656\", \"91640\", \"92021\", \"157264\", \"83768\", \"96700\", \"126362\", \"128269\", \"143331\", \"169914\", \"164178\"], \"outgoing_citations\": [\"212506\", \"219618\", \"220230\", \"230456\", \"233710\", \"234220\", \"241070\", \"244102\", \"266671\", \"280557\", \"284150\", \"295665\", \"296029\", \"298627\", \"311470\", \"353402\"]}","task_split":"paper_retrieval"}
{"document_id":"169541","document_content":"# A Queuing Approach to Parking: Modeling, Verification, and Prediction\n## Categories\n- Machine Learning\n- Systems and Control\n- Systems and Control\n## Abstract\nWe present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary real-time prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11479v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SY\", \"eess.SY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Systems and Control\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [\"52334\", \"163183\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"169544","document_content":"# Detecting and Reducing Bias in a High Stakes Domain\n## Categories\n- Computation and Language\n## Abstract\nGang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep learning approach can predict aggression and loss in posts. To address the possibility of bias in this sensitive application, we developed an approach to systematically interpret the state of the art model. We found, surprisingly, that it frequently bases its predictions on stop words such as \"a\" or \"on\", an approach that could harm social media users who have no aggressive intentions. To tackle this bias, domain experts annotated the rationales, highlighting words that explain why a tweet is labeled as \"aggression\". These new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. Our study shows that in high stake scenarios, accuracy alone cannot guarantee a good system and we need new evaluation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11474v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"171131\", \"218393\", \"220022\", \"220101\", \"222725\", \"232388\", \"234308\", \"234716\", \"234801\", \"247127\", \"259870\", \"260450\", \"277288\", \"287474\"]}","task_split":"paper_retrieval"}
{"document_id":"169634","document_content":"# DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThis paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. DeepDistance uses the inner distances estimated by this FCN in a detection algorithm to locate individual cells in a given image. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model. This extended model includes an auxiliary classification task and learns it in parallel to the two regression tasks by sharing feature representations with them. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify cell locations, even for the cell line that was not used in training, and improve the results of the previous deep learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11211v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"88508\"], \"outgoing_citations\": [\"232877\", \"234653\", \"279607\"]}","task_split":"paper_retrieval"}
{"document_id":"169660","document_content":"# Probing Representations Learned by Multimodal Recurrent and Transformer Models\n## Categories\n- Computation and Language\n- 68T50\n- I.2.7\n## Abstract\nRecent literature shows that large-scale language modeling provides excellent reusable sentence representations with both recurrent and self-attentive architectures. However, there has been less clarity on the commonalities and differences in the representational properties induced by the two architectures. It also has been shown that visual information serves as one of the means for grounding sentence representations. In this paper, we present a meta-study assessing the representational quality of models where the training signal is obtained from different modalities, in particular, language modeling, image features prediction, and both textual and multimodal machine translation. We evaluate textual and visual features of sentence representations obtained using predominant approaches on image retrieval and semantic textual similarity. Our experiments reveal that on moderate-sized datasets, a sentence counterpart in a target language or visual modality provides much stronger training signal for sentence representation than language modeling. Importantly, we observe that while the Transformer models achieve superior machine translation quality, representations from the recurrent neural network based models perform significantly better over tasks focused on semantic relevance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11125v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\", \"I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210454\", \"210458\", \"217233\", \"231811\", \"238963\", \"261224\", \"261239\", \"266669\", \"268344\", \"275603\", \"287281\", \"311336\", \"312643\", \"319159\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"169680","document_content":"# A Deep Neural Information Fusion Architecture for Textual Network Embeddings\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Computation and Language\n## Abstract\nTextual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutual influences between them into account. In this paper, a deep neural architecture is proposed to effectively fuse the two kinds of informations into one representation. The novelties of the proposed architecture are manifested in the aspects of a newly defined objective function, the complementary information fusion method for structural and textual features, and the mutual gate mechanism for textual feature extraction. Experimental results show that the proposed model outperforms the comparing methods on all three datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.11057v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"51318\", \"88925\", \"91062\"], \"outgoing_citations\": [\"219939\", \"230664\", \"234014\", \"268186\", \"282952\"]}","task_split":"paper_retrieval"}
{"document_id":"169710","document_content":"# Spectral Regularization for Combating Mode Collapse in GANs\n## Categories\n- Machine Learning\n## Abstract\nDespite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal solution to the discriminator has a strong relationship to the spectral distributions of the weight matrix.Therefore, we monitor the spectral distribution in the discriminator of spectral normalized GANs (SN-GANs), and discover a phenomenon which we refer to as spectral collapse, where a large number of singular values of the weight matrices drop dramatically when mode collapse occurs. We show that there are strong evidence linking mode collapse to spectral collapse; and based on this link, we set out to tackle spectral collapse as a surrogate of mode collapse. We have developed a spectral regularization method where we compensate the spectral distributions of the weight matrices to prevent them from collapsing, which in turn successfully prevents mode collapse in GANs. We provide theoretical explanations for why SR-GANs are more stable and can provide better performances than SN-GANs. We also present extensive experimental results and analysis to show that SR-GANs not only always outperform SN-GANs but also always succeed in combating mode collapse where SN-GANs fail. The code is available at https:\/\/github.com\/max-liu-112\/SRGANs-Spectral-Regularization-GANs-.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.10999v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1131\", \"8848\", \"9076\", \"18588\", \"25661\", \"64407\", \"85306\", \"88938\", \"105167\", \"25607\", \"41925\", \"64746\", \"84846\", \"117711\", \"124229\"], \"outgoing_citations\": [\"242055\", \"245177\", \"252621\", \"265970\", \"266988\", \"270105\", \"275665\", \"284132\", \"297130\", \"297625\"]}","task_split":"paper_retrieval"}
{"document_id":"169794","document_content":"# DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not accurate, informative and engaging for the users. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. Our proposed approach extends pointer-generator networks (See et al., 2017) by allowing the decoder to hierarchically attend and copy from external knowledge in addition to the dialogue context. We empirically show the effectiveness of the proposed model compared to several baselines including (Ghazvininejad et al., 2018; Zhang et al., 2018) through both automatic evaluation metrics and human evaluation on CONVAI2 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.10731v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"95561\", \"6459\", \"12510\", \"16075\", \"17278\", \"24593\", \"24847\", \"48093\", \"64249\", \"127566\", \"136417\", \"88051\", \"99635\", \"103430\", \"122948\", \"126563\", \"128145\", \"130818\", \"132332\", \"140423\", \"141639\", \"148271\", \"156340\", \"156438\", \"171990\", \"170864\", \"175250\", \"183857\"], \"outgoing_citations\": [\"68965\", \"221363\", \"233352\", \"244479\", \"268186\", \"274567\", \"281162\", \"284092\", \"292261\", \"296250\", \"300604\", \"309411\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"169885","document_content":"# A survey of cross-lingual features for zero-shot cross-lingual semantic parsing\n## Categories\n- Computation and Language\n## Abstract\nThe availability of corpora to train semantic parsers in English has lead to significant advances in the field. Unfortunately, for languages other than English, annotation is scarce and so are developed parsers. We then ask: could a parser trained in English be applied to language that it hasn't been trained on? To answer this question we explore zero-shot cross-lingual semantic parsing where we train an available coarse-to-fine semantic parser (Liu et al., 2018) using cross-lingual word embeddings and universal dependencies in English and test it on Italian, German and Dutch. Results on the Parallel Meaning Bank - a multilingual semantic graphbank, show that Universal Dependency features significantly boost performance when used in conjunction with other lexical features but modelling the UD structure directly when encoding the input does not.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.10461v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"212423\", \"215455\", \"230064\", \"232288\", \"241085\", \"253559\", \"260977\", \"267877\", \"268859\", \"274095\", \"274271\", \"290632\", \"300505\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"170053","document_content":"# Real-world Conversational AI for Hotel Bookings\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nIn this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.10001v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"238321\", \"267692\"]}","task_split":"paper_retrieval"}
{"document_id":"170071","document_content":"# Theory and Evaluation Metrics for Learning Disentangled Representations\n## Categories\n- Machine Learning\n## Abstract\nWe make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept \"disentangled representations\" used in supervised and unsupervised methods along three dimensions-informativeness, separability and interpretability - which can be expressed and quantified explicitly using information-theoretic constructs. This helps explain the behaviors of several well-known disentanglement learning models. We then propose robust metrics for measuring informativeness, separability and interpretability. Through a comprehensive suite of experiments, we show that our metrics correctly characterize the representations learned by different methods and are consistent with qualitative (visual) results. Thus, the metrics allow disentanglement learning methods to be compared on a fair ground. We also empirically uncovered new interesting properties of VAE-based methods and interpreted them with our formulation. These findings are promising and hopefully will encourage the design of more theoretically driven models for learning disentangled representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09961v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"26622\", \"59823\", \"155351\", \"70887\", \"73900\", \"80015\", \"90014\", \"12936\", \"17237\", \"17658\", \"18244\", \"29575\", \"32216\", \"39178\", \"44223\", \"57749\", \"61817\", \"156914\", \"95962\", \"103627\", \"91384\", \"93568\", \"100660\", \"119691\", \"154401\"], \"outgoing_citations\": [\"201724\", \"205697\", \"206218\", \"207094\", \"208041\", \"231018\", \"233977\", \"241933\", \"242142\", \"242239\", \"251597\", \"261512\", \"264782\", \"277856\", \"278987\", \"280734\", \"316183\", \"328112\", \"333469\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"170203","document_content":"# Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMachine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set.   In cases where the model is available at translation time (when the test set is provided), it can be adapted with a small subset of data, thereby achieving better performance than a generic model or a domain-adapted model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09532v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"91745\", \"164809\"], \"outgoing_citations\": [\"180116\", \"210107\", \"211064\", \"229654\", \"259541\", \"277537\", \"284263\"]}","task_split":"paper_retrieval"}
{"document_id":"170229","document_content":"# Adaptive Embedding Gate for Attention-Based Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition has attracted particular research interest because it is a very challenging problem and has various applications. The most cutting-edge methods are attentional encoder-decoder frameworks that learn the alignment between the input image and output sequences. In particular, the decoder recurrently outputs predictions, using the prediction of the previous step as a guidance for every time step. In this study, we point out that the inappropriate use of previous predictions in existing attention mechanisms restricts the recognition performance and brings instability. To handle this problem, we propose a novel module, namely adaptive embedding gate(AEG). The proposed AEG focuses on introducing high-order character language models to attention mechanism by controlling the information transmission between adjacent characters. AEG is a flexible module and can be easily integrated into the state-of-the-art attentional methods. We evaluate its effectiveness as well as robustness on a number of standard benchmarks, including the IIIT$5$K, SVT, SVT-P, CUTE$80$, and ICDAR datasets. Experimental results demonstrate that AEG can significantly boost recognition performance and bring better robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09475v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7580\", \"8109\", \"126431\", \"135021\"], \"outgoing_citations\": [\"203406\", \"205988\", \"210634\", \"232706\", \"250738\", \"256711\", \"256785\", \"260510\", \"264533\", \"294023\", \"296685\", \"296828\", \"307547\", \"320148\", \"320837\", \"322955\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"170285","document_content":"# A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase Task\n## Categories\n- Machine Learning\n- Computation and Language\n- I.2.6; I.2.7\n## Abstract\nThe following paper presents a method of comparing two sets of vectors. The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors. It may be applicable when we compare the meanings of two sentences as part of the problem of paraphrasing. This is the problem of measuring semantic similarity of two sentences (group of words). The existing methods are not sensible for the word order or syntactic connections in the considered sentences. The method appears to be advantageous because it neither presents a group of words as one scalar value, nor does it try to show the closeness through an aggregation vector, which is mean for the set of vectors. Instead of that we measure the cosine of the angle as the mean for the first group vectors projections (the context) on one side and each vector of the second group on the other side. The similarity of two sentences defined by these means does not lose any semantic characteristics and takes account of the words traits. The method was verified on the comparison of sentence pairs in Russian.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09341v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\", \"I.2.6; I.2.7\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\", \"I.2.6; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"182855\", \"237218\"]}","task_split":"paper_retrieval"}
{"document_id":"170295","document_content":"# Multilingual Neural Machine Translation with Language Clustering\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMultilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09324v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"725\", \"56654\", \"2663\", \"16207\", \"16439\", \"22254\", \"22374\", \"24303\", \"26400\", \"35543\", \"38687\", \"44974\", \"55514\", \"55895\", \"65027\", \"92489\", \"107968\", \"127879\", \"30974\", \"23414\", \"86571\", \"93229\", \"95012\", \"107405\", \"113832\", \"125626\", \"129165\", \"131513\", \"127876\", \"129799\", \"148542\", \"149588\", \"168557\"], \"outgoing_citations\": [\"171399\", \"182210\", \"187452\", \"197295\", \"219545\", \"220330\", \"229517\", \"234632\", \"238703\", \"238926\", \"259840\", \"277444\", \"280447\", \"300548\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"170370","document_content":"# Query-Based Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of \"which person is mentioned in the text ?\". Such a strategy comes with the advantage that it solves the long-standing issue of handling overlapping or nested entities (the same token that participates in more than one entity categories) with sequence-labeling techniques for NER. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances. We experiment the proposed model on five widely used NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04 and ACE05. The proposed model sets new SOTA results on all of these datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09138v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"201190\", \"215547\", \"215552\", \"227323\", \"233136\", \"251798\", \"263874\", \"270020\", \"278104\", \"281162\", \"284528\", \"285811\", \"302500\", \"312608\"]}","task_split":"paper_retrieval"}
{"document_id":"170396","document_content":"# Optimal $\u03b4$-Correct Best-Arm Selection for General Distributions\n## Categories\n- Machine Learning\n- Probability\n- 65C05, 60-08\n## Abstract\nGiven a finite set of unknown distributions, or arms, that can be sampled, we consider the problem of identifying the one with the largest mean using a delta-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified delta) that has minimum sample complexity. Lower bounds for delta-correct algorithms are well known. Delta-correct algorithms that match the lower bound asymptotically as delta reduces to zero have been previously developed when arm distributions are restricted to a single parameter exponential family. In this paper, we first observe a negative result that some restrictions are essential, as otherwise under a delta-correct algorithm, distributions with unbounded support would require an infinite number of samples in expectation. We then propose a delta-correct algorithm that matches the lower bound as delta reduces to zero under the mild restriction that a known bound on the expectation of a non-negative, continuous, increasing convex function (for example, the squared moment) of the underlying random variables, exists. We also propose batch processing and identify near-optimal batch sizes to substantially speed up the proposed algorithm. The best-arm problem has many learning applications, including recommendation systems and product selection. It is also a well studied classic problem in the simulation community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09094v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.PR\", \"stat.ML\", \"65C05, 60-08\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Probability\", \"Machine Learning\", \"65C05, 60-08\"], \"incoming_citations\": [], \"outgoing_citations\": [\"210176\", \"297526\", \"298287\", \"326956\", \"329474\", \"335987\"]}","task_split":"paper_retrieval"}
{"document_id":"170407","document_content":"# DAST Model: Deciding About Semantic Complexity of a Text\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMeasuring text complexity is an essential task in several fields and applications (such as NLP, semantic web, smart education, etc.). The semantic layer of text is more tacit than its syntactic structure and, as a result, calculation of semantic complexity is more difficult than syntactic complexity. While there are famous and powerful academic and commercial syntactic complexity measures, the problem of measuring semantic complexity is still a challenging one. In this paper, we introduce the DAST model, which stands for Deciding About Semantic Complexity of a Text. DAST proposes an intuitionistic approach to semantics that lets us have a well-defined model for the semantics of a text and its complexity: semantic is considered as a lattice of intuitions and, as a result, semantic complexity is defined as the result of a calculation on this lattice. A set theoretic formal definition of semantic complexity, as a 6-tuple formal system, is provided. By using this formal system, a method for measuring semantic complexity is presented. The evaluation of the proposed approach is done by a set of three human-judgment experiments. The results show that DAST model is capable of deciding about semantic complexity of text. Furthermore, the analysis of the results leads us to introduce a Markovian model for the process of common-sense, multiple-steps and semantic-complexity reasoning in people. The results of Experiments demonstrate that our method outperforms the random baseline with improvement in better precision and competes with other methods by less error percentage.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.09080v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"61819\", \"79816\", \"112693\"], \"outgoing_citations\": [\"182361\", \"295673\"]}","task_split":"paper_retrieval"}
{"document_id":"170492","document_content":"# Deep Learning Based Chatbot Models\n## Categories\n- Computation and Language\n## Abstract\nA conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence. While chatbots can be used for various tasks, in general they have to understand users' utterances and provide responses that are relevant to the problem at hand.   In my work, I conduct an in-depth survey of recent literature, examining over 70 publications related to chatbots published in the last 3 years. Then, I proceed to make the argument that the very nature of the general conversation domain demands approaches that are different from current state-of-of-the-art architectures. Based on several examples from the literature I show why current chatbot models fail to take into account enough priors when generating responses and how this affects the quality of the conversation. In the case of chatbots, these priors can be outside sources of information that the conversation is conditioned on like the persona or mood of the conversers. In addition to presenting the reasons behind this problem, I propose several ideas on how it could be remedied.   The next section focuses on adapting the very recent Transformer model to the chatbot domain, which is currently state-of-the-art in neural machine translation. I first present experiments with the vanilla model, using conversations extracted from the Cornell Movie-Dialog Corpus. Secondly, I augment the model with some of my ideas regarding the issues of encoder-decoder architectures. More specifically, I feed additional features into the model like mood or persona together with the raw conversation data. Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features affect the quality of the generated responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.08835v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"98551\", \"128718\", \"91706\", \"120219\", \"167399\", \"186426\"], \"outgoing_citations\": [\"256632\", \"257858\", \"258039\", \"259192\", \"259505\", \"259515\", \"259704\", \"260965\", \"261784\", \"262814\", \"262827\", \"263059\", \"263551\", \"263620\", \"264149\", \"264214\", \"264897\", \"266155\", \"266671\", \"267526\", \"267641\", \"267727\", \"267881\", \"269757\", \"270040\", \"271085\", \"272752\", \"274244\", \"274253\", \"274567\", \"275478\", \"275597\", \"275599\", \"276160\", \"276365\", \"276382\", \"279215\", \"280884\", \"281508\", \"281822\", \"282136\", \"289943\", \"290747\", \"290766\", \"291019\", \"291102\", \"291186\", \"291289\", \"291916\", \"292261\", \"294494\", \"294537\", \"295863\", \"296198\", \"296250\", \"299541\", \"301313\", \"302771\", \"304343\", \"309411\", \"310377\", \"310740\", \"310903\", \"316328\", \"317213\", \"320814\", \"322537\", \"342679\"]}","task_split":"paper_retrieval"}
{"document_id":"170604","document_content":"# Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nDiverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. Common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word position. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach to anticipate the sentence continuation on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t sentence quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.08529v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"35427\", \"113488\", \"16013\", \"93187\", \"28921\", \"31502\", \"42583\", \"61394\", \"168075\", \"96190\", \"89629\", \"92644\", \"110219\", \"139263\", \"141639\", \"141978\"], \"outgoing_citations\": [\"219342\", \"229734\", \"236907\", \"237271\", \"249394\", \"249972\", \"250449\", \"269122\", \"270154\", \"271234\", \"279002\", \"281152\", \"291936\", \"302621\", \"310136\", \"311643\", \"312704\", \"320003\", \"321621\", \"322022\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"170695","document_content":"# Reinforcement Learning in Healthcare: A Survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAs a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.08796v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"30438\", \"6053\", \"12572\", \"13854\", \"15648\", \"23398\", \"74313\", \"2340\", \"5324\", \"947\", \"16345\", \"17932\", \"31177\", \"33819\", \"34568\", \"37058\", \"40737\", \"43441\", \"43851\", \"45200\", \"61817\", \"64141\", \"69664\", \"73865\", \"77491\", \"84405\", \"92299\", \"98984\", \"112076\", \"114596\", \"120402\", \"121693\", \"81864\", \"93235\", \"94852\", \"105327\", \"105972\", \"110696\", \"117210\", \"130430\", \"134482\", \"204248\", \"156013\"], \"outgoing_citations\": [\"199665\", \"202934\", \"203360\", \"203739\", \"207739\", \"208939\", \"221387\", \"221419\", \"225382\", \"225819\", \"225954\", \"226169\", \"227255\", \"228753\", \"229839\", \"230082\", \"231157\", \"236488\", \"240750\", \"241769\", \"242890\", \"243308\", \"244024\", \"247677\", \"248533\", \"248911\", \"249233\", \"250234\", \"250811\", \"257850\", \"258656\", \"260733\", \"261983\", \"263272\", \"265649\", \"268060\", \"268399\", \"270473\", \"272437\", \"276143\", \"278978\", \"279096\", \"280296\", \"280457\", \"284713\", \"286253\", \"286557\", \"288237\", \"291025\", \"294621\", \"295197\", \"295281\", \"295435\", \"298907\", \"301647\", \"302857\", \"303049\", \"303539\", \"303550\", \"306219\", \"306647\", \"306835\", \"310171\", \"316293\", \"328305\", \"332438\", \"355682\", \"356455\", \"358578\", \"361624\"]}","task_split":"paper_retrieval"}
{"document_id":"170700","document_content":"# Progressive Face Super-Resolution via Attention to Facial Landmark\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. The main challenge of face SR is to restore essential facial features without distortion. We propose a novel face SR method that generates photo-realistic 8x super-resolved face images with fully retained facial details. To that end, we adopt a progressive training method, which allows stable training by splitting the network into successive steps, each producing output with a progressively higher resolution. We also propose a novel facial attention loss and apply it at each step to focus on restoring facial attributes in greater details by multiplying the pixel difference and heatmap values. Lastly, we propose a compressed version of the state-of-the-art face alignment network (FAN) for landmark heatmap extraction. With the proposed FAN, we can extract the heatmaps suitable for face SR and also reduce the overall training time. Experimental results verify that our method outperforms state-of-the-art methods in both qualitative and quantitative measurements, especially in perceptual quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.08239v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17463\", \"37459\", \"76110\", \"11143\", \"18974\", \"52528\", \"1773\", \"20148\", \"49874\", \"74756\", \"76015\", \"83295\", \"83477\", \"99898\", \"103761\", \"94977\", \"100258\", \"108335\", \"111131\", \"116182\", \"125816\", \"127680\", \"132093\", \"134378\", \"137881\", \"149962\"], \"outgoing_citations\": [\"236318\", \"240034\", \"248019\", \"248969\", \"270923\"]}","task_split":"paper_retrieval"}
{"document_id":"170711","document_content":"# Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nUnifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network named as Mask TextSpotter is presented. Different from the previous text spotters that follow the pipeline consisting of a proposal generation network and a sequence-to-sequence recognition network, Mask TextSpotter enjoys a simple and smooth end-to-end learning procedure, in which both detection and recognition can be achieved directly from two-dimensional space via semantic segmentation. Further, a spatial attention module is proposed to enhance the performance and universality. Benefiting from the proposed two-dimensional representation on both detection and recognition, it easily handles text instances of irregular shapes, for instance, curved text. We evaluate it on four English datasets and one multi-language dataset, achieving consistently superior performance over state-of-the-art methods in both detection and end-to-end text recognition tasks. Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.08207v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"170800","document_content":"# Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets\n## Categories\n- Computation and Language\n## Abstract\nCrowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a few workers generate the majority of examples raises concerns about data diversity, especially when workers freely generate sentences. In this paper, we perform a series of experiments showing these concerns are evident in three recent NLP datasets. We show that model performance improves when training with annotator identifiers as features, and that models are able to recognize the most productive annotators. Moreover, we show that often models do not generalize well to examples from annotators that did not contribute to the training set. Our findings suggest that annotator bias should be monitored during dataset creation, and that test set annotators should be disjoint from training set annotators.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.07898v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2372\", \"33114\", \"2601\", \"2432\", \"2767\", \"3092\", \"4186\", \"5296\", \"9357\", \"9635\", \"15862\", \"16498\", \"17166\", \"18058\", \"23557\", \"32080\", \"44795\", \"45294\", \"80072\", \"91712\", \"101277\", \"4745\", \"13292\", \"17812\", \"19115\", \"19139\", \"19957\", \"21332\", \"21249\", \"22976\", \"25862\", \"29086\", \"29789\", \"31613\", \"33588\", \"41253\", \"42117\", \"42161\", \"42662\", \"42580\", \"45420\", \"45477\", \"45583\", \"46606\", \"48217\", \"51322\", \"54565\", \"55232\", \"55188\", \"55256\", \"55510\", \"55804\", \"55988\", \"56479\", \"56463\", \"58881\", \"62508\", \"66058\", \"67313\", \"67498\", \"76684\", \"77585\", \"77629\", \"77573\", \"78504\", \"80696\", \"81551\", \"83028\", \"83776\", \"95425\", \"118058\", \"132550\", \"148371\", \"86624\", \"92778\", \"93660\", \"95013\", \"95890\", \"96106\", \"96515\", \"96995\", \"99278\", \"102079\", \"112349\", \"116151\", \"116399\", \"118406\", \"121866\", \"126188\", \"127447\", \"127537\", \"114670\", \"127708\", \"130525\", \"130767\", \"131629\", \"131864\", \"131846\", \"131952\", \"132773\", \"132681\", \"128406\", \"157244\", \"143050\", \"158701\", \"158744\", \"174583\", \"165930\", \"168621\", \"171572\", \"183410\", \"218247\"], \"outgoing_citations\": [\"196980\", \"208526\", \"211788\", \"211953\", \"218698\", \"233045\", \"233416\", \"233420\", \"234664\", \"239831\", \"274586\", \"279213\"]}","task_split":"paper_retrieval"}
{"document_id":"170805","document_content":"# Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound\n## Categories\n- Machine Learning\n- Image and Video Processing\n## Abstract\nOne of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are maximally informative with respect to relevant tasks, while an adversarial regularization encourages these features to be disentangled and minimally informative about irrelevant tasks. We aim to use the disentangled representations to generalize the applicability of deep neural networks. We demonstrate the advantages of the proposed method on synthetic data as well as fetal ultrasound images. Our experiments illustrate that our method is capable of learning disentangled internal representations. It outperforms baseline methods in multiple tasks, especially on images with new properties, e.g. previously unseen artifacts in fetal ultrasound.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.07885v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"27322\", \"78514\", \"156914\", \"90149\", \"119691\", \"139306\"], \"outgoing_citations\": [\"209361\", \"211948\", \"219142\", \"230691\", \"236093\", \"241933\", \"250037\", \"257180\", \"277171\", \"280734\", \"280882\"]}","task_split":"paper_retrieval"}
{"document_id":"170863","document_content":"# Improving Neural Machine Translation with Pre-trained Representation\n## Categories\n- Computation and Language\n## Abstract\nMonolingual data has been demonstrated to be helpful in improving the translation quality of neural machine translation (NMT). The current methods stay at the usage of word-level knowledge, such as generating synthetic parallel data or extracting information from word embedding. In contrast, the power of sentence-level contextual knowledge which is more complex and diverse, playing an important role in natural language generation, has not been fully exploited. In this paper, we propose a novel structure which could leverage monolingual data to acquire sentence-level contextual representations. Then, we design a framework for integrating both source and target sentence-level representations into NMT model to improve the translation quality. Experimental results on Chinese-English, German-English machine translation tasks show that our proposed model achieves improvement over strong Transformer baselines, while experiments on English-Turkish further demonstrate the effectiveness of our approach in the low-resource scenario.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.07688v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"100144\"], \"outgoing_citations\": [\"141949\", \"193295\", \"213165\", \"219545\", \"220160\", \"235312\", \"240377\", \"241611\", \"255999\", \"260929\", \"280884\", \"280973\", \"298750\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"171049","document_content":"# Feature Interaction-aware Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nInspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks. However, most real-world graphs often come with high-dimensional and sparse node features, rendering the learned node representations from existing GNN architectures less expressive. In this paper, we propose \\textit{Feature Interaction-aware Graph Neural Networks (FI-GNNs)}, a plug-and-play GNN framework for learning node representations encoded with informative feature interactions. Specifically, the proposed framework is able to highlight informative feature interactions in a personalized manner and further learn highly expressive node representations on feature-sparse graphs. Extensive experiments on various datasets demonstrate the superior capability of FI-GNNs for graph learning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.07110v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"112676\", \"170916\"], \"outgoing_citations\": [\"258393\", \"258416\", \"258567\", \"264424\", \"287508\"]}","task_split":"paper_retrieval"}
{"document_id":"171187","document_content":"# Reinforcement Learning Applications\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe start with a brief introduction to reinforcement learning (RL), about its successful stories, basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it, study material and an outlook. Then we discuss a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.06973v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"34882\", \"52134\", \"15472\", \"20946\", \"24082\", \"29746\", \"30463\", \"41444\", \"42791\", \"45015\", \"57924\", \"110593\", \"195153\", \"8041\", \"13841\", \"14518\", \"20789\", \"31793\", \"34755\", \"37058\", \"40116\", \"41002\", \"44791\", \"44969\", \"20450\", \"154720\", \"53943\", \"57965\", \"64383\", \"64536\", \"64770\", \"68391\", \"68877\", \"75694\", \"88549\", \"96593\", \"97468\", \"101868\", \"103481\", \"105259\", \"116293\", \"126018\", \"173896\", \"84808\", \"86275\", \"89232\", \"89661\", \"106665\", \"47007\", \"114813\", \"119012\", \"123336\", \"124703\", \"123759\", \"134432\", \"136694\", \"138403\", \"139508\", \"144260\", \"146122\", \"194039\", \"225557\", \"151700\", \"153438\", \"154020\", \"152103\", \"172393\", \"184540\", \"191187\", \"190572\", \"197905\", \"201935\", \"232429\", \"255359\", \"255357\", \"263288\", \"273531\", \"115175\", \"249790\", \"264051\"], \"outgoing_citations\": [\"307024\", \"342370\", \"343050\", \"350185\", \"361126\"]}","task_split":"paper_retrieval"}
{"document_id":"171308","document_content":"# Word and character segmentation directly in run-length compressed handwritten document images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFrom the literature, it is demonstrated that performing text-line segmentation directly in the run-length compressed handwritten document images significantly reduces the computational time and memory space. In this paper, we investigate the issues of word and character segmentation directly on the run-length compressed document images. Primarily, the spreads of the characters are intelligently extracted from the foreground runs of the compressed data and subsequently connected components are established. The spacing between the connected components would be larger between the adjacent words when compared to that of intra-words. With this knowledge, a threshold is empirically chosen for inter-word separation. Every connected component within a word is further analysed for character segmentation. Here, min-cut graph concept is used for separating the touching characters. Over-segmentation and under-segmentation issues are addressed by insertion and deletion operations respectively. The approach has been developed particularly for compressed handwritten English document images. However, the model has been tested on non-English document images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1909.05146v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204095\", \"258271\"]}","task_split":"paper_retrieval"}
{"document_id":"171475","document_content":"# ZeroER: Entity Resolution using Zero Labeled Examples\n## Categories\n- Databases\n- Machine Learning\n## Abstract\nEntity resolution (ER) refers to the problem of matching records in one or more relations that refer to the same real-world entity. While supervised machine learning (ML) approaches achieve the state-of-the-art results, they require a large amount of labeled examples that are expensive to obtain and often times infeasible. We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches? In this paper, we answer in the affirmative through our proposed approach dubbed ZeroER. Our approach is based on a simple observation -- the similarity vectors for matches should look different from that of unmatches. Operationalizing this insight requires a number of technical innovations. First, we propose a simple yet powerful generative model based on Gaussian Mixture Models for learning the match and unmatch distributions. Second, we propose an adaptive regularization technique customized for ER that ameliorates the issue of feature overfitting. Finally, we incorporate the transitivity property into the generative model in a novel way resulting in improved accuracy. On five benchmark ER datasets, we show that ZeroER greatly outperforms existing unsupervised approaches and achieves comparable performance to supervised approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3318464.3389743\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.LG\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Machine Learning\"], \"incoming_citations\": [\"580\", \"34615\", \"106744\", \"28970\", \"40718\", \"189622\", \"64612\", \"82783\", \"85712\"], \"outgoing_citations\": [\"216139\", \"233435\", \"246852\", \"249126\", \"254315\", \"325941\", \"339835\", \"355567\", \"357927\", \"360441\"]}","task_split":"paper_retrieval"}
{"document_id":"171483","document_content":"# Performing Deep Recurrent Double Q-Learning for Atari Games\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nCurrently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.06040v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"9836\"], \"outgoing_citations\": [\"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"171614","document_content":"# Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nPredictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the learning rate or step size). To begin to address this challenge, we examine the use of online step-size adaptation using a sensor-rich robotic arm. Our method of choice, Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step sizes on a feature level; importantly, TIDBD allows step-size tuning and representation learning to occur at the same time. We show that TIDBD is a practical alternative for classic Temporal-Difference (TD) learning via an extensive parameter search. Both approaches perform comparably in terms of predicting future aspects of a robotic data stream. Furthermore, the use of a step-size adaptation method like TIDBD appears to allow a system to automatically detect and characterize common sensor failures in a robotic application. Together, these results promise to improve the ability of robotic devices to learn from interactions with their environments in a robust way, providing key capabilities for autonomous agents and robots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.05751v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"90962\"], \"outgoing_citations\": [\"175636\", \"195994\", \"224222\", \"237929\", \"250883\", \"254494\", \"290182\"]}","task_split":"paper_retrieval"}
{"document_id":"171631","document_content":"# A Multimodal Vision Sensor for Autonomous Driving\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera. Each sensor provides a specific dimension of information: the stereo camera measures depth per pixel, the polarization obtains the degree of polarization, and the panoramic camera captures a 360-degree landscape. Data fusion and advanced environment perception could be built upon the combination of sensors. Designed especially for autonomous driving, this vision sensor is shipped with a robust semantic segmentation network. In addition, we demonstrate how cross-modal enhancement could be achieved by registering the color image and the polarization image. An example of water hazard detection is given. To prove the multimodal vision sensor's compatibility with different devices, a brief runtime performance analysis is carried out.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.05649v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"4150\", \"66713\", \"84503\", \"171558\"], \"outgoing_citations\": [\"171558\", \"186450\"]}","task_split":"paper_retrieval"}
{"document_id":"171770","document_content":"# Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Networking and Internet Architecture\n- Machine Learning\n## Abstract\nFacial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak.We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3308558.3313398\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Networking and Internet Architecture\", \"Machine Learning\"], \"incoming_citations\": [\"12777\", \"106842\", \"146159\"], \"outgoing_citations\": [\"237062\", \"251011\", \"285985\", \"320027\", \"328183\", \"342934\"]}","task_split":"paper_retrieval"}
{"document_id":"171783","document_content":"# Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach\n## Categories\n- Robotics\n- Artificial Intelligence\n- Systems and Control\n- Machine Learning\n- Systems and Control\n- 68T05, 68T40, 93C85\n## Abstract\nDeep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions may be hard to specify\/model and\/or to compute. Moreover, the transfer of policies learned in a simulator to the real-world has limitations (reality gap). On the other hand, machine learning methods that rely on the transfer of human knowledge to an agent have shown to be time efficient for obtaining well performing policies and do not require a reward function. In this context, we analyze the use of human corrective feedback during task execution to learn policies with high-dimensional state spaces, by using the D-COACH framework, and we propose new variants of this framework. D-COACH is a Deep Learning based extension of COACH (COrrective Advice Communicated by Humans), where humans are able to shape policies through corrective advice. The enhanced version of D-COACH, which is proposed in this paper, largely reduces the time and effort of a human for training a policy. Experimental results validate the efficiency of the D-COACH framework in three different problems (simulated and with real robots), and show that its enhanced version reduces the human training effort considerably, and makes it feasible to learn policies within periods of time in which a DRL agent do not reach any improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.05256v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"eess.SY\", \"cs.LG\", \"cs.SY\", \"cs.RO\", \"68T05, 68T40, 93C85\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Systems and Control\", \"Machine Learning\", \"Systems and Control\", \"Robotics\", \"68T05, 68T40, 93C85\"], \"incoming_citations\": [\"18315\", \"108289\"], \"outgoing_citations\": [\"215939\", \"246118\", \"253448\", \"254581\", \"263957\", \"275728\", \"283410\", \"306266\"]}","task_split":"paper_retrieval"}
{"document_id":"171811","document_content":"# Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nRecent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence embedding techniques learn a sentence-to-vector mapping and compute the similarity between the sentence vectors via simple elementary operations. In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. The outline of DSE is as follows: Given a cross-attentive teacher model (e.g. a fine-tuned BERT), we train a sentence embedding based student model to reconstruct the sentence-pair scores obtained by the teacher model. We empirically demonstrate the effectiveness of DSE on five GLUE sentence-pair tasks. DSE significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT. Furthermore, we show that DSE produces sentence embeddings that reach state-of-the-art performance on universal sentence representation benchmarks. Our code is made publicly available at https:\/\/github.com\/microsoft\/Distilled-Sentence-Embedding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.05161v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2999\", \"3642\", \"56187\", \"94572\", \"98578\", \"98453\", \"131610\", \"132613\", \"141570\", \"142115\"], \"outgoing_citations\": [\"189623\", \"200764\", \"237157\", \"238963\", \"253507\", \"259723\", \"267161\", \"296180\", \"296605\", \"303997\", \"310737\"]}","task_split":"paper_retrieval"}
{"document_id":"171823","document_content":"# Two-stage Federated Phenotyping and Patient Representation Learning\n## Categories\n- Information Retrieval\n- Computation and Language\n- Machine Learning\n## Abstract\nA large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.05596v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"9036\", \"4040\", \"6279\", \"29243\", \"71429\", \"80685\", \"3685\", \"25741\", \"33171\", \"68872\", \"84135\", \"83656\", \"85321\", \"96241\", \"141198\", \"156643\", \"207951\"], \"outgoing_citations\": [\"194178\", \"204950\", \"207951\", \"208335\", \"233107\"]}","task_split":"paper_retrieval"}
{"document_id":"171882","document_content":"# VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nEmbodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04950v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"23074\", \"36974\", \"112751\", \"85467\", \"121716\", \"124578\"], \"outgoing_citations\": [\"191394\", \"191878\", \"192738\", \"197653\", \"197658\", \"200941\", \"205641\", \"207866\", \"210374\", \"211939\", \"212893\", \"219048\", \"222356\", \"222620\", \"227507\", \"239633\", \"245731\", \"247418\", \"247900\", \"248742\", \"255134\", \"268586\", \"278655\", \"279420\", \"284449\", \"301767\", \"303837\", \"313143\"]}","task_split":"paper_retrieval"}
{"document_id":"171902","document_content":"# Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nAlthough significant progress has been made in the field of automatic image captioning, it is still a challenging task. Previous works normally pay much attention to improving the quality of the generated captions but ignore the diversity of captions. In this paper, we combine determinantal point process (DPP) and reinforcement learning (RL) and propose a novel reinforcing DPP (R-DPP) approach to generate a set of captions with high quality and diversity for an image. We show that R-DPP performs better on accuracy and diversity than using noise as a control signal (GANs, VAEs). Moreover, R-DPP is able to preserve the modes of the learned distribution. Hence, beam search algorithm can be applied to generate a single accurate caption, which performs better than other RL-based models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04919v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"28498\", \"42273\", \"89629\", \"112250\"], \"outgoing_citations\": [\"193328\", \"208615\", \"229734\", \"239242\", \"249972\", \"255878\", \"270154\", \"271234\", \"278604\", \"278954\", \"287504\", \"291817\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"171932","document_content":"# Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nConversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3342775.3342792\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"14656\", \"85615\", \"123020\"], \"outgoing_citations\": [\"88668\", \"174962\", \"204637\", \"245399\", \"256632\", \"257406\", \"296250\", \"310377\", \"310740\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"171960","document_content":"# Towards Self-Explainable Cyber-Physical Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nWith the increasing complexity of CPSs, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the MAB-EX framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04698v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"29849\"], \"outgoing_citations\": [\"239909\", \"273118\"]}","task_split":"paper_retrieval"}
{"document_id":"172062","document_content":"# Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04436v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"45163\", \"81276\", \"133479\"], \"outgoing_citations\": [\"200920\", \"226544\", \"229002\", \"243671\", \"257674\"]}","task_split":"paper_retrieval"}
{"document_id":"172079","document_content":"# Why Does a Visual Question Have Different Answers?\n## Categories\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nVisual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ~45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https:\/\/vizwiz.org.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04342v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"93187\", \"15076\", \"56499\", \"60150\", \"65303\", \"82839\", \"99374\", \"96085\", \"73544\", \"131617\", \"131885\", \"134604\", \"141296\"], \"outgoing_citations\": [\"241203\", \"250313\", \"258987\", \"259007\", \"267560\", \"267621\", \"270343\", \"276578\", \"281930\", \"301507\", \"313320\", \"317410\"]}","task_split":"paper_retrieval"}
{"document_id":"172088","document_content":"# Neural Text Generation with Unlikelihood Training\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nNeural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-$k$ and nucleus sampling, they do not address the fact that the token-level probabilities predicted by the model are poor. In this paper we show that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences containing repeats and frequent words, unlike those from the human training distribution. We propose a new objective, unlikelihood training, which forces unlikely generations to be assigned lower probability by the model. We show that both token and sequence level unlikelihood training give less repetitive, less dull text while maintaining perplexity, giving superior generations using standard greedy or beam search. According to human evaluations, our approach with standard beam search also outperforms the currently popular decoding methods of nucleus sampling or beam blocking, thus providing a strong alternative to existing techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04319v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8099\", \"43010\", \"63787\", \"2490\", \"2579\", \"2790\", \"3028\", \"3398\", \"3822\", \"3863\", \"12342\", \"15870\", \"16498\", \"16612\", \"25242\", \"30225\", \"35323\", \"50907\", \"56179\", \"77878\", \"2611\", \"11478\", \"14502\", \"15317\", \"15263\", \"17035\", \"22231\", \"22443\", \"22489\", \"22716\", \"23521\", \"23573\", \"24581\", \"24852\", \"25186\", \"25264\", \"27098\", \"27131\", \"29580\", \"30826\", \"31077\", \"31590\", \"39618\", \"40832\", \"42357\", \"42895\", \"43242\", \"44997\", \"46309\", \"47416\", \"50478\", \"50969\", \"51160\", \"53489\", \"55196\", \"55188\", \"56593\", \"60895\", \"168075\", \"68961\", \"70413\", \"71966\", \"75973\", \"77378\", \"77389\", \"77697\", \"77735\", \"78009\", \"77913\", \"88960\", \"91672\", \"105667\", \"120964\", \"128396\", \"129680\", \"132645\", \"132791\", \"184591\", \"78207\", \"79729\", \"89518\", \"91421\", \"92151\", \"94067\", \"94347\", \"94752\", \"96135\", \"97157\", \"98838\", \"99616\", \"102649\", \"105315\", \"108688\", \"115939\", \"116873\", \"120280\", \"121677\", \"124668\", \"127048\", \"127538\", \"127524\", \"127580\", \"128009\", \"131385\", \"131629\", \"132871\", \"132799\", \"134803\", \"143750\", \"150522\", \"157151\", \"157308\", \"157654\", \"157654\", \"189504\", \"165356\", \"167338\", \"168651\"], \"outgoing_citations\": [\"168228\", \"196340\", \"197924\", \"200725\", \"211804\", \"216217\", \"221363\", \"231949\", \"232274\", \"244479\", \"250505\", \"250577\", \"254855\", \"279543\", \"283618\", \"283971\", \"301840\", \"302815\"]}","task_split":"paper_retrieval"}
{"document_id":"172143","document_content":"# Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning\n## Categories\n- Computation and Language\n- Machine Learning\n- 68Uxx\n## Abstract\nNatural Language Understanding (NLU) models are typically trained in a supervised learning framework. In the case of intent classification, the predicted labels are predefined and based on the designed annotation schema while the labelling process is based on a laborious task where annotators manually inspect each utterance and assign the corresponding label. We propose an Active Annotation (AA) approach where we combine an unsupervised learning method in the embedding space, a human-in-the-loop verification process, and linguistic insights to create lexicons that can be open categories and adapted over time. In particular, annotators define the y-label space on-the-fly during the annotation using an iterative process and without the need for prior knowledge about the input data. We evaluate the proposed annotation paradigm in a real use-case NLU scenario. Results show that our Active Annotation paradigm achieves accurate and higher quality training data, with an annotation speed of an order of magnitude higher with respect to the traditional human-only driven baseline annotation methodology.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.04092v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"68Uxx\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"68Uxx\"], \"incoming_citations\": [], \"outgoing_citations\": [\"222981\", \"295576\"]}","task_split":"paper_retrieval"}
{"document_id":"172292","document_content":"# Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nOnline discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems -- predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as `cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions.   We employ four baselines -- first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement -- whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.03770v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"118809\"], \"outgoing_citations\": [\"267075\", \"267075\", \"290615\", \"290615\", \"295130\", \"295130\", \"299050\", \"299050\", \"332327\", \"332327\", \"347335\", \"347335\"]}","task_split":"paper_retrieval"}
{"document_id":"172343","document_content":"# A Generate-Validate Approach to Answering Questions about Qualitative Relationships\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nQualitative relationships describe how increasing or decreasing one property (e.g. altitude) affects another (e.g. temperature). They are an important aspect of natural language question answering and are crucial for building chatbots or voice agents where one may enquire about qualitative relationships. Recently a dataset about question answering involving qualitative relationships has been proposed, and a few approaches to answer such questions have been explored, in the heart of which lies a semantic parser that converts the natural language input to a suitable logical form. A problem with existing semantic parsers is that they try to directly convert the input sentences to a logical form. Since the output language varies with each application, it forces the semantic parser to learn almost everything from scratch. In this paper, we show that instead of using a semantic parser to produce the logical form, if we apply the generate-validate framework i.e. generate a natural language description of the logical form and validate if the natural language description is followed from the input text, we get a better scope for transfer learning and our method outperforms the state-of-the-art by a large margin of 7.93%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.03645v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"44782\", \"129807\"], \"outgoing_citations\": [\"188246\", \"209413\", \"284357\"]}","task_split":"paper_retrieval"}
{"document_id":"172372","document_content":"# BERT-based Ranking for Biomedical Entity Normalization\n## Categories\n- Information Retrieval\n- Computation and Language\n- Machine Learning\n## Abstract\nDeveloping high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings. Bidirectional Encoder Representations from Transformers (BERT), BERT for Biomedical Text Mining (BioBERT) and BERT for Clinical Text Mining (ClinicalBERT) were recently introduced to pre-train contextualized word representation models using bidirectional Transformers, advancing the state-of-the-art for many natural language processing tasks. In this study, we proposed an entity normalization architecture by fine-tuning the pre-trained BERT \/ BioBERT \/ ClinicalBERT models and conducted extensive experiments to evaluate the effectiveness of the pre-trained models for biomedical entity normalization using three different types of datasets. Our experimental results show that the best fine-tuned models consistently outperformed previous methods and advanced the state-of-the-art for biomedical entity normalization, with up to 1.17% increase in accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.03548v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"2937\", \"12775\", \"28469\", \"9763\", \"25606\", \"42207\", \"64374\", \"74057\", \"76012\", \"80172\", \"92185\", \"97522\", \"98792\", \"127623\", \"101295\", \"127709\", \"136153\"], \"outgoing_citations\": [\"191145\", \"191942\", \"197910\", \"325200\"]}","task_split":"paper_retrieval"}
{"document_id":"172394","document_content":"# Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions. We build a separate multi-modal embedding space for each PoS tag. The outputs of multiple PoS embeddings are then used as input to an integrated multi-modal space, where we perform action retrieval. All embeddings are trained jointly through a combination of PoS-aware and PoS-agnostic losses. Our proposal enables learning specialised embedding spaces that offer multiple views of the same embedded entities.   We report the first retrieval results on fine-grained actions for the large-scale EPIC dataset, in a generalised zero-shot setting. Results show the advantage of our approach for both video-to-text and text-to-video action retrieval. We also demonstrate the benefit of disentangling the PoS for the generic task of cross-modal video retrieval on the MSR-VTT dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.03477v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1081\", \"2284\", \"5478\", \"116568\", \"50907\", \"60281\", \"101354\", \"12199\", \"12778\", \"13933\", \"22104\", \"10669\", \"28085\", \"28057\", \"33374\", \"37316\", \"51492\", \"54595\", \"54820\", \"55460\", \"59465\", \"62053\", \"62318\", \"62806\", \"64186\", \"64249\", \"66734\", \"73046\", \"81508\", \"84992\", \"96230\", \"105055\", \"118223\", \"121870\", \"128066\", \"110610\", \"114022\", \"115367\", \"124223\", \"139259\", \"151281\", \"151327\", \"154316\", \"236377\"], \"outgoing_citations\": [\"174419\", \"181897\", \"204141\", \"217562\", \"221984\", \"234325\", \"236377\", \"236468\", \"244938\", \"245735\", \"256883\", \"263185\", \"263838\", \"265650\", \"269128\", \"269599\", \"283914\", \"286978\", \"292616\", \"294983\", \"295117\", \"295204\", \"303011\", \"306520\", \"317032\", \"322022\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"172526","document_content":"# FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n- http:\/\/www.acm.org\/about\/class\/1998\n## Abstract\nUsers increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3289600.3290990\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\", \"http:\/\/www.acm.org\/about\/class\/1998\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\", \"http:\/\/www.acm.org\/about\/class\/1998\"], \"incoming_citations\": [\"50291\", \"69255\", \"130459\", \"155463\"], \"outgoing_citations\": [\"298850\"]}","task_split":"paper_retrieval"}
{"document_id":"172538","document_content":"# Vision-based Navigation Using Deep Reinforcement Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nDeep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning architecture capable of navigating an agent, e.g. a mobile robot, to a target given by an image. To achieve this, we have extended the batched A2C algorithm with auxiliary tasks designed to improve visual navigation performance. We propose three additional auxiliary tasks: predicting the segmentation of the observation image and of the target image and predicting the depth-map. These tasks enable the use of supervised learning to pre-train a large part of the network and to reduce the number of training steps substantially. The training performance has been further improved by increasing the environment complexity gradually over time. An efficient neural network structure is proposed, which is capable of learning for multiple targets in multiple environments. Our method navigates in continuous state spaces and on the AI2-THOR environment simulator outperforms state-of-the-art goal-oriented visual navigation methods from the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ECMR.2019.8870964\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"stat.ML\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"70725\", \"84017\", \"88744\", \"92592\", \"93649\", \"127961\", \"155831\"], \"outgoing_citations\": [\"245731\", \"247418\", \"249137\", \"258384\", \"278195\", \"279420\", \"280296\", \"280681\", \"284565\"]}","task_split":"paper_retrieval"}
{"document_id":"172540","document_content":"# Editing Text in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look. This task is challenging, as the styles of both background and text need to be preserved so that the edited image is visually indistinguishable from the source image. Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module. The text conversion module changes the text content of the source image into the target text while keeping the original text style. The background inpainting module erases the original text, and fills the text region with appropriate texture. The fusion module combines the information from the two former modules, and generates the edited text images. To our knowledge, this work is the first attempt to edit text in natural images at the word level. Both visual effects and quantitative results on synthetic and real-world dataset (ICDAR 2013) fully confirm the importance and necessity of modular decomposition. We also conduct extensive experiments to validate the usefulness of our method in various real-world applications such as text image synthesis, augmented reality (AR) translation, information hiding, etc.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.03047v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"41858\", \"5857\", \"7829\", \"18970\", \"25406\", \"33798\", \"36052\", \"47572\", \"47763\", \"48702\", \"56713\", \"57555\", \"72298\", \"85941\", \"120486\", \"147463\", \"126431\", \"134664\", \"136190\", \"210634\"], \"outgoing_citations\": [\"190708\", \"191931\", \"196632\", \"205813\", \"207554\", \"210634\", \"234812\", \"248131\", \"248586\", \"250165\", \"262738\", \"269205\", \"279400\", \"294023\", \"294606\", \"300421\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"172586","document_content":"# Image Captioning using Facial Expression and Attention\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nBenefiting from advances in machine vision and natural language processing techniques, current image captioning systems are able to generate detailed visual descriptions. For the most part, these descriptions represent an objective characterisation of the image, although some models do incorporate subjective aspects related to the observer's view of the image, such as sentiment; current models, however, usually do not consider the emotional content of images during the caption generation process. This paper addresses this issue by proposing novel image captioning models which use facial expression features to generate image captions. The models generate image captions using long short-term memory networks applying facial features in addition to other visual features at different time steps. We compare a comprehensive collection of image captioning models with and without facial features using all standard evaluation metrics. The evaluation metrics indicate that applying facial features with an attention mechanism achieves the best performance, showing more expressive and more correlated image captions, on an image caption dataset extracted from the standard Flickr 30K dataset, consisting of around 11K images containing faces. An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.02923v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"74810\"], \"outgoing_citations\": [\"172581\", \"182725\", \"208888\", \"215181\", \"222042\", \"225101\", \"225585\", \"231647\", \"243816\", \"265677\", \"269654\", \"272531\", \"278399\", \"278604\", \"278954\", \"280246\", \"282943\", \"287504\", \"292831\", \"292925\", \"296681\", \"300099\", \"302621\", \"305473\", \"306659\", \"310816\", \"310903\", \"316435\", \"319781\", \"320003\", \"322022\", \"340958\", \"343074\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"172795","document_content":"# MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- 2010\n## Abstract\nDeep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3343031.3350887\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"2010\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"2010\"], \"incoming_citations\": [\"13971\", \"102763\", \"114938\", \"100515\", \"106416\"], \"outgoing_citations\": [\"188167\", \"209782\", \"231401\", \"239403\", \"239659\", \"240914\", \"242048\", \"244112\", \"245632\", \"245633\", \"247960\", \"250297\", \"252039\", \"256156\", \"259762\", \"261577\", \"264726\", \"265664\", \"269349\", \"271037\", \"273542\", \"274033\", \"280873\", \"291989\", \"310191\"]}","task_split":"paper_retrieval"}
{"document_id":"172818","document_content":"# Experiential AI\n## Categories\n- Computers and Society\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nExperiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.02619v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.LG\", \"cs.CY\", \"cs.AI\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"172825","document_content":"# Hermitian matrices for clustering directed graphs: insights and applications\n## Categories\n- Machine Learning\n## Abstract\nGraph clustering is a basic technique in machine learning, and has widespread applications in different domains. While spectral techniques have been successfully applied for clustering undirected graphs, the performance of spectral clustering algorithms for directed graphs (digraphs) is not in general satisfactory: these algorithms usually require symmetrising the matrix representing a digraph, and typical objective functions for undirected graph clustering do not capture cluster-structures in which the information given by the direction of the edges is crucial. To overcome these downsides, we propose a spectral clustering algorithm based on a complex-valued matrix representation of digraphs. We analyse its theoretical performance on a Stochastic Block Model for digraphs in which the cluster-structure is given not only by variations in edge densities, but also by the direction of the edges. The significance of our work is highlighted on a data set pertaining to internal migration in the United States: while previous spectral clustering algorithms for digraphs can only reveal that people are more likely to move between counties that are geographically close, our approach is able to cluster together counties with a similar socio-economical profile even when they are geographically distant, and illustrates how people tend to move from rural to more urbanised areas.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.02096v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"77906\", \"37050\", \"43332\", \"43356\", \"67592\", \"68117\", \"87888\", \"115322\", \"133384\", \"204157\"], \"outgoing_citations\": [\"277250\", \"314986\", \"317322\", \"322124\", \"341716\"]}","task_split":"paper_retrieval"}
{"document_id":"173028","document_content":"# A Fast Content-Based Image Retrieval Method Using Deep Visual Features\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n## Abstract\nFast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01505v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"48791\"], \"outgoing_citations\": [\"227036\"]}","task_split":"paper_retrieval"}
{"document_id":"173076","document_content":"# Deep Neural Network for Semantic-based Text Recognition in Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel \"semantic-based text recognition\" (STR) deep learning model that reads text in images with the help of understanding context. STR consists of several modules. We introduce the Text Grouping and Arranging (TGA) algorithm to connect and order isolated text regions. A text-recognition network interprets isolated words. Benefiting from semantic information, a sequenceto-sequence network model efficiently corrects inaccurate and uncertain phrases produced earlier in the STR pipeline. We present experiments on two new distinct datasets that contain scanned catalog images of interior designs and photographs of protesters with hand-written signs, respectively. Our results show that our STR model outperforms a baseline method that uses state-of-the-art single-wordrecognition techniques on both datasets. STR yields a high accuracy rate of 90% on the catalog images and 71% on the more difficult protest images, suggesting its generality in recognizing text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01403v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5857\", \"123550\"], \"outgoing_citations\": [\"216936\", \"232543\", \"232706\", \"255492\", \"255549\", \"258039\", \"294023\", \"296685\", \"310903\", \"319833\", \"320814\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"173132","document_content":"# A View on Deep Reinforcement Learning in System Optimization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Systems and Control\n- Systems and Control\n## Abstract\nMany real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often sequentially aggregated reward, are often inherently reinforcement learning problems and present the opportunity to leverage the recent substantial advances in deep reinforcement learning. However, in some cases, it is not clear why deep reinforcement learning is a good fit for the problem. Sometimes, it does not perform better than the state-of-the-art solutions. And in other cases, random search or greedy algorithms could outperform deep reinforcement learning. In this paper, we review, discuss, and evaluate the recent trends of using deep reinforcement learning in system optimization. We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization. Our evaluation includes challenges, the types of problems, their formulation in the deep reinforcement learning setting, embedding, the model used, efficiency, and robustness. We conclude with a discussion on open challenges and potential directions for pushing further the integration of reinforcement learning in system optimization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01275v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SY\", \"eess.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"30438\", \"124235\", \"109359\", \"139108\", \"157539\", \"165758\"], \"outgoing_citations\": [\"179728\", \"187437\", \"191754\", \"197359\", \"204842\", \"205913\", \"210901\", \"212016\", \"213852\", \"221810\", \"225125\", \"238049\", \"240484\", \"241814\", \"245178\", \"257206\", \"260842\", \"266004\", \"267980\", \"271671\", \"310211\"]}","task_split":"paper_retrieval"}
{"document_id":"173299","document_content":"# Network with Sub-Networks\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce network with sub-networks, a neural network which its weight layers could be detached into sub-neural networks during inference. To develop weights and biases which could be inserted in both base and sub-neural networks, firstly, the parameters are copied from sub-model to base-model. Each model is forward-propagated separately. Gradients from a pair of networks are averaged and, used to update both networks. Our base model achieves the test-accuracy which is comparable to the regularly trained models, while the model maintains the ability to detach weight layers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.2991\/jrnal.k.201215.006\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"205171\"]}","task_split":"paper_retrieval"}
{"document_id":"173463","document_content":"# JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English\n## Categories\n- Computation and Language\n## Abstract\nIn the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generated by a Statistical Machine Translation model. The current paper documents the architecture of our model, descriptions of the various modules and the results produced using the same. Our system garnered a BLEU score of 17.6.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01349v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"173466\", \"206271\", \"282931\"]}","task_split":"paper_retrieval"}
{"document_id":"173466","document_content":"# JUCBNMT at WMT2018 News Translation Task: Character Based Neural Machine Translation of Finnish to English\n## Categories\n- Computation and Language\n## Abstract\nIn the current work, we present a description of the system submitted to WMT 2018 News Translation Shared task. The system was created to translate news text from Finnish to English. The system used a Character Based Neural Machine Translation model to accomplish the given task. The current paper documents the preprocessing steps, the description of the submitted system and the results produced using the same. Our system garnered a BLEU score of 12.9.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.00323v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16027\", \"173463\"], \"outgoing_citations\": [\"282931\"]}","task_split":"paper_retrieval"}
{"document_id":"173673","document_content":"# InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based methods are simple, yet effective for obtaining fixed-length representations for graphs but they suffer from poor generalization due to hand-crafted designs. There are also some recent methods based on language models (e.g. graph2vec) but they tend to only consider certain substructures (e.g. subtrees) as graph representatives. Inspired by recent progress of unsupervised representation learning, in this paper we proposed a novel method called InfoGraph for learning graph-level representations. We maximize the mutual information between the graph-level representation and the representations of substructures of different scales (e.g., nodes, edges, triangles). By doing so, the graph-level representations encode aspects of the data that are shared across different scales of substructures. Furthermore, we further propose InfoGraph*, an extension of InfoGraph for semi-supervised scenarios. InfoGraph* maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned by existing supervised methods. As a result, the supervised encoder learns from unlabeled data while preserving the latent semantic space favored by the current supervised task. Experimental results on the tasks of graph classification and molecular property prediction show that InfoGraph is superior to state-of-the-art baselines and InfoGraph* can achieve performance competitive with state-of-the-art semi-supervised models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01000v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"18076\", \"33190\", \"34279\", \"47526\", \"49353\", \"66165\", \"67813\", \"94177\", \"118617\", \"222\", \"1511\", \"2025\", \"2094\", \"2524\", \"2832\", \"3205\", \"4125\", \"5189\", \"5282\", \"5283\", \"6002\", \"6154\", \"8265\", \"11343\", \"13106\", \"15441\", \"18175\", \"18914\", \"20365\", \"21405\", \"21990\", \"24963\", \"41310\", \"42926\", \"65306\", \"66588\", \"66762\", \"69784\", \"80211\", \"82775\", \"102775\", \"122995\", \"133777\", \"1162\", \"2973\", \"5740\", \"7914\", \"8235\", \"11318\", \"11375\", \"12874\", \"13779\", \"13961\", \"16194\", \"19370\", \"25345\", \"26075\", \"27319\", \"27805\", \"27877\", \"29221\", \"29202\", \"18909\", \"13637\", \"34643\", \"37775\", \"37805\", \"39993\", \"41406\", \"41932\", \"43059\", \"43244\", \"43042\", \"43277\", \"43872\", \"44064\", \"44493\", \"40616\", \"46443\", \"46926\", \"47300\", \"49084\", \"49800\", \"50036\", \"50362\", \"50859\", \"51716\", \"54670\", \"56361\", \"60996\", \"61210\", \"61869\", \"66867\", \"74440\", \"75522\", \"78815\", \"79731\", \"80836\", \"81505\", \"84204\", \"87870\", \"88159\", \"92214\", \"92652\", \"94952\", \"164939\", \"95455\", \"100975\", \"101265\", \"56175\", \"79045\", \"79289\", \"81711\", \"91654\", \"91794\", \"92075\", \"95191\", \"95635\", \"96691\", \"98636\", \"99187\", \"111026\", \"113666\", \"113751\", \"113411\", \"113746\", \"115132\", \"115700\", \"117754\", \"117926\", \"137073\", \"120250\", \"121166\", \"119843\", \"138048\", \"142825\", \"185430\", \"205283\", \"155422\"], \"outgoing_citations\": [\"180989\", \"183816\", \"195794\", \"228120\", \"229936\", \"234340\", \"241933\", \"242322\", \"242430\", \"249122\", \"260450\", \"261085\", \"290743\", \"291213\", \"291301\"]}","task_split":"paper_retrieval"}
{"document_id":"173711","document_content":"# Landmark Detection in Low Resolution Faces with Semi-Supervised Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLandmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images. This deters the performance of algorithms relying on quality landmarks, for example, face recognition. To the best of our knowledge, there does not exist any dataset consisting of low resolution face images along with their annotated landmarks, making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks on low resolution images by learning them from labeled high resolution images. The objective of this work is to show that predicting landmarks directly on low resolution images is more effective than the current practice of aligning images after rescaling or superresolution. In a two-step process, the proposed approach first learns to generate low resolution images by modeling the distribution of target low resolution images. In the second stage, the roles of generated images and real low resolution images are switched and the model learns to predict landmarks for real low resolution images from generated low resolution images. With extensive experimentation, we study the impact of each of the design choices and also show that prediction of landmarks directly on low resolution images improves the performance of important tasks such as face recognition in low resolution images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.13255v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"222871\", \"230415\", \"239321\", \"241654\", \"248019\", \"267612\", \"269567\", \"270105\", \"270923\", \"273864\", \"284986\", \"297167\", \"302868\", \"318646\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"173837","document_content":"# Towards Pure End-to-End Learning for Recognizing Multiple Text Sequences from an Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHere we address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, and we try to recognize all the text sequences contained in the image. 2) Pure end-to-end (PEE) learning.We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of all contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations.Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns multiple sequences of probability distribution conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding strategy.To evaluate the proposed method, we constructed several datasets mainly based on an existing public dataset andtwo real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.12791v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"232706\", \"234390\", \"239002\", \"239535\", \"245591\", \"245899\", \"250738\", \"256711\", \"257954\", \"261365\", \"262570\", \"269205\", \"272390\", \"279950\", \"294753\", \"296685\", \"296828\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"173877","document_content":"# Deep Non-Rigid Structure from Motion with Missing Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nNon-Rigid Structure from Motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from an ensemble of images with 2D correspondences. Current NRSfM algorithms are limited from two perspectives: (i) the number of images, and (ii) the type of shape variability they can handle. These difficulties stem from the inherent conflict between the condition of the system and the degrees of freedom needing to be modeled -- which has hampered its practical utility for many applications within vision. In this paper we propose a novel hierarchical sparse coding model for NRSFM which can overcome (i) and (ii) to such an extent, that NRSFM can be applied to problems in vision previously thought too ill posed. Our approach is realized in practice as the training of an unsupervised deep neural network (DNN) auto-encoder with a unique architecture that is able to disentangle pose from 3D structure. Using modern deep learning computational platforms allows us to solve NRSfM problems at an unprecedented scale and shape complexity. Our approach has no 3D supervision, relying solely on 2D point correspondences. Further, our approach is also able to handle missing\/occluded 2D points without the need for matrix completion. Extensive experiments demonstrate the impressive performance of our approach where we exhibit superior precision and robustness against all available state-of-the-art works in some instances by an order of magnitude. We further propose a new quality measure (based on the network weights) which circumvents the need for 3D ground-truth to ascertain the confidence we have in the reconstructability. We believe our work to be a significant advance over state-of-the-art in NRSFM.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.13123v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25139\", \"29208\"], \"outgoing_citations\": [\"197181\", \"220697\", \"244221\", \"287620\", \"293595\", \"302266\", \"312499\"]}","task_split":"paper_retrieval"}
{"document_id":"173882","document_content":"# Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nCurrent AI approaches have frequently been used to help personalize many aspects of medical experiences and tailor them to a specific individuals' needs. However, while such systems consider medically-relevant information, they ignore socially-relevant information about how this diagnosis should be communicated and discussed with the patient. The lack of this capability may lead to mis-communication, resulting in serious implications, such as patients opting out of the best treatment. Consider a case in which the same treatment is proposed to two different individuals. The manner in which this treatment is mediated to each should be different, depending on the individual patient's history, knowledge, and mental state. While it is clear that this communication should be conveyed via a human medical expert and not a software-based system, humans are not always capable of considering all of the relevant aspects and traversing all available information. We pose the challenge of creating Intelligent Agents (IAs) to assist medical service providers (MSPs) and consumers in establishing a more personalized human-to-human dialogue. Personalizing conversations will enable patients and MSPs to reach a solution that is best for their particular situation, such that a relation of trust can be built and commitment to the outcome of the interaction is assured. We propose a four-part conceptual framework for personalized social interactions, expand on which techniques are available within current AI research and discuss what has yet to be achieved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.12687v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"250010\"]}","task_split":"paper_retrieval"}
{"document_id":"174023","document_content":"# Multi-Granularity Fusion Network for Proposal and Activity Localization: Submission to ActivityNet Challenge 2019 Task 1 and Task 2\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis technical report presents an overview of our solution used in the submission to ActivityNet Challenge 2019 Task 1 (\\textbf{temporal action proposal generation}) and Task 2 (\\textbf{temporal action localization\/detection}). Temporal action proposal indicates the temporal intervals containing the actions and plays an important role in temporal action localization. Top-down and bottom-up methods are the two main categories used for proposal generation in the existing literature. In this paper, we devise a novel Multi-Granularity Fusion Network (MGFN) to combine the proposals generated from different frameworks for complementary filtering and confidence re-ranking. Specifically, we consider the diversity comprehensively from multiple perspectives, e.g. the characteristic aspect, the data aspect, the model aspect and the result aspect. Our MGFN achieves the state-of-the-art performance on the temporal action proposal task with 69.85 AUC score and the temporal action localization task with 38.90 mAP on the challenge testing set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.12223v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"228841\", \"253115\", \"260631\", \"271198\", \"272028\", \"300351\"]}","task_split":"paper_retrieval"}
{"document_id":"174126","document_content":"# Towards Model-based Reinforcement Learning for Industry-near Environments\n## Categories\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. While these algorithms are fundamentally different, both suffer from high variance, low sample efficiency, and hyperparameter sensitivity that in practice, make these algorithms a no-go for critical operations in the industry.   On the other hand, model-based reinforcement learning focuses on learning the transition dynamics between states in an environment. If these environment dynamics are adequately learned, a model-based approach is perhaps the most sample efficient method for learning agents to act in an environment optimally. The traits of model-based reinforcement are ideal for real-world environments where sampling is slow and for mission-critical operations. In the warehouse industry, there is an increasing motivation to minimise time and to maximise production. Currently, autonomous agents act suboptimally using handcrafted policies for significant portions of the state-space.   In this paper, we present The Dreaming Variational Autoencoder v2 (DVAE-2), a model-based reinforcement learning algorithm that increases sample efficiency, hence enable algorithms with low sample efficiency function better in real-world environments. We introduce Deep Warehouse, a simulated environment for industry-near testing of autonomous agents in grid-based warehouses. Finally, we illustrate that DVAE-2 improves the sample efficiency for the Deep Warehouse compared to model-free methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11971v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179594\", \"179948\", \"204844\", \"210517\", \"215743\", \"219180\", \"221283\", \"225762\", \"229890\", \"247242\", \"290433\"]}","task_split":"paper_retrieval"}
{"document_id":"174226","document_content":"# Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models\n## Categories\n- Artificial Intelligence\n- Methodology\n## Abstract\nDecision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is considered at the associative (probabilistic) level. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rational choice using associative information: maximize expected utility. There is an ongoing debate around the origin of probabilities involved in such calculation. In this work, we will show how the probabilities for decision-making can be grounded in causal models by considering decision problems in which the available actions and consequences are causally connected. In this setting, actions are regarded as an intervention over a causal model. Then, we extend a previous causal decision-making result, which relies on a known causal model, to the case in which the causal mechanism that controls some environment is unknown to a rational decision-maker. In this way, action-outcome probabilities can be grounded in causal models in known and unknown cases. Finally, as an application, we extend the well-known concept of Nash Equilibrium to the case in which the players of a strategic game consider causal information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11752v6\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ME\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Methodology\"], \"incoming_citations\": [], \"outgoing_citations\": [\"357931\"]}","task_split":"paper_retrieval"}
{"document_id":"174232","document_content":"# Environment Probing Interaction Policies\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the 'Environment-Probing' Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment's behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11740v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5373\", \"6152\", \"16608\", \"18892\", \"36626\", \"43678\", \"55156\", \"56639\", \"63262\", \"97906\", \"107437\", \"118638\", \"176979\", \"91372\", \"105878\", \"114309\", \"121390\", \"125165\", \"126647\", \"128103\", \"162155\", \"155001\", \"163450\", \"181647\"], \"outgoing_citations\": [\"234517\", \"236393\", \"238343\", \"243236\", \"253042\", \"253045\", \"261786\", \"271975\", \"272030\", \"274445\", \"280202\", \"280570\", \"280870\", \"281091\", \"282684\", \"283289\", \"293995\", \"296509\", \"326879\", \"357681\"]}","task_split":"paper_retrieval"}
{"document_id":"174277","document_content":"# Cooperative image captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhen describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a \"speaker network\" that generates sentences given an image, and a \"listener network\" that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate to achieve a joint task, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient. Second, joint training usually causes the vocabulary used during communication to drift and diverge from natural language.   We describe an approach that addresses both challenges. We first develop a new effective optimization based on partial-sampling from a multinomial distribution combined with straight-through gradient updates, which we name PSST for Partial-Sampling Straight-Through. Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions. Together, this approach creates descriptions that are both more discriminative and more natural than previous approaches. Evaluations on the standard COCO benchmark show that PSST Multinomial dramatically improve the recall@10 from 60% to 86% maintaining comparable language naturalness, and human evaluations show that it also increases naturalness while keeping the discriminative power of generated captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11565v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"93187\", \"28498\", \"86447\", \"112250\", \"120519\", \"133574\"], \"outgoing_citations\": [\"193328\", \"204730\", \"213330\", \"219639\", \"219687\", \"231619\", \"236907\", \"238125\", \"239242\", \"251792\", \"253508\", \"253934\", \"260926\", \"262814\", \"270154\", \"270915\", \"271085\", \"271234\", \"276436\", \"277491\", \"283076\", \"287504\", \"295368\", \"297855\", \"303240\", \"311008\"]}","task_split":"paper_retrieval"}
{"document_id":"174291","document_content":"# DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThe popularity of Convolutional Neural Network (CNN) in the field of Image Processing and Computer Vision has motivated researchers and industrialist experts across the globe to solve different challenges with high accuracy. The simplest way to train a CNN classifier is to directly feed the original RGB pixels images into the network. However, if we intend to classify images directly with its compressed data, the same approach may not work better, like in case of JPEG compressed images. This research paper investigates the issues of modifying the input representation of the JPEG compressed data, and then feeding into the CNN. The architecture is termed as DCT-CompCNN. This novel approach has shown that CNNs can also be trained with JPEG compressed DCT coefficients, and subsequently can produce a better performance in comparison with the conventional CNN approach. The efficiency of the modified input representation is tested with the existing ResNet-50 architecture and the proposed DCT-CompCNN architecture on a public image classification datasets like Dog Vs Cat and CIFAR-10 datasets, reporting a better performance","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11503v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"36284\", \"36782\", \"95417\", \"96694\", \"96694\"], \"outgoing_citations\": [\"248556\", \"297079\"]}","task_split":"paper_retrieval"}
{"document_id":"174386","document_content":"# A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHuman actions in videos are 3D signals. However, there are a few methods available for multiple human action recognition. For long videos, it's difficult to search within a video for a specific action and\/or person. For that, this paper proposes a new technic for multiple human action recognition and summarization for surveillance videos. The proposed approach proposes a new representation of the data by extracting the sequence of each person from the scene. This is followed by an analysis of each sequence to detect and recognize the corresponding actions using 3D convolutional neural networks (3DCNNs). Action-based video summarization is performed by saving each person's action at each time of the video. Results of this work revealed that the proposed method provides accurate multi human action recognition that easily used for summarization of any action. Further, for other videos that can be collected from the internet, which are complex and not built for surveillance applications, the proposed model was evaluated on some datasets like UCF101 and YouTube without any preprocessing. For this category of videos, the summarization is performed on the video sequences by summarizing the actions in each subsequence. The results obtained demonstrate its efficiency compared to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11272v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"8476\"], \"outgoing_citations\": [\"132836\", \"219422\", \"253704\", \"254968\", \"291753\", \"308931\", \"316528\"]}","task_split":"paper_retrieval"}
{"document_id":"174398","document_content":"# Prediction of Highway Lane Changes Based on Prototype Trajectories\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nThe vision of automated driving is to increase both road safety and efficiency, while offering passengers a convenient travel experience. This requires that autonomous systems correctly estimate the current traffic scene and its likely evolution. In highway scenarios early recognition of cut-in maneuvers is essential for risk-aware maneuver planning. In this paper, a statistical approach is proposed, which advantageously utilizes a set of prototypical lane change trajectories to realize both early maneuver detection and uncertainty-aware trajectory prediction for traffic participants. Generation of prototype trajectories from real traffic data is accomplished by Agglomerative Hierarchical Clustering. During clustering, the alignment of the cluster prototypes to each other is optimized and the cohesion of the resulting prototype is limited when two clusters merge. In the prediction stage, the similarity of observed vehicle motion and typical lane change patterns in the data base is evaluated to construct a set of significant features for maneuver classification via Boosted Decision Trees. The future trajectory is predicted combining typical lane change realizations in a mixture model. B-splines based trajectory adaptations guarantee continuity during transition from actually observed to predicted vehicle states. Quantitative evaluation results demonstrate the proposed concept's improved performance for both maneuver and trajectory prediction compared to a previously implemented reference approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11208v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"174419","document_content":"# Learning Visual Actions Using Multiple Verb-Only Labels\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work introduces verb-only representations for both recognition and retrieval of visual actions, in video. Current methods neglect legitimate semantic ambiguities between verbs, instead choosing unambiguous subsets of verbs along with objects to disambiguate the actions. We instead propose multiple verb-only labels, which we learn through hard or soft assignment as a regression. This enables learning a much larger vocabulary of verbs, including contextual overlaps of these verbs. We collect multi-verb annotations for three action video datasets and evaluate the verb-only labelling representations for action recognition and cross-modal retrieval (video-to-text and text-to-video). We demonstrate that multi-label verb-only representations outperform conventional single verb labels. We also explore other benefits of a multi-verb representation including cross-dataset retrieval and verb type manner and result verb types) retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.11117v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"172394\"], \"outgoing_citations\": [\"204141\", \"234222\", \"236377\", \"236918\", \"237201\", \"248855\", \"249394\", \"259012\", \"259867\", \"263838\", \"265650\", \"287573\", \"294983\", \"295117\", \"295598\", \"303209\", \"303377\", \"304481\", \"311647\", \"313196\", \"316803\", \"320003\", \"320332\", \"332079\", \"336346\"]}","task_split":"paper_retrieval"}
{"document_id":"174501","document_content":"# Performance Evaluation of Two-layer lossless HDR Coding using Histogram Packing Technique under Various Tone-mapping Operators\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nWe proposed a lossless two-layer HDR coding method using a histogram packing technique. The proposed method was demonstrated to outperform the normative JPEG XT encoder, under the use of the default tone-mapping operator. However, the performance under various tone-mapping operators has not been discussed. In this paper, we aim to compare the performance of the proposed method with that of the JPEG XT encoder under the use of various tone-mapping operators to clearly show the characteristic difference between them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.10889v1\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"188994\", \"222526\", \"248795\"]}","task_split":"paper_retrieval"}
{"document_id":"174508","document_content":"# Summary Refinement through Denoising\n## Categories\n- Computation and Language\n## Abstract\nWe propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.10873v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"218755\", \"219618\", \"230212\", \"234436\", \"241070\", \"280557\"]}","task_split":"paper_retrieval"}
{"document_id":"174554","document_content":"# Visual Interaction with Deep Learning Models through Collaborative Semantic Inference\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nAutomation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approaches lack explainable reasoning. We argue that both the visual interface and model structure of deep learning systems need to take into account interaction design. We propose a framework of collaborative semantic inference (CSI) for the co-design of interactions and models to enable visual collaboration between humans and algorithms. The approach exposes the intermediate reasoning process of models which allows semantic interactions with the visual metaphors of a problem, which means that a user can both understand and control parts of the model reasoning process. We demonstrate the feasibility of CSI with a co-designed case study of a document summarization system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.10739v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"10002\", \"15086\", \"26064\", \"45412\", \"55782\", \"56606\", \"58978\", \"74036\", \"102439\", \"122635\", \"102200\", \"128960\", \"153138\"], \"outgoing_citations\": [\"173919\", \"205056\", \"205161\", \"205684\", \"206169\", \"208621\", \"209451\", \"210518\", \"218181\", \"219618\", \"230325\", \"232274\", \"234308\", \"241273\", \"244557\", \"251595\", \"252033\", \"258731\", \"259890\", \"267258\", \"269127\", \"269542\", \"273959\", \"274749\", \"280282\", \"289715\", \"290515\", \"310759\", \"311845\"]}","task_split":"paper_retrieval"}
{"document_id":"174761","document_content":"# Structured Knowledge Discovery from Massive Text Corpus\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nNowadays, with the booming development of the Internet, people benefit from its convenience due to its open and sharing nature. A large volume of natural language texts is being generated by users in various forms, such as search queries, documents, and social media posts. As the unstructured text corpus is usually noisy and messy, it becomes imperative to correctly identify and accurately annotate structured information in order to obtain meaningful insights or better understand unstructured texts. On the other hand, the existing structured information, which embodies our knowledge such as entity or concept relations, often suffers from incompleteness or quality-related issues. Given a gigantic collection of texts which offers rich semantic information, it is also important to harness the massiveness of the unannotated text corpus to expand and refine existing structured knowledge with fewer annotation efforts.   In this dissertation, I will introduce principles, models, and algorithms for effective structured knowledge discovery from the massive text corpus. We are generally interested in obtaining insights and better understanding unstructured texts with the help of structured annotations or by structure-aware modeling. Also, given the existing structured knowledge, we are interested in expanding its scale and improving its quality harnessing the massiveness of the text corpus. In particular, four problems are studied in this dissertation: Structured Intent Detection for Natural Language Understanding, Structure-aware Natural Language Modeling, Generative Structured Knowledge Expansion, and Synonym Refinement on Structured Knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1908.01837v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"179881\", \"186123\", \"205014\", \"213168\", \"218202\", \"219441\", \"229259\", \"237938\", \"244722\", \"248279\", \"249299\", \"252703\", \"255999\", \"262328\", \"262896\", \"271675\", \"273380\", \"274120\", \"278104\", \"281119\", \"283732\", \"285317\", \"286797\", \"299541\", \"303469\", \"309411\", \"315446\", \"316630\", \"317401\"]}","task_split":"paper_retrieval"}
{"document_id":"174767","document_content":"# Exploring Factors for Improving Low Resolution Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates significantly when they are applied on low quality images, such as those acquired by surveillance cameras. A further challenge for low resolution face recognition for surveillance applications is the matching of recorded low resolution probe face images with high resolution reference images, which could be the case in watchlist scenarios. In this paper, we have addressed these problems and investigated the factors that would contribute to the identification performance of the state-of-the-art deep face recognition models when they are applied to low resolution face recognition under mismatched conditions. We have observed that the following factors affect performance in a positive way: appearance variety and resolution distribution of the training dataset, resolution matching between the gallery and probe images, and the amount of information included in the probe images. By leveraging this information, we have utilized deep face models trained on MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.10104v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"31799\", \"36730\", \"60697\", \"74756\", \"263345\"], \"outgoing_citations\": [\"209118\", \"290775\", \"300053\", \"318091\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"174906","document_content":"# A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Databases\n## Abstract\nFederated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TKDE.2021.3124599\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DB\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Databases\", \"Machine Learning\"], \"incoming_citations\": [\"9619\", \"11266\", \"37430\", \"196\", \"1154\", \"4079\", \"7921\", \"8859\", \"21385\", \"28219\", \"36972\", \"37180\", \"37702\", \"18313\", \"48513\", \"52799\", \"56473\", \"71707\", \"110259\", \"118784\", \"138025\", \"1964\", \"3685\", \"3990\", \"13408\", \"13449\", \"14468\", \"15294\", \"24849\", \"27374\", \"30490\", \"25048\", \"32427\", \"32980\", \"33532\", \"33823\", \"9725\", \"36880\", \"39350\", \"42201\", \"42569\", \"42825\", \"42929\", \"46867\", \"51457\", \"54391\", \"59584\", \"61466\", \"62742\", \"63272\", \"71546\", \"73211\", \"74949\", \"86772\", \"88156\", \"97129\", \"98267\", \"112927\", \"87186\", \"92901\", \"107570\", \"107500\", \"128895\", \"132169\", \"136391\", \"156968\", \"156972\", \"152856\"], \"outgoing_citations\": [\"59584\", \"66099\", \"71707\", \"74725\", \"91963\", \"92901\", \"96456\", \"97129\", \"100854\", \"102145\", \"103936\", \"109147\", \"109503\", \"110433\", \"112023\", \"113040\", \"118344\", \"118376\", \"118784\", \"119091\", \"120251\", \"120533\", \"130313\", \"133977\", \"138557\", \"138775\", \"139315\", \"142093\", \"143220\", \"151488\", \"151798\", \"153498\", \"154095\", \"155653\", \"156968\", \"156972\", \"157530\", \"161063\", \"161758\", \"161865\", \"163085\", \"163734\", \"164627\", \"164869\", \"166444\", \"169018\", \"170810\", \"172803\", \"172902\", \"173721\", \"174723\", \"174862\", \"176577\", \"177601\", \"178024\", \"178962\", \"179935\", \"180560\", \"180726\", \"181136\", \"183930\", \"184652\", \"186014\", \"186022\", \"187044\", \"195752\", \"196107\", \"199085\", \"200347\", \"200704\", \"201117\", \"201645\", \"202443\", \"204176\", \"204345\", \"204421\", \"205519\", \"205577\", \"205910\", \"206767\", \"206770\", \"206873\", \"207462\", \"207491\", \"207518\", \"207602\", \"207623\", \"208013\", \"208297\", \"210718\", \"210812\", \"210868\", \"214903\", \"217180\", \"217320\", \"221338\", \"221592\", \"223847\", \"226146\", \"228744\", \"229563\", \"229765\", \"232435\", \"232524\", \"233440\", \"234078\", \"234581\", \"234890\", \"235592\", \"239350\", \"239655\", \"241287\", \"241304\", \"242402\", \"245480\", \"246939\", \"247399\", \"248216\", \"248982\", \"252956\", \"257563\", \"260314\", \"263636\", \"265073\", \"265492\", \"268784\", \"273221\", \"280506\", \"282399\", \"282410\", \"282453\", \"283045\", \"285761\", \"290361\", \"291691\", \"301578\", \"302815\", \"305383\", \"316013\", \"316293\", \"328077\", \"329116\", \"353750\"]}","task_split":"paper_retrieval"}
{"document_id":"175014","document_content":"# Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Multimedia\n## Abstract\nThis paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different sizes which are evaluated in terms of retrieval performance. A robust experimental protocol using two public datasets (Tobacoo-800 and DocExplore) has shown that the proposed methods compare favorably against state-of-the-art document image retrieval and pattern spotting methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.09404v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Multimedia\"], \"incoming_citations\": [\"28390\", \"201134\"], \"outgoing_citations\": [\"179469\", \"179821\", \"249570\", \"295204\", \"314231\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"175042","document_content":"# Syntax-aware Neural Semantic Role Labeling\n## Categories\n- Computation and Language\n## Abstract\nSemantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.09312v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"4956\", \"6019\", \"16648\", \"77364\", \"94951\", \"92475\", \"100441\", \"100582\", \"131352\", \"156847\", \"159954\", \"183096\"], \"outgoing_citations\": [\"219714\", \"221642\", \"234631\", \"240157\", \"248279\", \"260977\", \"271527\", \"281121\", \"291941\", \"300593\", \"304281\", \"308043\", \"309148\"]}","task_split":"paper_retrieval"}
{"document_id":"175096","document_content":"# FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nWith the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.09173v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"9036\", \"9619\", \"33160\", \"124304\", \"334137\", \"2562\", \"2909\", \"5829\", \"6050\", \"7132\", \"8238\", \"16289\", \"23352\", \"24510\", \"36515\", \"44872\", \"47195\", \"49221\", \"51766\", \"66296\", \"80685\", \"83092\", \"99336\", \"99659\", \"110259\", \"1054\", \"3990\", \"12362\", \"18850\", \"26435\", \"15215\", \"26724\", \"30901\", \"33823\", \"34342\", \"39695\", \"45457\", \"50932\", \"54391\", \"54364\", \"55896\", \"58325\", \"15067\", \"63541\", \"65940\", \"69249\", \"72911\", \"74139\", \"80435\", \"85147\", \"98267\", \"98410\", \"101821\", \"82838\", \"83656\", \"85379\", \"87047\", \"87101\", \"88714\", \"88830\", \"89680\", \"91517\", \"98990\", \"109503\", \"133655\", \"134725\", \"135973\", \"140296\", \"156643\", \"166473\", \"91517\"], \"outgoing_citations\": [\"192670\", \"201645\", \"224094\", \"226118\", \"226787\", \"241304\", \"246939\", \"261491\", \"265073\", \"296192\", \"303155\"]}","task_split":"paper_retrieval"}
{"document_id":"175309","document_content":"# Structure-Invariant Testing for Machine Translation\n## Categories\n- Software Engineering\n- Computation and Language\n## Abstract\nIn recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, the complexity and intractability of neural machine translation (NMT) models that power modern machine translation make the robustness of these systems difficult to even assess, much less guarantee. Machine translation systems can return inferior results that lead to misunderstanding, medical misdiagnoses, threats to personal safety, or political conflicts. Despite its apparent importance, validating the robustness of machine translation systems is very difficult and has, therefore, been much under-explored.   To tackle this challenge, we introduce structure-invariant testing (SIT), a novel metamorphic testing approach for validating machine translation software. Our key insight is that the translation results of \"similar\" source sentences should typically exhibit similar sentence structures. Specifically, SIT (1) generates similar source sentences by substituting one word in a given sentence with semantically similar, syntactically equivalent words; (2) represents sentence structure by syntax parse trees (obtained via constituency or dependency parsing); (3) reports sentence pairs whose structures differ quantitatively by more than some threshold. To evaluate SIT, we use it to test Google Translate and Bing Microsoft Translator with 200 source sentences as input, which led to 64 and 70 buggy issues with 69.5\\% and 70\\% top-1 accuracy, respectively. The translation errors are diverse, including under-translation, over-translation, incorrect modification, word\/phrase mistranslation, and unclear logic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.08710v3\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.SE\", \"cs.CL\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Software Engineering\", \"Computation and Language\"], \"incoming_citations\": [\"5033\", \"18454\", \"83039\", \"5416\", \"31231\", \"36262\", \"95353\", \"84643\", \"129726\", \"79297\"], \"outgoing_citations\": [\"139551\", \"179903\", \"194080\", \"196616\", \"201954\", \"206004\", \"206156\", \"212775\", \"213362\", \"217321\", \"220017\", \"220346\", \"225546\", \"226990\", \"232097\", \"232158\", \"233535\", \"233739\", \"234716\", \"235345\", \"237770\", \"238354\", \"238702\", \"238926\", \"243083\", \"251240\", \"257512\", \"259984\", \"260450\", \"266128\", \"267867\", \"269735\", \"274817\", \"276358\", \"280973\", \"288229\", \"290789\", \"291899\", \"308857\"]}","task_split":"paper_retrieval"}
{"document_id":"175358","document_content":"# Predicting Human Activities from User-Generated Content\n## Categories\n- Computation and Language\n## Abstract\nThe activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.08540v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"39225\"], \"outgoing_citations\": [\"231675\", \"234778\", \"241383\", \"289706\", \"297807\", \"318602\"]}","task_split":"paper_retrieval"}
{"document_id":"175419","document_content":"# An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nInformation extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion. Being able to leverage this immense source of technical information helps to drastically reduce costs and time to solution in multiple application fields from food safety to pharmaceutics. We present a scalable document ingestion system that integrates data from databases and publications (in PDF format) in a biochemistry knowledge graph (BCKG). The BCKG is a comprehensive source of knowledge that can be queried to retrieve known biochemical facts and to generate novel insights. After describing the knowledge ingestion framework, we showcase an application of our system in the field of carbohydrate enzymes. The BCKG represents a way to scale knowledge ingestion and automatically exploit prior knowledge to accelerate discovery in biochemical sciences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.08400v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"35818\", \"79640\", \"103778\", \"121009\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"175560","document_content":"# Deep Neural Models for Medical Concept Normalization in User-Generated Texts\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform an existing state of the art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/P19-2055\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"88816\", \"73992\", \"74057\", \"96521\", \"101295\", \"117882\", \"120573\"], \"outgoing_citations\": [\"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"175563","document_content":"# Transfer Learning Across Simulated Robots With Different Sensors\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nFor a robot to learn a good policy, it often requires expensive equipment (such as sophisticated sensors) and a prepared training environment conducive to learning. However, it is seldom possible to perfectly equip robots for economic reasons, nor to guarantee ideal learning conditions, when deployed in real-life environments. A solution would be to prepare the robot in the lab environment, when all necessary material is available to learn a good policy. After training in the lab, the robot should be able to get by without the expensive equipment that used to be available to it, and yet still be guaranteed to perform well on the field. The transition between the lab (source) and the real-world environment (target) is related to transfer learning, where the state-space between the source and target tasks differ. We tackle a simulated task with continuous states and discrete actions presenting this challenge, using Bootstrapped Dual Policy Iteration, a model-free actor-critic reinforcement learning algorithm, and Policy Shaping. Specifically, we train a BDPI agent, embodied by a virtual robot performing a task in the V-Rep simulator, sensing its environment through several proximity sensors. The resulting policy is then used by a second agent learning the same task in the same environment, but with camera images as input. The goal is to obtain a policy able to perform the task relying on merely camera images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.07958v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"30316\"], \"outgoing_citations\": [\"195663\", \"199875\", \"260107\", \"275728\", \"302952\", \"302967\", \"310237\"]}","task_split":"paper_retrieval"}
{"document_id":"175565","document_content":"# What Should\/Do\/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?\n## Categories\n- Computation and Language\n## Abstract\nThere is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural parser adding insights to this question. We look at transitivity and agreement information of auxiliary verb constructions (AVCs) in comparison to finite main verbs (FMVs). This comparison is motivated by theoretical work in dependency grammar and in particular the work of Tesni\\`ere (1959) where AVCs and FMVs are both instances of a nucleus, the basic unit of syntax. An AVC is a dissociated nucleus, it consists of at least two words, and an FMV is its non-dissociated counterpart, consisting of exactly one word. We suggest that the representation of AVCs and FMVs should capture similar information. We use diagnostic classifiers to probe agreement and transitivity information in vectors learned by a transition-based neural parser in four typologically different languages. We find that the parser learns different information about AVCs and FMVs if only sequential models (BiLSTMs) are used in the architecture but similar information when a recursive layer is used. We find explanations for why this is the case by looking closely at how information is learned in the network and looking at what happens with different dependency representations of AVCs. We conclude that there may be benefits to using a recursive layer in dependency parsing and that we have not yet found the best way to integrate it in our parsers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.07950v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72886\"], \"outgoing_citations\": [\"195050\", \"197540\", \"205161\", \"218282\", \"218881\", \"232479\", \"237283\", \"256062\", \"256948\", \"263992\", \"269127\", \"280201\", \"281121\", \"281225\", \"286594\", \"296583\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"175817","document_content":"# Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nHigh-quality human annotations are necessary to create effective machine learning systems for social media. Low-quality human annotations indirectly contribute to the creation of inaccurate or biased learning systems. We show that human annotation quality is dependent on the ordering of instances shown to annotators (referred as 'annotation schedule'), and can be improved by local changes in the instance ordering provided to the annotators, yielding a more accurate annotation of the data stream for efficient real-time social media analytics. We propose an error-mitigating active learning algorithm that is robust with respect to some cases of human errors when deciding an annotation schedule. We validate the human error model and evaluate the proposed algorithm against strong baselines by experimenting on classification tasks of relevant social media posts during crises. According to these experiments, considering the order in which data instances are presented to human annotators leads to both an increase in accuracy for machine learning and awareness toward some potential biases in human learning that may affect the automated classifier.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3341161.3342931\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [\"113756\", \"13670\", \"116646\"], \"outgoing_citations\": [\"165714\", \"216878\", \"233507\", \"338843\"]}","task_split":"paper_retrieval"}
{"document_id":"175842","document_content":"# The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nPrivacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users.   In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.07157v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"81920\", \"30490\", \"42569\", \"62742\", \"81035\"], \"outgoing_citations\": [\"201645\", \"206720\", \"210868\"]}","task_split":"paper_retrieval"}
{"document_id":"175850","document_content":"# STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nThis paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model learns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary. As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive. Moreover, inference time is short and linear according to the number of sentences. As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries. On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.07323v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"4498\", \"109884\"], \"outgoing_citations\": [\"220230\", \"230212\", \"231869\", \"241070\", \"245154\", \"266671\", \"272080\", \"280557\", \"298514\"]}","task_split":"paper_retrieval"}
{"document_id":"176079","document_content":"# Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation\n## Categories\n- Information Theory\n- Information Retrieval\n- Cryptography and Security\n## Abstract\nWe investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization. In particular, we focus on a privacy property that is often overlooked, namely how much leakage exists about one specific binary property of one component of the feature vector. This property is e.g. the sign or an indicator that a threshold is exceeded.   We provide evidence that both approaches are able to protect such sensitive binary variables, and discuss how system parameters need to be set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.06388v2\", \"primary_category\": \"cs.IT\", \"categories\": [\"cs.IT\", \"cs.IR\", \"cs.CR\", \"math.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Information Theory\", \"Information Retrieval\", \"Cryptography and Security\", \"Information Theory\"], \"incoming_citations\": [], \"outgoing_citations\": [\"187219\", \"228895\", \"254537\", \"275359\"]}","task_split":"paper_retrieval"}
{"document_id":"176154","document_content":"# Lexical Simplification with Pretrained Encoders\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nLexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.06226v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"2947\", \"6218\", \"26460\", \"36870\", \"92547\", \"94357\", \"77841\", \"168495\"], \"outgoing_citations\": [\"199361\", \"214400\"]}","task_split":"paper_retrieval"}
{"document_id":"176158","document_content":"# Task Selection Policies for Multitask Learning\n## Categories\n- Machine Learning\n## Abstract\nOne of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work we provide an empirical evaluation of the performance of some common task selection policies in a synthetic bandit-style setting, as well as on the GLUE benchmark for natural language understanding. We connect task selection policy learning to existing work on automated curriculum learning and off-policy evaluation, and suggest a method based on counterfactual estimation that leads to improved model performance in our experimental settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.06214v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"35275\", \"24035\", \"99765\", \"98964\"], \"outgoing_citations\": [\"179401\", \"189623\", \"191589\", \"211293\", \"211737\", \"219180\", \"227323\", \"234631\", \"237157\", \"269253\", \"269842\", \"271200\", \"278531\", \"294694\", \"300548\", \"303003\", \"303539\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"176236","document_content":"# k-hop Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs. However, it was recently shown that this test cannot identify fundamental graph properties such as connectivity and triangle freeness. We show that GNNs also suffer from the same limitation. To address this limitation, we propose a more expressive architecture, k-hop GNNs, which updates a node's representation by aggregating information not only from its direct neighbors, but from its k-hop neighborhood. We show that the proposed architecture can identify fundamental graph properties. We evaluate the proposed architecture on standard node classification and graph classification datasets. Our experimental evaluation confirms our theoretical findings since the proposed model achieves performance better or comparable to standard GNNs and to state-of-the-art algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.06051v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"18144\", \"3105\", \"13814\", \"68717\", \"95034\", \"83801\", \"127546\"], \"outgoing_citations\": [\"188300\", \"188686\", \"199832\", \"203910\", \"215155\", \"215435\", \"229066\", \"265468\", \"269202\", \"272568\", \"291213\", \"297245\"]}","task_split":"paper_retrieval"}
{"document_id":"176321","document_content":"# Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems\n## Categories\n- Computation and Language\n## Abstract\nData scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data. In this paper, we demonstrate that recent progress in language modeling pre-training and transfer learning shows promise to overcome this problem. We propose a task-oriented dialogue model that operates solely on text input: it effectively bypasses explicit policy and language generation modules. Building on top of the TransferTransfo framework (Wolf et al., 2019) and generative model pre-training (Radford et al., 2019), we validate the approach on complex multi-domain task-oriented dialogues from the MultiWOZ dataset. Our automatic and human evaluations show that the proposed model is on par with a strong task-specific neural baseline. In the long run, our approach holds promise to mitigate the data scarcity problem, and to support the construction of more engaging and more eloquent task-oriented conversational agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.05774v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24760\", \"6635\", \"15935\", \"24608\", \"26335\", \"26647\", \"55305\", \"67993\", \"2039\", \"4657\", \"4825\", \"11561\", \"15797\", \"16075\", \"17373\", \"21191\", \"25282\", \"26534\", \"27094\", \"29406\", \"125732\", \"42317\", \"44249\", \"44409\", \"48100\", \"48120\", \"48795\", \"49726\", \"53415\", \"57932\", \"62145\", \"69895\", \"70413\", \"70610\", \"71089\", \"71092\", \"78854\", \"82284\", \"82737\", \"83056\", \"91625\", \"92938\", \"99215\", \"104906\", \"123916\", \"129261\", \"162696\", \"81778\", \"82594\", \"90175\", \"91128\", \"94114\", \"94435\", \"96092\", \"96753\", \"98658\", \"101840\", \"104375\", \"115538\", \"115759\", \"125674\", \"127463\", \"127848\", \"128145\", \"131150\", \"132799\", \"133049\", \"132332\", \"138737\", \"54270\", \"148608\", \"154192\", \"154477\", \"156814\", \"139107\", \"186662\", \"153178\", \"154141\", \"161395\", \"170345\"], \"outgoing_citations\": [\"182577\", \"183578\", \"190742\", \"197853\", \"201867\", \"211804\", \"216019\", \"244479\", \"267551\", \"268157\", \"270018\", \"270040\", \"275597\", \"290337\", \"292261\", \"294494\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"176538","document_content":"# Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nContextual reasoning is essential to understand events in long untrimmed videos. In this work, we systematically explore different captioning models with various contexts for the dense-captioning events in video task, which aims to generate captions for different events in the untrimmed video. We propose five types of contexts as well as two categories of event captioning models, and evaluate their contributions for event captioning from both accuracy and diversity aspects. The proposed captioning models are plugged into our pipeline system for the dense video captioning challenge. The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9.91 METEOR score on the challenge testing set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.05092v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"60281\", \"42920\", \"109275\", \"117188\", \"118764\", \"161619\"], \"outgoing_citations\": [\"191680\", \"223293\", \"227052\", \"267523\", \"278954\", \"316803\"]}","task_split":"paper_retrieval"}
{"document_id":"176543","document_content":"# MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nBuilding computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have methods from computer vision and natural language processing become powerful enough to make this vision seem more attainable. Pushed especially by developments in computer vision, many data sets and collection environments have recently been published that bring together verbal interaction and visual processing. Here, we argue that these datasets tend to oversimplify the dialogue part, and we propose a task---MeetUp!---that requires both visual and conversational grounding, and that makes stronger demands on representations of the discourse. MeetUp! is a two-player coordination game where players move in a visual environment, with the objective of finding each other. To do so, they must talk about what they see, and achieve mutual understanding. We describe a data collection and show that the resulting dialogues indeed exhibit the dialogue phenomena of interest, while also challenging the language & vision aspect.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.05084v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"24419\", \"24451\", \"55549\", \"97596\", \"137074\", \"155766\", \"161092\", \"162734\", \"169609\"], \"outgoing_citations\": [\"182584\", \"192738\", \"196049\", \"203398\", \"226321\", \"247749\", \"248742\", \"249906\", \"279518\", \"279678\", \"287427\", \"300099\", \"303833\", \"305371\", \"313196\", \"319159\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"176597","document_content":"# A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new tasks. In this work, the tasks correspond to reward functions for environments with the same (or similar) dynamical models. We propose to learn a dynamical model during the training process and use this model to perform sample-efficient adaptation to new tasks at test time. We use significantly fewer samples by performing policy optimization only in a \"virtual\" environment whose transitions are given by our learned dynamical model. Our algorithm sequentially trains against several tasks. Upon encountering a new task, we first warm-up a policy on our learned dynamical model, which requires no new samples from the environment. We then adapt the dynamical model with samples from this policy in the real environment. We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy over MAML, a state-of-the-art meta-learning algorithm, on these tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04964v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"48226\", \"18635\", \"29648\", \"31901\", \"34703\", \"45272\", \"118362\"], \"outgoing_citations\": [\"196921\", \"225108\", \"229890\", \"237229\", \"240495\", \"251819\", \"259045\", \"260407\", \"261316\", \"263650\", \"280202\", \"280870\", \"293995\", \"302952\", \"302967\"]}","task_split":"paper_retrieval"}
{"document_id":"176603","document_content":"# Vision-and-Dialog Navigation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nRobots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https:\/\/cvdn.dev\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04957v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"4499\", \"10293\", \"16536\", \"19631\", \"33721\", \"39368\", \"47577\", \"64409\", \"80120\", \"9686\", \"13764\", \"14346\", \"19456\", \"22556\", \"24419\", \"27520\", \"27522\", \"28518\", \"30277\", \"31155\", \"35300\", \"42743\", \"45665\", \"46468\", \"48662\", \"48758\", \"49578\", \"54802\", \"55072\", \"56049\", \"57305\", \"61178\", \"66404\", \"73451\", \"76247\", \"77485\", \"82685\", \"84618\", \"95168\", \"97596\", \"100277\", \"133099\", \"175031\", \"88444\", \"86813\", \"86578\", \"91773\", \"93950\", \"99877\", \"112798\", \"115960\", \"118832\", \"121303\", \"121544\", \"125969\", \"126597\", \"127495\", \"129809\", \"136760\", \"139227\", \"140318\", \"152659\", \"152871\", \"153162\", \"155676\", \"189381\", \"152372\", \"168651\"], \"outgoing_citations\": [\"162038\", \"168651\", \"183823\", \"191570\", \"196027\", \"196994\", \"197653\", \"206489\", \"208042\", \"208526\", \"210666\", \"211921\", \"219975\", \"220723\", \"220724\", \"225266\", \"247418\", \"247900\", \"248742\", \"249906\", \"255575\", \"279518\"]}","task_split":"paper_retrieval"}
{"document_id":"176605","document_content":"# Can Unconditional Language Models Recover Arbitrary Sentences?\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNeural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is it possible to adapt them in a similar way for use as general-purpose decoders? For this to be possible, it would need to be the case that for any target sentence of interest, there is some continuous representation that can be passed to the language model to cause it to reproduce that sentence. We set aside the difficult problem of designing an encoder that can produce such representations and, instead, ask directly whether such representations exist at all. To do this, we introduce a pair of effective, complementary methods for feeding representations into pretrained unconditional language models and a corresponding set of methods to map sentences into and out of this representation space, the reparametrized sentence space. We then investigate the conditions under which a language model can be made to generate a sentence through the identification of a point in such a space and find that it is possible to recover arbitrary sentences nearly perfectly with language models and representations of moderate size without modifying any model parameters.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04944v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"56832\", \"127741\", \"41767\", \"77463\", \"84406\", \"86969\", \"95588\", \"104915\", \"108688\", \"132243\", \"152729\"], \"outgoing_citations\": [\"187236\", \"187452\", \"194056\", \"234449\", \"234742\", \"258039\", \"260887\", \"263927\", \"267692\", \"268619\", \"286225\", \"289144\", \"292690\", \"295028\", \"303997\", \"316102\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"176612","document_content":"# Explaining an increase in predicted risk for clinical alerts\n## Categories\n- Machine Learning\n- Computers and Society\n- Applications\n## Abstract\nMuch work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step. When the estimated risk increases, the goal of the explanation is to attribute the increase to a few relevant inputs from the past. While our formal setup and techniques are general, we carry out an in-depth case study in a clinical setting. The goal here is to alert a clinician when a patient's risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment. Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert. We develop methods to lift static attribution techniques to the dynamical setting, where we identify and address challenges specific to dynamics. We then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04911v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CY\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"69673\", \"23211\", \"82862\", \"91124\", \"138322\"], \"outgoing_citations\": [\"186566\", \"206773\", \"232097\", \"241329\", \"244340\", \"250187\", \"251595\", \"264024\", \"265627\", \"286253\", \"287747\", \"291026\", \"301647\", \"303053\"]}","task_split":"paper_retrieval"}
{"document_id":"176840","document_content":"# DSNet: Automatic Dermoscopic Skin Lesion Segmentation\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation remains a challenging task due to their diverse color, texture, and indistinguishable boundaries and forms an open problem. Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used depth-wise separable convolution in lieu of standard convolution to project the learned discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed DSNet. We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is 77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and 6.8 % with respect to mIoU on the ISIC-2017 dataset. Our network for skin lesion segmentation outperforms other methods and can provide better segmented masks on two different test datasets which can lead to better performance in melanoma detection. Our trained model along with the source code and predicted masks are made publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04305v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"130639\", \"13243\", \"50742\", \"69952\", \"71829\", \"71830\"], \"outgoing_citations\": [\"147593\", \"195957\", \"214049\", \"230522\", \"254665\", \"263620\", \"271426\", \"271680\", \"272621\", \"272835\", \"299339\", \"309807\"]}","task_split":"paper_retrieval"}
{"document_id":"176936","document_content":"# Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n## Abstract\nFace recognition has evolved as a prominent biometric authentication modality. However, vulnerability to presentation attacks curtails its reliable deployment. Automatic detection of presentation attacks is essential for secure use of face recognition technology in unattended scenarios. In this work, we introduce a Convolutional Neural Network (CNN) based framework for presentation attack detection, with deep pixel-wise supervision. The framework uses only frame level information making it suitable for deployment in smart devices with minimal computational and time overhead. We demonstrate the effectiveness of the proposed approach in public datasets for both intra as well as cross-dataset experiments. The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0.42% in Protocol-1 of OULU dataset outperforming state of the art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.04047v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\"], \"incoming_citations\": [\"7596\", \"15541\", \"38932\", \"5991\", \"8093\", \"15509\", \"16691\", \"23048\", \"35846\", \"9939\", \"49673\", \"56417\", \"308\", \"10826\", \"24845\", \"25097\", \"27985\", \"35548\", \"38955\", \"51887\", \"61396\", \"65909\", \"65822\", \"66171\", \"73612\", \"85017\", \"86655\", \"89182\", \"96813\", \"110309\", \"121069\", \"125647\", \"129485\", \"226030\", \"95672\", \"99489\", \"110303\", \"111223\", \"114183\", \"115149\", \"130605\", \"137769\", \"141347\", \"1562\", \"149020\", \"159421\"], \"outgoing_citations\": [\"237298\", \"302959\", \"325438\"]}","task_split":"paper_retrieval"}
{"document_id":"177051","document_content":"# Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning\n## Categories\n- Machine Learning\n- Strongly Correlated Electrons\n- Optimization and Control\n- Quantum Physics\n## Abstract\nTensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning algorithms and for enhancing the mathematical understanding of existing methods. Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions. These factorizations include non-negative tensor-trains\/MPS, which are in correspondence with hidden Markov models, and Born machines, which are naturally related to local quantum circuits. When used to model probability distributions, they exhibit tractable likelihoods and admit efficient learning algorithms. Interestingly, we prove that there exist probability distributions for which there are unbounded separations between the resource requirements of some of these tensor-network factorizations. Particularly surprising is the fact that using complex instead of real tensors can lead to an arbitrarily large reduction in the number of parameters of the network. Additionally, we introduce locally purified states (LPS), a new factorization inspired by techniques for the simulation of quantum systems, with provably better expressive power than all other representations considered. The ramifications of this result are explored through numerical experiments. Our findings imply that LPS should be considered over hidden Markov models, and furthermore provide guidelines for the design of local quantum circuits for probabilistic modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.03741v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cond-mat.str-el\", \"math.OC\", \"quant-ph\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Strongly Correlated Electrons\", \"Optimization and Control\", \"Quantum Physics\", \"Machine Learning\"], \"incoming_citations\": [\"39783\", \"2629\", \"9355\", \"18063\", \"74381\", \"12615\", \"17900\", \"29499\", \"38654\", \"40282\", \"63612\", \"63668\", \"92719\", \"109971\", \"135543\", \"98572\", \"124938\", \"139626\", \"142619\", \"145985\", \"154078\", \"161398\", \"227886\", \"149137\", \"198472\"], \"outgoing_citations\": [\"198472\", \"203642\", \"227886\", \"235936\", \"237180\", \"246220\", \"251440\", \"251608\", \"251786\", \"254162\", \"256811\", \"257372\", \"267174\", \"269633\", \"282704\", \"287508\", \"292105\", \"297358\", \"306487\", \"320620\", \"352900\", \"353222\"]}","task_split":"paper_retrieval"}
{"document_id":"177158","document_content":"# Unbiased estimators for random design regression\n## Categories\n- Machine Learning\n## Abstract\nIn linear regression we wish to estimate the optimum linear least squares predictor for a distribution over $d$-dimensional input points and real-valued responses, based on a small sample. Under standard random design analysis, where the sample is drawn i.i.d. from the input distribution, the least squares solution for that sample can be viewed as the natural estimator of the optimum. Unfortunately, this estimator almost always incurs an undesirable bias coming from the randomness of the input points, which is a significant bottleneck in model averaging. In this paper we show that it is possible to draw a non-i.i.d. sample of input points such that, regardless of the response model, the least squares solution is an unbiased estimator of the optimum. Moreover, this sample can be produced efficiently by augmenting a previously drawn i.i.d. sample with an additional set of $d$ points, drawn jointly according to a certain determinantal point process constructed from the input distribution rescaled by the squared volume spanned by the points. Motivated by this, we develop a theoretical framework for studying volume-rescaled sampling, and in the process prove a number of new matrix expectation identities. We use them to show that for any input distribution and $\\epsilon>0$ there is a random design consisting of $O(d\\log d+ d\/\\epsilon)$ points from which an unbiased estimator can be constructed whose expected square loss over the entire distribution is bounded by $1+\\epsilon$ times the loss of the optimum. We provide efficient algorithms for generating such unbiased estimators in a number of practical settings and support our claims experimentally.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.03411v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"23746\", \"71632\", \"85606\", \"179853\", \"126557\", \"140735\", \"151933\"], \"outgoing_citations\": [\"114535\", \"115117\", \"126557\", \"130212\", \"159817\", \"181545\", \"183309\", \"200369\", \"204911\", \"210836\", \"215376\", \"229139\", \"241176\", \"241647\", \"242511\", \"249168\", \"253340\", \"265064\", \"266068\", \"267557\", \"273916\", \"300366\", \"307071\", \"308133\", \"351729\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"177196","document_content":"# Improving short text classification through global augmentation methods\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nWe study the effect of different approaches to text augmentation. To do this we use 3 datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of \\emph{mixup} further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-57321-8_21\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"36981\", \"20801\", \"10085\", \"21352\", \"25886\", \"31108\", \"32818\", \"44879\", \"45164\", \"96881\", \"134137\", \"141614\", \"155780\", \"157467\"], \"outgoing_citations\": [\"230755\", \"231898\", \"244368\", \"267601\"]}","task_split":"paper_retrieval"}
{"document_id":"177235","document_content":"# Joint Lifelong Topic Model and Manifold Ranking for Document Summarization\n## Categories\n- Computation and Language\n## Abstract\nDue to the manifold ranking method has a significant effect on the ranking of unknown data based on known data by using a weighted network, many researchers use the manifold ranking method to solve the document summarization task. However, their models only consider the original features but ignore the semantic features of sentences when they construct the weighted networks for the manifold ranking method. To solve this problem, we proposed two improved models based on the manifold ranking method. One is combining the topic model and manifold ranking method (JTMMR) to solve the document summarization task. This model not only uses the original feature, but also uses the semantic feature to represent the document, which can improve the accuracy of the manifold ranking method. The other one is combining the lifelong topic model and manifold ranking method (JLTMMR). On the basis of the JTMMR, this model adds the constraint of knowledge to improve the quality of the topic. At the same time, we also add the constraint of the relationship between documents to dig out a better document semantic features. The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature. Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task. At the same time, our models also have a good performance on the single document summarization task. After combining with a few basic surface features, our model significantly outperforms some model based on deep learning in recent years. After that, we also do an exploring work for lifelong machine learning by analyzing the effect of adding feedback. Experiments show that the effect of adding feedback to our model is significant.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.03224v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"32290\"], \"outgoing_citations\": [\"245571\", \"295470\"]}","task_split":"paper_retrieval"}
{"document_id":"177238","document_content":"# Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https:\/\/saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-981-15-3383-9_15\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85603\", \"36575\", \"100950\", \"131331\", \"159152\"], \"outgoing_citations\": [\"200967\", \"224155\"]}","task_split":"paper_retrieval"}
{"document_id":"177311","document_content":"# Generating Sentences from Disentangled Syntactic and Semantic Spaces\n## Categories\n- Computation and Language\n## Abstract\nVariational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE's latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax-transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.05789v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"177316","document_content":"# Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures\n## Categories\n- Computation and Language\n## Abstract\nWith the development of Natural Language Processing, Automatic question-answering system such as Waston, Siri, Alexa, has become one of the most important NLP applications. Nowadays, enterprises try to build automatic custom service chatbots to save human resources and provide a 24-hour customer service. Evaluation of chatbots currently relied greatly on human annotation which cost a plenty of time. Thus, has initiated a new Short Text Conversation subtask called Dialogue Quality (DQ) and Nugget Detection (ND) which aim to automatically evaluate dialogues generated by chatbots. In this paper, we solve the DQ and ND subtasks by deep neural network. We proposed two models for both DQ and ND subtasks which is constructed by hierarchical structure: embedding layer, utterance layer, context layer and memory layer, to hierarchical learn dialogue representation from word level, sentence level, context level to long range context level. Furthermore, we apply gating and attention mechanism at utterance layer and context layer to improve the performance. We also tried BERT to replace embedding layer and utterance layer as sentence representation. The result shows that BERT produced a better utterance representation than multi-stack CNN for both DQ and ND subtasks and outperform other models proposed by other researches. The evaluation measures are proposed by , that is, NMD, RSNOD for DQ and JSD, RNSS for ND, which is not traditional evaluation measures such as accuracy, precision, recall and f1-score. Thus, we have done a series of experiments by using traditional evaluation measures and analyze the performance and error.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.03070v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"203630\", \"208346\", \"220209\", \"253593\", \"256124\", \"267031\", \"270040\", \"275597\", \"296702\", \"343624\"]}","task_split":"paper_retrieval"}
{"document_id":"177339","document_content":"# Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMachine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently multiple annotated task-oriented human-machine dialogue datasets have been released, however their annotation schema varies across different collections, even for well-defined categories such as dialogue acts (DAs). We propose a Universal DA schema for task-oriented dialogues and align existing annotated datasets with our schema. Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues and use it for tagging human-human conversations. We investigate multiple datasets, propose manual and automated approaches for aligning the different schema, and present results on a target corpus of human-human dialogues. In unsupervised learning experiments we achieve an F1 score of 54.1% on system turns in human-human dialogues. In a semi-supervised setup, the F1 score increases to 57.7% which would otherwise require at least 1.7K manually annotated turns. For new domains, we show further improvements when unlabeled or labeled target domain data is available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.03020v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"6635\", \"27094\", \"131150\"], \"outgoing_citations\": [\"216019\", \"228410\", \"235207\", \"254660\", \"274253\", \"296702\"]}","task_split":"paper_retrieval"}
{"document_id":"177832","document_content":"# Adjustment Criteria for Recovering Causal Effects from Missing Data\n## Categories\n- Machine Learning\n## Abstract\nConfounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique for recovering casual effects from confounding bias. In this paper, we introduce a covariate adjustment formulation for controlling confounding bias in the presence of missing-not-at-random data and develop a necessary and sufficient condition for recovering causal effects using the adjustment. We also introduce an adjustment formulation for controlling both confounding and selection biases in the presence of missing data and develop a necessary and sufficient condition for valid adjustment. Furthermore, we present an algorithm that lists all valid adjustment sets and an algorithm that finds a valid adjustment set containing the minimum number of variables, which are useful for researchers interested in selecting adjustment sets with desired properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.01654v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"131938\", \"225056\"], \"outgoing_citations\": [\"357566\", \"360759\"]}","task_split":"paper_retrieval"}
{"document_id":"177943","document_content":"# Brno Mobile OCR Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical Character Recognition from low-quality images captured by handheld mobile devices. While OCR of high-quality scanned documents is a mature field where many commercial tools are available, and large datasets of text in the wild exist, no existing datasets can be used to develop and test document OCR methods robust to non-uniform lighting, image blur, strong noise, built-in denoising, sharpening, compression and other artifacts present in many photographs from mobile devices.   This dataset contains 2 113 unique pages from random scientific papers, which were photographed by multiple people using 23 different mobile devices. The resulting 19 728 photographs of various visual quality are accompanied by precise positions and text annotations of 500k text lines. We further provide an evaluation methodology, including an evaluation server and a testset with non-public annotations.   We provide a state-of-the-art text recognition baseline build on convolutional and recurrent neural networks trained with Connectionist Temporal Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates on easy, medium and hard parts of the dataset, respectively, confirming that the dataset is challenging.   The presented dataset will enable future development and evaluation of document analysis for low-quality images. It is primarily intended for line-level text recognition, and can be further used for line localization, layout analysis, image restoration and text binarization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.01307v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"67395\", \"107518\", \"107443\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"177961","document_content":"# Semi-Bagging Based Deep Neural Architecture to Extract Text from High Entropy Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n## Abstract\nExtracting texts of various size and shape from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in natural scene, etc. The existing works (based on only CNN) often perform sub-optimally when the image contains regions of high entropy having multiple objects. This paper presents an end-to-end text detection strategy combining a segmentation algorithm and an ensemble of multiple text detectors of different types to detect text in every individual image segments independently. The proposed strategy involves a super-pixel based image segmenter which splits an image into multiple regions. A convolutional deep neural architecture is developed which works on each of the segments and detects texts of multiple shapes, sizes, and structures. It outperforms the competing methods in terms of coverage in detecting texts in images especially the ones where the text of various types and sizes are compacted in a small region along with various other objects. Furthermore, the proposed text detection method along with a text recognizer outperforms the existing state-of-the-art approaches in extracting text from high entropy images. We validate the results on a dataset consisting of product images on an e-commerce website.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.01284v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"245591\", \"249641\", \"279950\", \"293979\", \"294606\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"178125","document_content":"# MULEX: Disentangling Exploitation from Exploration in Deep RL\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAn agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour. This trade-off is usually obtained by perturbing either the agent's actions (e.g., e-greedy or Gibbs sampling) or the agent's parameters (e.g., NoisyNet), or by modifying the reward it receives (e.g., exploration bonus, intrinsic motivation, or hand-shaped rewards). Here, we adopt a disruptive but simple and generic perspective, where we explicitly disentangle exploration and exploitation. Different losses are optimized in parallel, one of them coming from the true objective (maximizing cumulative rewards from the environment) and others being related to exploration. Every loss is used in turn to learn a policy that generates transitions, all shared in a single replay buffer. Off-policy methods are then applied to these transitions to optimize each loss. We showcase our approach on a hard-exploration environment, show its sample-efficiency and robustness, and discuss further implications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.00868v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"15365\", \"40884\", \"93557\", \"138675\", \"140259\", \"142272\"], \"outgoing_citations\": [\"200765\", \"200920\", \"205913\", \"212299\", \"221473\", \"242202\", \"260583\", \"262381\", \"264419\", \"280457\", \"291025\"]}","task_split":"paper_retrieval"}
{"document_id":"178308","document_content":"# A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Audio and Speech Processing\n## Abstract\nA spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1907.00390v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"23909\", \"74469\", \"1340\", \"22618\", \"27450\", \"28992\", \"50552\", \"70627\", \"127897\", \"18216\", \"18484\", \"23301\", \"26015\", \"27362\", \"29403\", \"30474\", \"33513\", \"45038\", \"46404\", \"47388\", \"53732\", \"58217\", \"62068\", \"63230\", \"63968\", \"65237\", \"67742\", \"160285\", \"79142\", \"79656\", \"80865\", \"95751\", \"95759\", \"98109\", \"93604\", \"75876\", \"82594\", \"84544\", \"89849\", \"95080\", \"96274\", \"96385\", \"97664\", \"127901\", \"129831\", \"129871\", \"133147\", \"128934\", \"157993\", \"168563\"], \"outgoing_citations\": [\"230543\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"178503","document_content":"# Learning Arbitration for Shared Autonomy by Hindsight Data Aggregation\n## Categories\n- Robotics\n## Abstract\nIn this paper we present a framework for the teleoperation of pick-and-place tasks. We define a shared control policy that allows to blend between direct user control and autonomous control based on user intent inference. One of the main challenges in shared autonomy systems is to define the arbitration function, which decides when to let the autonomous agent take over. In this work, we propose a model and training method to learn the arbitration function. Our model is based on a recurrent neural network that takes as input the state, intent prediction scores and user command to produce an arbitration between user and robot commands. This work extends our previous work on differentiable policies for shared autonomy. Differentiability of the policy is desirable to further train the shared autonomy system end-to-end. In this work we propose training of the arbitration function by using data from user performing the task with shared control. We present initial results by teleoperating a gripper in a virtual environment using pre-trained motion generation and intent prediction. We compare our data aggregation training procedure to a handcrafted arbitration function. Our preliminary results show the efficacy of the approach and shed light on limitations that we believe demonstrate the need for user adaptability in shared autonomy systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.12280v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"108840\"], \"outgoing_citations\": [\"208994\", \"243197\", \"264845\", \"271850\", \"275319\", \"290962\"]}","task_split":"paper_retrieval"}
{"document_id":"178575","document_content":"# Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThis work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.12068v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"383\", \"9883\", \"38588\", \"55676\", \"55858\", \"23257\", \"25140\", \"46671\", \"47184\", \"50482\", \"56479\", \"61132\", \"64743\", \"72477\", \"25231\", \"75293\", \"122048\", \"77696\", \"94688\", \"95566\", \"104063\", \"127679\", \"133899\"], \"outgoing_citations\": [\"167379\", \"218898\", \"235312\", \"259870\", \"271767\", \"286498\"]}","task_split":"paper_retrieval"}
{"document_id":"178605","document_content":"# BTEL: A Binary Tree Encoding Approach for Visual Localization\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nVisual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques. However, there remains one critical caveat: all current approaches that are based on image retrieval currently scale at best linearly with the size of the environment with respect to both storage, and consequentially in most approaches, query time. This limitation severely curtails the capability of autonomous systems in a wide range of compute, power, storage, size, weight or cost constrained applications such as drones. In this work, we present a novel binary tree encoding approach for visual localization which can serve as an alternative for existing quantization and indexing techniques. The proposed tree structure allows us to derive a compressed training scheme that achieves sub-linearity in both required storage and inference time. The encoding memory can be easily configured to satisfy different storage constraints. Moreover, our approach is amenable to an optional sequence filtering mechanism to further improve the localization results, while maintaining the same amount of storage. Our system is entirely agnostic to the front-end descriptors, allowing it to be used on top of recent state-of-the-art image representations. Experimental results show that the proposed method significantly outperforms state-of-the-art approaches under limited storage constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.11992v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"145924\"], \"outgoing_citations\": [\"243056\", \"246993\", \"259959\", \"294983\"]}","task_split":"paper_retrieval"}
{"document_id":"178648","document_content":"# Demystifying Inter-Class Disentanglement\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nLearning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence. We present a unified formulation for class and content disentanglement and use it to illustrate the limitations of current methods. We therefore introduce LORD, a novel method based on Latent Optimization for Representation Disentanglement. We find that latent optimization, along with an asymmetric noise regularization, is superior to amortized inference for achieving disentangled representations. In extensive experiments, our method is shown to achieve better disentanglement performance than both adversarial and non-adversarial methods that use the same level of supervision. We further introduce a clustering-based approach for extending our method for settings that exhibit in-class variation with promising results on the task of domain translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.11796v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"7149\", \"27322\", \"65191\", \"22992\", \"37638\", \"38668\", \"60627\", \"63434\", \"65007\", \"67912\", \"78548\", \"98093\", \"103627\", \"132972\", \"105034\", \"113080\", \"120772\", \"124845\", \"132433\", \"132719\", \"139925\", \"160471\"], \"outgoing_citations\": [\"183000\", \"205148\", \"208041\", \"229480\", \"233977\", \"241933\", \"242142\", \"242239\", \"251215\", \"257180\", \"260887\", \"264963\", \"265526\", \"276824\", \"280734\", \"282030\"]}","task_split":"paper_retrieval"}
{"document_id":"178885","document_content":"# A Deep Decoder Structure Based on WordEmbedding Regression for An Encoder-Decoder Based Model for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nGenerating textual descriptions for images has been an attractive problem for the computer vision and natural language processing researchers in recent years. Dozens of models based on deep learning have been proposed to solve this problem. The existing approaches are based on neural encoder-decoder structures equipped with the attention mechanism. These methods strive to train decoders to minimize the log likelihood of the next word in a sentence given the previous ones, which results in the sparsity of the output space. In this work, we propose a new approach to train decoders to regress the word embedding of the next word with respect to the previous ones instead of minimizing the log likelihood. The proposed method is able to learn and extract long-term information and can generate longer fine-grained captions without introducing any external memory cell. Furthermore, decoders trained by the proposed technique can take the importance of the generated words into consideration while generating captions. In addition, a novel semantic attention mechanism is proposed that guides attention points through the image, taking the meaning of the previously generated word into account. We evaluate the proposed approach with the MS-COCO dataset. The proposed model outperformed the state of the art models especially in generating longer captions. It achieved a CIDEr score equal to 125.0 and a BLEU-4 score equal to 50.5, while the best scores of the state of the art models are 117.1 and 48.0, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.12188v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"238125\", \"256347\", \"262828\", \"268218\", \"268356\", \"269030\", \"277511\", \"278604\", \"279518\", \"279676\", \"284230\", \"295307\", \"303837\", \"319781\", \"320332\", \"321621\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"178962","document_content":"# Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices\n## Categories\n- Cryptography and Security\n- Human-Computer Interaction\n- Networking and Internet Architecture\n- Systems and Control\n- Machine Learning\n- Systems and Control\n## Abstract\nHome appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.10893v4\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.HC\", \"cs.NI\", \"cs.CR\", \"cs.SY\", \"cs.LG\", \"eess.SY\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Networking and Internet Architecture\", \"Cryptography and Security\", \"Systems and Control\", \"Machine Learning\", \"Systems and Control\"], \"incoming_citations\": [\"6889\", \"9036\", \"9619\", \"174906\", \"2909\", \"19080\", \"72424\", \"74725\", \"102800\", \"147787\", \"9237\", \"12120\", \"18850\", \"36065\", \"53334\", \"58325\", \"61466\", \"107501\", \"130313\", \"103698\", \"153812\"], \"outgoing_citations\": [\"138557\", \"149546\", \"150179\", \"150516\", \"157950\", \"159202\", \"164869\", \"165264\", \"165657\", \"166528\", \"173134\", \"200201\", \"204801\", \"207518\", \"207623\", \"208847\", \"217320\", \"221338\", \"221592\", \"232524\", \"232933\", \"234581\", \"235592\", \"273221\", \"288989\", \"326583\", \"327079\"]}","task_split":"paper_retrieval"}
{"document_id":"179214","document_content":"# Learning to Interactively Learn and Assist\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Multiagent Systems\n## Abstract\nWhen deploying autonomous agents in the real world, we need effective ways of communicating objectives to them. Traditional skill learning has revolved around reinforcement and imitation learning, each with rigid constraints on the format of information exchanged between the human and the agent. While scalar rewards carry little information, demonstrations require significant effort to provide and may carry more information than is necessary. Furthermore, rewards and demonstrations are often defined and collected before training begins, when the human is most uncertain about what information would help the agent. In contrast, when humans communicate objectives with each other, they make use of a large vocabulary of informative behaviors, including non-verbal communication, and often communicate throughout learning, responding to observed behavior. In this way, humans communicate intent with minimal effort. In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. To accomplish this, we introduce a multi-agent training framework that enables an agent to learn from another agent who knows the current task. Through a series of experiments, we demonstrate the emergence of a variety of interactive learning behaviors, including information-sharing, information-seeking, and question-answering. Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.10187v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [\"3136\", \"28384\", \"37989\", \"35842\", \"53305\", \"61430\", \"64556\", \"78067\", \"80268\", \"86327\", \"87273\", \"93673\", \"111085\"], \"outgoing_citations\": [\"197982\", \"199302\", \"203662\", \"209926\", \"215102\", \"215235\", \"215939\", \"218582\", \"229737\", \"235431\", \"243236\", \"254581\", \"261260\", \"263264\", \"263957\", \"265505\", \"268223\", \"270925\", \"271497\", \"271952\", \"277491\", \"280202\", \"280870\", \"290744\", \"292127\", \"292262\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"179221","document_content":"# Training an Interactive Helper\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Multiagent Systems\n## Abstract\nDeveloping agents that can quickly adapt their behavior to new tasks remains a challenge. Meta-learning has been applied to this problem, but previous methods require either specifying a reward function which can be tedious or providing demonstrations which can be inefficient. In this paper, we investigate if, and how, a \"helper\" agent can be trained to interactively adapt their behavior to maximize the reward of another agent, whom we call the \"prime\" agent, without observing their reward or receiving explicit demonstrations. To this end, we propose to meta-learn a helper agent along with a prime agent, who, during training, observes the reward function and serves as a surrogate for a human prime. We introduce a distribution of multi-agent cooperative foraging tasks, in which only the prime agent knows the objects that should be collected. We demonstrate that, from the emerged physical communication, the trained helper rapidly infers and collects the correct objects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.10165v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215102\", \"226635\", \"233450\", \"243236\", \"271497\", \"280296\", \"280870\", \"293995\", \"297268\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"179233","document_content":"# Audio-Visual Kinship Verification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verification task, visual kinship verification is particularly difficult with low-quality found Internet data. Due to uncontrolled variations in background, pose, facial expression, blur, illumination and occlusion, state-of-the-art methods fail to provide high level of recognition accuracy. As with many other visual recognition tasks, kinship verification may benefit from combining visual and audio signals. However, voice-based kinship verification has received very little prior attention. We hypothesize that the human voice contains kin-related cues that are complementary to visual cues. In this paper, we address, for the first time, the use of audio-visual information from face and voice modalities to perform kinship verification. We first propose a new multi-modal kinship dataset, called TALking KINship (TALKIN), that contains several pairs of Internet-quality video sequences. Using TALKIN, we then study the utility of various kinship verification methods including traditional local feature based methods, statistical methods and more recent deep learning approaches. Then, early and late fusion methods are evaluated on the TALKIN dataset for the study of kinship verification with both face and voice modalities. Finally, we propose a deep Siamese fusion network with contrastive loss for multi-modal fusion of kinship relations. Extensive experiments on the TALKIN dataset indicate that by combining face and voice modalities, the proposed Siamese network can provide a significantly higher level of accuracy compared to baseline uni-modal and multi-modal fusion techniques. Experimental results also indicate that audio (vocal) information is complementary (to facial information) and useful for kinship verification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.10096v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"179444","document_content":"# DAL: Dual Adversarial Learning for Dialogue Generation\n## Categories\n- Computation and Language\n## Abstract\nIn open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning (DAL) for high-quality response generation. DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms the state-of-the-art methods regarding automatic metrics and human evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.09556v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23120\", \"45967\", \"47718\", \"67278\", \"127668\"], \"outgoing_citations\": [\"217640\", \"264394\", \"269757\", \"275597\", \"281472\", \"281749\", \"289061\", \"289943\", \"295863\", \"310740\", \"310903\", \"316165\", \"316328\", \"325200\"]}","task_split":"paper_retrieval"}
{"document_id":"179461","document_content":"# Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nA learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. We employ this likelihood-free importance weighting method to correct for the bias in generative models. We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art deep generative models, suggesting reduced bias. Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.09531v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"7181\", \"15233\", \"27746\", \"38380\", \"81138\", \"85786\", \"116594\", \"4799\", \"11366\", \"12152\", \"21401\", \"32757\", \"35080\", \"37433\", \"38126\", \"61050\", \"62850\", \"64268\", \"67326\", \"83472\", \"96190\", \"99660\", \"118930\", \"132791\", \"137424\", \"113479\", \"115305\", \"116953\", \"118689\", \"119254\", \"112623\", \"129534\", \"132506\", \"133055\", \"137050\", \"138084\", \"154092\", \"156042\", \"159651\", \"160466\", \"165328\", \"155348\"], \"outgoing_citations\": [\"147691\", \"154086\", \"202025\", \"206710\", \"208353\", \"208878\", \"210983\", \"214144\", \"228974\", \"230949\", \"236698\", \"240201\", \"242732\", \"244090\", \"245963\", \"249685\", \"250707\", \"256813\", \"263662\", \"265649\", \"266441\", \"272901\", \"282844\", \"288532\", \"291301\", \"293035\", \"295281\", \"305492\"]}","task_split":"paper_retrieval"}
{"document_id":"179469","document_content":"# Image Retrieval and Pattern Spotting using Siamese Neural Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a novel approach for image retrieval and pattern spotting in document image collections. The manual feature engineering is avoided by learning a similarity-based representation using a Siamese Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset. The learned representation is used to provide the similarity-based feature maps used to find relevant image candidates in the data collection given an image query. A robust experimental protocol based on the public Tobacco800 document image collection shows that the proposed method compares favorably against state-of-the-art document image retrieval methods, reaching 0.94 and 0.83 of mean average precision (mAP) for retrieval and pattern spotting (IoU=0.7), respectively. Besides, we have evaluated the proposed method considering feature maps of different sizes, showing the impact of reducing the number of features in the retrieval performance and time-consuming.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.09513v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"67375\", \"201134\", \"175014\"], \"outgoing_citations\": [\"249570\", \"295204\", \"314231\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"179538","document_content":"# Evaluating Computational Language Models with Scaling Properties of Natural Language\n## Categories\n- Computation and Language\n## Abstract\nIn this article, we evaluate computational models of natural language with respect to the universal statistical behaviors of natural language. Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text. We study whether five scaling properties (given by Zipf's law, Heaps' law, Ebeling's method, Taylor's law, and long-range correlation analysis) can serve for evaluation of computational models. Specifically, we test $n$-gram language models, a probabilistic context-free grammar (PCFG), language models based on Simon\/Pitman-Yor processes, neural language models, and generative adversarial networks (GANs) for text generation. Our analysis reveals that language models based on recurrent neural networks (RNNs) with a gating mechanism (i.e., long short-term memory, LSTM; a gated recurrent unit, GRU; and quasi-recurrent neural networks, QRNNs) are the only computational models that can reproduce the long memory behavior of natural language. Furthermore, through comparison with recently proposed model-based evaluation methods, we find that the exponent of Taylor's law is a good indicator of model quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.09379v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"45930\"], \"outgoing_citations\": [\"234750\", \"236053\", \"238143\", \"243154\", \"244368\", \"250815\", \"255035\", \"259145\", \"260929\", \"261140\", \"263935\", \"264937\", \"264957\", \"273062\", \"278065\", \"281176\", \"283971\", \"289878\", \"317675\", \"351626\"]}","task_split":"paper_retrieval"}
{"document_id":"179680","document_content":"# Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts\n## Categories\n- Human-Computer Interaction\n- Information Retrieval\n## Abstract\nData analytics software applications have become an integral part of the decision-making process of analysts. Users of such a software face challenges due to insufficient product and domain knowledge, and find themselves in need of help. To alleviate this, we propose a task-aware command recommendation system, to guide the user on what commands could be executed next. We rely on topic modeling techniques to incorporate information about user's task into our models. We also present a help prediction model to detect if a user is in need of help, in which case the system proactively provides the aforementioned command recommendations. We leverage the log data of a web-based analytics software to quantify the superior performance of our neural models, in comparison to competitive baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08973v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.IR\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Information Retrieval\"], \"incoming_citations\": [\"87405\"], \"outgoing_citations\": [\"249012\", \"258818\", \"267953\", \"281136\", \"349540\"]}","task_split":"paper_retrieval"}
{"document_id":"179700","document_content":"# Meta-learning of textual representations\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nRecent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to automate the design of supervised learning methods in the context of text mining. We introduce a meta learning methodology for automatically obtaining a representation for text mining tasks starting from raw text. We report experiments considering 60 different textual representations and more than 80 text mining datasets associated to a wide variety of tasks. Experimental results show the proposed methodology is a promising solution to obtain highly effective off the shell text classification pipelines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08934v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"39855\", \"82266\", \"196657\"], \"outgoing_citations\": [\"316639\", \"355279\"]}","task_split":"paper_retrieval"}
{"document_id":"179730","document_content":"# Informative Image Captioning with External Sources of Information\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nAn image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important \"informativeness\" dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08876v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"93187\", \"26827\", \"33417\", \"95829\", \"168082\", \"31505\", \"97767\", \"117131\", \"118456\", \"127707\", \"130657\", \"154933\"], \"outgoing_citations\": [\"232783\", \"234753\", \"281152\", \"291851\", \"296681\", \"302815\", \"321621\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"179799","document_content":"# Cooperative Lane Changing via Deep Reinforcement Learning\n## Categories\n- Systems and Control\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08662v1\", \"primary_category\": \"eess.SY\", \"categories\": [\"cs.LG\", \"cs.AI\", \"eess.SY\", \"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Systems and Control\", \"Systems and Control\"], \"incoming_citations\": [\"144965\", \"47946\", \"94703\", \"103825\", \"143673\", \"146405\"], \"outgoing_citations\": [\"218507\", \"234762\", \"262890\", \"278081\"]}","task_split":"paper_retrieval"}
{"document_id":"179957","document_content":"# Unsupervised State Representation Learning in Atari\n## Categories\n- Machine Learning\n## Abstract\nState representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at https:\/\/github.com\/mila-iqia\/atari-representation-learning","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08226v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"44400\", \"83187\", \"122\", \"11995\", \"32551\", \"34608\", \"44916\", \"115404\", \"8725\", \"12107\", \"13629\", \"13872\", \"17285\", \"17522\", \"32332\", \"35918\", \"40829\", \"42315\", \"43557\", \"43379\", \"43490\", \"43851\", \"43745\", \"44376\", \"45824\", \"51976\", \"52717\", \"62509\", \"63020\", \"63163\", \"63809\", \"64566\", \"67718\", \"70492\", \"78415\", \"82806\", \"94341\", \"97906\", \"100693\", \"112665\", \"137693\", \"78417\", \"91456\", \"92754\", \"95191\", \"96500\", \"96956\", \"102328\", \"104366\", \"104762\", \"108941\", \"109591\", \"109956\", \"109936\", \"111212\", \"113119\", \"113236\", \"118444\", \"119105\", \"121166\", \"125315\", \"127678\", \"128799\", \"131037\", \"132207\", \"136264\", \"137036\", \"138832\", \"138960\", \"139759\", \"153932\", \"155615\", \"161924\", \"164513\", \"124644\", \"152713\", \"156063\", \"167544\"], \"outgoing_citations\": [\"182813\", \"185954\", \"193391\", \"196894\", \"201585\", \"202065\", \"205630\", \"207094\", \"208041\", \"208351\", \"210077\", \"210636\", \"211558\", \"215702\", \"215718\", \"217368\", \"219016\", \"219180\", \"221473\", \"229302\", \"230068\", \"230557\", \"238963\", \"242478\", \"257688\", \"260136\", \"261697\", \"262216\", \"263811\", \"265263\", \"268248\", \"283223\", \"291302\", \"292009\", \"308711\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"179990","document_content":"# EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing\n## Categories\n- Computation and Language\n## Abstract\nWe present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans might perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.08104v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2208\", \"5053\", \"55412\", \"127662\", \"14944\", \"15122\", \"17436\", \"19800\", \"26460\", \"32732\", \"36870\", \"44703\", \"35122\", \"47416\", \"56593\", \"58358\", \"64358\", \"72846\", \"92547\", \"92961\", \"88817\", \"89344\", \"97282\", \"98424\", \"98965\", \"114691\", \"116150\", \"118053\", \"127065\", \"126870\", \"129016\", \"135231\", \"146955\", \"154650\", \"158869\", \"167980\", \"168922\", \"184361\"], \"outgoing_citations\": [\"212887\", \"214312\", \"214570\", \"266702\", \"269440\", \"270048\", \"296198\", \"302970\"]}","task_split":"paper_retrieval"}
{"document_id":"180097","document_content":"# Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model\n## Categories\n- Computation and Language\n## Abstract\nWhile deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word\/subword-level based models that adopt large-scale data-driven methods such as pre-trained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6% accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07854v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"174753\", \"178092\", \"181447\", \"200764\", \"220797\", \"235915\", \"237157\", \"281119\", \"284357\"]}","task_split":"paper_retrieval"}
{"document_id":"180158","document_content":"# Better transfer learning with inferred successor maps\n## Categories\n- Artificial Intelligence\n- Neurons and Cognition\n- Machine Learning\n## Abstract\nHumans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. We investigated two approaches that could enable this flexibility: factorized representations, which abstract away general aspects of a task from those prone to change, and nonparametric, memory-based approaches, which can provide a principled way of using similarity to past experiences to guide current behaviour. In particular, we combine the successor representation (SR) that factors the value of actions into expected outcomes and corresponding rewards with evaluating task similarity through clustering the space of reward functions. The proposed algorithm inverts a generative model over tasks, and dynamically samples from a flexible number of distinct SR maps while accumulating evidence about the current task context through amortized inference. It improves SR's transfer capabilities and outperforms competing algorithms and baselines in settings with both known and unsignalled rewards changes. Further, as a neurobiological model of spatial coding in the hippocampus, it explains important signatures of this representation, such as the \"flickering\" behaviour of hippocampal maps, and trajectory-dependent place cells (so-called splitter cells) and their dynamics. We thus provide a novel algorithmic approach for multi-task learning, as well as a common normative framework that links together these different characteristics of the brain's spatial representation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07663v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"q-bio.NC\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neurons and Cognition\", \"Machine Learning\"], \"incoming_citations\": [\"20487\", \"11406\", \"12745\", \"76687\", \"85115\"], \"outgoing_citations\": [\"200927\", \"205488\", \"208243\", \"233450\", \"236060\", \"240600\", \"259708\", \"261316\", \"272222\", \"290243\", \"298281\"]}","task_split":"paper_retrieval"}
{"document_id":"180181","document_content":"# Towards Robust Named Entity Recognition for Historic German\n## Categories\n- Computation and Language\n- I.2.7\n## Abstract\nRecent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with low-resource datasets. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6%. Our pre-trained language and NER models are publicly available under https:\/\/github.com\/stefan-it\/historic-ner .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07592v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7\"], \"incoming_citations\": [\"21526\", \"38003\"], \"outgoing_citations\": [\"220159\", \"259758\", \"267692\"]}","task_split":"paper_retrieval"}
{"document_id":"180184","document_content":"# Learning with Average Precision: Training Image Retrieval with a Listwise Loss\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage retrieval can be formulated as a ranking problem where the goal is to order database images by decreasing similarity to the query. Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain. First, rather than directly optimizing the global ranking, they minimize an upper-bound on the essential loss, which does not necessarily result in an optimal mean average precision (mAP). Second, these methods require significant engineering efforts to work well, e.g. special pre-training and hard-negative mining. In this paper we propose instead to directly optimize the global mAP by leveraging recent advances in listwise loss formulations. Using a histogram binning approximation, the AP can be differentiated and thus employed to end-to-end learning. Compared to existing losses, the proposed method considers thousands of images simultaneously at each iteration and eliminates the need for ad hoc tricks. It also establishes a new state of the art on many standard retrieval benchmarks. Models and evaluation scripts have been made available at https:\/\/europe.naverlabs.com\/Deep-Image-Retrieval\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07589v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1079\", \"3265\", \"5681\", \"12686\", \"27638\", \"45993\", \"56313\", \"70613\", \"73146\", \"85376\", \"97991\", \"1407\", \"4269\", \"4405\", \"7275\", \"13707\", \"15391\", \"19708\", \"24547\", \"25890\", \"28959\", \"29310\", \"30179\", \"31118\", \"31214\", \"31743\", \"35104\", \"36782\", \"37306\", \"40425\", \"42853\", \"43374\", \"28791\", \"48674\", \"48708\", \"52023\", \"52320\", \"52533\", \"52816\", \"56767\", \"51123\", \"59357\", \"59695\", \"61377\", \"62318\", \"62439\", \"63700\", \"63782\", \"65943\", \"69184\", \"70156\", \"70781\", \"84765\", \"124630\", \"176317\", \"174992\", \"78191\", \"87306\", \"85055\", \"94343\", \"103466\", \"109398\", \"109645\", \"110044\", \"111811\", \"112934\", \"117181\", \"133188\", \"135052\", \"135489\", \"145738\", \"147155\", \"152290\", \"153386\", \"200609\", \"180750\"], \"outgoing_citations\": [\"235575\", \"237248\", \"251473\", \"254741\", \"256474\", \"260926\", \"263034\", \"264978\", \"265613\", \"269599\", \"269692\", \"270894\", \"277635\", \"280333\", \"281341\", \"281481\", \"281953\", \"288173\", \"288549\", \"294983\", \"295204\", \"301572\", \"302694\", \"302895\", \"302923\", \"303054\", \"303067\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"180187","document_content":"# Differentiable probabilistic models of scientific imaging with the Fourier slice theorem\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nScientific imaging techniques such as optical and electron microscopy and computed tomography (CT) scanning are used to study the 3D structure of an object through 2D observations. These observations are related to the original 3D object through orthogonal integral projections. For common 3D reconstruction algorithms, computational efficiency requires the modeling of the 3D structures to take place in Fourier space by applying the Fourier slice theorem. At present, it is unclear how to differentiate through the projection operator, and hence current learning algorithms can not rely on gradient based methods to optimize 3D structure models. In this paper we show how back-propagation through the projection operator in Fourier space can be achieved. We demonstrate the validity of the approach with experiments on 3D reconstruction of proteins. We further extend our approach to learning probabilistic models of 3D objects. This allows us to predict regions of low sampling rates or estimate noise. A higher sample efficiency can be reached by utilizing the learned uncertainties of the 3D structure as an unsupervised estimate of the model fit. Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose. Through empirical studies we show that joint inference of the 3D structure and the object pose becomes more difficult when the ground truth object contains more symmetries. Due to the presence of for instance (approximate) rotational symmetries, the pose estimation can easily get stuck in local optima, inhibiting a fine-grained high-quality estimate of the 3D structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07582v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"37095\", \"167347\"], \"outgoing_citations\": [\"251693\", \"289119\", \"314533\"]}","task_split":"paper_retrieval"}
{"document_id":"180203","document_content":"# Multi-Graph Decoding for Code-Switching ASR\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn the FAME! Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's bilingual archives with CS speech. This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions. In this work, we propose a multi-graph decoding and rescoring strategy using bilingual and monolingual graphs together with a unified acoustic model for CS ASR. The proposed decoding scheme gives the freedom to design and employ alternative search spaces for each (monolingual or bilingual) recognition task and enables the effective use of monolingual resources of the high-resourced mixed language in low-resourced CS scenarios. In our scenario, Dutch is the high-resourced and Frisian is the low-resourced language. We therefore use additional monolingual Dutch text resources to improve the Dutch language model (LM) and compare the performance of single- and multi-graph CS ASR systems on Dutch segments using larger Dutch LMs. The ASR results show that the proposed approach outperforms baseline single-graph CS ASR systems, providing better performance on the monolingual Dutch segments without any accuracy loss on monolingual Frisian and code-mixed segments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07523v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"37725\", \"164559\"], \"outgoing_citations\": [\"213306\", \"223051\", \"223054\"]}","task_split":"paper_retrieval"}
{"document_id":"180304","document_content":"# A Structured Distributional Model of Sentence Meaning and Processing\n## Categories\n- Computation and Language\n## Abstract\nMost compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from Discourse Representation Theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modeled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension. We evaluate SDM on two recently introduced compositionality datasets, and our results show that combining a simple compositional model with event knowledge constantly improves performances, even with different types of word embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07280v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1345\", \"33859\", \"100224\"], \"outgoing_citations\": [\"233400\", \"254232\", \"260829\", \"265646\", \"286594\", \"288861\", \"298543\", \"302478\", \"312264\"]}","task_split":"paper_retrieval"}
{"document_id":"180340","document_content":"# NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe propose improving the cross-target and cross-scene generalization of visual navigation through learning an agent that is guided by conceiving the next observations it expects to see. This is achieved by learning a variational Bayesian model, called NeoNav, which generates the next expected observations (NEO) conditioned on the current observations of the agent and the target view. Our generative model is learned through optimizing a variational objective encompassing two key designs. First, the latent distribution is conditioned on current observations and the target view, leading to a model-based, target-driven navigation. Second, the latent space is modeled with a Mixture of Gaussians conditioned on the current observation and the next best action. Our use of mixture-of-posteriors prior effectively alleviates the issue of over-regularized latent space, thus significantly boosting the model generalization for new targets and in novel scenes. Moreover, the NEO generation models the forward dynamics of agent-environment interaction, which improves the quality of approximate inference and hence benefits data efficiency. We have conducted extensive evaluations on both real-world and synthetic benchmarks, and show that our model consistently outperforms the state-of-the-art models in terms of success rate, data efficiency, and generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07207v4\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"97665\", \"152072\", \"140039\"], \"outgoing_citations\": [\"185747\", \"192738\", \"214195\", \"217857\", \"220852\", \"224223\", \"227747\", \"231955\", \"237119\", \"240323\", \"242891\", \"245411\", \"245731\", \"260770\", \"260780\", \"261493\", \"266067\", \"272441\", \"272954\", \"274102\", \"277193\", \"277797\", \"279883\", \"280296\", \"280681\", \"281110\", \"283391\", \"284565\", \"291568\", \"298651\", \"308711\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"180372","document_content":"# Exemplar Guided Face Image Super-Resolution without Facial Landmarks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nNowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07078v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37459\", \"76110\", \"36730\", \"19343\", \"20279\", \"49874\", \"75333\", \"76015\", \"103761\", \"89019\", \"100258\", \"108335\", \"132093\"], \"outgoing_citations\": [\"211273\", \"217152\", \"235729\", \"258886\", \"288238\", \"298700\", \"303132\", \"303174\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"180450","document_content":"# Inspirational Adversarial Image Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nThe task of image generation started to receive some attention from artists and designers to inspire them in new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control on them. We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image. Specifically, we allow the generation given an inspirational image of the user choice by performing several optimization steps to recover optimal parameters from the model's latent space. We tested several exploration methods starting with classic gradient descents to gradient-free optimizers. Many gradient-free optimizers just need comparisons (better\/worse than another image), so that they can even be used without numerical criterion, without inspirational image, but with only with human preference. Thus, by iterating on one's preferences we could make robust Facial Composite or Fashion Generation algorithms. High resolution of the produced design generations are obtained using progressive growing of GANs. Our results on four datasets of faces, fashion images, and textures show that satisfactory images are effectively retrieved in most cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.11661v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"82957\", \"98595\", \"98156\", \"131628\"], \"outgoing_citations\": [\"124846\", \"149962\", \"154243\", \"155482\", \"190931\", \"217503\", \"217503\", \"217861\", \"227184\", \"227184\", \"236886\", \"236886\", \"240143\", \"240143\", \"240885\", \"244263\", \"245177\", \"245177\", \"248330\", \"252872\", \"252872\", \"260887\", \"260887\", \"263146\", \"264781\", \"264781\", \"268533\", \"271562\", \"271562\", \"272553\", \"277838\", \"280232\", \"281002\", \"281002\", \"284132\", \"284874\", \"287740\", \"291440\", \"291440\", \"291537\", \"291537\", \"292385\", \"296773\", \"296773\", \"298700\", \"298700\", \"337853\"]}","task_split":"paper_retrieval"}
{"document_id":"180459","document_content":"# Learning Interpretable Models Using an Oracle\n## Categories\n- Machine Learning\n## Abstract\nWe look at a specific aspect of model interpretability: models often need to be constrained in size for them to be considered interpretable. But smaller models also tend to have high bias. This suggests a trade-off between interpretability and accuracy. Our work addresses this by: (a) showing that learning a training distribution (often different from the test distribution) can often increase accuracy of small models, and therefore may be used as a strategy to compensate for small sizes, and (b) providing a model-agnostic algorithm to learn such training distributions. We pose the distribution learning problem as one of optimizing parameters for an Infinite Beta Mixture Model based on a Dirichlet Process, so that the held-out accuracy of a model trained on a sample from this distribution is maximized. To make computation tractable, we project the training data onto one dimension: prediction uncertainty scores as provided by a highly accurate oracle model. A Bayesian Optimizer is used for learning the parameters. Empirical results using multiple real world datasets, various oracles and interpretable models with different notions of model sizes, are presented. We observe significant relative improvements in the F1-score in most cases, occasionally seeing improvements greater than 100% over baselines. Additionally we show that the proposed algorithm provides the following benefits: (a) its a framework which allows for flexibility in implementation, (b) it can be used across feature spaces, e.g., the text classification accuracy of a Decision Tree using character n-grams is shown to improve when using a Gated Recurrent Unit as an oracle, which uses a sequence of characters as its input, (c) it can be used to train models that have a non-differentiable training loss, e.g., Decision Trees, and (d) reasonable defaults exist for most parameters of the algorithm, which makes it convenient to use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06852v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"324\", \"54540\", \"54740\", \"68615\", \"74642\", \"119873\", \"139553\", \"157698\", \"173739\", \"187772\", \"188493\", \"192122\", \"193619\", \"197223\", \"215409\", \"223501\", \"225410\", \"230061\", \"241329\", \"241330\", \"243647\", \"249872\", \"252363\", \"253295\", \"263144\", \"271988\", \"279535\", \"298766\", \"303951\", \"311195\", \"313106\", \"317469\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"180487","document_content":"# ASAC: Active Sensing using Actor-Critic models\n## Categories\n- Machine Learning\n## Abstract\nDeciding what and when to observe is critical when making observations is costly. In a medical setting where observations can be made sequentially, making these observations (or not) should be an active choice. We refer to this as the active sensing problem. In this paper, we propose a novel deep learning framework, which we call ASAC (Active Sensing using Actor-Critic models) to address this problem. ASAC consists of two networks: a selector network and a predictor network. The selector network uses previously selected observations to determine what should be observed in the future. The predictor network uses the observations selected by the selector network to predict a label, providing feedback to the selector network (well-selected variables should be predictive of the label). The goal of the selector network is then to select variables that balance the cost of observing the selected variables with their predictive power; we wish to preserve the conditional label distribution. During training, we use the actor-critic models to allow the loss of the selector to be \"back-propagated\" through the sampling process. The selector network \"acts\" by selecting future observations to make. The predictor network acts as a \"critic\" by feeding predictive errors for the selected variables back to the selector network. In our experiments, we show that ASAC significantly outperforms state-of-the-arts in two real-world medical datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06796v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"43317\", \"81873\"], \"outgoing_citations\": [\"228869\", \"241329\", \"269418\", \"282051\", \"334218\"]}","task_split":"paper_retrieval"}
{"document_id":"180491","document_content":"# Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nThe environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06789v5\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"68859\", \"3909\", \"28740\"], \"outgoing_citations\": [\"112562\", \"112577\", \"162430\", \"187297\", \"189914\", \"194576\", \"196618\", \"208156\", \"239891\", \"258687\", \"259864\", \"271271\"]}","task_split":"paper_retrieval"}
{"document_id":"180575","document_content":"# Multi-Hop Paragraph Retrieval for Open-Domain Question Answering\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThis paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06606v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"8561\", \"50552\", \"64447\", \"20965\", \"23599\", \"32476\", \"47485\", \"55377\", \"77192\", \"77341\", \"77656\", \"91626\", \"91823\", \"98369\", \"100407\", \"140601\", \"104940\", \"88899\", \"93719\", \"95832\", \"97393\", \"127255\", \"130183\", \"132681\", \"139630\", \"140317\", \"154601\", \"157277\", \"161554\", \"163072\"], \"outgoing_citations\": [\"186337\", \"200155\", \"204098\", \"208346\", \"215928\", \"216563\", \"220858\", \"231239\", \"234814\", \"235408\", \"235901\", \"238610\", \"250529\", \"252059\", \"253068\", \"261389\", \"266850\", \"268671\", \"290747\"]}","task_split":"paper_retrieval"}
{"document_id":"180623","document_content":"# Modeling of User Portrait Through Social Media\n## Categories\n- Social and Information Networks\n## Abstract\nNowadays, massive useful data of user information and social behavior have been accumulated on the Internet, providing a possibility of profiling user's personality traits online. In this paper, we propose a psychological modeling method based on computational linguistic features to profile Big Five personality traits of users on Sina Weibo (a Twitter-like microblogging service in China) and their correlations with user's social behaviors. To the best of our knowledge, this is the first research on investigating the potential relationship between profile information, social-network behaviors and personality traits of users on Sina Weibo. Our results demonstrate an effective modeling approach to understanding demographic and psychological portraits of users on social media without customer disruption, which is useful for commercial incorporations to provide better personalized products and services.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICME.2018.8486595\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"180645","document_content":"# High Speed and High Dynamic Range Video with an Event Camera\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent cameras are novel sensors that report brightness changes in the form of a stream of asynchronous \"events\" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images. In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams. Our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality (> 20%), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos (> 5,000 frames per second) of high-speed phenomena (e.g. a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. We also demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.07165v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"63922\", \"1798\", \"2176\", \"3311\", \"3629\", \"6051\", \"14984\", \"25451\", \"45856\", \"190036\", \"195521\", \"5751\", \"6280\", \"14929\", \"20007\", \"20637\", \"25803\", \"25767\", \"26495\", \"33539\", \"42545\", \"7584\", \"42312\", \"30764\", \"47252\", \"49930\", \"53538\", \"54348\", \"54653\", \"61160\", \"61609\", \"63967\", \"68472\", \"81097\", \"81435\", \"127359\", \"93777\", \"100061\", \"119565\", \"93747\", \"98108\", \"120116\", \"123529\", \"124413\", \"135873\", \"135840\", \"139039\", \"152420\", \"153050\"], \"outgoing_citations\": [\"189162\", \"190036\", \"190078\", \"211978\", \"222578\", \"223380\", \"224055\", \"238238\", \"241612\", \"243802\", \"255512\", \"264538\", \"281870\", \"287980\", \"288554\", \"301852\", \"308474\", \"308908\"]}","task_split":"paper_retrieval"}
{"document_id":"180695","document_content":"# Comparison of Diverse Decoding Methods from Conditional Language Models\n## Categories\n- Computation and Language\n## Abstract\nWhile conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that re-rank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from conditional language models. We also show how diversity can be improved without sacrificing quality by over-sampling additional candidates, then filtering to the desired number.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06362v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2579\", \"16293\", \"5954\", \"29406\", \"29970\", \"46988\", \"55960\", \"58358\", \"54487\", \"64676\", \"73917\", \"90310\", \"94230\", \"108978\", \"129680\", \"207215\", \"82594\", \"89861\", \"94752\", \"95282\", \"96200\", \"100503\", \"108688\", \"114461\", \"125626\", \"142985\", \"145384\", \"153233\", \"154192\", \"154342\", \"164270\"], \"outgoing_citations\": [\"192158\", \"192166\", \"217640\", \"219198\", \"232274\", \"259505\", \"274450\", \"279543\", \"280004\", \"283076\", \"287504\", \"292690\", \"295863\", \"300652\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"180823","document_content":"# Towards End-to-End Text Spotting in Natural Scenes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping.   In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end and is able to handle text of arbitrary shapes. The convolutional features are calculated only once and shared by both detection and recognition modules. Through multi-task training, the learned features become more discriminate and improve the overall performance. By employing the $2$D attention model in word recognition, the irregularity of text can be robustly addressed. It provides the spatial location for each character, which not only helps local feature extraction in word recognition, but also indicates an orientation angle to refine text localization. Our proposed method has achieved state-of-the-art performance on several standard text spotting benchmarks, including both regular and irregular ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.06013v6\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"50917\", \"236\", \"81960\", \"111035\", \"140478\"], \"outgoing_citations\": [\"140478\", \"161220\", \"170332\", \"190708\", \"204872\", \"205988\", \"209227\", \"210634\", \"211851\", \"232706\", \"239535\", \"240965\", \"245591\", \"245899\", \"250738\", \"252159\", \"256711\", \"257197\", \"261365\", \"269205\", \"272390\", \"272481\", \"279950\", \"284871\", \"289389\", \"294023\", \"294606\", \"294931\", \"296685\", \"296828\", \"299461\", \"309239\", \"311196\", \"320837\", \"328595\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"180851","document_content":"# Cross-View Policy Learning for Street Navigation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nThe ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL). Street View can be a sensible testbed for such RL agents, because it provides real-world photographic imagery at ground level, with diverse street appearances; it has been made into an interactive environment called StreetLearn and used for research on navigation. However, goal-driven street navigation agents have not so far been able to transfer to unseen areas without extensive retraining, and relying on simulation is not a scalable solution. Since aerial images are easily and globally accessible, we propose instead to train a multi-modal policy on ground and aerial views, then transfer the ground view policy to unseen (target) parts of the city by utilizing aerial view observations. Our core idea is to pair the ground view with an aerial view and to learn a joint policy that is transferable across views. We achieve this by learning a similar embedding space for both views, distilling the policy across views and dropping out visual modalities. We further reformulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only adaptation to a new area, when the agent is adapted to a held-out region using only the easily obtainable aerial view, and 3) ground view-only transfer, when the agent is tested on navigation tasks on unseen ground views, without aerial imagery. Experimental results suggest that the proposed cross-view policy learning enables better generalization of the agent and allows for more effective transfer to unseen environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05930v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"87445\", \"18400\", \"84961\", \"123303\", \"98224\", \"104815\", \"161521\", \"161660\", \"162505\", \"196909\"], \"outgoing_citations\": [\"196598\", \"196909\", \"199616\", \"208042\", \"208773\", \"227556\", \"231955\", \"237119\", \"237386\", \"238273\", \"241006\", \"241625\", \"243235\", \"246823\", \"247418\", \"248706\", \"249137\", \"249906\", \"262593\", \"263104\", \"263811\", \"264787\", \"270801\", \"272109\", \"272668\", \"277797\", \"280681\", \"284565\", \"291568\", \"310233\"]}","task_split":"paper_retrieval"}
{"document_id":"180880","document_content":"# Contrastive Multiview Coding\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nHumans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a \"dog\" can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks. Code is released at: http:\/\/github.com\/HobbitLong\/CMC\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05849v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"82674\", \"71714\", \"5917\", \"6636\", \"8964\", \"112015\", \"31230\", \"37051\", \"45692\", \"55739\", \"67813\", \"79763\", \"83187\", \"83941\", \"94177\", \"118617\", \"127183\", \"110082\", \"67\", \"315\", \"587\", \"1434\", \"1511\", \"2317\", \"2414\", \"2524\", \"3514\", \"3489\", \"3727\", \"4099\", \"4352\", \"4457\", \"4310\", \"4558\", \"4865\", \"4917\", \"5106\", \"5641\", \"6116\", \"5898\", \"7118\", \"7198\", \"7786\", \"10480\", \"11136\", \"11343\", \"13645\", \"14235\", \"15441\", \"15677\", \"16656\", \"17725\", \"19948\", \"21019\", \"21990\", \"22608\", \"22628\", \"23768\", \"26485\", \"28831\", \"29146\", \"29447\", \"29764\", \"32353\", \"32678\", \"32976\", \"34664\", \"35681\", \"35779\", \"37483\", \"38839\", \"39480\", \"40549\", \"41063\", \"41299\", \"42397\", \"43327\", \"43670\", \"44068\", \"44359\", \"44521\", \"48198\", \"48964\", \"49180\", \"49492\", \"49976\", \"50342\", \"53231\", \"53543\", \"55006\", \"61655\", \"62025\", \"63760\", \"63954\", \"64453\", \"65038\", \"65191\", \"65430\", \"66588\", \"66899\", \"69784\", \"74217\", \"77056\", \"82130\", \"85140\", \"85328\", \"85886\", \"86410\", \"86683\", \"98489\", \"102199\", \"103226\", \"103880\", \"109572\", \"113365\", \"115739\", \"115912\", \"137262\", \"1407\", \"2834\", \"3901\", \"4033\", \"4556\", \"6106\", \"5931\", \"6303\", \"6759\", \"6775\", \"7189\", \"7602\", \"8725\", \"8778\", \"9486\", \"9479\", \"10607\", \"10585\", \"10734\", \"11542\", \"11811\", \"11933\", \"12206\", \"12309\", \"12338\", \"12878\", \"12903\", \"12936\", \"13386\", \"13549\", \"13961\", \"14035\", \"14303\", \"14498\", \"14589\", \"15034\", \"15493\", \"16187\", \"16373\", \"16522\", \"16549\", \"16619\", \"17328\", \"17517\", \"17823\", \"18284\", \"18733\", \"19237\", \"9819\", \"115947\", \"3077\", \"17604\", \"19866\", \"20375\", \"20384\", \"20846\", \"20937\", \"21382\", \"21611\", \"21823\", \"22507\", \"23696\", \"23625\", \"24085\", \"24216\", \"24857\", \"25153\", \"26075\", \"26453\", \"26668\", \"26558\", \"27345\", \"27347\", \"27578\", \"27821\", \"27905\", \"28136\", \"28262\", \"28561\", \"28840\", \"28766\", \"29104\", \"29181\", \"29220\", \"29208\", \"29613\", \"29688\", \"30073\", \"30524\", \"30588\", \"30779\", \"31078\", \"31213\", \"31452\", \"31874\", \"32332\", \"32559\", \"33405\", \"33281\", \"33574\", \"34442\", \"35181\", \"36894\", \"37058\", \"37234\", \"37316\", \"34469\", \"37931\", \"37938\", \"38101\", \"38125\", \"38369\", \"38467\", \"38716\", \"38999\", \"39122\", \"39618\", \"39993\", \"40223\", \"40533\", \"40501\", \"40708\", \"40814\", \"41104\", \"41520\", \"41795\", \"42387\", \"42693\", \"43023\", \"43059\", \"43013\", \"43651\", \"43847\", \"43867\", \"44050\", \"44294\", \"44459\", \"44818\", \"45087\", \"45044\", \"40762\", \"42924\", \"40498\", \"43185\", \"43454\", \"45876\", \"45977\", \"46336\", \"48202\", \"48458\", \"48497\", \"48501\", \"48591\", \"48850\", \"49042\", \"49329\", \"49387\", \"49739\", \"50036\", \"50043\", \"50100\", \"50219\", \"50222\", \"50859\"], \"outgoing_citations\": [\"123821\", \"145027\", \"151327\", \"152543\", \"152790\", \"160122\", \"177483\", \"180920\", \"181925\", \"182813\", \"183952\", \"185155\", \"185954\", \"188176\", \"191890\", \"193227\", \"197714\", \"201585\", \"202960\", \"210636\", \"210761\", \"222918\", \"224270\", \"245283\", \"257948\", \"259415\", \"268248\", \"268631\", \"276665\", \"279211\", \"283406\", \"285093\", \"291440\", \"295738\", \"299534\", \"300855\", \"302787\", \"302802\", \"345732\", \"357235\"]}","task_split":"paper_retrieval"}
{"document_id":"181052","document_content":"# Uncovering Dominant Social Class in Neighborhoods through Building Footprints: A Case Study of Residential Zones in Massachusetts using Computer Vision\n## Categories\n- Computer Vision and Pattern Recognition\n- Computers and Society\n## Abstract\nIn urban theory, urban form is related to social and economic status. This paper explores to uncover zip-code level income through urban form by analyzing figure-ground map, a simple, prevailing and precise representation of urban form in the field of urban study. Deep learning in computer vision enables such representation maps to be studied at a large scale. We propose to train a DCNN model to identify and uncover the internal bridge between social class and urban form. Further, using hand-crafted informative visual features related with urban form properties (building size, building density, etc.), we apply a random forest classifier to interpret how morphological properties are related with social class.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05352v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CY\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"181078","document_content":"# Representation Learning for Words and Entities\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nThis thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05651v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"161151\", \"178230\", \"228842\", \"231768\", \"232329\", \"243732\", \"265526\", \"265739\", \"267123\", \"267351\", \"268728\", \"273396\", \"283235\", \"288041\", \"289508\", \"291575\", \"294617\", \"296214\", \"298784\", \"300498\", \"302943\", \"303029\", \"304901\", \"316630\", \"318839\", \"320008\", \"322879\", \"323173\", \"323696\", \"325742\", \"328698\", \"336326\", \"342907\", \"345692\", \"350905\", \"352900\", \"354437\", \"357162\", \"357920\"]}","task_split":"paper_retrieval"}
{"document_id":"181081","document_content":"# Search on the Replay Buffer: Bridging Planning and Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nThe history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectively reason over long horizons, but assume access to a local policy and distance metric over collision-free paths. Reinforcement learning excels at learning policies and the relative values of states, but fails to plan over long horizons. Despite the successes of each method in various domains, tasks that require reasoning over long horizons with limited feedback and high-dimensional observations remain exceedingly challenging for both planning and reinforcement learning algorithms. Frustratingly, these sorts of tasks are potentially the most useful, as they are simple to design (a human only need to provide an example goal state) and avoid reward shaping, which can bias the agent towards finding a sub-optimal solution. We introduce a general control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks. Our aim is to decompose the task of reaching a distant goal state into a sequence of easier tasks, each of which corresponds to reaching a subgoal. Planning algorithms can automatically find these waypoints, but only if provided with suitable abstractions of the environment -- namely, a graph consisting of nodes and edges. Our main insight is that this graph can be constructed via reinforcement learning, where a goal-conditioned value function provides edge weights, and nodes are taken to be previously seen observations in a replay buffer. Using graph search over our replay buffer, we can automatically generate this sequence of subgoals, even in image-based environments. Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over one hundred steps, and generalizes substantially better than standard RL algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05253v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"12555\", \"44400\", \"65465\", \"58\", \"1752\", \"7709\", \"11364\", \"14284\", \"15877\", \"18176\", \"63862\", \"75621\", \"78473\", \"97832\", \"105111\", \"109508\", \"952\", \"1414\", \"5820\", \"8634\", \"13615\", \"15365\", \"15383\", \"21975\", \"26076\", \"31589\", \"38161\", \"39279\", \"39395\", \"39688\", \"43324\", \"45153\", \"21619\", \"54546\", \"55721\", \"60708\", \"63319\", \"67087\", \"69303\", \"77736\", \"84844\", \"86305\", \"88041\", \"94561\", \"128664\", \"142303\", \"151443\", \"44089\", \"85766\", \"90880\", \"108713\", \"112129\", \"112949\", \"117297\", \"117936\", \"87362\", \"119375\", \"123031\", \"125870\", \"126377\", \"131891\", \"132719\", \"136039\", \"136916\", \"139633\", \"140129\", \"143410\", \"148914\", \"167147\", \"175488\", \"178186\", \"181498\"], \"outgoing_citations\": [\"196387\", \"204013\", \"212130\", \"214717\", \"215425\", \"216429\", \"227747\", \"229890\", \"231175\", \"231209\", \"231925\", \"236969\", \"240323\", \"240348\", \"240495\", \"240918\", \"248619\", \"249906\", \"252334\", \"253600\", \"254596\", \"259045\", \"260583\", \"260770\", \"270667\", \"272464\", \"274102\", \"279420\", \"280681\", \"283391\", \"284565\", \"284567\", \"289719\", \"294142\", \"298281\", \"298651\", \"308711\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"181089","document_content":"# Reinforcement Knowledge Graph Reasoning for Explainable Recommendation\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nRecent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3331184.3331203\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"7627\", \"20875\", \"53372\", \"72315\", \"75234\", \"129299\", \"5838\", \"12342\", \"18335\", \"31255\", \"66586\", \"83553\", \"95561\", \"137054\", \"139564\", \"144530\", \"3657\", \"6878\", \"8309\", \"11787\", \"16141\", \"18031\", \"21810\", \"22373\", \"25074\", \"25805\", \"27889\", \"30681\", \"33204\", \"36940\", \"36944\", \"38755\", \"43762\", \"47567\", \"48355\", \"50017\", \"50291\", \"50401\", \"50702\", \"54275\", \"58386\", \"68044\", \"69255\", \"69504\", \"73917\", \"76185\", \"76257\", \"76941\", \"106824\", \"90451\", \"93764\", \"115010\", \"115107\", \"115735\", \"118742\", \"121346\", \"122086\", \"123124\", \"127552\", \"130658\", \"137147\", \"145029\", \"150471\", \"155463\", \"174276\", \"233732\", \"179473\"], \"outgoing_citations\": [\"210521\", \"219691\", \"232728\", \"233732\", \"239537\", \"250322\", \"260644\", \"261756\", \"276004\", \"305418\"]}","task_split":"paper_retrieval"}
{"document_id":"181122","document_content":"# General Video Game Rule Generation\n## Categories\n- Artificial Intelligence\n## Abstract\nWe introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition. The problem is, given a game level as input, to generate the rules of a game that fits that level. This can be seen as the inverse of the General Video Game Level Generation problem. Conceptualizing these two problems as separate helps breaking the very hard problem of generating complete games into smaller, more manageable subproblems. The proposed framework builds on the GVGAI software and thus asks the rule generator for rules defined in the Video Game Description Language. We describe the API, and three different rule generators: a random, a constructive and a search-based generator. Early results indicate that the constructive generator generates playable and somewhat interesting game rules but has a limited expressive range, whereas the search-based generator generates remarkably diverse rulesets, but with an uneven quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.05160v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"31098\", \"86180\", \"33228\", \"37296\", \"68080\", \"139814\", \"163503\", \"168225\", \"176932\", \"178386\", \"181330\", \"186290\", \"191836\", \"202740\", \"240572\", \"218887\", \"229002\", \"229359\", \"237653\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"181254","document_content":"# Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nAn accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabilistic forecasting, medium-term load forecasting (MTLF) demands more attention due to its vital role in power system operation and planning such as optimal scheduling of generation units, robust planning program for customer service, and economic supply. In this study, a hybrid method, composed of Support Vector Regression (SVR) and Symbiotic Organism Search Optimization (SOSO) method, is proposed for MTLF. In the proposed forecasting model, SVR is the main part of the forecasting algorithm while SOSO is embedded into it to optimize the parameters of SVR. In addition, a minimum redundancy-maximum relevance feature selection algorithm is used to in the preprocessing of input data. The proposed method is tested on EUNITE competition dataset to demonstrate its proper performance. Furthermore, it is compared with some previous works to show eligibility of our method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04818v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"181255","document_content":"# Position-aware Graph Neural Networks\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nLearning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (GNN) architectures have limited power in capturing the position\/location of a given node with respect to all other nodes of the graph. Here we propose Position-aware Graph Neural Networks (P-GNNs), a new class of GNNs for computing position-aware node embeddings. P-GNN first samples sets of anchor nodes, computes the distance of a given target node to each anchor-set,and then learns a non-linear distance-weighted aggregation scheme over the anchor-sets. This way P-GNNs can capture positions\/locations of nodes with respect to the anchor nodes. P-GNNs have several advantages: they are inductive, scalable,and can incorporate node feature information. We apply P-GNNs to multiple prediction tasks including link prediction and community detection. We show that P-GNNs consistently outperform state of the art GNNs, with up to 66% improvement in terms of the ROC AUC score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04817v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"18977\", \"120407\", \"16180\", \"18706\", \"22079\", \"30914\", \"34810\", \"37770\", \"38038\", \"74616\", \"80211\", \"97457\", \"101759\", \"102194\", \"133777\", \"135535\", \"191904\", \"882\", \"1006\", \"1466\", \"4995\", \"5326\", \"5787\", \"7914\", \"9015\", \"13834\", \"17969\", \"19609\", \"21895\", \"24738\", \"29682\", \"4786\", \"39605\", \"40734\", \"43342\", \"43338\", \"43688\", \"47665\", \"18019\", \"49630\", \"49956\", \"50822\", \"50720\", \"52255\", \"54110\", \"46420\", \"56829\", \"61780\", \"64367\", \"62817\", \"66570\", \"67519\", \"71159\", \"73530\", \"73619\", \"77517\", \"80220\", \"80899\", \"88245\", \"90277\", \"90641\", \"91881\", \"92001\", \"105139\", \"143473\", \"159538\", \"79849\", \"81245\", \"81468\", \"81711\", \"83230\", \"86320\", \"90139\", \"91654\", \"62816\", \"91408\", \"101267\", \"103085\", \"104223\", \"111026\", \"112378\", \"111790\", \"114134\", \"115132\", \"115834\", \"117690\", \"118087\", \"118788\", \"120250\", \"119843\", \"126374\", \"127525\", \"131562\", \"135856\", \"138048\", \"139136\", \"139344\", \"141321\", \"142226\", \"143081\", \"144037\", \"144992\", \"145523\", \"146069\", \"160169\", \"163945\", \"183816\", \"192622\", \"138999\", \"150059\", \"159199\", \"161189\", \"165475\", \"183719\", \"195767\"], \"outgoing_citations\": [\"228992\", \"241029\", \"242648\", \"261201\", \"318072\"]}","task_split":"paper_retrieval"}
{"document_id":"181399","document_content":"# Relationship-Embedded Representation Learning for Grounding Referring Expressions\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nGrounding referring expressions in images aims to locate the object instance in an image described by a referring expression. It involves a joint understanding of natural language and image content, and is essential for a range of visual tasks related to human-computer interaction. As a language-to-vision matching task, the core of this problem is to not only extract all the necessary information (i.e., objects and the relationships among them) in both the image and referring expression, but also make full use of context information to align cross-modal semantic concepts in the extracted information. Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image. In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph. In addition, we propose a Gated Graph Convolutional Network (GGCN) to compute multimodal semantic contexts by fusing information from different modes and propagating multimodal information in the structured relation graph. Experimental results on three common benchmark datasets show that our Cross-Modal Relationship Inference Network, which consists of CMRE and GGCN, significantly surpasses all existing state-of-the-art methods. Code is available at https:\/\/github.com\/sibeiyang\/sgmn\/tree\/master\/lib\/cmrin_models","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04464v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"49435\", \"59533\", \"61081\", \"130336\"], \"outgoing_citations\": [\"176990\", \"197658\", \"207034\", \"212327\", \"213330\", \"217310\", \"222631\", \"222661\", \"226275\", \"227507\", \"237145\", \"237214\", \"237959\", \"238203\", \"244272\", \"248215\", \"250108\", \"250712\", \"259346\", \"263034\", \"264533\", \"264856\", \"268511\", \"269216\", \"276299\", \"277060\", \"277968\", \"278604\", \"279177\", \"284449\", \"285190\", \"287377\", \"287427\", \"287454\", \"291430\", \"302662\", \"303203\", \"303534\", \"303603\", \"303714\", \"303833\", \"303837\", \"312506\", \"313320\"]}","task_split":"paper_retrieval"}
{"document_id":"181410","document_content":"# Reinforcement Learning of Minimalist Numeral Grammars\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSpeech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication technologies, simple slot-filling should be replaced by utterance meaning transducers (UMT) that are based on semantic parsers and a \\emph{mental lexicon}, comprising syntactic, phonetic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. We outline a reinforcement learning algorithm for the acquisition of the syntactic morphology and arithmetic semantics of English numerals, based on minimalist grammar (MG), a recent computational implementation of generative linguistics. Number words are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as arithmetic terms from a suitable term algebra. Since MG encodes universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04447v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"104334\", \"127913\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"181434","document_content":"# Few-Shot Point Cloud Region Annotation with Human in the Loop\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe propose a point cloud annotation framework that employs human-in-loop learning to enable the creation of large point cloud datasets with per-point annotations. Sparse labels from a human annotator are iteratively propagated to generate a full segmentation of the network by fine-tuning a pre-trained model of an allied task via a few-shot learning paradigm. We show that the proposed framework significantly reduces the amount of human interaction needed in annotating point clouds, without sacrificing on the quality of the annotations. Our experiments also suggest the suitability of the framework in annotating large datasets by noting a reduction in human interaction as the number of full annotations completed by the system increases. Finally, we demonstrate the flexibility of the framework to support multiple different annotations of the same point cloud enabling the creation of datasets with different granularities of annotation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04409v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"206936\", \"248727\", \"249190\", \"269040\", \"298730\"]}","task_split":"paper_retrieval"}
{"document_id":"181437","document_content":"# Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVisual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding functions that map an instance to a single point in the shared space. Unfortunately, injective embedding cannot effectively handle polysemous instances with multiple possible meanings; at best, it would find an average representation of different meanings. This hinders its use in real-world scenarios where individual instances and their cross-modal associations are often ambiguous. In this work, we introduce Polysemous Instance Embedding Networks (PIE-Nets) that compute multiple and diverse representations of an instance by combining global context with locally-guided features via multi-head self-attention and residual learning. To learn visual-semantic embedding, we tie-up two PIE-Nets and optimize them jointly in the multiple instance learning framework. Most existing work on cross-modal retrieval focuses on image-text data. Here, we also tackle a more challenging case of video-text retrieval. To facilitate further research in video-text retrieval, we release a new dataset of 50K video-sentence pairs collected from social media, dubbed MRW (my reaction when). We demonstrate our approach on both image-text and video-text retrieval scenarios using MS-COCO, TGIF, and our new MRW dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04402v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5478\", \"9191\", \"101354\", \"72957\", \"33374\", \"33924\", \"42740\", \"50613\", \"55096\", \"59840\", \"84765\", \"88202\", \"105055\", \"130722\", \"133787\", \"138177\", \"161696\", \"86006\", \"111384\", \"111530\", \"95983\", \"162227\", \"163456\", \"156399\"], \"outgoing_citations\": [\"215998\", \"235882\", \"238205\", \"244081\", \"248171\", \"250098\", \"250183\", \"259318\", \"268871\", \"271260\", \"280249\", \"285796\", \"288000\", \"292486\", \"293220\", \"294898\", \"302943\", \"303011\", \"314050\", \"322022\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"181514","document_content":"# Identifying Visible Actions in Lifestyle Vlogs\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWe consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing actions while verbally describing them. Our goal is to identify if actions mentioned in the speech description of a video are visually present. We construct a dataset with crowdsourced manual annotations of visible actions, and introduce a multimodal algorithm that leverages information derived from visual and linguistic clues to automatically infer which actions are visible in a video. We demonstrate that our multimodal algorithm outperforms algorithms based only on one modality at a time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/P19-1643\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25416\", \"81390\", \"93929\", \"126167\", \"128128\", \"134177\"], \"outgoing_citations\": [\"219048\", \"236377\", \"245503\", \"248108\", \"265650\", \"268871\", \"270674\", \"270890\", \"274833\", \"288062\", \"292471\", \"295117\", \"303534\", \"317994\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"181590","document_content":"# \"Did You Hear That?\" Learning to Play Video Games from Audio Cues\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nGame-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information. However, humans benefit from a wider array of sensors which we utilise in order to navigate the world around us. In particular, sounds and music are key to how many of us perceive the world and influence the decisions we make. In this paper, we present initial experiments on game-playing agents learning to play video games solely from audio cues. We expand the Video Game Description Language to allow for audio specification, and the General Video Game AI framework to provide new audio games and an API for learning agents to make use of audio observations. We analyse the games and the audio game design process, include initial results with simple Q~Learning agents, and encourage further research in this area.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.04027v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"37438\", \"158301\"], \"outgoing_citations\": [\"240572\", \"242616\"]}","task_split":"paper_retrieval"}
{"document_id":"181620","document_content":"# A Survey of Reinforcement Learning Informed by Natural Language\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nTo be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally, we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03926v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8099\", \"8658\", \"1704\", \"4639\", \"16536\", \"22323\", \"39368\", \"47401\", \"56667\", \"79803\", \"11352\", \"14182\", \"14861\", \"22124\", \"26076\", \"31177\", \"35181\", \"37989\", \"41041\", \"41627\", \"42423\", \"44473\", \"47013\", \"53305\", \"55605\", \"64051\", \"64961\", \"69616\", \"69948\", \"71959\", \"74771\", \"83197\", \"89875\", \"95168\", \"97596\", \"97289\", \"105761\", \"124867\", \"132041\", \"144521\", \"119120\", \"82624\", \"160608\", \"88351\", \"142053\", \"92228\", \"108770\", \"110974\", \"111265\", \"112980\", \"115436\", \"119188\", \"119169\", \"116357\", \"124075\", \"127678\", \"128393\", \"131013\", \"137584\", \"138524\", \"140901\", \"145784\", \"161143\", \"157435\", \"162602\", \"162609\", \"166082\", \"168951\", \"180270\"], \"outgoing_citations\": [\"182870\", \"186130\", \"196377\", \"198208\", \"198373\", \"208042\", \"208773\", \"209859\", \"211664\", \"212893\", \"213745\", \"220159\", \"221212\", \"226321\", \"226327\", \"229002\", \"229145\", \"242597\", \"243782\", \"244457\", \"247002\", \"247900\", \"248742\", \"249906\", \"251348\", \"251691\", \"259693\", \"261378\", \"262106\", \"263104\", \"263270\", \"263650\", \"266342\", \"267779\", \"271868\", \"281106\", \"288699\", \"290891\", \"303714\", \"307569\", \"310370\", \"311272\", \"334986\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"181770","document_content":"# Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWord embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. We present a large dataset based on manual Wikipedia annotations and word senses, where word senses from different words are related by semantic classes. This is the basis for novel diagnostic tests for an embedding's content: we probe word embeddings for semantic classes and analyze the embedding space by classifying embeddings into semantic classes. Our main findings are: (i) Information about a sense is generally represented well in a single-vector embedding - if the sense is frequent. (ii) A classifier can accurately predict whether a word is single-sense or multi-sense, based only on its embedding. (iii) Although rare senses are not well represented in single-vector embeddings, this does not have negative impact on an NLP application whose performance depends on frequent senses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03608v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"39187\", \"46808\", \"56526\", \"103917\", \"67678\", \"100467\", \"41806\", \"128999\", \"142276\"], \"outgoing_citations\": [\"186130\", \"208560\", \"226692\", \"232527\", \"233400\", \"234203\", \"238963\", \"246513\", \"249109\", \"251721\", \"259706\", \"274521\", \"278993\", \"282036\", \"287036\", \"289600\", \"289601\", \"291175\", \"300128\", \"308446\", \"311843\", \"313947\"]}","task_split":"paper_retrieval"}
{"document_id":"181782","document_content":"# A Hierarchical Network for Diverse Trajectory Proposals\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nAutonomous explorative robots frequently encounter scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either manoeuvring around the obstacles or moving towards various frontiers. In this work, we propose a 2 stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment which can be occupied in the future. The second stage is a Trajectory Sampling network which provides a finegrained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings. For the outdoor case, we use the KITTI dataset and our own outdoor driving dataset. In the indoor setting, we use an autonomous drone to navigate various scenarios and also a ground robot which can explore the environment using the trajectories proposed by our framework. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons portray the performance gain of the proposed architecture over a diverse set of methods against which it is compared.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03584v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"55457\", \"109691\"], \"outgoing_citations\": [\"203036\", \"223299\", \"329296\", \"331340\"]}","task_split":"paper_retrieval"}
{"document_id":"181804","document_content":"# Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims\n## Categories\n- Computation and Language\n## Abstract\nOne key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as \"animals should have lawful rights\", and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03538v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2746\", \"7052\", \"16320\", \"10531\", \"14889\", \"21518\", \"23714\", \"8948\", \"38188\", \"43534\", \"44625\", \"131897\", \"55843\", \"58881\", \"68276\", \"77577\", \"78080\", \"98792\", \"148371\", \"83050\", \"88762\", \"88767\", \"123990\", \"122156\", \"127851\", \"132773\", \"157352\", \"154039\", \"161848\", \"168163\", \"169340\", \"177061\", \"175192\", \"181273\"], \"outgoing_citations\": [\"221735\", \"221930\", \"228156\", \"238986\", \"267552\", \"267707\", \"268073\", \"293849\", \"300302\"]}","task_split":"paper_retrieval"}
{"document_id":"181897","document_content":"# HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLearning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models will be publicly available at: www.di.ens.fr\/willow\/research\/howto100m\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03327v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79061\", \"90690\", \"1081\", \"2212\", \"3805\", \"4359\", \"4348\", \"5626\", \"5960\", \"6414\", \"6562\", \"7218\", \"8438\", \"8991\", \"12103\", \"14600\", \"116568\", \"15441\", \"17178\", \"22148\", \"24775\", \"33472\", \"41063\", \"43252\", \"44352\", \"47057\", \"53303\", \"55118\", \"57298\", \"60281\", \"64841\", \"101354\", \"85536\", \"96353\", \"103226\", \"1284\", \"5726\", \"5927\", \"7454\", \"7938\", \"8845\", \"9060\", \"10271\", \"10218\", \"10348\", \"12778\", \"13933\", \"14882\", \"16519\", \"16751\", \"16825\", \"17403\", \"18220\", \"20039\", \"20409\", \"20908\", \"22104\", \"22807\", \"24718\", \"25416\", \"25794\", \"25818\", \"10669\", \"16315\", \"26968\", \"27337\", \"27865\", \"28085\", \"28518\", \"31452\", \"34329\", \"37183\", \"39008\", \"40525\", \"40597\", \"41192\", \"41153\", \"41795\", \"43644\", \"44055\", \"44668\", \"46584\", \"46967\", \"48259\", \"49913\", \"50219\", \"50518\", \"51750\", \"52603\", \"52720\", \"53510\", \"54108\", \"54595\", \"54862\", \"54820\", \"55444\", \"55460\", \"56558\", \"57216\", \"57466\", \"58701\", \"59021\", \"58876\", \"59465\", \"59462\", \"59654\", \"60135\", \"62053\", \"62053\", \"61949\", \"62260\", \"62318\", \"62806\", \"64249\", \"66630\", \"67133\", \"67375\", \"69945\", \"70426\", \"71446\", \"72354\", \"73317\", \"76641\", \"81390\", \"83575\", \"84992\", \"85426\", \"85540\", \"90913\", \"96230\", \"101575\", \"106968\", \"107454\", \"118223\", \"126474\", \"128066\", \"135766\", \"137693\", \"138177\", \"175031\", \"81067\", \"86006\", \"86777\", \"87181\", \"87767\", \"88988\", \"89825\", \"93929\", \"104563\", \"104663\", \"86729\", \"108108\", \"108197\", \"108985\", \"109375\", \"110610\", \"112096\", \"112504\", \"112860\", \"114022\", \"115367\", \"117153\", \"119114\", \"120859\", \"87013\", \"119347\", \"123824\", \"124561\", \"126167\", \"126373\", \"127506\", \"127728\", \"131626\", \"128128\", \"134177\", \"134387\", \"136352\", \"137412\", \"142128\", \"146888\", \"151281\", \"151327\", \"178006\", \"180920\", \"172394\", \"173844\", \"162966\"], \"outgoing_citations\": [\"194508\", \"196110\", \"211144\", \"211986\", \"217562\", \"220529\", \"221984\", \"233440\", \"236377\", \"236468\", \"236772\", \"237765\", \"259318\", \"259835\", \"263034\", \"263185\", \"264711\", \"267523\", \"269128\", \"270325\", \"270713\", \"272075\", \"274379\", \"277530\", \"282943\", \"283914\", \"287541\", \"294898\", \"295117\", \"296681\", \"301767\", \"302621\", \"303011\", \"303593\", \"304481\", \"310322\", \"310452\", \"313189\", \"313320\", \"316472\", \"316564\", \"319169\", \"322022\", \"328051\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"182082","document_content":"# Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nAs autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles using a dual long short term memory (LSTM) network, which is capable of effectively improving prediction accuracy in strongly interactive driving environments. In contrast to traditional approaches which require trajectory matching and manual feature selection, this method can automatically learn high-level spatial-temporal features of driver behaviors from naturalistic driving data through sequence learning. By employing two blocks of LSTMs, the proposed method feeds the sequential trajectory to the first LSTM for driver intention recognition as an intermediate indicator, which is immediately followed by a second LSTM for future trajectory prediction. Test results from real-world highway driving data show that the proposed method can, in comparison to state-of-art methods, output more accurate and reasonable estimate of different future trajectories over 5s time horizon with root mean square error (RMSE) for longitudinal and lateral prediction less than 5.77m and 0.49m, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02815v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"20916\", \"149568\", \"21817\", \"23300\", \"32112\", \"59422\", \"71863\", \"88536\", \"91801\", \"128967\", \"143953\", \"167345\", \"182303\"], \"outgoing_citations\": [\"244314\", \"268197\"]}","task_split":"paper_retrieval"}
{"document_id":"182090","document_content":"# A novel approach to model exploration for value function learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nPlanning and Learning are complementary approaches. Planning relies on deliberative reasoning about the current state and sequence of future reachable states to solve the problem. Learning, on the other hand, is focused on improving system performance based on experience or available data. Learning to improve the performance of planning based on experience in similar, previously solved problems, is ongoing research. One approach is to learn Value function (cost-to-go) which can be used as heuristics for speeding up search-based planning. Existing approaches in this direction use the results of the previous search for learning the heuristics. In this work, we present a search-inspired approach of systematic model exploration for the learning of the value function which does not stop when a plan is available but rather prolongs search such that not only resulting optimal path is used but also extended region around the optimal path. This, in turn, improves both the efficiency and robustness of successive planning. Additionally, the effect of losing admissibility by using ML heuristic is managed by bounding ML with other admissible heuristics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02789v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"211425\", \"212130\", \"223312\", \"229140\", \"230608\", \"239113\", \"242317\", \"250190\", \"255779\", \"257819\", \"260770\", \"261604\", \"298651\"]}","task_split":"paper_retrieval"}
{"document_id":"182132","document_content":"# Toward a Characterization of Loss Functions for Distribution Learning\n## Categories\n- Machine Learning\n## Abstract\nIn this work we study loss functions for learning and evaluating probability distributions over large discrete domains. Unlike classification or regression where a wide variety of loss functions are used, in the distribution learning and density estimation literature, very few losses outside the dominant $log\\ loss$ are applied. We aim to understand this fact, taking an axiomatic approach to the design of loss functions for learning distributions. We start by proposing a set of desirable criteria that any good loss function should satisfy. Intuitively, these criteria require that the loss function faithfully evaluates a candidate distribution, both in expectation and when estimated on a few samples. Interestingly, we observe that \\emph{no loss function} possesses all of these criteria. However, one can circumvent this issue by introducing a natural restriction on the set of candidate distributions. Specifically, we require that candidates are $calibrated$ with respect to the target distribution, i.e., they may contain less information than the target but otherwise do not significantly distort the truth. We show that, after restricting to this set of distributions, the log loss, along with a large variety of other losses satisfy the desired criteria. These results pave the way for future investigations of distribution learning that look beyond the log loss, choosing a loss function based on application or domain need.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02652v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"314194\", \"325852\", \"337067\", \"353894\", \"355223\"]}","task_split":"paper_retrieval"}
{"document_id":"182163","document_content":"# Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMany complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02564v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15958\", \"44795\", \"46998\", \"127705\", \"169617\"], \"outgoing_citations\": [\"123021\", \"208636\", \"218966\", \"221142\", \"223417\", \"241616\", \"259758\", \"268433\", \"290378\", \"309411\"]}","task_split":"paper_retrieval"}
{"document_id":"182227","document_content":"# One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Logic in Computer Science\n## Abstract\nOur interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc. In practice users are able to provide a very small number of example images labeled with the information that needs to be extracted. We adopt a novel two-level neuro-deductive, approach where (a) we use pre-trained deep neural networks to populate a relational database with facts about each document-image; and (b) we use a form of deductive reasoning, related to meta-interpretive learning of transition systems to learn extraction programs: Given task-specific transitions defined using the entities and relations identified by the neural detectors and a small number of instances (usually 1, sometimes 2) of images and the desired outputs, a resource-bounded meta-interpreter constructs proofs for the instance(s) via logical deduction; a set of logic programs that extract each desired entity is easily synthesized from such proofs. In most cases a single training example together with a noisy-clone of itself suffices to learn a program-set that generalizes well on test documents, at which time the value of each entity is determined by a majority vote across its program-set. We demonstrate our two-level neuro-deductive approach on publicly available datasets (\"Patent\" and \"Doctor's Bills\") and also describe its use in a real-life industrial problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02427v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Logic in Computer Science\"], \"incoming_citations\": [\"21025\", \"101629\", \"138476\", \"142949\"], \"outgoing_citations\": [\"206404\", \"235417\", \"248275\", \"284871\"]}","task_split":"paper_retrieval"}
{"document_id":"182282","document_content":"# DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification\n## Categories\n- Machine Learning\n## Abstract\nGraph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of the existing graph neural networks suffer from the following limitations: (1) there is limited analysis regarding the graph convolution properties, such as seed-oriented, degree-aware and order-free; (2) the node's degree-specific graph structure is not explicitly expressed in graph convolution for distinguishing structure-aware node neighborhoods; (3) the theoretical explanation regarding the graph-level pooling schemes is unclear.   To address these problems, we propose a generic degree-specific graph neural network named DEMO-Net motivated by Weisfeiler-Lehman graph isomorphism test that recursively identifies 1-hop neighborhood structures. In order to explicitly capture the graph topology integrated with node attributes, we argue that graph convolution should have three properties: seed-oriented, degree-aware, order-free. To this end, we propose multi-task graph convolution where each task represents node representation learning for nodes with a specific degree value, thus leading to preserving the degree-specific graph structure. In particular, we design two multi-task learning methods: degree-specific weight and hashing functions for graph convolution. In addition, we propose a novel graph-level pooling\/readout scheme for learning graph representation provably lying in a degree-specific Hilbert kernel space. The experimental results on several node and graph classification benchmark data sets demonstrate the effectiveness and efficiency of our proposed DEMO-Net over state-of-the-art graph neural network models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.02319v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"84759\", \"968\", \"16309\", \"17349\", \"34735\", \"42873\", \"63730\", \"80211\", \"85438\", \"97503\", \"135535\", \"191904\", \"4237\", \"16183\", \"16287\", \"30453\", \"37775\", \"40214\", \"41734\", \"41635\", \"42715\", \"78629\", \"96703\", \"119293\", \"161931\", \"92294\", \"101267\", \"111322\", \"114134\", \"115602\", \"120019\", \"144216\", \"157117\"], \"outgoing_citations\": [\"210119\", \"265468\", \"269202\", \"303863\"]}","task_split":"paper_retrieval"}
{"document_id":"182408","document_content":"# Towards Document Image Quality Assessment: A Text Line Based Framework and A Synthetic Text Line Image Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSince the low quality of document images will greatly undermine the chances of success in automatic text recognition and analysis, it is necessary to assess the quality of document images uploaded in online business process, so as to reject those images of low quality. In this paper, we attempt to achieve document image quality assessment and our contributions are twofold. Firstly, since document image quality assessment is more interested in text, we propose a text line based framework to estimate document image quality, which is composed of three stages: text line detection, text line quality prediction, and overall quality assessment. Text line detection aims to find potential text lines with a detector. In the text line quality prediction stage, the quality score is computed for each text line with a CNN-based prediction model. The overall quality of document images is finally assessed with the ensemble of all text line quality. Secondly, to train the prediction model, a large-scale dataset, comprising 52,094 text line images, is synthesized with diverse attributes. For each text line image, a quality label is computed with a piece-wise function. To demonstrate the effectiveness of the proposed framework, comprehensive experiments are evaluated on two popular document image quality assessment benchmarks. Our framework significantly outperforms the state-of-the-art methods by large margins on the large and complicated dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.01907v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"42711\"], \"outgoing_citations\": [\"225048\", \"279950\", \"284871\"]}","task_split":"paper_retrieval"}
{"document_id":"182506","document_content":"# Improving Neural Language Models by Segmenting, Attending, and Predicting the Future\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nCommon language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does not require any linguistic annotation of phrase segmentation. Instead, we define syntactic heights and phrase segmentation rules, enabling the model to automatically induce phrases, recognize their task-specific heads, and generate phrase embeddings in an unsupervised learning manner. Our method can easily be applied to language models with different network architectures since an independent module is used for phrase induction and context-phrase alignment, and no change is required in the underlying language modeling network. Experiments have shown that our model outperformed several strong baseline models on different data sets. We achieved a new state-of-the-art performance of 17.4 perplexity on the Wikitext-103 dataset. Additionally, visualizing the outputs of the phrase induction module showed that our model is able to learn approximate phrase-level structural knowledge without any annotation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.01702v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"63381\", \"83446\", \"100441\", \"124030\", \"158621\"], \"outgoing_citations\": [\"213362\", \"216217\", \"228464\", \"230846\", \"237584\", \"238143\", \"250815\", \"251565\", \"259145\", \"260929\", \"278065\", \"281176\", \"281205\", \"283971\", \"288551\", \"297648\", \"299541\", \"300508\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"182525","document_content":"# Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nThe ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to efficiently process information at the edge. A key challenge, however, is to understand which learning rules are best suited for specific tasks and how the relevant hyperparameters can be fine-tuned. In this work, we introduce a conceptual framework in which the learning process is integrated into the network itself. This allows us to cast meta-learning as a mathematical optimization problem. We employ DeepHyper, a scalable, asynchronous model-based search, to simultaneously optimize the choice of meta-learning rules and their hyperparameters. We demonstrate our approach with two different datasets, MNIST and FashionMNIST, using a network architecture inspired by the learning center of the insect brain. Our results show that optimal learning rules can be dataset-dependent even within similar tasks. This dependency demonstrates the importance of introducing versatility and flexibility in the learning algorithms. It also illuminates experimental findings in insect neuroscience that have shown a heterogeneity of learning rules within the insect mushroom body.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3354265.3354270\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"182544","document_content":"# Transferable Neural Projection Representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNeural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character embedding matrices. While useful, such representations occupy huge memory making it hard to deploy on-device and often do not generalize to unknown words due to vocabulary pruning.   In this paper, we propose a skip-gram based architecture coupled with Locality-Sensitive Hashing (LSH) projections to learn efficient dynamically computable representations. Our model does not need to store lookup tables as representations are computed on-the-fly and require low memory footprint. The representations can be trained in an unsupervised fashion and can be easily transferred to other NLP tasks. For qualitative evaluation, we analyze the nearest neighbors of the word representations and discover semantically similar words even with misspellings. For quantitative evaluation, we plug our transferable projections into a simple LSTM and run it on multiple NLP tasks and show how our transferable projections achieve better performance compared to prior work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.01605v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"41582\", \"171816\", \"131519\"], \"outgoing_citations\": [\"245196\", \"259562\"]}","task_split":"paper_retrieval"}
{"document_id":"182677","document_content":"# Boosting Entity Linking Performance by Leveraging Unlabeled Documents\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nModern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia. Our approach consists of two stages. First, we construct a high recall list of candidate entities for each mention in an unlabeled document. Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model. The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document. The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets. It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems. By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.01250v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"21023\", \"26715\", \"121898\", \"24055\", \"77042\", \"97185\", \"100131\", \"21536\", \"106440\", \"41806\", \"135977\", \"148404\", \"167165\", \"169340\"], \"outgoing_citations\": [\"178230\", \"233941\", \"253068\", \"268728\", \"300498\", \"312828\", \"335270\"]}","task_split":"paper_retrieval"}
{"document_id":"182691","document_content":"# Content Adaptive Optimization for Neural Image Compression\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThe field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, so far, research has mainly focused on architecture and model improvements, in this work we explore content adaptive optimization. To this end, we introduce an iterative procedure which adapts the latent representation to the specific content we wish to compress while keeping the parameters of the network and the predictive model fixed. Our experiments show that this allows for an overall increase in rate-distortion performance, independently of the specific architecture used. Furthermore, we also evaluate this strategy in the context of adapting a pretrained network to other content that is different in visual appearance or resolution. Here, our experiments show that our adaptation strategy can largely close the gap as compared to models specifically trained for the given content while having the benefit that no additional data in the form of model parameter updates has to be transmitted.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.01223v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"49272\", \"8452\", \"2555\", \"28077\", \"65570\", \"74237\", \"113821\", \"104873\", \"108590\", \"120449\", \"135025\"], \"outgoing_citations\": [\"218722\", \"243653\", \"245227\", \"281135\", \"286385\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"182724","document_content":"# A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n- Signal Processing\n## Abstract\nIt is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JSTSP.2019.2952087\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\", \"eess.SP\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\", \"Signal Processing\"], \"incoming_citations\": [\"94762\"], \"outgoing_citations\": [\"189226\", \"209770\", \"213397\", \"237274\", \"267161\", \"272080\", \"283235\"]}","task_split":"paper_retrieval"}
{"document_id":"182977","document_content":"# Sentiment Tagging with Partial Labels using Modular Architectures\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMany NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00534v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"92795\", \"158025\", \"167703\"], \"outgoing_citations\": [\"151216\", \"244272\", \"256158\", \"268586\", \"269624\", \"274183\", \"275005\", \"300421\", \"302247\", \"303003\", \"312608\", \"313465\"]}","task_split":"paper_retrieval"}
{"document_id":"183006","document_content":"# Deep Unknown Intent Detection with Margin Loss\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIdentifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00434v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"46822\", \"74469\", \"25264\", \"24089\", \"33208\", \"33513\", \"41745\", \"46417\", \"46404\", \"50065\", \"50869\", \"52620\", \"62790\", \"76104\", \"79656\", \"80112\", \"83081\", \"123561\", \"149188\", \"155266\", \"138059\", \"168621\"], \"outgoing_citations\": [\"204763\", \"226294\", \"244877\", \"254900\", \"265561\", \"270376\", \"277590\", \"278526\", \"302985\"]}","task_split":"paper_retrieval"}
{"document_id":"183045","document_content":"# Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nWhile mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed models. The prediction framework is deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to other social app platforms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00355v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"76638\", \"36731\", \"40261\", \"42900\", \"51756\", \"61072\", \"187000\", \"125219\", \"179732\", \"119812\"], \"outgoing_citations\": [\"164380\", \"240954\", \"316274\", \"321968\", \"324931\"]}","task_split":"paper_retrieval"}
{"document_id":"183066","document_content":"# Automated Video Game Testing Using Synthetic and Human-Like Agents\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00317v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"33420\", \"66886\", \"67378\", \"47009\", \"103422\", \"136331\"], \"outgoing_citations\": [\"206197\", \"224955\", \"228012\", \"240362\", \"240572\", \"277390\", \"309396\"]}","task_split":"paper_retrieval"}
{"document_id":"183087","document_content":"# Learning to Generate Grounded Visual Captions without Localization Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nWhen automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the correct image regions to output particular words, or if the model is hallucinating based on priors in the dataset and\/or the language model. The most common way of relating image regions with words in caption models is through an attention mechanism over the regions that are used as input to predict the next word. The model must therefore learn to predict the attentional weights without knowing the word it should localize. This is difficult to train without grounding supervision since recurrent models can propagate past information and there is no explicit signal to force the captioning model to properly ground the individual decoded words. In this work, we help the model to achieve this via a novel cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it, and then reconstruct the sentence from the localized image region(s) to match the ground-truth. Our proposed framework only requires learning one extra fully-connected layer (the localizer), a layer that can be removed at test time. We show that our model significantly improves grounding accuracy without relying on grounding supervision or introducing extra computation during inference, for both image and video captioning tasks. Code is available at https:\/\/github.com\/chihyaoma\/cyclical-visual-captioning .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00283v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"7692\", \"29678\", \"31878\", \"98018\", \"81859\", \"110219\", \"120471\", \"132774\", \"133695\"], \"outgoing_citations\": [\"198870\", \"199453\", \"205750\", \"206058\", \"206567\", \"218912\", \"222631\", \"223389\", \"226179\", \"231212\", \"232882\", \"234602\", \"236918\", \"237201\", \"237655\", \"250211\", \"267379\", \"269640\", \"270154\", \"281152\", \"281472\", \"282532\", \"287504\", \"289627\", \"290392\", \"296681\", \"303452\", \"303534\", \"312643\", \"313435\"]}","task_split":"paper_retrieval"}
{"document_id":"183110","document_content":"# Adversarial Generation and Encoding of Nested Texts\n## Categories\n- Computation and Language\n## Abstract\nIn this paper we propose a new language model called AGENT, which stands for Adversarial Generation and Encoding of Nested Texts. AGENT is designed for encoding, generating and refining documents that consist of a long and coherent text, such as an entire book, provided they are hierarchically annotated (nested). i.e. divided into sentences, paragraphs and chapters. The core idea of our system is learning vector representations for each level of the text hierarchy (sentences, paragraphs, etc...), and train each such representation to perform 3 tasks: The task of reconstructing the sequence of vectors from a lower level that was used to create the representation, and generalized versions of the Masked Language Modeling (MLM) and \"Next Sentence Prediction\" tasks from BERT Devlin et al. [2018]. Additionally we present a new adversarial model for long text generation and suggest a way to improve the coherence of the generated text by traversing its vector representation tree.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00238v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"200893\", \"234631\"]}","task_split":"paper_retrieval"}
{"document_id":"183187","document_content":"# Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nAs deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? To approach this question, deep multi-task learning is extended in this paper to the setting where there is no obvious overlap between task architectures. The idea is that any set of (architecture,task) pairs can be decomposed into a set of potentially related subproblems, whose sharing is optimized by an efficient stochastic algorithm. The approach is first validated in a classic synthetic multi-task learning benchmark, and then applied to sharing across disparate architectures for vision, NLP, and genomics tasks. It discovers regularities across these domains, encodes them into sharable modules, and combines these modules systematically to improve performance in the individual tasks. The results confirm that sharing learned functionality across diverse domains and architectures is indeed beneficial, thus establishing a key ingredient for general problem solving in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00097v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"5322\", \"16608\", \"131702\", \"27289\", \"62577\", \"96468\", \"122091\", \"94626\", \"114909\"], \"outgoing_citations\": [\"237578\", \"239335\", \"239450\", \"251209\", \"251499\", \"251797\", \"259145\", \"261316\", \"263620\", \"265734\", \"275599\", \"280296\", \"280302\", \"281169\", \"284134\", \"291055\", \"292199\", \"294694\", \"297167\", \"303003\", \"311668\", \"313998\", \"319827\", \"357176\"]}","task_split":"paper_retrieval"}
{"document_id":"183197","document_content":"# Scoring Sentence Singletons and Pairs for Abstractive Summarization\n## Categories\n- Computation and Language\n## Abstract\nWhen writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.00077v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"87999\", \"2553\", \"16045\", \"17749\", \"84343\", \"20965\", \"27024\", \"30911\", \"31578\", \"45305\", \"55285\", \"56583\", \"58358\", \"56512\", \"102910\", \"121667\", \"132794\", \"88286\", \"89344\", \"95174\", \"95843\", \"103859\", \"119817\", \"127753\", \"130347\", \"149732\", \"154649\", \"159889\", \"164036\", \"183200\"], \"outgoing_citations\": [\"183200\", \"212506\", \"219618\", \"220230\", \"220955\", \"227983\", \"227985\", \"230212\", \"230234\", \"231869\", \"237494\", \"250690\", \"259563\", \"266671\", \"268186\", \"281847\", \"289673\", \"290789\", \"295665\", \"296029\", \"311754\"]}","task_split":"paper_retrieval"}
{"document_id":"183231","document_content":"# Pre-Training Graph Neural Networks for Generic Structural Feature Extraction\n## Categories\n- Machine Learning\n## Abstract\nGraph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of labeled data and expressive features, which might be inaccessible for some applications. To tackle this problem, we propose a pre-training framework that captures generic graph structural information that is transferable across tasks. Our framework can leverage the following three tasks: 1) denoising link reconstruction, 2) centrality score ranking, and 3) cluster preserving. The pre-training procedure can be conducted purely on the synthetic graphs, and the pre-trained GNN is then adapted for downstream applications. With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks. Comprehensive experiments demonstrate that our proposed framework can significantly enhance the performance of various tasks at the level of node, link, and graph.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.13728v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"49353\", \"52113\", \"66588\", \"67159\", \"80211\", \"27805\", \"66264\", \"66867\", \"101265\", \"113751\"], \"outgoing_citations\": [\"195157\", \"261201\", \"277250\", \"310475\"]}","task_split":"paper_retrieval"}
{"document_id":"183257","document_content":"# On the Fairness of Disentangled Representations\n## Categories\n- Machine Learning\n## Abstract\nRecently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an \\emph{unobserved} sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than \\num{12600} trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.13662v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"168861\", \"146184\", \"315\", \"1785\", \"7041\", \"11436\", \"16722\", \"3904\", \"20686\", \"27322\", \"33203\", \"38128\", \"52735\", \"77775\", \"80015\", \"81138\", \"89035\", \"227779\", \"14234\", \"14809\", \"18189\", \"18244\", \"20955\", \"25150\", \"32216\", \"32757\", \"11676\", \"35918\", \"36764\", \"38455\", \"38668\", \"43030\", \"44406\", \"46569\", \"52004\", \"52722\", \"54154\", \"56003\", \"57749\", \"62850\", \"63434\", \"68053\", \"69932\", \"70171\", \"70211\", \"90921\", \"102593\", \"110493\", \"118768\", \"118930\", \"119060\", \"119081\", \"142178\", \"81157\", \"87002\", \"88027\", \"91004\", \"91259\", \"100660\", \"108962\", \"109786\", \"110825\", \"114553\", \"116379\", \"118721\", \"123681\", \"128431\", \"109266\", \"137157\", \"139925\", \"143581\", \"147421\", \"180811\", \"183696\", \"187863\", \"154401\", \"159312\", \"179803\", \"181909\", \"183732\", \"193061\"], \"outgoing_citations\": [\"181909\", \"183732\", \"185954\", \"186021\", \"201724\", \"205889\", \"206218\", \"206460\", \"206907\", \"207094\", \"208041\", \"210434\", \"224840\", \"225801\", \"228466\", \"231027\", \"233751\", \"236595\", \"239658\", \"241812\", \"241933\", \"242142\", \"242239\", \"244542\", \"251597\", \"253197\", \"253486\", \"255129\", \"260136\", \"261512\", \"264219\", \"264782\", \"264963\", \"265526\", \"271038\", \"278987\", \"280734\", \"281848\", \"291302\", \"292209\", \"292736\", \"297838\", \"300606\", \"303062\", \"304094\", \"311052\", \"311482\", \"312484\", \"316183\", \"320031\", \"321465\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"183383","document_content":"# Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n## Abstract\nGetting an overview over the legal domain has become challenging, especially in a broad, international context. Legal question answering systems have the potential to alleviate this task by automatically retrieving relevant legal texts for a specific statement and checking whether the meaning of the statement can be inferred from the found documents. We investigate a combination of the BM25 scoring method of Elasticsearch with word embeddings trained on English translations of the German and Japanese civil law. For this, we define criteria which select a dynamic number of relevant documents according to threshold scores. Exploiting two deep learning classifiers and their respective prediction bias with a threshold-based answer inclusion criterion has shown to be beneficial for the textual entailment task, when compared to the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.13350v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"16953\", \"39615\", \"89141\"], \"outgoing_citations\": [\"220723\", \"242676\", \"257973\", \"259120\", \"271360\", \"285451\", \"291575\", \"306182\"]}","task_split":"paper_retrieval"}
{"document_id":"183403","document_content":"# MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms\n## Categories\n- Computation and Language\n## Abstract\nWe introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diverse problem types. We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models. Using this representation language, our new dataset, MathQA, significantly enhances the AQuA dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model enhanced with automatic problem categorization. Our experiments show improvements over competitive baselines in our MathQA as well as the AQuA dataset. The results are still significantly lower than human performance indicating that the dataset poses new challenges for future research. Our dataset is available at: https:\/\/math-qa.github.io\/math-QA\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.13319v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15891\", \"15847\", \"32042\", \"32821\", \"48678\", \"96833\", \"719\", \"9241\", \"12817\", \"13400\", \"13374\", \"16185\", \"21843\", \"24666\", \"25239\", \"26234\", \"26483\", \"29470\", \"32476\", \"101113\", \"43967\", \"46289\", \"50647\", \"55131\", \"61074\", \"60990\", \"61450\", \"63475\", \"64908\", \"73266\", \"78252\", \"79536\", \"94695\", \"98051\", \"102856\", \"97282\", \"98821\", \"101855\", \"38612\", \"124449\", \"127454\", \"132440\", \"132063\", \"134061\", \"158744\", \"163188\", \"168630\", \"165103\"], \"outgoing_citations\": [\"238788\", \"246516\", \"266702\", \"267877\", \"284114\", \"286868\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"183570","document_content":"# Using Restart Heuristics to Improve Agent Performance in Angry Birds\n## Categories\n- Artificial Intelligence\n## Abstract\nOver the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12877v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"114433\"], \"outgoing_citations\": [\"329437\"]}","task_split":"paper_retrieval"}
{"document_id":"183617","document_content":"# Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function\n## Categories\n- Computation and Language\n## Abstract\nGender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the probabilities of male and female words in the output. Using an array of bias evaluation metrics, we provide empirical evidence that our approach successfully mitigates gender bias in language models without increasing perplexity. In comparison to existing debiasing strategies, data augmentation, and word embedding debiasing, our method performs better in several aspects, especially in reducing gender bias in occupation words. Finally, we introduce a combination of data augmentation and our approach, and show that it outperforms existing strategies in all bias evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12801v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15927\", \"516\", \"18940\", \"25033\", \"44149\", \"50710\", \"63715\", \"67075\", \"75424\", \"83768\", \"122414\", \"122375\", \"124692\", \"127565\", \"128960\", \"125066\", \"157164\", \"157497\"], \"outgoing_citations\": [\"192069\", \"195777\", \"219818\", \"222795\", \"259870\"]}","task_split":"paper_retrieval"}
{"document_id":"183685","document_content":"# Graph Learning Network: A Structure Learning Algorithm\n## Categories\n- Machine Learning\n## Abstract\nRecently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps recursively to enhance the prediction and the embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12665v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"131202\", \"113086\"], \"outgoing_citations\": [\"189851\", \"221106\", \"229927\", \"237455\", \"239581\", \"240268\", \"241029\", \"242322\", \"242739\", \"249839\", \"252195\", \"264326\", \"268287\", \"271527\"]}","task_split":"paper_retrieval"}
{"document_id":"183696","document_content":"# Unsupervised Model Selection for Variational Disentangled Representation Learning\n## Categories\n- Machine Learning\n## Abstract\nDisentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning and model selection of existing unsupervised approaches without requiring access to ground truth attribute labels, which are not available for most datasets. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. Our approach, Unsupervised Disentanglement Ranking (UDR), leverages the recent theoretical results that explain why variational autoencoders disentangle (Rolinek et al, 2019), to quantify the quality of disentanglement by performing pairwise comparisons between trained model representations. We show that our approach performs comparably to the existing supervised alternatives across 5,400 models from six state of the art unsupervised disentangled representation learning model classes. Furthermore, we show that the ranking produced by our approach correlates well with the final task performance on two different domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12614v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1785\", \"2524\", \"7546\", \"27322\", \"34142\", \"80015\", \"10202\", \"57749\", \"58514\", \"90586\", \"110493\", \"120747\", \"69639\", \"80538\", \"91004\", \"91259\", \"112145\", \"116650\", \"109266\", \"143581\", \"180811\"], \"outgoing_citations\": [\"183257\", \"183732\", \"185153\", \"202065\", \"205697\", \"206907\", \"207094\", \"208041\", \"210434\", \"212703\", \"220865\", \"224840\", \"225801\", \"227957\", \"234807\", \"236093\", \"241933\", \"242142\", \"242239\", \"243235\", \"246129\", \"249196\", \"251597\", \"260136\", \"261512\", \"263444\", \"284470\", \"297682\", \"302634\", \"355279\"]}","task_split":"paper_retrieval"}
{"document_id":"183719","document_content":"# On the equivalence between graph isomorphism testing and function approximation with GNNs\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) have achieved much success on graph-structured data. In light of this, there have been increasing interests in studying their expressive power. One line of work studies the capability of GNNs to approximate permutation-invariant functions on graphs, and another focuses on the their power as tests for graph isomorphism. Our work connects these two perspectives and proves their equivalence. We further develop a framework of the expressive power of GNNs that incorporates both of these viewpoints using the language of sigma-algebra, through which we compare the expressive power of different types of GNNs together with other graph isomorphism tests. In particular, we prove that the second-order Invariant Graph Network fails to distinguish non-isomorphic regular graphs with the same degree. Then, we extend it to a new architecture, Ring-GNN, which succeeds in distinguishing these graphs and achieves good performances on real-world datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12560v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"27897\", \"44078\", \"118201\", \"120407\", \"16180\", \"18144\", \"18213\", \"18515\", \"18640\", \"45929\", \"69149\", \"74464\", \"91450\", \"98743\", \"2056\", \"10071\", \"13814\", \"16076\", \"19609\", \"20859\", \"27805\", \"31192\", \"39957\", \"42645\", \"42699\", \"46936\", \"49388\", \"50499\", \"58289\", \"68391\", \"69656\", \"73530\", \"80611\", \"90277\", \"90641\", \"90849\", \"93509\", \"101265\", \"107838\", \"115600\", \"134727\", \"143456\", \"80776\", \"82471\", \"91654\", \"103085\", \"112378\", \"115834\", \"118087\", \"121424\", \"125230\", \"126374\", \"127256\", \"132211\", \"137151\", \"137772\", \"141107\", \"141179\", \"143081\", \"149610\", \"151458\", \"142425\", \"159285\", \"163945\", \"167884\", \"177249\", \"184180\", \"192622\", \"205283\", \"138999\", \"184194\"], \"outgoing_citations\": [\"110102\", \"143081\", \"181255\", \"181265\", \"184286\", \"186624\", \"196221\", \"201448\", \"203857\", \"204871\", \"215435\", \"234036\", \"242092\", \"245763\", \"263068\", \"301645\", \"335498\", \"343291\"]}","task_split":"paper_retrieval"}
{"document_id":"183732","document_content":"# Are Disentangled Representations Helpful for Abstract Visual Reasoning?\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n- I.2.6\n## Abstract\nA disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there is little empirical evidence that supports this claim. In this paper, we conduct a large-scale study that investigates whether disentangled representations are more suitable for abstract reasoning tasks. Using two new tasks similar to Raven's Progressive Matrices, we evaluate the usefulness of the representations learned by 360 state-of-the-art unsupervised disentanglement models. Based on these representations, we train 3600 abstract reasoning models and observe that disentangled representations do in fact lead to better down-stream performance. In particular, they enable quicker learning using fewer samples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12506v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\", \"stat.ML\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Machine Learning\", \"I.2.6\"], \"incoming_citations\": [\"315\", \"1785\", \"24756\", \"38128\", \"80015\", \"131804\", \"161560\", \"12936\", \"14234\", \"18244\", \"18189\", \"20747\", \"20955\", \"29613\", \"11676\", \"33748\", \"35918\", \"36764\", \"38455\", \"38668\", \"301461\", \"51087\", \"51288\", \"52004\", \"52846\", \"57749\", \"60627\", \"61452\", \"63431\", \"68053\", \"69932\", \"70211\", \"70421\", \"80098\", \"90921\", \"95962\", \"118768\", \"141879\", \"139836\", \"80538\", \"80819\", \"81157\", \"81601\", \"84506\", \"87002\", \"87061\", \"91004\", \"91259\", \"91429\", \"96496\", \"97865\", \"100660\", \"102328\", \"109667\", \"109786\", \"110105\", \"112561\", \"132719\", \"109266\", \"137635\", \"138664\", \"141826\", \"143581\", \"173765\", \"180811\", \"183696\", \"187863\", \"118449\", \"144684\", \"153300\", \"155715\", \"169585\", \"181909\", \"183257\", \"190475\", \"193061\"], \"outgoing_citations\": [\"183257\", \"186021\", \"187863\", \"196894\", \"200715\", \"201724\", \"205889\", \"206218\", \"207094\", \"208041\", \"210434\", \"212169\", \"220865\", \"224981\", \"228466\", \"229064\", \"231018\", \"231027\", \"233751\", \"236093\", \"236595\", \"239658\", \"241933\", \"242142\", \"242239\", \"251597\", \"253197\", \"255129\", \"260136\", \"261512\", \"264533\", \"264782\", \"264963\", \"265526\", \"273869\", \"280734\", \"289880\", \"292209\", \"295736\", \"297838\", \"300606\", \"311052\", \"311482\", \"316183\", \"320031\", \"333469\"]}","task_split":"paper_retrieval"}
{"document_id":"183747","document_content":"# Uncertainty Based Detection and Relabeling of Noisy Image Labels\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nDeep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. Data with noisy labels can therefore be cleaned in an iterative process. Our proposed method can be easily implemented, and shows promising performance on the task of noisy label detection on CIFAR-10 and CIFAR-100.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.11876v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"61003\", \"13596\", \"21770\", \"61468\", \"69044\", \"98970\", \"152500\", \"164954\"], \"outgoing_citations\": [\"189025\", \"219108\", \"228948\", \"237153\", \"237905\", \"238064\", \"242145\", \"247500\", \"249618\", \"249957\", \"251661\", \"264889\", \"271988\", \"272109\", \"272670\", \"302730\", \"320027\", \"321153\", \"328627\"]}","task_split":"paper_retrieval"}
{"document_id":"183883","document_content":"# Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nSolving tasks with sparse rewards is one of the most important challenges in reinforcement learning. In the single-agent setting, this challenge is addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces; however, applying these techniques naively to the multi-agent setting results in agents exploring independently, without any coordination among themselves. Exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate their exploration. In this paper we introduce a framework for designing intrinsic rewards which consider what other agents have explored such that the agents can coordinate. Then, we develop an approach for learning how to dynamically select between several exploration modalities to maximize extrinsic rewards. Concretely, we formulate the approach as a hierarchical policy where a high-level controller selects among sets of policies trained on diverse intrinsic rewards and the low-level controllers learn the action policies of all agents under these specific rewards. We demonstrate the effectiveness of the proposed approach in cooperative domains with sparse rewards where state-of-the-art methods fail and challenging multi-stage tasks that necessitate changing modes of coordination.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.12127v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"41571\", \"17155\", \"106428\", \"8098\", \"18862\", \"17137\", \"27099\", \"32373\", \"43040\", \"49692\", \"60082\", \"74956\", \"162100\", \"171112\"], \"outgoing_citations\": [\"161370\", \"187966\", \"200920\", \"212299\", \"213637\", \"215222\", \"231395\", \"237193\", \"263576\", \"272422\", \"280457\", \"291025\", \"291460\", \"291892\", \"292127\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"183906","document_content":"# LeagueAI: Improving object detector performance and flexibility through automatically generated training data and domain randomization\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n## Abstract\nIn this technical report I present my method for automatic synthetic dataset generation for object detection and demonstrate it on the video game League of Legends. This report furthermore serves as a handbook on how to automatically generate datasets and as an introduction on the dataset generation part of the LeagueAI framework. The LeagueAI framework is a software framework that provides detailed information about the game League of Legends based on the same input a human player would have, namely vision. The framework allows researchers and enthusiasts to develop their own intelligent agents or to extract detailed information about the state of the game. A big problem of machine vision applications usually is the laborious work of gathering large amounts of hand labeled data. Thus, a crucial part of the vision pipeline of the LeagueAI framework, the dataset generation, is presented in this report. The method involves extracting image raw data from the game's 3D models and combining them with the game background to create game-like synthetic images and to generate the corresponding labels automatically. In an experiment I compared a model trained on synthetic data to a model trained on hand labeled data and a model trained on a combined dataset. The model trained on the synthetic data showed higher detection precision on more classes and more reliable tracking performance of the player character. The model trained on the combined dataset did not perform better because of the different formats of the older hand labeled dataset and the synthetic data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.13546v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"165015\"], \"outgoing_citations\": [\"213206\", \"263211\"]}","task_split":"paper_retrieval"}
{"document_id":"184076","document_content":"# Triple2Vec: Learning Triple Embeddings from Knowledge Graphs\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGraph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes in a (knowledge) graph. To the best of our knowledge, none of them has tackled the problem of embedding of graph edges, that is, knowledge graph triples. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed edges in (knowledge) graphs. Trple2Vec builds upon three main ingredients. The first is the notion of line graph. The line graph of a graph is another graph representing the adjacency between edges of the original graph. In particular, the nodes of the line graph are the edges of the original graph. We show that directly applying existing embedding techniques on the nodes of the line graph to learn edge embeddings is not enough in the context of knowledge graphs. Thus, we introduce the notion of triple line graph. The second is an edge weighting mechanism both for line graphs derived from knowledge graphs and homogeneous graphs. The third is a strategy based on graph walks on the weighted triple line graph that can preserve proximity between nodes. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real world (knowledge) graphs and compared it with related work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.11691v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"17235\"], \"outgoing_citations\": [\"275900\"]}","task_split":"paper_retrieval"}
{"document_id":"184148","document_content":"# Target-Guided Open-Domain Conversation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMany real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.11553v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"16359\", \"24344\", \"50552\", \"81985\", \"95561\", \"24229\", \"24852\", \"24657\", \"34843\", \"48120\", \"48802\", \"55442\", \"61157\", \"62527\", \"80354\", \"104906\", \"114380\", \"127566\", \"91095\", \"92291\", \"95678\", \"95728\", \"123799\", \"126246\", \"127048\", \"127592\", \"144206\", \"157654\", \"157654\", \"143589\", \"186662\", \"219301\", \"201374\"], \"outgoing_citations\": [\"68965\", \"182107\", \"197853\", \"201374\", \"205147\", \"205490\", \"219301\", \"219937\", \"230014\", \"234138\", \"235975\", \"244479\", \"245399\", \"247337\", \"263265\", \"263622\", \"265309\", \"268168\", \"270040\", \"272531\", \"274253\", \"278671\", \"285454\", \"291102\", \"292261\", \"295863\", \"296250\", \"310740\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"184190","document_content":"# XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWhile natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a $1.0\\%$ performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.11471v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"4248\", \"6596\", \"26658\", \"1282\", \"8723\", \"20273\", \"22194\", \"41912\", \"45238\", \"51130\", \"73442\", \"86408\", \"90967\", \"98508\", \"126368\", \"128230\", \"131679\", \"131993\", \"133009\", \"157862\", \"161377\"], \"outgoing_citations\": [\"217957\", \"218485\", \"221514\", \"231863\", \"234577\", \"242125\", \"259706\", \"290778\", \"323451\", \"330941\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"184286","document_content":"# Provably Powerful Graph Networks\n## Categories\n- Machine Learning\n## Abstract\nRecently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many simple instances of graphs are indistinguishable by the 1-WL test.   In search for more expressive graph learning models we build upon the recent k-order invariant and equivariant graph neural networks (Maron et al. 2019a,b) and present two results:   First, we show that such k-order networks can distinguish between non-isomorphic graphs as good as the k-WL tests, which are provably stronger than the 1-WL test for k>2. This makes these models strictly stronger than message passing models. Unfortunately, the higher expressiveness of these models comes with a computational cost of processing high order tensors.   Second, setting our goal at building a provably stronger, simple and scalable model we show that a reduced 2-order network containing just scaled identity operator, augmented with a single quadratic operation (matrix multiplication) has a provable 3-WL expressive power. Differently put, we suggest a simple model that interleaves applications of standard Multilayer-Perceptron (MLP) applied to the feature dimension and matrix multiplication. We validate this model by presenting state of the art results on popular graph classification and regression tasks. To the best of our knowledge, this is the first practical invariant\/equivariant model with guaranteed 3-WL expressiveness, strictly stronger than message passing models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.11136v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"18977\", \"45478\", \"118201\", \"120407\", \"4065\", \"6661\", \"16180\", \"18144\", \"18515\", \"41342\", \"56646\", \"69149\", \"97457\", \"98743\", \"102194\", \"146216\", \"164881\", \"275\", \"1222\", \"2056\", \"4410\", \"7776\", \"10071\", \"11288\", \"13295\", \"13274\", \"13814\", \"15396\", \"16076\", \"16517\", \"18304\", \"19609\", \"23825\", \"37531\", \"39957\", \"41578\", \"42645\", \"42876\", \"43155\", \"43338\", \"43757\", \"43981\", \"33840\", \"45332\", \"46936\", \"49388\", \"49956\", \"50499\", \"52701\", \"55114\", \"57010\", \"58289\", \"63555\", \"64367\", \"65206\", \"65828\", \"68391\", \"68697\", \"70447\", \"73530\", \"73619\", \"90277\", \"90641\", \"96407\", \"97047\", \"106490\", \"107810\", \"120001\", \"134727\", \"143456\", \"144943\", \"149742\", \"174370\", \"80776\", \"82471\", \"83801\", \"86320\", \"91654\", \"93290\", \"103085\", \"110102\", \"111026\", \"111646\", \"113666\", \"115834\", \"116959\", \"117810\", \"118122\", \"118087\", \"118788\", \"120250\", \"121424\", \"129410\", \"137151\", \"137772\", \"138212\", \"141179\", \"141107\", \"141628\", \"142226\", \"143081\", \"145025\", \"151458\", \"163150\", \"144582\", \"167884\", \"177249\", \"177593\", \"192622\", \"138999\", \"159599\", \"182965\", \"183719\", \"184110\", \"184194\"], \"outgoing_citations\": [\"186624\", \"192622\", \"193372\", \"196221\", \"201448\", \"204871\", \"215435\", \"229498\", \"229936\", \"245763\", \"265468\", \"265828\", \"269273\", \"272626\", \"301645\", \"303863\", \"311030\", \"359939\"]}","task_split":"paper_retrieval"}
{"document_id":"184367","document_content":"# Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability\n## Categories\n- Machine Learning\n## Abstract\nData-driven approaches which aim to identify and predict player engagement are becoming increasingly popular in games industry contexts. This is due to the growing practice of tracking and storing large volumes of in-game telemetries coupled with a desire to tailor the gaming experience to the end-user's needs. These approaches are particularly useful not just for companies adopting Game-as-a-Service (GaaS) models (e.g. for re-engagement strategies) but also for those working under persistent content-delivery regimes (e.g. for better audience targeting). A major challenge for the latter is to build engagement models of the user which are data-efficient, holistic and can generalize across multiple game titles and genres with minimal adjustments. This work leverages a theoretical framework rooted in engagement and behavioural science research for building a model able to estimate engagement-related behaviours employing only a minimal set of game-agnostic metrics. Through a series of experiments we show how, by modelling early user-game interactions, this approach can make joint estimates of long-term survival time and churn probability across several single-player games in a range of genres. The model proposed is very suitable for industry applications since it relies on a minimal set of metrics and observations, scales well with the number of users and is explicitly designed to work across a diverse range of titles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10998v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"103422\", \"128769\"], \"outgoing_citations\": [\"220847\", \"276841\", \"288625\"]}","task_split":"paper_retrieval"}
{"document_id":"184390","document_content":"# Explainable Reinforcement Learning Through a Causal Lens\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nPrevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen. In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generate explanations of behaviour based on counterfactual analysis of the causal model. We report on a study with 120 participants who observe agents playing a real-time strategy game (Starcraft II) and then receive explanations of the agents' behaviour. We investigated: 1) participants' understanding gained by explanations through task prediction; 2) explanation satisfaction and 3) trust. Our results show that causal model explanations perform better on these measures compared to two other baseline explanation models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10958v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"28573\", \"71823\", \"129980\", \"4944\", \"5114\", \"8851\", \"19211\", \"22966\", \"23036\", \"32730\", \"34072\", \"51298\", \"68339\", \"75603\", \"136611\", \"89512\", \"119682\", \"144265\", \"153952\", \"952\", \"2258\", \"2495\", \"4271\", \"10993\", \"13525\", \"18388\", \"23098\", \"24272\", \"25074\", \"30095\", \"31060\", \"35142\", \"36970\", \"37153\", \"45824\", \"48235\", \"51293\", \"56227\", \"59886\", \"63032\", \"66879\", \"68016\", \"69162\", \"72722\", \"74858\", \"105190\", \"105871\", \"116402\", \"121691\", \"124327\", \"211032\", \"42996\", \"85487\", \"87254\", \"95709\", \"96766\", \"97648\", \"102965\", \"111326\", \"113870\", \"115735\", \"125366\", \"126862\", \"137820\", \"139877\", \"142603\", \"145283\", \"150447\"], \"outgoing_citations\": [\"196464\", \"206349\", \"211032\", \"213583\", \"223640\", \"258472\", \"335124\", \"348631\", \"357901\"]}","task_split":"paper_retrieval"}
{"document_id":"184417","document_content":"# Non-Determinism in Neural Networks for Adversarial Robustness\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nRecent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other areas. In most instances where these tasks are deployed in real-world scenarios, the models used in them have been shown to be susceptible to adversarial attacks, making it imperative for us to address the challenge of their adversarial robustness. Existing techniques for adversarial robustness fall into three broad categories: defensive distillation techniques, adversarial training techniques, and randomized or non-deterministic model based techniques. In this paper, we propose a novel neural network paradigm that falls under the category of randomized models for adversarial robustness, but differs from all existing techniques under this category in that it models each parameter of the network as a statistical distribution with learnable parameters. We show experimentally that this framework is highly robust to a variety of white-box and black-box adversarial attacks, while preserving the task-specific performance of the traditional neural network model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10906v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"221704\", \"231780\", \"235580\", \"237389\", \"251284\", \"251795\", \"252039\", \"259984\", \"261323\", \"277638\", \"288416\"]}","task_split":"paper_retrieval"}
{"document_id":"184448","document_content":"# Integration of Text-maps in Convolutional Neural Networks for Region Detection among Different Textual Categories\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this work, we propose a new technique that combines appearance and text in a Convolutional Neural Network (CNN), with the aim of detecting regions of different textual categories. We define a novel visual representation of the semantic meaning of text that allows a seamless integration in a standard CNN architecture. This representation, referred to as text-map, is integrated with the actual image to provide a much richer input to the network. Text-maps are colored with different intensities depending on the relevance of the words recognized over the image. Concretely, these words are previously extracted using Optical Character Recognition (OCR) and they are colored according to the probability of belonging to a textual category of interest. In this sense, this solution is especially relevant in the context of item coding for supermarket products, where different types of textual categories must be identified, such as ingredients or nutritional facts. We evaluated our solution in the proprietary item coding dataset of Nielsen Brandbank, which contains more than 10,000 images for train and 2,000 images for test. The reported results demonstrate that our approach focused on visual and textual data outperforms state-of-the-art algorithms only based on appearance, such as standard Faster R-CNN. These enhancements are reflected in precision and recall, which are improved in 42 and 33 points respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10858v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"234595\", \"268829\", \"309067\"]}","task_split":"paper_retrieval"}
{"document_id":"184477","document_content":"# Deep Online Learning with Stochastic Constraints\n## Categories\n- Machine Learning\n## Abstract\nDeep learning models are considered to be state-of-the-art in many offline machine learning tasks. However, many of the techniques developed are not suitable for online learning tasks. The problem of using deep learning models with sequential data becomes even harder when several loss functions need to be considered simultaneously, as in many real-world applications. In this paper, we, therefore, propose a novel online deep learning training procedure which can be used regardless of the neural network's architecture, aiming to deal with the multiple objectives case. We demonstrate and show the effectiveness of our algorithm on the Neyman-Pearson classification problem on several benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10817v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"250875\", \"262449\", \"272308\", \"313465\", \"321079\", \"324363\", \"328545\"]}","task_split":"paper_retrieval"}
{"document_id":"184520","document_content":"# Variational Bayes: A report on approaches and applications\n## Categories\n- Machine Learning\n## Abstract\nDeep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model uncertainty. Variational methods have been used for approximating intractable integrals that arise in Bayesian inference for neural networks. In this report, we review the major variational inference concepts pertinent to Bayesian neural networks and compare various approximation methods used in literature. We also talk about the applications of variational bayes in Reinforcement learning and continual learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10744v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"179754\"], \"outgoing_citations\": [\"252086\", \"262381\", \"264523\", \"272234\", \"296515\", \"298281\", \"303805\", \"311660\"]}","task_split":"paper_retrieval"}
{"document_id":"184531","document_content":"# Topological Data Analysis of Time Series Data for B2B Customer Relationship Management\n## Categories\n- Machine Learning\n## Abstract\nTopological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited to the field of financial time series primarily and as a method for feature generation in machine learning applications. In this work, TDA is presented as a technique to gain additional understanding of the customers' loyalty for business-to-business customer relationship management. Increasing loyalty and strengthening relationships with key accounts remain an active topic of discussion both for researchers and managers. Using two public and two proprietary data sets of commercial data, this research shows that the technique enables analysts to better understand their customer base and identify prospective opportunities. In addition, the approach can be used as a clustering method to increase the accuracy of a predictive model for loyalty scoring. This work thus seeks to introduce TDA as a viable tool for data analysis to the quantitate marketing practitioner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03956v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"95212\", \"101864\", \"101772\", \"184130\", \"184483\"], \"outgoing_citations\": [\"231154\", \"253573\"]}","task_split":"paper_retrieval"}
{"document_id":"184544","document_content":"# Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nThe rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers whenever needed. To investigate the effectiveness and limitations of such a chatbot in conducting surveys, we conducted a field study involving about 600 participants. In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey. Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses measured by Gricean Maxims in terms of their informativeness, relevance, specificity, and clarity. Based on our results, we discuss design implications for creating AI-powered chatbots to conduct effective surveys and beyond.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3381804\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"57014\", \"117715\", \"138402\", \"143976\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"184573","document_content":"# Exploring Temporal Information for Improved Video Understanding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and semantic segmentation. For action recognition, I have proposed a framework, termed hidden two-stream networks, to learn an optimal motion representation that does not require the computation of optical flow. My framework alleviates several challenges faced in video classification, such as learning motion representations, real-time inference, multi-framerate handling, generalizability to unseen actions, etc. For semantic segmentation, I have introduced a general framework that uses video prediction models to synthesize new training samples. By scaling up the training dataset, my trained models are more accurate and robust than previous models even without modifications to the network architectures or objective functions. I believe videos have much more potential to be mined, and temporal information is one of the most important cues for machines to perceive the visual world better.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10654v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"196680\", \"207281\", \"211884\", \"212444\", \"212445\", \"219260\", \"222802\", \"223019\", \"225950\", \"232918\", \"233266\", \"237349\", \"237499\", \"237959\", \"238082\", \"238886\", \"247535\", \"248038\", \"248541\", \"249406\", \"249748\", \"250005\", \"250311\", \"258894\", \"259939\", \"260747\", \"262632\", \"265219\", \"269121\", \"269276\", \"269502\", \"269678\", \"269928\", \"270832\", \"274079\", \"274483\", \"275433\", \"277178\", \"277459\", \"277625\", \"278206\", \"278370\", \"278505\", \"278753\", \"279147\", \"279394\", \"279521\", \"279623\", \"279975\", \"281333\", \"283389\", \"283807\", \"285631\", \"286231\", \"286564\", \"288919\", \"291917\", \"292471\", \"293365\", \"293790\", \"294503\", \"296745\", \"298565\", \"299386\", \"299702\", \"301624\", \"302126\", \"302831\", \"302909\", \"303015\", \"303174\", \"303392\", \"303623\", \"303728\", \"303866\", \"304534\", \"304596\", \"305615\", \"309902\", \"311148\", \"312644\", \"314890\", \"316005\", \"317994\", \"318846\", \"321340\", \"321377\", \"321798\", \"323833\", \"325323\"]}","task_split":"paper_retrieval"}
{"document_id":"184591","document_content":"# Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nExposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency. Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed. Both automatic and human evaluations are conducted in our experiments. On the contrary to the popular belief in exposure bias, we find that the the distortion induced by the prefix discrepancy is limited, and does not seem to be incremental during the generation. Moreover, our analysis reveals an interesting self-recovery ability of the LM, which we hypothesize to be countering the harmful effects from exposure bias.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10617v10\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"42619\", \"100503\", \"126380\", \"129680\", \"132002\", \"157308\", \"161927\", \"163994\", \"167338\", \"172088\", \"182215\", \"184909\", \"196340\", \"211205\", \"212371\", \"217640\", \"228201\", \"232274\", \"232355\", \"240174\", \"243154\", \"255035\", \"264937\", \"264957\", \"268454\", \"274450\", \"275597\", \"278954\", \"281749\", \"284481\", \"290766\", \"302815\", \"303250\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"184635","document_content":"# Soft Contextual Data Augmentation for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nWhile data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In this paper, we present a novel data augmentation method for neural machine translation. Different from previous augmentation methods that randomly drop, swap or replace words with other words in a sentence, we softly augment a randomly chosen word in a sentence by its contextual mixture of multiple related words. More accurately, we replace the one-hot representation of a word by a distribution (provided by a language model) over the vocabulary, i.e., replacing the embedding of this word by a weighted combination of multiple semantically similar words. Since the weights of those words depend on the contextual information of the word to be replaced, the newly generated sentences capture much richer information than previous augmentation methods. Experimental results on both small scale and large scale machine translation datasets demonstrate the superiority of our method over strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10523v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"36981\", \"42229\", \"18991\", \"24755\", \"38855\", \"55305\", \"8723\", \"17070\", \"22194\", \"23762\", \"25034\", \"25829\", \"43140\", \"43377\", \"45164\", \"47759\", \"51130\", \"51739\", \"56965\", \"59303\", \"60381\", \"65915\", \"77353\", \"127037\", \"86408\", \"89639\", \"86257\", \"92217\", \"95549\", \"122165\", \"126294\", \"128185\", \"132986\", \"157631\"], \"outgoing_citations\": [\"205715\", \"220611\", \"231898\", \"251816\", \"251974\", \"267601\", \"272068\", \"281472\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"184834","document_content":"# Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification\n## Categories\n- Machine Learning\n## Abstract\nExtreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semantic relationship between each document and extreme labels by taking advantage of both document content and label correlation. Our objective is to establish an explicit label-aware representation for each document with a hybrid attention deep neural network model(LAHA). LAHA consists of three parts. The first part adopts a multi-label self-attention mechanism to detect the contribution of each word to labels. The second part exploits the label structure and document content to determine the semantic connection between words and labels in a same latent space. An adaptive fusion strategy is designed in the third part to obtain the final label-aware document representation so that the essence of previous two parts can be sufficiently integrated. Extensive experiments have been conducted on six benchmark datasets by comparing with the state-of-the-art methods. The results show the superiority of our proposed LAHA method, especially on the tail labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.10070v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"73795\"], \"outgoing_citations\": [\"208991\", \"211954\", \"228250\", \"261474\", \"269062\", \"271934\", \"279991\", \"288415\", \"300803\", \"302425\", \"357238\", \"361480\"]}","task_split":"paper_retrieval"}
{"document_id":"184899","document_content":"# Scene Induced Multi-Modal Trajectory Forecasting via Planning\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nWe address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem. We present an approach consisting of three models; a goal prediction model to identify potential goals of the agent, an inverse reinforcement learning model to plan optimal paths to each goal, and a trajectory generator to obtain future trajectories along the planned paths. Analysis of predictions on the Stanford drone dataset, shows generalizability of our approach to novel scenes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.09949v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"154903\", \"99442\", \"112263\", \"144247\", \"166501\", \"170388\"], \"outgoing_citations\": [\"191356\", \"214011\", \"217456\", \"227342\", \"229266\", \"232095\", \"232096\", \"268891\", \"288804\"]}","task_split":"paper_retrieval"}
{"document_id":"184957","document_content":"# Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining\n## Categories\n- Quantitative Methods\n- Computation and Language\n- Applications\n- Information Retrieval\n## Abstract\nBackground: A large number of neurology case reports have been published, but it is a challenging task for human medical experts to explore all of these publications. Text mining offers a computational approach to investigate neurology literature and capture meaningful patterns. The overarching goal of this study is to provide a new perspective on case reports of neurological disease and syndrome analysis over the last six decades using text mining.   Methods: We extracted diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017. Text mining was applied to reports on the detected DsSs to investigate high-frequency DsSs, categorize them, and explore the linear trends over the 63-year time frame.   Results: The text mining methods explored high-frequency neurologic DsSs and their trends and the relationships between them from 1955 to 2017. We detected more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While the trend analysis showed the increasing trends in the case reports for top-10 high-frequency DsSs, the categories had mixed trends.   Conclusion: Our study provided new insights into the application of text mining methods to investigate DsSs in a large number of medical case reports that occur over several decades. The proposed approach can be used to provide a macro level analysis of medical literature by discovering interesting patterns and tracking them over several years to help physicians explore these case reports more efficiently.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03183v1\", \"primary_category\": \"q-bio.QM\", \"categories\": [\"cs.CL\", \"stat.AP\", \"q-bio.QM\", \"cs.IR\"], \"primary_category_human_readable\": \"Quantitative Methods\", \"categories_human_readable\": [\"Computation and Language\", \"Applications\", \"Quantitative Methods\", \"Information Retrieval\"], \"incoming_citations\": [\"151730\", \"165673\"], \"outgoing_citations\": [\"154394\", \"196165\", \"206754\", \"206755\", \"221024\", \"221025\", \"226805\", \"237874\", \"237875\", \"243184\", \"255123\", \"255124\", \"267465\"]}","task_split":"paper_retrieval"}
{"document_id":"184987","document_content":"# Meta-GNN: On Few-shot Node Classification in Graph Meta-learning\n## Categories\n- Machine Learning\n## Abstract\nMeta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no demonstration. Most of the existing meta-learning methods are proposed to tackle few-shot learning problems such as image and text, in rather Euclidean domain. However, there are very few works applying meta-learning to non-Euclidean domains, and the recently proposed graph neural networks (GNNs) models do not perform effectively on graph few-shot learning problems. Towards this, we propose a novel graph meta-learning framework -- Meta-GNN -- to tackle the few-shot node classification problem in graph meta-learning settings. It obtains the prior knowledge of classifiers by training on many similar few-shot learning tasks and then classifies the nodes from new classes with only few labeled samples. Additionally, Meta-GNN is a general model that can be straightforwardly incorporated into any existing state-of-the-art GNN. Our experiments conducted on three benchmark datasets demonstrate that our proposed approach not only improves the node classification performance by a large margin on few-shot learning problems in meta-learning paradigm, but also learns a more general and flexible model for task adaption.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.09718v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"6316\", \"1070\", \"2132\", \"42791\", \"82032\", \"14302\", \"28343\", \"33347\", \"42253\", \"42554\", \"49630\", \"65047\", \"66264\", \"66570\", \"69111\", \"80992\", \"92597\", \"113849\", \"116748\", \"118766\", \"119358\", \"136162\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"185052","document_content":"# MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions\n## Categories\n- Machine Learning\n## Abstract\nPredicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.24963\/ijcai.2019\/551\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2964\", \"11471\", \"13009\", \"39747\", \"46593\", \"113849\", \"125643\", \"132828\", \"147572\", \"205283\"], \"outgoing_citations\": [\"216419\", \"221106\", \"243529\", \"256259\", \"267865\", \"280783\", \"291892\", \"297245\", \"303863\", \"311030\"]}","task_split":"paper_retrieval"}
{"document_id":"185117","document_content":"# Cognitive Model Priors for Predicting Human Decisions\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHuman decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Economic Sciences. However, theoretical models of this kind have developed slowly, and robust, high-precision predictive models of human decisions remain a challenge. While machine learning is a natural candidate for solving these problems, it is currently unclear to what extent it can improve predictions obtained by current theories. We argue that this is mainly due to data scarcity, since noisy human behavior requires massive sample sizes to be accurately captured by off-the-shelf machine learning methods. To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets. We offer two contributions towards this end: first, we construct \"cognitive model priors\" by pretraining neural networks with synthetic data generated by cognitive models (i.e., theoretical models developed by cognitive psychologists). We find that fine-tuning these networks on small datasets of real human decisions results in unprecedented state-of-the-art improvements on two benchmark datasets. Second, we present the first large-scale dataset for human decision-making, containing over 240,000 human judgments across over 13,000 decision problems. This dataset reveals the circumstances where cognitive model priors are useful, and provides a new standard for benchmarking prediction of human decisions under uncertainty.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.09397v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"95022\", \"106776\", \"5572\", \"26479\", \"83531\", \"1939\", \"65863\", \"70873\", \"71872\", \"117340\", \"183675\", \"188032\", \"114314\", \"127461\", \"201520\", \"161349\", \"161546\"], \"outgoing_citations\": [\"190582\", \"229604\", \"261162\", \"263159\", \"279119\"]}","task_split":"paper_retrieval"}
{"document_id":"185337","document_content":"# Toward Learning a Unified Many-to-Many Mapping for Diverse Image Translation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage-to-image translation, which translates input images to a different domain with a learned one-to-one mapping, has achieved impressive success in recent years. The success of translation mainly relies on the network architecture to reserve the structural information while modify the appearance slightly at the pixel level through adversarial training. Although these networks are able to learn the mapping, the translated images are predictable without exclusion. It is more desirable to diversify them using image-to-image translation by introducing uncertainties, i.e., the generated images hold potential for variations in colors and textures in addition to the general similarity to the input images, and this happens in both the target and source domains. To this end, we propose a novel generative adversarial network (GAN) based model, InjectionGAN, to learn a many-to-many mapping. In this model, the input image is combined with latent variables, which comprise of domain-specific attribute and unspecific random variations. The domain-specific attribute indicates the target domain of the translation, while the unspecific random variations introduce uncertainty into the model. A unified framework is proposed to regroup these two parts and obtain diverse generations in each domain. Extensive experiments demonstrate that the diverse generations have high quality for the challenging image-to-image translation tasks where no pairing information of the training dataset exits. Both quantitative and qualitative results prove the superior performance of InjectionGAN over the state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.08766v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3813\", \"931\", \"14789\", \"29271\", \"32062\", \"53424\", \"77292\", \"153820\", \"112594\", \"139664\", \"148316\", \"148475\", \"151944\", \"152831\", \"247571\", \"163302\", \"163323\", \"170048\", \"163301\"], \"outgoing_citations\": [\"198908\", \"219025\", \"237588\", \"248730\", \"270751\", \"272314\", \"272912\", \"277822\", \"280996\", \"283651\", \"284874\", \"291302\", \"296355\"]}","task_split":"paper_retrieval"}
{"document_id":"185479","document_content":"# Predicting TED Talk Ratings from Language and Prosody\n## Categories\n- Multimedia\n- Computation and Language\n## Abstract\nWe use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over 200 thousand sentences), audio features and the associated meta information including about 5.5 Million ratings from spontaneous visitors of the website. We propose three neural network architectures and compare with statistical machine learning. Our experiments reveal that it is possible to predict all the 14 different ratings with an average AUC of 0.83 using the transcripts and prosody features only. The dataset and the complete source code is available for further analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1906.03940v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"9724\"], \"outgoing_citations\": [\"235090\", \"259145\", \"260462\", \"271223\", \"290471\", \"296283\", \"314563\"]}","task_split":"paper_retrieval"}
{"document_id":"185815","document_content":"# Don't Blame Distributional Semantics if it can't do Entailment\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nDistributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.07356v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"185877","document_content":"# End-to-end Adaptation with Backpropagation through WFST for On-device Speech Recognition System\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n## Abstract\nAn on-device DNN-HMM speech recognition system efficiently works with a limited vocabulary in the presence of a variety of predictable noise. In such a case, vocabulary and environment adaptation is highly effective. In this paper, we propose a novel method of end-to-end (E2E) adaptation, which adjusts not only an acoustic model (AM) but also a weighted finite-state transducer (WFST). We convert a pretrained WFST to a trainable neural network and adapt the system to target environments\/vocabulary by E2E joint training with an AM. We replicate Viterbi decoding with forward--backward neural network computation, which is similar to recurrent neural networks (RNNs). By pooling output score sequences, a vocabulary posterior for each utterance is obtained and used for discriminative loss computation. Experiments using 2--10 hours of English\/Japanese adaptation datasets indicate that the fine-tuning of only WFSTs and that of only AMs are both comparable to a state-of-the-art adaptation method, and E2E joint training of the two components achieves the best recognition performance. We also adapt each language system to the other language using the adaptation data, and the results show that the proposed method also works well for language adaptations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.07149v3\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.SD\", \"cs.CL\", \"eess.AS\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Sound\", \"Computation and Language\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"59904\"], \"outgoing_citations\": [\"219764\", \"249138\", \"262568\", \"284201\", \"308796\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"185991","document_content":"# Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation\n## Categories\n- Machine Learning\n## Abstract\nWe consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computationally expensive. Recent generative adversarial methods based on matching the policy distribution between the expert and the agent could be unstable during training. We propose a new framework for imitation learning by estimating the support of the expert policy to compute a fixed reward function, which allows us to re-frame imitation learning within the standard reinforcement learning setting. We demonstrate the efficacy of our reward function on both discrete and continuous domains, achieving comparable or better performance than the state of the art under different reinforcement learning algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.06750v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3724\", \"15090\", \"30327\", \"39368\", \"42781\", \"115575\", \"3384\", \"31582\", \"41463\", \"43475\", \"44355\", \"47373\", \"79130\", \"86496\", \"93063\", \"120259\", \"130096\", \"88065\", \"110525\", \"116206\", \"133647\", \"141096\", \"142074\", \"146158\", \"184304\", \"154297\"], \"outgoing_citations\": [\"206814\", \"212299\", \"249069\", \"254597\", \"259045\", \"272501\", \"280655\", \"304250\"]}","task_split":"paper_retrieval"}
{"document_id":"186017","document_content":"# What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue\n## Categories\n- Computation and Language\n## Abstract\nHumans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why. In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entity-centric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.06649v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"6214\", \"54510\", \"86291\"], \"outgoing_citations\": [\"220029\", \"220460\", \"232127\", \"233400\", \"237283\", \"263120\", \"278163\", \"281158\", \"281225\", \"291177\", \"294814\", \"295323\", \"297423\", \"316576\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"186054","document_content":"# FH-GAN: Face Hallucination and Recognition using Generative Adversarial Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThere are many factors affecting visual face recognition, such as low resolution images, aging, illumination and pose variance, etc. One of the most important problem is low resolution face images which can result in bad performance on face recognition. Most of the general face recognition algorithms usually assume a sufficient resolution for the face images. However, in practice many applications often do not have sufficient image resolutions. The modern face hallucination models demonstrate reasonable performance to reconstruct high-resolution images from its corresponding low resolution images. However, they do not consider identity level information during hallucination which directly affects results of the recognition of low resolution faces. To address this issue, we propose a Face Hallucination Generative Adversarial Network (FH-GAN) which improves the quality of low resolution face images and accurately recognize those low quality images. Concretely, we make the following contributions: 1) we propose FH-GAN network, an end-to-end system, that improves both face hallucination and face recognition simultaneously. The novelty of this proposed network depends on incorporating identity information in a GAN-based face hallucination algorithm via combining a face recognition network for identity preserving. 2) We also propose a new face hallucination network, namely Dense Sparse Network (DSNet), which improves upon the state-of-art in face hallucination. 3) We demonstrate benefits of training the face recognition and GAN-based DSNet jointly by reporting good result on face hallucination and recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.06537v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\"], \"outgoing_citations\": [\"211273\", \"244812\", \"300053\", \"308687\", \"318091\", \"328120\"]}","task_split":"paper_retrieval"}
{"document_id":"186238","document_content":"# Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text detection attracts much attention in computer vision, because it can be widely used in many applications such as real-time text translation, automatic information entry, blind person assistance, robot sensing and so on. Though many methods have been proposed for horizontal and oriented texts, detecting irregular shape texts such as curved texts is still a challenging problem. To solve the problem, we propose a robust scene text detection method with adaptive text region representation. Given an input image, a text region proposal network is first used for extracting text proposals. Then, these proposals are verified and refined with a refinement network. Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found. In this way, text regions of arbitrary shapes are detected and represented with adaptive number of boundary points. This gives more accurate description of text regions. Experimental results on five benchmarks, namely, CTW1500, TotalText, ICDAR2013, ICDAR2015 and MSRATD500, show that the proposed method achieves state-of-the-art in scene text detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.05980v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155242\", \"6741\", \"13927\", \"14003\", \"31775\", \"50917\", \"54017\", \"66255\", \"25116\", \"25132\", \"33142\", \"33498\", \"58673\", \"43191\", \"113025\", \"136651\", \"150258\", \"155066\", \"155848\", \"59016\", \"81667\", \"106579\", \"131896\", \"135263\", \"136466\", \"140006\", \"140478\", \"154995\", \"210634\"], \"outgoing_citations\": [\"225795\", \"231139\", \"233366\", \"239002\", \"240965\", \"245591\", \"245899\", \"245970\", \"250729\", \"252159\", \"268580\", \"270668\", \"271112\", \"272390\", \"272481\", \"279950\", \"293979\", \"294606\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"186454","document_content":"# Expression Conditional GAN for Facial Expression-to-Expression Translation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Multimedia\n## Abstract\nIn this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.05416v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Multimedia\"], \"incoming_citations\": [\"144279\", \"74251\", \"19673\", \"26938\", \"162138\", \"151156\"], \"outgoing_citations\": [\"190613\", \"193255\", \"201302\", \"202959\", \"204590\", \"220842\", \"221244\", \"221342\", \"221485\", \"223504\", \"229037\", \"245594\", \"248730\", \"264685\", \"268494\", \"268963\", \"274550\", \"278895\", \"280070\", \"280996\", \"283070\", \"303715\"]}","task_split":"paper_retrieval"}
{"document_id":"186654","document_content":"# Synchronous Bidirectional Neural Machine Translation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nExisting approaches to neural machine translation (NMT) generate the target language sequence token by token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can be produced in a right-to-left decoding direction, and thus suffers from the issue of unbalanced outputs. In this paper, we introduce a synchronous bidirectional neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time. Specifically, we first propose a new algorithm that enables synchronous bidirectional decoding in a single model. Then, we present an interactive decoding model in which left-to-right (right-to-left) generation does not only depend on its previously generated outputs, but also relies on future contexts predicted by right-to-left (left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on large-scale NIST Chinese-English, WMT14 English-German, and WMT18 Russian-English translation tasks. Experimental results demonstrate that our model achieves significant improvements over the strong Transformer model by 3.92, 1.49 and 1.04 BLEU points respectively, and obtains the state-of-the-art performance on Chinese-English and English-German translation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.04847v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"72647\", \"1816\", \"13221\", \"22017\", \"26906\", \"34029\", \"37740\", \"38323\", \"42619\", \"51408\", \"139541\", \"152859\", \"77667\", \"95605\", \"106720\", \"106960\", \"117258\", \"131513\", \"143501\", \"151021\", \"152219\", \"197819\", \"171535\", \"179425\", \"189555\", \"200268\"], \"outgoing_citations\": [\"219952\", \"234164\", \"244989\", \"249268\", \"257361\", \"257947\", \"263927\", \"268370\", \"275595\", \"276439\", \"282494\", \"287823\", \"290778\", \"292831\", \"299936\", \"301840\"]}","task_split":"paper_retrieval"}
{"document_id":"186698","document_content":"# A Benchmark Study of Machine Learning Models for Online Fake News Detection\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nThe proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites\/blogs to select the most appropriate fake news detection method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.mlwa.2021.100032\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"22211\", \"41216\", \"54503\"], \"outgoing_citations\": [\"122812\", \"139404\", \"145400\", \"150286\", \"163399\", \"163765\", \"180997\", \"181527\", \"186250\", \"186386\", \"190038\", \"193848\", \"211841\", \"259202\", \"267552\", \"268073\", \"271021\", \"288921\", \"297806\"]}","task_split":"paper_retrieval"}
{"document_id":"186873","document_content":"# A logical-based corpus for cross-lingual evaluation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAt present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.05704v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"5253\", \"18779\", \"43938\", \"58662\", \"97326\", \"97669\", \"166494\"], \"outgoing_citations\": [\"200764\", \"211890\", \"217957\", \"233045\", \"233416\", \"239503\", \"239831\", \"241099\", \"305591\", \"311098\"]}","task_split":"paper_retrieval"}
{"document_id":"186969","document_content":"# Learning Motor Resonance in Human-Human and Human-Robot Interaction with Coupled Dynamical System\n## Categories\n- Robotics\n## Abstract\nHuman interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuroscience refers to this mechanism as motor resonance, in the sense that the perception of another person's actions and sensory experiences activates the observer's brain as if (s)he would be performing the same actions and having the same experiences. We analyze and model non-verbal cues (arm movements) exchanged between two humans that interact and execute handover actions. The contributions of this paper are the following: (i) computational models, using recorded motion data, describing the motor behaviour of each actor in action-in-interaction situations, (ii) a computational model that captures the behaviour if the \"giver\" and \"receiver\" during an object handover action, by coupling the arm motion of both actors, and (iii) embedded these models in the iCub robot for both action execution and recognition. Our results show that: (i) the robot can interpret the human arm motion and recognize handover actions; and (ii) behave in a \"human-like\" manner to receive the object of the recognized handover action.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.04072v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"242940\"]}","task_split":"paper_retrieval"}
{"document_id":"187146","document_content":"# Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, which is the international standard to compress HDR images, adopts a two-layer coding method for backward compatibility to the legacy JPEG standard. However, there are two problems with this two-layer coding method. One is that it does not exhibit better near-lossless performance than other methods for HDR image compression with single-layer structure. The other problem is that the determining the appropriate values of the coding parameters may be required for each input image to achieve good compression performance of near-lossless compression with the two-layer coding method of the JPEG XT. To solve these problems, we focus on a histogram-packing technique that takes into account the histogram sparseness of HDR images. We used zero-skip quantization, which is an extension of the histogram-packing technique proposed for lossless coding, for implementing the proposed near-lossless coding method. The experimental results indicate that the proposed method exhibits not only a better near-lossless compression performance than that of the two-layer coding method of the JPEG XT, but also there are no issue regarding the combination of parameter values without losing backward compatibility to the JPEG standard.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1587\/transfun.E102.A.1842\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"106681\"], \"outgoing_citations\": [\"226538\"]}","task_split":"paper_retrieval"}
{"document_id":"187188","document_content":"# A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art. The code for key-point estimation model is available at https:\/\/github.com\/Pirazh\/Vehicle_Key_Point_Orientation_Estimation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.03397v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"16146\", \"35985\", \"54440\", \"142870\", \"147598\", \"52640\", \"64211\", \"99269\", \"122185\", \"167075\", \"185263\", \"23064\", \"28319\", \"28691\", \"30375\", \"48358\", \"64203\", \"68302\", \"64004\", \"81979\", \"84051\", \"93323\", \"104009\", \"131344\", \"136582\", \"78822\", \"87409\", \"103990\", \"121150\", \"94858\", \"129639\", \"130424\", \"131360\", \"131859\", \"150069\", \"150505\", \"155380\", \"162085\"], \"outgoing_citations\": [\"226906\", \"232732\", \"241654\", \"258687\", \"270376\", \"275164\", \"285257\", \"310367\"]}","task_split":"paper_retrieval"}
{"document_id":"187195","document_content":"# 2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval\n## Categories\n- Artificial Intelligence\n## Abstract\nImage retrieval utilizes image descriptors to retrieve the most similar images to a given query image. Convolutional neural network (CNN) is becoming the dominant approach to extract image descriptors for image retrieval. For low-power hardware implementation of image retrieval, the drawback of CNN-based feature descriptor is that it requires hundreds of megabytes of storage. To address this problem, this paper applies deep model quantization and compression to CNN in ASIC chip for image retrieval. It is demonstrated that the CNN-based features descriptor can be extracted using as few as 2-bit weights quantization to deliver a similar performance as floating-point model for image retrieval. In addition, to implement CNN in ASIC, especially for large scale images, the limited buffer size of chips should be considered. To retrieve large scale images, we propose an improved pooling strategy, region nested invariance pooling (RNIP), which uses cropped sub-images for CNN. Testing results on chip show that integrating RNIP with the proposed 2-bit CNN model compression approach is capable of retrieving large scale images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.03362v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"252610\", \"267922\", \"275976\", \"295204\", \"320127\"]}","task_split":"paper_retrieval"}
{"document_id":"187326","document_content":"# Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations\n## Categories\n- Computation and Language\n## Abstract\nSyntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, which may suffer from error propagation. In this work, we propose a novel method to integrate source-side syntax implicitly for NMT. The basic idea is to use the intermediate hidden representations of a well-trained end-to-end dependency parser, which are referred to as syntax-aware word representations (SAWRs). Then, we simply concatenate such SAWRs with ordinary word embeddings to enhance basic NMT models. The method can be straightforwardly integrated into the widely-used sequence-to-sequence (Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline system, and test the effectiveness of our proposed method on two benchmark datasets of the Chinese-English and English-Vietnamese translation tasks, respectively. Experimental results show that the proposed approach is able to bring significant BLEU score improvements on the two datasets compared with the baseline, 1.74 points for Chinese-English translation and 0.80 point for English-Vietnamese translation, respectively. In addition, the approach also outperforms the explicit Tree-RNN and Tree-Linearization methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.02878v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"14960\", \"48402\", \"6019\", \"7836\", \"72647\", \"94453\", \"99477\", \"921\", \"75179\", \"105826\", \"117465\", \"138172\", \"168201\", \"156847\", \"168539\", \"171238\"], \"outgoing_citations\": [\"231932\", \"249268\", \"260977\", \"261055\", \"262535\", \"263927\", \"265779\", \"267365\", \"267460\", \"268370\", \"268788\", \"268812\", \"274499\", \"276668\", \"279947\", \"281121\", \"290777\", \"292831\", \"296274\", \"300593\", \"301840\", \"304229\", \"316741\"]}","task_split":"paper_retrieval"}
{"document_id":"187331","document_content":"# Automatic Inference of Minimalist Grammars using an SMT-Solver\n## Categories\n- Computation and Language\n## Abstract\nWe introduce (1) a novel parser for Minimalist Grammars (MG), encoded as a system of first-order logic formulae that may be evaluated using an SMT-solver, and (2) a novel procedure for inferring Minimalist Grammars using this parser. The input to this procedure is a sequence of sentences that have been annotated with syntactic relations such as semantic role labels (connecting arguments to predicates) and subject-verb agreement. The output of this procedure is a set of minimalist grammars, each of which is able to parse the sentences in the input sequence such that the parse for a sentence has the same syntactic relations as those specified in the annotation for that sentence. We applied this procedure to a set of sentences annotated with syntactic relations and evaluated the inferred grammars using cost functions inspired by the Minimum Description Length principle and the Subset principle. Inferred grammars that were optimal with respect to certain combinations of these cost functions were found to align with contemporary theories of syntax.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.02869v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"187357","document_content":"# Development of Deep Learning Based Natural Language Processing Model for Turkish\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNatural language is one of the most fundamental features that distinguish people from other living things and enable people to communicate each other. Language is a tool that enables people to express their feelings and thoughts and to transfers cultures through generations. Texts and audio are examples of natural language in daily life. In the natural language, many words disappear in time, on the other hand new words are derived. Therefore, while the process of natural language processing (NLP) is complex even for human, it is difficult to process in computer system. The area of linguistics examines how people use language. NLP, which requires the collaboration of linguists and computer scientists, plays an important role in human computer interaction. Studies in NLP have increased with the use of artificial intelligence technologies in the field of linguistics. With the deep learning methods which are one of the artificial intelligence study areas, platforms close to natural language are being developed. Developed platforms for language comprehension, machine translation and part of speech (POS) tagging benefit from deep learning methods. Recurrent Neural Network (RNN), one of the deep learning architectures, is preferred for processing sequential data such as text or audio data. In this study, Turkish POS tagging model has been proposed by using Bidirectional Long-Short Term Memory (BLSTM) which is an RNN type. The proposed POS tagging model is provided to natural language researchers with a platform that allows them to perform and use their own analysis. In the development phase of the platform developed by using BLSTM, the error rate of the POS tagger has been reduced by taking feedback with expert opinion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.05699v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"304701\"]}","task_split":"paper_retrieval"}
{"document_id":"187454","document_content":"# Interactive Video Retrieval with Dialog\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nNow that everyone can easily record videos, the quantity of which is continuously increasing, research on methods for improved video retrieval is important in the contemporary world. In cases where target videos are to be identified within a large collection gathered by individuals, the appropriate information must be obtained to retrieve the correct video within a large number of similar items in the target database. The purpose of this research is to retrieve target videos in such cases by introducing an interaction, or a dialog, between the system and the user. We propose a system to retrieve videos by asking questions about the content of the videos and leveraging the user's responses to the questions. Additionally, we confirmed the usefulness of the proposed system through experiments using the dataset called AVSD which includes videos and dialogs about the videos.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.02442v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39527\", \"65855\"], \"outgoing_citations\": [\"201552\", \"218099\", \"221984\", \"227167\", \"236468\", \"259318\", \"260926\", \"267973\", \"268871\", \"271085\", \"279518\", \"286978\", \"288000\", \"293986\", \"295117\"]}","task_split":"paper_retrieval"}
{"document_id":"187465","document_content":"# Design Space Exploration via Answer Set Programming Modulo Theories\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nThe design of embedded systems, that are ubiquitously used in mobile devices and cars, is becoming continuously more complex such that efficient system-level design methods are becoming crucial. My research aims at developing systems that help the designer express the complex design problem in a declarative way and explore the design space to obtain divers sets of solutions with desirable properties. To that end, we employ knowledge representation and reasoning capabilities of ASP in combination with background theories. As a result, for the first time, we proposed a sophisticated methodology that allows for the direct integration of multi-objective optimization of non-linear objectives into ASP. This includes unique results of diverse sub-problems covered in several publications which I will present in this work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.05248v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"261343\"]}","task_split":"paper_retrieval"}
{"document_id":"187516","document_content":"# Analysis of Gene Interaction Graphs as Prior Knowledge for Machine Learning Models\n## Categories\n- Genomics\n- Quantitative Methods\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGene interaction graphs aim to capture various relationships between genes and can represent decades of biology research. When trying to make predictions from genomic data, those graphs could be used to overcome the curse of dimensionality by making machine learning models sparser and more consistent with biological common knowledge. In this work, we focus on assessing how well those graphs capture dependencies seen in gene expression data to evaluate the adequacy of the prior knowledge provided by those graphs. We propose a condition graphs should satisfy to provide good prior knowledge and test it using `Single Gene Inference' tasks. We also compare with randomly generated graphs, aiming to measure the true benefit of using biologically relevant graphs in this context, and validate our findings with five clinical tasks. We find some graphs capture relevant dependencies for most genes while being very sparse. Our analysis with random graphs finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.02295v2\", \"primary_category\": \"q-bio.GN\", \"categories\": [\"q-bio.GN\", \"q-bio.QM\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Genomics\", \"categories_human_readable\": [\"Genomics\", \"Quantitative Methods\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"160981\", \"227612\", \"250241\", \"284264\", \"298033\"]}","task_split":"paper_retrieval"}
{"document_id":"187617","document_content":"# Image Captioning with Clause-Focused Metrics in a Multi-Modal Setting for Marketing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MIPR.2019.00085\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"125349\"], \"outgoing_citations\": [\"243142\", \"259007\", \"284230\", \"302621\", \"303003\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"187624","document_content":"# Distributional Semantics and Linguistic Theory\n## Categories\n- Computation and Language\n## Abstract\nDistributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are of relevance for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar-semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The review aims at fostering greater cross-fertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1146\/annurev-linguistics-011619-030303\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18764\", \"20003\", \"48324\", \"41709\", \"61937\", \"71164\", \"77745\", \"43318\", \"94905\", \"112496\", \"113263\", \"131433\", \"156490\", \"156495\"], \"outgoing_citations\": [\"181125\", \"192036\", \"209985\", \"218524\", \"228662\", \"232527\", \"260829\", \"276843\", \"291574\", \"300128\", \"319428\", \"329672\"]}","task_split":"paper_retrieval"}
{"document_id":"187775","document_content":"# Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into regular patches and independently apply LR to HR mapping to each patch, we implement deep reinforcement learning and develop a novel attention-aware face hallucination (Attention-FH) framework, which recurrently learns to attend a sequence of patches and performs facial part enhancement by fully exploiting the global interdependency of the image. Specifically, our proposed framework incorporates two components: a recurrent policy network for dynamically specifying a new attended region at each time step based on the status of the super-resolved image and the past attended region sequence, and a local enhancement network for selected patch hallucination and global state updating. The Attention-FH model jointly learns the recurrent policy network and local enhancement network through maximizing a long-term reward that reflects the hallucination result with respect to the whole HR image. Extensive experiments demonstrate that our Attention-FH significantly outperforms the state-of-the-art methods on in-the-wild face images with large pose and illumination variations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.01509v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"1972\", \"72246\", \"104519\", \"97923\", \"99938\", \"140134\", \"146578\", \"154701\"], \"outgoing_citations\": [\"206561\", \"212327\", \"237776\", \"246959\", \"247268\", \"248969\", \"251088\", \"258886\", \"259136\", \"259660\", \"271821\", \"271958\", \"272029\", \"274838\", \"288238\", \"296033\", \"297133\", \"303040\", \"318091\", \"318826\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"187822","document_content":"# A Survey on Neural Architecture Search\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nThe growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning techniques are computationally intensive and their application requires a high level of domain knowledge. Therefore, even partial automation of this process helps to make deep learning more accessible to both researchers and practitioners. With this survey, we provide a formalism which unifies and categorizes the landscape of existing methods along with a detailed analysis that compares and contrasts the different approaches. We achieve this via a comprehensive discussion of the commonly adopted architecture search spaces and architecture optimization algorithms based on principles of reinforcement learning and evolutionary algorithms along with approaches that incorporate surrogate and one-shot models. Additionally, we address the new research directions which include constrained and multi-objective architecture search as well as automated data augmentation, optimizer and activation function search.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.01392v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"100853\", \"104162\", \"117609\", \"9920\", \"30430\", \"42168\", \"107932\", \"120953\", \"2370\", \"3097\", \"12948\", \"13943\", \"16666\", \"22577\", \"23410\", \"25483\", \"25926\", \"31439\", \"31617\", \"32086\", \"33036\", \"36755\", \"39125\", \"46232\", \"51997\", \"119207\", \"55878\", \"58639\", \"69010\", \"69745\", \"71443\", \"72918\", \"73977\", \"84839\", \"85497\", \"86502\", \"97885\", \"114938\", \"120278\", \"129028\", \"130251\", \"173324\", \"78782\", \"87080\", \"90392\", \"99330\", \"113542\", \"119595\", \"120871\", \"121756\", \"134572\", \"134725\", \"135349\", \"141767\", \"142965\", \"143377\", \"143708\", \"146674\", \"153695\", \"157314\", \"174269\", \"175226\", \"175460\", \"149052\", \"148183\", \"158452\", \"160964\", \"162953\", \"165264\", \"185816\", \"200601\"], \"outgoing_citations\": [\"189505\", \"192613\", \"193726\", \"195884\", \"198072\", \"198231\", \"198831\", \"199021\", \"200619\", \"200893\", \"203414\", \"204859\", \"209785\", \"214405\", \"220678\", \"224067\", \"226624\", \"227216\", \"228272\", \"228939\", \"229657\", \"234369\", \"239716\", \"240372\", \"242604\", \"249360\", \"250658\", \"251071\", \"251701\", \"255255\", \"255606\", \"258268\", \"258332\", \"261128\", \"261948\", \"264988\", \"265872\", \"267780\", \"269847\", \"272361\", \"278779\", \"280213\", \"281001\", \"281006\", \"290872\", \"300723\", \"303141\", \"303849\"]}","task_split":"paper_retrieval"}
{"document_id":"187952","document_content":"# Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDomain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.01966v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23264\", \"62029\", \"127104\"], \"outgoing_citations\": [\"153217\"]}","task_split":"paper_retrieval"}
{"document_id":"188041","document_content":"# Egocentric Hand Track and Object-based Human Action Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEgocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly, without further use of visual features.   Initially, we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions. We employ object detection and region tracking techniques to locate hands and capture their movements. Prior knowledge about egocentric views facilitates hand identification between left and right. With regard to detection and tracking, we contribute a pipeline that successfully operates on unseen egocentric videos to find the camera wearer's hands and associate them through time. Moreover, we emphasize on the value of scene information for action recognition. We acknowledge that the presence of objects is significant for the execution of actions by humans and in general for the description of a scene. To acquire this information, we utilize object detection for specific classes that are relevant to the actions we want to recognize.   Our experiments are targeted on videos of kitchen activities from the Epic-Kitchens dataset. We model action recognition as a sequence learning problem of the detected spatial positions in the frames. Our results show that explicit hand and object detections with no other visual information can be relied upon to classify hand-related human actions. Testing against methods fully dependent on visual features, signals that for actions where hand motions are conceptually important, a region-of-interest-based description of a video contains equally expressive information with comparable classification performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.00742v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"27721\", \"29961\", \"32831\", \"55636\", \"128066\", \"149819\", \"166787\"], \"outgoing_citations\": [\"191180\", \"222952\", \"227837\", \"236377\", \"237660\", \"246962\", \"277156\", \"287986\", \"292731\", \"296487\"]}","task_split":"paper_retrieval"}
{"document_id":"188074","document_content":"# On two existing approaches to statistical analysis of social media data\n## Categories\n- Applications\n- Social and Information Networks\n## Abstract\nUsing social media data for statistical analysis of general population faces commonly two basic obstacles: firstly, social media data are collected for different objects than the population units of interest; secondly, the relevant measures are typically not available directly but need to be extracted by algorithms or machine learning techniques. In this paper we examine and summarise two existing approaches to statistical analysis based on social media data, which can be discerned in the literature. In the first approach, analysis is applied to the social media data that are organised around the objects directly observed in the data; in the second one, a different analysis is applied to a constructed pseudo survey dataset, aimed to transform the observed social media data to a set of units from the target population. We elaborate systematically the relevant data quality frameworks, exemplify their applications, and highlight some typical challenges associated with social media data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.00635v1\", \"primary_category\": \"stat.AP\", \"categories\": [\"stat.AP\", \"cs.SI\"], \"primary_category_human_readable\": \"Applications\", \"categories_human_readable\": [\"Applications\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"235788\"]}","task_split":"paper_retrieval"}
{"document_id":"188111","document_content":"# Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nRepresenting entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for link prediction models: identifying the fact to add into or remove from the knowledge graph that changes the prediction for a target fact after the model is retrained. Using these single modifications of the graph, we identify the most influential fact for a predicted link and evaluate the sensitivity of the model to the addition of fake facts. We introduce an efficient approach to estimate the effect of such modifications by approximating the change in the embeddings when the knowledge graph changes. To avoid the combinatorial search over all possible facts, we train a network to decode embeddings to their corresponding graph components, allowing the use of gradient-based optimization to identify the adversarial modification. We use these techniques to evaluate the robustness of link prediction models (by measuring sensitivity to additional facts), study interpretability through the facts most responsible for predictions (by identifying the most influential neighbors), and detect incorrect facts in the knowledge base.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.00563v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"204706\", \"10043\", \"11401\", \"91809\", \"57317\", \"60179\", \"66870\", \"94973\", \"97282\", \"127904\", \"137543\", \"200424\"], \"outgoing_citations\": [\"214994\", \"219150\", \"229027\", \"231277\", \"248147\", \"250785\", \"251893\", \"256006\", \"256639\", \"257097\", \"260374\", \"265002\", \"289536\", \"329290\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"188207","document_content":"# The relationship between Biological and Artificial Intelligence\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIntelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful.   In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.00547v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215960\", \"240617\", \"315023\"]}","task_split":"paper_retrieval"}
{"document_id":"188300","document_content":"# MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nExisting popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances. Mixhop requires no additional memory or computational complexity, and outperforms on challenging baselines. In addition, we propose sparsity regularization that allows us to visualize how the network prioritizes neighborhood information across different graph datasets. Our analysis of the learned architectures reveals that neighborhood mixing varies per datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1905.00067v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"47526\", \"49891\", \"66165\", \"120407\", \"138382\", \"82050\", \"84759\", \"222\", \"365\", \"404\", \"783\", \"751\", \"4763\", \"7330\", \"7683\", \"9898\", \"12375\", \"12727\", \"13009\", \"14546\", \"16062\", \"16947\", \"18144\", \"19379\", \"24963\", \"31660\", \"36313\", \"42144\", \"42873\", \"42923\", \"64027\", \"66762\", \"80211\", \"82325\", \"93679\", \"120682\", \"135535\", \"141153\", \"742\", \"2159\", \"5070\", \"5292\", \"5736\", \"5763\", \"6953\", \"8529\", \"9382\", \"10060\", \"12231\", \"12824\", \"13257\", \"13814\", \"16183\", \"16517\", \"19609\", \"24104\", \"24664\", \"29338\", \"260\", \"30453\", \"31870\", \"32870\", \"34086\", \"35738\", \"41635\", \"42715\", \"43244\", \"44322\", \"46303\", \"46662\", \"47368\", \"49201\", \"49635\", \"50505\", \"56361\", \"56980\", \"58522\", \"56240\", \"59700\", \"63525\", \"67856\", \"68821\", \"70728\", \"72929\", \"80486\", \"88245\", \"91606\", \"96464\", \"98076\", \"113226\", \"117823\", \"118719\", \"140399\", \"141774\", \"143339\", \"159538\", \"164796\", \"164464\", \"171841\", \"184856\", \"77861\", \"78173\", \"79309\", \"83230\", \"83801\", \"85459\", \"86905\", \"89160\", \"17991\", \"90518\", \"95564\", \"96429\", \"99280\", \"102283\", \"103085\", \"111026\", \"113891\", \"114134\", \"114193\", \"114415\", \"114798\", \"115265\", \"115393\", \"117308\", \"117690\", \"118096\", \"118493\", \"119707\", \"120250\", \"122955\", \"123274\", \"124721\", \"126374\", \"127546\", \"127864\", \"132014\", \"133957\", \"134133\", \"134890\", \"136103\", \"139815\", \"141516\", \"145256\", \"155249\", \"124460\", \"173348\", \"176236\", \"176611\", \"185430\", \"192622\", \"241565\", \"155315\", \"156733\", \"159315\", \"157002\", \"164915\", \"177574\", \"185055\", \"195676\", \"240985\", \"240287\"], \"outgoing_citations\": [\"218204\", \"240985\", \"250056\", \"252363\", \"274969\", \"303863\"]}","task_split":"paper_retrieval"}
{"document_id":"188313","document_content":"# Active Manifolds: A non-linear analogue to Active Subspaces\n## Categories\n- Machine Learning\n## Abstract\nWe present an approach to analyze $C^1(\\mathbb{R}^m)$ functions that addresses limitations present in the Active Subspaces (AS) method of Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active Manifolds (AM) method identifies a 1-D curve in the domain (the active manifold) on which nearly all values of the unknown function are attained, and which can be exploited for approximation or analysis, especially when $m$ is large (high-dimensional input space). We provide theorems justifying our AM technique and an algorithm permitting functional approximation and sensitivity analysis. Using accessible, low-dimensional functions as initial examples, we show AM reduces approximation error by an order of magnitude compared to AS, at the expense of more computation. Following this, we revisit the sensitivity analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic power generator model, and compare the performance of AM on the same data. Our analysis provides detailed information not captured by AS, exhibiting the influence of each parameter individually along an active manifold. Overall, AM represents a novel technique for analyzing functional models with benefits including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more accurate regression than AS (at more computational expense), enabling more informative sensitivity analysis, and granting accessible visualizations(2-D plots) of parameter sensitivity along the AM.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.13386v4\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"75229\", \"103715\", \"13281\", \"33883\", \"49637\", \"52935\", \"119075\", \"196074\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"188316","document_content":"# Very Deep Self-Attention Networks for End-to-End Speech Recognition\n## Categories\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nRecently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve around time-delay neural networks (TDNN) and long short-term memory (LSTM) recurrent neural networks, we propose to use self-attention via the Transformer architecture as an alternative. Our analysis shows that deep Transformer networks with high learning capacity are able to exceed performance from previous end-to-end approaches and even match the conventional hybrid systems. Moreover, we trained very deep models with up to 48 Transformer layers for both encoder and decoders combined with stochastic residual connections, which greatly improve generalizability and training efficiency. The resulting models outperform all previous end-to-end ASR approaches on the Switchboard benchmark. An ensemble of these models achieve 9.9% and 17.7% WER on Switchboard and CallHome test sets respectively. This finding brings our end-to-end models to competitive levels with previous hybrid systems. Further, with model ensembling the Transformers can outperform certain hybrid systems, which are more complicated in terms of both structure and training procedure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.13377v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"43668\", \"77439\", \"138783\", \"23339\", \"88348\", \"34512\", \"35701\", \"41489\", \"51167\", \"58159\", \"61421\", \"67488\", \"68540\", \"68815\", \"69401\", \"69766\", \"70578\", \"71248\", \"73307\", \"75683\", \"78310\", \"84468\", \"88346\", \"91988\", \"93368\", \"95964\", \"104181\", \"112617\", \"123732\", \"129046\", \"207396\", \"89151\", \"92598\", \"95833\", \"109001\", \"117729\", \"120908\", \"122457\", \"123079\", \"123464\", \"123742\", \"123744\", \"123936\", \"124457\", \"125020\", \"128241\", \"132577\", \"135483\", \"143720\", \"147019\", \"151647\", \"159142\", \"159854\", \"164998\", \"153516\", \"164154\", \"166863\", \"169570\"], \"outgoing_citations\": [\"202047\", \"220598\", \"232327\", \"232748\", \"237754\", \"282929\", \"292186\", \"307902\"]}","task_split":"paper_retrieval"}
{"document_id":"188327","document_content":"# The Role of User Profile for Fake News Detection\n## Categories\n- Social and Information Networks\n- Information Retrieval\n## Abstract\nConsuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also enables the widespread of fake news. Because of the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the challenging problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.13355v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.IR\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Information Retrieval\"], \"incoming_citations\": [\"5197\", \"53995\", \"79642\", \"88313\", \"129247\", \"129853\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"188362","document_content":"# Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement learning has seen great advancements in the past five years. The successful introduction of deep learning in place of more traditional methods allowed reinforcement learning to scale to very complex domains achieving super-human performance in environments like the game of Go or numerous video games. Despite great successes in multiple domains, these new methods suffer from their own issues that make them often inapplicable to the real world problems. Extreme lack of data efficiency, together with huge variance and difficulty in enforcing safety constraints, is one of the three most prominent issues in the field. Usually, millions of data points sampled from the environment are necessary for these algorithms to converge to acceptable policies.   This thesis proposes novel Generative Adversarial Imaginative Reinforcement Learning algorithm. It takes advantage of the recent introduction of highly effective generative adversarial models, and Markov property that underpins reinforcement learning setting, to model dynamics of the real environment within the internal imagination module. Rollouts from the imagination are then used to artificially simulate the real environment in a standard reinforcement learning process to avoid, often expensive and dangerous, trial and error in the real environment. Experimental results show that the proposed algorithm more economically utilises experience from the real environment than the current state-of-the-art Rainbow DQN algorithm, and thus makes an important step towards sample efficient deep reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.13255v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"196921\", \"227949\", \"234575\", \"249069\", \"255427\", \"258199\", \"260583\", \"260770\", \"265677\", \"269458\", \"279883\", \"283158\", \"297268\", \"308711\"]}","task_split":"paper_retrieval"}
{"document_id":"188442","document_content":"# Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nEncoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood~(MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., \"Maybe, I don't know.\" Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.13015v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"188466","document_content":"# Predictive Situation Awareness for Ebola Virus Disease using a Collective Intelligence Multi-Model Integration Platform: Bayes Cloud\n## Categories\n- Artificial Intelligence\n- Physics and Society\n- Populations and Evolution\n## Abstract\nThe humanity has been facing a plethora of challenges associated with infectious diseases, which kill more than 6 million people a year. Although continuous efforts have been applied to relieve the potential damages from such misfortunate events, it is unquestionable that there are many persisting challenges yet to overcome. One related issue we particularly address here is the assessment and prediction of such epidemics. In this field of study, traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW), characterized by understanding the current situations and predicting the future situations. Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. In this paper, we develop a computing system platform focusing on collective intelligence causal modeling, in order to support PSAW in the domain of infectious disease. Analyses of global epidemics require integration of multiple different data and models, which can be originated from multiple independent researchers. These models should be integrated to accurately assess and predict the infectious disease in terms of holistic view. The system shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning. These functions are supported by subject-matter expert and artificial intelligence (AI), with uncertainty treatment. Subject-matter experts, as collective intelligence, develop causal models and integrate them as one joint causal model. The integrated causal model shall be used to reason about: (1) the past, regarding how the causal factors have occurred; (2) the present, regarding how the spread is going now; and (3) the future, regarding how it will proceed. Finally, we introduce one use case of predictive situation awareness for the Ebola virus disease.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12958v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"physics.soc-ph\", \"q-bio.PE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Physics and Society\", \"Populations and Evolution\"], \"incoming_citations\": [], \"outgoing_citations\": [\"275239\", \"348256\", \"360445\"]}","task_split":"paper_retrieval"}
{"document_id":"188492","document_content":"# Unsupervised Data Augmentation for Consistency Training\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nSemi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at https:\/\/github.com\/google-research\/uda.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12848v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"42429\", \"82674\", \"7596\", \"17583\", \"36981\", \"37270\", \"41514\", \"42229\", \"59457\", \"127659\", \"8080\", \"164475\", \"40274\", \"52113\", \"2\", \"1328\", \"1670\", \"2173\", \"2567\", \"3203\", \"3832\", \"4310\", \"4329\", \"4499\", \"6596\", \"6975\", \"7112\", \"7198\", \"8140\", \"10480\", \"11670\", \"14401\", \"14665\", \"16404\", \"16935\", \"17095\", \"17725\", \"18482\", \"18700\", \"18991\", \"20526\", \"21524\", \"22130\", \"22884\", \"23465\", \"24654\", \"26818\", \"28831\", \"29718\", \"30577\", \"30905\", \"31515\", \"33669\", \"36181\", \"36293\", \"38859\", \"40674\", \"42896\", \"43114\", \"43406\", \"43595\", \"45881\", \"46852\", \"48797\", \"55305\", \"55926\", \"57352\", \"57703\", \"59233\", \"62200\", \"64533\", \"66261\", \"66358\", \"68973\", \"70931\", \"76366\", \"80688\", \"83456\", \"85140\", \"85840\", \"93564\", \"95876\", \"60783\", \"105167\", \"110662\", \"130054\", \"134781\", \"136802\", \"140804\", \"213445\", \"588\", \"615\", \"1907\", \"2543\", \"104012\", \"2816\", \"4409\", \"7861\", \"7886\", \"8323\", \"8488\", \"10378\", \"11318\", \"11600\", \"13242\", \"13233\", \"13574\", \"13728\", \"14079\", \"14303\", \"15912\", \"16201\", \"16199\", \"16544\", \"16928\", \"17271\", \"18629\", \"18843\", \"19235\", \"19237\", \"16745\", \"17604\", \"19840\", \"20189\", \"20607\", \"20733\", \"22451\", \"23368\", \"23822\", \"24008\", \"24196\", \"24120\", \"24425\", \"24671\", \"24719\", \"25465\", \"26384\", \"26257\", \"26455\", \"26544\", \"27277\", \"28071\", \"28008\", \"28262\", \"28401\", \"28561\", \"29818\", \"29993\", \"31083\", \"31985\", \"32318\", \"32757\", \"33574\", \"33682\", \"33710\", \"34008\", \"34121\", \"34403\", \"35467\", \"35687\", \"38107\", \"38454\", \"38572\", \"38796\", \"38882\", \"39592\", \"40367\", \"40341\", \"40612\", \"41007\", \"41191\", \"41554\", \"41603\", \"41566\", \"41572\", \"41912\", \"42706\", \"42930\", \"43165\", \"43680\", \"43663\", \"43847\", \"43607\", \"44552\", \"45179\", \"45157\", \"45273\", \"116472\", \"136292\", \"40919\", \"45667\", \"45741\", \"45660\", \"45895\", \"46080\", \"46102\", \"46503\", \"46634\", \"46755\", \"46744\", \"46733\", \"46988\", \"47475\", \"47624\", \"48682\", \"48659\", \"49019\", \"49329\", \"49616\", \"51130\", \"51287\", \"51758\", \"52422\", \"52671\", \"52816\", \"53450\", \"53500\", \"53926\", \"54454\", \"54845\", \"55047\", \"54970\", \"55226\", \"55134\", \"55945\", \"56274\", \"56562\", \"56736\", \"56812\", \"56965\", \"57257\", \"57297\", \"57664\", \"58583\", \"58863\", \"58860\", \"58998\", \"59388\", \"59942\", \"60381\", \"60429\", \"60379\", \"61481\", \"61727\", \"62202\", \"62286\", \"62767\", \"62939\", \"63347\", \"63590\", \"63984\", \"19871\", \"65315\", \"65673\", \"66278\", \"66932\", \"67648\", \"67730\", \"68383\", \"68638\", \"68761\", \"56695\", \"70399\", \"70509\", \"71089\", \"71458\", \"71639\", \"71888\", \"72093\", \"72317\", \"72546\", \"72507\", \"72854\", \"73135\", \"74310\", \"74679\", \"74716\", \"74923\", \"75137\", \"75202\", \"76856\", \"77353\", \"77368\"], \"outgoing_citations\": [\"101677\", \"182013\", \"182964\", \"183227\", \"183233\", \"184775\", \"185155\", \"187091\", \"191890\", \"195137\", \"195794\", \"198185\", \"199965\", \"213954\", \"216925\", \"220017\", \"220611\", \"224289\", \"228002\", \"228536\", \"230755\", \"234340\", \"234577\", \"243059\", \"251743\", \"261446\", \"265273\", \"265872\", \"272827\", \"274513\", \"281472\", \"290407\", \"291899\", \"295676\", \"298118\", \"303997\", \"309807\", \"311119\", \"320202\", \"320317\", \"328112\"]}","task_split":"paper_retrieval"}
{"document_id":"188510","document_content":"# Graph Matching Networks for Learning the Similarity of Graph Structured Objects\n## Categories\n- Machine Learning\n## Abstract\nThis paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12787v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"56502\", \"72729\", \"113308\", \"113797\", \"3164\", \"5718\", \"9364\", \"9448\", \"9993\", \"10233\", \"13106\", \"17757\", \"21241\", \"41310\", \"59178\", \"85345\", \"82031\", \"944\", \"2056\", \"6569\", \"9467\", \"10843\", \"107\", \"13961\", \"19409\", \"28066\", \"30129\", \"35436\", \"38534\", \"42232\", \"43488\", \"44322\", \"47279\", \"47578\", \"49956\", \"50702\", \"50859\", \"103965\", \"54697\", \"58484\", \"59075\", \"59936\", \"59985\", \"61023\", \"61261\", \"63886\", \"64998\", \"68391\", \"69526\", \"74144\", \"74167\", \"77653\", \"77995\", \"81077\", \"83811\", \"85822\", \"87870\", \"90053\", \"95455\", \"105502\", \"124702\", \"130341\", \"132041\", \"143339\", \"143473\", \"145749\", \"149594\", \"80126\", \"80231\", \"85593\", \"84385\", \"160391\", \"88150\", \"52845\", \"95587\", \"97000\", \"100867\", \"101629\", \"107793\", \"109611\", \"115239\", \"115975\", \"116959\", \"119550\", \"120274\", \"121287\", \"119843\", \"38035\", \"125073\", \"125610\", \"125643\", \"135305\", \"141676\", \"143322\", \"141151\", \"145482\", \"154106\", \"155249\", \"205283\", \"218399\", \"150212\", \"155741\", \"154044\", \"163097\", \"168755\", \"170344\"], \"outgoing_citations\": [\"189544\", \"193372\", \"215435\", \"239581\", \"257997\", \"269617\", \"272574\", \"279026\", \"314520\", \"328183\"]}","task_split":"paper_retrieval"}
{"document_id":"188660","document_content":"# X-Ray Image Compression Using Convolutional Recurrent Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nIn the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of large volumes of health-care data, especially, high-resolution medical images, particularly challenging. Effective image compression for medical images thus plays a vital role in today's healthcare information system, particularly in teleradiology. In this work, an X-ray image compression method based on a Convolutional Recurrent Neural Networks RNN-Conv is presented. The proposed architecture can provide variable compression rates during deployment while it requires each network to be trained only once for a specific dimension of X-ray images. The model uses a multi-level pooling scheme that learns contextualized features for effective compression. We perform our image compression experiments on the National Institute of Health (NIH) ChestX-ray8 dataset and compare the performance of the proposed architecture with a state-of-the-art RNN based technique and JPEG 2000. The experimental results depict improved compression performance achieved by the proposed method in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12271v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"231681\", \"241131\", \"272675\", \"286385\", \"293570\"]}","task_split":"paper_retrieval"}
{"document_id":"188699","document_content":"# Linearized two-layers neural networks in high dimension\n## Categories\n- Statistics Theory\n- Machine Learning\n## Abstract\nWe consider the problem of learning an unknown function $f_{\\star}$ on the $d$-dimensional sphere with respect to the square loss, given i.i.d. samples $\\{(y_i,{\\boldsymbol x}_i)\\}_{i\\le n}$ where ${\\boldsymbol x}_i$ is a feature vector uniformly distributed on the sphere and $y_i=f_{\\star}({\\boldsymbol x}_i)+\\varepsilon_i$. We study two popular classes of models that can be regarded as linearizations of two-layers neural networks around a random initialization: the random features model of Rahimi-Recht (RF); the neural tangent kernel model of Jacot-Gabriel-Hongler (NT). Both these approaches can also be regarded as randomized approximations of kernel ridge regression (with respect to different kernels), and enjoy universal approximation properties when the number of neurons $N$ diverges, for a fixed dimension $d$.   We consider two specific regimes: the approximation-limited regime, in which $n=\\infty$ while $d$ and $N$ are large but finite; and the sample size-limited regime in which $N=\\infty$ while $d$ and $n$ are large but finite. In the first regime we prove that if $d^{\\ell + \\delta} \\le N\\le d^{\\ell+1-\\delta}$ for small $\\delta > 0$, then \\RF\\, effectively fits a degree-$\\ell$ polynomial in the raw features, and \\NT\\, fits a degree-$(\\ell+1)$ polynomial. In the second regime, both RF and NT reduce to kernel methods with rotationally invariant kernels. We prove that, if the number of samples is $d^{\\ell + \\delta} \\le n \\le d^{\\ell +1-\\delta}$, then kernel methods can fit at most a a degree-$\\ell$ polynomial in the raw features. This lower bound is achieved by kernel ridge regression. Optimal prediction error is achieved for vanishing ridge regularization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12191v3\", \"primary_category\": \"math.ST\", \"categories\": [\"cs.LG\", \"math.ST\", \"stat.TH\"], \"primary_category_human_readable\": \"Statistics Theory\", \"categories_human_readable\": [\"Machine Learning\", \"Statistics Theory\", \"Statistics Theory\"], \"incoming_citations\": [\"100318\", \"131099\", \"3912\", \"5225\", \"9707\", \"10201\", \"10511\", \"23144\", \"60308\", \"109814\", \"118515\", \"123841\", \"129429\", \"194532\", \"14622\", \"16627\", \"23536\", \"30257\", \"30544\", \"31140\", \"32522\", \"35590\", \"36413\", \"38066\", \"42724\", \"42947\", \"43560\", \"44121\", \"44323\", \"45947\", \"49706\", \"56688\", \"57270\", \"59188\", \"59724\", \"61662\", \"64271\", \"64564\", \"66060\", \"66810\", \"71592\", \"77683\", \"78261\", \"79149\", \"82446\", \"82446\", \"91443\", \"92149\", \"95158\", \"96278\", \"96801\", \"97723\", \"98711\", \"104070\", \"108103\", \"116580\", \"121723\", \"153048\", \"184410\", \"216484\", \"77826\", \"79809\", \"88992\", \"90342\", \"92188\", \"97081\", \"100141\", \"103794\", \"108676\", \"105828\", \"113219\", \"114410\", \"116050\", \"116481\", \"116496\", \"117921\", \"118643\", \"120384\", \"123172\", \"137787\", \"138520\", \"143093\", \"144436\", \"147393\", \"161573\", \"163499\", \"166250\", \"169937\", \"202189\", \"174587\", \"179719\", \"180106\", \"180145\", \"181034\", \"182015\", \"183011\", \"183263\", \"183863\", \"184469\", \"192892\", \"205396\"], \"outgoing_citations\": [\"179719\", \"192892\", \"194690\", \"198526\", \"198779\", \"201703\", \"204540\", \"205396\", \"209138\", \"210734\", \"210798\", \"215486\", \"226861\", \"230745\", \"232941\", \"235187\", \"243280\", \"252186\", \"298323\", \"317000\", \"319606\", \"355548\"]}","task_split":"paper_retrieval"}
{"document_id":"188875","document_content":"# Fake News Early Detection: An Interdisciplinary Study\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nMassive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this paper, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception\/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.11679v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"141447\", \"158439\", \"181527\", \"187975\", \"199511\", \"211841\", \"217900\", \"222721\", \"257863\", \"267552\", \"271021\", \"273723\", \"281571\", \"304737\", \"316630\", \"319035\"]}","task_split":"paper_retrieval"}
{"document_id":"188994","document_content":"# JPEG XT Image Compression with Hue Compensation for Two-Layer HDR Coding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a novel JPEG XT image compression with hue compensation for two-layer HDR coding. LDR images produced from JPEG XT bitstreams have some distortion in hue due to tone mapping operations. In order to suppress the color distortion, we apply a novel hue compensation method based on the maximally saturated colors. Moreover, the bitstreams generated by using the proposed method are fully compatible with the JPEG XT standard. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three kinds of criterion: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.11315v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"173524\", \"174344\", \"174501\"], \"outgoing_citations\": [\"226538\"]}","task_split":"paper_retrieval"}
{"document_id":"189070","document_content":"# Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOpioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient's evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.11115v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"103648\", \"156013\", \"173828\"], \"outgoing_citations\": [\"220787\", \"249233\", \"250798\", \"263133\", \"265649\"]}","task_split":"paper_retrieval"}
{"document_id":"189084","document_content":"# Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRepresenting images by compact hash codes is an attractive approach for large-scale content-based image retrieval. In most state-of-the-art hashing-based image retrieval systems, for each image, local descriptors are first aggregated as a global representation vector. This global vector is then subjected to a hashing function to generate a binary hash code. In previous works, the aggregating and the hashing processes are designed independently. Hence these frameworks may generate suboptimal hash codes. In this paper, we first propose a novel unsupervised hashing framework in which feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization generates aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, the proposed method is flexible. It can be extended for supervised hashing. When the data label is available, the framework can be adapted to learn binary codes which minimize the reconstruction loss w.r.t. label vectors. Furthermore, we also propose a fast version of the state-of-the-art hashing method Binary Autoencoder to be used in our proposed frameworks. Extensive experiments on benchmark datasets under various settings show that the proposed methods outperform state-of-the-art unsupervised and supervised hashing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2019.2913509\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85376\", \"108421\", \"196949\", \"241679\", \"247969\"], \"outgoing_citations\": [\"241439\", \"242722\", \"243056\", \"264939\", \"269814\", \"284208\", \"288218\", \"291400\", \"292069\", \"303067\", \"306406\", \"307882\", \"311835\", \"314568\", \"316473\", \"318535\", \"319454\", \"320359\", \"328917\", \"331485\"]}","task_split":"paper_retrieval"}
{"document_id":"189179","document_content":"# A CNN-RNN Architecture for Multi-Label Weather Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nWeather Recognition plays an important role in our daily lives and many computer vision applications. However, recognizing the weather conditions from a single image remains challenging and has not been studied thoroughly. Generally, most previous works treat weather recognition as a single-label classification task, namely, determining whether an image belongs to a specific weather class or not. This treatment is not always appropriate, since more than one weather conditions may appear simultaneously in a single image. To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i.e., assigning an image more than one labels according to the displayed weather conditions. Specifically, a CNN-RNN based multi-label classification approach is proposed in this paper. The convolutional neural network (CNN) is extended with a channel-wise attention model to extract the most correlated visual features. The Recurrent Neural Network (RNN) further processes the features and excavates the dependencies among weather classes. Finally, the weather labels are predicted step by step. Besides, we construct two datasets for the weather recognition task and explore the relationships among different weather conditions. Experimental results demonstrate the superiority and effectiveness of the proposed approach. The new constructed datasets will be available at https:\/\/github.com\/wzgwzg\/Multi-Label-Weather-Recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10709v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"59368\", \"40994\", \"52952\", \"64196\", \"145397\", \"160413\", \"219310\"], \"outgoing_citations\": [\"270624\", \"271767\"]}","task_split":"paper_retrieval"}
{"document_id":"189201","document_content":"# Tile Pattern KL-Divergence for Analysing and Evolving Game Levels\n## Categories\n- Neural and Evolutionary Computing\n## Abstract\nThis paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objective function of an evolutionary algorithm to evolve new levels. We describe the benefits of its asymmetry for level analysis and demonstrate how (not surprisingly) the quality of the results depends on the features used. Here we use tile-patterns of various sizes as features.   When using the measure for evolution-based level generation, we demonstrate that the choice of variation operator is critical in order to provide an efficient search process, and introduce a novel convolutional mutation operator to facilitate this. We compare the results with alternative generators, including evolving in the latent space of generative adversarial networks, and Wave Function Collapse. The results clearly show the proposed method to provide competitive performance, providing reasonable quality results with very fast training and reasonably fast generation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3321707.3321781\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"38564\", \"41052\", \"95477\", \"112786\", \"94524\", \"107909\", \"111464\", \"117983\", \"122922\", \"125408\", \"133812\", \"139814\", \"143545\"], \"outgoing_citations\": [\"233501\", \"274886\", \"289709\"]}","task_split":"paper_retrieval"}
{"document_id":"189264","document_content":"# Fine-Grained Named Entity Recognition using ELMo and Wikidata\n## Categories\n- Information Retrieval\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. We observe that when the type set spans several domains the accuracy of the entity detection becomes a limitation for supervised learning models. The primary reason being the lack of datasets where entity boundaries are properly annotated, whilst covering a large spectrum of entity types. Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata). Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10503v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"205022\", \"5406\", \"88174\", \"170852\"], \"outgoing_citations\": [\"243378\", \"243397\", \"294249\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"189265","document_content":"# Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nUnderstanding passenger intents and extracting relevant slots are important building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV). In this work, we explored AMIE (Automated-vehicle Multi-modal In-cabin Experience), the in-cabin agent responsible for handling certain passenger-vehicle interactions. When the passengers give instructions to AMIE, the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our current explorations, we focused on AMIE scenarios describing usages around setting or changing the destination and route, updating driving behavior or speed, finishing the trip and other use-cases to support various natural commands. We collected a multi-modal in-cabin dataset with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme via a realistic scavenger hunt game activity. After exploring various recent Recurrent Neural Networks (RNN) based techniques, we introduced our own hierarchical joint models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results outperformed certain competitive baselines and achieved overall F1 scores of 0.91 for utterance-level intent detection and 0.96 for slot filling tasks. In addition, we conducted initial speech-to-text explorations by comparing intent\/slot models trained and tested on human transcriptions versus noisy Automatic Speech Recognition (ASR) outputs. Finally, we compared the results with single passenger rides versus the rides with multiple passengers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10500v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"74469\", \"39936\", \"76740\", \"113489\", \"165839\"], \"outgoing_citations\": [\"206026\", \"257499\", \"266411\", \"270825\", \"282492\", \"285311\", \"300822\"]}","task_split":"paper_retrieval"}
{"document_id":"189334","document_content":"# Improving benchmarks for autonomous vehicles testing using synthetically generated images\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n- 65K10\n## Abstract\nNowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of the counties of the world. Meaning autonomous vehicle from the USA will not be able to drive though Lithuania recognizing all road signs on the way. In this paper, we propose a solution on how to update model using a small dataset from the country vehicle will be used in. It is important to mention that is not panacea, rather small upgrade which can boost autonomous car development in countries with limited data access. We achieved about 10 percent quality raise and expect even better results during future experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10261v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\", \"65K10\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\", \"65K10\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254314\"]}","task_split":"paper_retrieval"}
{"document_id":"189378","document_content":"# End-to-End Spoken Language Translation\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn this paper, we address the task of spoken language understanding. We present a method for translating spoken sentences from one language into spoken sentences in another language. Given spectrogram-spectrogram pairs, our model can be trained completely from scratch to translate unseen sentences. Our method consists of a pyramidal-bidirectional recurrent network combined with a convolutional network to output sentence-level spectrograms in the target language. Empirically, our model achieves competitive performance with state-of-the-art methods on multiple languages and can generalize to unseen speakers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10760v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"38483\", \"143066\", \"151567\", \"190888\"], \"outgoing_citations\": [\"273119\", \"279215\", \"280004\", \"319833\", \"320332\", \"333638\"]}","task_split":"paper_retrieval"}
{"document_id":"189386","document_content":"# DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nDriver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations. In safe driving, an essential task is to predict the incoming accidents as early as possible. BDD-A was aware of this problem and collected the driver attention in laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses the critical situations which do not encounter actual accidents, and just faces the driver attention prediction task, without a close step for accident prediction. In contrast to this, we explore the view of drivers' eyes for capturing multiple kinds of accidents, and construct a more diverse and larger video benchmark than ever before with the driver attention and the driving accident annotation simultaneously (named as DADA-2000), which has 2000 video clips owning about 658,476 frames on 54 kinds of accidents. These clips are crowd-sourced and captured in various occasions (highway, urban, rural, and tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and nighttime). For the driver attention representation, we collect the maps of fixations, saccade scan path and focusing time. The accidents are annotated by their categories, the accident window in clips and spatial locations of the crash-objects. Based on the analysis, we obtain a quantitative and positive answer for the question in this paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.12634v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"11453\", \"8578\", \"150619\", \"34098\", \"150868\", \"108369\", \"131781\"], \"outgoing_citations\": [\"196856\", \"208385\", \"236413\", \"236451\", \"243029\", \"245223\", \"250185\", \"266771\", \"274178\", \"279629\"]}","task_split":"paper_retrieval"}
{"document_id":"189504","document_content":"# The Curious Case of Neural Text Degeneration\n## Categories\n- Computation and Language\n## Abstract\nDespite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive.   In this paper, we reveal surprising distributional differences between human text and machine text. In addition, we find that decoding strategies alone can dramatically effect the quality of machine text, even when generated from exactly the same neural language model. Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09751v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"3606\", \"8099\", \"19141\", \"24322\", \"48963\", \"63787\", \"98756\", \"242024\", \"7637\", \"610\", \"657\", \"814\", \"2341\", \"2624\", \"2579\", \"2490\", \"2601\", \"3028\", \"3540\", \"3322\", \"3398\", \"3822\", \"3863\", \"4121\", \"7112\", \"7692\", \"8926\", \"9862\", \"10902\", \"11792\", \"12342\", \"15870\", \"15889\", \"15998\", \"15941\", \"15974\", \"16015\", \"16339\", \"16498\", \"16293\", \"16404\", \"17187\", \"17696\", \"17856\", \"18006\", \"19078\", \"19990\", \"20391\", \"20447\", \"20586\", \"21743\", \"23747\", \"24935\", \"25308\", \"25415\", \"30346\", \"31453\", \"32189\", \"34300\", \"37870\", \"38804\", \"39368\", \"39414\", \"41924\", \"42171\", \"44191\", \"44352\", \"50907\", \"51875\", \"55214\", \"56179\", \"56210\", \"58794\", \"119164\", \"2611\", \"3784\", \"3869\", \"5202\", \"6839\", \"6795\", \"7939\", \"8753\", \"9320\", \"11478\", \"12510\", \"12964\", \"13891\", \"14362\", \"14502\", \"15681\", \"9187\", \"16064\", \"16586\", \"16630\", \"17035\", \"17278\", \"17214\", \"17962\", \"21045\", \"21178\", \"21679\", \"21740\", \"22231\", \"22368\", \"22443\", \"22489\", \"22716\", \"23087\", \"23179\", \"23268\", \"23348\", \"23471\", \"23560\", \"23573\", \"23563\", \"23711\", \"23812\", \"24004\", \"24280\", \"24400\", \"24581\", \"24657\", \"25282\", \"25495\", \"25460\", \"25788\", \"25949\", \"26628\", \"27098\", \"27291\", \"27454\", \"27403\", \"27415\", \"29406\", \"29356\", \"30210\", \"30172\", \"30826\", \"31077\", \"31808\", \"125732\", \"34008\", \"34659\", \"34586\", \"34955\", \"35113\", \"35402\", \"35848\", \"35611\", \"35783\", \"36889\", \"39106\", \"39618\", \"39416\", \"39513\", \"39916\", \"40302\", \"40991\", \"41869\", \"42007\", \"42357\", \"42174\", \"42273\", \"42406\", \"42580\", \"42790\", \"42802\", \"43140\", \"43242\", \"43047\", \"43184\", \"43785\", \"43646\", \"43892\", \"44364\", \"44409\", \"44891\", \"44668\", \"45134\", \"45198\", \"44997\", \"45377\", \"45432\", \"115567\", \"132431\", \"45586\", \"98128\", \"91650\", \"46142\", \"45930\", \"45984\", \"46324\", \"47416\", \"48659\", \"48682\", \"48927\", \"48738\", \"48894\", \"49033\", \"48999\", \"49294\", \"49449\", \"50710\", \"50687\", \"50646\", \"51160\", \"51252\", \"51288\", \"141592\", \"53415\", \"53443\", \"53489\", \"54783\", \"54788\", \"55134\", \"55367\", \"55679\", \"55442\", \"55491\", \"55960\", \"55695\", \"55799\", \"55812\", \"55821\", \"56043\", \"56578\", \"56593\", \"56761\", \"56830\", \"56800\", \"56832\", \"58831\", \"58885\", \"60207\", \"60797\", \"61030\", \"61182\", \"61917\", \"61872\", \"62145\", \"62484\", \"62920\", \"63298\", \"128292\", \"63848\", \"63928\", \"64398\", \"54487\", \"64910\", \"168075\", \"68331\", \"68626\", \"68880\", \"69362\", \"70450\", \"70413\", \"70610\", \"70826\", \"71369\", \"71358\", \"71422\", \"71522\", \"71700\", \"71966\", \"72075\", \"72412\", \"72937\", \"72991\", \"73820\", \"74541\", \"74625\", \"75109\", \"75973\", \"76607\", \"77164\", \"77165\", \"77378\", \"77389\", \"77352\", \"77487\", \"77697\", \"77587\", \"77735\", \"77913\", \"78009\", \"78260\", \"79155\"], \"outgoing_citations\": [\"170013\", \"172088\", \"192158\", \"211205\", \"211804\", \"212371\", \"228201\", \"231949\", \"232274\", \"234728\", \"243154\", \"250451\", \"260247\", \"261692\", \"263927\", \"265309\", \"275597\", \"277320\", \"279543\", \"280916\", \"291102\"]}","task_split":"paper_retrieval"}
{"document_id":"189600","document_content":"# PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe consider open-domain queston answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e.g., ``multi-hop'') reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve (from the KB and\/or corpus) and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an {iterative} process to construct a question-specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or ``pull'') operations on the corpus and\/or KB. After the subgraph is complete, a similar graph CNN is used to extract the answer from the subgraph. This retrieve-and-reason process allows us to answer multi-hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question-answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state-of-the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB-only setting or a text-only setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09537v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3809\", \"8356\", \"10264\", \"12316\", \"13090\", \"13997\", \"15964\", \"17151\", \"17867\", \"18380\", \"22719\", \"28754\", \"29573\", \"61369\", \"78029\", \"97239\", \"97239\", \"174296\", \"10232\", \"12774\", \"13340\", \"13589\", \"22590\", \"22624\", \"22975\", \"23997\", \"23992\", \"28745\", \"32724\", \"37494\", \"41142\", \"45225\", \"45863\", \"47547\", \"49206\", \"50364\", \"55178\", \"55916\", \"55799\", \"56037\", \"64146\", \"69510\", \"76113\", \"77192\", \"91626\", \"91610\", \"91739\", \"92800\", \"98369\", \"98792\", \"95011\", \"3264\", \"104940\", \"103442\", \"109697\", \"111343\", \"114439\", \"114729\", \"127255\", \"127675\", \"129128\", \"129112\", \"130961\", \"132413\", \"134017\", \"140317\", \"142243\", \"154601\", \"157144\"], \"outgoing_citations\": [\"216563\", \"219306\", \"234577\", \"234814\", \"238610\", \"250322\", \"250511\", \"250529\", \"252130\", \"253068\", \"256171\", \"261389\", \"264636\", \"266850\", \"267878\", \"268671\", \"270519\", \"290747\", \"291099\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"189615","document_content":"# Model-free Deep Reinforcement Learning for Urban Autonomous Driving\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nUrban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09503v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"53008\", \"62235\", \"67530\", \"1342\", \"14153\", \"21579\", \"21649\", \"23029\", \"30226\", \"52272\", \"57924\", \"60310\", \"68506\", \"76756\", \"77457\", \"104679\", \"144508\", \"11137\", \"22569\", \"22806\", \"23710\", \"24151\", \"27279\", \"28886\", \"38166\", \"46459\", \"48253\", \"62673\", \"63178\", \"70139\", \"75241\", \"88224\", \"93822\", \"105685\", \"106495\", \"163665\", \"95016\", \"97614\", \"113006\", \"115020\", \"119012\", \"126603\", \"135243\", \"135686\", \"137882\", \"138452\", \"139098\", \"145842\", \"153686\", \"166815\", \"157402\", \"145308\", \"172655\"], \"outgoing_citations\": [\"196849\", \"200152\", \"206814\", \"221071\", \"253953\", \"267981\", \"269382\", \"290092\"]}","task_split":"paper_retrieval"}
{"document_id":"189642","document_content":"# Personalized sentence generation using generative adversarial networks with author-specific word usage\n## Categories\n- Computation and Language\n## Abstract\nThe author-specific word usage is a vital feature to let readers perceive the writing style of the author. In this work, a personalized sentence generation method based on generative adversarial networks (GANs) is proposed to cope with this issue. The frequently used function word and content word are incorporated not only as the input features but also as the sentence structure constraint for the GAN training. For the sentence generation with the related topics decided by the user, the Named Entity Recognition (NER) information of the input words is also used in the network training. We compared the proposed method with the GAN-based sentence generation methods, and the experimental results showed that the generated sentences using our method are more similar to the original sentences of the same author based on the objective evaluation such as BLEU and SimHash score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09442v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"168075\"], \"outgoing_citations\": [\"243154\", \"255035\", \"264937\", \"272531\", \"273062\", \"275597\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"189707","document_content":"# Unifying Question Answering, Text Classification, and Regression via Span Extraction\n## Categories\n- Computation and Language\n## Abstract\nEven as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks, We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09286v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11600\", \"92112\", \"92548\", \"86113\", \"92420\", \"97745\", \"98293\", \"125364\", \"127513\", \"159825\", \"176642\"], \"outgoing_citations\": [\"200764\", \"211737\", \"211788\", \"217957\", \"227323\", \"259706\", \"259723\", \"263874\", \"266850\", \"281162\"]}","task_split":"paper_retrieval"}
{"document_id":"189807","document_content":"# An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n## Abstract\nSequence-to-sequence (S2S) modeling is becoming a popular paradigm for automatic speech recognition (ASR) because of its ability to jointly optimize all the conventional ASR components in an end-to-end (E2E) fashion. This report investigates the ability of E2E ASR from standard close-talk to far-field applications by encompassing entire multichannel speech enhancement and ASR components within the S2S model. There have been previous studies on jointly optimizing neural beamforming alongside E2E ASR for denoising. It is clear from both recent challenge outcomes and successful products that far-field systems would be incomplete without solving both denoising and dereverberation simultaneously. This report uses a recently developed architecture for far-field ASR by composing neural extensions of dereverberation and beamforming modules with the S2S ASR module as a single differentiable neural network and also clearly defining the role of each subnetwork. The original implementation of this architecture was successfully applied to the noisy speech recognition task (CHiME-4), while we applied this implementation to noisy reverberant tasks (DIRHA and REVERB). Our investigation shows that the method achieves better performance than conventional pipeline methods on the DIRHA English dataset and comparable performance on the REVERB dataset. It also has additional advantages of being neither iterative nor requiring parallel noisy and clean speech data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09049v3\", \"primary_category\": \"eess.AS\", \"categories\": [\"cs.SD\", \"eess.AS\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Sound\", \"Audio and Speech Processing\", \"Computation and Language\"], \"incoming_citations\": [\"13416\", \"54292\", \"67526\", \"78492\", \"86192\", \"88493\", \"123724\", \"126276\", \"129570\", \"161692\"], \"outgoing_citations\": [\"195370\", \"210397\", \"221967\", \"227467\", \"237174\", \"237421\", \"237567\", \"248246\", \"253930\", \"271544\", \"284201\", \"307902\"]}","task_split":"paper_retrieval"}
{"document_id":"189817","document_content":"# When is a Prediction Knowledge?\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWithin Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural proposals, but also as the beginnings of a theory of machine knowledge in reinforcement learning. Recent work has expanded what can be expressed using predictions, and developed applications which use predictions to inform decision-making on a variety of synthetic and real-world problems. While promising, we here suggest that the notion of predictions as knowledge in reinforcement learning is as yet underdeveloped: some work explicitly refers to predictions as knowledge, what the requirements are for considering a prediction to be knowledge have yet to be well explored. This specification of the necessary and sufficient conditions of knowledge is important; even if claims about the nature of knowledge are left implicit in technical proposals, the underlying assumptions of such claims have consequences for the systems we design. These consequences manifest in both the way we choose to structure predictive knowledge architectures, and how we evaluate them. In this paper, we take a first step to formalizing predictive knowledge by discussing the relationship of predictive knowledge learning methods to existing theories of knowledge in epistemology. Specifically, we explore the relationships between Generalized Value Functions and epistemic notions of Justification and Truth.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.09024v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8368\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"189821","document_content":"# Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands\n## Categories\n- Computation and Language\n## Abstract\nTo understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3314221.3314594\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11597\", \"15856\", \"100214\", \"25466\", \"49102\", \"55174\", \"59690\", \"95195\", \"95359\", \"104285\", \"109177\", \"127406\", \"135438\", \"146930\"], \"outgoing_citations\": [\"218853\", \"220801\", \"227323\", \"233274\", \"250689\", \"257206\", \"259706\", \"267790\", \"268067\", \"268356\", \"280884\", \"281081\", \"281169\", \"290632\", \"296138\", \"300505\", \"308610\", \"336720\", \"340201\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"189835","document_content":"# Exploring the Limitations of Behavior Cloning for Autonomous Driving\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nDriving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https:\/\/github.com\/felipecode\/coiltraine .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08980v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"113488\", \"352\", \"6202\", \"13755\", \"23013\", \"23360\", \"30226\", \"75603\", \"82102\", \"136922\", \"4575\", \"6763\", \"8072\", \"9119\", \"9308\", \"10178\", \"13426\", \"13581\", \"14495\", \"16267\", \"22916\", \"23376\", \"24698\", \"27279\", \"28886\", \"33869\", \"36422\", \"39701\", \"42771\", \"46314\", \"48253\", \"52145\", \"54293\", \"54365\", \"54933\", \"55951\", \"65769\", \"62457\", \"66896\", \"71419\", \"74992\", \"75357\", \"57444\", \"79130\", \"87200\", \"89880\", \"92812\", \"106323\", \"124212\", \"128789\", \"149829\", \"150868\", \"163665\", \"78388\", \"80133\", \"89603\", \"90726\", \"99033\", \"103411\", \"104762\", \"106160\", \"106604\", \"108553\", \"112808\", \"115020\", \"115896\", \"118092\", \"119595\", \"123870\", \"126289\", \"127001\", \"138449\", \"139719\", \"144835\", \"152470\", \"153475\", \"154418\", \"175521\", \"214194\", \"149380\", \"152241\", \"167405\", \"183941\"], \"outgoing_citations\": [\"183941\", \"206814\", \"213646\", \"216131\", \"218009\", \"225121\", \"227720\", \"231487\", \"234287\", \"237546\", \"245430\", \"253953\", \"255613\", \"278822\", \"287219\", \"313499\"]}","task_split":"paper_retrieval"}
{"document_id":"189837","document_content":"# Intentional Computational Level Design\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nThe procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and\/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08972v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"138151\", \"36213\", \"68080\", \"81984\", \"107909\", \"127100\", \"143545\", \"152647\", \"153614\", \"168225\", \"176932\", \"181330\", \"193434\"], \"outgoing_citations\": [\"202740\", \"224233\", \"224955\", \"228285\", \"230005\", \"289709\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"189870","document_content":"# Understanding Neural Networks via Feature Visualization: A survey\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nA neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation Maximization (AM) or Feature Visualization via Optimization. In this chapter, we (1) review existing AM techniques in the literature; (2) discuss a probabilistic interpretation for AM; and (3) review the applications of AM in debugging and explaining networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08939v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"2288\", \"17948\", \"49427\", \"32811\", \"42519\", \"98834\", \"7415\", \"15401\", \"36849\", \"40012\", \"46056\", \"77117\", \"87223\", \"95447\", \"99238\", \"134283\", \"141332\", \"146400\", \"93629\", \"110550\", \"114626\", \"118186\", \"118985\", \"149785\", \"162406\", \"154544\", \"164784\"], \"outgoing_citations\": [\"208277\", \"212942\", \"234534\", \"245433\", \"266128\", \"268506\", \"279091\", \"284132\", \"284874\", \"291537\", \"298512\", \"298700\", \"301913\", \"302634\", \"309854\", \"310759\", \"319938\", \"356259\"]}","task_split":"paper_retrieval"}
{"document_id":"189887","document_content":"# (De)Constructing Bias on Skin Lesion Datasets\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma is the deadliest form of skin cancer. Automated skin lesion analysis plays an important role for early detection. Nowadays, the ISIC Archive and the Atlas of Dermoscopy dataset are the most employed skin lesion sources to benchmark deep-learning based tools. However, all datasets contain biases, often unintentional, due to how they were acquired and annotated. Those biases distort the performance of machine-learning models, creating spurious correlations that the models can unfairly exploit, or, contrarily destroying cogent correlations that the models could learn. In this paper, we propose a set of experiments that reveal both types of biases, positive and negative, in existing skin lesion datasets. Our results show that models can correctly classify skin lesion images without clinically-meaningful information: disturbingly, the machine-learning model learned over images where no information about the lesion remains, presents an accuracy above the AI benchmark curated with dermatologists' performances. That strongly suggests spurious correlations guiding the models. We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations. Our main findings raise awareness of the limitations of models trained and evaluated in small datasets such as the ones we evaluated, and may suggest future guidelines for models intended for real-world deployment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08818v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"22199\", \"41481\", \"45387\", \"54640\", \"65859\", \"99183\", \"105831\", \"126890\", \"96875\", \"103536\", \"129337\", \"152363\"], \"outgoing_citations\": [\"199630\", \"199669\", \"219115\", \"271595\", \"299339\"]}","task_split":"paper_retrieval"}
{"document_id":"190015","document_content":"# Learning Interpretable Disentangled Representations using Adversarial VAEs\n## Categories\n- Machine Learning\n## Abstract\nLearning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of 81.50% in terms of disentanglement, 11.60% in clustering, and 2% in supervised classification with a few amounts of labeled data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08491v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"33863\", \"19599\", \"30090\", \"81807\", \"84768\", \"97331\", \"84506\", \"137157\"], \"outgoing_citations\": [\"187863\", \"208041\", \"241933\", \"242239\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"190036","document_content":"# Event-based Vision: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nEvent cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TPAMI.2020.3008413\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"63922\", \"178009\", \"37060\", \"769\", \"897\", \"1798\", \"2176\", \"3370\", \"3629\", \"4150\", \"4724\", \"6051\", \"6185\", \"8317\", \"14984\", \"20552\", \"20489\", \"20528\", \"21564\", \"23739\", \"25451\", \"35301\", \"37495\", \"45856\", \"50625\", \"51930\", \"59089\", \"62109\", \"90960\", \"120928\", \"121969\", \"195521\", \"530\", \"2308\", \"4806\", \"6280\", \"10040\", \"12278\", \"13718\", \"13886\", \"14929\", \"17108\", \"17417\", \"18751\", \"11105\", \"19738\", \"20007\", \"20544\", \"20849\", \"22524\", \"25099\", \"25767\", \"25803\", \"26495\", \"27882\", \"29295\", \"29503\", \"30236\", \"32109\", \"33539\", \"33411\", \"35281\", \"38892\", \"42545\", \"44143\", \"45063\", \"7584\", \"118857\", \"46164\", \"47252\", \"50066\", \"50853\", \"52244\", \"52214\", \"52456\", \"54348\", \"54653\", \"57984\", \"59890\", \"61609\", \"63928\", \"63985\", \"63967\", \"64013\", \"64369\", \"65538\", \"65393\", \"66711\", \"66966\", \"67368\", \"67882\", \"68472\", \"70708\", \"73520\", \"80223\", \"81085\", \"81097\", \"81435\", \"127359\", \"86089\", \"89867\", \"90479\", \"93777\", \"98999\", \"100061\", \"104948\", \"108769\", \"130934\", \"132435\", \"180645\", \"88657\", \"93747\", \"98108\", \"107151\", \"111652\", \"114257\", \"123529\", \"129415\", \"130942\", \"133220\", \"133552\", \"133705\", \"135144\", \"135873\", \"135840\", \"136017\", \"136148\", \"137858\", \"138214\", \"138334\", \"139135\", \"142320\", \"142716\", \"145902\", \"147740\", \"152420\", \"156450\", \"169531\", \"181133\", \"182025\", \"188446\"], \"outgoing_citations\": [\"108769\", \"124413\", \"152420\", \"152991\", \"180645\", \"189162\", \"190078\", \"190090\", \"190497\", \"192704\", \"193519\", \"194707\", \"203195\", \"205351\", \"206851\", \"206879\", \"207245\", \"207669\", \"208731\", \"209333\", \"211978\", \"219128\", \"223057\", \"223380\", \"224055\", \"229822\", \"235474\", \"236796\", \"236799\", \"237914\", \"238238\", \"239208\", \"241612\", \"243802\", \"250626\", \"251425\", \"252319\", \"255512\", \"257940\", \"258616\", \"262755\", \"263555\", \"264249\", \"264538\", \"268747\", \"271427\", \"273248\", \"274990\", \"277783\", \"278822\", \"281870\", \"285642\", \"287980\", \"288554\", \"289307\", \"295786\", \"301852\", \"305392\", \"308474\", \"308908\"]}","task_split":"paper_retrieval"}
{"document_id":"190184","document_content":"# People infer recursive visual concepts from just a few examples\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Neurons and Cognition\n## Abstract\nMachine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer causal \"programs\" -- latent generating processes with nontrivial algorithmic properties -- from one, two, or three visual examples. People were asked to extrapolate the programs in several ways, for both classifying and generating new examples. As a theory of these inductive abilities, we present a Bayesian program learning model that searches the space of programs for the best explanation of the observations. Although variable, people's judgments are broadly consistent with the model and inconsistent with several alternatives, including a pre-trained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmic abstractions from sparse input data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.08034v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"q-bio.NC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Neurons and Cognition\", \"Machine Learning\"], \"incoming_citations\": [\"14183\", \"250219\", \"8319\", \"41543\", \"42116\", \"48311\", \"70922\", \"96214\", \"116081\", \"85432\", \"107129\", \"135912\"], \"outgoing_citations\": [\"181037\", \"199577\", \"235826\", \"250206\", \"259826\", \"284747\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"190237","document_content":"# End-to-End Robotic Reinforcement Learning without Reward Engineering\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nThe combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.07854v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"7249\", \"9312\", \"22458\", \"64415\", \"109082\", \"129693\", \"138348\", \"3907\", \"13872\", \"21975\", \"26076\", \"30979\", \"30961\", \"35306\", \"35867\", \"35918\", \"36302\", \"38095\", \"39688\", \"40884\", \"42015\", \"43409\", \"43971\", \"155602\", \"47984\", \"49757\", \"52000\", \"53903\", \"54546\", \"59341\", \"61195\", \"61875\", \"66036\", \"71397\", \"79175\", \"86496\", \"92122\", \"94181\", \"94561\", \"96719\", \"123568\", \"124867\", \"135951\", \"81007\", \"80924\", \"87299\", \"88033\", \"90880\", \"91106\", \"92636\", \"105961\", \"107165\", \"115363\", \"118561\", \"128799\", \"132719\", \"133171\", \"103314\", \"140124\", \"151978\", \"154734\", \"160619\", \"164743\", \"163689\", \"165176\", \"167147\", \"175488\", \"180884\", \"148969\", \"150902\", \"157304\", \"160156\", \"163364\", \"171962\", \"182138\", \"185610\"], \"outgoing_citations\": [\"203662\", \"207601\", \"215643\", \"215931\", \"218636\", \"226635\", \"227314\", \"229737\", \"230042\", \"233450\", \"251924\", \"254597\", \"263957\", \"272987\", \"280383\", \"280655\", \"282684\", \"283026\", \"283394\", \"283427\", \"297317\", \"306156\", \"309396\", \"343196\"]}","task_split":"paper_retrieval"}
{"document_id":"190473","document_content":"# Tutorial: Safe and Reliable Machine Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis document serves as a brief overview of the \"Safe and Reliable Machine Learning\" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). The talk slides can be found here: https:\/\/bit.ly\/2Gfsukp, while a video of the talk is available here: https:\/\/youtu.be\/FGLOCkC4KmE, and a complete list of references for the tutorial here: https:\/\/bit.ly\/2GdLPme.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.07204v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"34352\", \"20051\", \"24709\", \"138885\", \"46043\", \"50893\", \"51935\", \"78936\", \"104316\", \"150740\", \"153099\", \"155578\", \"186556\", \"204156\", \"206355\"], \"outgoing_citations\": [\"204156\", \"206355\", \"215230\", \"229995\", \"237923\", \"238640\", \"252103\", \"270120\"]}","task_split":"paper_retrieval"}
{"document_id":"190604","document_content":"# Pun Generation with Surprise\n## Categories\n- Computation and Language\n## Abstract\nWe tackle the problem of generating a pun sentence given a pair of homophones (e.g., \"died\" and \"dyed\"). Supervised text generation is inappropriate due to the lack of a large corpus of puns, and even if such a corpus existed, mimicry is at odds with generating novel content. In this paper, we propose an unsupervised approach to pun generation using a corpus of unhumorous text and what we call the local-global surprisal principle: we posit that in a pun sentence, there is a strong association between the pun word (e.g., \"dyed\") and the distant context, as well as a strong association between the alternative word (e.g., \"died\") and the immediate context. This contrast creates surprise and thus humor. We instantiate this principle for pun generation in two ways: (i) as a measure based on the ratio of probabilities under a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Human evaluation shows that our retrieve-and-edit approach generates puns successfully 31% of the time, tripling the success rate of a neural generation baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.06828v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23152\", \"22718\", \"23560\", \"24396\", \"55273\", \"69820\", \"77376\", \"99824\", \"102063\", \"121481\", \"128082\", \"128507\", \"160013\", \"169288\", \"176604\"], \"outgoing_citations\": [\"207427\", \"210155\", \"232274\", \"234138\", \"235238\", \"250035\", \"254855\", \"265309\"]}","task_split":"paper_retrieval"}
{"document_id":"190860","document_content":"# Face De-occlusion using 3D Morphable Model and Generative Adversarial Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent decades, 3D morphable model (3DMM) has been commonly used in image-based photorealistic 3D face reconstruction. However, face images are often corrupted by serious occlusion by non-face objects including eyeglasses, masks, and hands. Such objects block the correct capture of landmarks and shading information. Therefore, the reconstructed 3D face model is hardly reusable. In this paper, a novel method is proposed to restore de-occluded face images based on inverse use of 3DMM and generative adversarial network. We utilize the 3DMM prior to the proposed adversarial network and combine a global and local adversarial convolutional neural network to learn face de-occlusion model. The 3DMM serves not only as geometric prior but also proposes the face region for the local discriminator. Experiment results confirm the effectiveness and robustness of the proposed algorithm in removing challenging types of occlusions with various head poses and illumination. Furthermore, the proposed method reconstructs the correct 3D face model with de-occluded textures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.06109v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2870\", \"26240\", \"1917\", \"14983\", \"34450\", \"431\", \"9169\", \"41296\", \"130175\", \"149962\"], \"outgoing_citations\": [\"198793\", \"218619\", \"236073\", \"247495\", \"247558\", \"268405\", \"268494\", \"270923\", \"273729\", \"277232\", \"277837\", \"280394\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"190863","document_content":"# Adapting Sequence to Sequence models for Text Normalization in Social Media\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nSocial media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.06100v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"12940\", \"18538\", \"121671\", \"89123\", \"127045\"], \"outgoing_citations\": [\"246487\", \"251240\", \"253678\", \"254008\", \"261850\", \"264814\", \"282931\", \"286225\", \"295308\", \"296251\", \"299936\", \"302056\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"190941","document_content":"# Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders\n## Categories\n- Information Retrieval\n- Machine Learning\n- I.2.4\n## Abstract\nProduct recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional \"offline\" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach. Finally, we will present a couple methods that solve known retail & distribution pain-points based on the proposed architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.07687v4\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"I.2.4\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"I.2.4\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"193964\", \"199292\", \"231008\", \"283711\", \"289543\", \"289794\", \"309886\"]}","task_split":"paper_retrieval"}
{"document_id":"190976","document_content":"# GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Machine Learning\n## Abstract\nThe huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical systems models that are generated from data using a Genetic Programming (GP) framework. A fitness function suitable for the health domain is exploited. An evaluation of the approach in the mental health domain shows that performance of the model generated by the GP is on par with a dynamical systems model developed based on domain knowledge, significantly outperforms a generic Long Term Short Term Memory (LSTM) model and in some cases also outperforms an individualized LSTM model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.05815v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"303550\"]}","task_split":"paper_retrieval"}
{"document_id":"191058","document_content":"# Reasoning Visual Dialogs with Structural and Partial Observations\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nWe propose a novel model to address the task of Visual Dialog which exhibits complex dialog structures. To obtain a reasonable answer based on the current question and the dialog history, the underlying semantic dependencies between dialog entities are essential. In this paper, we explicitly formalize this task as inference in a graphical model with partially observed nodes and unknown graph structures (relations in dialog). The given dialog entities are viewed as the observed nodes. The answer to a given question is represented by a node with missing value. We first introduce an Expectation Maximization algorithm to infer both the underlying dialog structures and the missing node values (desired answers). Based on this, we proceed to propose a differentiable graph neural network (GNN) solution that approximates this process. Experiment results on the VisDial and VisDial-Q datasets show that our model outperforms comparative methods. It is also observed that our method can infer the underlying dialog structure for better dialog reasoning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.05548v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"30389\", \"20917\", \"22804\", \"23922\", \"55825\", \"66234\", \"82221\", \"91630\", \"108343\", \"111075\", \"131202\", \"175031\", \"77867\", \"52845\", \"96106\", \"106574\", \"111800\", \"114276\", \"113691\", \"118565\", \"97349\", \"128248\", \"128534\", \"132988\", \"136760\", \"137480\", \"137708\", \"130231\", \"140428\", \"152659\", \"154236\", \"154592\", \"155927\", \"146524\", \"179860\", \"200553\"], \"outgoing_citations\": [\"211939\", \"219014\", \"220496\", \"222620\", \"225101\", \"231647\", \"237271\", \"239242\", \"242322\", \"242597\", \"249820\", \"250195\", \"253057\", \"255095\", \"259007\", \"264514\", \"268356\", \"269273\", \"271085\", \"278604\", \"279026\", \"279678\", \"281409\", \"284449\", \"289503\", \"291430\", \"291892\", \"302621\", \"302662\", \"303603\", \"306883\", \"311670\", \"312506\", \"313143\", \"315007\", \"320003\", \"323775\"]}","task_split":"paper_retrieval"}
{"document_id":"191113","document_content":"# A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nUnsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to \\textit{ground} their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the Malopa (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.05426v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"44917\", \"55615\", \"91991\", \"119083\"], \"outgoing_citations\": [\"251816\", \"251974\", \"267605\", \"278226\", \"281121\", \"298944\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"191120","document_content":"# Attentive Action and Context Factorization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a method for human action recognition, one that can localize the spatiotemporal regions that `define' the actions. This is a challenging task due to the subtlety of human actions in video and the co-occurrence of contextual elements. To address this challenge, we utilize conjugate samples of human actions, which are video clips that are contextually similar to human action samples but do not contain the action. We introduce a novel attentional mechanism that can spatially and temporally separate human actions from the co-occurring contextual factors. The separation of the action and context factors is weakly supervised, eliminating the need for laboriously detailed annotation of these two factors in training samples. Our method can be used to build human action classifiers with higher accuracy and better interpretability. Experiments on several human action recognition datasets demonstrate the quantitative and qualitative benefits of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.05410v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"191330\"], \"outgoing_citations\": [\"206227\", \"212776\", \"227145\", \"227719\", \"229179\", \"246521\", \"249077\", \"250068\", \"251423\", \"264781\", \"264963\", \"268963\", \"276018\", \"280734\", \"281003\", \"287380\", \"294078\", \"311683\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"191163","document_content":"# Advances in Natural Language Question Answering: A Review\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nQuestion Answering has recently received high attention from artificial intelligence communities due to the advancements in learning technologies. Early question answering models used rule-based approaches and moved to the statistical approach to address the vastly available information. However, statistical approaches are shown to underperform in handling the dynamic nature and the variation of language. Therefore, learning models have shown the capability of handling the dynamic nature and variations in language. Many deep learning methods have been introduced to question answering. Most of the deep learning approaches have shown to achieve higher results compared to machine learning and statistical methods. The dynamic nature of language has profited from the nonlinear learning in deep learning. This has created prominent success and a spike in work on question answering. This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.05276v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"2606\", \"75106\"], \"outgoing_citations\": [\"218313\", \"303469\", \"313427\", \"316576\", \"317213\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"191332","document_content":"# Characterizing the impact of geometric properties of word embeddings on task performance\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAnalysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.04866v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"18531\", \"97505\"], \"outgoing_citations\": [\"154631\", \"234203\", \"252324\", \"264335\", \"265631\", \"267228\", \"289632\", \"293018\", \"300574\", \"311696\", \"319144\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"191471","document_content":"# Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nTraditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., \\emph{person} or \\emph{location}) and have equipped language models with access to an external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.04458v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"7555\", \"11540\", \"32315\", \"71008\", \"127037\", \"90494\", \"96765\", \"77370\", \"115664\", \"129702\", \"150253\", \"169327\"], \"outgoing_citations\": [\"177232\", \"197767\", \"212491\", \"232273\", \"259145\", \"267692\", \"281158\", \"287413\"]}","task_split":"paper_retrieval"}
{"document_id":"191570","document_content":"# Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nA grand goal in AI is to build a robot that can accurately navigate based on natural language instructions, which requires the agent to perceive the scene, understand and ground language, and act in the real-world environment. One key challenge here is to learn to navigate in new environments that are unseen during training. Most of the existing approaches perform dramatically worse in unseen environments as compared to seen ones. In this paper, we present a generalizable navigational agent. Our agent is trained in two stages. The first stage is training via mixed imitation and reinforcement learning, combining the benefits from both off-policy and on-policy optimization. The second stage is fine-tuning via newly-introduced 'unseen' triplets (environment, path, instruction). To generate these unseen triplets, we propose a simple but effective 'environmental dropout' method to mimic unseen environments, which overcomes the problem of limited seen environment variability. Next, we apply semi-supervised learning (via back-translation) on these dropped-out environments to generate new paths and instructions. Empirically, we show that our agent is substantially better at generalizability when fine-tuned with these triplets, outperforming the state-of-art approaches by a large margin on the private unseen test set of the Room-to-Room task, and achieving the top rank on the leaderboard.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.04195v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"4499\", \"7377\", \"10293\", \"27350\", \"33721\", \"35623\", \"39368\", \"47577\", \"59461\", \"93187\", \"6339\", \"6778\", \"9686\", \"13414\", \"13764\", \"17160\", \"17198\", \"18840\", \"19911\", \"26281\", \"27522\", \"28518\", \"30309\", \"35300\", \"35938\", \"36974\", \"42082\", \"44196\", \"45665\", \"48662\", \"48758\", \"49578\", \"49746\", \"54330\", \"54802\", \"57305\", \"59216\", \"61081\", \"65095\", \"66217\", \"69518\", \"73451\", \"81503\", \"81574\", \"82453\", \"84618\", \"100277\", \"105761\", \"114991\", \"153902\", \"175031\", \"77336\", \"85467\", \"86950\", \"87706\", \"87806\", \"88444\", \"91773\", \"93277\", \"93950\", \"98224\", \"109092\", \"110525\", \"111800\", \"112798\", \"125969\", \"126597\", \"127852\", \"132774\", \"132720\", \"133919\", \"136760\", \"139037\", \"139227\", \"140039\", \"140318\", \"152871\", \"153162\", \"155676\", \"155908\", \"159921\", \"168528\", \"166959\", \"172416\", \"176603\", \"177351\", \"177674\", \"211921\"], \"outgoing_citations\": [\"203398\", \"208773\", \"220017\", \"220611\", \"224223\", \"228916\", \"234391\", \"238287\", \"245731\", \"247900\", \"248742\", \"249906\", \"255575\", \"263104\", \"265722\", \"266671\", \"267779\", \"268563\", \"274102\", \"280570\", \"284565\", \"302815\", \"307569\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"191573","document_content":"# Unsupervised learning of action classes with continuous temporal embedding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe task of temporally detecting and segmenting actions in untrimmed videos has seen an increased attention recently. One problem in this context arises from the need to define and label action boundaries to create annotations for training which is very time and cost intensive. To address this issue, we propose an unsupervised approach for learning action classes from untrimmed video sequences. To this end, we use a continuous temporal embedding of framewise features to benefit from the sequential nature of activities. Based on the latent space created by the embedding, we identify clusters of temporal segments across all videos that correspond to semantic meaningful action classes. The approach is evaluated on three challenging datasets, namely the Breakfast dataset, YouTube Instructions, and the 50Salads dataset. While previous works assumed that the videos contain the same high level activity, we furthermore show that the proposed approach can also be applied to a more general setting where the content of the videos is unknown.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.04189v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19821\", \"1960\", \"3517\", \"5676\", \"17178\", \"44352\", \"46493\", \"46854\", \"7027\", \"19925\", \"24718\", \"29561\", \"39008\", \"44055\", \"52435\", \"57923\", \"58191\", \"58193\", \"61811\", \"87742\", \"135766\", \"145028\", \"126373\", \"134333\", \"140008\"], \"outgoing_citations\": [\"227523\", \"231686\", \"237402\", \"237765\", \"259415\", \"259428\", \"265791\", \"269502\", \"270713\", \"271891\", \"272360\", \"283115\", \"283384\", \"287541\", \"292797\", \"302753\", \"307001\", \"310322\", \"313397\", \"313481\", \"316472\"]}","task_split":"paper_retrieval"}
{"document_id":"191634","document_content":"# Early warning in egg production curves from commercial hens: A SVM approach\n## Categories\n- Applications\n- Machine Learning\n## Abstract\nArtificial Intelligence allows the improvement of our daily life, for instance, speech and handwritten text recognition, real time translation and weather forecasting are common used applications. In the livestock sector, machine learning algorithms have the potential for early detection and warning of problems, which represents a significant milestone in the poultry industry. Production problems generate economic loss that could be avoided by acting in a timely manner. In the current study, training and testing of support vector machines are addressed, for an early detection of problems in the production curve of commercial eggs, using farm's egg production data of 478,919 laying hens grouped in 24 flocks. Experiments using support vector machines with a 5 k-fold cross-validation were performed at different previous time intervals, to alert with up to 5 days of forecasting interval, whether a flock will experience a problem in production curve. Performance metrics such as accuracy, specificity, sensitivity, and positive predictive value were evaluated, reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on unseen data (test-set). The optimal forecasting interval was from zero to three days, performance metrics decreases as the forecasting interval is increased. It should be emphasized that this technique was able to issue an alert a day in advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel application embedded in a computer system of poultry management is able to provide significant improvements in early detection and warning of problems related to the production curve.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.compag.2015.12.009\", \"primary_category\": \"stat.AP\", \"categories\": [\"stat.AP\", \"cs.LG\"], \"primary_category_human_readable\": \"Applications\", \"categories_human_readable\": [\"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"14294\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"191641","document_content":"# Jointly Measuring Diversity and Quality in Text Generation Models\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nText generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator's density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03971v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8099\", \"16013\", \"50907\", \"94351\", \"9320\", \"17035\", \"115567\", \"76748\", \"105667\", \"170376\", \"94487\", \"95532\", \"99616\", \"105599\", \"114461\", \"115939\", \"169267\", \"211205\", \"161927\", \"204159\"], \"outgoing_citations\": [\"211205\", \"243154\", \"255035\", \"263935\", \"264937\", \"273062\", \"277021\", \"281002\", \"303250\"]}","task_split":"paper_retrieval"}
{"document_id":"191869","document_content":"# The Mathematics of Text Structure\n## Categories\n- Computation and Language\n- Category Theory\n- Quantum Physics\n## Abstract\nIn previous work we gave a mathematical foundation, referred to as DisCoCat, for how words interact in a sentence in order to produce the meaning of that sentence. To do so, we exploited the perfect structural match of grammar and categories of meaning spaces. Here, we give a mathematical foundation, referred to as DisCoCirc, for how sentences interact in texts in order to produce the meaning of that text. First we revisit DisCoCat. While in DisCoCat all meanings are fixed as states (i.e. have no input), in DisCoCirc word meanings correspond to a type, or system, and the states of this system can evolve. Sentences are gates within a circuit which update the variable meanings of those words. Like in DisCoCat, word meanings can live in a variety of spaces e.g. propositional, vectorial, or cognitive. The compositional structure are string diagrams representing information flows, and an entire text yields a single string diagram in which word meanings lift to the meaning of an entire text. While the developments in this paper are independent of a physical embodiment (cf. classical vs. quantum computing), both the compositional formalism and suggested meaning model are highly quantum-inspired, and implementation on a quantum computer would come with a range of benefits. We also praise Jim Lambek for his role in mathematical linguistics in general, and the development of the DisCo program more specifically.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03478v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"math.CT\", \"quant-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Category Theory\", \"Quantum Physics\"], \"incoming_citations\": [\"82194\", \"17040\", \"17333\", \"17910\", \"23688\", \"37961\", \"42233\", \"50008\", \"131615\", \"43318\", \"82195\", \"122385\", \"125845\", \"125735\", \"129586\", \"136974\", \"148638\", \"156326\"], \"outgoing_citations\": [\"148638\", \"200990\", \"210969\", \"210970\", \"210971\", \"212840\", \"291108\", \"297835\", \"299918\", \"310767\", \"313529\", \"318101\", \"328199\", \"329786\", \"330808\", \"334694\", \"338185\", \"342527\", \"345221\", \"349396\"]}","task_split":"paper_retrieval"}
{"document_id":"191888","document_content":"# Reinforced Imitation in Heterogeneous Action Space\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nImitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's policy based on either mimicking expert behavior or maximizing sparse reward. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when its actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert, due to the optimized usage of sparse rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03438v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"84270\", \"122822\", \"158515\", \"163689\", \"168853\"], \"outgoing_citations\": [\"224411\", \"229554\", \"230068\", \"233167\", \"234517\", \"240800\", \"242141\", \"254596\", \"260018\", \"261522\", \"261805\", \"263264\", \"270529\", \"270925\", \"271975\", \"272298\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"191966","document_content":"# Weakly Supervised Video Moment Retrieval From Text Queries\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nThere have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03282v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"4080\", \"14600\", \"21501\", \"66667\", \"10836\", \"17011\", \"23020\", \"23769\", \"25742\", \"26968\", \"33654\", \"38434\", \"40616\", \"48860\", \"49800\", \"59050\", \"61663\", \"72065\", \"74091\", \"11516\", \"82839\", \"105055\", \"110615\", \"164525\", \"99938\", \"104424\", \"105236\", \"105151\", \"107961\", \"110926\", \"111530\", \"105753\", \"117153\", \"136619\", \"145623\", \"146489\", \"151281\", \"155521\", \"173844\"], \"outgoing_citations\": [\"220529\", \"223200\", \"234836\", \"247497\", \"259318\", \"267223\", \"268408\", \"269128\", \"270802\", \"271891\", \"281407\", \"293986\", \"295117\", \"302943\", \"303011\", \"303452\", \"312423\", \"313189\", \"313435\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"191970","document_content":"# Synthesized Policies for Transfer and Adaptation across Tasks and Environments\n## Categories\n- Machine Learning\n## Abstract\nThe ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simultaneously transfer across both environments (ENV) and tasks (TASK), probably more importantly, by learning from only sparse (ENV, TASK) pairs out of all the possible combinations. We propose a novel compositional neural network architecture which depicts a meta rule for composing policies from the environment and task embeddings. Notably, one of the main challenges is to learn the embeddings jointly with the meta rule. We further propose new training methods to disentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies. Experiments on GridWorld and Thor, of which the agent takes as input an egocentric view, show that our approach gives rise to high success rates on all the (ENV, TASK) pairs after learning from only 40% of them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03276v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"37290\", \"125969\", \"143504\", \"168078\", \"159036\", \"171778\"], \"outgoing_citations\": [\"235092\", \"247418\", \"258472\", \"261316\", \"263650\", \"265722\", \"274487\", \"280296\", \"281106\", \"284134\", \"284565\", \"290243\", \"290904\", \"292616\", \"297304\", \"302952\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"192009","document_content":"# The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We~adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11263-021-01511-6\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4032\", \"7382\", \"7625\", \"38121\", \"154345\", \"1067\", \"7570\", \"11354\", \"21800\", \"31669\", \"33718\", \"46710\", \"51768\", \"57807\", \"64190\", \"64989\", \"132880\", \"85411\", \"86767\", \"77336\", \"80552\", \"142625\", \"146096\", \"157799\", \"173425\", \"148110\", \"157570\", \"169643\", \"173199\"], \"outgoing_citations\": [\"31789\", \"64190\", \"79372\", \"118538\", \"122379\", \"131338\", \"138578\", \"141103\", \"151836\", \"173199\", \"175919\", \"183445\", \"190328\", \"192206\", \"203887\", \"207914\", \"215681\", \"220375\", \"220552\", \"222344\", \"225094\", \"225481\", \"227078\", \"229003\", \"229995\", \"230254\", \"239137\", \"240532\", \"240914\", \"242269\", \"249008\", \"249330\", \"254651\", \"256630\", \"257694\", \"259334\", \"264232\", \"284665\", \"295576\", \"322496\"]}","task_split":"paper_retrieval"}
{"document_id":"192055","document_content":"# Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations\n## Categories\n- Computer Vision and Pattern Recognition\n- 68U10\n- I.4.6; I.5.3\n## Abstract\nLesion segmentation from the surrounding skin is the first task for developing automatic Computer-Aided Diagnosis of skin cancer. Variant features of lesion like uneven distribution of color, irregular shape, border and texture make this task challenging. The contribution of this paper is to present and compare two different approaches to skin lesion segmentation. The first approach uses watershed, while the second approach uses mean-shift. Pre-processing steps were performed in both approaches for removing hair and dark borders of microscopic images. The Evaluation of the proposed approaches was performed using Jaccard Index (Intersection over Union or IoU). An additional contribution of this paper is to present pipelines for performing pre-processing and segmentation applying existing segmentation and morphological algorithms which led to promising results. On average, the first approach showed better performance than the second one with average Jaccard Index over 200 ISIC-2017 challenge images are 89.16% and 76.94% respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.03075v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68U10\", \"I.4.6; I.5.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68U10\", \"I.4.6; I.5.3\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254674\", \"284947\"]}","task_split":"paper_retrieval"}
{"document_id":"192158","document_content":"# Unifying Human and Statistical Evaluation for Natural Language Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHow can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity but not quality, as models that occasionally emit low quality samples would be insufficiently penalized. In this paper, we propose a unified framework which evaluates both diversity and quality, based on the optimal error rate of predicting whether a sentence is human- or machine-generated. We demonstrate that this error rate can be efficiently estimated by combining human and statistical evaluation, using an evaluation metric which we call HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects diversity defects which fool pure human evaluation and that (ii) techniques such as annealing for improving quality actually decrease HUSE due to decreased diversity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02792v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2863\", \"9000\", \"37870\", \"42171\", \"75094\", \"75094\", \"2611\", \"3784\", \"8753\", \"9320\", \"9832\", \"17825\", \"23776\", \"25312\", \"28808\", \"30826\", \"31768\", \"38382\", \"41866\", \"132431\", \"47214\", \"47416\", \"56830\", \"61872\", \"63298\", \"69615\", \"70159\", \"71888\", \"71966\", \"91642\", \"94230\", \"107302\", \"108978\", \"127232\", \"129680\", \"132683\", \"132791\", \"223084\", \"94327\", \"94534\", \"95532\", \"99445\", \"100285\", \"100344\", \"100503\", \"108688\", \"114461\", \"115939\", \"118062\", \"119586\", \"126346\", \"128009\", \"128733\", \"131919\", \"128049\", \"142670\", \"143317\", \"145384\", \"157125\", \"158107\", \"166562\", \"170123\", \"186662\", \"189504\", \"189542\", \"153439\", \"165308\", \"168852\", \"174596\", \"180695\", \"179568\", \"181585\", \"183694\", \"192786\", \"204159\"], \"outgoing_citations\": [\"207427\", \"211205\", \"219825\", \"221328\", \"229730\", \"230976\", \"235238\", \"247257\", \"257858\", \"260587\", \"275228\", \"275597\", \"295863\", \"298750\", \"303913\", \"310717\", \"310740\"]}","task_split":"paper_retrieval"}
{"document_id":"192166","document_content":"# Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification\n## Categories\n- Computation and Language\n## Abstract\nSentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02767v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"3392\", \"17436\", \"23560\", \"24637\", \"56593\", \"64358\", \"92547\", \"92961\", \"129680\", \"78896\", \"108688\", \"118053\", \"126870\", \"129016\", \"127956\", \"142985\", \"154650\", \"158340\", \"180695\"], \"outgoing_citations\": [\"212887\", \"214312\", \"232274\", \"247368\", \"270048\", \"276365\", \"279543\", \"283076\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"192172","document_content":"# ExCL: Extractive Clip Localization Using Natural Language Descriptions\n## Categories\n- Computation and Language\n## Abstract\nThe task of retrieving clips within videos based on a given natural language query requires cross-modal reasoning over multiple frames. Prior approaches such as sliding window classifiers are inefficient, while text-clip similarity driven ranking-based approaches such as segment proposal networks are far more complicated. In order to select the most relevant video clip corresponding to the given text description, we propose a novel extractive approach that predicts the start and end frames by leveraging cross-modal interactions between the text and video - this removes the need to retrieve and re-rank multiple proposal segments. Using recurrent networks we encode the two modalities into a joint representation which is then used in different variants of start-end frame predictor networks. Through extensive experimentation and ablative analysis, we demonstrate that our simple and elegant approach significantly outperforms state of the art on two datasets and has comparable performance on a third.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02755v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24485\", \"66667\", \"82708\", \"99220\", \"2022\", \"10836\", \"12466\", \"12662\", \"17168\", \"17011\", \"21807\", \"22104\", \"23020\", \"24639\", \"32325\", \"33654\", \"34329\", \"35933\", \"40616\", \"48860\", \"49800\", \"51213\", \"59050\", \"59325\", \"63086\", \"85540\", \"85822\", \"94658\", \"105055\", \"164525\", \"98929\", \"102267\", \"130880\", \"132452\", \"128305\", \"145702\", \"170995\", \"189453\", \"173844\"], \"outgoing_citations\": [\"207818\", \"209133\", \"217021\", \"219151\", \"235648\", \"259318\", \"267223\", \"267523\", \"270802\", \"295117\", \"302753\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"192217","document_content":"# 3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a new color photometric stereo (CPS) method that recovers high quality, detailed 3D face geometry in a single shot. Our system uses three uncalibrated near point lights of different colors and a single camera. For robust self-calibration of the light sources, we use 3D morphable model (3DMM) and semantic segmentation of facial parts. We address the spectral ambiguity problem by incorporating albedo consensus, albedo similarity, and proxy prior into a unified framework. We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile. Experiments show that our new approach produces state-of-the-art results from single image with high-fidelity geometry that includes details such as wrinkles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02605v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"134658\"], \"outgoing_citations\": [\"193656\", \"247495\", \"248918\", \"254971\", \"259476\", \"262122\", \"270235\", \"273729\", \"280394\", \"284716\", \"284788\"]}","task_split":"paper_retrieval"}
{"document_id":"192337","document_content":"# Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nThe overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been dominantly used in previous approaches for unsupervised neural machine translation, where pseudo sentence pairs are generated to train the models with a reconstruction loss. However, the pseudo sentences are usually of low quality as translation errors accumulate during training. To avoid this fundamental issue, we propose an alternative but more effective approach, extract-edit, to extract and then edit real sentences from the target monolingual corpora. Furthermore, we introduce a comparative translation loss to evaluate the translated target sentences and thus train the unsupervised translation systems. Experiments show that the proposed approach consistently outperforms the previous state-of-the-art unsupervised machine translation systems across two benchmarks (English-French and English-German) and two low-resource language pairs (English-Romanian and English-Russian) by more than 2 (up to 3.63) BLEU points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02331v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25308\", \"38855\", \"89936\", \"104906\", \"132055\", \"132595\", \"158975\", \"77667\", \"118104\", \"146003\", \"152767\", \"170174\", \"171533\"], \"outgoing_citations\": [\"207427\", \"227586\", \"228171\", \"231932\", \"234377\", \"234807\", \"235238\", \"251816\", \"251974\", \"254855\", \"263927\", \"264937\", \"265947\", \"268098\", \"278016\", \"281472\", \"282136\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"192342","document_content":"# Guiding Extractive Summarization with Question-Answering Rewards\n## Categories\n- Computation and Language\n## Abstract\nHighlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth. Manual annotation of extraction units is cost-prohibitive, whereas acquiring labels by automatically aligning human abstracts and source documents can yield inferior results. In this paper we describe a novel framework to guide a supervised, extractive summarization system with question-answering rewards. We argue that quality summaries should serve as a document surrogate to answer important questions, and such question-answer pairs can be conveniently obtained from human abstracts. The system learns to promote summaries that are informative, fluent, and perform competitively on question-answering. Our results compare favorably with those reported by strong summarization baselines as evaluated by automatic metrics and human assessors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.02321v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9385\", \"8747\", \"47416\", \"53191\", \"86055\", \"88217\", \"127281\", \"127538\", \"140815\", \"159144\", \"161321\", \"167137\", \"168744\", \"168908\"], \"outgoing_citations\": [\"212506\", \"219618\", \"220230\", \"220955\", \"225556\", \"227983\", \"230234\", \"230464\", \"230466\", \"232355\", \"233710\", \"235041\", \"237494\", \"241070\", \"250690\", \"250722\", \"263243\", \"266671\", \"279351\", \"280557\", \"281847\", \"289673\", \"290515\", \"290789\", \"296029\", \"300867\"]}","task_split":"paper_retrieval"}
{"document_id":"192399","document_content":"# Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions\n## Categories\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMy research lies in the intersection of security and machine learning. This overview summarizes one component of my research: combining computer vision with malware exploit detection for enhanced security solutions. I will present the perspectives of efficacy, reliability and resiliency to formulate threat detection as computer vision problems and develop state-of-the-art image-based malware classification. Representing malware binary as images provides a direct visualization of data samples, reduces the efforts for feature extraction, and consumes the whole binary for holistic structural analysis. Employing transfer learning of deep neural networks effective for large scale image classification to malware classification demonstrates superior classification efficacy compared with classical machine learning algorithms. To enhance reliability of these vision-based malware detectors, interpretation frameworks can be constructed on the malware visual representations and useful for extracting faithful explanation, so that security practitioners have confidence in the model before deployment. In cyber-security applications, we should always assume that a malware writer constantly modifies code to bypass detection. Addressing the resiliency of the malware detectors is equivalently important as efficacy and reliability. Via understanding the attack surfaces of machine learning models used for malware detection, we can greatly improve the robustness of the algorithms to combat malware adversaries in the wild. Finally I will discuss future research directions worth pursuing in this research community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.10504v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"205044\", \"205493\", \"241256\", \"245698\", \"257627\", \"268474\"]}","task_split":"paper_retrieval"}
{"document_id":"192471","document_content":"# Character Region Awareness for Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text detection methods based on neural networks have emerged recently and have shown promising results. Previous methods trained with rigid word-level bounding boxes exhibit limitations in representing the text region in an arbitrary shape. In this paper, we propose a new scene text detection method to effectively detect text area by exploring each character and affinity between characters. To overcome the lack of individual character level annotations, our proposed framework exploits both the given character-level annotations for synthetic images and the estimated character-level ground-truths for real images acquired by the learned interim model. In order to estimate affinity between characters, the network is trained with the newly proposed representation for affinity. Extensive experiments on six benchmarks, including the TotalText and CTW-1500 datasets which contain highly curved texts in natural images, demonstrate that our character-level text detection significantly outperforms the state-of-the-art detectors. According to the results, our proposed method guarantees high flexibility in detecting complicated scene text images, such as arbitrarily-oriented, curved, or deformed texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01941v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"91575\", \"155242\", \"2918\", \"6241\", \"6741\", \"7829\", \"13927\", \"14003\", \"31775\", \"37135\", \"50917\", \"52248\", \"54017\", \"56985\", \"66255\", \"79138\", \"80383\", \"83916\", \"84505\", \"6843\", \"8735\", \"11650\", \"17176\", \"18970\", \"25116\", \"25132\", \"24978\", \"29640\", \"31607\", \"33142\", \"33498\", \"33798\", \"35713\", \"35782\", \"38795\", \"38854\", \"43153\", \"47089\", \"48702\", \"50136\", \"53987\", \"53992\", \"54412\", \"55556\", \"55562\", \"58673\", \"58994\", \"59727\", \"59992\", \"70328\", \"70646\", \"72402\", \"43191\", \"77578\", \"81960\", \"82221\", \"85941\", \"103980\", \"108814\", \"113025\", \"113794\", \"123348\", \"127548\", \"136651\", \"150258\", \"155066\", \"155848\", \"59016\", \"81667\", \"83582\", \"84406\", \"92285\", \"95982\", \"101668\", \"105295\", \"106579\", \"111035\", \"111116\", \"117076\", \"120420\", \"119526\", \"122981\", \"122949\", \"123078\", \"126431\", \"129173\", \"129484\", \"131896\", \"135263\", \"136466\", \"139566\", \"140006\", \"140478\", \"151049\", \"156029\", \"174062\", \"210634\", \"153142\", \"163175\", \"169411\", \"172530\", \"177984\", \"178095\", \"189470\"], \"outgoing_citations\": [\"215090\", \"225795\", \"239535\", \"240965\", \"245591\", \"245899\", \"245970\", \"247316\", \"248133\", \"252159\", \"257197\", \"257954\", \"262570\", \"270668\", \"271112\", \"272390\", \"279950\", \"289389\", \"294023\", \"294606\", \"295564\"]}","task_split":"paper_retrieval"}
{"document_id":"192512","document_content":"# Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer\n## Categories\n- Machine Learning\n## Abstract\nAn important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable combination of high-fidelity photographic observations, massive amounts of different environment configurations and fast simulation speeds. In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations. We present a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Our scenarios combine two key advantages: (i) they are based on a simple but highly efficient 3D environment (ViZDoom) which allows high speed simulation (12000fps); (ii) the scenarios provide the user with a range of difficulty settings, in order to identify the limitations of current state of the art algorithms and network architectures. We aim to increase accessibility to the field of Deep-RL by providing baselines for challenging scenarios where new ideas can be iterated on quickly. We argue that the community should be able to address challenging problems in reasoning of mobile agents without the need for a large compute infrastructure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01806v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"6613\", \"35753\", \"34913\", \"82126\", \"117151\", \"133619\", \"145753\", \"168254\", \"159298\", \"214440\", \"190256\"], \"outgoing_citations\": [\"192738\", \"212893\", \"219658\", \"235092\", \"239703\", \"243235\", \"247323\", \"247418\", \"247749\", \"248885\", \"249906\", \"255531\", \"258384\", \"272930\", \"278195\", \"280202\", \"280296\", \"280681\", \"284468\", \"293071\", \"315055\", \"317841\"]}","task_split":"paper_retrieval"}
{"document_id":"192597","document_content":"# Generating Labels for Regression of Subjective Constructs using Triplet Embeddings\n## Categories\n- Machine Learning\n## Abstract\nHuman annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trends focus on correcting artifacts and biases introduced by annotators during the annotation process while fusing them into a single annotation. In this work, we propose a novel annotation approach using triplet embeddings. By lifting the absolute annotation process to relative annotations where the annotator compares individual target constructs in triplets, we leverage the accuracy of comparisons over absolute ratings by human annotators. We then build a 1-dimensional embedding in Euclidean space that is indexed in time and serves as a label for regression. In this setting, the annotation fusion occurs naturally as a union of sets of sampled triplet comparisons among different annotators. We show that by using our proposed sampling method to find an embedding, we are able to accurately represent synthetic hidden constructs in time under noisy sampling conditions. We further validate this approach using human annotations collected from Mechanical Turk and show that we can recover the underlying structure of the hidden construct up to bias and scaling factors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patrec.2019.10.003\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"289813\", \"354448\"]}","task_split":"paper_retrieval"}
{"document_id":"192648","document_content":"# Good News, Everyone! Context driven entity-aware captioning for news images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCurrent image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce `GoodNews', the largest news image captioning dataset in the literature and demonstrate state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01475v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"8982\", \"19075\", \"55418\", \"148583\", \"3784\", \"24039\", \"25348\", \"33417\", \"55207\", \"95829\", \"31505\", \"100311\", \"111530\", \"130657\", \"153745\", \"171726\", \"182699\"], \"outgoing_citations\": [\"227741\", \"231647\", \"234753\", \"277419\", \"278604\", \"284230\", \"295639\", \"296049\", \"300099\", \"305473\", \"312643\", \"312704\", \"313196\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"192671","document_content":"# A Holistic Representation Guided Attention Network for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet strong approach for scene text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder which guided by holistic representation. The holistic representation can guide the attention-based decoder focus on more accurate area. As no recurrent module is adopted, our model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01375v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3394\", \"5857\", \"6379\", \"121818\", \"191\", \"42497\", \"48901\", \"163092\", \"99077\"], \"outgoing_citations\": [\"203406\", \"205988\", \"211851\", \"217463\", \"220129\", \"232706\", \"234577\", \"241203\", \"256711\", \"261365\", \"268829\", \"294023\", \"296685\", \"296828\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"192719","document_content":"# Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. The traditional hashing methods in RS usually exploit hand-crafted features to learn hash functions to obtain binary codes, which can be insufficient to optimally represent the information content of RS images. To overcome this problem, in this paper we introduce a metric-learning based hashing network, which learns: 1) a semantic-based metric space for effective feature representation; and 2) compact binary hash codes for fast archive search. Our network considers an interplay of multiple loss functions that allows to jointly learn a metric based semantic space facilitating similar images to be clustered together in that target space and at the same time producing compact final activations that lose negligible information when binarized. Experiments carried out on two benchmark RS archives point out that the proposed network significantly improves the retrieval performance under the same retrieval time when compared to the state-of-the-art hashing methods in RS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LGRS.2020.2974629\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"50903\", \"133251\"], \"outgoing_citations\": [\"286380\", \"303512\", \"310303\"]}","task_split":"paper_retrieval"}
{"document_id":"192753","document_content":"# Person Identification with Visual Summary for a Safe Access to a Smart Home\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nSafeAccess is an integrated system designed to provide easier and safer access to a smart home for people with or without disabilities. The system is designed to enhance safety and promote the independence of people with disability (i.e., visually impaired). The key functionality of the system includes the detection and identification of human and generating contextual visual summary from the real-time video streams obtained from the cameras placed in strategic locations around the house. In addition, the system classifies human into groups (i.e. friends\/families\/caregiver versus intruders\/burglars\/unknown). These features allow the user to grant\/deny remote access to the premises or ability to call emergency services. In this paper, we focus on designing a prototype system for the smart home and building a robust recognition engine that meets the system criteria and addresses speed, accuracy, deployment and environmental challenges under a wide variety of practical and real-life situations. To interact with the system, we implemented a dialog enabled interface to create a personalized profile using face images or video of friend\/families\/caregiver. To improve computational efficiency, we apply change detection to filter out frames and use Faster-RCNN to detect the human presence and extract faces using Multitask Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP\/FaceNet to identify a person and groups by matching extracted faces with the profile. SafeAccess sends a visual summary to the users with an MMS containing a person's name if any match found or as \"Unknown\", scene image, facial description, and contextual information. SafeAccess identifies friends\/families\/caregiver versus intruders\/unknown with an average F-score 0.97 and generates a visual summary from 10 classes with an average accuracy of 98.01%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01178v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225585\", \"318091\", \"321117\", \"336860\"]}","task_split":"paper_retrieval"}
{"document_id":"192755","document_content":"# A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations\n## Categories\n- Computation and Language\n## Abstract\nWe propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We also investigate the effect of moving from bag-of-words to recurrent neural network modules. We evaluate our models as well as several popular pretrained embeddings on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01173v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19990\", \"18913\", \"44100\", \"46005\", \"56003\", \"56882\", \"73415\", \"80697\", \"79090\", \"91598\", \"129567\", \"78655\", \"94543\", \"94843\", \"96726\", \"96501\", \"106553\", \"119533\", \"124496\", \"124868\", \"126962\", \"157122\", \"159742\", \"169719\", \"179201\", \"182958\"], \"outgoing_citations\": [\"179458\", \"219615\", \"219789\", \"221478\", \"227326\", \"235345\", \"236895\", \"242133\", \"250035\", \"250360\", \"250449\", \"254855\", \"255129\", \"263848\", \"265309\", \"267622\", \"268157\", \"269609\", \"269899\", \"270040\", \"272531\", \"278987\", \"279000\", \"280734\", \"294248\", \"302516\", \"303029\"]}","task_split":"paper_retrieval"}
{"document_id":"192777","document_content":"# Benchmarking Approximate Inference Methods for Neural Structured Prediction\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nExact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016). Another approach, proposed recently, is to train a neural network (an \"inference network\") to perform inference (Tu and Gimpel, 2018). In this paper, we compare these two families of inference methods on three sequence labeling datasets. We choose sequence labeling because it permits us to use exact inference as a benchmark in terms of speed, accuracy, and search error. Across datasets, we demonstrate that inference networks achieve a better speed\/accuracy\/search error trade-off than gradient descent, while also being faster than exact inference at similar accuracy levels. We find further benefit by combining inference networks and gradient descent, using the former to provide a warm start for the latter.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.01138v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"84948\", \"95234\", \"115375\", \"96251\", \"100079\", \"113240\", \"127430\", \"157573\", \"168146\", \"170543\", \"190175\"], \"outgoing_citations\": [\"179458\", \"228348\", \"239565\", \"243020\", \"250577\", \"259704\", \"264202\", \"274533\", \"276439\", \"294531\", \"302947\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"192875","document_content":"# GAN You Do the GAN GAN?\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nGenerative Adversarial Networks (GANs) have become a dominant class of generative models. In recent years, GAN variants have yielded especially impressive results in the synthesis of a variety of forms of data. Examples include compelling natural and artistic images, textures, musical sequences, and 3D object files. However, one obvious synthesis candidate is missing. In this work, we answer one of deep learning's most pressing questions: GAN you do the GAN GAN? That is, is it possible to train a GAN to model a distribution of GANs? We release the full source code for this project under the MIT license.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.00724v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"226031\", \"245833\"]}","task_split":"paper_retrieval"}
{"document_id":"192928","document_content":"# Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle\n## Categories\n- Computation and Language\n## Abstract\nWe introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack --i.e. a data structure with linear-time sequential access-- the proposed system uses a set of parsing items, with constant-time random access. This change makes it possible to construct any discontinuous constituency tree in exactly $4n - 2$ transitions for a sentence of length $n$. At each parsing step, the parser considers every item in the set to be combined with a focus item and to construct a new constituent in a bottom-up fashion. The parsing strategy is based on the assumption that most syntactic structures can be parsed incrementally and that the set --the memory of the parser-- remains reasonably small on average. Moreover, we introduce a provably correct dynamic oracle for the new transition system, and present the first experiments in discontinuous constituency parsing using a dynamic oracle. Our parser obtains state-of-the-art results on three English and German discontinuous treebanks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.00615v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"14960\", \"99477\", \"56383\", \"92862\", \"89832\", \"97308\", \"134103\", \"143988\", \"169688\"], \"outgoing_citations\": [\"197834\", \"212972\", \"234729\", \"252425\", \"266642\", \"266758\", \"277612\", \"289951\", \"296583\", \"296712\", \"302803\", \"309633\", \"316788\", \"324579\", \"357112\"]}","task_split":"paper_retrieval"}
{"document_id":"193061","document_content":"# Symmetry-Based Disentangled Representation Learning requires Interaction with Environments\n## Categories\n- Machine Learning\n## Abstract\nFinding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. Higgins et al. recently proposed Symmetry-Based Disentangled Representation Learning, a definition based on a characterization of symmetries in the environment using group theory. We build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries. Our experiments can be reproduced in Colab and the code is available on GitHub.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.00243v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"27322\", \"87490\", \"5578\", \"44223\", \"52082\", \"68053\", \"70171\", \"105871\", \"142440\", \"84506\", \"105383\", \"116650\", \"141826\", \"180811\", \"152148\", \"171112\"], \"outgoing_citations\": [\"183257\", \"183732\", \"197666\", \"201682\", \"207094\", \"208041\", \"219180\", \"219397\", \"260136\", \"270823\"]}","task_split":"paper_retrieval"}
{"document_id":"193311","document_content":"# Learning to Weight for Text Classification\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nIn information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a score reflecting the importance of the term for the document. In tasks characterized by the presence of training data (such as text classification) it seems logical that the term weighting function should take into account the distribution (as estimated from training data) of the term across the classes of interest. Although `supervised term weighting' approaches that use this intuition have been described before, they have failed to show consistent improvements. In this article we analyse the possible reasons for this failure, and call consolidated assumptions into question. Following this criticism we propose a novel supervised term weighting approach that, instead of relying on any predefined formula, learns a term weighting function optimised on the training set of interest; we dub this approach \\emph{Learning to Weight} (LTW). The experiments that we run on several well-known benchmarks, and using different learning methods, show that our method outperforms previous term weighting approaches in text classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.12090v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"56986\", \"154182\"], \"outgoing_citations\": [\"294609\", \"317454\", \"329671\"]}","task_split":"paper_retrieval"}
{"document_id":"193328","document_content":"# Describing like humans: on diversity in image captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, the state-of-the-art models for image captioning have overtaken human performance based on the most popular metrics, such as BLEU, METEOR, ROUGE, and CIDEr. Does this mean we have solved the task of image captioning? The above metrics only measure the similarity of the generated caption to the human annotations, which reflects its accuracy. However, an image contains many concepts and multiple levels of detail, and thus there is a variety of captions that express different concepts and details that might be interesting for different humans. Therefore only evaluating accuracy is not sufficient for measuring the performance of captioning models --- the diversity of the generated captions should also be considered. In this paper, we proposed a new metric for measuring the diversity of image captions, which is derived from latent semantic analysis and kernelized to use CIDEr similarity. We conduct extensive experiments to re-evaluate recent captioning models in the context of both diversity and accuracy. We find that there is still a large gap between the model and human performance in terms of both accuracy and diversity and the models that have optimized accuracy (CIDEr) have low diversity. We also show that balancing the cross-entropy loss and CIDEr reward in reinforcement learning during training can effectively control the tradeoff between diversity and accuracy of the generated captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.12020v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"3461\", \"5789\", \"38766\", \"159645\", \"15263\", \"28498\", \"29109\", \"31502\", \"42357\", \"53992\", \"55460\", \"61394\", \"80728\", \"89629\", \"91237\", \"5781\", \"101699\", \"111062\", \"112250\", \"139263\", \"139825\", \"141978\", \"174277\", \"171902\"], \"outgoing_citations\": [\"212441\", \"227741\", \"230900\", \"238125\", \"239242\", \"249394\", \"249972\", \"270154\", \"271234\", \"277419\", \"278604\", \"279002\", \"279676\", \"281152\", \"296681\", \"311824\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"193442","document_content":"# Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image retrieval as a `downstream' task to guide the process of phrase localization. Our method, as a first step, infers the latent correspondences between regions-of-interest (RoIs) and phrases in the caption and creates a discriminative image representation using these matched RoIs. In a subsequent step, this (learned) representation is aligned with the caption. Our key contribution lies in building this `caption-conditioned' image encoding which tightly couples both the tasks and allows the weak supervision to effectively guide visual grounding. We provide an extensive empirical and qualitative analysis to investigate the different components of our proposed model and compare it with competitive baselines. For phrase localization, we report an improvement of 4.9% (absolute) over the prior state-of-the-art on the VisualGenome dataset. We also report results that are at par with the state-of-the-art on the downstream caption-to-image retrieval task on COCO and Flickr30k datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.11649v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6030\", \"50048\", \"4860\", \"14882\", \"18440\", \"29314\", \"34531\", \"54685\", \"61057\", \"61785\", \"62101\", \"63186\", \"77697\", \"85620\", \"106415\", \"114274\", \"100907\", \"101085\", \"105922\", \"117945\", \"108056\", \"133695\", \"150513\", \"185910\", \"190250\", \"278894\"], \"outgoing_citations\": [\"208242\", \"225830\", \"230386\", \"235875\", \"236696\", \"238205\", \"239400\", \"260926\", \"262814\", \"267379\", \"269128\", \"280249\", \"281407\", \"285796\", \"287380\", \"295738\", \"302943\", \"303534\", \"303603\", \"303833\", \"312643\", \"314050\", \"316584\", \"317841\", \"320003\", \"321621\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"193626","document_content":"# Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace super-resolution methods usually aim at producing visually appealing results rather than preserving distinctive features for further face identification. In this work, we propose a deep learning method for face verification on very low-resolution face images that involves identity-preserving face super-resolution. Our framework includes a super-resolution network and a feature extraction network. We train a VGG-based deep face recognition network (Parkhi et al. 2015) to be used as feature extractor. Our super-resolution network is trained to minimize the feature distance between the high resolution ground truth image and the super-resolved image, where features are extracted using our pre-trained feature extraction network. We carry out experiments on FRGC, Multi-PIE, LFW-a, and MegaFace datasets to evaluate our method in controlled and uncontrolled settings. The results show that the presented method outperforms conventional super-resolution methods in low-resolution face verification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.10974v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"74756\", \"127354\"], \"outgoing_citations\": [\"258886\", \"267612\", \"288238\", \"302157\", \"303402\"]}","task_split":"paper_retrieval"}
{"document_id":"193784","document_content":"# Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nQuestion-answering systems and voice assistants are becoming major part of client service departments of many organizations, helping them to reduce the labor costs of staff. In many such systems, there is always natural language understanding module that solves intent classification task. This task is complicated because of its case-dependency - every subject area has its own semantic kernel. The state of art approaches for intent classification are different machine learning and deep learning methods that use text vector representations as input. The basic vector representation models such as Bag of words and TF-IDF generate sparse matrixes, which are becoming very big as the amount of input data grows. Modern methods such as word2vec and FastText use neural networks to evaluate word embeddings with fixed dimension size. As we are developing a question-answering system for students and enrollees of the Perm National Research Polytechnic University, we have faced the problem of user's intent detection. The subject area of our system is very specific, that is why there is a lack of training data. This aspect makes intent classification task more challenging for using state of the art deep learning methods. In this paper, we propose an approach of the questions embeddings representation based on calculation of Shannon entropy.The goal of the approach is to produce low dimensional question vectors as neural approaches do and to outperform related methods, described above in condition of small dataset. We evaluate and compare our model with existing ones using logistic regression and dataset that contains questions asked by students and enrollees. The data is labeled into six classes. Experimental comparison of proposed approach and other models revealed that proposed model performed better in the given task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.25673\/13485\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"214025\", \"272080\", \"278226\", \"332095\"]}","task_split":"paper_retrieval"}
{"document_id":"193865","document_content":"# Computational and Robotic Models of Early Language Development: A Review\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments.   Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.10246v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"179314\"], \"outgoing_citations\": [\"240517\", \"287521\", \"301334\", \"320715\"]}","task_split":"paper_retrieval"}
{"document_id":"193877","document_content":"# Deep Shape from Polarization\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThis paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely physics-based. We see value in these principled models, and blend these physical models as priors into a neural network architecture. This proposed approach achieves results that exceed the previous state-of-the-art on a challenging dataset we introduce. This dataset consists of polarization images taken over a range of object textures, paints, and lighting conditions. We report that our proposed method achieves the lowest test error on each tested condition in our dataset, showing the value of blending data-driven and physics-driven approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.10210v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"137457\", \"1531\", \"4615\", \"29497\", \"17711\", \"22621\", \"47562\", \"51214\", \"111226\", \"154071\", \"164038\"], \"outgoing_citations\": [\"193319\", \"212719\", \"213300\", \"219267\", \"219800\", \"223643\", \"235442\", \"240584\", \"248572\", \"251860\", \"257717\", \"295571\", \"313465\", \"339203\"]}","task_split":"paper_retrieval"}
{"document_id":"194115","document_content":"# Aggregated Deep Local Features for Remote Sensing Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRemote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/rs11050493\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"50903\", \"66717\", \"133251\"], \"outgoing_citations\": [\"237248\", \"277635\", \"282928\", \"303067\", \"320578\"]}","task_split":"paper_retrieval"}
{"document_id":"194147","document_content":"# Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model\n## Categories\n- Machine Learning\n## Abstract\nFor autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal probabilistic prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.09381v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"5374\", \"73900\", \"132642\", \"6122\", \"12281\", \"52930\", \"54107\", \"60750\", \"75349\", \"165295\", \"116670\", \"118382\", \"174742\", \"175311\"], \"outgoing_citations\": [\"212451\", \"217456\", \"218432\", \"218634\", \"218635\", \"227342\", \"232095\", \"236083\", \"241801\", \"251921\", \"255129\", \"268197\", \"279793\"]}","task_split":"paper_retrieval"}
{"document_id":"194199","document_content":"# Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nThe speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. However, existing methods still attempt to reason over a representation of the environment that is flat and unnecessarily detailed, which limits scalability. An open problem is then to develop methods capable of producing the most compact environment model sufficient for accurate and efficient natural language understanding. We propose a model that leverages environment-related information encoded within instructions to identify the subset of observations and perceptual classifiers necessary to perceive a succinct, instruction-specific environment representation. The framework uses three probabilistic graphical models trained from a corpus of annotated instructions to infer salient scene semantics, perceptual classifiers, and grounded symbols. Experimental results on two robots operating in different environments demonstrate that by exploiting the content and the structure of the instructions, our method learns compact environment representations that significantly improve the efficiency of natural language symbol grounding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.09243v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Robotics\"], \"incoming_citations\": [\"48063\", \"12943\", \"35938\", \"160368\", \"165623\"], \"outgoing_citations\": [\"315839\"]}","task_split":"paper_retrieval"}
{"document_id":"194220","document_content":"# Towards automatic construction of multi-network models for heterogeneous multi-task learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- 68T99\n- I.2.6\n## Abstract\nMulti-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks. From classifying hand-written characters of different alphabets to figuring out how to play several Atari games using reinforcement learning, multi-task models have been able to widen their performance range across different tasks, although these tasks are usually of a similar nature. In this work, we attempt to widen this range even further, by including heterogeneous tasks in a single learning procedure. To do so, we firstly formally define a multi-network model, identifying the necessary components and characteristics to allow different adaptations of said model depending on the tasks it is required to fulfill. Secondly, employing the formal definition as a starting point, we develop an illustrative model example consisting of three different tasks (classification, regression and data sampling). The performance of this model implementation is then analyzed, showing its capabilities. Motivated by the results of the analysis, we enumerate a set of open challenges and future research lines over which the full potential of the proposed model definition can be exploited.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.09171v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"68T99\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"68T99\", \"I.2.6\"], \"incoming_citations\": [\"41588\"], \"outgoing_citations\": [\"190464\", \"236093\", \"239450\", \"240321\", \"245937\", \"251797\", \"260314\", \"264600\", \"266583\", \"269847\", \"272636\", \"275087\", \"277021\", \"296236\", \"302162\", \"355279\", \"361902\"]}","task_split":"paper_retrieval"}
{"document_id":"194325","document_content":"# Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMedical imaging based prostate cancer diagnosis procedure uses intra-operative transrectal ultrasound (TRUS) imaging to visualize the prostate shape and location to collect tissue samples. Correct tissue sampling from prostate requires accurate prostate segmentation in TRUS images. To achieve this, this study uses a novel residual connection based fully convolutional network. The advantage of this segmentation technique is that it requires no pre-processing of TRUS images to perform the segmentation. Thus, it offers a faster and straightforward prostate segmentation from TRUS images. Results show that the proposed technique can achieve around 86% Dice Similarity accuracy using only few TRUS datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.08814v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32869\"], \"outgoing_citations\": [\"216096\", \"278659\"]}","task_split":"paper_retrieval"}
{"document_id":"194366","document_content":"# Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nIn this paper, we address the problem of 3D object mesh reconstruction from RGB videos. Our approach combines the best of multi-view geometric and data-driven methods for 3D reconstruction by optimizing object meshes for multi-view photometric consistency while constraining mesh deformations with a shape prior. We pose this as a piecewise image alignment problem for each mesh face projection. Our approach allows us to update shape parameters from the photometric error without any depth or mask information. Moreover, we show how to avoid a degeneracy of zero photometric gradients via rasterizing from a virtual viewpoint. We demonstrate 3D object mesh reconstruction results from both synthetic and real-world videos with our photometric mesh optimization, which is unachievable with either na\\\"ive mesh generation networks or traditional pipelines of surface reconstruction without heavy manual post-processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.08642v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"34802\", \"28127\", \"14623\", \"25833\", \"28367\", \"56324\", \"60501\", \"80766\", \"81334\", \"81544\", \"93605\", \"74622\", \"81597\", \"82460\", \"92774\", \"93153\", \"105438\", \"109671\", \"116937\", \"125790\", \"130379\", \"135758\", \"137564\", \"148326\", \"153557\", \"150023\"], \"outgoing_citations\": [\"18227\", \"228126\", \"236720\", \"238344\", \"245324\", \"249836\", \"251421\", \"261184\", \"263147\", \"268221\", \"268404\", \"278180\", \"279050\", \"292758\", \"295727\", \"298730\"]}","task_split":"paper_retrieval"}
{"document_id":"194402","document_content":"# Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.08527v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2870\", \"5306\", \"26240\", \"42280\", \"54087\", \"18495\", \"172991\", \"761\", \"2507\", \"2904\", \"3093\", \"3900\", \"5179\", \"7345\", \"9493\", \"12184\", \"17613\", \"25419\", \"57572\", \"431\", \"9232\", \"12174\", \"15251\", \"12561\", \"16773\", \"22840\", \"29054\", \"29040\", \"31599\", \"36483\", \"31474\", \"41152\", \"41321\", \"44440\", \"42098\", \"40450\", \"48333\", \"50204\", \"50329\", \"51399\", \"54129\", \"55402\", \"57586\", \"59791\", \"61913\", \"221215\", \"63042\", \"67560\", \"71608\", \"53101\", \"76574\", \"76752\", \"81239\", \"82106\", \"84585\", \"87603\", \"96763\", \"84411\", \"84680\", \"84709\", \"97923\", \"106246\", \"109928\", \"111131\", \"112307\", \"129267\", \"129244\", \"133828\", \"134658\", \"137181\", \"140504\", \"168874\"], \"outgoing_citations\": [\"227856\", \"236050\", \"237222\", \"238254\", \"247495\", \"248005\", \"248572\", \"250292\", \"257846\", \"259476\", \"268703\", \"270042\", \"270134\", \"270235\", \"270795\", \"270923\", \"277948\", \"280394\", \"284716\", \"288147\", \"294926\", \"296384\", \"299047\", \"302701\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"194746","document_content":"# Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nEncoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.07377v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"82145\", \"3120\", \"8345\", \"4468\", \"17060\", \"122798\", \"8129\", \"21983\", \"27465\", \"48016\", \"55703\", \"64171\", \"68713\", \"78094\", \"247723\", \"81703\", \"118987\", \"138393\", \"166115\", \"167895\"], \"outgoing_citations\": [\"204309\", \"223954\", \"247430\", \"258039\", \"266671\", \"269836\", \"284201\", \"286876\", \"293655\", \"307902\", \"310136\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"194846","document_content":"# A Weighted Multi-Criteria Decision Making Approach for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning aims at automatically generating descriptions of an image in natural language. This is a challenging problem in the field of artificial intelligence that has recently received significant attention in the computer vision and natural language processing. Among the existing approaches, visual retrieval based methods have been proven to be highly effective. These approaches search for similar images, then build a caption for the query image based on the captions of the retrieved images. In this study, we present a method for visual retrieval based image captioning, in which we use a multi criteria decision making algorithm to effectively combine several criteria with proportional impact weights to retrieve the most relevant caption for the query image. The main idea of the proposed approach is to design a mechanism to retrieve more semantically relevant captions with the query image and then selecting the most appropriate caption by imitation of the human act based on a weighted multi-criteria decision making algorithm. Experiments conducted on MS COCO benchmark dataset have shown that proposed method provides much more effective results in compare to the state-of-the-art models by using criteria with proportional impact weights .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1904.00766v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"279676\", \"300099\", \"313196\", \"321621\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"194861","document_content":"# Audio De-identification: A New Entity Recognition Task\n## Categories\n- Computation and Language\n## Abstract\nNamed Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline's results on it.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.07037v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"8424\", \"16304\", \"48718\", \"50569\", \"123465\", \"145761\"], \"outgoing_citations\": [\"229907\", \"249909\", \"290680\"]}","task_split":"paper_retrieval"}
{"document_id":"194948","document_content":"# Generate What You Can't See - a View-dependent Image Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nIn order to operate autonomously, a robot should explore the environment and build a model of each of the surrounding objects. A common approach is to carefully scan the whole workspace. This is time-consuming. It is also often impossible to reach all the viewpoints required to acquire full knowledge about the environment. Humans can perform shape completion of occluded objects by relying on past experience. Therefore, we propose a method that generates images of an object from various viewpoints using a single input RGB image. A deep neural network is trained to imagine the object appearance from many viewpoints. We present the whole pipeline, which takes a single RGB image as input and returns a sequence of RGB and depth images of the object. The method utilizes a CNN-based object detector to extract the object from the natural scene. Then, the proposed network generates a set of RGB and depth images. We show the results both on a synthetic dataset and on real images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.06814v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"70019\"], \"outgoing_citations\": [\"255435\", \"259890\", \"283824\", \"321448\"]}","task_split":"paper_retrieval"}
{"document_id":"194991","document_content":"# Matching Entities Across Different Knowledge Graphs with Graph Embeddings\n## Categories\n- Computation and Language\n- Databases\n## Abstract\nThis paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world entity in another knowledge graph. We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data. The contributions of our work are datasets for examining this problem and strong baselines on which future work can be based.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.06607v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DB\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Databases\"], \"incoming_citations\": [\"50876\", \"140188\", \"146640\", \"161666\"], \"outgoing_citations\": [\"248193\"]}","task_split":"paper_retrieval"}
{"document_id":"195043","document_content":"# Numerical computation of formal solutions to interval linear systems of equations\n## Categories\n- Numerical Analysis\n- 65G40, 65J15, 65F10\n## Abstract\nThe work is devoted to the development of numerical methods for computing \"formal solutions\" of interval systems of linear algebraic equations. These solutions are found in Kaucher interval arithmetic, which extends and completes the classical interval arithmetic algebraically. The need to solve these problems naturally arises, for example, in inner and outer estimation of various solution sets to interval linear systems of equations. The work develops two approaches to the construction of stationary iterative methods for computing the formal solutions that are based on splitting the matrix of the system. We consider their convergence and implementation issues, compare with the other approaches to computing formal solutions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.10272v1\", \"primary_category\": \"math.NA\", \"categories\": [\"math.NA\", \"cs.NA\", \"65G40, 65J15, 65F10\"], \"primary_category_human_readable\": \"Numerical Analysis\", \"categories_human_readable\": [\"Numerical Analysis\", \"Numerical Analysis\", \"65G40, 65J15, 65F10\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"195088","document_content":"# Adaptive Variance for Changing Sparse-Reward Environments\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nRobots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.06309v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"124148\", \"115363\", \"180885\"], \"outgoing_citations\": [\"253045\", \"253658\", \"254597\", \"255807\", \"272290\", \"272785\", \"272987\", \"274445\", \"280457\", \"280870\", \"283289\", \"291025\", \"298281\", \"300847\"]}","task_split":"paper_retrieval"}
{"document_id":"195089","document_content":"# A Research Agenda: Dynamic Models to Defend Against Correlated Attacks\n## Categories\n- Machine Learning\n## Abstract\nIn this article I describe a research agenda for securing machine learning models against adversarial inputs at test time. This article does not present results but instead shares some of my thoughts about where I think that the field needs to go. Modern machine learning works very well on I.I.D. data: data for which each example is drawn {\\em independently} and for which the distribution generating each example is {\\em identical}. When these assumptions are relaxed, modern machine learning can perform very poorly. When machine learning is used in contexts where security is a concern, it is desirable to design models that perform well even when the input is designed by a malicious adversary. So far most research in this direction has focused on an adversary who violates the {\\em identical} assumption, and imposes some kind of restricted worst-case distribution shift. I argue that machine learning security researchers should also address the problem of relaxing the {\\em independence} assumption and that current strategies designed for robustness to distribution shift will not do so. I recommend {\\em dynamic models} that change each time they are run as a potential solution path to this problem, and show an example of a simple attack using correlated data that can be mitigated by a simple dynamic defense. This is not intended as a real-world security measure, but as a recommendation to explore this research direction and develop more realistic defenses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.06293v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"13165\", \"81137\", \"26553\", \"41857\", \"48766\", \"99754\", \"136662\", \"187448\", \"170768\"], \"outgoing_citations\": [\"224235\", \"228463\", \"230507\", \"244005\", \"272670\"]}","task_split":"paper_retrieval"}
{"document_id":"195126","document_content":"# Deep Residual Autoencoder for quality independent JPEG restoration\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) to remove artifacts in JPEG compressed images that is independent from the Quality Factor (QF) used. The proposed approach leverages both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. The proposed model operates in the YCbCr color space and performs JPEG artifact restoration in two phases using two different autoencoders: the first one restores the luma channel exploiting 2D convolutions; the second one, using the restored luma channel as a guide, restores the chroma channels explotining 3D convolutions. Extensive experimental results on three widely used benchmark datasets (i.e. LIVE1, BDS500, and CLASSIC-5) show that our model is able to outperform the state of the art with respect to all the evaluation metrics considered (i.e. PSNR, PSNR-B, and SSIM). This results is remarkable since the approaches in the state of the art use a different set of weights for each compression quality, while the proposed model uses the same weights for all of them, making it applicable to images in the wild where the QF used for compression is unkwnown. Furthermore, the proposed model shows a greater robustness than state-of-the-art methods when applied to compression qualities not seen during training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2020.2984387\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"130705\"], \"outgoing_citations\": [\"213816\", \"228748\", \"230874\", \"231629\", \"249153\", \"259136\", \"268506\", \"269384\", \"278546\", \"279844\", \"294611\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"195147","document_content":"# Deep Distribution Regression\n## Categories\n- Machine Learning\n- Methodology\n## Abstract\nDue to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distribution, which contains uncertainty information that might be crucial for decision making. In this article, we provide a general solution by transforming a conditional distribution estimation problem into a constrained multi-class classification problem, in which tools such as deep neural networks. We propose a novel joint binary cross-entropy loss function to accomplish this goal. We demonstrate its performance in various simulation studies comparing to state-of-the-art competing methods. Additionally, our method shows improved accuracy in a probabilistic solar energy forecasting problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.06023v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\", \"stat.ME\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Methodology\"], \"incoming_citations\": [\"46378\", \"41348\", \"44002\", \"105496\", \"110117\", \"142391\"], \"outgoing_citations\": [\"220206\", \"357554\"]}","task_split":"paper_retrieval"}
{"document_id":"195170","document_content":"# Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nOur goal in this work is to train an image captioning model that generates more dense and informative captions. We introduce \"relational captioning,\" a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in an image. Relational captioning is a framework that is advantageous in both diversity and amount of information, leading to image understanding based on relationships. Part-of speech (POS, i.e. subject-object-predicate categories) tags can be assigned to every English word. We leverage the POS as a prior to guide the correct sequence of words in a caption. To this end, we propose a multi-task triple-stream network (MTTSNet) which consists of three recurrent units for the respective POS and jointly performs POS prediction and captioning. We demonstrate more diverse and richer representations generated by the proposed model against several baselines and competing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.05942v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"35427\", \"5625\", \"93187\", \"14760\", \"17157\", \"21531\", \"24867\", \"48826\", \"56499\", \"61394\", \"82892\", \"175031\", \"95771\", \"110219\", \"111456\", \"111475\", \"117131\", \"132390\", \"133574\", \"152418\", \"168553\", \"207049\"], \"outgoing_citations\": [\"209956\", \"217310\", \"223302\", \"224759\", \"242228\", \"249972\", \"250108\", \"253934\", \"259807\", \"259865\", \"259882\", \"266825\", \"270154\", \"271234\", \"274665\", \"276530\", \"278604\", \"279914\", \"287454\", \"289627\", \"296681\", \"302621\"]}","task_split":"paper_retrieval"}
{"document_id":"195217","document_content":"# Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nRecently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the realism of the synthetic image. Different from the previous methods, this paper takes the first step to purify real images. Through the style transfer task, the distribution of outdoor real images is converted into indoor synthetic images, thereby reducing the influence of light. Therefore, this paper proposes a real-time style transfer network that preserves image content information (eg, gaze direction, pupil center position) of an input image (real image) while inferring style information (eg, image color structure, semantic features) of style image (synthetic image). In addition, the network accelerates the convergence speed of the model and adapts to multi-scale images. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. Qualitatively, it compares the proposed method with the available methods in a series of indoor and outdoor scenarios of the LPW dataset. In quantitative terms, it evaluates the purified image by training a gaze estimation model on the cross data set. The results show a significant improvement over the baseline method compared to the raw real image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.05820v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215082\", \"233793\", \"249423\", \"256929\", \"270879\", \"275954\", \"286290\", \"302917\", \"303104\", \"312452\", \"314659\"]}","task_split":"paper_retrieval"}
{"document_id":"195271","document_content":"# GASC: Genre-Aware Semantic Change for Ancient Greek\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWord meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/W19-4707\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"56152\", \"52054\", \"55324\", \"74072\", \"74650\", \"91742\", \"85704\", \"110305\", \"147843\", \"182513\", \"209985\"], \"outgoing_citations\": [\"182513\", \"209985\", \"235202\", \"243873\", \"283543\", \"291574\", \"321913\", \"329536\", \"329672\", \"356534\", \"360707\"]}","task_split":"paper_retrieval"}
{"document_id":"195767","document_content":"# GNNExplainer: Generating Explanations for Graph Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nGraph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models, and explaining predictions made by GNNs remains unsolved. Here we propose GNNExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms baselines by 17.1% on average. GNNExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.03894v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"35996\", \"44144\", \"77699\", \"153143\", \"193\", \"2094\", \"5870\", \"9648\", \"10337\", \"16062\", \"22339\", \"27470\", \"35187\", \"38311\", \"38282\", \"39834\", \"41550\", \"42591\", \"44728\", \"59556\", \"62062\", \"64027\", \"66831\", \"71193\", \"73199\", \"80211\", \"82325\", \"85438\", \"93679\", \"97503\", \"99908\", \"120789\", \"122995\", \"129924\", \"146722\", \"148583\", \"1521\", \"2097\", \"3578\", \"5470\", \"7315\", \"7396\", \"8285\", \"8568\", \"12320\", \"12282\", \"14249\", \"15225\", \"18623\", \"21965\", \"23297\", \"25781\", \"26807\", \"27319\", \"27462\", \"28866\", \"30707\", \"30916\", \"31059\", \"32104\", \"32727\", \"34086\", \"34147\", \"36145\", \"36605\", \"39603\", \"39929\", \"40268\", \"40905\", \"41789\", \"41734\", \"46926\", \"50362\", \"51946\", \"55147\", \"55157\", \"55553\", \"56223\", \"56228\", \"57492\", \"58446\", \"58484\", \"59194\", \"59879\", \"60657\", \"61059\", \"61210\", \"61869\", \"64696\", \"70398\", \"72929\", \"73530\", \"76589\", \"78815\", \"80217\", \"81304\", \"84773\", \"84643\", \"84957\", \"85095\", \"90985\", \"92174\", \"94973\", \"94952\", \"95034\", \"97322\", \"106033\", \"116651\", \"118517\", \"122107\", \"143141\", \"144943\", \"148058\", \"149594\", \"154721\", \"77655\", \"80075\", \"83315\", \"65710\", \"86320\", \"88158\", \"88103\", \"91214\", \"94864\", \"104985\", \"114344\", \"114373\", \"114950\", \"114873\", \"115054\", \"117374\", \"117690\", \"118086\", \"118653\", \"118766\", \"120374\", \"121171\", \"122497\", \"127904\", \"127844\", \"128281\", \"130994\", \"132211\", \"134907\", \"136848\", \"138048\", \"143081\", \"143408\", \"154575\", \"211845\", \"150890\", \"31888\", \"159599\", \"165236\", \"201425\"], \"outgoing_citations\": [\"181255\", \"189496\", \"207734\", \"208922\", \"217873\", \"228992\", \"239469\", \"241329\", \"242322\", \"243529\", \"243791\", \"248459\", \"252105\", \"256044\", \"262092\", \"273959\", \"296356\"]}","task_split":"paper_retrieval"}
{"document_id":"195957","document_content":"# A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.03313v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2317\", \"17043\", \"33863\", \"72064\", \"127366\", \"5510\", \"44185\", \"54755\", \"71829\", \"84169\", \"121077\", \"176840\"], \"outgoing_citations\": [\"187364\", \"220792\", \"227163\", \"230522\", \"232376\", \"254665\", \"265219\", \"270629\", \"271531\", \"271680\", \"271944\", \"272225\", \"272506\", \"296268\", \"311067\"]}","task_split":"paper_retrieval"}
{"document_id":"195963","document_content":"# You Only Recognize Once: Towards Fast Video Text Spotting\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideo text spotting is still an important research topic due to its various real-applications. Previous approaches usually fall into the four-staged pipeline: text detection in individual images, framewisely recognizing localized text regions, tracking text streams and generating final results with complicated post-processing skills, which might suffer from the huge computational cost as well as the interferences of low-quality text. In this paper, we propose a fast and robust video text spotting framework by only recognizing the localized text one-time instead of frame-wisely recognition. Specifically, we first obtain text regions in videos with a well-designed spatial-temporal detector. Then we concentrate on developing a novel text recommender for selecting the highest-quality text from text streams and only recognizing the selected ones. Here, the recommender assembles text tracking, quality scoring and recognition into an end-to-end trainable module, which not only avoids the interferences from low-quality text but also dramatically speeds up the video text spotting process. In addition, we collect a larger scale video text dataset (LSVTD) for promoting the video text spotting community, which contains 100 text videos from 22 different real-life scenarios. Extensive experiments on two public benchmarks show that our method greatly speeds up the recognition process averagely by 71 times compared with the frame-wise manner, and also achieves the remarkable state-of-the-art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.03299v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"236\", \"4188\", \"10580\", \"25406\", \"33439\", \"59917\", \"107518\", \"123348\", \"85935\", \"122765\"], \"outgoing_citations\": [\"162242\", \"232706\", \"234390\", \"237616\", \"239002\", \"239535\", \"245899\", \"250738\", \"256711\", \"257954\", \"261365\", \"262570\", \"269149\", \"270264\", \"270668\", \"271112\", \"272390\", \"272481\", \"296685\", \"299461\", \"328595\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"196047","document_content":"# Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples\n## Categories\n- Machine Learning\n## Abstract\nFew-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models' ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.03096v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"29617\", \"37430\", \"49008\", \"83941\", \"105371\", \"2822\", \"3387\", \"18695\", \"20702\", \"24689\", \"32725\", \"32972\", \"38239\", \"38319\", \"38393\", \"42598\", \"43312\", \"50845\", \"72850\", \"77056\", \"87195\", \"118036\", \"130651\", \"131702\", \"148364\", \"184092\", \"950\", \"948\", \"1349\", \"1776\", \"5055\", \"6628\", \"7674\", \"7703\", \"13378\", \"13600\", \"15911\", \"16345\", \"16597\", \"17196\", \"18054\", \"19866\", \"20392\", \"19527\", \"22166\", \"767\", \"22693\", \"23207\", \"24085\", \"24842\", \"26468\", \"30588\", \"30872\", \"31658\", \"33187\", \"35313\", \"35162\", \"37950\", \"40091\", \"44480\", \"153684\", \"45914\", \"47469\", \"47624\", \"48018\", \"49630\", \"49507\", \"54756\", \"55131\", \"55946\", \"56596\", \"56755\", \"57430\", \"57948\", \"58277\", \"58895\", \"60626\", \"60701\", \"62728\", \"65914\", \"66024\", \"67535\", \"69358\", \"69491\", \"70924\", \"72181\", \"72558\", \"72579\", \"73135\", \"73254\", \"75270\", \"76452\", \"76555\", \"77291\", \"77916\", \"79802\", \"94201\", \"94342\", \"95935\", \"96214\", \"96590\", \"97351\", \"98921\", \"107285\", \"108079\", \"111560\", \"112069\", \"117183\", \"117816\", \"117992\", \"137669\", \"113383\", \"182011\", \"80589\", \"81329\", \"83051\", \"85622\", \"90042\", \"90056\", \"92287\", \"92827\", \"95608\", \"96589\", \"97251\", \"101376\", \"104561\", \"109081\", \"110289\", \"111046\", \"111468\", \"111389\", \"111473\", \"112851\", \"112992\", \"114014\", \"114084\", \"114668\", \"115254\", \"117366\", \"118021\", \"118766\", \"137073\", \"119017\", \"121352\", \"679\", \"128120\", \"129987\", \"130677\", \"129574\", \"134943\", \"135783\", \"138141\", \"138216\", \"139591\", \"139738\", \"139804\", \"141047\", \"142796\", \"144444\", \"145837\", \"146098\", \"151037\", \"152322\", \"152543\", \"154496\", \"157147\", \"161188\", \"162941\", \"164934\", \"165886\", \"168361\", \"180867\", \"183547\", \"157209\", \"159036\", \"160981\", \"163588\", \"163906\", \"164217\", \"167406\", \"166877\", \"167551\", \"180147\", \"183881\", \"191258\", \"197936\", \"231222\"], \"outgoing_citations\": [\"168361\", \"213370\", \"224464\", \"228540\", \"230792\", \"231222\", \"234262\", \"240315\", \"244924\", \"247038\", \"250796\", \"261577\", \"269126\", \"290800\", \"297625\", \"337853\", \"343358\"]}","task_split":"paper_retrieval"}
{"document_id":"196191","document_content":"# A Character-Level Approach to the Text Normalization Problem Based on a New Causal Encoder\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nText normalization is a ubiquitous process that appears as the first step of many Natural Language Processing problems. However, previous Deep Learning approaches have suffered from so-called silly errors, which are undetectable on unsupervised frameworks, making those models unsuitable for deployment. In this work, we make use of an attention-based encoder-decoder architecture that overcomes these undetectable errors by using a fine-grained character-level approach rather than a word-level one. Furthermore, our new general-purpose encoder based on causal convolutions, called Causal Feature Extractor (CFE), is introduced and compared to other common encoders. The experimental results show the feasibility of this encoder, which leverages the attention mechanisms the most and obtains better results in terms of accuracy, number of parameters and convergence time. While our method results in a slightly worse initial accuracy (92.74%), errors can be automatically detected and, thus, more readily solved, obtaining a more robust model for deployment. Furthermore, there is still plenty of room for future improvements that will push even further these advantages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.02642v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"281496\", \"282931\", \"296251\"]}","task_split":"paper_retrieval"}
{"document_id":"196257","document_content":"# KBQA: Learning Question Answering over QA Corpora and Knowledge Bases\n## Categories\n- Computation and Language\n## Abstract\nQuestion answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of \"canned\" questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates, over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city's population, we learn templates such as What's the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.14778\/3055540.3055549\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"84384\", \"1053\", \"29573\", \"32110\", \"38258\", \"45539\", \"854\", \"7774\", \"9487\", \"34997\", \"44106\", \"48639\", \"49295\", \"56037\", \"24814\", \"58241\", \"63653\", \"76915\", \"98055\", \"99699\", \"188713\", \"95011\", \"91061\", \"109967\", \"25047\", \"92667\", \"93489\", \"102738\", \"137898\", \"143469\", \"157274\", \"161662\", \"177173\", \"216454\", \"155968\", \"161045\", \"168182\", \"169683\", \"179672\", \"185278\", \"170709\", \"194274\", \"211723\", \"213780\", \"223728\", \"230022\"], \"outgoing_citations\": [\"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"196267","document_content":"# Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care\n## Categories\n- Artificial Intelligence\n- Applications\n## Abstract\nIn this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018; Bos, Azoulay, & Martin-Loeches, 2019; Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.02345v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.AP\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Applications\"], \"incoming_citations\": [\"95628\", \"183473\"], \"outgoing_citations\": [\"199665\", \"202934\", \"225954\", \"230082\", \"230891\", \"290019\", \"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"196295","document_content":"# Efficient Deep Neural Network for Photo-realistic Image Super-Resolution\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. In addition, our proposed model adopts group convolution and recursive schemes in order to achieve extreme efficiency. We further improve the perceptual quality of the output by employing the adversarial learning paradigm and a multi-scale discriminator approach. The performance of our method is investigated through extensive internal experiments and benchmarks using various datasets. Our results show that our models outperform the recent methods with similar complexity, for both traditional pixel-based and perception-based tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.02240v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26629\", \"112154\", \"112716\", \"86183\", \"113326\"], \"outgoing_citations\": [\"28002\", \"33893\", \"60633\", \"83477\", \"119873\", \"133888\", \"158170\", \"171325\", \"211988\", \"215599\", \"216269\", \"217152\", \"219505\", \"222716\", \"225426\", \"226061\", \"228870\", \"236318\", \"238021\", \"241016\", \"248731\", \"250250\", \"254116\", \"255790\", \"259136\", \"269027\", \"277357\", \"277722\", \"284563\", \"287403\", \"303360\", \"303384\", \"305006\", \"312178\"]}","task_split":"paper_retrieval"}
{"document_id":"196377","document_content":"# Using Natural Language for Reward Shaping in Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRecent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal. However, designing appropriate shaping rewards is known to be difficult as well as time-consuming. In this work, we address this problem by using natural language instructions to perform reward shaping. We propose the LanguagE-Action Reward Network (LEARN), a framework that maps free-form natural language instructions to intermediate rewards based on actions taken by the agent. These intermediate language-based rewards can seamlessly be integrated into any standard reinforcement learning algorithm. We experiment with Montezuma's Revenge from the Atari Learning Environment, a popular benchmark in RL. Our experiments on a diverse set of 15 tasks demonstrate that, for the same number of interactions with the environment, language-based rewards lead to successful completion of the task 60% more often on average, compared to learning without language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.02020v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"1269\", \"4639\", \"14182\", \"17680\", \"19456\", \"26076\", \"34439\", \"44473\", \"64051\", \"67594\", \"69518\", \"72877\", \"97596\", \"105761\", \"124867\", \"74639\", \"91773\", \"96491\", \"103709\", \"108770\", \"110974\", \"115960\", \"127494\", \"128388\", \"131013\", \"140901\", \"157664\", \"157435\", \"162602\", \"163457\", \"181620\"], \"outgoing_citations\": [\"208773\", \"229145\", \"251798\", \"260018\", \"264940\", \"266671\", \"267779\", \"268335\", \"268584\", \"291102\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"196480","document_content":"# Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nMotion Planning, as a fundamental technology of automatic navigation for the autonomous vehicle, is still an open challenging issue in the real-life traffic situation and is mostly applied by the model-based approaches. However, due to the complexity of the traffic situations and the uncertainty of the edge cases, it is hard to devise a general motion planning system for the autonomous vehicle. In this paper, we proposed a motion planning model based on deep learning (named as spatiotemporal LSTM network), which is able to generate a real-time reflection based on spatiotemporal information extraction. To be specific, the model based on spatiotemporal LSTM network has three main structure. Firstly, the Convolutional Long-short Term Memory (Conv-LSTM) is used to extract hidden features through sequential image data. Then, the 3D Convolutional Neural Network(3D-CNN) is applied to extract the spatiotemporal information from the multi-frame feature information. Finally, the fully connected neural networks are used to construct a control model for autonomous vehicle steering angle. The experiments demonstrated that the proposed method can generate a robust and accurate visual motion planning results for the autonomous vehicle.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.01712v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"63083\", \"166790\"], \"outgoing_citations\": [\"270783\"]}","task_split":"paper_retrieval"}
{"document_id":"196521","document_content":"# Adversarial Examples on Graph Data: Deep Insights into Attack and Defense\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nGraph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.01610v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"16829\", \"40267\", \"47224\", \"154562\", \"117094\", \"52113\", \"2760\", \"12336\", \"14939\", \"18640\", \"40735\", \"41139\", \"42144\", \"48505\", \"74969\", \"80990\", \"82775\", \"84075\", \"88565\", \"153811\", \"91033\", \"91700\", \"204706\", \"882\", \"3616\", \"9954\", \"10842\", \"11491\", \"12824\", \"13470\", \"24144\", \"28343\", \"30916\", \"33693\", \"37537\", \"41353\", \"44560\", \"52486\", \"55629\", \"61367\", \"63937\", \"66867\", \"70251\", \"75088\", \"86002\", \"91611\", \"96703\", \"97696\", \"103074\", \"104269\", \"111023\", \"126927\", \"142789\", \"81246\", \"90444\", \"91654\", \"96709\", \"98256\", \"100999\", \"103053\", \"118237\", \"118349\", \"118653\", \"120084\", \"123312\", \"123835\", \"125407\", \"136764\", \"137133\", \"137543\", \"139118\", \"143325\", \"170916\", \"156910\", \"158776\", \"160564\", \"166889\", \"188800\"], \"outgoing_citations\": [\"71722\", \"213013\", \"229027\", \"237370\", \"239137\", \"251284\", \"279797\", \"283734\", \"311030\"]}","task_split":"paper_retrieval"}
{"document_id":"196540","document_content":"# Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Multimedia\n## Abstract\nWe propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation. Such compact hash codes enable the complete elimination of real-valued feature storage and allow for significant reduction of the computation complexity and storage cost of large-scale image retrieval applications. Specifically, we learn a neural network-based model, which transforms the input representation into a binary representation. We formalize the training objective of the network in an intuitive and effective way, considering each training sample as a query and aiming to obtain the same retrieval results using the produced hash codes as those obtained with the original features. This training formulation directly optimizes the hashing model for the target usage of the hash codes it produces. We further explore the addition of a decoder trained to obtain an approximated reconstruction of the original features. At test time, we retrieved the most promising database samples with an efficient graph-based search procedure using only our hash codes and perform re-ranking using the reconstructed features, thus without needing to access the original features at all. Experiments conducted on multiple publicly available large-scale datasets show that our method consistently outperforms all compared state-of-the-art unsupervised hashing methods and that the reconstruction procedure can effectively boost the search accuracy with a minimal constant additional cost.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.01545v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [\"159926\"], \"outgoing_citations\": [\"234121\", \"258390\", \"265613\", \"271159\", \"277432\", \"284208\", \"285231\", \"291400\", \"295576\", \"318535\", \"319454\"]}","task_split":"paper_retrieval"}
{"document_id":"196671","document_content":"# NoRML: No-Reward Meta Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nEfficiently adapting to new environments and changes in dynamics is critical for agents to successfully operate in the real world. Reinforcement learning (RL) based approaches typically rely on external reward feedback for adaptation. However, in many scenarios this reward signal might not be readily available for the target task, or the difference between the environments can be implicit and only observable from the dynamics. To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML). NoRML extends Model Agnostic Meta Learning (MAML) for RL and uses observable dynamics of the environment instead of an explicit reward function in MAML's finetune step. Our method has a more expressive update step than MAML, while maintaining MAML's gradient based foundation. Additionally, in order to allow more targeted exploration, we implement an extension to MAML that effectively disconnects the meta-policy parameters from the fine-tuned policies' parameters. We first study our method on a number of synthetic control problems and then validate our method on common benchmark environments, showing that NoRML outperforms MAML when the dynamics change between tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.01063v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"78473\", \"131702\", \"50304\", \"71397\", \"74675\", \"88552\", \"94181\", \"106120\", \"107437\", \"117531\", \"142989\", \"138908\", \"141204\", \"155443\", \"164478\", \"164936\", \"157061\"], \"outgoing_citations\": [\"217919\", \"234025\", \"237229\", \"238343\", \"240177\", \"241496\", \"242281\", \"253045\", \"259762\", \"261577\", \"262591\", \"274445\", \"280202\", \"280570\", \"280870\", \"283289\", \"293995\", \"306153\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"196898","document_content":"# A Behavioral Approach to Visual Navigation with Graph Localization Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nInspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment. We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.00445v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"6637\", \"16494\", \"31846\", \"36507\", \"74570\", \"75621\", \"186811\", \"6704\", \"15383\", \"9192\", \"25684\", \"32831\", \"36974\", \"39279\", \"50415\", \"48479\", \"57729\", \"65095\", \"68278\", \"78923\", \"81574\", \"82597\", \"92592\", \"135780\", \"135815\", \"85766\", \"109880\", \"110466\", \"123031\", \"127984\", \"136916\", \"142025\", \"157491\", \"164699\", \"186151\", \"159881\", \"167688\", \"196148\", \"196226\"], \"outgoing_citations\": [\"212935\", \"214195\", \"219658\", \"225224\", \"239320\", \"240323\", \"245731\", \"246823\", \"247749\", \"272930\", \"274102\", \"274783\", \"280681\", \"281106\", \"281110\", \"303714\"]}","task_split":"paper_retrieval"}
{"document_id":"196909","document_content":"# Learning To Follow Directions in Street View\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nNavigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train\/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.00401v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39368\", \"80120\", \"17198\", \"18840\", \"27522\", \"45665\", \"77756\", \"95168\", \"163326\", \"160608\", \"86813\", \"131013\", \"132720\", \"137412\", \"139112\", \"147685\", \"162505\", \"159154\", \"172416\", \"180851\", \"183378\"], \"outgoing_citations\": [\"180851\", \"208042\", \"219658\", \"222620\", \"224223\", \"225266\", \"228916\", \"231925\", \"237119\", \"237386\", \"239633\", \"241006\", \"241625\", \"242033\", \"243090\", \"243235\", \"244457\", \"245731\", \"247418\", \"247749\", \"248742\", \"248885\", \"249906\", \"255134\", \"255575\", \"255613\", \"259007\", \"262593\", \"263104\", \"263270\", \"263650\", \"269640\", \"278195\", \"280296\", \"280681\", \"284565\", \"289265\", \"291568\", \"292386\", \"292920\", \"293071\", \"302621\", \"303837\", \"311272\", \"313143\", \"323775\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"196994","document_content":"# Improving Grounded Natural Language Understanding through Human-Robot Dialog\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nNatural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically---continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICRA.2019.8794287\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"8070\", \"15289\", \"34439\", \"36974\", \"113500\", \"59690\", \"62460\", \"74858\", \"78377\", \"86652\", \"95145\", \"104479\", \"135461\", \"138944\", \"153162\", \"188484\", \"104483\", \"165623\", \"176603\", \"177549\", \"178034\", \"188341\", \"215220\"], \"outgoing_citations\": [\"201788\", \"209784\", \"215818\", \"231925\", \"238287\", \"247849\", \"248742\", \"248885\", \"249906\", \"261871\", \"263947\", \"267790\", \"267881\", \"271085\", \"271625\", \"271868\", \"272294\", \"283665\", \"288543\", \"288978\", \"290632\", \"290891\", \"295041\", \"300505\", \"302731\", \"303020\", \"303452\", \"307630\", \"308446\", \"311272\", \"313934\", \"314762\", \"319159\", \"323775\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"197042","document_content":"# Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers\n## Categories\n- Machine Learning\n## Abstract\nThe predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of supervised learning. However, when the models are constructed by the means of empirical risk minimization (ERM), surrogate functions such as the logistic loss or hinge loss are optimized instead. In this work, we show that in the case of linear predictors, the expected error and the expected ranking loss can be effectively approximated by smooth functions whose closed form expressions and those of their first (and second) order derivatives depend on the first and second moments of the data distribution, which can be precomputed. Hence, the complexity of an optimization algorithm applied to these functions does not depend on the size of the training data. These approximation functions are derived under the assumption that the output of the linear classifier for a given data set has an approximately normal distribution. We argue that this assumption is significantly weaker than the Gaussian assumption on the data itself and we support this claim by demonstrating that our new approximation is quite accurate on data sets that are not necessarily Gaussian. We present computational results that show that our proposed approximations and related optimization algorithms can produce linear classifiers with similar or better test accuracy or AUC, than those obtained using state-of-the-art approaches, in a fraction of the time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1903.00359v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"197145","document_content":"# High dynamic range image forensics using cnn\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHigh dynamic range (HDR) imaging has recently drawn much attention in multimedia community. In this paper, we proposed a HDR image forensics method based on convolutional neural network (CNN).To our best knowledge, this is the first time to apply deep learning method on HDR image forensics. The proposed algorithm uses CNN to distinguish HDR images generated by multiple low dynamic range (LDR) images from that expanded by single LDR image using inverse tone mapping (iTM). To do this, we learn the change of statistical characteristics extracted by the proposed CNN architectures and classify two kinds of HDR images. Comparision results with some traditional statistical characteristics shows efficiency of the proposed method in HDR image source identification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.10938v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"139663\"], \"outgoing_citations\": [\"244729\"]}","task_split":"paper_retrieval"}
{"document_id":"197154","document_content":"# BERT for Joint Intent Classification and Slot Filling\n## Categories\n- Computation and Language\n## Abstract\nIntent classification and slot filling are two essential tasks for natural language understanding. They often suffer from small-scale human-labeled training data, resulting in poor generalization capability, especially for rare words. Recently a new language representation model, BERT (Bidirectional Encoder Representations from Transformers), facilitates pre-training deep bidirectional representations on large-scale unlabeled corpora, and has created state-of-the-art models for a wide variety of natural language processing tasks after simple fine-tuning. However, there has not been much effort on exploring BERT for natural language understanding. In this work, we propose a joint intent classification and slot filling model based on BERT. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on several public benchmark datasets, compared to the attention-based recurrent neural network models and slot-gated models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.10909v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23909\", \"74469\", \"24344\", \"24608\", \"24691\", \"27450\", \"38538\", \"45998\", \"55305\", \"55947\", \"59368\", \"70627\", \"74570\", \"2793\", \"3107\", \"4155\", \"5301\", \"10513\", \"12049\", \"12125\", \"14126\", \"16007\", \"16880\", \"19392\", \"20273\", \"21525\", \"21740\", \"22655\", \"27041\", \"27478\", \"28828\", \"29403\", \"29819\", \"37332\", \"39094\", \"42441\", \"44438\", \"44947\", \"46404\", \"47388\", \"49366\", \"52620\", \"53443\", \"54462\", \"56006\", \"58217\", \"62790\", \"63230\", \"63968\", \"65237\", \"67742\", \"69680\", \"69895\", \"73442\", \"77499\", \"79142\", \"81520\", \"83777\", \"86043\", \"88786\", \"89247\", \"91161\", \"91635\", \"95751\", \"95759\", \"98109\", \"93604\", \"99215\", \"104803\", \"125024\", \"129606\", \"164953\", \"83392\", \"84441\", \"84921\", \"86350\", \"87723\", \"87925\", \"89849\", \"92551\", \"95239\", \"96274\", \"97253\", \"97664\", \"98073\", \"100121\", \"100528\", \"100483\", \"101933\", \"103458\", \"105837\", \"107503\", \"108214\", \"124328\", \"127901\", \"129871\", \"130105\", \"132711\", \"128934\", \"143978\", \"145190\", \"157993\", \"158970\", \"166702\", \"169144\", \"168563\", \"189770\", \"194488\"], \"outgoing_citations\": [\"230543\", \"268518\", \"285317\", \"289624\"]}","task_split":"paper_retrieval"}
{"document_id":"197165","document_content":"# Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nFace recognition system is one of the esteemed research areas in pattern recognition and computer vision as long as its major challenges. A few challenges in recognizing faces are blur, illumination, and varied expressions. Blur is natural while taking photographs using cameras, mobile phones, etc. Blur can be uniform and non-uniform. Usually non-uniform blur happens in images taken using handheld image devices. Distinguishing or handling a blurred image in a face recognition system is generally tough. Under varying lighting conditions, it is challenging to identify the person correctly. Diversified facial expressions such as happiness, sad, surprise, fear, anger changes or deforms the faces from normal images. Identifying faces with facial expressions is also a challenging task, due to the deformation caused by the facial expressions. To solve these issues, a pre-processing step was carried out after which Blur and Illumination-Robust Face recognition (BIRFR) algorithm was performed. The test image and training images with facial expression are transformed to neutral face using Facial expression removal (FER) peration. Every training image is transformed based on the optimal Transformation Spread Function (TSF), and illumination coefficients. Local Binary Pattern (LBP) features extracted from test image and transformed training image is used for classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.10885v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"197295","document_content":"# Multilingual Neural Machine Translation with Knowledge Distillation\n## Categories\n- Computation and Language\n## Abstract\nMultilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diversity and model capacity limitations. In this paper, we propose a distillation-based approach to boost the accuracy of multilingual machine translation. Specifically, individual models are first trained and regarded as teachers, and then the multilingual model is trained to fit the training data and match the outputs of individual models simultaneously through knowledge distillation. Experiments on IWSLT, WMT and Ted talk translation datasets demonstrate the effectiveness of our method. Particularly, we show that one model is enough to handle multiple languages (up to 44 languages in our experiment), with comparable or even better accuracy than individual models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.10461v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11597\", \"2603\", \"5390\", \"16002\", \"16612\", \"25329\", \"26981\", \"38855\", \"46629\", \"55233\", \"103356\", \"106423\", \"131467\", \"4777\", \"8141\", \"10734\", \"11790\", \"16207\", \"19914\", \"19880\", \"21033\", \"21201\", \"24303\", \"24644\", \"33360\", \"34325\", \"38687\", \"39954\", \"44551\", \"46744\", \"47003\", \"48458\", \"48536\", \"55514\", \"58866\", \"58872\", \"68902\", \"71092\", \"78250\", \"80727\", \"83275\", \"95234\", \"100542\", \"100560\", \"119873\", \"79805\", \"81702\", \"163696\", \"64823\", \"90711\", \"90967\", \"93229\", \"88303\", \"95012\", \"96097\", \"96153\", \"96159\", \"100297\", \"100500\", \"107024\", \"106960\", \"107405\", \"121034\", \"121907\", \"124230\", \"126296\", \"127086\", \"127829\", \"128706\", \"128868\", \"129679\", \"130968\", \"132321\", \"123740\", \"129799\", \"139514\", \"143564\", \"148542\", \"154843\", \"157171\", \"157862\", \"149588\", \"157631\", \"151022\", \"170278\", \"170295\", \"171034\", \"171399\", \"176571\", \"176642\", \"182740\", \"186464\", \"187452\", \"189623\", \"190164\", \"191884\", \"206314\"], \"outgoing_citations\": [\"204947\", \"206314\", \"219545\", \"220351\", \"221514\", \"228234\", \"228310\", \"229517\", \"232071\", \"232302\", \"234632\", \"235275\", \"236228\", \"238926\", \"242125\", \"264787\", \"272109\", \"274601\", \"280447\", \"281093\", \"289589\", \"300548\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"197534","document_content":"# Semantic Hilbert Space for Text Representation Learning\n## Categories\n- Computation and Language\n## Abstract\nCapturing the meaning of sentences has long been a challenging task. Current models tend to apply linear combinations of word features to conduct semantic composition for bigger-granularity units e.g. phrases, sentences, and documents. However, the semantic linearity does not always hold in human language. For instance, the meaning of the phrase `ivory tower' can not be deduced by linearly combining the meanings of `ivory' and `tower'. To address this issue, we propose a new framework that models different levels of semantic units (e.g. sememe, word, sentence, and semantic abstraction) on a single \\textit{Semantic Hilbert Space}, which naturally admits a non-linear semantic composition by means of a complex-valued vector word representation. An end-to-end neural network~\\footnote{https:\/\/github.com\/wabyking\/qnn} is proposed to implement the framework in the text classification task, and evaluation results on six benchmarking text classification datasets demonstrate the effectiveness, robustness and self-explanation power of the proposed model. Furthermore, intuitive case studies are conducted to help end users to understand how the framework works.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3308558.3313516\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"75896\", \"62119\", \"113322\", \"101732\", \"107604\", \"149374\"], \"outgoing_citations\": [\"220043\", \"272080\", \"298543\", \"315078\", \"348819\", \"358186\"]}","task_split":"paper_retrieval"}
{"document_id":"197564","document_content":"# Syntactic Recurrent Neural Network for Authorship Attribution\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWriting style is a combination of consistent decisions at different levels of language production including lexical, syntactic, and structural associated to a specific author (or author groups). While lexical-based models have been widely explored in style-based text classification, relying on content makes the model less scalable when dealing with heterogeneous data comprised of various topics. On the other hand, syntactic models which are content-independent, are more robust against topic variance. In this paper, we introduce a syntactic recurrent neural network to encode the syntactic patterns of a document in a hierarchical structure. The model first learns the syntactic representation of sentences from the sequence of part-of-speech tags. For this purpose, we exploit both convolutional filters and long short-term memories to investigate the short-term and long-term dependencies of part-of-speech tags in the sentences. Subsequently, the syntactic representations of sentences are aggregated into document representation using recurrent neural networks. Our experimental results on PAN 2012 dataset for authorship attribution task shows that syntactic recurrent neural network outperforms the lexical model with the identical architecture by approximately 14% in terms of accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.09723v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"20709\", \"94798\", \"125726\"], \"outgoing_citations\": [\"267552\", \"274586\", \"286525\", \"298159\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"197577","document_content":"# Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions\n## Categories\n- Robotics\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Computation and Language\n## Abstract\nWe propose a developmental approach that allows a robot to interpret and describe the actions of human agents by reusing previous experience. The robot first learns the association between words and object affordances by manipulating the objects in its environment. It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment. It finally fuses the information from these two models to interpret and describe human actions in light of its own experience. In our experiments, we show that the model can be used flexibly to do inference on different aspects of the scene. We can predict the effects of an action on the basis of object properties. We can revise the belief that a certain action occurred, given the observed effects of the human action. In an early action recognition fashion, we can anticipate the effects when the action has only been partially observed. By estimating the probability of words given the evidence and feeding them into a pre-defined grammar, we can generate relevant descriptions of the scene. We believe that this is a step towards providing robots with the fundamental skills to engage in social collaboration with humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TCDS.2018.2882140\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.CV\", \"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"36633\"], \"outgoing_citations\": [\"249215\", \"249413\", \"264533\", \"353848\"]}","task_split":"paper_retrieval"}
{"document_id":"197647","document_content":"# Lost in Machine Translation: A Method to Reduce Meaning Loss\n## Categories\n- Computation and Language\n## Abstract\nA desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, state-of-the-art systems often fail in this regard, particularly in cases where the source and target languages partition the \"meaning space\" in different ways. For instance, \"I cut my finger.\" and \"I cut my finger off.\" describe different states of the world but are translated to French (by both Fairseq and Google Translate) as \"Je me suis coupe le doigt.\", which is ambiguous as to whether the finger is detached. More generally, translation systems are typically many-to-one (non-injective) functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pre-trained neural sequence-to-sequence model. This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality (measured by BLEU score).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.09514v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22651\", \"48306\", \"131868\", \"131507\", \"128086\", \"179210\"], \"outgoing_citations\": [\"235544\", \"276436\", \"279543\", \"280973\", \"281076\", \"295368\", \"303833\"]}","task_split":"paper_retrieval"}
{"document_id":"197866","document_content":"# Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nOver the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation. One of the primary challenges in this regard is structuring a graph that encodes and encompasses the features of textual data for the effective machine learning algorithm. Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications. However, most of the traditional text mining methods perform somewhat poor in terms of employing such relations. In this paper, we propose a sentence-level graph-based text representation which includes stop words to consider semantic and term relations. Then, we employ a representation learning approach on the combined graphs of sentences to extract the latent and continuous features of the documents. Eventually, the learned features of the documents are fed into a deep neural network for the sentiment classification task. The experimental results demonstrate that the proposed method substantially outperforms the related sentiment analysis approaches based on several benchmark datasets. Furthermore, our method can be generalized on different datasets without any dependency on pre-trained word embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.eswa.2019.113090\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"72229\", \"122957\", \"115503\"], \"outgoing_citations\": [\"153036\", \"265819\", \"271993\", \"278226\", \"285806\"]}","task_split":"paper_retrieval"}
{"document_id":"197869","document_content":"# Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition in real life situations like low illumination condition is still an open challenge in biometric security. It is well established that the state-of-the-art methods in face recognition provide low accuracy in the case of poor illumination. In this work, we propose an algorithm for a more robust illumination invariant face recognition using a multi-modal approach. We propose a new dataset consisting of aligned faces of thermal and visual images of a hundred subjects. We then apply face detection on thermal images using the biggest blob extraction method and apply them for fusing images of different modalities for the purpose of face recognition. An algorithm is proposed to implement fusion of thermal and visual images. We reason for why relying on only one modality can give erroneous results. We use a lighter and faster CNN model called MobileNet for the purpose of face recognition with faster inferencing and to be able to be use it in real time biometric systems. We test our proposed method on our own created dataset to show that real-time face recognition on fused images shows far better results than using visual or thermal images separately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.08802v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"198011","document_content":"# Learning to Learn Semantic Parsers from Natural Language Supervision\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nAs humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users' corrections such as \"no, what I really meant was before his job, not after\", by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.08373v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3727\", \"24345\", \"40308\", \"69518\", \"126793\", \"134017\", \"162157\", \"170083\"], \"outgoing_citations\": [\"260107\", \"266702\", \"267790\", \"290632\", \"302516\", \"308610\", \"310370\", \"334986\", \"345732\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"198022","document_content":"# FAVAE: Sequence Disentanglement using Information Bottleneck Principle\n## Categories\n- Machine Learning\n## Abstract\nWe propose the factorized action variational autoencoder (FAVAE), a state-of-the-art generative model for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data. We focused on the disentangled representation of sequential data since there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock market. Sequential data are characterized by dynamic and static factors: dynamic factors are time dependent, and static factors are independent of time. Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors. However, these models cannot disentangle representations between dynamic factors, such as disentangling \"picking up\" and \"throwing\" in robotic tasks. FAVAE can disentangle multiple dynamic factors. Since it does not require modeling priors, it can disentangle \"between\" dynamic factors. We conducted experiments to show that FAVAE can extract disentangled dynamic factors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.08341v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"194152\"], \"outgoing_citations\": [\"236093\", \"239658\", \"241933\", \"242239\", \"255129\", \"272919\", \"278987\", \"281176\", \"281227\", \"290184\", \"290396\", \"291936\", \"311643\", \"317401\", \"321177\"]}","task_split":"paper_retrieval"}
{"document_id":"198266","document_content":"# Resolving Conflicts in Clinical Guidelines using Argumentation\n## Categories\n- Artificial Intelligence\n## Abstract\nAutomatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.07526v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"30542\", \"171695\"], \"outgoing_citations\": [\"282927\"]}","task_split":"paper_retrieval"}
{"document_id":"198292","document_content":"# Predicting customer's gender and age depending on mobile phone data\n## Categories\n- Machine Learning\n- Computers and Society\n## Abstract\nIn the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1186\/s40537-019-0180-9\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computers and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"198332","document_content":"# Extending the Anonymity of Zcash\n## Categories\n- Cryptography and Security\n## Abstract\nAlthough Bitcoin in its original whitepaper stated that it offers anonymous transactions, de-anonymization techniques have found otherwise. Therefore, alternative cryptocurrencies, like Dash, Monero, and Zcash, were developed to provide better privacy. As Edward Snowden stated, \"Zcash's privacy tech makes it the most interesting Bitcoin alternative (...) because the privacy properties of it are truly unique\". Zcash's privacy is based on peer-reviewed cryptographic constructions, hence it is considered to provide the foundations for the best anonymity. However, even Zcash makes some privacy concessions. It does not protect users' privacy in the presence of a global adversary who is able to observe the whole network, and hence correlate the parties exchanging money, by using their network addresses. The recent empirical analysis of Zcash shows, that users often choose naive ways while performing the protocol operations, not realizing that it degrades their anonymity. In this talk, we will discuss an extension of Zcash using mix networks to enhance the privacy guarantees of users that choose to remain anonymous by tackling two major security challenges: one at the application layer of the scheme and one at its network layer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.07337v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"232777\", \"272640\"]}","task_split":"paper_retrieval"}
{"document_id":"198728","document_content":"# Making Convex Loss Functions Robust to Outliers using $e$-Exponentiated Transformation\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we propose a novel {\\em $e$-exponentiated} transformation, $0 \\le e<1$, for loss functions. When the transformation is applied to a convex loss function, the transformed loss function become more robust to outliers. Using a novel generalization error bound, we have theoretically shown that the transformed loss function has a tighter bound for datasets corrupted by outliers. Our empirical observation shows that the accuracy obtained using the transformed loss function can be significantly better than the same obtained using the original loss function and comparable to that obtained by some other state of the art methods in the presence of label noise.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.06127v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"228948\", \"312135\", \"320027\", \"332456\"]}","task_split":"paper_retrieval"}
{"document_id":"198744","document_content":"# Exploring Language Similarities with Dimensionality Reduction Technique\n## Categories\n- Computation and Language\n## Abstract\nIn recent years several novel models were developed to process natural language, development of accurate language translation systems have helped us overcome geographical barriers and communicate ideas effectively. These models are developed mostly for a few languages that are widely used while other languages are ignored. Most of the languages that are spoken share lexical, syntactic and sematic similarity with several other languages and knowing this can help us leverage the existing model to build more specific and accurate models that can be used for other languages, so here I have explored the idea of representing several known popular languages in a lower dimension such that their similarities can be visualized using simple 2 dimensional plots. This can even help us understand newly discovered languages that may not share its vocabulary with any of the existing languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.06092v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"198754","document_content":"# Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma is one of the ten most common cancers in the US. Early detection is crucial for survival, but often the cancer is diagnosed in the fatal stage. Deep learning has the potential to improve cancer detection rates, but its applicability to melanoma detection is compromised by the limitations of the available skin lesion databases, which are small, heavily imbalanced, and contain images with occlusions. We build deep-learning-based tools for data purification and augmentation to counter-act these limitations. The developed tools can be utilized in a deep learning system for lesion classification and we show how to build such a system. The system heavily relies on the processing unit for removing image occlusions and the data generation unit, based on generative adversarial networks, for populating scarce lesion classes, or equivalently creating virtual patients with pre-defined types of lesions. We empirically verify our approach and show that incorporating these two units into melanoma detection system results in the superior performance over common baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.06061v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"54640\", \"87658\", \"129337\", \"128346\"], \"outgoing_citations\": [\"270887\", \"271426\", \"271531\", \"271595\", \"271944\", \"272621\", \"273341\", \"282613\", \"289678\"]}","task_split":"paper_retrieval"}
{"document_id":"198822","document_content":"# Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1080\/2150704X.2019.1647368\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"264032\", \"281953\", \"299079\"]}","task_split":"paper_retrieval"}
{"document_id":"199085","document_content":"# Federated Machine Learning: Concept and Applications\n## Categories\n- Artificial Intelligence\n- Cryptography and Security\n- Machine Learning\n## Abstract\nToday's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.04885v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"2963\", \"5803\", \"6700\", \"9036\", \"9619\", \"11900\", \"16829\", \"16700\", \"19284\", \"20802\", \"24894\", \"35996\", \"36937\", \"34478\", \"40978\", \"46353\", \"59691\", \"71172\", \"82174\", \"95956\", \"103684\", \"107463\", \"108881\", \"146382\", \"174906\", \"184843\", \"175528\", \"196\", \"1576\", \"2760\", \"3410\", \"3778\", \"4040\", \"4079\", \"5829\", \"6050\", \"7548\", \"8155\", \"8232\", \"8363\", \"8794\", \"10068\", \"13693\", \"14389\", \"14519\", \"15428\", \"16779\", \"19157\", \"22527\", \"23969\", \"24991\", \"26780\", \"28148\", \"28219\", \"28611\", \"29011\", \"29232\", \"29681\", \"30370\", \"34352\", \"35088\", \"35312\", \"36515\", \"36865\", \"37702\", \"39036\", \"40618\", \"40674\", \"42329\", \"42516\", \"42791\", \"18313\", \"43741\", \"47195\", \"47765\", \"49144\", \"50023\", \"50951\", \"51046\", \"51766\", \"52164\", \"52799\", \"55141\", \"56473\", \"56467\", \"57316\", \"63861\", \"64210\", \"66296\", \"66561\", \"66843\", \"68836\", \"71429\", \"71707\", \"72424\", \"72802\", \"74732\", \"75395\", \"191444\", \"75743\", \"76170\", \"76623\", \"80596\", \"80685\", \"82241\", \"83105\", \"84761\", \"86356\", \"87900\", \"91349\", \"92728\", \"99336\", \"99659\", \"104208\", \"105807\", \"107773\", \"109491\", \"110259\", \"117293\", \"123895\", \"135151\", \"138025\", \"139447\", \"140640\", \"149736\", \"164701\", \"166152\", \"7595\", \"441\", \"833\", \"1054\", \"2960\", \"3289\", \"3990\"], \"outgoing_citations\": [\"226146\", \"228744\", \"232524\", \"239350\", \"241304\", \"245480\", \"246939\", \"248216\", \"248982\", \"250510\", \"265073\", \"265492\", \"273221\"]}","task_split":"paper_retrieval"}
{"document_id":"199302","document_content":"# Deep Reinforcement Learning from Policy-Dependent Human Feedback\n## Categories\n- Machine Learning\n## Abstract\nTo widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will need to learn these behaviors within a reasonable amount of time while efficiently leveraging the sparse feedback a human trainer is capable of providing. Recent work has shown that human feedback can be characterized as a critique of an agent's current behavior rather than as an alternative reward signal to be maximized, culminating in the COnvergent Actor-Critic by Humans (COACH) algorithm for making direct policy updates based on human feedback. Our work builds on COACH, moving to a setting where the agent's policy is represented by a deep neural network. We employ a series of modifications on top of the original COACH algorithm that are critical for successfully learning behaviors from high-dimensional observations, while also satisfying the constraint of obtaining reduced sample complexity. We demonstrate the effectiveness of our Deep COACH algorithm in the rich 3D world of Minecraft with an agent that learns to complete tasks by mapping from raw pixels to actions using only real-time human feedback in 10-15 minutes of interaction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.04257v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"353397\", \"18315\", \"26479\", \"99547\", \"11431\", \"30094\", \"30309\", \"35842\", \"43409\", \"115937\", \"86323\", \"110680\", \"47007\", \"103314\", \"145848\", \"146535\", \"179214\", \"189416\", \"196851\", \"209453\", \"56246\"], \"outgoing_citations\": [\"254581\", \"263957\", \"268910\", \"291568\", \"293876\", \"297317\"]}","task_split":"paper_retrieval"}
{"document_id":"199454","document_content":"# Peeking into the Future: Predicting Future Person Activities and Locations in Videos\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeciphering human behaviors to predict their future paths\/trajectories and what they would do from videos is important in many applications. Motivated by this idea, this paper studies predicting a pedestrian's future path jointly with future activities. We propose an end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings. To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen. Experimental results demonstrate our state-of-the-art performance over two public benchmarks on future trajectory prediction. Moreover, our method is able to produce meaningful future activity prediction in addition to the path. The result provides the first empirical evidence that joint modeling of paths and activities benefits future path prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03748v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"223\", \"5198\", \"64110\", \"12209\", \"16624\", \"30353\", \"41031\", \"60595\", \"68567\", \"86653\", \"113579\", \"226421\", \"5124\", \"13032\", \"13516\", \"26175\", \"28890\", \"29772\", \"32241\", \"32559\", \"32561\", \"36122\", \"38000\", \"40248\", \"40266\", \"139151\", \"51104\", \"55496\", \"55496\", \"57738\", \"58456\", \"59257\", \"60589\", \"63204\", \"64854\", \"67184\", \"71119\", \"71212\", \"73408\", \"74323\", \"82421\", \"83006\", \"75719\", \"82921\", \"83794\", \"85367\", \"88879\", \"109124\", \"122980\", \"124545\", \"136580\", \"153627\", \"173243\", \"267294\", \"80587\", \"86175\", \"87955\", \"94097\", \"94978\", \"95755\", \"98690\", \"102706\", \"112263\", \"115066\", \"119595\", \"122175\", \"122751\", \"128967\", \"129703\", \"132131\", \"137027\", \"139777\", \"142189\", \"142298\", \"112902\", \"148116\", \"151320\", \"164839\", \"166501\", \"173508\", \"168054\", \"174738\", \"184456\"], \"outgoing_citations\": [\"221569\", \"226868\", \"229164\", \"229266\", \"244219\", \"248734\", \"248822\", \"269240\", \"271003\", \"279059\", \"303203\", \"312442\", \"314670\"]}","task_split":"paper_retrieval"}
{"document_id":"199499","document_content":"# A Bandit Framework for Optimal Selection of Reinforcement Learning Agents\n## Categories\n- Machine Learning\n## Abstract\nDeep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many practical applications, however, interactions with the environment are costly and a good simulator of the environment is not available. Further, as environments differ by application, the optimal inductive bias (architecture, hyperparameters, etc.) of a reinforcement agent depends on the application. In this work, we propose a multi-arm bandit framework that selects from a set of different reinforcement learning agents to choose the one with the best inductive bias. To alleviate the problem of sparse rewards, the reinforcement learning agents are augmented with surrogate rewards. This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward. The bandit has the double objective of maximizing the reward while the agents are learning and selecting the best agent after a finite number of learning steps. Our experimental results on standard environments show that the proposed framework is able to consistently select the optimal agent after a finite number of steps, while collecting more cumulative reward compared to selecting a sub-optimal architecture or uniformly alternating between different agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03657v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"86824\", \"95355\"], \"outgoing_citations\": [\"272785\", \"309498\"]}","task_split":"paper_retrieval"}
{"document_id":"199625","document_content":"# Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nEvaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse. Though existing studies on learning patient similarity from EHRs have shown being useful in solving real clinical problems, their applicability is limited due to the lack of medical interpretations. Moreover, most previous methods assume a vector-based representation for patients, which typically requires aggregation of medical events over a certain time period. As a consequence, temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based on the temporal matching of longitudinal patient EHRs. Two efficient methods are presented, unsupervised and supervised, both of which preserve the temporal properties in EHRs. The supervised scheme takes a convolutional neural network architecture and learns an optimal representation of patient clinical records with medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement over the baselines. We make our code and sample data available for further study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03376v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"61905\", \"37082\", \"10115\", \"102327\", \"78556\", \"125175\", \"125283\", \"136869\", \"141510\", \"153049\", \"196679\", \"163088\", \"186566\", \"189987\", \"208650\", \"230988\"], \"outgoing_citations\": [\"256812\", \"275412\", \"330994\"]}","task_split":"paper_retrieval"}
{"document_id":"199630","document_content":"# Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin. The challenge was hosted in 2018 at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Granada, Spain. The dataset included over 12,500 images across 3 tasks. 900 users registered for data download, 115 submitted to the lesion segmentation task, 25 submitted to the lesion attribute detection task, and 159 submitted to the disease classification task. Novel evaluation protocols were established, including a new test for segmentation algorithm performance, and a test for algorithm ability to generalize. Results show that top segmentation algorithms still fail on over 10% of images on average, and algorithms with equal performance on test data can have different abilities to generalize. This is an important consideration for agencies regulating the growing set of machine learning tools in the healthcare domain, and sets a new standard for future public challenges in healthcare.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03368v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"11266\", \"13140\", \"1089\", \"1511\", \"2016\", \"6022\", \"6227\", \"6649\", \"7352\", \"9635\", \"12333\", \"12414\", \"17028\", \"33412\", \"34473\", \"42641\", \"44315\", \"47730\", \"58401\", \"59068\", \"72064\", \"73680\", \"129076\", \"130639\", \"140640\", \"7624\", \"7938\", \"9528\", \"12014\", \"13479\", \"16904\", \"17693\", \"17731\", \"20306\", \"23263\", \"23534\", \"34639\", \"35786\", \"36056\", \"39135\", \"39822\", \"40703\", \"42126\", \"44961\", \"44426\", \"52801\", \"54627\", \"55685\", \"152754\", \"56562\", \"56650\", \"57431\", \"57664\", \"58611\", \"58895\", \"59488\", \"60851\", \"64980\", \"65010\", \"65131\", \"66774\", \"71481\", \"76050\", \"80696\", \"80781\", \"84476\", \"84697\", \"90709\", \"92067\", \"99192\", \"129028\", \"183668\", \"29487\", \"77847\", \"83051\", \"84663\", \"92753\", \"94557\", \"96875\", \"98861\", \"103536\", \"105227\", \"110938\", \"112437\", \"111482\", \"117366\", \"120412\", \"123452\", \"124972\", \"125254\", \"128574\", \"129081\", \"129120\", \"129337\", \"131575\", \"132481\", \"132649\", \"90077\", \"128346\", \"130710\", \"136085\", \"137548\", \"137763\", \"148569\", \"151037\", \"157779\", \"160504\", \"160933\", \"162635\", \"164211\", \"152170\", \"72537\", \"156689\", \"159152\", \"167593\", \"170812\", \"172832\", \"178129\", \"182234\", \"182674\", \"188256\", \"188540\", \"189887\"], \"outgoing_citations\": [\"271531\", \"272225\", \"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"199665","document_content":"# Does the \"Artificial Intelligence Clinician\" learn optimal treatment strategies for sepsis in intensive care?\n## Categories\n- Artificial Intelligence\n- Applications\n## Abstract\nFrom 2017 to 2018 the number of scientific publications found via PubMed search using the keyword \"Machine Learning\" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an uncontested opportunity to satisfy these demands with relatively little oversight. In a recent Nature Medicine article, \"The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care,\" Komorowski and his coauthors propose methods to train an artificial intelligence clinician to treat sepsis patients with vasopressors and IV fluids. In this post, we will closely examine the claims laid out in this paper. In particular, we will study the individual treatment profiles suggested by their AI Clinician to gain insight into how their AI Clinician intends to treat patients on an individual level.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03271v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.AP\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Applications\"], \"incoming_citations\": [\"170695\", \"74313\", \"184991\", \"95628\", \"156013\", \"196267\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"199764","document_content":"# Generating the support with extreme value losses\n## Categories\n- Machine Learning\n## Abstract\nWhen optimizing against the mean loss over a distribution of predictions in the context of a regression task, then even if there is a distribution of targets the optimal prediction distribution is always a delta function at a single value. Methods of constructing generative models need to overcome this tendency. We consider a simple method of summarizing the prediction error, such that the optimal strategy corresponds to outputting a distribution of predictions with a support that matches the support of the distribution of targets --- optimizing against the minimal value of the loss given a set of samples from the prediction distribution, rather than the mean. We show that models trained against this loss learn to capture the support of the target distribution and, when combined with an auxiliary classifier-like prediction task, can be projected via rejection sampling to reproduce the full distribution of targets. The resulting method works well compared to other generative modeling approaches particularly in low dimensional spaces with highly non-trivial distributions, due to mode collapse solutions being globally suboptimal with respect to the extreme value loss. However, the method is less suited to high-dimensional spaces such as images due to the scaling of the number of samples needed in order to accurately estimate the extreme value loss when the dimension of the data manifold becomes large.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.02940v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"220531\", \"226061\", \"269126\", \"270105\", \"280893\", \"281002\", \"281681\", \"290092\", \"336223\"]}","task_split":"paper_retrieval"}
{"document_id":"199895","document_content":"# Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper presents an adaptive level generation algorithm for the physics-based puzzle game Angry Birds. The proposed algorithm is based on a pre-existing level generator for this game, but where the difficulty of the generated levels can be adjusted based on the player's performance. This allows for the creation of personalised levels tailored specifically to the player's own abilities. The effectiveness of our proposed method is evaluated using several agents with differing strategies and AI techniques. By using these agents as models \/ representations of real human player's characteristics, we can optimise level properties efficiently over a large number of generations. As a secondary investigation, we also demonstrate that by combining the performance of several agents together it is possible to generate levels that are especially challenging for certain players but not others.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.02518v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"56433\"], \"outgoing_citations\": [\"218647\", \"226544\", \"239033\", \"325494\", \"329437\"]}","task_split":"paper_retrieval"}
{"document_id":"199925","document_content":"# Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset prediction with respect to the number of CHF case patients in the cohort and the test set (3,332 CHF patients) on which the AUC metrics are reported. We explore the extent to which LSTM (Long Short Term Memory) based model, a variant of RNNs, can accurately predict the onset of CHF when compared to known linear baselines like Logistic Regression, Random Forests and deep learning based models such as Multi-Layer Perceptron and Convolutional Neural Networks. We utilize demographics, medical diagnosis and procedure data from 21,405 CHF and 194,989 control patients to as our features. We describe our feature embedding strategy for medical diagnosis codes that accommodates the sparse, irregular, longitudinal, and high-dimensional characteristics of EHR data. We empirically show that LSTMs can capture the longitudinal aspects of EHR data better than the proposed baselines. As an attempt to interpret the model, we present a temporal data analysis-based technique on false positives to attribute feature importance. A model capable of predicting the onset of congestive heart failure months in the future with this level of accuracy and precision can support efforts of practitioners to implement risk factor reduction strategies and researchers to begin to systematically evaluate interventions to potentially delay or avert development of the disease with high mortality, morbidity and significant costs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.02443v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"156592\"], \"outgoing_citations\": [\"275412\", \"287354\", \"290520\", \"303550\"]}","task_split":"paper_retrieval"}
{"document_id":"199975","document_content":"# A Guiding Principle for Causal Decision Problems\n## Categories\n- Artificial Intelligence\n## Abstract\nWe define a Causal Decision Problem as a Decision Problem where the available actions, the family of uncertain events and the set of outcomes are related through the variables of a Causal Graphical Model $\\mathcal{G}$. A solution criteria based on Pearl's Do-Calculus and the Expected Utility criteria for rational preferences is proposed. The implementation of this criteria leads to an on-line decision making procedure that has been shown to have similar performance to classic Reinforcement Learning algorithms while allowing for a causal model of an environment to be learned. Thus, we aim to provide the theoretical guarantees of the usefulness and optimality of a decision making procedure based on causal information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.02279v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"225891\", \"276458\", \"290722\"]}","task_split":"paper_retrieval"}
{"document_id":"200032","document_content":"# Word Embeddings for Entity-annotated Texts\n## Categories\n- Computation and Language\n## Abstract\nLearned vector representations of words are useful tools for many information retrieval and natural language processing tasks due to their ability to capture lexical semantics. However, while many such tasks involve or even rely on named entities as central components, popular word embedding models have so far failed to include entities as first-class citizens. While it seems intuitive that annotating named entities in the training corpus should result in more intelligent word features for downstream tasks, performance issues arise when popular embedding approaches are naively applied to entity annotated corpora. Not only are the resulting entity embeddings less useful than expected, but one also finds that the performance of the non-entity word embeddings degrades in comparison to those trained on the raw, unannotated corpus. In this paper, we investigate approaches to jointly train word and entity embeddings on a large corpus with automatically annotated and linked entities. We discuss two distinct approaches to the generation of such embeddings, namely the training of state-of-the-art embeddings on raw-text and annotated versions of the corpus, as well as node embeddings of a co-occurrence graph representation of the annotated corpus. We compare the performance of annotated embeddings and classical word embeddings on a variety of word similarity, analogy, and clustering evaluation tasks, and investigate their performance in entity-specific tasks. Our findings show that it takes more than training popular word embedding models on an annotated corpus to create entity embeddings with acceptable performance on common test cases. Based on these results, we discuss how and when node embeddings of the co-occurrence graph representation of the text can restore the performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-15712-8_20\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54510\", \"55194\", \"127749\"], \"outgoing_citations\": [\"233613\", \"239146\", \"244586\", \"291855\", \"300498\", \"315078\", \"325742\", \"333603\"]}","task_split":"paper_retrieval"}
{"document_id":"200163","document_content":"# Interactively shaping robot behaviour with unlabeled human instructions\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nIn this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular architecture, named TICS (Task-Instruction-Contingency-Shaping) that combines different information sources: a predefined reward function, human evaluative feedback and unlabeled instructions. This approach provides a novel perspective for robotic task learning that lies between Reinforcement Learning and Supervised Learning paradigms. We evaluate our framework both in simulation and with a real robot. The experimental results demonstrate the effectiveness of our framework in accelerating the task-learning process and in reducing the number of required teaching signals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10458-020-09459-6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"35842\", \"123527\", \"137316\"], \"outgoing_citations\": [\"275728\", \"289831\"]}","task_split":"paper_retrieval"}
{"document_id":"200196","document_content":"# Learning to Learn in Simulation\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nDeep learning often requires the manual collection and annotation of a training set. On robotic platforms, can we partially automate this task by training the robot to be curious, i.e., to seek out beneficial training information in the environment? In this work, we address the problem of curiosity as it relates to online, real-time, human-in-the-loop training of an object detection algorithm onboard a drone, where motion is constrained to two dimensions. We use a 3D simulation environment and deep reinforcement learning to train a curiosity agent to, in turn, train the object detection model. This agent could have one of two conflicting objectives: train as quickly as possible, or train with minimal human input. We outline a reward function that allows the curiosity agent to learn either of these objectives, while taking into account some of the physical characteristics of the drone platform on which it is meant to run. In addition, We show that we can weigh the importance of achieving these objectives by adjusting a parameter in the reward function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.01569v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"26939\", \"218770\", \"237493\", \"257128\", \"259094\", \"268322\", \"268446\", \"280570\", \"283001\", \"293491\", \"302753\", \"303040\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"200369","document_content":"# Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression\n## Categories\n- Machine Learning\n## Abstract\nIn experimental design, we are given a large collection of vectors, each with a hidden response value that we assume derives from an underlying linear model, and we wish to pick a small subset of the vectors such that querying the corresponding responses will lead to a good estimator of the model. A classical approach in statistics is to assume the responses are linear, plus zero-mean i.i.d. Gaussian noise, in which case the goal is to provide an unbiased estimator with smallest mean squared error (A-optimal design). A related approach, more common in computer science, is to assume the responses are arbitrary but fixed, in which case the goal is to estimate the least squares solution using few responses, as quickly as possible, for worst-case inputs. Despite many attempts, characterizing the relationship between these two approaches has proven elusive. We address this by proposing a framework for experimental design where the responses are produced by an arbitrary unknown distribution. We show that there is an efficient randomized experimental design procedure that achieves strong variance bounds for an unbiased estimator using few responses in this general model. Nearly tight bounds for the classical A-optimality criterion, as well as improved bounds for worst-case responses, emerge as special cases of this result. In the process, we develop a new algorithm for a joint sampling distribution called volume sampling, and we propose a new i.i.d. importance sampling method: inverse score sampling. A key novelty of our analysis is in developing new expected error bounds for worst-case regression by controlling the tail behavior of i.i.d. sampling via the jointness of volume sampling. Our result motivates a new minimax-optimality criterion for experimental design which can be viewed as an extension of both A-optimal design and sampling for worst-case regression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00995v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"23746\", \"87115\", \"71632\", \"85606\", \"140989\", \"114535\", \"126557\", \"140372\", \"151933\", \"181545\", \"177158\"], \"outgoing_citations\": [\"210836\", \"215376\", \"229139\", \"241176\", \"241647\", \"246625\", \"249168\", \"253340\", \"266068\", \"300366\", \"307071\", \"321716\", \"343323\", \"361709\"]}","task_split":"paper_retrieval"}
{"document_id":"200441","document_content":"# Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nEarly detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the numbers of skin cancers, there is a growing need of computerized analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with very limited amount of segmentation ground truth labeling as it is laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images. We trained the Mask-RCNN and DeepLabv3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set. Our results showed that the best proposed ensemble method segmented the skin lesions with Jaccard index of 79.58% for the ISIC-2017 testing set. The proposed ensemble method outperformed FrCN, FCN, U-Net, and SegNet in Jaccard Index by 2.48%, 7.42%, 17.95%, and 9.96% respectively. Furthermore, the proposed ensemble method achieved an accuracy of 95.6% for some representative clinically benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCN, U-Net, and SegNet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00809v2\", \"primary_category\": \"eess.IV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"148569\", \"161488\"], \"outgoing_citations\": [\"223511\", \"246192\", \"249038\", \"249039\", \"259214\", \"312185\"]}","task_split":"paper_retrieval"}
{"document_id":"200476","document_content":"# Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nInteractive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the form of a human operator providing some type of feedback to the user, which can be explicit feedback, implicit feedback, or a combination of both. Explicit feedback, such as through a mouse click, carries a high cognitive load. The focus of this study is to extend the current state of the art in interactive machine learning by demonstrating that agents can learn a human user's behavior and adapt to preferences with a reduced amount of explicit human feedback in a mixed feedback setting. The learning agent perceives a value of its own behavior from hand gestures given via a spatial interface. This feedback mechanism is termed Spatial Interface Valuing. This method is evaluated experimentally in a simulated environment for a grasping task using a robotic arm with variable grip settings. Preliminary results indicate that learning agents using spatial interface valuing can learn a value function mapping spatial gestures to expected future rewards much more quickly as compared to those same agents just receiving explicit feedback, demonstrating that an agent perceiving feedback from a human user via a spatial interface can serve as an effective complement to existing approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00719v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"244313\", \"263957\", \"276143\", \"289831\", \"290804\"]}","task_split":"paper_retrieval"}
{"document_id":"200545","document_content":"# Efficient Learning of Discrete Graphical Models\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n- Statistics Theory\n- Statistics Theory\n## Abstract\nGraphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for which the maximum likelihood approach is intractable. In this work, we provide the first sample-efficient method based on the Interaction Screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis. We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models. Importantly, our bounds make explicit distinction between parameters that are proper to the model and priors used as an input to the algorithm. Finally, we show that the Interaction Screening framework includes all models previously considered in the literature as special cases, and for which our analysis shows a systematic improvement in sample complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00600v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"math.IT\", \"math.ST\", \"stat.ML\", \"stat.TH\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\", \"Statistics Theory\", \"Machine Learning\", \"Statistics Theory\"], \"incoming_citations\": [\"12892\", \"58699\", \"68525\", \"81052\", \"90729\", \"90670\", \"120488\", \"117074\", \"129568\", \"149609\", \"271504\", \"165723\", \"202111\", \"212650\"], \"outgoing_citations\": [\"117074\", \"212650\", \"263334\", \"264916\", \"271504\", \"277910\", \"291997\", \"321432\"]}","task_split":"paper_retrieval"}
{"document_id":"200682","document_content":"# Learning Action Representations for Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nMost model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00183v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"18199\", \"21832\", \"24177\", \"37110\", \"78473\", \"12287\", \"14870\", \"22368\", \"23741\", \"37671\", \"44490\", \"45523\", \"143967\", \"51151\", \"59809\", \"65294\", \"67615\", \"67614\", \"79599\", \"85855\", \"90812\", \"95530\", \"96855\", \"121446\", \"142643\", \"155590\", \"91844\", \"95821\", \"100477\", \"107604\", \"89234\", \"120561\", \"120731\", \"126089\", \"126543\", \"128144\", \"140833\", \"158667\", \"163329\", \"164513\", \"165708\", \"182477\", \"159110\", \"164032\", \"166532\", \"168512\", \"200321\"], \"outgoing_citations\": [\"200321\", \"218114\", \"232233\", \"262449\", \"265946\", \"277472\", \"280296\", \"292022\", \"301005\", \"315231\", \"351390\"]}","task_split":"paper_retrieval"}
{"document_id":"200687","document_content":"# Dating Documents using Graph Convolution Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDocument date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.00175v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"16892\", \"80211\", \"42889\", \"43574\", \"44245\", \"76227\", \"122331\", \"157487\", \"157513\", \"168038\", \"181307\", \"182540\", \"197770\", \"201818\", \"218179\", \"230130\", \"206411\", \"202289\"], \"outgoing_citations\": [\"206411\", \"263243\", \"268812\", \"271527\"]}","task_split":"paper_retrieval"}
{"document_id":"200734","document_content":"# Rhythm Zone Theory: Speech Rhythms are Physical after all\n## Categories\n- Neurons and Cognition\n- Computation and Language\n- Sound\n## Abstract\nSpeech rhythms have been dealt with in three main ways: from the introspective analyses of rhythm as a correlate of syllable and foot timing in linguistics and applied linguistics, through analyses of durations of segments of utterances associated with consonantal and vocalic properties, syllables, feet and words, to models of rhythms in speech production and perception as physical oscillations. The present study avoids introspection and human-filtered annotation methods and extends the signal processing paradigm of amplitude envelope spectrum analysis by adding an additional analytic step of edge detection, and postulating the co-existence of multiple speech rhythms in rhythm zones marked by identifiable edges (Rhythm Zone Theory, RZT). An exploratory investigation of the utility of RZT is conducted, suggesting that native and non-native readings of the same text are distinct sub-genres of read speech: a reading by a US native speaker and non-native readings by relatively low-performing Cantonese adult learners of English. The study concludes by noting that with the methods used, RZT can distinguish between the speech rhythms of well-defined sub-genres of native speaker reading vs. non-native learner reading, but needs further refinement in order to be applied to the paradoxically more complex speech of low-performing language learners, whose speech rhythms are co-determined by non-fluency and disfluency factors in addition to well-known linguistic factors of grammar, vocabulary and discourse constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.01267v2\", \"primary_category\": \"q-bio.NC\", \"categories\": [\"cs.CL\", \"cs.SD\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Neurons and Cognition\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Neurons and Cognition\"], \"incoming_citations\": [\"168887\", \"194749\"], \"outgoing_citations\": [\"284475\"]}","task_split":"paper_retrieval"}
{"document_id":"200925","document_content":"# Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nTo solve its task, a robot needs to have the ability to interpret its perceptions. In vision, this interpretation is particularly difficult and relies on the understanding of the structure of the scene, at least to the extent of its task and sensorimotor abilities. A robot with the ability to build and adapt this interpretation process according to its own tasks and capabilities would push away the limits of what robots can achieve in a non controlled environment. A solution is to provide the robot with processes to build such representations that are not specific to an environment or a situation. A lot of works focus on objects segmentation, recognition and manipulation. Defining an object solely on the basis of its visual appearance is challenging given the wide range of possible objects and environments. Therefore, current works make simplifying assumptions about the structure of a scene. Such assumptions reduce the adaptivity of the object extraction process to the environments in which the assumption holds. To limit such assumptions, we introduce an exploration method aimed at identifying moveable elements in a scene without considering the concept of object. By using the interactive perception framework, we aim at bootstrapping the acquisition process of a representation of the environment with a minimum of context specific assumptions. The robotic system builds a perceptual map called relevance map which indicates the moveable parts of the current scene. A classifier is trained online to predict the category of each region (moveable or non-moveable). It is also used to select a region with which to interact, with the goal of minimizing the uncertainty of the classification. A specific classifier is introduced to fit these needs: the collaborative mixture models classifier. The method is tested on a set of scenarios of increasing complexity, using both simulations and a PR2 robot.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10968v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"195600\", \"125378\", \"104852\"], \"outgoing_citations\": [\"260235\", \"294660\", \"319427\", \"321486\", \"321611\", \"322867\"]}","task_split":"paper_retrieval"}
{"document_id":"200927","document_content":"# Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe ability to transfer skills across tasks has the potential to scale up reinforcement learning (RL) agents to environments currently out of reach. Recently, a framework based on two ideas, successor features (SFs) and generalised policy improvement (GPI), has been introduced as a principled way of transferring skills. In this paper we extend the SFs & GPI framework in two ways. One of the basic assumptions underlying the original formulation of SFs & GPI is that rewards for all tasks of interest can be computed as linear combinations of a fixed set of features. We relax this constraint and show that the theoretical guarantees supporting the framework can be extended to any set of tasks that only differ in the reward function. Our second contribution is to show that one can use the reward functions themselves as features for future tasks, without any loss of expressiveness, thus removing the need to specify a set of features beforehand. This makes it possible to combine SFs & GPI with deep learning in a more stable way. We empirically verify this claim on a complex 3D environment where observations are images from a first-person perspective. We show that the transfer promoted by SFs & GPI leads to very good policies on unseen tasks almost instantaneously. We also describe how to learn policies specialised to the new tasks in a way that allows them to be added to the agent's set of skills, and thus be reused in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10964v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"218\", \"20487\", \"36644\", \"11406\", \"12745\", \"12959\", \"17153\", \"26551\", \"31901\", \"34787\", \"42563\", \"46516\", \"53903\", \"65615\", \"65435\", \"67153\", \"70741\", \"85115\", \"96180\", \"119760\", \"90884\", \"90966\", \"100245\", \"114800\", \"126363\", \"143204\", \"157007\", \"167687\", \"181163\", \"197465\", \"200791\", \"148522\", \"39730\", \"154419\", \"160289\", \"168512\", \"171332\", \"177381\", \"179483\", \"180158\", \"204013\", \"205488\", \"207100\", \"227313\", \"208372\", \"237929\"], \"outgoing_citations\": [\"252334\", \"261316\", \"261780\", \"263650\", \"272464\", \"277797\", \"282503\", \"284134\", \"284567\", \"290243\", \"290904\"]}","task_split":"paper_retrieval"}
{"document_id":"200967","document_content":"# Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nIn this paper, we studied extensively on different deep learning based methods to detect melanoma and skin lesion cancers. Melanoma, a form of malignant skin cancer is very threatening to health. Proper diagnosis of melanoma at an earlier stage is crucial for the success rate of complete cure. Dermoscopic images with Benign and malignant forms of skin cancer can be analyzed by computer vision system to streamline the process of skin cancer detection. In this study, we experimented with various neural networks which employ recent deep learning based models like PNASNet-5-Large, InceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properly processed and augmented before feeding them into the network. We tested our methods on International Skin Imaging Collaboration (ISIC) 2018 challenge dataset. Our system has achieved best validation score of 0.76 for PNASNet-5-Large model. Further improvement and optimization of the proposed methods with a bigger training dataset and carefully chosen hyper-parameter could improve the performances. The code available for download at https:\/\/github.com\/miltonbd\/ISIC_2018_classification","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10802v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"44643\", \"124256\", \"178321\", \"177238\"], \"outgoing_citations\": [\"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"201077","document_content":"# Diversity in Faces\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings due to confounding factors related to pose, resolution, illumination, occlusion, and viewpoint. However, with recent advances in neural networks, face recognition has achieved unprecedented accuracy, largely built on data-driven deep learning methods. While this is encouraging, a critical aspect that is limiting facial recognition accuracy and fairness is inherent facial diversity. Every face is different. Every face reflects something unique about us. Aspects of our heritage - including race, ethnicity, culture, geography - and our individual identify - age, gender, and other visible manifestations of self-expression, are reflected in our faces. We expect face recognition to work equally accurately for every face. Face recognition needs to be fair. As we rely on data-driven methods to create face recognition technology, we need to ensure necessary balance and coverage in training data. However, there are still scientific questions about how to represent and extract pertinent facial features and quantitatively measure facial diversity. Towards this goal, Diversity in Faces (DiF) provides a data set of one million annotated human face images for advancing the study of facial diversity. The annotations are generated using ten well-established facial coding schemes from the scientific literature. The facial coding schemes provide human-interpretable quantitative measures of facial features. We believe that by making the extracted coding schemes available on a large set of faces, we can accelerate research and development towards creating more fair and accurate facial recognition systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10436v6\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6240\", \"15218\", \"15781\", \"35007\", \"45615\", \"81138\", \"115438\", \"129666\", \"7019\", \"12349\", \"14417\", \"24135\", \"25548\", \"30249\", \"31121\", \"31225\", \"40456\", \"43206\", \"50911\", \"53058\", \"58322\", \"60882\", \"62698\", \"64571\", \"72234\", \"109196\", \"171905\", \"96484\", \"100253\", \"101311\", \"107610\", \"112392\", \"113250\", \"116281\", \"112554\", \"120590\", \"125012\", \"130272\", \"131265\", \"138418\", \"139925\", \"142067\", \"147885\", \"148613\", \"148615\", \"152832\", \"159152\", \"180239\", \"183899\", \"184965\", \"187353\", \"187837\"], \"outgoing_citations\": [\"205908\", \"222824\", \"235399\", \"243156\", \"248676\", \"259870\", \"264659\", \"272912\", \"276480\", \"281201\", \"281848\", \"302157\", \"310635\", \"313299\", \"316430\", \"321122\", \"336184\"]}","task_split":"paper_retrieval"}
{"document_id":"201189","document_content":"# Structuring an unordered text document\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nSegmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc. This paper proposes structuring of an unordered text document based on the keywords in the document. We test our approach on Wikipedia documents using both statistical and predictive methods such as the TextRank algorithm and Google's USE (Universal Sentence Encoder). From our experimental results, we show that the proposed model can effectively structure an unordered document into sections.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10133v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"201191","document_content":"# Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Computers and Society\n## Abstract\nCitizen engagement and technology usage are two emerging trends driven by smart city initiatives. Governments around the world are adopting technology for faster resolution of civic issues. Typically, citizens report issues, such as broken roads, garbage dumps, etc. through web portals and mobile apps, in order for the government authorities to take appropriate actions. Several mediums -- text, image, audio, video -- are used to report these issues. Through a user study with 13 citizens and 3 authorities, we found that image is the most preferred medium to report civic issues. However, analyzing civic issue related images is challenging for the authorities as it requires manual effort. Moreover, previous works have been limited to identifying a specific set of issues from images. In this work, given an image, we propose to generate a Civic Issue Graph consisting of a set of objects and the semantic relations between them, which are representative of the underlying civic issue. We also release two multi-modal (text and images) datasets, that can help in further analysis of civic issues from images. We present a novel approach for adversarial training of existing scene graph models that enables the use of scene graphs for new applications in the absence of any labelled training data. We conduct several experiments to analyze the efficacy of our approach, and using human evaluation, we establish the appropriateness of our model at representing different civic issues.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.10124v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.CY\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Computers and Society\"], \"incoming_citations\": [\"62580\", \"86391\", \"181820\", \"192684\"], \"outgoing_citations\": [\"219188\", \"239599\", \"243011\", \"243975\", \"250108\", \"259807\", \"267476\", \"276530\", \"286181\"]}","task_split":"paper_retrieval"}
{"document_id":"201199","document_content":"# Measuring Long-term Impact of Ads on LinkedIn Feed\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n## Abstract\nOrganic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting objectives. Hence, it is important to design a good Revenue-Engagement Tradeoff (RENT) mechanism to blend ads in the feed. In this paper, we design experiments to understand how members' behavior evolve over time given different ads experiences. These experiences vary on ads density, while the quality of ads (ensured by relevance models) is held constant. Our experiments have been conducted on randomized member buckets and we use two experimental designs to measure the short term and long term effects of the various treatments. Based on the first three months' data, we observe that the long term impact is at a much smaller scale than the short term impact in our application. Furthermore, we observe different member cohorts (based on user activity level) adapt and react differently over time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.03098v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"201345","document_content":"# Learning to Clean: A GAN Perspective\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, watermarkings, coffee stains, or faded text. These artifacts pose many readability challenges to current text recognition algorithms and significantly degrade their performance. Existing learning based denoising techniques require a dataset comprising of noisy documents paired with cleaned versions. In such scenarios, a model can be trained to generate clean documents from noisy versions. However, very often in the real world such a paired dataset is not available, and all we have for training our denoising model are unpaired sets of noisy and clean images. This paper explores the use of GANs to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, translating a document from noisy domain ( i.e., background noise, blurred, faded, watermarked ) to a target clean document using Generative Adversarial Networks (GAN). However, in the absence of paired images for training, we employed CycleGAN which is known to learn a mapping between the distributions of the noisy images to the denoised images using unpaired data to achieve image-to-image translation for cleaning the noisy documents. We compare the performance of CycleGAN for document cleaning tasks using unpaired images with a Conditional GAN trained on paired data from the same dataset. Experiments were performed on a public document dataset on which different types of noise were artificially induced, results demonstrate that CycleGAN learns a more robust mapping from the space of noisy to clean documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.11382v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5077\", \"27598\", \"48483\", \"129050\"], \"outgoing_citations\": [\"235293\", \"277175\", \"290576\"]}","task_split":"paper_retrieval"}
{"document_id":"201512","document_content":"# Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these methods produce coarse segmentation masks due to the loss of location information during the encoding layers. Inspired by Pyramid Scene Parsing Network (PSP-Net), we propose an encoder-decoder model that utilizes pyramid pooling modules in the deep skip connections which aggregate the global context and compensate for the lost spatial information. We trained and validated our approach using ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge dataset. Our approach showed a validation accuracy with a Jaccard index of 0.837, which outperforms U-Net. We believe that with this reported reliable accuracy, this method can be introduced for clinical practice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISBI.2019.8759172\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"125254\", \"90077\"], \"outgoing_citations\": [\"220335\", \"224155\", \"254665\"]}","task_split":"paper_retrieval"}
{"document_id":"201542","document_content":"# The informal semantics of Answer Set Programming: A Tarskian perspective\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nIn Knowledge Representation, it is crucial that knowledge engineers have a good understanding of the formal expressions that they write. What formal expressions state intuitively about the domain of discourse is studied in the theory of the informal semantics of a logic. In this paper we study the informal semantics of Answer Set Programming. The roots of answer set programming lie in the language of Extended Logic Programming, which was introduced initially as an epistemic logic for default and autoepistemic reasoning. In 1999, the seminal papers on answer set programming proposed to use this logic for a different purpose, namely, to model and solve search problems. Currently, the language is used primarily in this new role. However, the original epistemic intuitions lose their explanatory relevance in this new context. How answer set programs are connected to the specifications of problems they model is more easily explained in a classical Tarskian semantics, in which models correspond to possible worlds, rather than to belief states of an epistemic agent. In this paper, we develop a new theory of the informal semantics of answer set programming, which is formulated in the Tarskian setting and based on Frege's compositionality principle. It differs substantially from the earlier epistemic theory of informal semantics, providing a different view on the meaning of the connectives in answer set programming and on its relation to other logics, in particular classical logic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.09125v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [\"34877\", \"10033\"], \"outgoing_citations\": [\"335274\", \"339800\", \"353474\"]}","task_split":"paper_retrieval"}
{"document_id":"201645","document_content":"# SecureBoost: A Lossless Federated Learning Framework\n## Categories\n- Machine Learning\n## Abstract\nThe protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non-privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that require centralized data and thus it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.08755v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"24894\", \"174906\", \"175528\", \"334137\", \"6050\", \"17474\", \"37702\", \"50023\", \"56467\", \"66561\", \"82241\", \"101803\", \"104208\", \"118784\", \"140640\", \"149736\", \"166152\", \"2960\", \"3999\", \"4437\", \"11278\", \"13444\", \"14750\", \"22858\", \"25548\", \"27377\", \"27562\", \"29423\", \"21024\", \"30490\", \"33171\", \"36588\", \"39741\", \"40808\", \"41102\", \"42569\", \"42825\", \"48442\", \"51895\", \"52788\", \"61519\", \"62742\", \"64899\", \"66166\", \"73932\", \"92902\", \"124465\", \"149546\", \"151798\", \"56467\", \"81035\", \"82378\", \"83656\", \"86147\", \"87952\", \"88714\", \"92672\", \"102077\", \"106037\", \"100412\", \"107237\", \"109503\", \"113592\", \"113626\", \"106055\", \"106069\", \"117888\", \"126083\", \"123163\", \"133633\", \"134725\", \"140744\", \"141728\", \"143888\", \"146045\", \"156643\", \"156972\", \"175096\", \"110331\", \"149190\", \"153327\", \"151488\", \"159558\", \"161126\", \"161865\", \"174723\", \"175842\", \"179589\"], \"outgoing_citations\": [\"72911\", \"96456\", \"142093\", \"156972\", \"183930\", \"200704\", \"206720\", \"248982\", \"265492\", \"289430\"]}","task_split":"paper_retrieval"}
{"document_id":"201647","document_content":"# BioBERT: a pre-trained biomedical language representation model for biomedical text mining\n## Categories\n- Computation and Language\n## Abstract\nBiomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. We make the pre-trained weights of BioBERT freely available at https:\/\/github.com\/naver\/biobert-pretrained, and the source code for fine-tuning BioBERT available at https:\/\/github.com\/dmis-lab\/biobert.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1093\/bioinformatics\/btz682\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2951\", \"24555\", \"26103\", \"32774\", \"51432\", \"66846\", \"70327\", \"82136\", \"124916\"], \"outgoing_citations\": [\"191942\", \"193702\", \"197659\", \"213077\", \"243880\", \"247316\", \"249726\", \"259706\", \"263980\"]}","task_split":"paper_retrieval"}
{"document_id":"201656","document_content":"# Deep Multimodality Model for Multi-task Multi-view Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMany real-world problems exhibit the coexistence of multiple types of heterogeneity, such as view heterogeneity (i.e., multi-view property) and task heterogeneity (i.e., multi-task property). For example, in an image classification problem containing multiple poses of the same object, each pose can be considered as one view, and the detection of each type of object can be treated as one task. Furthermore, in some problems, the data type of multiple views might be different. In a web classification problem, for instance, we might be provided an image and text mixed data set, where the web pages are characterized by both images and texts. A common strategy to solve this kind of problem is to leverage the consistency of views and the relatedness of tasks to build the prediction model. In the context of deep neural network, multi-task relatedness is usually realized by grouping tasks at each layer, while multi-view consistency is usually enforced by finding the maximal correlation coefficient between views. However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.). In this paper, we bridge this gap by proposing a deep multi-task multi-view learning framework that learns a deep representation for such dual-heterogeneity problems. Empirical studies on multiple real-world data sets demonstrate the effectiveness of our proposed Deep-MTMV algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.08723v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"260314\", \"48497\", \"69185\"], \"outgoing_citations\": [\"252170\", \"280302\", \"294072\", \"312090\"]}","task_split":"paper_retrieval"}
{"document_id":"201860","document_content":"# Causal Reasoning from Meta-reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDiscovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinforcement learning. We train a recurrent network with model-free reinforcement learning to solve a range of problems that each contain causal structure. We find that the trained agent can perform causal reasoning in novel situations in order to obtain rewards. The agent can select informative interventions, draw causal inferences from observational data, and make counterfactual predictions. Although established formal causal reasoning algorithms also exist, in this paper we show that such reasoning can arise from model-free reinforcement learning, and suggest that causal reasoning in complex settings may benefit from the more end-to-end learning-based approaches presented here. This work also offers new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform -- and interpret -- experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.08162v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"4639\", \"83792\", \"25074\", \"33207\", \"38037\", \"45824\", \"67971\", \"71387\", \"96490\", \"113898\", \"121684\", \"91959\", \"96305\", \"96956\", \"99114\", \"104822\", \"110678\", \"115013\", \"113549\", \"115541\", \"122822\", \"130677\", \"135778\", \"136859\", \"139684\", \"200942\", \"152946\", \"154589\", \"163450\", \"163690\", \"187278\", \"198248\"], \"outgoing_citations\": [\"244112\", \"280202\", \"284747\", \"290722\", \"303714\", \"304236\"]}","task_split":"paper_retrieval"}
{"document_id":"202305","document_content":"# Generating Text Sequence Images for Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre\/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11063-019-10166-x\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"120486\"], \"outgoing_citations\": [\"192483\", \"225336\", \"232706\", \"256711\", \"294023\", \"296685\", \"296828\", \"302303\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"202383","document_content":"# Hierarchical Attentional Hybrid Neural Networks for Document Classification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nDocument classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not take into account the contexting importance of words and sentences. In this paper, we propose a new approach based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms for document classification tasks. The main contribution of this work is the use of convolution layers to extract more meaningful, generalizable and abstract features by the hierarchical representation. The proposed method in this paper improves the results of the current attention-based approaches for document classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-30493-5_39\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"312\", \"33445\", \"41085\", \"76806\", \"86992\", \"109314\"], \"outgoing_citations\": [\"220891\", \"291045\", \"300822\", \"307902\"]}","task_split":"paper_retrieval"}
{"document_id":"202400","document_content":"# Adaptive Artificial Intelligent Q&A Platform\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe paper presents an approach to build a question and answer system that is capable of processing the information in a large dataset and allows the user to gain knowledge from this dataset by asking questions in natural language form. Key content of this research covers four dimensions which are; Corpus Preprocessing, Question Preprocessing, Deep Neural Network for Answer Extraction and Answer Generation. The system is capable of understanding the question, responds to the user's query in natural language form as well. The goal is to make the user feel as if they were interacting with a person than a machine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1902.02162v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"202453","document_content":"# Combating Fake News: A Survey on Identification and Mitigation Techniques\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nThe proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.06437v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"96167\", \"4418\", \"34849\", \"2638\", \"4126\", \"10531\", \"11296\", \"12864\", \"15325\", \"20626\", \"20841\", \"21011\", \"25782\", \"36187\", \"40327\", \"49745\", \"109315\", \"57020\", \"62658\", \"67722\", \"70620\", \"71581\", \"104042\", \"107162\", \"132615\", \"88313\", \"98501\", \"109033\", \"109303\", \"109331\", \"145167\", \"145540\", \"171462\"], \"outgoing_citations\": [\"219169\", \"228585\", \"234529\", \"238986\", \"239988\", \"244279\", \"249184\", \"256855\", \"257863\", \"267552\", \"268073\", \"268465\", \"270399\", \"270799\", \"271021\", \"273723\", \"299078\", \"299889\", \"302653\", \"306628\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"202556","document_content":"# Theory of Minds: Understanding Behavior in Groups Through Inverse Planning\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nHuman social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and how to cooperate in order to compete. Towards the goal of building machine-learning algorithms with human-like social intelligence, we develop a generative model of multi-agent action understanding based on a novel representation for these latent relationships called Composable Team Hierarchies (CTH). This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning. We use CTH as a target for Bayesian inference yielding a new algorithm for understanding behavior in groups that can both infer hidden relationships as well as predict future actions for multiple agents interacting together. Our algorithm rapidly recovers an underlying causal model of how agents relate in spatial stochastic games from just a few observations. The patterns of inference made by this algorithm closely correspond with human judgments and the algorithm makes the same rapid generalizations that people do.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.06085v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [\"15988\", \"26479\", \"1428\", \"44930\", \"49692\", \"93035\", \"134858\", \"93409\", \"100419\", \"111552\", \"111527\", \"111876\", \"118468\", \"138270\", \"130231\", \"201506\", \"182278\", \"184303\", \"192377\", \"242322\"], \"outgoing_citations\": [\"225886\", \"240780\", \"241354\", \"251601\", \"255077\", \"256103\", \"256504\", \"260667\", \"262105\", \"274315\", \"301305\", \"302108\", \"357900\"]}","task_split":"paper_retrieval"}
{"document_id":"202558","document_content":"# DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization. Unlike the traditional methods which predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce the uniform images of the degraded input images, which allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) which uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) which uses a stack of different neural networks for iterative output refinement. Given the learned uniform and enhanced image, the binarization map can be easy to obtain by a global or local threshold. The experimental results on several public benchmark data sets show that our proposed methods provide a new clean version of the degraded image which is suitable for visualization and promising results of binarization using the global Otsu's threshold based on the enhanced images learned iteratively by the neural network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.patcog.2019.01.025\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"47109\", \"63562\", \"5077\", \"10407\", \"42711\", \"9739\", \"48608\", \"49071\", \"50103\", \"43191\", \"82740\", \"83298\", \"103980\", \"112161\", \"92929\", \"138519\", \"156477\"], \"outgoing_citations\": [\"258854\", \"262398\", \"283926\"]}","task_split":"paper_retrieval"}
{"document_id":"202645","document_content":"# SAFE: Scale Aware Feature Encoder for Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we address the problem of having characters with different scales in scene text recognition. We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales. SAFE is composed of a multi-scale convolutional encoder and a scale attention network. The multi-scale convolutional encoder targets at extracting character features under multiple scales, and the scale attention network is responsible for selecting features from the most relevant scale(s). SAFE has two main advantages over the traditional single-CNN encoder used in current state-of-the-art text recognizers. First, it explicitly tackles the scale problem by extracting scale-invariant features from the characters. This allows the recognizer to put more effort in handling other challenges in scene text recognition, like those caused by view distortion and poor image quality. Second, it can transfer the learning of feature encoding across different character scales. This is particularly important when the training set has a very unbalanced distribution of character scales, as training with such a dataset will make the encoder biased towards extracting features from the predominant scale. To evaluate the effectiveness of SAFE, we design a simple text recognizer named scale-spatial attention network (S-SAN) that employs SAFE as its feature encoder, and carry out experiments on six public benchmarks. Experimental results demonstrate that S-SAN can achieve state-of-the-art (or, in some cases, extremely competitive) performance without any post-processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.05770v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"133054\"], \"outgoing_citations\": [\"232706\", \"256711\", \"276130\", \"294023\", \"296685\", \"296828\", \"303621\", \"311196\", \"320148\", \"320837\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"202748","document_content":"# Learning from Dialogue after Deployment: Feed Yourself, Chatbot!\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n- Machine Learning\n## Abstract\nThe majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.05415v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"16341\", \"31927\", \"50552\", \"13808\", \"16931\", \"21067\", \"21737\", \"24007\", \"36866\", \"40489\", \"44917\", \"45883\", \"57670\", \"64838\", \"66657\", \"66630\", \"74913\", \"89002\", \"122762\", \"128396\", \"128718\", \"136417\", \"89813\", \"94268\", \"94846\", \"99128\", \"102649\", \"105315\", \"115695\", \"116873\", \"119635\", \"127524\", \"126793\", \"121404\", \"121400\", \"137928\", \"148271\", \"155361\", \"157211\", \"161092\", \"166480\", \"186970\", \"121402\", \"164786\", \"166082\", \"170864\", \"175055\", \"178278\", \"179346\", \"184854\", \"202381\"], \"outgoing_citations\": [\"202381\", \"205893\", \"210078\", \"210493\", \"212046\", \"213042\", \"218963\", \"223802\", \"232341\", \"232584\", \"234141\", \"235207\", \"238640\", \"241921\", \"244479\", \"249126\", \"252860\", \"256632\", \"265222\", \"266155\", \"271367\", \"277941\", \"279215\", \"294144\", \"296250\", \"301313\"]}","task_split":"paper_retrieval"}
{"document_id":"202795","document_content":"# Sentence transition matrix: An efficient approach that preserves sentence semantics\n## Categories\n- Computation and Language\n## Abstract\nSentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. Therefore, various sentence embedding models based on supervised and unsupervised learning have been proposed after the advent of researches regarding the distributed representation of words. They were evaluated through semantic textual similarity (STS) tasks, which measure the degree of semantic preservation of a sentence and neural network-based supervised embedding models generally yielded state-of-the-art performance. However, these models have a limitation in that they have multiple parameters to update, thereby requiring a tremendous amount of labeled training data. In this study, we propose an efficient approach that learns a transition matrix that refines a sentence embedding vector to reflect the latent semantic meaning of a sentence. The proposed method has two practical advantages; (1) it can be applied to any sentence embedding method, and (2) it can achieve robust performance in STS tasks irrespective of the number of training examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.05219v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"135296\", \"218392\", \"218851\", \"221153\", \"234807\", \"237274\", \"251816\", \"251974\", \"253559\", \"255894\", \"259723\", \"267161\", \"272080\", \"274111\", \"282914\", \"283908\", \"290387\", \"291045\", \"298543\", \"302507\", \"302516\", \"307756\", \"310737\", \"310738\", \"316165\", \"329586\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"202837","document_content":"# ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing the agent to explore and instead having a human explorer collect the data. Supervised learning has been explored for AV control, but it encounters the issue of the covariate shift. That is, training data collected from an optimal demonstration consists only of the states induced by the optimal control policy, but at runtime, the trained agent may encounter a vastly different state distribution with little relevant training data. To mitigate this issue, we have our human explorer make sub-optimal decisions. In order to have our agent not replicate these sub-optimal decisions, supervised learning requires that we either erase these actions, or replace these action with the correct action. Erasing is wasteful and replacing is difficult, since it is not easy to know the correct action without driving. We propose an alternate framework that includes continuous scalar feedback for each action, marking which actions we should replicate, which we should avoid, and how sure we are. Our framework learns continuous control from sub-optimal demonstration and evaluative feedback collected before training. We find that a human demonstrator can explore sub-optimal states in a safe manner, while still getting enough gradation to benefit learning. The collection method for data and feedback we call \"Backseat Driver.\" We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples. We empirically validate several models in the ReNeg framework, testing on lane-following with limited data. We find that the best solution is a generalization of mean-squared error and outperforms supervised learning on the positive examples alone.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.05101v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"242141\", \"272501\", \"275728\", \"327996\", \"352572\"]}","task_split":"paper_retrieval"}
{"document_id":"202934","document_content":"# Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nSepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.04670v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"170695\", \"74313\", \"8061\", \"17932\", \"31177\", \"36421\", \"64122\", \"104740\", \"110793\", \"126139\", \"133335\", \"147531\", \"156013\", \"184671\", \"186326\", \"196267\"], \"outgoing_citations\": [\"229839\", \"249361\", \"265649\", \"268399\", \"295281\"]}","task_split":"paper_retrieval"}
{"document_id":"203110","document_content":"# Image retrieval method based on CNN and dimension reduction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAn image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.03924v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"264220\", \"331445\", \"350247\"]}","task_split":"paper_retrieval"}
{"document_id":"203144","document_content":"# What comes next? Extractive summarization by next-sentence prediction\n## Categories\n- Computation and Language\n## Abstract\nExisting approaches to automatic summarization assume that a length limit for the summary is given, and view content selection as an optimization problem to maximize informativeness and minimize redundancy within this budget. This framework ignores the fact that human-written summaries have rich internal structure which can be exploited to train a summarization system. We present NEXTSUM, a novel approach to summarization based on a model that predicts the next sentence to include in the summary using not only the source article, but also the summary produced so far. We show that such a model successfully captures summary-specific discourse moves, and leads to better content selection performance, in addition to automatically predicting how long the target summary should be. We perform experiments on the New York Times Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model summarization baselines by significant margins. We also show that the lengths of summaries produced by our system correlates with the lengths of the human-written gold standards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.03859v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96263\", \"128252\", \"183942\", \"157321\", \"170449\"], \"outgoing_citations\": [\"266671\", \"280557\", \"292379\", \"294830\", \"296029\", \"297955\"]}","task_split":"paper_retrieval"}
{"document_id":"203211","document_content":"# ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nWe present ParaBank, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of ParaNMT, we train a Czech-English neural machine translation (NMT) system to generate novel paraphrases of English reference sentences. By adding lexical constraints to the NMT decoding procedure, however, we are able to produce multiple high-quality sentential paraphrases per source sentence, yielding an English paraphrase resource with more than 4 billion generated tokens and exhibiting greater lexical diversity. Using human judgments, we also demonstrate that ParaBank's paraphrases improve over ParaNMT on both semantic similarity and fluency. Finally, we use ParaBank to train a monolingual NMT model with the same support for lexically-constrained decoding for sentence rewriting tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.03644v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18454\", \"25308\", \"50552\", \"13799\", \"24424\", \"26384\", \"26367\", \"26460\", \"43878\", \"46005\", \"56312\", \"64398\", \"73415\", \"91599\", \"104826\", \"114947\", \"127566\", \"89861\", \"94843\", \"106553\", \"34255\", \"126962\", \"133423\", \"128053\", \"128024\", \"164127\", \"183200\"], \"outgoing_citations\": [\"229355\", \"235175\", \"247368\", \"250360\", \"268166\"]}","task_split":"paper_retrieval"}
{"document_id":"203265","document_content":"# Answer Interaction in Non-factoid Question Answering Systems\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nInformation retrieval systems are evolving from document retrieval to answer retrieval. Web search logs provide large amounts of data about how people interact with ranked lists of documents, but very little is known about interaction with answer texts. In this paper, we use Amazon Mechanical Turk to investigate three answer presentation and interaction approaches in a non-factoid question answering setting. We find that people perceive and react to good and bad answers very differently, and can identify good answers relatively quickly. Our results provide the basis for further investigation of effective answer interaction and feedback methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3295750.3298946\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"31772\", \"123404\", \"170238\", \"177468\"], \"outgoing_citations\": [\"233646\", \"234472\", \"245908\", \"320868\"]}","task_split":"paper_retrieval"}
{"document_id":"203387","document_content":"# PVSS: A Progressive Vehicle Search System for Video Surveillance Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper is focused on the task of searching for a specific vehicle that appeared in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, attribute-based search cannot accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be misrecognized in surveillance scenes due to the low resolution and noise. In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules: the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of the PVSS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.03062v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"131344\", \"118940\"], \"outgoing_citations\": [\"258511\", \"258687\", \"271295\", \"275643\", \"287103\", \"293365\", \"293596\", \"296989\", \"310367\"]}","task_split":"paper_retrieval"}
{"document_id":"203505","document_content":"# Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial information for image-text retrieval, while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval. In the DSMHN, two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels. Specifically, with the assumption that the learned hash codes should be optimal for the classification task, two stream networks are jointly trained to learn the hash functions by embedding the semantic labels on the resultant hash codes. Moreover, a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning, intermodality similarity-preserving learning, semantic label-preserving learning, and hash function learning with different types of loss functions simultaneously. The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions. We conduct extensive experiments for both single modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets. Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2020.2997020\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"24781\", \"100185\", \"137813\", \"49184\", \"255486\"], \"outgoing_citations\": [\"187156\", \"191827\", \"191832\", \"232982\", \"260176\", \"264939\", \"265811\", \"280246\", \"287103\", \"288218\", \"297863\", \"298792\", \"302163\", \"303512\", \"314568\", \"318535\", \"321682\", \"332151\", \"356778\"]}","task_split":"paper_retrieval"}
{"document_id":"203509","document_content":"# What do Language Representations Really Represent?\n## Categories\n- Computation and Language\n## Abstract\nA neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just like it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, while genetic relationships---a convenient benchmark used for evaluation in previous work---appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.02646v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"4777\", \"16207\", \"22023\", \"23210\", \"44974\", \"72915\", \"104894\", \"144897\", \"118167\", \"89177\", \"91648\", \"92100\", \"93794\", \"122592\", \"127876\", \"138349\", \"137472\", \"145018\", \"180249\", \"189148\", \"193634\"], \"outgoing_citations\": [\"226011\", \"241113\", \"259840\", \"267321\", \"268162\", \"277444\", \"298944\", \"319833\", \"347538\"]}","task_split":"paper_retrieval"}
{"document_id":"203612","document_content":"# Fusion Strategies for Learning User Embeddings with Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGrowing amounts of online user data motivate the need for automated processing techniques. In case of user ratings, one interesting option is to use neural networks for learning to predict ratings given an item and a user. While training for prediction, such an approach at the same time learns to map each user to a vector, a so-called user embedding. Such embeddings can for example be valuable for estimating user similarity. However, there are various ways how item and user information can be combined in neural networks, and it is unclear how the way of combining affects the resulting embeddings. In this paper, we run an experiment on movie ratings data, where we analyze the effect on embedding quality caused by several fusion strategies in neural networks. For evaluating embedding quality, we propose a novel measure, Pair-Distance Correlation, which quantifies the condition that similar users should have similar embedding vectors. We find that the fusion strategy affects results in terms of both prediction performance and embedding quality. Surprisingly, we find that prediction performance not necessarily reflects embedding quality. This suggests that if embeddings are of interest, the common tendency to select models based on their prediction ability should be reconsidered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.02322v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"257563\", \"259007\", \"266120\", \"267631\", \"289057\", \"290261\", \"293693\", \"313405\"]}","task_split":"paper_retrieval"}
{"document_id":"203774","document_content":"# Vector representations of text data in deep learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nIn this dissertation we report results of our research on dense distributed representations of text data. We propose two novel neural models for learning such representations. The first model learns representations at the document level, while the second model learns word-level representations.   For document-level representations we propose Binary Paragraph Vector: a neural network models for learning binary representations of text documents, which can be used for fast document retrieval. We provide a thorough evaluation of these models and demonstrate that they outperform the seminal method in the field in the information retrieval task. We also report strong results in transfer learning settings, where our models are trained on a generic text corpus and then used to infer codes for documents from a domain-specific dataset. In contrast to previously proposed approaches, Binary Paragraph Vector models learn embeddings directly from raw text data.   For word-level representations we propose Disambiguated Skip-gram: a neural network model for learning multi-sense word embeddings. Representations learned by this model can be used in downstream tasks, like part-of-speech tagging or identification of semantic relations. In the word sense induction task Disambiguated Skip-gram outperforms state-of-the-art models on three out of four benchmarks datasets. Our model has an elegant probabilistic interpretation. Furthermore, unlike previous models of this kind, it is differentiable with respect to all its parameters and can be trained with backpropagation. In addition to quantitative results, we present qualitative evaluation of Disambiguated Skip-gram, including two-dimensional visualisations of selected word-sense embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.01695v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"55581\"], \"outgoing_citations\": [\"256219\", \"262903\", \"270915\", \"275596\", \"281169\", \"281277\", \"288174\", \"293018\", \"298805\", \"300904\", \"311843\", \"313947\", \"316936\", \"317213\", \"320814\", \"321994\", \"325742\", \"325820\", \"328469\", \"352060\", \"356778\"]}","task_split":"paper_retrieval"}
{"document_id":"203811","document_content":"# Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNeural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2,3], could be said in natural language processing (NLP) tasks, such as named entity recognition. However, the success of NNs remains dependent on the availability of large labelled datasets, which is a significant hurdle in many important applications. One such case are electronic health records (EHRs), which are arguably the largest source of medical data, most of which lies hidden in natural text [4,5]. Data access is difficult due to data privacy concerns, and therefore annotated datasets are scarce. With scarce data, NNs will likely not be able to extract this hidden information with practical accuracy. In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4.3 above the architecture that won the competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms. To reach this state-of-the-art accuracy, our approach applies transfer learning to leverage on datasets annotated for other I2B2 tasks, and designs and trains embeddings that specially benefit from such transfer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.01592v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"6965\", \"37082\", \"16200\", \"138895\", \"93713\", \"164217\"], \"outgoing_citations\": [\"210283\", \"242400\", \"244586\", \"296713\", \"316314\"]}","task_split":"paper_retrieval"}
{"document_id":"203855","document_content":"# Efforts estimation of doctors annotating medical image\n## Categories\n- Machine Learning\n- Image and Video Processing\n## Abstract\nAccurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But such methods just cut the number of annotation candidates and do not study how many efforts the doctor will exactly take, which is not enough since even annotating a small amount of medical data will take a lot of time for the doctor.   In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image. First, by coming active learning and U-shape network, we employ a suggestive annotation strategy to choose the most effective annotation candidates. Then we exploit a fine annotation platform to alleviate annotating efforts on each candidate and first utilize a new criterion to quantitatively calculate the efforts taken by doctors. In our work, we take MR brain tissue segmentation as an example to evaluate the proposed method.   Extensive experiments on the well-known IBSR18 dataset and MRBrainS18 Challenge dataset show that, using proposed strategy, state-of-the-art segmentation performance can be achieved by using only 60% annotation candidates and annotation efforts can be alleviated by at least 44%, 44%, 47% on CSF, GM, WM separately.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.02355v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227420\", \"263706\", \"288623\", \"294919\", \"311067\"]}","task_split":"paper_retrieval"}
{"document_id":"203983","document_content":"# A Joint Model for Multimodal Document Quality Assessment\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Digital Libraries\n## Abstract\nThe quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text --- such as images, font choices, and visual layout --- we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.01010v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.DL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Digital Libraries\"], \"incoming_citations\": [\"22543\", \"12018\", \"77332\", \"9542\", \"42711\", \"79124\", \"94378\", \"106275\", \"127768\"], \"outgoing_citations\": [\"152460\", \"205209\", \"232545\", \"234220\", \"250568\", \"290471\", \"310772\"]}","task_split":"paper_retrieval"}
{"document_id":"204048","document_content":"# Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data\n## Categories\n- Computers and Society\n- Machine Learning\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nInformal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3306618.3314253\", \"primary_category\": \"cs.CY\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.CY\", \"cs.CV\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Computers and Society\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"40464\", \"99047\", \"103791\", \"170351\"], \"outgoing_citations\": [\"305683\", \"309293\"]}","task_split":"paper_retrieval"}
{"document_id":"204070","document_content":"# Generating Multiple Objects at Spatially Distinct Locations\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nRecent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve. This is especially true for images that should contain multiple distinct objects at different spatial locations. We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator. Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed. The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes. The global pathway focuses on the image background and the general image layout. We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations. We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.00686v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"15670\", \"36534\", \"71277\", \"134939\", \"159132\", \"93187\", \"8969\", \"17887\", \"20364\", \"37316\", \"51639\", \"58980\", \"59356\", \"60753\", \"62944\", \"72165\", \"73658\", \"75826\", \"80189\", \"97163\", \"116018\", \"116970\", \"134951\", \"175031\", \"82103\", \"113313\", \"116807\", \"141033\", \"149491\", \"152402\", \"157078\", \"213877\", \"168416\", \"170922\", \"171225\", \"174560\", \"176457\"], \"outgoing_citations\": [\"219211\", \"220605\", \"225097\", \"236736\", \"240885\", \"241205\", \"245000\", \"249031\", \"259890\", \"279030\", \"279091\", \"283070\", \"295736\", \"303714\", \"303715\"]}","task_split":"paper_retrieval"}
{"document_id":"204141","document_content":"# Action2Vec: A Crossmodal Embedding Approach to Action Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train our embedding using a joint loss that combines classification accuracy with similarity to Word2Vec semantics. We evaluate Action2Vec by performing zero shot action recognition and obtain state of the art results on three standard datasets. In addition, we present two novel analogy tests which quantify the extent to which our joint embedding captures distributional semantics. This is the first joint embedding space to combine verbs and action videos, and the first to be thoroughly evaluated with respect to its distributional semantics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.00484v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"44352\", \"13933\", \"26190\", \"28057\", \"40564\", \"43853\", \"64186\", \"65576\", \"68620\", \"81508\", \"114219\", \"129450\", \"152245\", \"166950\", \"103606\", \"122752\", \"138805\", \"172394\", \"174419\"], \"outgoing_citations\": [\"238069\", \"279521\", \"293365\", \"293986\", \"303011\", \"303392\", \"303593\", \"313189\", \"317994\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"204556","document_content":"# The role of grammar in transition-probabilities of subsequent words in English text\n## Categories\n- Computation and Language\n- Physics and Society\n## Abstract\nSentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf's law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject--predicate--object) locally re-order the word order in sentences that are sampled with a SSR word generation process. We demonstrate that superimposing grammatical structure -- as a local word re-ordering (permutation) process -- on a sample-space reducing process is sufficient to explain both, word frequencies and word-to-word transition probabilities. We compare the quality of the grammatically ordered SSR model in reproducing several test statistics of real texts with other text generation models, such as the Bernoulli model, the Simon model, and the Monkey typewriting model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.10991v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Physics and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"326939\"]}","task_split":"paper_retrieval"}
{"document_id":"204584","document_content":"# Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReal-world face recognition requires an ability to perceive the unique features of an individual face across multiple, variable images. The primate visual system solves the problem of image invariance using cascades of neurons that convert images of faces into categorical representations of facial identity. Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons. DCNN representations can be examined in a multidimensional \"face space\", with identities and image parameters quantified via their projections onto the axes that define the space. We examined the organization of viewpoint, illumination, gender, and identity in this space. We show that the network creates a highly organized, hierarchically nested, face similarity structure in which information about face identity and imaging characteristics coexist. Natural image variation is accommodated in this hierarchy, with face identity nested under gender, illumination nested under identity, and viewpoint nested under illumination. To examine identity, we caricatured faces and found that network identification accuracy increased with caricature level, and--mimicking human perception--a caricatured distortion of a face \"resembled\" its veridical counterpart. Caricatures improved performance by moving the identity away from other identities in the face space and minimizing the effects of illumination and viewpoint. Deep networks produce face representations that solve long-standing computational problems in generalized face recognition. They also provide a unitary theoretical framework for reconciling decades of behavioral and neural results that emphasized either the image or the object\/face in representations, without understanding how a neural code could seamlessly accommodate both.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1038\/s42256-019-0111-7\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"99428\", \"136271\", \"142176\", \"162020\", \"155785\"], \"outgoing_citations\": [\"236834\", \"281201\", \"281332\", \"294284\", \"308384\"]}","task_split":"paper_retrieval"}
{"document_id":"204587","document_content":"# Identifying Computer-Translated Paragraphs using Coherence Features\n## Categories\n- Computation and Language\n## Abstract\nWe have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.10896v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"150456\", \"161677\", \"189214\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"204763","document_content":"# A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIntent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or \"encoder-decoder\" models), and generate the intents and semantic tags either using separate models or a joint model. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. Most of these approaches use one (joint) NN based model (including encoder-decoder structure) to model two tasks, hence may not fully take advantage of the cross-impact between them. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-the-art result on the benchmark ATIS data, with about 0.5$\\%$ intent accuracy improvement and 0.9 $\\%$ slot filling improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.10235v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"74469\", \"1340\", \"22618\", \"27450\", \"28992\", \"50552\", \"70627\", \"127897\", \"10513\", \"19392\", \"25203\", \"39094\", \"42875\", \"44438\", \"45038\", \"45832\", \"47388\", \"52620\", \"63968\", \"65237\", \"77774\", \"80112\", \"95759\", \"75876\", \"84441\", \"88667\", \"89849\", \"93873\", \"97253\", \"100483\", \"104522\", \"120737\", \"123561\", \"129831\", \"128934\", \"143978\", \"135816\", \"157993\", \"138059\", \"166702\", \"161533\", \"168563\", \"177377\", \"178445\", \"183006\", \"194488\", \"204764\", \"217486\"], \"outgoing_citations\": [\"285311\", \"285317\", \"300449\", \"312009\"]}","task_split":"paper_retrieval"}
{"document_id":"204844","document_content":"# VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nRecent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks. Among them, the low efficiency of data utilization in model-free reinforcement algorithms is of great concern. In contrast, the model-based reinforcement learning algorithms can reveal underlying dynamics in learning environments and seldom suffer the data utilization problem. To address the problem, a model-based reinforcement learning algorithm with attention mechanism embedded is proposed as an extension of World Models in this paper. We learn the environment model through Mixture Density Network Recurrent Network(MDN-RNN) for agents to interact, with combinations of variational auto-encoder(VAE) and attention incorporated in state value estimates during the process of learning policy. In this way, agent can learn optimal policies through less interactions with actual environment, and final experiments demonstrate the effectiveness of our model in control problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.09968v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"123032\", \"148737\", \"174126\"], \"outgoing_citations\": [\"210517\", \"217919\", \"224840\", \"229302\", \"231360\", \"240495\", \"240918\", \"245479\", \"259045\", \"263288\", \"264542\", \"269126\", \"271952\", \"282884\", \"283289\", \"291920\", \"304250\"]}","task_split":"paper_retrieval"}
{"document_id":"204872","document_content":"# TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nReading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text. Most of existing approaches attempt to solve the problem in two or multiple stages, which is considered to be the bottleneck to optimize the overall performance. To address this issue, we propose an end-to-end trainable network architecture, named TextNet, which is able to simultaneously localize and recognize irregular text from images. Specifically, we develop a scale-aware attention mechanism to learn multi-scale image features as a backbone network, sharing fully convolutional features and computation for localization and recognition. In text detection branch, we directly generate text proposals in quadrangles, covering oriented, perspective and curved text regions. To preserve text features for recognition, we introduce a perspective RoI transform layer, which can align quadrangle proposals into small feature maps. Furthermore, in order to extract effective features for recognition, we propose to encode the aligned RoI features by RNN into context information, combining spatial attention mechanism to generate text sequences. This overall pipeline is capable of handling both regular and irregular cases. Finally, text localization and recognition tasks can be jointly trained in an end-to-end fashion with designed multi-task loss. Experiments on standard benchmarks show that the proposed TextNet can achieve state-of-the-art performance, and outperform existing approaches on irregular datasets by a large margin.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.09900v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"50917\", \"52248\", \"180823\", \"56713\", \"58673\", \"81960\", \"93345\", \"113025\", \"111035\", \"140478\", \"141887\", \"154995\", \"156029\", \"194581\", \"161220\", \"166353\", \"165784\", \"169711\", \"170332\", \"171036\", \"190708\"], \"outgoing_citations\": [\"239535\", \"245591\", \"245899\", \"252159\", \"256364\", \"257954\", \"261365\", \"262570\", \"269108\", \"270668\", \"271112\", \"272390\", \"272481\", \"279950\", \"284871\", \"293655\", \"294023\", \"294753\", \"296685\", \"296828\", \"303621\", \"306502\", \"311196\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"204935","document_content":"# Inference in Graded Bayesian Networks\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- 62F15, 68T05, 16Y60\n## Abstract\nMachine learning provides algorithms that can learn from data and make inferences or predictions on data. Bayesian networks are a class of graphical models that allow to represent a collection of random variables and their condititional dependencies by directed acyclic graphs. In this paper, an inference algorithm for the hidden random variables of a Bayesian network is given by using the tropicalization of the marginal distribution of the observed variables. By restricting the topological structure to graded networks, an inference algorithm for graded Bayesian networks will be established that evaluates the hidden random variables rank by rank and in this way yields the most probable states of the hidden variables. This algorithm can be viewed as a generalized version of the Viterbi algorithm for graded Bayesian networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.01837v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ML\", \"62F15, 68T05, 16Y60\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"62F15, 68T05, 16Y60\"], \"incoming_citations\": [], \"outgoing_citations\": [\"144568\"]}","task_split":"paper_retrieval"}
{"document_id":"205014","document_content":"# Joint Slot Filling and Intent Detection via Capsule Neural Networks\n## Categories\n- Computation and Language\n## Abstract\nBeing able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.09471v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23909\", \"74469\", \"22618\", \"27450\", \"70627\", \"109960\", \"127897\", \"18484\", \"29403\", \"30474\", \"42875\", \"44032\", \"44438\", \"45038\", \"47388\", \"50869\", \"52419\", \"52620\", \"56558\", \"56973\", \"57812\", \"58217\", \"62068\", \"63968\", \"65237\", \"67742\", \"71388\", \"75121\", \"79656\", \"88786\", \"89247\", \"95759\", \"98109\", \"110206\", \"75876\", \"83392\", \"95239\", \"95845\", \"96521\", \"101045\", \"120737\", \"122165\", \"127901\", \"129831\", \"133147\", \"128934\", \"129221\", \"136051\", \"143978\", \"135816\", \"144895\", \"157993\", \"138872\", \"161707\", \"166043\", \"166702\", \"161533\", \"168563\", \"174761\", \"177377\", \"190005\", \"194488\"], \"outgoing_citations\": [\"219441\", \"229259\", \"237346\", \"248279\", \"252703\", \"255999\", \"285317\", \"299541\"]}","task_split":"paper_retrieval"}
{"document_id":"205052","document_content":"# Whittemore: An embedded domain specific language for causal programming\n## Categories\n- Programming Languages\n- Methodology\n- Artificial Intelligence\n## Abstract\nThis paper introduces Whittemore, a language for causal programming. Causal programming is based on the theory of structural causal models and consists of two primary operations: identification, which finds formulas that compute causal queries, and estimation, which applies formulas to transform probability distributions to other probability distribution. Causal programming provides abstractions to declare models, queries, and distributions with syntax similar to standard mathematical notation, and conducts rigorous causal inference, without requiring detailed knowledge of the underlying algorithms. Examples of causal inference with real data are provided, along with discussion of the implementation and possibilities for future extension.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.11918v1\", \"primary_category\": \"cs.PL\", \"categories\": [\"stat.ME\", \"cs.PL\", \"cs.AI\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"Methodology\", \"Programming Languages\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"357538\", \"357921\", \"358911\"]}","task_split":"paper_retrieval"}
{"document_id":"205074","document_content":"# Symbolic inductive bias for visually grounded learning of spoken language\n## Categories\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nA widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech\/text task leads to substantial performance improvements on image retrieval when compared to training the speech\/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.09244v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"683\", \"22962\", \"53303\", \"44917\", \"58270\", \"84995\", \"85004\", \"96244\", \"127278\", \"131070\", \"132251\", \"154976\", \"166011\", \"169266\", \"186418\", \"189109\"], \"outgoing_citations\": [\"217125\", \"219714\", \"220126\", \"227323\", \"234421\", \"236276\", \"236772\", \"241449\", \"252631\", \"260373\", \"263945\", \"265708\", \"269624\", \"270711\", \"272944\", \"274549\", \"275409\", \"281169\", \"282931\", \"288551\", \"303003\", \"303546\"]}","task_split":"paper_retrieval"}
{"document_id":"205188","document_content":"# Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nCross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using the conditional variational autoencoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.08879v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"6839\", \"97181\", \"163725\", \"186370\"], \"outgoing_citations\": [\"267641\", \"270040\", \"292261\", \"297170\", \"308378\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"205357","document_content":"# Generating Diverse and Meaningful Captions\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nImage Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty. We make our source code publicly available online.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-01418-6_18\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"81082\", \"83854\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"205461","document_content":"# Learning Symmetry Consistent Deep CNNs for Face Completion\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global consistency among face components and recovering fine facial details. On the other hand, reflectional symmetry is a prominent property of face image and benefits face recognition and consistency modeling, yet remaining uninvestigated in deep face completion. In this work, we leverage two kinds of symmetry-enforcing subnets to form a symmetry-consistent CNN model (i.e., SymmFCNet) for effective face completion. For missing pixels on only one of the half-faces, an illumination-reweighted warping subnet is developed to guide the warping and illumination reweighting of the other half-face. As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures. The SymmFCNet is constructed by stacking generative reconstruction subnet upon illumination-reweighted warping subnet, and can be end-to-end learned from training set of unaligned face images. Experiments show that SymmFCNet can generate high quality results on images with synthetic and real occlusion, and performs favorably against state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.07741v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26240\", \"14983\", \"10613\", \"147666\", \"209731\"], \"outgoing_citations\": [\"168994\", \"178859\", \"190170\", \"190371\", \"195652\", \"202600\", \"204216\", \"213592\", \"218619\", \"223624\", \"235729\", \"243990\", \"249537\", \"268494\", \"268970\", \"270923\", \"279178\", \"279182\", \"282432\", \"287740\", \"287801\", \"292758\", \"294531\", \"321122\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"205610","document_content":"# Deep reinforcement learning for search, recommendation, and online advertising: a survey\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nSearch, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques. These DRL based techniques have two key advantages -- (1) they are able to continuously update information seeking strategies according to users' real-time feedback, and (2) they can maximize the expected cumulative long-term reward from users where reward has different definitions according to information seeking applications such as click-through rate, revenue, user satisfaction and engagement. In this paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3320496.3320500\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"75234\", \"73578\", \"96050\", \"22866\", \"105259\", \"124622\", \"104791\", \"120950\", \"131223\", \"139406\", \"199383\", \"174929\", \"199149\"], \"outgoing_citations\": [\"199383\", \"210118\", \"212600\", \"213358\", \"216280\", \"217540\", \"218531\", \"222498\", \"226934\", \"230811\", \"233018\", \"240298\", \"240405\", \"240736\", \"241160\", \"241732\", \"244898\", \"246249\", \"251005\", \"257737\", \"268845\", \"271114\", \"276513\", \"298598\", \"301005\", \"317770\", \"342608\", \"351390\"]}","task_split":"paper_retrieval"}
{"document_id":"205616","document_content":"# Composing Text and Image for Image Retrieval - An Empirical Odyssey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we learn a similarity metric between a target image and a source image plus source text, an embedding and composing function such that target image feature is close to the source image plus text composition feature. We propose a new way to combine image and text using such function that is designed for the retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to classify input queries, in addition to image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.07119v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19259\", \"1560\", \"4896\", \"8381\", \"9431\", \"42657\", \"63954\", \"102580\", \"93187\", \"605\", \"6827\", \"23496\", \"30600\", \"33627\", \"38693\", \"43864\", \"41274\", \"45983\", \"54198\", \"45942\", \"56489\", \"57769\", \"61317\", \"65855\", \"68658\", \"70460\", \"77995\", \"81451\", \"81968\", \"85889\", \"98051\", \"106682\", \"117426\", \"183623\", \"105896\", \"111567\", \"112422\", \"112428\", \"113665\", \"115043\", \"123991\", \"130237\", \"131626\", \"134661\", \"155210\", \"158332\", \"160571\", \"196779\"], \"outgoing_citations\": [\"229164\", \"233661\", \"234262\", \"244272\", \"250221\", \"253708\", \"255134\", \"259007\", \"259403\", \"263185\", \"270894\", \"277060\", \"281407\", \"287460\", \"291119\", \"294983\", \"295204\", \"303011\", \"303106\", \"303452\", \"303833\", \"306520\", \"313465\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"205715","document_content":"# Conditional BERT Contextual Augmentation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWe propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model\\footnote{The term \"conditional masked language model\" appeared once in original BERT paper, which indicates context-conditional, is equivalent to term \"masked language model\". In our paper, \"conditional masked language model\" indicates we apply extra label-conditional constraint to the \"masked language model\".} task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.06705v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"36981\", \"42229\", \"8926\", \"18991\", \"24730\", \"26658\", \"42896\", \"55926\", \"13866\", \"615\", \"2024\", \"8723\", \"16499\", \"17070\", \"21352\", \"22603\", \"25264\", \"25886\", \"26808\", \"40348\", \"43377\", \"46724\", \"47759\", \"48618\", \"59303\", \"60381\", \"74463\", \"89828\", \"93612\", \"93798\", \"100386\", \"128551\", \"132345\", \"77841\", \"86408\", \"89639\", \"90408\", \"92217\", \"95359\", \"96712\", \"96881\", \"98508\", \"99332\", \"99950\", \"114239\", \"122165\", \"126294\", \"129289\", \"132687\", \"127924\", \"132808\", \"136153\", \"138517\", \"139638\", \"157726\", \"159316\", \"164142\", \"165441\", \"157467\", \"159333\", \"184635\"], \"outgoing_citations\": [\"231898\", \"232164\", \"234391\", \"247127\", \"267601\", \"272068\", \"272531\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"205750","document_content":"# Grounded Video Description\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideo description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or \"true\" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.06587v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6030\", \"7692\", \"8991\", \"9633\", \"116568\", \"29678\", \"60281\", \"77056\", \"5837\", \"7027\", \"7204\", \"8514\", \"14882\", \"16669\", \"21873\", \"24639\", \"25304\", \"29109\", \"31878\", \"42920\", \"46309\", \"59458\", \"62270\", \"63186\", \"66032\", \"67555\", \"72227\", \"77697\", \"80826\", \"114274\", \"138796\", \"175031\", \"83901\", \"91773\", \"5843\", \"97645\", \"104610\", \"110219\", \"87013\", \"125719\", \"127728\", \"132879\", \"133695\", \"134024\", \"135265\", \"152418\", \"165195\", \"183087\", \"185799\", \"188920\", \"173844\", \"181008\"], \"outgoing_citations\": [\"218912\", \"222631\", \"223293\", \"232882\", \"234602\", \"236918\", \"237655\", \"242033\", \"250211\", \"260810\", \"265650\", \"267523\", \"267973\", \"269640\", \"279740\", \"281152\", \"282532\", \"287302\", \"290392\", \"303534\", \"312643\", \"316803\"]}","task_split":"paper_retrieval"}
{"document_id":"205940","document_content":"# Measuring Similarity: Computationally Reproducing the Scholar's Interests\n## Categories\n- Computation and Language\n## Abstract\nComputerized document classification already orders the news articles that Apple's \"News\" app or Google's \"personalized search\" feature groups together to match a reader's interests. The invisible and therefore illegible decisions that go into these tailored searches have been the subject of a critique by scholars who emphasize that our intelligence about documents is only as good as our ability to understand the criteria of search. This article will attempt to unpack the procedures used in computational classification of texts, translating them into term legible to humanists, and examining opportunities to render the computational text classification process subject to expert critique and improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.05984v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"205943","document_content":"# Scaling shared model governance via model splitting\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Neural and Evolutionary Computing\n## Abstract\nCurrently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation. Unfortunately, neither of these techniques is applicable to the training of large neural networks due to their large computational and communication overheads. As a scalable technique for shared model governance, we propose splitting deep learning model between multiple parties. This paper empirically investigates the security guarantee of this technique, which is introduced as the problem of model completion: Given the entire training data set or an environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model's original performance? We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind~Lab. Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent's trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location. Our results suggest that model splitting might be a feasible technique for shared model governance in some settings where training is very expensive.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.05979v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"122106\"], \"outgoing_citations\": [\"239419\", \"241503\", \"242292\", \"247336\", \"249196\", \"260583\", \"261316\", \"262381\", \"273552\", \"278195\", \"290235\", \"302952\", \"302967\"]}","task_split":"paper_retrieval"}
{"document_id":"205988","document_content":"# ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary variation of text appearances in perspective distortion, text line curvature, text styles and different types of imaging artifacts. The recent deep networks are capable of learning robust representations with respect to imaging artifacts and text style changes, but still face various problems while dealing with scene texts with perspective and curvature distortions. This paper presents an end-to-end trainable scene text recognition system (ESIR) that iteratively removes perspective distortion and text line curvature as driven by better scene text recognition performance. An innovative rectification network is developed which employs a novel line-fitting transformation to estimate the pose of text lines in scenes. In addition, an iterative rectification pipeline is developed where scene text distortions are corrected iteratively towards a fronto-parallel view. The ESIR is also robust to parameter initialization and the training needs only scene text images and word-level annotations as required by most scene text recognition systems. Extensive experiments over a number of public datasets show that the proposed ESIR is capable of rectifying scene text distortions accurately, achieving superior recognition performance for both normal scene text images and those suffering from perspective and curvature distortions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.05824v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6379\", \"13927\", \"37053\", \"50917\", \"52248\", \"66255\", \"68083\", \"44535\", \"121818\", \"180823\", \"191\", \"7580\", \"8109\", \"9220\", \"21983\", \"24824\", \"28270\", \"29428\", \"33363\", \"33358\", \"35666\", \"36977\", \"38101\", \"38469\", \"39482\", \"39737\", \"39759\", \"42497\", \"40901\", \"43037\", \"48822\", \"48901\", \"50591\", \"53635\", \"53750\", \"55703\", \"56713\", \"57569\", \"63761\", \"64528\", \"64650\", \"67749\", \"79320\", \"81960\", \"93345\", \"116951\", \"122764\", \"133054\", \"145945\", \"147463\", \"154282\", \"163092\", \"90549\", \"100072\", \"100417\", \"100709\", \"110306\", \"111035\", \"111991\", \"120420\", \"123550\", \"126431\", \"134664\", \"135021\", \"136843\", \"136831\", \"141887\", \"144173\", \"145587\", \"150060\", \"152219\", \"154995\", \"158242\", \"162477\", \"170229\", \"192671\", \"210634\", \"155455\", \"174894\", \"174928\", \"194314\", \"201515\", \"205980\"], \"outgoing_citations\": [\"194314\", \"201515\", \"205980\", \"225195\", \"225336\", \"231139\", \"232706\", \"239535\", \"247460\", \"250738\", \"256711\", \"278180\", \"294023\", \"296685\", \"296828\", \"311196\", \"320148\", \"320837\", \"322955\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"206026","document_content":"# Conversational Intent Understanding for Passengers in Autonomous Vehicles\n## Categories\n- Computation and Language\n## Abstract\nUnderstanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Experience), the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our AMIE scenarios, we describe usages and support various natural commands for interacting with the vehicle. We collected a multimodal in-cabin data-set with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme. We explored various recent Recurrent Neural Networks (RNN) based techniques and built our own hierarchical models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results achieved F1-score of 0.91 on utterance-level intent recognition and 0.96 on slot extraction models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1901.04899v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"165839\", \"189265\"], \"outgoing_citations\": [\"270825\", \"285311\"]}","task_split":"paper_retrieval"}
{"document_id":"206042","document_content":"# Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nRepresentation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new model that learns coupled representations of domains, intents, and slots by taking advantage of their hierarchical dependency in a Spoken Language Understanding system. Our proposed model learns the vector representation of intents based on the slots tied to these intents by aggregating the representations of the slots. Similarly, the vector representation of a domain is learned by aggregating the representations of the intents tied to a specific domain. To the best of our knowledge, it is the first approach to jointly learning the representations of domains, intents, and slots using their hierarchical relationships. The experimental results demonstrate the effectiveness of the representations learned by our model, as evidenced by improved performance on the contextual cross-domain reranking task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.06083v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"74469\", \"135568\", \"161533\"], \"outgoing_citations\": [\"234683\", \"234684\", \"244978\", \"248968\", \"251669\", \"285317\"]}","task_split":"paper_retrieval"}
{"document_id":"206151","document_content":"# Dynamic Transfer Learning for Named Entity Recognition\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nState-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.05288v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"117882\", \"144494\", \"161585\", \"206157\"], \"outgoing_citations\": [\"161585\", \"206157\", \"208066\", \"227323\", \"227488\", \"230234\", \"234384\", \"240504\", \"240692\", \"249726\", \"256158\", \"263808\", \"271158\", \"274533\", \"286922\", \"287246\", \"295323\", \"296221\", \"302067\", \"302500\", \"309148\"]}","task_split":"paper_retrieval"}
{"document_id":"206404","document_content":"# Deep Reader: Information extraction from Document images via relation extraction and Natural Language\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nRecent advancements in the area of Computer Vision with state-of-art Neural Networks has given a boost to Optical Character Recognition (OCR) accuracies. However, extracting characters\/text alone is often insufficient for relevant information extraction as documents also have a visual structure that is not captured by OCR. Extracting information from tables, charts, footnotes, boxes, headings and retrieving the corresponding structured representation for the document remains a challenge and finds application in a large number of real-world use cases. In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image. The model schema allows for an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables, lines and boxes. Deep Reader maps the extracted entities into a rich relational schema so as to capture all the relevant relationships between entities (words, textboxes, lines etc) detected in the document. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added on top of the relationship schema so that a non-technical user, specifying the queries in natural language, can fetch the information with minimal effort. In this paper, we also demonstrate many different capabilities of Deep Reader and report results on a real-world use case.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.04377v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"14174\", \"48483\", \"182227\"], \"outgoing_citations\": [\"223954\", \"234193\", \"275747\", \"284871\", \"294531\", \"310903\"]}","task_split":"paper_retrieval"}
{"document_id":"206489","document_content":"# Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nWe present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level end-goals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines in both seen and unseen environments. Our code and data are publicly available at https:\/\/github.com\/debadeepta\/vnla .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.04155v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"4499\", \"10293\", \"16536\", \"33721\", \"39368\", \"59461\", \"15829\", \"19456\", \"19039\", \"27522\", \"28518\", \"36974\", \"45665\", \"46839\", \"56489\", \"59216\", \"69518\", \"84618\", \"89875\", \"100277\", \"163326\", \"175031\", \"86813\", \"91773\", \"118832\", \"127495\", \"139227\", \"140901\", \"152871\", \"157302\", \"189381\", \"151605\", \"165930\", \"168651\", \"176603\", \"208773\", \"211921\"], \"outgoing_citations\": [\"192738\", \"208042\", \"208773\", \"209452\", \"210666\", \"212893\", \"218182\", \"219305\", \"219658\", \"225266\", \"226321\", \"227594\", \"228916\", \"238287\", \"242141\", \"245731\", \"247418\", \"247749\", \"247849\", \"248742\", \"248885\", \"249906\", \"250190\", \"254596\", \"255575\", \"261378\", \"263270\", \"272501\", \"293071\", \"299936\", \"317874\", \"321007\", \"327996\", \"334986\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"206517","document_content":"# An Automatic System for Unconstrained Video-Based Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAlthough deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra\/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of modules for face\/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.04058v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32886\", \"93610\", \"187375\"], \"outgoing_citations\": [\"188770\", \"198338\", \"217136\", \"236834\", \"258667\", \"269149\", \"281201\", \"281332\", \"287611\", \"288159\", \"292924\", \"294284\", \"296384\", \"297167\", \"302868\", \"308384\", \"320918\", \"321122\", \"328183\"]}","task_split":"paper_retrieval"}
{"document_id":"206538","document_content":"# An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nNowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions\/situations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-37309-2_26\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215542\", \"218290\", \"242064\", \"256222\", \"290957\", \"307968\"]}","task_split":"paper_retrieval"}
{"document_id":"206648","document_content":"# A Deep Multi-task Learning Approach to Skin Lesion Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin lesion identification is a key step toward dermatological diagnosis. When describing a skin lesion, it is very important to note its body site distribution as many skin diseases commonly affect particular parts of the body. To exploit the correlation between skin lesions and their body site distributions, in this study, we investigate the possibility of improving skin lesion classification using the additional context information provided by body location. Specifically, we build a deep multi-task learning (MTL) framework to jointly optimize skin lesion classification and body location classification (the latter is used as an inductive bias). Our MTL framework uses the state-of-the-art ImageNet pretrained model with specialized loss functions for the two related tasks. Our experiments show that the proposed MTL based method performs more robustly than its standalone (single-task) counterpart.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.03527v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"87658\", \"100571\", \"110872\", \"129987\", \"173413\"], \"outgoing_citations\": [\"206649\", \"293853\", \"297167\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"206649","document_content":"# Skin Disease Classification versus Skin Lesion Characterization: Achieving Robust Diagnosis using Multi-label Deep Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this study, we investigate what a practically useful approach is in order to achieve robust skin disease diagnosis. A direct approach is to target the ground truth diagnosis labels, while an alternative approach instead focuses on determining skin lesion characteristics that are more visually consistent and discernible. We argue that, for computer-aided skin disease diagnosis, it is both more realistic and more useful that lesion type tags should be considered as the target of an automated diagnosis system such that the system can first achieve a high accuracy in describing skin lesions, and in turn facilitate disease diagnosis using lesion characteristics in conjunction with other evidence. To further meet such an objective, we employ convolutional neural networks (CNNs) for both the disease-targeted and lesion-targeted classifications. We have collected a large-scale and diverse dataset of 75,665 skin disease images from six publicly available dermatology atlantes. Then we train and compare both disease-targeted and lesion-targeted classifiers, respectively. For disease-targeted classification, only 27.6% top-1 accuracy and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of 0.42. In contrast, for lesion-targeted classification, we can achieve a much higher mAP of 0.70.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICPR.2016.7899659\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"87658\", \"112061\", \"129987\", \"206648\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"206699","document_content":"# Efficient transfer learning and online adaptation with latent variable models for continuous control\n## Categories\n- Machine Learning\n## Abstract\nTraditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.03399v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"129370\", \"137547\", \"143504\", \"161071\", \"161304\"], \"outgoing_citations\": [\"229890\", \"259045\", \"263272\", \"341343\"]}","task_split":"paper_retrieval"}
{"document_id":"206799","document_content":"# Backdooring Convolutional Neural Networks via Targeted Weight Perturbations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a new type of backdoor attack that exploits a vulnerability of convolutional neural networks (CNNs) that has been previously unstudied. In particular, we examine the application of facial recognition. Deep learning techniques are at the top of the game for facial recognition, which means they have now been implemented in many production-level systems. Alarmingly, unlike other commercial technologies such as operating systems and network devices, deep learning-based facial recognition algorithms are not presently designed with security requirements or audited for security vulnerabilities before deployment. Given how young the technology is and how abstract many of the internal workings of these algorithms are, neural network-based facial recognition systems are prime targets for security breaches. As more and more of our personal information begins to be guarded by facial recognition (e.g., the iPhone X), exploring the security vulnerabilities of these systems from a penetration testing standpoint is crucial. Along these lines, we describe a general methodology for backdooring CNNs via targeted weight perturbations. Using a five-layer CNN and ResNet-50 as case studies, we show that an attacker is able to significantly increase the chance that inputs they supply will be falsely accepted by a CNN while simultaneously preserving the error rates for legitimate enrolled classes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.03128v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"10543\", \"55437\", \"9167\", \"26001\", \"111465\", \"111676\", \"50866\", \"54364\", \"55877\", \"57186\", \"60995\", \"70340\", \"116283\", \"136413\", \"159202\", \"162909\", \"82872\", \"110549\", \"117230\", \"129306\", \"135932\", \"226146\", \"155748\", \"201983\"], \"outgoing_citations\": [\"229877\", \"247399\", \"254903\", \"257949\", \"259984\", \"280112\", \"280873\", \"296288\"]}","task_split":"paper_retrieval"}
{"document_id":"206800","document_content":"# Taking the Scenic Route: Automatic Exploration for Videogames\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nMachine playtesting tools and game moment search engines require exposure to the diversity of a game's state space if they are to report on or index the most interesting moments of possible play. Meanwhile, mobile app distribution services would like to quickly determine if a freshly-uploaded game is fit to be published. Having access to a semantic map of reachable states in the game would enable efficient inference in these applications. However, human gameplay data is expensive to acquire relative to the coverage of a game that it provides. We show that off-the-shelf automatic exploration strategies can explore with an effectiveness comparable to human gameplay on the same timescale. We contribute generic methods for quantifying exploration quality as a function of time and demonstrate our metric on several elementary techniques and human players on a collection of commercial games sampled from multiple game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of states reached and the semantic map extracted, this work makes productive contrast with the focus on finding a behavior policy or optimizing game score used in most automatic game playing research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.03125v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [\"13197\", \"60722\", \"90466\", \"200920\"], \"outgoing_citations\": [\"221473\", \"230068\", \"245453\", \"261387\", \"261529\", \"291025\", \"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"206814","document_content":"# ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nOur goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and\/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.03079v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"48121\", \"1978\", \"4129\", \"4576\", \"5033\", \"7681\", \"15731\", \"20577\", \"21649\", \"21960\", \"22122\", \"23013\", \"26052\", \"30226\", \"35378\", \"50175\", \"60310\", \"62061\", \"75603\", \"76756\", \"82541\", \"103234\", \"132642\", \"136922\", \"144508\", \"1487\", \"4575\", \"4616\", \"9925\", \"9841\", \"10178\", \"12236\", \"13426\", \"18926\", \"20692\", \"17981\", \"23376\", \"23300\", \"23710\", \"25916\", \"26007\", \"28886\", \"29921\", \"34925\", \"36033\", \"22099\", \"36226\", \"36423\", \"36400\", \"36810\", \"36974\", \"37621\", \"40291\", \"41518\", \"42771\", \"89951\", \"46459\", \"47265\", \"47264\", \"48253\", \"52145\", \"52676\", \"54933\", \"55121\", \"59663\", \"62261\", \"62612\", \"63102\", \"64437\", \"64848\", \"65247\", \"65769\", \"62457\", \"66140\", \"66191\", \"71419\", \"72656\", \"74982\", \"75031\", \"75053\", \"75097\", \"75109\", \"75357\", \"78478\", \"79130\", \"79811\", \"85572\", \"86607\", \"87200\", \"88738\", \"89880\", \"97138\", \"98313\", \"103594\", \"105685\", \"106863\", \"112756\", \"114946\", \"117375\", \"128789\", \"135780\", \"137081\", \"149829\", \"161638\", \"163665\", \"165015\", \"198176\", \"262834\", \"68258\", \"80133\", \"86749\", \"88068\", \"90726\", \"92081\", \"103411\", \"103790\", \"104303\", \"104307\", \"105013\", \"106127\", \"106160\", \"106162\", \"107775\", \"108553\", \"109433\", \"112041\", \"112295\", \"112808\", \"113006\", \"113767\", \"115020\", \"115796\", \"116072\", \"117327\", \"117772\", \"117991\", \"118382\", \"119341\", \"119595\", \"120207\", \"120396\", \"121146\", \"122175\", \"122615\", \"124842\", \"126140\", \"126297\", \"126578\", \"126603\", \"127001\", \"102054\", \"86743\", \"128169\", \"128967\", \"129687\", \"130378\", \"131366\", \"133078\", \"134237\", \"135686\", \"135831\", \"136834\", \"137006\", \"137718\", \"137715\", \"138073\", \"138169\", \"138374\", \"138752\", \"139098\", \"139158\", \"141942\", \"144512\", \"145842\", \"145993\", \"148658\", \"150096\", \"151320\", \"152470\", \"153475\", \"153686\", \"154697\", \"156622\", \"156917\", \"161127\", \"166354\", \"173508\", \"174408\", \"183641\", \"193963\", \"81691\", \"149380\", \"152241\", \"153930\", \"155834\", \"158047\", \"158269\", \"159949\", \"160270\", \"162130\", \"162423\", \"161193\", \"165689\", \"166003\", \"169068\", \"169678\", \"170789\", \"173527\", \"179692\", \"183941\", \"185991\", \"186874\", \"189615\", \"189835\", \"191356\", \"193618\", \"193693\", \"194269\", \"196226\", \"196584\", \"196849\", \"197258\"], \"outgoing_citations\": [\"78890\", \"79134\", \"214194\", \"225121\", \"227720\", \"234287\", \"253953\", \"257512\", \"266128\", \"267981\", \"269012\", \"270414\", \"275566\", \"278822\", \"282884\", \"283365\", \"313499\"]}","task_split":"paper_retrieval"}
{"document_id":"206845","document_content":"# Improving Retrieval-Based Question Answering with Deep Inference Models\n## Categories\n- Computation and Language\n## Abstract\nQuestion answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.02971v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"50013\", \"169135\", \"200370\"], \"outgoing_citations\": [\"229792\", \"238962\", \"291019\", \"294137\", \"294554\", \"309736\", \"313465\"]}","task_split":"paper_retrieval"}
{"document_id":"207042","document_content":"# Trustworthy Smart Band: Security Requirement Analysis with Threat Modeling\n## Categories\n- Cryptography and Security\n## Abstract\nAs smart bands make life more convenient and provide a positive lifestyle, many people are now using them. Since smart bands deal with private information, security design and implementation for smart band system become necessary. To make a trustworthy smart band, we must derive the security requirements of the system first, and then design the system satisfying the security requirements. In this paper, we apply threat modeling techniques such as Data Flow Diagram, STRIDE, and Attack Tree to the smart band system to identify threats and derive security requirements accordingly. Through threat modeling, we found the vulnerabilities of the smart band system and successfully exploited smart bands with them. To defend against these threats, we propose security measures and verify that they are secure by using Scyther which is a tool for automatic verification of security protocol.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.02361v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"345726\"]}","task_split":"paper_retrieval"}
{"document_id":"207062","document_content":"# Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nSkin lesions are conditions that appear on a patient due to many different reasons. One of these can be because of an abnormal growth in skin tissue, defined as cancer. This disease plagues more than 14.1 million patients and had been the cause of more than 8.2 million deaths, worldwide. Therefore, the construction of a classification model for 12 lesions, including Malignant Melanoma and Basal Cell Carcinoma, is proposed. Furthermore, in this work, it is used a ResNet-152 architecture, which was trained over 3,797 images, later augmented by a factor of 29 times, using positional, scale, and lighting transformations. Finally, the network was tested with 956 images and achieve an area under the curve (AUC) of 0.96 for Melanoma and 0.91 for Basal Cell Carcinoma.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.02316v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"230919\", \"237660\", \"258792\", \"270887\", \"271944\"]}","task_split":"paper_retrieval"}
{"document_id":"207071","document_content":"# Simultaneous Recognition of Horizontal and Vertical Text in Natural Images\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nRecent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.07059v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"49128\", \"50591\", \"120420\"], \"outgoing_citations\": [\"245899\", \"256711\", \"269108\", \"294023\", \"296685\", \"296828\", \"320148\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"207094","document_content":"# Towards a Definition of Disentangled Representations\n## Categories\n- Machine Learning\n## Abstract\nHow can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. However, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. In particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. Similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. By connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. Our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. While this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.02230v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"33274\", \"5409\", \"6554\", \"12073\", \"12721\", \"26622\", \"27322\", \"33203\", \"39463\", \"63757\", \"65191\", \"68339\", \"70887\", \"71525\", \"72458\", \"87490\", \"103983\", \"230922\", \"952\", \"4575\", \"7376\", \"12943\", \"12936\", \"19106\", \"21063\", \"25951\", \"25164\", \"28666\", \"29575\", \"29923\", \"11676\", \"33786\", \"33921\", \"39999\", \"42895\", \"44223\", \"44100\", \"45624\", \"53207\", \"56003\", \"61327\", \"61416\", \"61817\", \"63434\", \"65861\", \"66198\", \"66829\", \"67572\", \"68053\", \"69932\", \"70421\", \"72074\", \"75643\", \"87413\", \"89316\", \"89349\", \"92381\", \"95962\", \"103627\", \"105643\", \"105871\", \"110493\", \"120747\", \"129567\", \"142440\", \"69639\", \"162060\", \"71648\", \"81601\", \"82608\", \"82703\", \"84506\", \"85166\", \"91004\", \"91279\", \"91259\", \"91567\", \"100660\", \"103891\", \"105383\", \"109667\", \"83549\", \"116297\", \"116650\", \"123820\", \"128113\", \"129067\", \"130407\", \"132719\", \"109266\", \"133373\", \"138664\", \"139759\", \"141826\", \"142769\", \"143360\", \"143581\", \"143775\", \"148803\", \"152394\", \"160471\", \"161795\", \"164155\", \"168518\", \"170071\", \"176649\", \"180811\", \"183696\", \"184690\", \"187863\", \"201588\", \"206756\", \"215956\", \"149182\", \"152148\", \"154401\", \"151741\", \"159243\", \"164167\", \"154419\", \"165021\", \"168312\", \"169878\", \"174976\", \"177310\", \"179296\", \"179803\", \"179957\", \"181335\", \"181388\", \"181909\", \"182615\", \"183257\", \"183732\", \"185954\", \"193061\", \"196894\", \"202216\", \"206907\", \"212169\", \"224873\"], \"outgoing_citations\": [\"208041\", \"218117\", \"220865\", \"225801\", \"236093\", \"236595\", \"241933\", \"242142\", \"242239\", \"246129\", \"251597\", \"260136\", \"261512\", \"264549\", \"264963\", \"267761\", \"278987\", \"280296\", \"281227\", \"284470\", \"297838\", \"300606\", \"311052\", \"311482\", \"315703\", \"316183\", \"319797\", \"320031\", \"333469\", \"348554\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"207124","document_content":"# Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications\n## Categories\n- Computation and Language\n## Abstract\nA growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems -- just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines. In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step we address are word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing too much runtime performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4230\/OASIcs.LDK.2019.11\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"195197\", \"209718\"], \"outgoing_citations\": [\"233080\", \"350356\"]}","task_split":"paper_retrieval"}
{"document_id":"207184","document_content":"# A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nNamed entity discovery and linking is the fundamental and core component of question answering. In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement. To overcome these difficulties, we proposed a knowledge graph based solution for QEDL and developed a system consists of Question Entity Discovery (QED) module and Entity Linking (EL) module. The method of QED module is a tradeoff and ensemble of two methods. One is the method based on knowledge graph retrieval, which could extract more entities in questions and guarantee the recall rate, the other is the method based on Conditional Random Field (CRF), which improves the precision rate. The EL module is treated as a ranking problem and Learning to Rank (LTR) method with features such as semantic similarity, text similarity and entity popularity is utilized to extract and make full use of the information in short texts. On the official dataset of a shared QEDL evaluation task, our approach could obtain 64.44% F1 score of QED and 64.86% accuracy of EL, which ranks the 2nd place and indicates its practical use for QEDL problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.01889v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"295325\"]}","task_split":"paper_retrieval"}
{"document_id":"207324","document_content":"# Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nHierarchical reinforcement learning deals with the problem of breaking down large tasks into meaningful sub-tasks. Autonomous discovery of these sub-tasks has remained a challenging problem. We propose a novel method of learning sub-tasks by combining paradigms of routing in computer networks and graph based skill discovery within the options framework to define meaningful sub-goals. We apply the recent advancements of learning embeddings using Riemannian optimisation in the hyperbolic space to embed the state set into the hyperbolic space and create a model of the environment. In doing so we enforce a global topology on the states and are able to exploit this topology to learn meaningful sub-tasks. We demonstrate empirically, both in discrete and continuous domains, how these embeddings can improve the learning of meaningful sub-tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.01487v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"228417\", \"228704\", \"230068\", \"236182\", \"236843\", \"252334\", \"260297\", \"265739\", \"272464\", \"272530\", \"284567\", \"294142\"]}","task_split":"paper_retrieval"}
{"document_id":"207610","document_content":"# Universal Perturbation Attack Against Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nUniversal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00552v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"155021\", \"8730\", \"40839\", \"47857\", \"73146\", \"121565\", \"21092\", \"32156\", \"34476\", \"44110\", \"49247\", \"64267\", \"65308\", \"65935\", \"152734\", \"174992\", \"218749\", \"124662\", \"126300\", \"131122\", \"140049\", \"205613\", \"170359\", \"201111\"], \"outgoing_citations\": [\"237248\", \"244295\", \"245634\", \"251284\", \"251473\", \"266440\", \"277635\", \"287103\", \"291400\", \"301572\", \"303067\", \"320036\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"207617","document_content":"# Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data\n## Categories\n- Machine Learning\n- Quantitative Methods\n## Abstract\nAlzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, personalized care and treatment planning, as well as patient counseling about their individual progression is limited. Recent developments in machine learning to detect hidden patterns in complex, multi-dimensional datasets provides significant opportunities to address this critical need. In this work, we use unsupervised and supervised machine learning approaches for subtype identification and prediction. We apply machine learning methods to the extensive clinical observations available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to predict disease progression. Our analysis depicts the progression space for the Alzheimer's disease into low, moderate and high disease progression zones. The proposed work will enable early detection and characterization of distinct disease subtypes based on clinical heterogeneity. We anticipate that our models will enable patient counseling, clinical trial design, and ultimately individualized clinical care.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00546v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"q-bio.QM\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Quantitative Methods\", \"Machine Learning\"], \"incoming_citations\": [\"31375\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"207627","document_content":"# Generalization in anti-causal learning\n## Categories\n- Machine Learning\n## Abstract\nThe ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i.d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a causal model. In such a framework, we want to find a cause that leads to the observed effect. Anti-causal models are used to drive this search, but a causal model is required for validation. We investigate the fundamental differences between causal and anti-causal tasks, discuss implications for topics ranging from adversarial attacks to disentangling factors of variation, and provide extensive evidence from the literature to substantiate our view. We advocate for incorporating causal models in supervised learning to shift the paradigm from inference only, to search and validation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00524v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"34887\", \"13855\", \"24977\", \"41344\", \"42883\", \"89349\", \"82872\", \"116023\", \"127307\", \"130767\", \"154589\", \"164060\", \"177371\", \"195216\"], \"outgoing_citations\": [\"204885\", \"231780\", \"241933\", \"245296\", \"246541\", \"248617\", \"249555\", \"251597\", \"252039\", \"263890\", \"267196\", \"273390\", \"281041\", \"295651\", \"295736\", \"301465\", \"316517\", \"353174\", \"353236\", \"357230\", \"360719\"]}","task_split":"paper_retrieval"}
{"document_id":"207720","document_content":"# Multi-modal Capsule Routing for Actor and Action Video Segmentation Conditioned on Natural Language Queries\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose an end-to-end capsule network for pixel level localization of actors and actions present in a video. The localization is performed based on a natural language query through which an actor and action are specified. We propose to encode both the video as well as textual input in the form of capsules, which provide more effective representation in comparison with standard convolution based features. We introduce a novel capsule based attention mechanism for fusion of video and text capsules for text selected video segmentation. The attention mechanism is performed via joint EM routing over video and text capsules for text selected actor and action localization. The existing works on actor-action localization are mainly focused on localization in a single frame instead of the full video. Different from existing works, we propose to perform the localization on all frames of the video. To validate the potential of the proposed network for actor and action localization on all the frames of a video, we extend an existing actor-action dataset (A2D) with annotations for all the frames. The experimental evaluation demonstrates the effectiveness of the proposed capsule network for text selective actor and action localization in videos, and it also improves upon the performance of the existing state-of-the art works on single frame-based localization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00303v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32683\", \"72701\", \"102396\"], \"outgoing_citations\": [\"231215\", \"235911\", \"237346\", \"238366\", \"259318\", \"265650\", \"267223\", \"267280\", \"267973\", \"270117\", \"273696\", \"292471\", \"295117\", \"296243\"]}","task_split":"paper_retrieval"}
{"document_id":"207730","document_content":"# Learning Curriculum Policies for Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nCurriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an open problem that has been the subject of much recent work in this area. In this paper, we build upon a recent method for curriculum design, which formulates the curriculum sequencing problem as a Markov Decision Process. We extend this model to handle multiple transfer learning algorithms, and show for the first time that a curriculum policy over this MDP can be learned from experience. We explore various representations that make this possible, and evaluate our approach by learning curriculum policies for multiple agents in two different domains. The results show that our method produces curricula that can train agents to perform on a target task as fast or faster than existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00285v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"9804\", \"18466\", \"73516\", \"104784\", \"33029\", \"43616\", \"63182\", \"66832\", \"88744\", \"114931\", \"117801\", \"138823\", \"201315\", \"80827\", \"95611\", \"96714\", \"108295\", \"111886\", \"114905\", \"116978\", \"126123\", \"126363\", \"129185\", \"137441\", \"142432\", \"147597\", \"168328\", \"189573\", \"162705\", \"162994\", \"164505\"], \"outgoing_citations\": [\"240506\", \"261017\", \"262325\", \"266182\", \"269253\", \"271375\", \"357294\"]}","task_split":"paper_retrieval"}
{"document_id":"207767","document_content":"# Traversing the Continuous Spectrum of Image Retrieval with Deep Dynamic Models\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce the first work to tackle the image retrieval problem as a continuous operation. While the proposed approaches in the literature can be roughly categorized into two main groups: category- and instance-based retrieval, in this work we show that the retrieval task is much richer and more complex. Image similarity goes beyond this discrete vantage point and spans a continuous spectrum among the classical operating points of category and instance similarity. However, current retrieval models are static and incapable of exploring this rich structure of the retrieval space since they are trained and evaluated with a single operating point as a target objective. Hence, we introduce a novel retrieval model that for a given query is capable of producing a dynamic embedding that can target an arbitrary point along the continuous retrieval spectrum. Our model disentangles the visual signal of a query image into its basic components of categorical and attribute information. Furthermore, using a continuous control parameter our model learns to reconstruct a dynamic embedding of the query by mixing these components with different proportions to target a specific point along the retrieval simplex. We demonstrate our idea in a comprehensive evaluation of the proposed model and highlight the advantages of our approach against a set of well-established discrete retrieval models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00202v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"236817\", \"255095\", \"277635\", \"279518\", \"290747\", \"294983\", \"295204\", \"295909\", \"297133\", \"301767\", \"303067\", \"303224\", \"310639\", \"311684\", \"312090\", \"320036\", \"330936\", \"331445\", \"332726\", \"336312\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"207804","document_content":"# Explore-Exploit: A Framework for Interactive and Online Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nInteractive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00116v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"129982\"], \"outgoing_citations\": [\"225737\", \"278174\", \"333185\", \"338202\"]}","task_split":"paper_retrieval"}
{"document_id":"207829","document_content":"# GDPP: Learning Diverse Generations Using Determinantal Point Process\n## Categories\n- Machine Learning\n## Abstract\nGenerative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteristic of generative models is their ability to produce multi-modal outputs. However, while training, they are often susceptible to mode collapse, that is models are limited in mapping input noise to only a few modes of the true data distribution. In this work, we draw inspiration from Determinantal Point Process (DPP) to propose an unsupervised penalty loss that alleviates mode collapse while producing higher quality samples. DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity. We use DPP kernel to model the diversity in real data as well as in synthetic data. Then, we devise an objective term that encourages generators to synthesize data with similar diversity to real data. In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, generation quality, and convergence-time whereas being 5.8x faster than its closest competitor.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1812.00068v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1734\", \"57129\", \"5884\", \"25661\", \"70294\", \"207810\", \"26428\", \"37696\", \"43442\", \"43475\", \"45681\", \"136319\", \"83794\", \"117973\", \"118261\", \"94245\", \"113528\", \"114354\", \"121535\", \"136109\", \"140042\", \"176596\", \"164584\", \"203956\"], \"outgoing_citations\": [\"227526\", \"229773\", \"231428\", \"247713\", \"248034\", \"256229\", \"264374\", \"269271\", \"272783\", \"278560\", \"281002\", \"281144\", \"291302\", \"291817\"]}","task_split":"paper_retrieval"}
{"document_id":"207921","document_content":"# An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDue to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.12695v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"270618\"]}","task_split":"paper_retrieval"}
{"document_id":"207951","document_content":"# LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data\n## Categories\n- Machine Learning\n## Abstract\nIntensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.12629v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"9036\", \"29243\", \"45580\", \"92438\", \"110259\", \"10455\", \"34610\", \"57772\", \"79711\", \"80280\", \"88714\", \"102197\", \"100412\", \"132414\", \"140335\", \"141728\", \"156643\", \"205910\", \"161850\", \"171823\", \"194178\"], \"outgoing_citations\": [\"163298\", \"171823\", \"194178\", \"204950\", \"208335\", \"226146\", \"265073\", \"280506\", \"303573\", \"329400\", \"339350\", \"340028\", \"359693\"]}","task_split":"paper_retrieval"}
{"document_id":"207983","document_content":"# An Introduction to Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1561\/2200000071\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"16116\", \"24905\", \"41588\", \"64244\", \"6909\", \"11719\", \"13681\", \"22043\", \"33383\", \"38070\", \"42321\", \"49145\", \"53788\", \"58517\", \"61931\", \"77439\", \"80551\", \"101350\", \"107773\", \"111065\", \"115868\", \"124148\", \"130543\", \"150685\", \"2179\", \"4334\", \"6632\", \"15055\", \"18204\", \"18635\", \"19159\", \"25729\", \"27533\", \"42509\", \"20450\", \"47296\", \"48351\", \"50631\", \"53142\", \"54028\", \"54459\", \"58757\", \"62159\", \"63975\", \"67061\", \"68621\", \"69405\", \"70860\", \"71981\", \"70806\", \"75808\", \"77491\", \"81581\", \"86239\", \"112665\", \"170489\", \"172131\", \"175053\", \"183481\", \"152854\", \"142374\", \"78995\", \"97067\", \"99058\", \"105562\", \"92764\", \"112328\", \"109538\", \"116790\", \"118092\", \"118444\", \"120465\", \"122670\", \"124375\", \"124294\", \"125326\", \"125317\", \"126662\", \"129206\", \"134588\", \"135645\", \"136880\", \"138897\", \"138714\", \"142272\", \"148658\", \"149376\", \"159013\", \"164419\", \"166815\", \"175157\", \"177206\", \"187408\", \"205583\", \"224848\", \"152118\", \"156121\", \"159217\", \"165374\", \"214440\", \"171112\", \"172378\", \"172691\", \"174609\", \"179610\", \"182781\"], \"outgoing_citations\": [\"212016\", \"212299\", \"213637\", \"214419\", \"214592\", \"215425\", \"218114\", \"218124\", \"218770\", \"225886\", \"226635\", \"227301\", \"230068\", \"233926\", \"234025\", \"235092\", \"240506\", \"241436\", \"241503\", \"247726\", \"252249\", \"253042\", \"254581\", \"254920\", \"255154\", \"255613\", \"256103\", \"258472\", \"258580\", \"258834\", \"259045\", \"260136\", \"260583\", \"260770\", \"260780\", \"261316\", \"261493\", \"261522\", \"262106\", \"262381\", \"263576\", \"263811\", \"263957\", \"264419\", \"264786\", \"266182\", \"266825\", \"268361\", \"268563\", \"268584\", \"268821\", \"269012\", \"269251\", \"269458\", \"270925\", \"271783\", \"271864\", \"272422\", \"272530\", \"272636\", \"272788\", \"272987\", \"277193\", \"278195\", \"279191\", \"279790\", \"280202\", \"280296\", \"280570\", \"280681\", \"280870\", \"280992\", \"281110\", \"281257\", \"281484\", \"282684\", \"283410\", \"283428\", \"284468\", \"284470\", \"284565\", \"284567\", \"284713\", \"286255\", \"287823\", \"290371\", \"290744\", \"290825\", \"291025\", \"291460\", \"291568\", \"291892\", \"292009\", \"293071\", \"293876\", \"294142\", \"295281\", \"297268\", \"297317\", \"298281\", \"298562\", \"298651\", \"300847\", \"301917\", \"302720\", \"302815\", \"302952\", \"302967\", \"302970\", \"303141\", \"303306\", \"303539\", \"304250\", \"305802\", \"306812\", \"308711\", \"310171\", \"310624\", \"317505\", \"317863\", \"328869\", \"329835\", \"349544\", \"349880\", \"350523\", \"357569\"]}","task_split":"paper_retrieval"}
{"document_id":"208021","document_content":"# A rule-based system proposal to aid in the evaluation and decision-making in external beam radiation treatment planning\n## Categories\n- Software Engineering\n- Artificial Intelligence\n## Abstract\nAs part of a plan launched by the Ministry of Health of Brazil to increase the availability of linear accelerators for radiotherapy treatment for the whole country, for which Varian Medical Systems company has won the bidding, a technical cooperation agreement was signed inviting Brazilian Scientific and Technological Institutions to participate in a technology transfer program. As a result, jointly, the Eldorado Research Institute and the Center for Biomedical Engineering of the University of Campinas presents in this work, the concepts behind of a proposed rule engine to aid in the evaluation and decision-making in radiotherapy treatment planning. Normally, the determination of the radiation dose for a given patient is a complex and intensive procedure, which requires a lot of domain knowledge and subjective experience from the oncologists' team. In order to help them in this complex task, and additionally, provide an auxiliary tool for less experienced oncologists, it is presented a project conception of a software system that will make use of a hybrid data-oriented approach. The proposed rule engine will apply both inference mechanism and expression evaluation to verify and accredit the quality of an external beam radiation treatment plan by considering, at first, the 3D-conformal radiotherapy (3DCRT) technique.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.12454v1\", \"primary_category\": \"cs.SE\", \"categories\": [\"cs.AI\", \"cs.SE\"], \"primary_category_human_readable\": \"Software Engineering\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Software Engineering\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"208041","document_content":"# Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.12359v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8357\", \"65457\", \"81165\", \"113369\", \"168861\", \"102277\", \"315\", \"1785\", \"3852\", \"4316\", \"5274\", \"6554\", \"9974\", \"12721\", \"14348\", \"14588\", \"16710\", \"16722\", \"17295\", \"17675\", \"17963\", \"18254\", \"21642\", \"22548\", \"23287\", \"24683\", \"25914\", \"27322\", \"27417\", \"28495\", \"31291\", \"33203\", \"34142\", \"34887\", \"38128\", \"43433\", \"45970\", \"49754\", \"50058\", \"56250\", \"65191\", \"155351\", \"67913\", \"70887\", \"72458\", \"73900\", \"75603\", \"76023\", \"80015\", \"83792\", \"87490\", \"89936\", \"97544\", \"103983\", \"119167\", \"146022\", \"150201\", \"160326\", \"161560\", \"230922\", \"4575\", \"4894\", \"4898\", \"5668\", \"6590\", \"7221\", \"10004\", \"9992\", \"10442\", \"12936\", \"13426\", \"13493\", \"14234\", \"14278\", \"14809\", \"15469\", \"17072\", \"17445\", \"18244\", \"18189\", \"19486\", \"19599\", \"20747\", \"20955\", \"22992\", \"23076\", \"23400\", \"24977\", \"26306\", \"26673\", \"27458\", \"28401\", \"29142\", \"29575\", \"29931\", \"30112\", \"30287\", \"32107\", \"32807\", \"33468\", \"11676\", \"33786\", \"35216\", \"36326\", \"36764\", \"37469\", \"38455\", \"38467\", \"38668\", \"39178\", \"39665\", \"39999\", \"41320\", \"41873\", \"43049\", \"43333\", \"43355\", \"43335\", \"43651\", \"43900\", \"44100\", \"44506\", \"45681\", \"45629\", \"45831\", \"46435\", \"47080\", \"47197\", \"46569\", \"52722\", \"52846\", \"54538\", \"117483\", \"45942\", \"56003\", \"57749\", \"59449\", \"59530\", \"59802\", \"59862\", \"60282\", \"61327\", \"61452\", \"61416\", \"61509\", \"62256\", \"62588\", \"62794\", \"63434\", \"64367\", \"64978\", \"65007\", \"65861\", \"66198\", \"67170\", \"67560\", \"67912\", \"68053\", \"68067\", \"68669\", \"69721\", \"69932\", \"69963\", \"70171\", \"70211\", \"70382\", \"72074\", \"72199\", \"72983\", \"73927\", \"74139\", \"74357\", \"74730\", \"75323\", \"76739\", \"79090\", \"82171\", \"83066\", \"84068\", \"84768\", \"84985\", \"86412\", \"87413\", \"89349\", \"89316\", \"90921\", \"93022\", \"94031\", \"95962\", \"95944\", \"96114\", \"97331\", \"149306\", \"98093\", \"98752\", \"103627\", \"105871\", \"107985\", \"108741\", \"108902\", \"110493\", \"111236\", \"112073\", \"117883\", \"118255\", \"118768\", \"120747\", \"129567\", \"72041\", \"133796\", \"141540\", \"145172\", \"145349\", \"156136\", \"158503\", \"139836\", \"183113\", \"210854\", \"93244\", \"69639\", \"162060\", \"34278\", \"80538\", \"82209\", \"82669\", \"83044\", \"83493\", \"86628\", \"87061\", \"88024\", \"88259\", \"88046\", \"91259\", \"91384\", \"91429\", \"92004\", \"93568\", \"82753\", \"95821\", \"96305\", \"96496\", \"97865\", \"99542\", \"100165\", \"100660\", \"100782\", \"101063\", \"101596\", \"101955\", \"103891\", \"104295\", \"104568\", \"105241\", \"105383\", \"105296\", \"105668\", \"105720\", \"105882\", \"107530\", \"107832\", \"108960\", \"109667\", \"109786\", \"109753\", \"109953\", \"110924\", \"111212\", \"111712\", \"111868\", \"111965\", \"112145\", \"112398\", \"101658\", \"113870\", \"114524\", \"116650\", \"117038\", \"118821\", \"118888\", \"119003\", \"119691\", \"119641\", \"119937\", \"121768\", \"116023\", \"116505\", \"117940\", \"121844\", \"123374\", \"124375\", \"124684\", \"124845\", \"126318\", \"126558\", \"126836\", \"128671\", \"129067\", \"129244\", \"130407\", \"131678\", \"132719\", \"109266\", \"128175\", \"130199\", \"132495\", \"133900\", \"135906\", \"136839\", \"137476\", \"138086\", \"138328\", \"138404\", \"139756\", \"140081\", \"139944\", \"139925\", \"140573\", \"141924\", \"141826\", \"142769\", \"143360\", \"143581\", \"143831\", \"143775\", \"146278\", \"147292\", \"147210\", \"147237\", \"147421\", \"148302\", \"150005\", \"151681\", \"152402\", \"154412\", \"155582\", \"155453\", \"156282\", \"158575\", \"160471\", \"161214\", \"163466\", \"163661\", \"164600\", \"168518\", \"168874\", \"170071\", \"176649\", \"178648\", \"180811\", \"182775\", \"183696\", \"183943\", \"184045\", \"184464\", \"184690\", \"187093\", \"187863\", \"190842\", \"193850\", \"200942\", \"201881\", \"206756\", \"208520\", \"144684\", \"149182\", \"151284\", \"151710\", \"152148\", \"152731\", \"153208\", \"153300\", \"153486\", \"154101\", \"154401\", \"154589\", \"155597\", \"156867\", \"158217\", \"151741\", \"158482\", \"158823\", \"161911\", \"162928\", \"160312\", \"163906\", \"164905\", \"172919\", \"165584\", \"165689\", \"168312\", \"168885\", \"169878\", \"170441\", \"172552\", \"174808\", \"175699\", \"176254\", \"176342\", \"177310\", \"178826\", \"179296\", \"179803\", \"179957\", \"181388\", \"181909\", \"181916\", \"181921\", \"183257\", \"183732\", \"182154\", \"185954\", \"190015\", \"190475\", \"191000\", \"193061\", \"195230\", \"196894\", \"199494\", \"202065\", \"202216\", \"203215\", \"205697\", \"206907\", \"212169\", \"206218\", \"207094\", \"210846\", \"233751\"], \"outgoing_citations\": [\"206218\", \"210434\", \"212169\", \"224840\", \"225801\", \"231027\", \"233751\", \"239658\", \"241933\", \"242142\", \"242239\", \"251597\", \"253197\", \"255129\", \"260136\", \"261512\", \"264782\", \"264963\", \"265526\", \"280734\", \"292209\", \"297838\", \"300606\", \"311052\", \"311482\", \"316183\", \"319797\", \"320031\", \"321465\", \"333469\", \"353174\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"208243","document_content":"# Experience Replay for Continual Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nContinual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions. This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills. We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence. Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience. While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning. We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities. When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.11682v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"24448\", \"68316\", \"74019\", \"90584\", \"102469\", \"102782\", \"111192\", \"1070\", \"2277\", \"4353\", \"8773\", \"11176\", \"11761\", \"14514\", \"15121\", \"15140\", \"16606\", \"16558\", \"22458\", \"22567\", \"26377\", \"44495\", \"46533\", \"50173\", \"52133\", \"52904\", \"53747\", \"56927\", \"72850\", \"73496\", \"78473\", \"97832\", \"120568\", \"128670\", \"166134\", \"39789\", \"523\", \"902\", \"3165\", \"3273\", \"7252\", \"9455\", \"11861\", \"14593\", \"15424\", \"18988\", \"16255\", \"19728\", \"25785\", \"27318\", \"27991\", \"29905\", \"31901\", \"32824\", \"29534\", \"38286\", \"38582\", \"41277\", \"41965\", \"42843\", \"44694\", \"45234\", \"46195\", \"46863\", \"48814\", \"50541\", \"53848\", \"54353\", \"54582\", \"57184\", \"57588\", \"59797\", \"60826\", \"61313\", \"61389\", \"61562\", \"63370\", \"68930\", \"73883\", \"78843\", \"78768\", \"80768\", \"82278\", \"83250\", \"84800\", \"86746\", \"89248\", \"95506\", \"101182\", \"103106\", \"112059\", \"112076\", \"113136\", \"115768\", \"119370\", \"134365\", \"141358\", \"207011\", \"129829\", \"2046\", \"44049\", \"80589\", \"84673\", \"87091\", \"92241\", \"96426\", \"96376\", \"97411\", \"101780\", \"102426\", \"112186\", \"115958\", \"116914\", \"117390\", \"117623\", \"120010\", \"116357\", \"127469\", \"130043\", \"130876\", \"137119\", \"137673\", \"141948\", \"143775\", \"153104\", \"155007\", \"155615\", \"161521\", \"164924\", \"147344\", \"182889\", \"200812\", \"137047\", \"163028\", \"153054\", \"172207\", \"180158\", \"184546\", \"189213\", \"191374\", \"197283\", \"200656\"], \"outgoing_citations\": [\"200656\", \"217663\", \"231847\", \"240600\", \"241499\", \"264786\", \"281159\", \"281257\", \"290825\"]}","task_split":"paper_retrieval"}
{"document_id":"208278","document_content":"# Link Prediction in Networks with Core-Fringe Data\n## Categories\n- Social and Information Networks\n- Physics and Society\n- Machine Learning\n- Machine Learning\n## Abstract\nData collection often involves the partial measurement of a larger system. A common example arises in collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes along with a potentially much larger set of fringe nodes that have links to the core. Given the ubiquity of this process for assembling network data, it is crucial to understand the role of such a `core-fringe' structure.   Here we study how the inclusion of fringe nodes affects the standard task of network link prediction. One might initially think the inclusion of any additional data is useful, and hence that it should be beneficial to include all fringe nodes that are available. However, we find that this is not true; in fact, there is substantial variability in the value of the fringe nodes for prediction. Once an algorithm is selected, in some datasets, including any additional data from the fringe can actually hurt prediction performance; in other datasets, including some amount of fringe information is useful before prediction performance saturates or even declines; and in further cases, including the entire fringe leads to the best performance. While such variety might seem surprising, we show that these behaviors are exhibited by simple random graph models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3308558.3313626\", \"primary_category\": \"cs.SI\", \"categories\": [\"physics.soc-ph\", \"cs.SI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Physics and Society\", \"Social and Information Networks\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"13759\", \"74616\", \"36868\", \"186308\", \"113891\"], \"outgoing_citations\": [\"233354\", \"240500\", \"249126\", \"291900\", \"324413\", \"329725\", \"340653\"]}","task_split":"paper_retrieval"}
{"document_id":"208351","document_content":"# Unsupervised Control Through Non-Parametric Discriminative Rewards\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.11359v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"44400\", \"94177\", \"122\", \"7709\", \"11364\", \"13252\", \"13151\", \"18599\", \"32551\", \"36644\", \"46165\", \"56916\", \"79803\", \"106764\", \"13872\", \"14068\", \"15116\", \"17658\", \"26551\", \"34799\", \"35306\", \"37058\", \"39374\", \"43379\", \"45285\", \"45630\", \"53903\", \"54546\", \"61166\", \"63088\", \"63319\", \"64566\", \"75117\", \"77736\", \"80569\", \"82278\", \"82806\", \"84068\", \"93791\", \"96855\", \"90622\", \"107437\", \"135951\", \"201315\", \"77975\", \"113883\", \"116671\", \"119169\", \"126377\", \"132207\", \"142272\", \"143204\", \"143954\", \"175488\", \"177830\", \"181163\", \"186749\", \"195849\", \"152041\", \"152835\", \"155441\", \"158110\", \"160120\", \"163364\", \"171112\", \"177345\", \"179957\", \"185410\", \"185607\", \"196387\", \"198915\", \"206945\", \"228299\"], \"outgoing_citations\": [\"224840\", \"225801\", \"227065\", \"229145\", \"231175\", \"231209\", \"234517\", \"236849\", \"236969\", \"240233\", \"240282\", \"240506\", \"241900\", \"246118\", \"266182\", \"268248\", \"269251\", \"269458\", \"272464\", \"272530\", \"279790\", \"291908\", \"297268\", \"308711\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"208371","document_content":"# Skin lesion segmentation using U-Net and good training strategies\n## Categories\n- Computer Vision and Pattern Recognition\n- 68T45\n- I.2.10\n## Abstract\nIn this paper we approach the problem of skin lesion segmentation using a convolutional neural network based on the U-Net architecture. We present a set of training strategies that had a significant impact on the performance of this model. We evaluated this method on the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection, obtaining threshold Jaccard index of 77.5%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.11314v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68T45\", \"I.2.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68T45\", \"I.2.10\"], \"incoming_citations\": [\"150556\"], \"outgoing_citations\": [\"237660\", \"263368\", \"321475\"]}","task_split":"paper_retrieval"}
{"document_id":"208474","document_content":"# Dataset Distillation\n## Categories\n- Machine Learning\n## Abstract\nModel distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10959v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"17936\"], \"outgoing_citations\": [\"17936\", \"33002\", \"57855\", \"58158\", \"68946\", \"99752\", \"100192\", \"106716\", \"117601\", \"118482\", \"119380\", \"119670\", \"120650\", \"121168\", \"128239\", \"131003\", \"150871\", \"163095\", \"206200\", \"209859\", \"234262\", \"236393\", \"239655\", \"259604\", \"287664\", \"290679\", \"317642\", \"337405\", \"360716\"]}","task_split":"paper_retrieval"}
{"document_id":"208521","document_content":"# Part-level Car Parsing and Reconstruction from Single Street View\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPart information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and DeepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10837v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"111750\", \"176731\"], \"outgoing_citations\": [\"206936\", \"208084\", \"222674\", \"225992\", \"228415\", \"235203\", \"255272\", \"259211\", \"267795\", \"270859\", \"271575\", \"276764\", \"278972\", \"279147\", \"284450\", \"300920\", \"303379\", \"306596\", \"310367\", \"312499\", \"313495\", \"328649\"]}","task_split":"paper_retrieval"}
{"document_id":"208555","document_content":"# Unsupervised Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep neural networks have achieved great successes on the image captioning task. However, most of the existing models depend heavily on paired image-sentence datasets, which are very expensive to acquire. In this paper, we make the first attempt to train an image captioning model in an unsupervised manner. Instead of relying on manually labeled image-sentence pairs, our proposed model merely requires an image set, a sentence corpus, and an existing visual concept detector. The sentence corpus is used to teach the captioning model how to generate plausible sentences. Meanwhile, the knowledge in the visual concept detector is distilled into the captioning model to guide the model to recognize the visual concepts in an image. In order to further encourage the generated captions to be semantically consistent with the image, the image and caption are projected into a common latent space so that they can reconstruct each other. Given that the existing sentence corpora are mainly designed for linguistic research and are thus with little reference to image contents, we crawl a large-scale image description corpus of two million natural sentences to facilitate the unsupervised image captioning scenario. Experimental results show that our proposed model is able to produce quite promising results without any caption annotations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10787v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"5842\", \"5789\", \"6697\", \"14401\", \"67993\", \"96985\", \"101277\", \"93187\", \"6500\", \"26968\", \"27283\", \"32962\", \"37041\", \"40810\", \"42740\", \"91643\", \"47764\", \"53028\", \"55413\", \"61057\", \"168075\", \"80867\", \"93826\", \"112161\", \"88096\", \"5781\", \"99988\", \"101091\", \"102731\", \"116404\", \"129340\", \"134565\", \"134875\", \"155210\", \"163008\", \"168553\", \"169277\", \"170020\", \"170299\", \"171342\", \"171726\", \"175142\", \"193738\", \"193909\"], \"outgoing_citations\": [\"223302\", \"227880\", \"234807\", \"236897\", \"237199\", \"238941\", \"244368\", \"251816\", \"251974\", \"267476\", \"279676\", \"280323\", \"281152\", \"295639\", \"302815\", \"303209\"]}","task_split":"paper_retrieval"}
{"document_id":"208585","document_content":"# Environments for Lifelong Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nTo achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10732v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15121\", \"78473\", \"49371\"], \"outgoing_citations\": [\"217105\", \"224223\", \"227594\", \"244457\", \"245731\", \"247749\", \"248885\", \"249906\", \"255613\", \"274783\", \"279420\", \"293071\", \"293876\"]}","task_split":"paper_retrieval"}
{"document_id":"208658","document_content":"# Sentence Encoding with Tree-constrained Relation Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10475v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"152767\"], \"outgoing_citations\": [\"243765\", \"249694\", \"256948\", \"261640\", \"265431\", \"265434\", \"268812\", \"268848\", \"271527\", \"271610\", \"274120\", \"274817\", \"276860\", \"279384\", \"280916\", \"281121\", \"291019\", \"296289\", \"299541\", \"309965\", \"310837\"]}","task_split":"paper_retrieval"}
{"document_id":"208668","document_content":"# Combining neural and knowledge-based approaches to Named Entity Recognition in Polish\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity recognition framework composed of knowledge-based feature extractors and a deep learning model including contextual word embeddings, long short-term memory (LSTM) layers and conditional random fields (CRF) inference layer. We use an entity linking module to integrate our system with Wikipedia. The combination of effective neural architecture and external resources allows us to obtain state-of-the-art results on recognition of Polish proper names. We evaluate our model on data from PolEval 2018 NER challenge on which it outperforms other methods, reducing the error rate by 22.4% compared to the winning solution. Our work shows that combining neural NER model and entity linking model with a knowledge base is more effective in recognizing named entities than using NER model alone.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-20912-4_4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"109913\", \"120456\", \"158017\"], \"outgoing_citations\": [\"221739\", \"228348\", \"232527\", \"256158\", \"260613\", \"267692\", \"268157\", \"312608\"]}","task_split":"paper_retrieval"}
{"document_id":"208687","document_content":"# Matchable Image Retrieval by Learning from Surface Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction. In this paper, we narrow down this gap by presenting an efficient CNN-based method to retrieve images with overlaps, which we refer to as the matchable image retrieval problem. Different from previous methods that generates training data based on sparse reconstruction, we create a large-scale image database with rich 3D geometrics and exploit information from surface reconstruction to obtain fine-grained training data. We propose a batched triplet-based loss function combined with mesh re-projection to effectively learn the CNN representation. The proposed method significantly accelerates the image retrieval process in 3D reconstruction and outperforms the state-of-the-art CNN-based and BoW methods for matchable image retrieval. The code and data are available at https:\/\/github.com\/hlzz\/mirror.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.10343v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73146\", \"28671\", \"100159\", \"103697\", \"106210\", \"111002\", \"135253\", \"135459\", \"154756\", \"156417\", \"191610\", \"197756\", \"201300\"], \"outgoing_citations\": [\"224369\", \"224551\", \"272867\", \"280378\", \"281341\", \"281953\", \"294983\", \"301572\", \"303067\", \"320036\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"208860","document_content":"# Hardware Conditioned Policies for Multi-Robot Transfer Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDeep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms. We propose a novel approach called \\textit{Hardware Conditioned Policies} where we train a universal policy conditioned on a vector representation of robot hardware. We considered robots in simulation with varied dynamics, kinematic structure, kinematic lengths and degrees-of-freedom. First, we use the kinematic structure directly as the hardware encoding and show great zero-shot transfer to completely novel robots not seen during training. For robots with lower zero-shot success rate, we also demonstrate that fine-tuning the policy network is significantly more sample-efficient than training a model from scratch. In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well. The code and videos are available on the project webpage: https:\/\/sites.google.com\/view\/robot-transfer-hcp.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.09864v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"7646\", \"16608\", \"34544\", \"95451\", \"109259\", \"6081\", \"53844\", \"58889\", \"62577\", \"64984\", \"78956\", \"89252\", \"96663\", \"87588\", \"88947\", \"91719\", \"113076\", \"138724\", \"164419\", \"183019\", \"151617\", \"171778\", \"196576\", \"198914\", \"214403\"], \"outgoing_citations\": [\"229333\", \"253045\", \"253658\", \"259392\", \"260087\", \"270419\", \"271975\", \"274445\", \"283289\", \"283724\", \"284134\", \"290378\", \"297285\", \"306153\"]}","task_split":"paper_retrieval"}
{"document_id":"208899","document_content":"# Mean Local Group Average Precision (mLGAP): A New Performance Metric for Hashing-based Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThe research on hashing techniques for visual data is gaining increased attention in recent years due to the need for compact representations supporting efficient search\/retrieval in large-scale databases such as online images. Among many possibilities, Mean Average Precision(mAP) has emerged as the dominant performance metric for hashing-based retrieval. One glaring shortcoming of mAP is its inability in balancing retrieval accuracy and utilization of hash codes: pushing a system to attain higher mAP will inevitably lead to poorer utilization of the hash codes. Poor utilization of the hash codes hinders good retrieval because of increased collision of samples in the hash space. This means that a model giving a higher mAP values does not necessarily do a better job in retrieval. In this paper, we introduce a new metric named Mean Local Group Average Precision (mLGAP) for better evaluation of the performance of hashing-based retrieval. The new metric provides a retrieval performance measure that also reconciles the utilization of hash codes, leading to a more practically meaningful performance metric than conventional ones like mAP. To this end, we start by mathematical analysis of the deficiencies of mAP for hashing-based retrieval. We then propose mLGAP and show why it is more appropriate for hashing-based retrieval. Experiments on image retrieval are used to demonstrate the effectiveness of the proposed metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.09763v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"125848\", \"172160\"], \"outgoing_citations\": [\"303512\", \"314568\", \"316473\", \"358798\"]}","task_split":"paper_retrieval"}
{"document_id":"208967","document_content":"# A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data\n## Categories\n- Computers and Society\n## Abstract\nIn recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potential to redefine how transportation system (i.e. design, planning and operations) is done. While researchers in both academia and industry are making advances in using this data to transportation system ends (e.g. information inference from collected data), little attention has been paid to four larger scale challenges that will need to be overcome if the potential for Big Transportation Data is to be harnessed for transportation decision-making purposes. This paper aims to provide awareness of these large-scale challenges and provides insight into how we believe these challenges are likely to be met.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s42421-019-00001-z\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\"], \"incoming_citations\": [\"131165\", \"80272\", \"172408\", \"202174\"], \"outgoing_citations\": [\"217730\", \"343104\"]}","task_split":"paper_retrieval"}
{"document_id":"208994","document_content":"# A Blended Human-Robot Shared Control Framework to Handle Drift and Latency\n## Categories\n- Robotics\n## Abstract\nMaximizing the utility of human-robot teams in disaster response and search and rescue (SAR) missions remains to be a challenging problem. This is due to the dynamic, uncertain nature of the environment and the variability in cognitive performance of the human operators. By having an autonomous agent share control with the operator, we can achieve near-optimal performance by augmenting the operator's input and compensate for the factors resulting in degraded performance. What this solution does not consider though is the human input latency and errors caused by potential hardware failures that can occur during task completion when operating in disaster response and SAR scenarios. In this paper, we propose the use of blended shared control (BSC) architecture to address these issues and investigate the architecture's performance in constrained, dynamic environments with a differential drive robot that has input latency and erroneous odometry feedback. We conduct a validation study (n=12) for our control architecture and then a user study (n=14) in 2 different environments that are unknown to both the human operator and the autonomous agent. The results demonstrate that the BSC architecture can prevent collisions and enhance operator performance without the need of a complete transfer of control between the human operator and autonomous agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.09382v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"27840\", \"178503\"], \"outgoing_citations\": [\"220420\", \"243197\"]}","task_split":"paper_retrieval"}
{"document_id":"209098","document_content":"# Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.09019v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"102723\", \"2120\", \"3046\", \"51487\", \"59002\", \"61168\", \"64558\", \"97923\", \"110872\", \"120894\", \"159733\", \"160255\", \"173743\", \"230252\", \"176984\"], \"outgoing_citations\": [\"235857\", \"239574\", \"248969\", \"255682\", \"257567\", \"258886\", \"259659\", \"259660\", \"260755\", \"277357\", \"288238\", \"308687\"]}","task_split":"paper_retrieval"}
{"document_id":"209148","document_content":"# Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays\n## Categories\n- Machine Learning\n## Abstract\nMachine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network. The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data. When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.08840v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"223207\"]}","task_split":"paper_retrieval"}
{"document_id":"209333","document_content":"# Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent cameras have a lot of advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range. However, since the outputs of event cameras are the sequences of asynchronous events overtime rather than actual intensity images, existing algorithms could not be directly applied. Therefore, it is demanding to generate intensity images from events for other tasks. In this paper, we unlock the potential of event camera-based conditional generative adversarial networks to create images\/videos from an adjustable portion of the event data stream. The stacks of space-time coordinates of events are used as inputs and the network is trained to reproduce images based on the spatio-temporal intensity changes. The usefulness of event cameras to generate high dynamic range(HDR) images even in extreme illumination conditions and also non blurred images under rapid motion is also shown.In addition, the possibility of generating very high frame rate videos is demonstrated, theoretically up to 1 million frames per second (FPS) since the temporal resolution of event cameras are about 1{\\mu}s. Proposed methods are evaluated by comparing the results with the intensity images captured on the same pixel grid-line of events using online available real datasets and synthetic datasets produced by the event camera simulator.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2019.01032\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3311\", \"6185\", \"14984\", \"25451\", \"131467\", \"190036\", \"195521\", \"18686\", \"5801\", \"20637\", \"25803\", \"30441\", \"7584\", \"49930\", \"127359\", \"100061\", \"115507\", \"131521\", \"133705\", \"136384\", \"139039\", \"139135\", \"148389\", \"153050\", \"175694\", \"197597\"], \"outgoing_citations\": [\"211978\", \"231930\", \"236796\", \"241612\", \"251425\", \"254763\", \"257940\", \"279030\", \"281870\", \"287980\", \"289307\", \"294531\", \"296355\"]}","task_split":"paper_retrieval"}
{"document_id":"209452","document_content":"# Guiding Policies with Language via Meta-Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nBehavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require a human expert to be able to actually perform the task in order to generate the demonstration. Instruction following from natural language instructions provides an appealing alternative: in the same way that we can specify goals to other humans simply by speaking or writing, we would like to be able to specify tasks for our machines. However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task. In this work, we propose an interactive formulation of the task specification problem, where iterative language corrections are provided to an autonomous agent, guiding it in acquiring the desired skill. Our proposed language-guided policy learning algorithm can integrate an instruction and a sequence of corrections to acquire new skills very quickly. In our experiments, we show that this method can enable a policy to follow instructions and corrections for simulated navigation and manipulation tasks, substantially outperforming direct, non-interactive instruction following.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07882v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"71663\", \"11352\", \"14861\", \"26076\", \"42161\", \"55157\", \"74771\", \"89875\", \"80096\", \"160608\", \"89599\", \"140901\", \"144372\", \"161143\", \"151842\", \"151605\", \"171822\", \"182138\", \"184898\", \"206489\"], \"outgoing_citations\": [\"215931\", \"229737\", \"230042\", \"243197\", \"250567\", \"251691\", \"251924\", \"254581\", \"261378\", \"261522\", \"262591\", \"263650\", \"263957\", \"267779\", \"280202\", \"280870\", \"289260\", \"290891\", \"307569\", \"355682\"]}","task_split":"paper_retrieval"}
{"document_id":"209453","document_content":"# Scalable agent alignment via reward modeling: a research direction\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nOne obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user's intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07871v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"170092\", \"1860\", \"36644\", \"46528\", \"2329\", \"5616\", \"11431\", \"17175\", \"20461\", \"21737\", \"30094\", \"40489\", \"43409\", \"45923\", \"47013\", \"60332\", \"67123\", \"72694\", \"75318\", \"83169\", \"85867\", \"171948\", \"74639\", \"79914\", \"80268\", \"82620\", \"86327\", \"86323\", \"47009\", \"99754\", \"102649\", \"111066\", \"112041\", \"47007\", \"118985\", \"122106\", \"128643\", \"142715\", \"147139\", \"147483\", \"148318\", \"152542\", \"157309\", \"117347\", \"150632\", \"151605\", \"166082\", \"178291\", \"178953\", \"179212\", \"179798\", \"182450\", \"186139\", \"187845\", \"198373\", \"198521\", \"200660\"], \"outgoing_citations\": [\"87237\", \"199302\", \"207267\", \"207866\", \"209926\", \"213652\", \"222567\", \"224235\", \"224770\", \"225886\", \"226220\", \"229145\", \"229347\", \"229749\", \"230068\", \"230077\", \"230362\", \"230507\", \"231157\", \"233924\", \"234575\", \"235092\", \"238640\", \"239105\", \"239539\", \"240233\", \"242048\", \"245825\", \"246129\", \"247344\", \"248213\", \"249185\", \"251082\", \"251691\", \"251785\", \"254581\", \"258058\", \"261038\", \"263957\", \"265010\", \"265053\", \"265196\", \"265642\", \"265652\", \"266406\", \"268223\", \"268336\", \"270029\", \"272298\", \"272464\", \"274487\", \"275728\", \"276143\", \"279628\", \"284591\", \"290744\", \"295376\", \"297317\", \"298539\", \"298696\", \"301305\", \"304902\", \"311571\", \"334786\", \"355682\"]}","task_split":"paper_retrieval"}
{"document_id":"209471","document_content":"# Deeper Interpretability of Deep Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep Convolutional Neural Networks (CNNs) have been one of the most influential recent developments in computer vision, particularly for categorization. There is an increasing demand for explainable AI as these systems are deployed in the real world. However, understanding the information represented and processed in CNNs remains in most cases challenging. Within this paper, we explore the use of new information theoretic techniques developed in the field of neuroscience to enable novel understanding of how a CNN represents information. We trained a 10-layer ResNet architecture to identify 2,000 face identities from 26M images generated using a rigorously controlled 3D face rendering model that produced variations of intrinsic (i.e. face morphology, gender, age, expression and ethnicity) and extrinsic factors (i.e. 3D pose, illumination, scale and 2D translation). With our methodology, we demonstrate that unlike human's network overgeneralizes face identities even with extreme changes of face shape, but it is more sensitive to changes of texture. To understand the processing of information underlying these counterintuitive properties, we visualize the features of shape and texture that the network processes to identify faces. Then, we shed a light into the inner workings of the black box and reveal how hidden layers represent these features and whether the representations are invariant to pose. We hope that our methodology will provide an additional valuable tool for interpretability of CNNs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07807v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"93190\", \"108139\", \"184173\"], \"outgoing_citations\": [\"220339\", \"269271\", \"298201\", \"318826\", \"328423\"]}","task_split":"paper_retrieval"}
{"document_id":"209504","document_content":"# Intention Oriented Image Captions with Guiding Objects\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAlthough existing image caption models can produce promising results using recurrent neural networks (RNNs), it is difficult to guarantee that an object we care about is contained in generated descriptions, for example in the case that the object is inconspicuous in the image. Problems become even harder when these objects did not appear in training stage. In this paper, we propose a novel approach for generating image captions with guiding objects (CGO). The CGO constrains the model to involve a human-concerned object when the object is in the image. CGO ensures that the object is in the generated description while maintaining fluency. Instead of generating the sequence from left to right, we start the description with a selected object and generate other parts of the sequence based on this object. To achieve this, we design a novel framework combining two LSTMs in opposite directions. We demonstrate the characteristics of our method on MSCOCO where we generate descriptions for each detected object in the images. With CGO, we can extend the ability of description to the objects being neglected in image caption labels and provide a set of more comprehensive and diverse descriptions for an image. CGO shows advantages when applied to the task of describing novel objects. We show experimental results on both MSCOCO and ImageNet datasets. Evaluations show that our method outperforms the state-of-the-art models in the task with average F1 75.8, leading to better descriptions in terms of both content accuracy and fluency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07662v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"16013\", \"101277\", \"113677\", \"93187\", \"15902\", \"17327\", \"31378\", \"31502\", \"61394\", \"74508\", \"82846\", \"139263\"], \"outgoing_citations\": [\"219342\", \"229734\", \"236042\", \"237655\", \"249972\", \"258345\", \"262564\", \"269122\", \"270154\", \"271234\", \"278604\", \"278948\", \"279002\", \"283076\", \"289061\", \"289627\", \"302621\", \"302815\", \"303209\", \"313196\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"209513","document_content":"# Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations\n## Categories\n- Computation and Language\n## Abstract\nTo build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence. Many research efforts have been dedicated to building such dialogue systems, yet few shed light on modeling the conversation flow in an ongoing dialogue. Besides, it is common for people to talk about highly relevant aspects during a conversation. And the topics are coherent and drift naturally, which demonstrates the necessity of dialogue flow modeling. To this end, we present the multi-turn cue-words driven conversation system with reinforcement learning method (RLCw), which strives to select an adaptive cue word with the greatest future credit, and therefore improve the quality of generated responses. We introduce a new reward to measure the quality of cue words in terms of effectiveness and relevance. To further optimize the model for long-term conversations, a reinforcement approach is adopted in this paper. Experiments on real-life dataset demonstrate that our model consistently outperforms a set of competitive baselines in terms of simulated turns, diversity and human evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07631v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24852\", \"182968\"], \"outgoing_citations\": [\"248966\", \"263622\", \"270040\", \"276160\", \"276365\", \"278671\", \"279215\", \"285454\", \"289061\", \"291102\", \"292261\", \"294494\", \"295863\", \"296250\", \"303997\", \"310903\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"209557","document_content":"# NSEEN: Neural Semantic Embedding for Entity Normalization\n## Categories\n- Information Retrieval\n- Computation and Language\n- Neural and Evolutionary Computing\n- Machine Learning\n- Databases\n## Abstract\nMuch of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07514v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.NE\", \"cs.LG\", \"cs.IR\", \"cs.DB\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\", \"Machine Learning\", \"Information Retrieval\", \"Databases\"], \"incoming_citations\": [\"88816\", \"127709\", \"146640\", \"183088\", \"170279\"], \"outgoing_citations\": [\"159867\", \"183088\", \"254315\", \"257933\", \"262989\", \"307740\", \"361366\"]}","task_split":"paper_retrieval"}
{"document_id":"209636","document_content":"# Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe way to accurately and effectively identify people has always been an interesting topic in research and industry. With the rapid development of artificial intelligence in recent years, facial recognition gains lots of attention due to prompting the development of emerging identification methods. Compared to traditional card recognition, fingerprint recognition and iris recognition, face recognition has many advantages including non-contact interface, high concurrency, and user-friendly usage. It has high potential to be used in government, public facilities, security, e-commerce, retailing, education and many other fields. With the development of deep learning and the introduction of deep convolutional neural networks, the accuracy and speed of face recognition have made great strides. However, the results from different networks and models are very different with different system architecture. Furthermore, it could take significant amount of data storage space and data processing time for the face recognition system with video feed, if the system stores images and features of human faces. In this paper, facial features are extracted by merging and comparing multiple models, and then a deep neural network is constructed to train and construct the combined features. In this way, the advantages of multiple models can be combined to mention the recognition accuracy. After getting a model with high accuracy, we build a product model. The model will take a human face image and extract it into a vector. Then the distance between vectors are compared to determine if two faces on different picture belongs to the same person. The proposed approach reduces data storage space and data processing time for the face recognition system with video feed scientifically with our proposed system architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07339v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"209767","document_content":"# Improving Rotated Text Detection with Rotation Region Proposal Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nA significant number of images shared on social media platforms such as Facebook and Instagram contain text in various forms. It's increasingly becoming commonplace for bad actors to share misinformation, hate speech or other kinds of harmful content as text overlaid on images on such platforms. A scene-text understanding system should hence be able to handle text in various orientations that the adversary might use. Moreover, such a system can be incorporated into screen readers used to aid the visually impaired. In this work, we extend the scene-text extraction system at Facebook, Rosetta, to efficiently handle text in various orientations. Specifically, we incorporate the Rotation Region Proposal Networks (RRPN) in our text extraction pipeline and offer practical suggestions for building and deploying a model for detecting and recognizing text in arbitrary orientations efficiently. Experimental results show a significant improvement on detecting rotated text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07031v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"80383\", \"75104\", \"110101\", \"111561\", \"140006\", \"154995\"], \"outgoing_citations\": [\"162242\", \"272481\"]}","task_split":"paper_retrieval"}
{"document_id":"209787","document_content":"# Exploring Gameplay With AI Agents\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- 68T42\n## Abstract\nThe process of playtesting a game is subjective, expensive and incomplete. In this paper, we present a playtesting approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06962v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"68T42\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"68T42\"], \"incoming_citations\": [\"37296\", \"41863\", \"177843\", \"193790\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"209806","document_content":"# On the Complexity of Exploration in Goal-Driven Navigation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nBuilding agents that can explore their environments intelligently is a challenging open problem. In this paper, we make a step towards understanding how a hierarchical design of the agent's policy can affect its exploration capabilities. First, we design EscapeRoom environments, where the agent must figure out how to navigate to the exit by accomplishing a number of intermediate tasks (\\emph{subgoals}), such as finding keys or opening doors. Our environments are procedurally generated and vary in complexity, which can be controlled by the number of subgoals and relationships between them. Next, we propose to measure the complexity of each environment by constructing dependency graphs between the goals and analytically computing \\emph{hitting times} of a random walk in the graph. We empirically evaluate Proximal Policy Optimization (PPO) with sparse and shaped rewards, a variation of policy sketches, and a hierarchical version of PPO (called HiPPO) akin to h-DQN. We show that analytically estimated \\emph{hitting time} in goal dependency graphs is an informative metric of the environment complexity. We conjecture that the result should hold for environments other than navigation. Finally, we show that solving environments beyond certain level of complexity requires hierarchical approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06889v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"6899\", \"15829\", \"171112\"], \"outgoing_citations\": [\"262381\", \"264419\", \"279922\", \"281106\", \"284567\", \"291025\", \"294142\"]}","task_split":"paper_retrieval"}
{"document_id":"209926","document_content":"# Reward learning from human preferences and demonstrations in Atari\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nTo solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games without using game rewards. Additionally, we investigate the goodness of fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06521v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"53008\", \"163\", \"1860\", \"10858\", \"21027\", \"30327\", \"40128\", \"46528\", \"116233\", \"129693\", \"5616\", \"5892\", \"11431\", \"13366\", \"16039\", \"16665\", \"21737\", \"22916\", \"30094\", \"32223\", \"32785\", \"34461\", \"35842\", \"40489\", \"43409\", \"54347\", \"55951\", \"57391\", \"59140\", \"60332\", \"61416\", \"64583\", \"65405\", \"67123\", \"82750\", \"116304\", \"116575\", \"139572\", \"167888\", \"80268\", \"81341\", \"86153\", \"87679\", \"100946\", \"102649\", \"111876\", \"115011\", \"115852\", \"122106\", \"126773\", \"128389\", \"103314\", \"140983\", \"142761\", \"152542\", \"154933\", \"158515\", \"160619\", \"164743\", \"179214\", \"165614\", \"176970\", \"179705\", \"182023\", \"182138\", \"182450\", \"173805\", \"186139\", \"186947\", \"190766\", \"198373\", \"209453\"], \"outgoing_citations\": [\"239539\", \"240800\", \"241900\", \"243714\", \"252249\", \"254581\", \"254596\", \"256193\", \"260018\", \"260583\", \"261038\", \"263957\", \"272433\", \"275728\", \"279790\", \"280296\", \"291025\", \"305802\", \"339382\", \"355682\"]}","task_split":"paper_retrieval"}
{"document_id":"209930","document_content":"# Verified Runtime Validation for Partially Observable Hybrid Systems\n## Categories\n- Logic in Computer Science\n- F.4.1; I.2.2; J.2\n## Abstract\nFormal verification provides strong safety guarantees but only for models of cyber-physical systems. Hybrid system models describe the required interplay of computation and physical dynamics, which is crucial to guarantee what computations lead to safe physical behavior (e.g., cars should not collide). Control computations that affect physical dynamics must act in advance to avoid possibly unsafe future circumstances. Formal verification then ensures that the controllers correctly identify and provably avoid unsafe future situations under a certain model of physics. But any model of physics necessarily deviates from reality and, moreover, any observation with real sensors and manipulation with real actuators is subject to uncertainty. This makes runtime validation a crucial step to monitor whether the model assumptions hold for the real system implementation.   The key question is what property needs to be runtime-monitored and what a satisfied runtime monitor entails about the safety of the system: the observations of a runtime monitor only relate back to the safety of the system if they are themselves accompanied by a proof of correctness! For an unbroken chain of correctness guarantees, we, thus, synthesize runtime monitors in a provably correct way from provably safe hybrid system models. This paper addresses the inevitable challenge of making the synthesized monitoring conditions robust to partial observability of sensor uncertainty and partial controllability due to actuator disturbance. We show that the monitoring conditions result in provable safety guarantees with fallback controllers that react to monitor violation at runtime.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06502v2\", \"primary_category\": \"cs.LO\", \"categories\": [\"F.4.1; I.2.2; J.2\", \"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"F.4.1; I.2.2; J.2\", \"Logic in Computer Science\"], \"incoming_citations\": [\"11643\", \"125064\", \"159944\", \"195434\"], \"outgoing_citations\": [\"243333\", \"270913\", \"293392\", \"299686\", \"310497\", \"319164\", \"326324\", \"341244\"]}","task_split":"paper_retrieval"}
{"document_id":"210053","document_content":"# Automatic Grammar Augmentation for Robust Voice Command Recognition\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nThis paper proposes a novel pipeline for automatic grammar augmentation that provides a significant improvement in the voice command recognition accuracy for systems with small footprint acoustic model (AM). The improvement is achieved by augmenting the user-defined voice command set, also called grammar set, with alternate grammar expressions. For a given grammar set, a set of potential grammar expressions (candidate set) for augmentation is constructed from an AM-specific statistical pronunciation dictionary that captures the consistent patterns and errors in the decoding of AM induced by variations in pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions. Using this candidate set, greedy optimization based and cross-entropy-method (CEM) based algorithms are considered to search for an augmented grammar set with improved recognition accuracy utilizing a command-specific dataset. Our experiments show that the proposed pipeline along with algorithms considered in this paper significantly reduce the mis-detection and mis-classification rate without increasing the false-alarm rate. Experiments also demonstrate the consistent superior performance of CEM method over greedy-based algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06096v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"140204\", \"150920\"], \"outgoing_citations\": [\"260414\", \"284965\", \"352346\"]}","task_split":"paper_retrieval"}
{"document_id":"210077","document_content":"# Natural Environment Benchmarks for Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWhile current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data. By testing increasingly complex RL algorithms on low-complexity simulation environments, we often end up with brittle RL policies that generalize poorly beyond the very specific domain. To combat this, we propose three new families of benchmark RL domains that contain some of the complexity of the natural world, while still supporting fast and extensive data acquisition. The proposed domains also permit a characterization of generalization through fair train\/test separation, and easy comparison and replication of results. Through this work, we challenge the RL research community to develop more robust algorithms that meet high standards of evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.06032v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"11995\", \"66783\", \"9444\", \"35440\", \"35657\", \"40969\", \"69480\", \"75606\", \"76491\", \"117599\", \"163705\", \"82954\", \"92636\", \"93006\", \"94475\", \"123054\", \"136185\", \"136936\", \"137062\", \"140619\", \"145405\", \"152469\", \"152918\", \"159298\", \"167468\", \"179957\", \"183053\", \"187351\", \"189119\", \"201313\"], \"outgoing_citations\": [\"227301\", \"237119\", \"240817\", \"247418\", \"248885\", \"258384\", \"272043\", \"275034\", \"282684\", \"303040\", \"315745\"]}","task_split":"paper_retrieval"}
{"document_id":"210148","document_content":"# Neural Based Statement Classification for Biased Language\n## Categories\n- Computation and Language\n## Abstract\nBiased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups.   In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus.   In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias.   We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3289600.3291018\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"843\", \"5912\", \"3111\", \"31432\", \"48329\", \"70692\", \"82967\", \"141988\", \"154952\", \"168387\", \"181313\"], \"outgoing_citations\": [\"190348\", \"249094\", \"270179\", \"270183\"]}","task_split":"paper_retrieval"}
{"document_id":"210184","document_content":"# Translating a Math Word Problem to an Expression Tree\n## Categories\n- Computation and Language\n## Abstract\nSequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.05632v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26187\", \"32042\", \"32821\", \"48678\", \"50898\", \"96833\", \"20765\", \"21843\", \"24666\", \"25273\", \"101113\", \"37796\", \"46289\", \"50647\", \"56186\", \"78252\", \"79536\", \"91766\", \"94434\", \"98821\", \"132440\", \"220655\", \"161669\", \"169320\", \"200693\"], \"outgoing_citations\": [\"220655\", \"246516\", \"266702\", \"287181\"]}","task_split":"paper_retrieval"}
{"document_id":"210210","document_content":"# Automated Pain Detection from Facial Expressions using FACS: A Review\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Image and Video Processing\n- Machine Learning\n## Abstract\nFacial pain expression is an important modality for assessing pain, especially when the patient's verbal ability to communicate is impaired. The facial muscle-based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable as a method for detecting facial expressions (FE) including valid detection of pain. Unfortunately, FACS coding by humans is a very time-consuming task that makes its clinical use prohibitive. Significant progress on automated facial expression recognition (AFER) has led to its numerous successful applications in FACS-based affective computing problems. However, only a handful of studies have been reported on automated pain detection (APD), and its application in clinical settings is still far from a reality. In this paper, we review the progress in research that has contributed to automated pain detection, with focus on 1) the framework-level similarity between spontaneous AFER and APD problems; 2) the evolution of system design including the recent development of deep learning methods; 3) the strategies and considerations in developing a FACS-based pain detection framework from existing research; and 4) introduction of the most relevant databases that are available for AFER and APD studies. We attempt to present key considerations in extending a general AFER framework to an APD framework in clinical settings. In addition, the performance metrics are also highlighted in evaluating an AFER or an APD system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.07988v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"eess.IV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Image and Video Processing\", \"Machine Learning\"], \"incoming_citations\": [\"75782\", \"76316\", \"118080\"], \"outgoing_citations\": [\"258976\", \"268873\", \"269237\", \"274047\", \"276050\", \"289208\", \"295162\"]}","task_split":"paper_retrieval"}
{"document_id":"210213","document_content":"# An Analysis of the Semantic Annotation Task on the Linked Data Cloud\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nSemantic annotation, the process of identifying key-phrases in texts and linking them to concepts in a knowledge base, is an important basis for semantic information retrieval and the Semantic Web uptake. Despite the emergence of semantic annotation systems, very few comparative studies have been published on their performance. In this paper, we provide an evaluation of the performance of existing systems over three tasks: full semantic annotation, named entity recognition, and keyword detection. More specifically, the spotting capability (recognition of relevant surface forms in text) is evaluated for all three tasks, whereas the disambiguation (correctly associating an entity from Wikipedia or DBpedia to the spotted surface forms) is evaluated only for the first two tasks. Our evaluation is twofold: First, we compute standard precision and recall on the output of semantic annotators on diverse datasets, each best suited for one of the identified tasks. Second, we build a statistical model using logistic regression to identify significant performance differences. Our results show that systems that provide full annotation perform better than named entities annotators and keyword extractors, for all three tasks. However, there is still much room for improvement for the identification of the most relevant entities described in a text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.05549v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"210279","document_content":"# Pose Invariant 3D Face Reconstruction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\n3D face reconstruction is an important task in the field of computer vision. Although 3D face reconstruction has being developing rapidly in recent years, it is still a challenge for face reconstruction under large pose. That is because much of the information about a face in a large pose will be unknowable. In order to address this issue, this paper proposes a novel 3D face reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input a single face image, it generates a frontal image by normalizing the image. Then we set weighted sum of the 3D parameters of the two images. Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction. Experiments on the challenging AFW, LFPW and AFLW database show that our algorithm significantly improves the accuracy of 3D face reconstruction even under extreme poses .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.05295v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"221177\", \"250081\", \"270795\", \"302701\", \"311317\", \"323646\", \"354038\"]}","task_split":"paper_retrieval"}
{"document_id":"210354","document_content":"# Private Model Compression via Knowledge Distillation\n## Categories\n- Machine Learning\n## Abstract\nThe soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\\times$ compression ratio and 19$\\times$ speed-up with merely 0.97% accuracy loss.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.05072v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"74019\", \"68918\", \"174933\", \"6890\", \"14288\", \"30490\", \"36588\", \"47365\", \"54740\", \"54946\", \"107541\", \"107501\", \"108682\", \"119873\", \"120499\", \"124154\", \"96607\", \"108167\", \"111831\", \"129081\", \"132990\", \"134178\", \"137809\", \"175927\", \"180812\", \"196418\", \"199616\", \"94582\", \"153992\", \"182288\"], \"outgoing_citations\": [\"218449\", \"237938\", \"239622\", \"240981\", \"251120\", \"261446\", \"271973\", \"273221\", \"276281\", \"282410\"]}","task_split":"paper_retrieval"}
{"document_id":"210450","document_content":"# Universal Marginalizer for Amortised Inference and Embedding of Generative Models\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nProbabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models; however, they can be computationally costly due to significant time burden and\/or storage requirements; or they lack theoretical guarantees of convergence and accuracy when applied to large scale graphical models. To this end, we propose the Universal Marginaliser Importance Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a deep neural network trained on samples from the model and inherits the asymptotic guarantees of importance sampling. We show how combining samples drawn from the graphical model with an appropriate masking function allows us to train a single neural network to approximate any of the corresponding conditional marginal distributions, and thus amortise the cost of inference. We also show that the graph embeddings can be applied for tasks such as: clustering, classification and interpretation of relationships between the nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing that UM-IS outperforms sampling-based methods by a large margin while being computationally efficient.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.04727v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"161185\", \"161379\"], \"outgoing_citations\": [\"272546\", \"282411\", \"311424\", \"317636\", \"334299\", \"346888\", \"346933\", \"349845\", \"350477\"]}","task_split":"paper_retrieval"}
{"document_id":"210455","document_content":"# Gauges, Loops, and Polynomials for Partition Functions of Graphical Models\n## Categories\n- Machine Learning\n- Mathematical Physics\n- Mathematical Physics\n## Abstract\nGraphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating the PF, we consider Multi-Graph Models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge Function technique with the technique from the field of real stable polynomials. We show that the Gauge Function has a natural polynomial representation in terms of gauges\/variables associated with edges of the multi-graph. Moreover, it can be used to recover the Partition Function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists in application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination of edges resulting in a sequence of models on decreasing in size graphs with the same Partition Function. Even though complexity of computing factors in the sequence models grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that Belief Propagation estimations in the sequence do not decrease, each low-bounding the Partition Function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.04713v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math-ph\", \"math.MP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Mathematical Physics\", \"Mathematical Physics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"259047\", \"268355\", \"274334\", \"276758\", \"286590\", \"288631\", \"289022\", \"309419\", \"325986\", \"330067\", \"334357\", \"337742\", \"339895\", \"353358\", \"360885\", \"361378\"]}","task_split":"paper_retrieval"}
{"document_id":"210473","document_content":"# Distributionally Robust Semi-Supervised Learning for People-Centric Sensing\n## Categories\n- Human-Computer Interaction\n- Computer Vision and Pattern Recognition\n## Abstract\nSemi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.05299v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.CV\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"12465\", \"12853\", \"95876\", \"146286\", \"42148\", \"145294\", \"150887\", \"191850\"], \"outgoing_citations\": [\"231790\", \"257981\", \"350301\"]}","task_split":"paper_retrieval"}
{"document_id":"210599","document_content":"# Deep Face Quality Assessment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace image quality is an important factor in facial recognition systems as its verification and recognition accuracy is highly dependent on the quality of image presented. Rejecting low quality images can significantly increase the accuracy of any facial recognition system. In this project, a simple approach is presented to train a deep convolutional neural network to perform end-to-end face image quality assessment. The work is done in 2 stages : First, generation of quality score label and secondly, training a deep convolutional neural network in a supervised manner to predict quality score between 0 and 1. The generation of quality labels is done by comparing the face image with a template of best quality images and then evaluating the normalized score based on the similarity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.04346v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"102723\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"210931","document_content":"# Transformative Machine Learning\n## Categories\n- Machine Learning\n## Abstract\nThe key to success in machine learning (ML) is the use of effective data representations. Traditionally, data representations were hand-crafted. Recently it has been demonstrated that, given sufficient data, deep neural networks can learn effective implicit representations from simple input representations. However, for most scientific problems, the use of deep learning is not appropriate as the amount of available data is limited, and\/or the output models must be explainable. Nevertheless, many scientific problems do have significant amounts of data available on related tasks, which makes them amenable to multi-task learning, i.e. learning many related problems simultaneously. Here we propose a novel and general representation learning approach for multi-task learning that works successfully with small amounts of data. The fundamental new idea is to transform an input intrinsic data representation (i.e., handcrafted features), to an extrinsic representation based on what a pre-trained set of models predict about the examples. This transformation has the dual advantages of producing significantly more accurate predictions, and providing explainable models. To demonstrate the utility of this transformative learning approach, we have applied it to three real-world scientific problems: drug-design (quantitative structure activity relationship learning), predicting human gene expression (across different tissue types and drug treatments), and meta-learning for machine learning (predicting which machine learning methods work best for a given problem). In all three problems, transformative machine learning significantly outperforms the best intrinsic representation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.03392v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"215015\", \"256221\", \"326456\"]}","task_split":"paper_retrieval"}
{"document_id":"210952","document_content":"# Discovering Power Laws in Entity Length\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis paper presents a discovery that the length of the entities in various datasets follows a family of scale-free power law distributions. The concept of entity here broadly includes the named entity, entity mention, time expression, aspect term, and domain-specific entity that are well investigated in natural language processing and related areas. The entity length denotes the number of words in an entity. The power law distributions in entity length possess the scale-free property and have well-defined means and finite variances. We explain the phenomenon of power laws in entity length by the principle of least effort in communication and the preferential mechanism.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.03325v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"210964","document_content":"# Doc2Im: document to image conversion through self-attentive embedding\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nText classification is a fundamental task in NLP applications. Latest research in this field has largely been divided into two major sub-fields. Learning representations is one sub-field and learning deeper models, both sequential and convolutional, which again connects back to the representation is the other side. We posit the idea that the stronger the representation is, the simpler classifier models are needed to achieve higher performance. In this paper we propose a completely novel direction to text classification research, wherein we convert text to a representation very similar to images, such that any deep network able to handle images is equally able to handle text. We take a deeper look at the representation of documents as an image and subsequently utilize very simple convolution based models taken as is from computer vision domain. This image can be cropped, re-scaled, re-sampled and augmented just like any other image to work with most of the state-of-the-art large convolution based models which have been designed to handle large image datasets. We show impressive results with some of the latest benchmarks in the related fields. We perform transfer learning experiments, both from text to text domain and also from image to text domain. We believe this is a paradigm shift from the way document understanding and text classification has been traditionally done, and will drive numerous novel research ideas in the community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.03291v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"301397\", \"320868\", \"360727\"]}","task_split":"paper_retrieval"}
{"document_id":"211064","document_content":"# Data Selection with Feature Decay Algorithms Using an Approximated Target Side\n## Categories\n- Computation and Language\n## Abstract\nData selection techniques applied to neural machine translation (NMT) aim to increase the performance of a model by retrieving a subset of sentences for use as training data.   One of the possible data selection techniques are transductive learning methods, which select the data based on the test set, i.e. the document to be translated. A limitation of these methods to date is that using the source-side test set does not by itself guarantee that sentences are selected with correct translations, or translations that are suitable given the test-set domain. Some corpora, such as subtitle corpora, may contain parallel sentences with inaccurate translations caused by localization or length restrictions.   In order to try to fix this problem, in this paper we propose to use an approximated target-side in addition to the source-side when selecting suitable sentence-pairs for training a model. This approximated target-side is built by pre-translating the source-side.   In this work, we explore the performance of this general idea for one specific data selection approach called Feature Decay Algorithms (FDA).   We train German-English NMT models on data selected by using the test set (source), the approximated target side, and a mixture of both. Our findings reveal that models built using a combination of outputs of FDA (using the test set and an approximated target side) perform better than those solely using the test set. We obtain a statistically significant improvement of more than 1.5 BLEU points over a model trained with all data, and more than 0.5 BLEU points over a strong FDA baseline that uses source-side information only.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.03039v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56086\", \"24770\", \"129331\", \"133033\", \"164809\", \"170203\", \"180116\"], \"outgoing_citations\": [\"259541\", \"284263\"]}","task_split":"paper_retrieval"}
{"document_id":"211084","document_content":"# Behavioural Repertoire via Generative Adversarial Policy Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nLearning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this paper, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment. We demonstrate this idea with an application of robust ball-throwing in the presence of obstacles. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of obstacles.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICDL-EpiRob44920.2019\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"204049\", \"241900\", \"247158\", \"253077\", \"261617\", \"261637\", \"266613\", \"270024\", \"270529\", \"290620\", \"290675\", \"290679\", \"292385\", \"302907\", \"306835\", \"322163\", \"327091\", \"341316\"]}","task_split":"paper_retrieval"}
{"document_id":"211205","document_content":"# Language GANs Falling Short\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nGenerating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality \/ diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality \/ diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com\/pclucas14\/GansFallingShort","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.02549v6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"28060\", \"3382\", \"16590\", \"20586\", \"37870\", \"42171\", \"50907\", \"9320\", \"24581\", \"26417\", \"30826\", \"38841\", \"41866\", \"42802\", \"49444\", \"71966\", \"72075\", \"75537\", \"77389\", \"78009\", \"79155\", \"80747\", \"94230\", \"101963\", \"120964\", \"127232\", \"132645\", \"132683\", \"132791\", \"170376\", \"184591\", \"84410\", \"93331\", \"93245\", \"94789\", \"100365\", \"100503\", \"102106\", \"108688\", \"114461\", \"115939\", \"119254\", \"120280\", \"112623\", \"126682\", \"127236\", \"127447\", \"128009\", \"132799\", \"133013\", \"132978\", \"135296\", \"137197\", \"138350\", \"140414\", \"149063\", \"156196\", \"78439\", \"177775\", \"189504\", \"152767\", \"155575\", \"159960\", \"161927\", \"163994\", \"164449\", \"165308\", \"169469\", \"171014\", \"183694\", \"184909\", \"190452\", \"191641\", \"192158\", \"192208\", \"200162\", \"204159\", \"206058\", \"212371\", \"233685\", \"205684\", \"210197\"], \"outgoing_citations\": [\"184909\", \"191641\", \"216118\", \"217526\", \"228201\", \"234728\", \"236053\", \"238906\", \"240486\", \"243154\", \"243291\", \"244368\", \"245825\", \"255035\", \"263935\", \"264937\", \"272464\", \"273062\", \"278560\", \"283971\", \"295863\", \"302815\", \"303913\", \"311470\"]}","task_split":"paper_retrieval"}
{"document_id":"211345","document_content":"# TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nTo safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances' movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.02146v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"5374\", \"23013\", \"28267\", \"24208\", \"40717\", \"53587\", \"68567\", \"105409\", \"132642\", \"134963\", \"144528\", \"149568\", \"182562\", \"4263\", \"9254\", \"11075\", \"13054\", \"23190\", \"32241\", \"3430\", \"40612\", \"41580\", \"40266\", \"45469\", \"139151\", \"52676\", \"54392\", \"58456\", \"61254\", \"63204\", \"69857\", \"78478\", \"81152\", \"83006\", \"85367\", \"86413\", \"87994\", \"92521\", \"92812\", \"93453\", \"121363\", \"124545\", \"127156\", \"87593\", \"173577\", \"90374\", \"95755\", \"99205\", \"99442\", \"102032\", \"106162\", \"108183\", \"109691\", \"111397\", \"112768\", \"114017\", \"116072\", \"116670\", \"119595\", \"121741\", \"120248\", \"120882\", \"120880\", \"122751\", \"124451\", \"123762\", \"126686\", \"128967\", \"129703\", \"132886\", \"133645\", \"134029\", \"134054\", \"134965\", \"135373\", \"136423\", \"136753\", \"137027\", \"137715\", \"137647\", \"138169\", \"142189\", \"144247\", \"153095\", \"164301\", \"173508\", \"193612\", \"194576\", \"197149\", \"238743\", \"151395\", \"158269\", \"161620\", \"164246\", \"167345\", \"170487\", \"173280\", \"174716\", \"175291\", \"175311\", \"175629\", \"182014\", \"157689\", \"201953\", \"206321\"], \"outgoing_citations\": [\"206321\", \"232096\", \"236312\", \"240248\", \"241801\", \"244314\", \"253435\", \"268197\", \"268891\", \"303203\", \"311643\"]}","task_split":"paper_retrieval"}
{"document_id":"211624","document_content":"# Elastic CRFs for Open-ontology Slot Filling\n## Categories\n- Computation and Language\n## Abstract\nSlot filling is a crucial component in task-oriented dialog systems, which is to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The widely-used practice of treating slot filling as a sequence labeling task suffers from two drawbacks. First, the ontology is usually pre-defined and fixed. Most current methods are unable to predict new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the semantic meanings and relations for slots, which are implicit in their natural language descriptions. These observations motivate us to propose a novel model called elastic conditional random field (eCRF), for open-ontology slot filling. eCRFs can leverage the neural features of both the utterance and the slot descriptions, and are able to model the interactions between different slots. Experimental results show that eCRFs outperforms existing models on both the in-domain and the cross-doamin tasks, especially in predictions of unseen slots and values.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/app112210675\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"74469\"], \"outgoing_citations\": [\"50621\", \"129221\", \"131502\", \"159862\", \"180447\", \"232285\", \"245042\", \"246324\", \"261770\", \"285317\", \"295473\", \"297147\", \"297150\", \"300449\", \"302947\", \"310667\"]}","task_split":"paper_retrieval"}
{"document_id":"211713","document_content":"# A Marauder's Map of Security and Privacy in Machine Learning\n## Categories\n- Cryptography and Security\n## Abstract\nThere is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first encompasses a spectrum of approaches to verification and admission control, which is a prerequisite to enable fail-safe defaults in machine learning systems. The second seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desiderata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.01134v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"211753","document_content":"# What evidence does deep learning model use to classify Skin Lesions?\n## Categories\n- Computer Vision and Pattern Recognition\n- Quantitative Methods\n## Abstract\nMelanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions. Secondly, we utilize a prediction difference analysis method that examines each patch on the image through patch-wised corrupting to detect the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings can be significant and useful to guide clinical diagnosis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.01051v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"q-bio.QM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Quantitative Methods\"], \"incoming_citations\": [\"17043\", \"65859\", \"126969\", \"182414\"], \"outgoing_citations\": [\"220571\", \"272645\", \"273959\"]}","task_split":"paper_retrieval"}
{"document_id":"211839","document_content":"# A Bayesian Approach for Sequence Tagging with Crowds\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nCurrent methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation errors. To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels. By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks. We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art. We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00780v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"54016\", \"24680\", \"46042\", \"127795\", \"127943\", \"189535\"], \"outgoing_citations\": [\"145987\", \"304686\", \"357145\"]}","task_split":"paper_retrieval"}
{"document_id":"211841","document_content":"# A Survey on Natural Language Processing for Fake News Detection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00770v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23929\", \"96167\", \"7744\", \"15754\", \"27370\", \"7737\", \"10531\", \"11296\", \"15325\", \"21630\", \"25782\", \"42117\", \"186698\", \"45669\", \"47955\", \"69141\", \"73907\", \"207715\", \"86946\", \"95139\", \"103057\", \"126416\", \"72956\", \"148729\", \"168707\", \"166227\", \"181527\", \"182490\", \"188875\", \"172920\", \"197826\"], \"outgoing_citations\": [\"219169\", \"219367\", \"221735\", \"227367\", \"228585\", \"238986\", \"267336\", \"267552\", \"268073\", \"270020\", \"271021\", \"273723\", \"302653\"]}","task_split":"paper_retrieval"}
{"document_id":"211851","document_content":"# Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and\/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a $31$-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks. Code is available at: https:\/\/tinyurl.com\/ShowAttendRead","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00751v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"5857\", \"38506\", \"50775\", \"50917\", \"52248\", \"59368\", \"78763\", \"121818\", \"180823\", \"191\", \"7580\", \"8109\", \"9220\", \"24824\", \"28270\", \"29640\", \"29777\", \"33360\", \"33363\", \"33358\", \"39759\", \"42497\", \"43037\", \"48822\", \"48901\", \"49128\", \"50591\", \"53750\", \"55703\", \"58277\", \"63761\", \"64528\", \"64650\", \"67749\", \"73620\", \"93345\", \"116951\", \"120627\", \"122764\", \"133054\", \"147463\", \"150258\", \"163092\", \"99077\", \"100417\", \"110306\", \"111991\", \"120420\", \"123550\", \"126431\", \"134664\", \"135021\", \"135263\", \"136218\", \"136831\", \"136843\", \"143280\", \"144173\", \"149329\", \"150060\", \"151319\", \"158242\", \"162477\", \"189663\", \"192671\", \"210634\", \"155455\", \"165784\", \"169411\", \"180931\", \"229426\"], \"outgoing_citations\": [\"232706\", \"250738\", \"256711\", \"261365\", \"294023\", \"296685\", \"296828\", \"299461\", \"311196\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"211865","document_content":"# Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems\n## Categories\n- Computation and Language\n## Abstract\nSolving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10% accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00720v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26187\", \"32821\", \"96833\", \"13400\", \"20765\", \"23950\", \"24666\", \"25273\", \"25239\", \"101113\", \"37796\", \"56186\", \"78252\", \"79536\", \"80713\", \"94434\", \"98821\", \"220655\"], \"outgoing_citations\": [\"233912\", \"246516\", \"266702\", \"278226\", \"283758\", \"284114\", \"287181\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"212031","document_content":"# Survey on Vision-based Path Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nPath prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-91131-1_4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"18339\", \"149568\", \"32831\", \"34076\", \"36970\", \"63204\", \"71863\", \"129703\", \"186194\", \"151550\"], \"outgoing_citations\": [\"248822\", \"254458\", \"263418\", \"266218\", \"267118\", \"268891\", \"271683\", \"273754\", \"277390\", \"283970\", \"295183\", \"296086\", \"299434\", \"318131\", \"321729\", \"358782\"]}","task_split":"paper_retrieval"}
{"document_id":"212041","document_content":"# Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone cannot meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00218v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"68390\", \"102602\", \"136945\", \"111163\", \"133113\", \"158246\"], \"outgoing_citations\": [\"191827\", \"200534\", \"243754\", \"258937\", \"287103\", \"293545\", \"295099\", \"302868\", \"303512\", \"306406\", \"310303\", \"316473\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"212048","document_content":"# Attention-Aware Generalized Mean Pooling for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIt has been shown that image descriptors extracted by convolutional neural networks (CNNs) achieve remarkable results for retrieval problems. In this paper, we apply attention mechanism to CNN, which aims at enhancing more relevant features that correspond to important keypoints in the input image. The generated attention-aware features are then aggregated by the previous state-of-the-art generalized mean (GeM) pooling followed by normalization to produce a compact global descriptor, which can be efficiently compared to other image descriptors by the dot product. An extensive comparison of our proposed approach with state-of-the-art methods is performed on the new challenging ROxford5k and RParis6k retrieval benchmarks. Results indicate significant improvement over previous work. In particular, our attention-aware GeM (AGeM) descriptor outperforms state-of-the-art method on ROxford5k under the `Hard' evaluation protocal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00202v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"34971\", \"18577\", \"18636\", \"176317\", \"105117\", \"111811\", \"167361\", \"181615\", \"193735\"], \"outgoing_citations\": [\"222623\", \"231046\", \"237248\", \"251473\", \"277635\", \"280378\", \"281953\", \"294367\", \"294983\", \"295204\", \"301572\", \"303067\", \"304501\", \"320036\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"212169","document_content":"# Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness\n## Categories\n- Machine Learning\n## Abstract\nThe ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks. While various approaches aiming towards this goal have been proposed in recent times, a commonly accepted definition and validation procedure is missing. We provide a causal perspective on representation learning which covers disentanglement and domain shift robustness as special cases. Our causal framework allows us to introduce a new metric for the quantitative evaluation of deep latent variable models. We show how this metric can be estimated from labeled observational data and further provide an efficient estimation algorithm that scales linearly in the dataset size.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.00007v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"315\", \"1785\", \"3852\", \"6554\", \"7525\", \"27322\", \"51953\", \"70887\", \"71525\", \"80015\", \"10442\", \"12936\", \"18189\", \"19975\", \"24977\", \"33786\", \"43651\", \"117483\", \"63431\", \"65077\", \"66198\", \"68053\", \"71100\", \"75048\", \"75323\", \"77425\", \"84768\", \"90921\", \"69639\", \"79537\", \"82388\", \"89108\", \"96305\", \"98272\", \"98464\", \"109786\", \"114524\", \"116297\", \"116650\", \"118821\", \"121221\", \"116023\", \"130407\", \"130199\", \"139756\", \"139925\", \"140573\", \"141826\", \"206756\", \"151710\", \"153486\", \"154589\", \"181909\", \"183732\", \"208041\"], \"outgoing_citations\": [\"187863\", \"206756\", \"207094\", \"208041\", \"231027\", \"236093\", \"241933\", \"242142\", \"242239\", \"251597\", \"264782\", \"265526\", \"267394\", \"270823\", \"280734\", \"320031\", \"357230\"]}","task_split":"paper_retrieval"}
{"document_id":"212203","document_content":"# Infrastructure for the representation and electronic exchange of design knowledge\n## Categories\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nThis paper develops the concept of knowledge and its exchange using Semantic Web technologies. It points out that knowledge is more than information because it embodies the meaning, that is to say semantic and context. These characteristics will influence our approach to represent and to treat the knowledge. In order to be adopted, the developed system needs to be simple and to use standards. The goal of the paper is to find standards to model knowledge and exchange it with an other person. Therefore, we propose to model knowledge using UML models to show a graphical representation and to exchange it with XML to ensure the portability at low cost. We introduce the concept of ontology for organizing knowledge and for facilitating the knowledge exchange. Proposals have been tested by implementing an application on the design knowledge of a pen.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.13191v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Multiagent Systems\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"212286","document_content":"# Automated Remote Patient Monitoring: Data Sharing and Privacy Using Blockchain\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nThe revolution of Internet of Things (IoT) devices and wearable technology has opened up great possibilities in remote patient monitoring. To streamline the diagnosis and treatment process, healthcare professionals are now adopting the wearable technology. However, these technologies also pose grave privacy risks and security concerns about the transfer and the logging of data transactions. One solution to protect privacy in healthcare is the use of blockchain technology. However, one of the primary problems with blockchain is its highly limited scalability. In this work here, we propose the utilization of a blockchain based protocol to provide secure management and analysis of data. In this paper we use recently introduced PoW based protocol GHOSTDAG, that generalizes Satoshi's blockchain to a direct acyclic graph of blocks (blockDAG) and provides high throughput while also avoiding the security-scalability problem. We use two blockchains based on the original GHOSTDAG protocol, one that is private and one that is public. Using a private blockchain, we create a system where we use smart contracts to analyze patient health data. If the smart contract for any reason issues an alert for an abnormal reading then the system makes the record of that event to the public blockchain. This would resolve the privacy and security vulnerabilities associated with remote patient monitoring and also the limited scalability problem of Satoshi's original blockchain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1811.03417v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CR\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"212370","document_content":"# Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe antinuclear antibody detection with human epithelial cells is a popular approach for autoimmune diseases diagnosis. The manual evaluation demands time, effort and capital, and automation in screening can greatly aid the physicians in these respects. In this work, we employ simple, efficient and visually more interpretable, class-specific features which defined based on the visual characteristics of each class. We believe that defining features with a good visual interpretation, is indeed important in a scenario, where such an approach is used in an interactive CAD system for pathologists. Considering that problem consists of few classes, and our rather simplistic feature definitions, frameworks can be structured as hierarchies of various binary classifiers. These variants include frameworks which are earlier explored and some which are not explored for this task. We perform various experiments which include traditional texture features and demonstrate the effectiveness of class-specific features in various frameworks. We make insightful comparisons between different types of classification frameworks given their silent aspects and pros and cons over each other. We also demonstrate an experiment with only intermediates samples for testing. The proposed work yields encouraging results with respect to the state-of-the-art and highlights the role of class-specific features in different classification frameworks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.12690v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"314717\", \"332431\", \"346413\"]}","task_split":"paper_retrieval"}
{"document_id":"212426","document_content":"# Rain Removal in Traffic Surveillance: Does it Matter?\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVarying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TITS.2018.2872502\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"16477\", \"60777\", \"88981\", \"124201\", \"166155\", \"99725\", \"106364\", \"135144\", \"162666\", \"175713\", \"179513\", \"172169\", \"238365\"], \"outgoing_citations\": [\"218523\", \"258537\", \"275747\", \"279732\", \"283989\", \"285187\"]}","task_split":"paper_retrieval"}
{"document_id":"212451","document_content":"# A Framework for Probabilistic Generic Traffic Scene Prediction\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nIn a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.12506v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"5374\", \"132642\", \"4263\", \"12281\", \"103954\", \"118382\", \"174742\", \"175311\", \"194147\", \"218432\"], \"outgoing_citations\": [\"218634\", \"236083\", \"241801\", \"244314\", \"268197\", \"289605\"]}","task_split":"paper_retrieval"}
{"document_id":"212537","document_content":"# ReviewQA: a relational aspect-based opinion reading dataset\n## Categories\n- Computation and Language\n## Abstract\nDeep reading models for question-answering have demonstrated promising performance over the last couple of years. However current systems tend to learn how to cleverly extract a span of the source document, based on its similarity with the question, instead of seeking for the appropriate answer. Indeed, a reading machine should be able to detect relevant passages in a document regarding a question, but more importantly, it should be able to reason over the important pieces of the document in order to produce an answer when it is required. To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that we expect a model to master. Indeed, each question comes with an associated type that characterizes the required competency. With this framework, it is possible to benchmark the main families of models and to get an overview of what are the strengths and the weaknesses of a given model on the set of tasks evaluated in this dataset. Our corpus contains more than 500.000 questions in natural language over 100.000 hotel reviews. Our setup is projective, the answer of a question does not need to be extracted from a document, like in most of the recent datasets, but selected among a set of candidates that contains all the possible answers to the questions of the dataset. Finally, we present several baselines over this dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.12196v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"117098\", \"128155\"], \"outgoing_citations\": [\"250547\", \"253068\", \"279213\", \"280973\", \"291617\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"212658","document_content":"# Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation\n## Categories\n- Computational Physics\n- Machine Learning\n- Materials Science\n## Abstract\nAn active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1103\/PhysRevMaterials.3.023804\", \"primary_category\": \"physics.comp-ph\", \"categories\": [\"cs.LG\", \"physics.comp-ph\", \"cond-mat.mtrl-sci\"], \"primary_category_human_readable\": \"Computational Physics\", \"categories_human_readable\": [\"Machine Learning\", \"Computational Physics\", \"Materials Science\"], \"incoming_citations\": [\"30351\", \"22757\", \"45381\", \"102133\", \"129245\", \"137453\", \"153899\", \"111998\", \"121156\", \"98862\", \"152113\", \"196066\", \"153929\", \"160299\"], \"outgoing_citations\": [\"244012\", \"247799\", \"247850\", \"259838\"]}","task_split":"paper_retrieval"}
{"document_id":"212708","document_content":"# Middle-Out Decoding\n## Categories\n- Computation and Language\n## Abstract\nDespite being virtually ubiquitous, sequence-to-sequence models are challenged by their lack of diversity and inability to be externally controlled. In this paper, we speculate that a fundamental shortcoming of sequence generation models is that the decoding is done strictly from left-to-right, meaning that outputs values generated earlier have a profound effect on those generated later. To address this issue, we propose a novel middle-out decoder architecture that begins from an initial middle-word and simultaneously expands the sequence in both directions. To facilitate information flow and maintain consistent decoding, we introduce a dual self-attention mechanism that allows us to model complex dependencies between the outputs. We illustrate the performance of our model on the task of video captioning, as well as a synthetic sequence de-noising task. Our middle-out decoder achieves significant improvements on de-noising and competitive performance in the task of video captioning, while quantifiably improving the caption diversity. Furthermore, we perform a qualitative analysis that demonstrates our ability to effectively control the generation process of our decoder.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.11735v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"13221\", \"77667\", \"94026\", \"108688\", \"139805\", \"152767\", \"158619\", \"200268\"], \"outgoing_citations\": [\"219572\", \"235175\", \"242442\", \"243154\", \"255954\", \"264568\", \"265431\", \"268083\", \"268166\", \"271234\", \"273977\", \"282800\", \"282943\", \"299541\", \"303593\", \"304481\", \"313189\", \"313196\", \"313320\", \"313435\", \"316803\", \"320332\"]}","task_split":"paper_retrieval"}
{"document_id":"212719","document_content":"# Flash Photography for Data-Driven Hidden Scene Recovery\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these tasks can be done around corners with consumer cameras. Recent techniques for NLOS imaging using consumer cameras have not been able to both localize and identify the hidden object. We introduce a method that couples traditional geometric understanding and data-driven techniques. To avoid the limitation of large dataset gathering, we train the data-driven models on rendered samples to computationally recover the hidden scene on real data. The method has three independent operating modes: 1) a regression output to localize a hidden object in 2D, 2) an identification output to identify the object type or pose, and 3) a generative network to reconstruct the hidden scene from a new viewpoint. The method is able to localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also identifies the hidden object class with 87.7% accuracy (compared to 33.3% random accuracy). This paper also provides an analysis on the distribution of information that encodes the occluded object in the accessible scene. We show that, unlike previously thought, the area that extends beyond the corner is essential for accurate object localization and identification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.11710v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"53056\", \"26876\", \"47985\", \"107438\", \"193877\", \"133869\", \"153685\", \"162043\", \"162710\", \"163201\", \"184123\"], \"outgoing_citations\": [\"255287\", \"255291\", \"260417\", \"273351\", \"278889\", \"292178\", \"294023\", \"301973\"]}","task_split":"paper_retrieval"}
{"document_id":"212808","document_content":"# Parsing Coordination for Spoken Language Understanding\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nTypical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by downstream domain applications. In this work we discuss expanding such systems to handle compound entities and intents by introducing a domain-agnostic shallow parser that handles linguistic coordination. We show that our model for parsing coordination learns domain-independent and slot-independent features and is able to segment conjunct boundaries of many different phrasal categories. We also show that using adversarial training can be effective for improving generalization across different slot types for coordination parsing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.11497v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"195574\"], \"outgoing_citations\": [\"232363\", \"233407\", \"268454\", \"275597\", \"281472\", \"294731\", \"311571\"]}","task_split":"paper_retrieval"}
{"document_id":"212887","document_content":"# Integrating Transformer and Paraphrase Rules for Sentence Simplification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nSentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from ma- chine translation studies and implicitly learned simplification mapping rules from normal- simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we pro- pose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state- of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https:\/\/github.com\/ Sanqiang\/text_simplification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.11193v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"12701\", \"127662\", \"17436\", \"20448\", \"24637\", \"26460\", \"32732\", \"55802\", \"64358\", \"73415\", \"92547\", \"78896\", \"82994\", \"90024\", \"108688\", \"114691\", \"118053\", \"123457\", \"127613\", \"126870\", \"127956\", \"135231\", \"163045\", \"162482\", \"172006\", \"179990\", \"192166\"], \"outgoing_citations\": [\"249315\", \"259192\", \"270048\", \"273977\", \"278065\", \"290747\"]}","task_split":"paper_retrieval"}
{"document_id":"212891","document_content":"# TarMAC: Targeted Multi-Agent Communication\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Multiagent Systems\n## Abstract\nWe propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision. We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.11187v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.MA\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Multiagent Systems\", \"Machine Learning\"], \"incoming_citations\": [\"8055\", \"1172\", \"4736\", \"7388\", \"15988\", \"22304\", \"24222\", \"30710\", \"40573\", \"67099\", \"118649\", \"142598\", \"142993\", \"172206\", \"1881\", \"5777\", \"9906\", \"12359\", \"12902\", \"12734\", \"17137\", \"21525\", \"23074\", \"28315\", \"30955\", \"31596\", \"77350\", \"37786\", \"37932\", \"41813\", \"44981\", \"200130\", \"40482\", \"70359\", \"70700\", \"73543\", \"96855\", \"97037\", \"110006\", \"167727\", \"59482\", \"76986\", \"86099\", \"90925\", \"98200\", \"98618\", \"102464\", \"105474\", \"110000\", \"113074\", \"119117\", \"120519\", \"121221\", \"119445\", \"122089\", \"132745\", \"133667\", \"134367\", \"135688\", \"136238\", \"138976\", \"140780\", \"156055\", \"162173\", \"164599\", \"176493\", \"182701\", \"198468\", \"203131\", \"213438\", \"152946\", \"151570\", \"154533\", \"158678\", \"160980\", \"190948\"], \"outgoing_citations\": [\"204914\", \"225886\", \"231395\", \"245731\", \"262814\", \"263378\", \"270254\", \"271085\", \"271497\", \"277491\", \"280774\", \"291892\", \"292127\", \"303714\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"213126","document_content":"# Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nPrevious studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems. However, there are inevitably noises in the external information, severely reducing the benefit that the existing methods could receive from the incorporation. To tackle the problem, this study pays special attention to the discrimination of the noises during the incorporation. We argue that there exist two kinds of noise in this external information, i.e. global noise and local noise, which affect the translations for the whole sentence and for some specific words, respectively. Accordingly, we propose a general framework that learns to jointly discriminate both the global and local noises, so that the external information could be better leveraged. Our model is trained on the dataset derived from the original parallel corpus without any external labeled data or annotation. Experimental results in various real-world scenarios, language pairs, and neural architectures indicate that discriminating noises contributes to significant improvements in translation quality by being able to better incorporate the external information, even in very noisy conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.10317v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"99505\", \"114991\", \"171969\", \"150615\", \"177134\", \"195512\"], \"outgoing_citations\": [\"230556\", \"232603\", \"235175\", \"249315\", \"259199\", \"259268\", \"260977\", \"265947\", \"268166\", \"268370\", \"268909\", \"271637\", \"282494\", \"282509\", \"287823\", \"290995\", \"290996\", \"292831\", \"295815\", \"299936\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"213282","document_content":"# Time-Aware and Corpus-Specific Entity Relatedness\n## Categories\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nEntity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but flexible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.10004v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"213100\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"213306","document_content":"# Semi-supervised acoustic model training for speech with code-switching\n## Categories\n- Computation and Language\n## Abstract\nIn the FAME! project, we aim to develop an automatic speech recognition (ASR) system for Frisian-Dutch code-switching (CS) speech extracted from the archives of a local broadcaster with the ultimate goal of building a spoken document retrieval system. Unlike Dutch, Frisian is a low-resourced language with a very limited amount of manually annotated speech data. In this paper, we describe several automatic annotation approaches to enable using of a large amount of raw bilingual broadcast data for acoustic model training in a semi-supervised setting. Previously, it has been shown that the best-performing ASR system is obtained by two-stage multilingual deep neural network (DNN) training using 11 hours of manually annotated CS speech (reference) data together with speech data from other high-resourced languages. We compare the quality of transcriptions provided by this bilingual ASR system with several other approaches that use a language recognition system for assigning language labels to raw speech segments at the front-end and using monolingual ASR resources for transcription. We further investigate automatic annotation of the speakers appearing in the raw broadcast data by first labeling with (pseudo) speaker tags using a speaker diarization system and then linking to the known speakers appearing in the reference data using a speaker recognition system. These speaker labels are essential for speaker-adaptive training in the proposed setting. We train acoustic models using the manually and automatically annotated data and run recognition experiments on the development and test data of the FAME! speech corpus to quantify the quality of the automatic annotations. The ASR and CS detection results demonstrate the potential of using automatic language and speaker tagging in semi-supervised bilingual acoustic model training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.09699v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42172\", \"89131\", \"132348\", \"133032\", \"138203\", \"177319\", \"179806\", \"180203\", \"191716\", \"223054\", \"223051\", \"223052\"], \"outgoing_citations\": [\"223054\", \"283117\"]}","task_split":"paper_retrieval"}
{"document_id":"213340","document_content":"# Neural Transition-based Syntactic Linearization\n## Categories\n- Computation and Language\n## Abstract\nThe task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multi-layer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed-forward neural network, observing significantly better results compared to LSTM language models on this task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.09609v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24491\", \"176190\"], \"outgoing_citations\": [\"226756\", \"232980\", \"232981\", \"293610\", \"296122\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"213344","document_content":"# Biomedical Document Clustering and Visualization based on the Concepts of Diseases\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDocument clustering is a text mining technique used to provide better document search and browsing in digital libraries or online corpora. A lot of research has been done on biomedical document clustering that is based on using existing ontology. But, associations and co-occurrences of the medical concepts are not well represented by using ontology. In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed. They identify the closest concepts of diseases in the context of a corpus. Each document is represented by using the vector space model. A weight scheme is proposed to consider both local content and associations between concepts. A Self-Organizing Map is used as document clustering algorithm. The vector projection and visualization features of SOM enable visualization and analysis of the clusters distributions and relationships on the two dimensional space. The experimental results show that the proposed document clustering framework generates meaningful clusters and facilitate visualization of the clusters based on the concepts of diseases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.09597v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"81887\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"213394","document_content":"# Unsupervised Learning of Shape and Pose with Differentiable Point Clouds\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the shape and the pose from a single image by minimizing the reprojection error: given several views of an object, the projections of the predicted shapes to the predicted camera poses should match the provided views. To deal with pose ambiguity, we introduce an ensemble of pose predictors which we then distill to a single \"student\" model. To allow for efficient learning of high-fidelity shapes, we represent the shapes by point clouds and devise a formulation allowing for differentiable projection of these. Our experiments show that the distilled ensemble of pose predictors learns to estimate the pose accurately, while the point cloud representation allows to predict detailed shape models. The supplementary video can be found at https:\/\/www.youtube.com\/watch?v=LuIGovKeo60","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.09381v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"645\", \"4051\", \"16742\", \"23050\", \"6397\", \"8263\", \"9860\", \"12571\", \"12616\", \"14623\", \"16380\", \"21349\", \"26296\", \"30375\", \"30735\", \"32753\", \"10256\", \"34337\", \"37574\", \"40078\", \"44397\", \"130893\", \"49060\", \"54836\", \"56087\", \"59466\", \"59862\", \"65137\", \"65475\", \"65734\", \"67381\", \"68665\", \"81117\", \"95454\", \"110238\", \"115706\", \"133354\", \"161134\", \"180606\", \"191339\", \"58660\", \"82772\", \"86627\", \"86452\", \"91114\", \"93153\", \"95940\", \"97658\", \"97806\", \"104897\", \"105007\", \"105438\", \"110472\", \"110798\", \"111572\", \"112366\", \"112560\", \"113538\", \"117017\", \"117893\", \"118898\", \"118888\", \"120791\", \"126997\", \"127456\", \"132253\", \"132197\", \"133193\", \"133612\", \"135485\", \"135609\", \"135465\", \"136895\", \"137225\", \"138804\", \"150515\", \"151076\", \"151681\", \"153398\", \"153557\", \"154009\", \"154173\", \"111573\", \"151534\", \"155280\", \"158372\", \"156204\", \"169258\", \"170931\", \"173162\", \"174782\", \"178047\", \"181617\", \"182539\", \"183957\", \"184536\", \"192520\", \"192689\", \"200777\", \"202449\", \"203725\", \"208181\", \"208590\", \"128840\"], \"outgoing_citations\": [\"235794\", \"236936\", \"245324\", \"249836\", \"259890\", \"263147\", \"267228\", \"268404\", \"270389\", \"278990\", \"279050\", \"289119\", \"298480\"]}","task_split":"paper_retrieval"}
{"document_id":"213573","document_content":"# Abstractive Summarization Using Attentive Neural Techniques\n## Categories\n- Computation and Language\n## Abstract\nIn a world of proliferating data, the ability to rapidly summarize text is growing in importance. Automatic summarization of text can be thought of as a sequence to sequence problem. Another area of natural language processing that solves a sequence to sequence problem is machine translation, which is rapidly evolving due to the development of attention-based encoder-decoder networks. This work applies these modern techniques to abstractive summarization. We perform analysis on various attention mechanisms for summarization with the goal of developing an approach and architecture aimed at improving the state of the art. In particular, we modify and optimize a translation model with self-attention for generating abstractive sentence summaries. The effectiveness of this base model along with attention variants is compared and analyzed in the context of standardized evaluation sets and test metrics. However, we show that these metrics are limited in their ability to effectively score abstractive summaries, and propose a new approach based on the intuition that an abstractive model requires an abstractive evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.08838v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18840\", \"223084\", \"184180\", \"189816\"], \"outgoing_citations\": [\"237477\", \"238482\", \"239950\", \"243803\", \"280557\", \"280735\"]}","task_split":"paper_retrieval"}
{"document_id":"213710","document_content":"# Temporal Action Detection by Joint Identification-Verification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTemporal action detection aims at not only recognizing action category but also detecting start time and end time for each action instance in an untrimmed video. The key challenge of this task is to accurately classify the action and determine the temporal boundaries of each action instance. In temporal action detection benchmark: THUMOS 2014, large variations exist in the same action category while many similarities exist in different action categories, which always limit the performance of temporal action detection. To address this problem, we propose to use joint Identification-Verification network to reduce the intra-action variations and enlarge inter-action differences. The joint Identification-Verification network is a siamese network based on 3D ConvNets, which can simultaneously predict the action categories and the similarity scores for the input pairs of video proposal segments. Extensive experimental results on the challenging THUMOS 2014 dataset demonstrate the effectiveness of our proposed method compared to the existing state-of-art methods for temporal action detection in untrimmed videos.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.08375v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"253115\", \"268408\", \"270802\", \"272028\", \"272360\", \"277459\", \"280225\", \"282939\", \"287580\", \"299439\", \"300351\", \"309902\", \"311710\"]}","task_split":"paper_retrieval"}
{"document_id":"213751","document_content":"# Large-scale Hierarchical Alignment for Data-driven Text Rewriting\n## Categories\n- Computation and Language\n## Abstract\nWe propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.08237v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"89936\", \"125835\", \"173799\", \"168922\"], \"outgoing_citations\": [\"214312\", \"228171\", \"234391\", \"265309\", \"272080\", \"310136\"]}","task_split":"paper_retrieval"}
{"document_id":"213822","document_content":"# Semantic Parsing for Task Oriented Dialog using Hierarchical Representations\n## Categories\n- Computation and Language\n## Abstract\nTask oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (fb.me\/semanticparsingdialog), and show that parsing models outperform sequence-to-sequence approaches on this dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07942v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"46822\", \"8144\", \"15856\", \"15945\", \"17314\", \"21953\", \"24691\", \"13600\", \"15894\", \"16880\", \"21740\", \"23791\", \"24645\", \"24757\", \"36321\", \"42875\", \"43704\", \"44032\", \"44176\", \"44583\", \"44832\", \"46794\", \"48161\", \"55935\", \"55962\", \"56973\", \"58181\", \"73442\", \"72973\", \"80300\", \"83777\", \"91750\", \"104803\", \"82073\", \"89187\", \"89214\", \"89849\", \"91687\", \"95239\", \"95845\", \"95921\", \"96106\", \"98073\", \"98858\", \"120737\", \"123561\", \"127406\", \"128897\", \"129258\", \"129574\", \"144895\", \"154477\", \"152874\", \"162565\", \"164340\", \"168129\", \"170083\", \"176848\", \"195574\", \"198784\", \"200340\", \"207678\", \"204820\", \"206188\"], \"outgoing_citations\": [\"263808\", \"280201\", \"285317\", \"290766\", \"296150\", \"297648\", \"319833\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"213824","document_content":"# Unsupervised Neural Text Simplification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThe paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders and gains knowledge of simplification through discrimination based-losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on a public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. Addition of a few labelled pairs also improves the performance further.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07931v6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"12701\", \"127662\", \"17436\", \"26460\", \"32732\", \"36870\", \"50687\", \"64358\", \"82994\", \"88487\", \"105099\", \"108688\", \"113240\", \"118053\", \"127511\", \"127956\", \"97102\"], \"outgoing_citations\": [\"219175\", \"220515\", \"232164\", \"235275\", \"251974\", \"265309\"]}","task_split":"paper_retrieval"}
{"document_id":"213852","document_content":"# Applications of Deep Reinforcement Learning in Communications and Networking: A Survey\n## Categories\n- Networking and Internet Architecture\n- Machine Learning\n## Abstract\nThis paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07862v1\", \"primary_category\": \"cs.NI\", \"categories\": [\"cs.NI\", \"cs.LG\"], \"primary_category_human_readable\": \"Networking and Internet Architecture\", \"categories_human_readable\": [\"Networking and Internet Architecture\", \"Machine Learning\"], \"incoming_citations\": [\"12923\", \"14155\", \"18982\", \"27958\", \"30438\", \"39366\", \"40940\", \"45979\", \"48112\", \"50943\", \"65989\", \"193282\", \"97479\", \"962\", \"7548\", \"9602\", \"10483\", \"12990\", \"13681\", \"21440\", \"22825\", \"24531\", \"30416\", \"30463\", \"38940\", \"41453\", \"45015\", \"46010\", \"46717\", \"64617\", \"65436\", \"77178\", \"77439\", \"85957\", \"101789\", \"108684\", \"135053\", \"136080\", \"145459\", \"147787\", \"204007\", \"4616\", \"19232\", \"20196\", \"21810\", \"27249\", \"27285\", \"27718\", \"31177\", \"31285\", \"32097\", \"32641\", \"16256\", \"37058\", \"301670\", \"40358\", \"40551\", \"41367\", \"41814\", \"42022\", \"42509\", \"42913\", \"20450\", \"47327\", \"50697\", \"50691\", \"50892\", \"51864\", \"52553\", \"55470\", \"56476\", \"58172\", \"95470\", \"59976\", \"60209\", \"61670\", \"63975\", \"64958\", \"66405\", \"69249\", \"69485\", \"70282\", \"72552\", \"72706\", \"135631\", \"73517\", \"74045\", \"75694\", \"77117\", \"77435\", \"79103\", \"80930\", \"83198\", \"86095\", \"88090\", \"88549\", \"93919\", \"105200\", \"105233\", \"106495\", \"107223\", \"107783\", \"108953\", \"110643\", \"111100\", \"116424\", \"119740\", \"129055\", \"130894\", \"135215\", \"136414\", \"177912\", \"140341\", \"141897\", \"147323\", \"162204\", \"162252\", \"180897\", \"203506\", \"64365\", \"73042\", \"95155\", \"99980\", \"100937\", \"101878\", \"100720\", \"102944\", \"7324\", \"104698\", \"105620\", \"113272\", \"114762\", \"115511\", \"244399\", \"242951\", \"120557\", \"123169\", \"123900\", \"76222\", \"138897\", \"140542\", \"145318\", \"149745\", \"150860\", \"156625\", \"156888\", \"157589\", \"164869\", \"165316\", \"166291\", \"175157\", \"175196\", \"179879\", \"205665\", \"154862\", \"170897\", \"173132\", \"177981\", \"178344\", \"181409\", \"186982\", \"187147\", \"187535\", \"188610\", \"188871\", \"189139\", \"197367\", \"211105\", \"222573\", \"239264\", \"253822\"], \"outgoing_citations\": [\"205659\", \"206601\", \"206745\", \"214848\", \"218910\", \"222164\", \"222573\", \"231787\", \"231928\", \"232394\", \"233432\", \"235037\", \"236207\", \"236590\", \"237232\", \"237826\", \"240208\", \"240405\", \"240425\", \"240445\", \"240626\", \"241582\", \"243883\", \"243885\", \"244805\", \"244838\", \"244906\", \"245784\", \"246655\", \"246820\", \"246987\", \"251484\", \"251564\", \"253397\", \"253412\", \"253822\", \"255332\", \"257520\", \"258081\", \"258199\", \"260583\", \"261675\", \"262381\", \"262706\", \"267580\", \"269360\", \"273337\", \"276628\", \"278766\", \"281549\", \"283228\", \"283410\", \"285817\", \"286765\", \"289539\", \"296055\", \"297186\", \"298642\", \"301005\", \"309111\", \"311147\", \"314451\", \"320173\"]}","task_split":"paper_retrieval"}
{"document_id":"213912","document_content":"# On Evaluating Embedding Models for Knowledge Base Completion\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nKnowledge bases contribute to many web search and mining tasks, yet they are often incomplete. To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature. Perhaps surprisingly, relatively simple models with limited expressiveness often performed remarkably well under today's most commonly used evaluation protocols. In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion. We show that when focusing on a different prediction task for evaluating knowledge base completion, the performance of current embedding models is unsatisfactory even on datasets previously thought to be too easy. This is especially true when embedding models are compared against a simple rule-based baseline. This work indicates the need for more research into the embedding models and evaluation protocols for knowledge base completion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07180v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"38327\", \"106688\", \"65730\", \"80138\", \"121488\", \"144356\", \"133426\", \"136260\", \"71900\", \"188873\", \"205803\"], \"outgoing_citations\": [\"238203\", \"252012\", \"255967\", \"265002\", \"267141\", \"297226\", \"304901\", \"311834\", \"316630\", \"329290\"]}","task_split":"paper_retrieval"}
{"document_id":"213954","document_content":"# Sequence to Sequence Mixture Model for Diverse Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nSequence to sequence (SEQ2SEQ) models often lack diversity in their generated translations. This can be attributed to the limitation of SEQ2SEQ models in capturing lexical and syntactic variations in a parallel corpus resulting from different styles, genres, topics, or ambiguity of the translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal loglikelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted beam search. Our mixture model uses negligible additional parameters and incurs no extra computation cost during decoding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07391v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24310\", \"25140\", \"29197\", \"33697\", \"115707\", \"89726\", \"92747\", \"93839\", \"123670\", \"125260\", \"133899\", \"153474\", \"155080\", \"188492\", \"152767\", \"164879\", \"160521\", \"168448\", \"168634\", \"198185\", \"205684\"], \"outgoing_citations\": [\"230284\", \"233206\", \"236907\", \"238926\", \"250577\", \"250815\", \"270154\", \"275596\", \"276358\", \"279543\", \"281076\", \"283076\", \"291858\"]}","task_split":"paper_retrieval"}
{"document_id":"213994","document_content":"# At Human Speed: Deep Reinforcement Learning with Action Delay\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThere has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of tasks, from video games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning and reinforcement learning, that learn to play from experience with minimal prior knowledge. However, these machines often do not win through intelligence alone -- they possess vastly superior speed and precision, allowing them to act in ways a human never could. To level the playing field, we restrict the machine's reaction time to a human level, and find that standard deep reinforcement learning methods quickly drop in performance. We propose a solution to the action delay problem inspired by human perception -- to endow agents with a neural predictive model of the environment which \"undoes\" the delay inherent in their environment -- and demonstrate its efficacy against professional players in Super Smash Bros. Melee, a popular console fighting game.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07286v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"72872\", \"96184\", \"105363\", \"125696\", \"131417\", \"133479\"], \"outgoing_citations\": [\"273563\", \"276687\"]}","task_split":"paper_retrieval"}
{"document_id":"214089","document_content":"# Neural Morphological Tagging for Estonian\n## Categories\n- Computation and Language\n## Abstract\nWe develop neural morphological tagging and disambiguation models for Estonian. First, we experiment with two neural architectures for morphological tagging - a standard multiclass classifier which treats each morphological tag as a single unit, and a sequence model which handles the morphological tags as sequences of morphological category values. Secondly, we complement these models with the analyses generated by a rule-based Estonian morphological analyser (MA) VABAMORF , thus performing a soft morphological disambiguation. We compare two ways of supplementing a neural morphological tagger with the MA outputs: firstly, by adding the combined analyses embeddings to the word representation input to the neural tagging model, and secondly, by adopting an attention mechanism to focus on the most relevant analyses generated by the MA. Experiments on three Estonian datasets show that our neural architectures consistently outperform the non-neural baselines, including HMM-disambiguated VABAMORF, while augmenting models with MA outputs results in a further performance boost for both models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06908v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"112575\"], \"outgoing_citations\": [\"213581\", \"264459\", \"294248\", \"308310\"]}","task_split":"paper_retrieval"}
{"document_id":"214121","document_content":"# Named Entity Analysis and Extraction with Uncommon Words\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nMost previous research treats named entity extraction and classification as an end-to-end task. We argue that the two sub-tasks should be addressed separately. Entity extraction lies at the level of syntactic analysis while entity classification lies at the level of semantic analysis. According to Noam Chomsky's \"Syntactic Structures,\" pp. 93-94 (Chomsky 1957), syntax is not appealed to semantics and semantics does not affect syntax. We analyze two benchmark datasets for the characteristics of named entities, finding that uncommon words can distinguish named entities from common text; where uncommon words are the words that hardly appear in common text and they are mainly the proper nouns. Experiments validate that lexical and syntactic features achieve state-of-the-art performance on entity extraction and that semantic features do not further improve the extraction performance, in both of our model and the state-of-the-art baselines. With Chomsky's view, we also explain the failure of joint syntactic and semantic parsings in other works.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06818v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"256158\", \"267692\", \"274533\", \"289403\", \"308310\", \"312608\", \"330797\"]}","task_split":"paper_retrieval"}
{"document_id":"214149","document_content":"# Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nDeep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area of research and many concurrent inventions, we decided to focus on a relatively simple robotic task to evaluate a set of ideas that might help to solve recent reinforcement learning problems. We test a newly created combination of two commonly used reinforcement learning methods, whether it is able to learn more effectively than a baseline. We also compare different ideas to preprocess information before it is fed to the reinforcement learning algorithm. The goal of this strategy is to reduce training time and eventually help the algorithm to converge. The concluding evaluation proves the general applicability of the described concepts by testing them using a simulated environment. These concepts might be reused for future experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06746v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"280992\", \"281110\", \"281159\", \"283410\", \"289719\", \"303521\"]}","task_split":"paper_retrieval"}
{"document_id":"214156","document_content":"# Robust Neural Machine Translation with Joint Textual and Phonetic Embedding\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06729v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16217\", \"27278\", \"31388\", \"44551\", \"54462\", \"54693\", \"57095\", \"60797\", \"80348\", \"77667\", \"139549\", \"89153\", \"92971\", \"123394\", \"127581\", \"158298\", \"165042\", \"173177\", \"173791\", \"179900\", \"182218\", \"179591\"], \"outgoing_citations\": [\"189099\", \"189103\", \"211863\", \"213698\", \"231932\", \"251240\"]}","task_split":"paper_retrieval"}
{"document_id":"214174","document_content":"# Deep Transfer Reinforcement Learning for Text Summarization\n## Categories\n- Machine Learning\n- Computation and Language\n- I.2.6; I.2.7; I.2.10\n## Abstract\nDeep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-of-the-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06667v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\", \"I.2.6; I.2.7; I.2.10\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\", \"I.2.6; I.2.7; I.2.10\"], \"incoming_citations\": [\"207215\", \"79729\", \"131391\", \"143330\", \"230774\", \"188948\"], \"outgoing_citations\": [\"207215\", \"214221\", \"218235\", \"219618\", \"220509\", \"225556\", \"227323\", \"230212\", \"230774\", \"233710\", \"238703\", \"241070\", \"244679\", \"249726\", \"251736\", \"266671\", \"270925\", \"280557\", \"290261\"]}","task_split":"paper_retrieval"}
{"document_id":"214226","document_content":"# Deep Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nWe discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.06339v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"214400","document_content":"# A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification\n## Categories\n- Computation and Language\n## Abstract\nCurrent lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.05754v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"6424\", \"36517\", \"45719\", \"48746\", \"92547\", \"94357\", \"116150\", \"127104\", \"125582\", \"154650\", \"156306\", \"176154\"], \"outgoing_citations\": [\"227488\", \"234352\", \"234907\", \"248226\", \"270048\", \"280708\", \"308756\", \"328319\"]}","task_split":"paper_retrieval"}
{"document_id":"214403","document_content":"# Policy Transfer with Strategy Optimization\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nComputer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification. In this paper, we present a different approach that leverages domain randomization for transferring control policies to unknown environments. The key idea that, instead of learning a single policy in the simulation, we simultaneously learn a family of policies that exhibit different behaviors. When tested in the target environment, we directly search for the best policy in the family based on the task performance, without the need to identify the dynamic parameters. We evaluate our method on five simulated robotic control problems with different discrepancies in the training and testing environment and demonstrate that our method can overcome larger modeling errors compared to training a robust policy or an adaptive policy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.05751v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"5373\", \"12230\", \"18174\", \"66783\", \"95451\", \"109259\", \"20646\", \"20208\", \"36626\", \"50983\", \"70142\", \"81039\", \"89252\", \"107895\", \"105878\", \"118690\", \"133560\", \"138908\", \"120991\", \"153614\", \"155443\", \"164419\", \"164478\", \"183019\", \"171778\", \"185289\", \"193396\", \"196576\"], \"outgoing_citations\": [\"208860\", \"234025\", \"236393\", \"241900\", \"244294\", \"253045\", \"260284\", \"261780\", \"272030\", \"274445\", \"282684\", \"283289\", \"327091\"]}","task_split":"paper_retrieval"}
{"document_id":"214505","document_content":"# Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry where the goal is to learn the mapping between a face sketch image and its corresponding photo-realistic image. However, the limited number of paired sketch-photo training data usually prevents the current frameworks to learn a robust mapping between the geometry of sketches and their matching photo-realistic images. Consequently, in this work, we present an approach for learning to synthesize a photo-realistic image from a face sketch in an unsupervised fashion. In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face. Learning facial prior information empowers the network to remove the geometrical artifacts in the face sketch. We demonstrate that a simultaneous optimization of the face photo generator network, employing the proposed perceptual discriminator in combination with a texture-wise discriminator, results in a significant improvement in quality and recognition rate of the synthesized photos. We evaluate the proposed network by conducting extensive experiments on multiple baseline sketch-photo datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.05361v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"66\", \"59351\", \"59711\", \"82752\", \"171598\", \"164412\", \"172936\", \"172947\", \"29859\"], \"outgoing_citations\": [\"234787\", \"236391\", \"278895\", \"290749\", \"296773\", \"318394\"]}","task_split":"paper_retrieval"}
{"document_id":"214546","document_content":"# Efficient Augmentation via Data Subsampling\n## Categories\n- Machine Learning\n## Abstract\nData augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply. In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset. We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.05222v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"42295\", \"127518\", \"144910\", \"156680\", \"238777\"], \"outgoing_citations\": [\"229919\", \"240260\", \"250707\", \"256788\", \"256813\", \"290407\", \"320129\", \"327865\", \"343323\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"214557","document_content":"# The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews\n## Categories\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nAnnotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to the existing annotation guidelines with a goal of making the extracted app features more useful and informative to the app developers. We test the proposed changes via simulating the application of the new annotation guidelines and then evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to the app developers. Secondly, we are interested in what kind of annotated training data is necessary for training an automatic app feature extraction model. In particular, we explore whether the training set should contain annotated app reviews from those apps\/app categories on which the model is subsequently planned to be applied, or is it sufficient to have annotated app reviews from any app available for training, even when these apps are from very different categories compared to the test app. Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall. Furthermore, we test whether augmenting the training set with annotated product reviews helps to improve the performance of app feature extraction. We find that the models trained on augmented training set lead to improved recall but at the cost of the drop in precision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.05187v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"274562\", \"312665\"]}","task_split":"paper_retrieval"}
{"document_id":"214805","document_content":"# Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation\n## Categories\n- Machine Learning\n## Abstract\nDeep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to a centralized location faces various legal, privacy, technical, and data-ownership challenges, especially among international institutions. In this study, we introduce the first use of federated learning for multi-institutional collaboration, enabling deep learning modeling without sharing patient data. Our quantitative results demonstrate that the performance of federated semantic segmentation models (Dice=0.852) on multimodal brain scans is similar to that of models trained by sharing data (Dice=0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.04304v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2963\", \"37270\", \"203074\", \"1828\", \"11904\", \"16289\", \"27416\", \"36865\", \"37349\", \"49221\", \"60494\", \"66561\", \"71429\", \"76669\", \"77479\", \"99336\", \"101803\", \"110259\", \"112625\", \"166152\", \"3289\", \"6737\", \"12088\", \"19152\", \"27798\", \"28782\", \"29660\", \"34937\", \"36915\", \"37227\", \"39822\", \"41753\", \"49219\", \"49756\", \"49967\", \"51220\", \"102145\", \"60953\", \"62872\", \"63956\", \"64978\", \"65658\", \"66782\", \"68872\", \"72995\", \"73648\", \"75782\", \"78786\", \"85147\", \"99226\", \"104264\", \"108307\", \"133696\", \"80401\", \"83656\", \"87101\", \"98209\", \"102450\", \"103131\", \"105490\", \"109503\", \"118615\", \"129815\", \"131944\", \"134725\", \"136201\", \"141728\", \"146915\", \"153695\", \"160794\", \"165424\", \"170810\", \"110331\", \"160401\", \"162051\", \"163734\", \"185996\", \"231375\"], \"outgoing_citations\": [\"226146\", \"246939\", \"252956\", \"273975\", \"289558\"]}","task_split":"paper_retrieval"}
{"document_id":"214874","document_content":"# The combination of context information to enhance simple question answering\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nWith the rapid development of knowledge base,question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type,outdegree. Experimental results show that context information can improve the result of simple question answering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.04000v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"222416\", \"236190\", \"246677\", \"260530\", \"264121\", \"290304\", \"290690\", \"290997\", \"295325\", \"311684\", \"328352\", \"330994\"]}","task_split":"paper_retrieval"}
{"document_id":"214879","document_content":"# Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nThis work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modeling. In the state-of-the-art methods, source and target sentences are treated as one-dimensional sequences over time, while we view translation as a two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence between source and target words. We extend beyond the current sequence to sequence backbone NMT models to a 2D structure in which the source and target sentences are aligned with each other in a 2D grid. Our proposed topology shows consistent improvements over attention-based sequence to sequence model on two WMT 2017 tasks, German$\\leftrightarrow$English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.03975v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"84965\", \"119740\", \"142031\", \"152767\", \"155272\", \"155267\", \"212127\", \"221621\"], \"outgoing_citations\": [\"232154\", \"234164\", \"292490\", \"299936\", \"300543\", \"310023\", \"311926\", \"320692\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"214976","document_content":"# Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.03707v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"65191\", \"12571\", \"7584\", \"52404\", \"57499\", \"60151\", \"90268\", \"98407\", \"126366\", \"131277\", \"177876\", \"167491\", \"169549\", \"196774\", \"209671\", \"209623\"], \"outgoing_citations\": [\"200297\", \"234234\", \"234358\", \"235982\", \"237212\", \"247753\", \"247761\", \"248391\", \"249175\", \"249466\", \"252062\", \"257553\", \"264846\", \"267376\", \"269476\", \"270057\", \"275848\", \"282124\", \"283716\", \"286181\", \"296192\", \"307879\", \"310233\", \"350529\"]}","task_split":"paper_retrieval"}
{"document_id":"215116","document_content":"# Reinforcement Evolutionary Learning Method for self-learning\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate the model periodically as the variable patterns for the model changes significantly due to market change or consumer behavior change etc. Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is majorly available for gaming or online systems, to learn from the live feedback. However, there are some research happened on the area of online advertisement, pricing etc where due to the nature of the online learning environment scope of reinforcement learning is explored. Our proposed solution is a reinforcement learning based, true self-learning algorithm which can adapt to the data change or concept drift and auto learn and self-calibrate for the new patterns of the data solving the problem of concept drift.   Keywords - Reinforcement learning, Genetic Algorithm, Q-learning, Classification modelling, CMA-ES, NES, Multi objective optimization, Concept drift, Population stability index, Incremental learning, F1-measure, Predictive Modelling, Self-learning, MCTS, AlphaGo, AlphaZero","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.03198v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"218305\"], \"outgoing_citations\": [\"237601\", \"247158\", \"249018\", \"283732\"]}","task_split":"paper_retrieval"}
{"document_id":"215181","document_content":"# A Comprehensive Survey of Deep Learning for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nGenerating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.04020v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"19259\", \"28573\", \"35427\", \"3727\", \"12465\", \"18326\", \"43280\", \"47195\", \"74810\", \"82963\", \"90027\", \"90723\", \"95561\", \"101277\", \"118036\", \"6500\", \"11824\", \"27365\", \"32961\", \"33663\", \"66883\", \"36970\", \"39406\", \"40534\", \"41801\", \"50613\", \"52654\", \"59671\", \"72906\", \"73658\", \"75008\", \"75336\", \"75733\", \"86413\", \"86689\", \"91944\", \"93826\", \"100023\", \"102192\", \"118540\", \"118569\", \"130722\", \"175031\", \"223084\", \"31178\", \"67532\", \"79141\", \"83901\", \"78599\", \"85583\", \"89648\", \"91111\", \"97539\", \"102772\", \"108688\", \"110219\", \"112491\", \"115939\", \"117530\", \"118456\", \"119495\", \"124829\", \"114521\", \"131931\", \"132390\", \"135490\", \"140872\", \"141196\", \"144619\", \"152322\", \"154807\", \"162598\", \"163770\", \"172586\", \"175715\", \"183406\", \"201514\", \"150187\", \"152816\", \"151549\", \"157229\", \"180681\", \"184640\", \"185910\", \"193531\", \"197370\"], \"outgoing_citations\": [\"230900\", \"233518\", \"244368\", \"248686\", \"249394\", \"258345\", \"262564\", \"267476\", \"268096\", \"268218\", \"268356\", \"269030\", \"270154\", \"271234\", \"277511\", \"278604\", \"278846\", \"279002\", \"279676\", \"279914\", \"280973\", \"281152\", \"284230\", \"286364\", \"289627\", \"291851\", \"293365\", \"295307\", \"295639\", \"296867\", \"298750\", \"300099\", \"301120\", \"302621\", \"302815\", \"303003\", \"303209\", \"303833\", \"305473\", \"306073\", \"310816\", \"311824\", \"312643\", \"313196\", \"313934\", \"317304\", \"317401\", \"319656\", \"320003\", \"321621\", \"322022\", \"323634\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"215435","document_content":"# Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nIn recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically -- showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the $1$-dimensional Weisfeiler-Leman graph isomorphism heuristic ($1$-WL). We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called $k$-dimensional GNNs ($k$-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.02244v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"18977\", \"19723\", \"26217\", \"27897\", \"65286\", \"118201\", \"120407\", \"127269\", \"51256\", \"52113\", \"2558\", \"3246\", \"3222\", \"3348\", \"4065\", \"4620\", \"5884\", \"6661\", \"7330\", \"7929\", \"7993\", \"8875\", \"14352\", \"15899\", \"16180\", \"18144\", \"18174\", \"18515\", \"18640\", \"18893\", \"22079\", \"22043\", \"25810\", \"38282\", \"39387\", \"40269\", \"41342\", \"42923\", \"44918\", \"45929\", \"66831\", \"67697\", \"69149\", \"74464\", \"80211\", \"80688\", \"91450\", \"94914\", \"102194\", \"120682\", \"126868\", \"133777\", \"146216\", \"168353\", \"178142\", \"191904\", \"193031\", \"193032\", \"201944\", \"80793\", \"275\", \"1222\", \"2056\", \"3105\", \"4410\", \"4696\", \"7295\", \"7776\", \"10071\", \"10697\", \"11288\", \"11491\", \"12967\", \"13133\", \"126243\", \"13295\", \"13779\", \"13814\", \"15576\", \"16517\", \"18031\", \"18304\", \"18623\", \"18592\", \"18830\", \"19609\", \"23232\", \"23332\", \"24738\", \"25781\", \"26197\", \"27558\", \"27805\", \"27860\", \"28034\", \"30707\", \"32104\", \"35463\", \"35897\", \"37535\", \"38400\", \"39279\", \"41578\", \"42305\", \"42645\", \"42699\", \"42715\", \"43155\", \"43059\", \"43757\", \"44235\", \"43981\", \"33840\", \"45032\", \"45295\", \"45332\", \"45755\", \"116597\", \"46303\", \"47156\", \"47793\", \"47935\", \"48519\", \"49388\", \"49956\", \"52019\", \"52310\", \"52701\", \"46420\", \"55553\", \"57357\", \"57782\", \"58289\", \"59866\", \"136319\", \"62249\", \"62578\", \"63555\", \"63431\", \"63814\", \"64367\", \"65206\", \"65828\", \"66858\", \"67228\", \"67519\", \"68391\", \"68697\", \"69656\", \"72934\", \"73530\", \"73619\", \"77151\", \"74607\", \"15011\", \"88245\", \"90277\", \"90641\", \"91828\", \"93505\", \"93509\", \"96407\", \"97047\", \"99184\", \"101393\", \"107810\", \"109488\", \"112479\", \"115890\", \"117122\", \"118149\", \"119868\", \"120001\", \"132809\", \"130836\", \"134727\", \"136491\", \"140442\", \"140399\", \"141226\", \"143456\", \"144385\", \"149389\", \"149742\", \"188686\", \"78173\", \"79438\", \"80776\", \"82471\", \"83801\", \"85092\", \"86320\", \"88158\", \"88586\", \"89502\", \"91251\", \"92363\", \"93290\", \"91408\", \"95023\", \"95564\", \"95587\", \"95635\", \"103085\", \"104821\", \"106670\", \"109933\", \"110102\", \"111026\", \"111646\", \"113666\", \"114934\", \"115540\", \"115834\", \"116141\", \"116219\", \"116927\", \"116959\", \"117584\", \"117810\", \"118122\", \"118788\", \"119057\", \"119251\", \"120084\", \"120250\", \"123843\", \"125230\", \"126374\", \"127256\", \"131562\", \"133183\", \"134585\", \"135428\", \"136186\", \"136778\", \"136848\", \"137772\", \"138212\", \"139045\", \"140828\", \"141107\", \"141179\", \"142226\", \"143081\", \"145273\", \"145482\", \"145731\", \"148699\", \"155249\", \"155744\", \"155642\", \"159913\", \"161896\", \"162941\", \"163945\", \"144582\", \"168172\", \"176236\", \"177249\", \"177593\", \"184180\", \"184368\", \"190484\", \"192186\", \"192622\", \"193372\", \"205283\", \"138999\", \"150692\", \"155315\", \"156406\", \"157117\", \"161576\", \"164676\", \"156634\", \"159599\", \"171776\", \"76233\", \"175185\", \"176579\", \"182965\", \"183719\", \"184194\", \"184286\", \"187289\", \"188510\", \"189544\", \"189913\", \"190159\", \"191340\", \"190468\", \"196221\", \"196255\", \"210730\"], \"outgoing_citations\": [\"249443\", \"265468\", \"272626\", \"291213\", \"315215\", \"359939\"]}","task_split":"paper_retrieval"}
{"document_id":"215459","document_content":"# A Single Approach to Decide Chase Termination on Linear Existential Rules\n## Categories\n- Logic in Computer Science\n## Abstract\nExistential rules, long known as tuple-generating dependencies in database theory, have been intensively studied in the last decade as a powerful formalism to represent ontological knowledge in the context of ontology-based query answering. A knowledge base is then composed of an instance that contains incomplete data and a set of existential rules, and answers to queries are logically entailed from the knowledge base. This brought again to light the fundamental chase tool, and its different variants that have been proposed in the literature. It is well-known that the problem of determining, given a chase variant and a set of existential rules, whether the chase will halt on any instance, is undecidable. Hence, a crucial issue is whether it becomes decidable for known subclasses of existential rules. In this work, we consider linear existential rules, a simple yet important subclass of existential rules that generalizes inclusion dependencies. We show the decidability of the all instance chase termination problem on linear rules for three main chase variants, namely semi-oblivious, restricted and core chase. To obtain these results, we introduce a novel approach based on so-called derivation trees and a single notion of forbidden pattern. Besides the theoretical interest of a unified approach and new proofs, we provide the first positive decidability results concerning the termination of the restricted chase, proving that chase termination on linear existential rules is decidable for both versions of the problem: Does every fair chase sequence terminate? Does some fair chase sequence terminate?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.02132v1\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\"], \"incoming_citations\": [\"71457\", \"125709\", \"203124\"], \"outgoing_citations\": [\"329791\", \"334185\", \"337915\", \"347016\"]}","task_split":"paper_retrieval"}
{"document_id":"215524","document_content":"# Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem\n## Categories\n- Machine Learning\n## Abstract\nDesigning optimal controllers continues to be challenging as systems are becoming complex and are inherently nonlinear. The principal advantage of reinforcement learning (RL) is its ability to learn from the interaction with the environment and provide optimal control strategy. In this paper, RL is explored in the context of control of the benchmark cartpole dynamical system with no prior knowledge of the dynamics. RL algorithms such as temporal-difference, policy gradient actor-critic, and value function approximation are compared in this context with the standard LQR solution. Further, we propose a novel approach to integrate RL and swing-up controllers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICACCI.2017.8125811\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"55470\", \"105916\", \"125326\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"215546","document_content":"# Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAn activation function has crucial role in a deep neural network.   A simple rectified linear unit (ReLU) are widely used for the activation function.   In this paper, a weighted sigmoid gate unit (WiG) is proposed as the activation function.   The proposed WiG consists of a multiplication of inputs and the weighted sigmoid gate.   It is shown that the WiG includes the ReLU and same activation functions as a special case.   Many activation functions have been proposed to overcome the performance of the ReLU.   In the literature, the performance is mainly evaluated with an object recognition task.   The proposed WiG is evaluated with the object recognition task and the image restoration task.   Then, the expeirmental comparisons demonstrate the proposed WiG overcomes the existing activation functions including the ReLU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.01829v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6965\", \"95493\", \"118654\", \"188412\", \"204966\"], \"outgoing_citations\": [\"218343\", \"252168\", \"269179\", \"274283\", \"277320\", \"289897\", \"321736\", \"333672\"]}","task_split":"paper_retrieval"}
{"document_id":"215675","document_content":"# Sketching for Latent Dirichlet-Categorical Models\n## Categories\n- Machine Learning\n## Abstract\nRecent work has explored transforming data sets into smaller, approximate summaries in order to scale Bayesian inference. We examine a related problem in which the parameters of a Bayesian model are very large and expensive to store in memory, and propose more compact representations of parameter values that can be used during inference. We focus on a class of graphical models that we refer to as latent Dirichlet-Categorical models, and show how a combination of two sketching algorithms known as count-min sketch and approximate counters provide an efficient representation for them. We show that this sketch combination -- which, despite having been used before in NLP applications, has not been previously analyzed -- enjoys desirable properties. We prove that for this class of models, when the sketches are used during Markov Chain Monte Carlo inference, the equilibrium of sketched MCMC converges to that of the exact chain as sketch parameters are tuned to reduce the error rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.01400v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"283057\", \"292188\", \"297714\", \"304158\", \"304335\", \"314038\", \"317342\"]}","task_split":"paper_retrieval"}
{"document_id":"215677","document_content":"# Rough set based lattice structure for knowledge representation in medical expert systems: low back pain management case study\n## Categories\n- Artificial Intelligence\n## Abstract\nThe aim of medical knowledge representation is to capture the detailed domain knowledge in a clinically efficient manner and to offer a reliable resolution with the acquired knowledge. The knowledge base to be used by a medical expert system should allow incremental growth with inclusion of updated knowledge over the time. As knowledge are gathered from a variety of knowledge sources by different knowledge engineers, the problem of redundancy is an important concern here due to increased processing time of knowledge and occupancy of large computational storage to accommodate all the gathered knowledge. Also there may exist many inconsistent knowledge in the knowledge base. In this paper, we have proposed a rough set based lattice structure for knowledge representation in medical expert systems which overcomes the problem of redundancy and inconsistency in knowledge and offers computational efficiency with respect to both time and space. We have also generated an optimal set of decision rules that would be used directly by the inference engine. The reliability of each rule has been measured using a new metric called credibility factor, and the certainty and coverage factors of a decision rule have been re-defined. With a set of decisions rules arranged in descending order according to their reliability measures, the medical expert system will consider the highly reliable and certain rules at first, then it would search for the possible and uncertain rules at later stage, if recommended by physicians. The proposed knowledge representation technique has been illustrated using an example from the domain of low back pain. The proposed scheme ensures completeness, consistency, integrity, non-redundancy, and ease of access.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.01560v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"104466\"], \"outgoing_citations\": [\"349757\", \"349874\"]}","task_split":"paper_retrieval"}
{"document_id":"215737","document_content":"# Predicate learning in neural systems: Discovering latent generative structures\n## Categories\n- Artificial Intelligence\n## Abstract\nHumans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and flexibility of human behavior. But how do the structures that these models invoke arise in neural systems in the first place? To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data. During the process of predicate learning, an artificial neural network exploits the naturally occurring dynamic properties of distributed computing across neuronal assemblies in order to learn predicates, but also to combine them compositionally, two computational aspects which appear to be necessary for human behavior as per formal theories in multiple domains. We describe how predicates can be combined generatively using neural oscillations to achieve human-like extrapolation and compositionality in an artificial neural network. The ability to learn predicates from experience, to represent structures compositionally, and to extrapolate to unseen data offers an inroads to understanding and modeling the most complex human behaviors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.01127v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"229203\", \"246129\"]}","task_split":"paper_retrieval"}
{"document_id":"215775","document_content":"# PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones\n## Categories\n- Cryptography and Security\n## Abstract\nNowadays many people store photos in smartphones. Many of the photos contain sensitive, private information, such as a photocopy of driver's license and credit card. An arising privacy concern is with the unauthorized accesses to such private photos by installed apps. Coarse-grained access control systems such as the Android permission system offer all-or-nothing access to photos stored on smartphones, and users are unaware of the exact behavior of installed apps. Our analysis finds that 82% of the top 200 free apps in a popular Android app store have complete access to stored photos and network on a user's smartphone, which indicates possible private photo leakage. In addition, our user survey reveals that 87.5% of the 112 respondents are not aware that certain apps can access their photos without informing users, and all the respondents believe that the stored photos on their smartphones contain different types of private information. Hence, we propose PhotoSafer, a content-based, context-aware private photo protection system for Android phones. PhotoSafer can detect private photos based on photo content with a well-trained deep convolutional neural network, and control access to photos based on system status (e.g., screen locked or not) and app-running status (e.g., app in the background). Evaluations demonstrate that PhotoSafer can accurately identify private photos in real time. The efficacy and efficiency of the implemented prototype system show the potential for practical use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/PAC.2018.00008\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"123281\", \"288420\", \"348561\"]}","task_split":"paper_retrieval"}
{"document_id":"215818","document_content":"# Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition\n## Categories\n- Robotics\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nIn an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work, we develop an agent empowered with visual curiosity, i.e. the ability to ask questions to an Oracle (e.g., human) about the contents in images (e.g., What is the object on the left side of the red cube?) and build visual recognition model based on the answers received (e.g., Cylinder). In order to do this, the agent must (1) understand what it recognizes and what it does not, (2) formulate a valid, unambiguous and informative language query (a question) to ask the Oracle, (3) derive the parameters of visual classifiers from the Oracle response and (4) leverage the updated visual classifiers to ask more clarified questions. Specifically, we propose a novel framework and formulate the learning of visual curiosity as a reinforcement learning problem. In this framework, all components of our agent, visual recognition module (to see), question generation policy (to ask), answer digestion module (to understand) and graph memory module (to memorize), are learned entirely end-to-end to maximize the reward derived from the scene graph obtained by the agent as a consequence of the dialog with the Oracle. Importantly, the question generation policy is disentangled from the visual recognition system and specifics of the environment. Consequently, we demonstrate a sort of double generalization. Our question generation policy generalizes to new environments and a new pair of eyes, i.e., new visual system. Trained on a synthetic dataset, our results show that our agent learns new visual concepts significantly faster than several heuristic baselines, even when tested on synthetic environments with novel objects, as well as in a realistic environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00912v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"17137\", \"25613\", \"34439\", \"77567\", \"83765\", \"96781\", \"103568\", \"109847\", \"113074\", \"118153\", \"124829\", \"132369\", \"189699\", \"192592\", \"193573\", \"207755\", \"196994\", \"278894\"], \"outgoing_citations\": [\"237655\", \"247900\", \"248742\", \"249906\", \"254516\", \"255644\", \"259094\", \"259711\", \"262855\", \"274102\", \"279518\", \"284565\", \"303603\"]}","task_split":"paper_retrieval"}
{"document_id":"215834","document_content":"# How Powerful are Graph Neural Networks?\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nGraph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00826v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"2093\", \"6316\", \"11367\", \"11546\", \"17850\", \"18076\", \"18977\", \"22090\", \"26217\", \"27897\", \"34279\", \"36074\", \"40267\", \"47224\", \"47526\", \"49891\", \"55039\", \"55644\", \"56502\", \"65286\", \"66165\", \"67813\", \"77699\", \"109417\", \"112333\", \"113308\", \"118201\", \"120407\", \"127269\", \"129299\", \"129270\", \"138382\", \"142936\", \"51256\", \"153143\"], \"outgoing_citations\": [\"211417\", \"224981\", \"229936\", \"231240\", \"265468\", \"279026\", \"297245\", \"301645\", \"303863\"]}","task_split":"paper_retrieval"}
{"document_id":"215889","document_content":"# One-Click Annotation with Guided Hierarchical Object Detection\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nThe increase in data collection has made data annotation an interesting and valuable task in the contemporary world. This paper presents a new methodology for quickly annotating data using click-supervision and hierarchical object detection. The proposed work is semi-automatic in nature where the task of annotations is split between the human and a neural network. We show that our improved method of annotation reduces the time, cost and mental stress on a human annotator. The research also highlights how our method performs better than the current approach in different circumstances such as variation in number of objects, object size and different datasets. Our approach also proposes a new method of using object detectors making it suitable for data annotation task. The experiment conducted on PASCAL VOC dataset revealed that annotation created from our approach achieves a mAP of 0.995 and a recall of 0.903. The Our Approach has shown an overall improvement by 8.5%, 18.6% in mean average precision and recall score for KITTI and 69.6%, 36% for CITYSCAPES dataset. The proposed framework is 3-4 times faster as compared to the standard annotation method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00609v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.HC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"189755\"], \"outgoing_citations\": [\"242426\", \"245505\", \"246830\", \"250796\", \"258992\", \"268417\", \"268580\", \"272424\", \"272819\", \"289035\"]}","task_split":"paper_retrieval"}
{"document_id":"215978","document_content":"# Spontaneous Facial Expression Recognition using Sparse Representation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5220\/0006118000640074\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"215980\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"215979","document_content":"# Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLearning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards. One promising strategy is inspired by human curiosity, requiring the agent to learn to predict the future. In this paper a curiosity-driven agent is extended to use these predictions directly for training. To achieve this, the agent predicts the value function of the next state at any point in time. Subsequently, the consistency of this prediction with the current value function is measured, which is then used as a regularization term in the loss function of the algorithm. Experiments were made on grid-world environments as well as on a 3D navigation task, both with sparse rewards. In the first case the extended agent is able to learn significantly faster than the baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00361v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"164883\"], \"outgoing_citations\": [\"241436\", \"244430\", \"271375\", \"272422\", \"272437\", \"280296\", \"291025\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"216014","document_content":"# Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Space\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC challenge 2017 Training Dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00871v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"230522\", \"254674\"]}","task_split":"paper_retrieval"}
{"document_id":"216027","document_content":"# Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThe use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.01266v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"163\", \"35649\", \"35834\", \"63523\", \"2483\", \"35842\", \"43198\", \"75117\", \"88081\", \"109958\", \"115366\", \"120189\", \"141010\", \"144836\", \"158622\", \"180894\", \"159675\", \"165811\", \"168178\", \"196870\", \"207327\"], \"outgoing_citations\": [\"240209\", \"253256\", \"265069\", \"270529\", \"270667\", \"281106\"]}","task_split":"paper_retrieval"}
{"document_id":"216073","document_content":"# Robot Vision: Calibration of Wide-Angle Lens Cameras Using Collinearity Condition and K-Nearest Neighbour Regression\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n- Machine Learning\n## Abstract\nVisual perception is regularly used by humans and robots for navigation. By either implicitly or explicitly mapping the environment, ego-motion can be determined and a path of actions can be planned. The process of mapping and navigation are delicately intertwined; therefore, improving one can often lead to an improvement of the other. Both processes are sensitive to the interior orientation parameters of the camera system and mathematically modelling these systematic errors can often improve the precision and accuracy of the overall solution. This paper presents an automatic camera calibration method suitable for any lens, without having prior knowledge about the sensor. Statistical inference is performed to map the environment and localize the camera simultaneously. K-nearest neighbour regression is used to model the geometric distortions of the images. A normal-angle lens Nikon camera and wide-angle lens GoPro camera were calibrated using the proposed method, as well as the conventional bundle adjustment with self-calibration method (for comparison). Results showed that the mapping error was reduced from an average of 14.9 mm to 1.2 mm (i.e. a 92% improvement) and 66.6 mm to 1.5 mm (i.e. a 98% improvement) using the proposed method for the Nikon and GoPro cameras, respectively. In contrast, the conventional approach achieved an average 3D error of 0.9 mm (i.e. 94% improvement) and 3.3 mm (i.e. 95% improvement) for the Nikon and GoPro cameras, respectively. Thus, the proposed method performs well irrespective of the lens\/sensor used: it yields results that are comparable to the conventional approach for normal-angle lens cameras, and it has the additional benefit of improving calibration results for wide-angle lens cameras.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5194\/isprs-archives-XLII-1-93-2018\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"eess.IV\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"216097","document_content":"# Adversarial Attacks and Defences: A Survey\n## Categories\n- Machine Learning\n- Cryptography and Security\n## Abstract\nDeep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00069v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"1240\", \"3541\", \"19284\", \"35996\", \"38615\", \"101129\", \"123263\", \"184843\", \"117094\", \"933\", \"4415\", \"5114\", \"6261\", \"15726\", \"15632\", \"16572\", \"32747\", \"34124\", \"37702\", \"52354\", \"57706\", \"62062\", \"65718\", \"69176\", \"69129\", \"73985\", \"117632\", \"134209\", \"552\", \"2848\", \"7167\", \"8458\", \"8522\", \"9266\", \"11949\", \"13451\", \"22960\", \"23954\", \"24318\", \"34122\", \"35148\", \"35795\", \"38973\", \"39125\", \"39257\", \"41871\", \"42066\", \"44057\", \"46222\", \"49477\", \"51782\", \"36316\", \"56887\", \"60276\", \"60995\", \"62255\", \"64258\", \"66415\", \"66648\", \"60298\", \"68327\", \"68760\", \"69447\", \"69408\", \"70596\", \"71746\", \"73576\", \"74561\", \"75537\", \"76420\", \"77425\", \"78355\", \"80228\", \"83168\", \"88900\", \"96038\", \"97113\", \"97696\", \"100890\", \"104058\", \"106513\", \"111608\", \"114938\", \"118297\", \"126493\", \"134074\", \"137190\", \"72967\", \"77966\", \"80536\", \"81452\", \"85555\", \"91167\", \"91493\", \"92064\", \"218749\", \"95868\", \"96709\", \"99353\", \"101635\", \"102830\", \"101921\", \"104781\", \"107309\", \"112527\", \"112925\", \"113557\", \"114524\", \"114784\", \"114802\", \"115256\", \"122568\", \"122733\", \"123804\", \"124614\", \"127805\", \"128727\", \"128968\", \"132490\", \"129858\", \"136268\", \"138562\", \"139272\", \"140545\", \"142486\", \"142715\", \"143003\", \"148952\", \"156706\", \"157923\", \"169981\", \"183641\", \"183850\", \"183921\", \"183406\", \"197699\", \"143294\", \"151284\", \"156070\", \"159164\", \"162680\", \"174677\", \"175789\", \"182040\", \"181838\", \"184039\", \"188107\"], \"outgoing_citations\": [\"231780\", \"237370\", \"247960\", \"257097\", \"260827\", \"261567\", \"265456\", \"266440\", \"271646\", \"273221\", \"280659\", \"285761\", \"302937\", \"303176\", \"334382\", \"343470\", \"351732\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"216118","document_content":"# SALSA-TEXT : self attentive latent space based adversarial text generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nInspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to their promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-18305-9_10\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"27415\", \"96175\", \"87746\", \"174139\", \"211205\", \"155575\", \"190452\", \"191373\"], \"outgoing_citations\": [\"189305\", \"190452\", \"221788\", \"233720\", \"234577\", \"234728\", \"240955\", \"243154\", \"263848\", \"263935\", \"272531\", \"273062\", \"281749\", \"291019\", \"304241\"]}","task_split":"paper_retrieval"}
{"document_id":"216210","document_content":"# The Rule of Three: Abstractive Text Summarization in Three Bullet Points\n## Categories\n- Computation and Language\n## Abstract\nNeural network-based approaches have become widespread for abstractive text summarization. Though previously proposed models for abstractive text summarization addressed the problem of repetition of the same contents in the summary, they did not explicitly consider its information structure. One of the reasons these previous models failed to account for information structure in the generated summary is that standard datasets include summaries of variable lengths, resulting in problems in analyzing information flow, specifically, the manner in which the first sentence is related to the following sentences. Therefore, we use a dataset containing summaries with only three bullet points, and propose a neural network-based abstractive summarization model that considers the information structures of the generated summaries. Our experimental results show that the information structure of a summary can be controlled, thus improving the performance of the overall summarization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10867v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"280557\", \"299936\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"216221","document_content":"# Learning and Planning with a Semantic Model\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nBuilding deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10842v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"16270\", \"102971\", \"104772\", \"114824\", \"139227\", \"159038\", \"207499\"], \"outgoing_citations\": [\"229038\", \"237119\", \"240323\", \"240348\", \"240506\", \"245731\", \"253291\", \"263650\", \"263811\", \"272464\", \"272930\", \"274102\", \"279420\", \"280296\", \"280681\", \"280870\", \"283391\", \"284565\", \"284567\", \"298651\", \"308711\", \"317861\"]}","task_split":"paper_retrieval"}
{"document_id":"216233","document_content":"# Direct optimization of F-measure for retrieval-based personal question answering\n## Categories\n- Information Retrieval\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nRecent advances in spoken language technologies and the introduction of many customer facing products, have given rise to a wide customer reliance on smart personal assistants for many of their daily tasks. In this paper, we present a system to reduce users' cognitive load by extending personal assistants with long-term personal memory where users can store and retrieve by voice, arbitrary pieces of information. The problem is framed as a neural retrieval based question answering system where answers are selected from previously stored user memories. We propose to directly optimize the end-to-end retrieval performance, measured by the F1-score, using reinforcement learning, leading to better performance on our experimental test set(s).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.00679v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"154109\"], \"outgoing_citations\": [\"241975\", \"246513\", \"248937\", \"267351\", \"274120\", \"281162\", \"290937\", \"291102\", \"298750\", \"307547\", \"309148\", \"313465\", \"317213\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"216235","document_content":"# Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWe present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural network architecture trained on 600,000 doctor notes in German. We compare two approaches, one that uses the full text of the medical notes and one that uses only a selected list of medical entities extracted from the text. These approaches achieve 79% and 66% precision, respectively, but on a confidence threshold of 0.6, precision increases to 85% and 75%, respectively. In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective. The method is based on the learning of attention scores and a method of automatic validation using the same data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10804v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"69186\", \"88115\", \"183077\"], \"outgoing_citations\": [\"248156\", \"262139\", \"291045\", \"302425\", \"310639\", \"314946\"]}","task_split":"paper_retrieval"}
{"document_id":"216241","document_content":"# Cursive Scene Text Analysis by Deep Convolutional Linear Pyramids\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe camera captured images have various aspects to investigate. Generally, the emphasis of research depends on the interesting regions. Sometimes the focus could be on color segmentation, object detection or scene text analysis. The image analysis, visibility and layout analysis are the tasks easier for humans as suggested by behavioral trait of humans, but in contrast when these same tasks are supposed to perform by machines then it seems to be challenging. The learning machines always learn from the properties associated to provided samples. The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy. The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images. The work presented in this manuscript exploited the strength of linear pyramids by considering each pyramid as a feature of the provided sample. Each pyramid image process through various empirically selected kernels. The performance was investigated by considering Arabic text on each image pyramid of EASTR-42k dataset. The error rate of 0.17% was reported on Arabic scene text recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10792v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268281\"]}","task_split":"paper_retrieval"}
{"document_id":"216384","document_content":"# Iterative Document Representation Learning Towards Summarization with Polishing\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN\/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10324v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72957\", \"30911\", \"40912\", \"43953\", \"80697\", \"80700\", \"207215\", \"95033\", \"125951\", \"165983\", \"186043\", \"206114\"], \"outgoing_citations\": [\"149557\", \"235041\", \"241070\", \"259639\", \"280557\", \"296029\", \"297133\"]}","task_split":"paper_retrieval"}
{"document_id":"216386","document_content":"# Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nVision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors\/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems of the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adversarial inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicle by unexpected roadway hazards, such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional roadway hazards) that can compromise the DNN-based navigational system of an autonomous vehicle, and produces an incorrect steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazardous environment, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system including hazardous object detection and semantic segmentation improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared to the traditional DNN-based autonomous vehicle driving system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.03967v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"78484\", \"191504\"], \"outgoing_citations\": [\"244611\", \"253628\", \"257512\", \"259984\", \"261749\", \"277405\", \"278822\", \"280275\", \"284665\", \"287717\", \"295969\", \"316005\", \"316457\", \"334092\"]}","task_split":"paper_retrieval"}
{"document_id":"216387","document_content":"# Vector Learning for Cross Domain Representations\n## Categories\n- Machine Learning\n## Abstract\nRecently, generative adversarial networks have gained a lot of popularity for image generation tasks. However, such models are associated with complex learning mechanisms and demand very large relevant datasets. This work borrows concepts from image and video captioning models to form an image generative framework. The model is trained in a similar fashion as recurrent captioning model and uses the learned weights for image generation. This is done in an inverse direction, where the input is a caption and the output is an image. The vector representation of the sentence and frames are extracted from an encoder-decoder model which is initially trained on similar sentence and image pairs. Our model conditions image generation on a natural language caption. We leverage a sequence-to-sequence model to generate synthetic captions that have the same meaning for having a robust image generation. One key advantage of our method is that the traditional image captioning datasets can be used for synthetic sentence paraphrases. Results indicate that images generated through multiple captions are better at capturing the semantic meaning of the family of captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10312v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"216400\", \"256219\", \"274665\", \"279091\", \"291537\", \"295204\", \"302621\", \"313435\", \"314240\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"216400","document_content":"# Semantic Sentence Embeddings for Paraphrasing and Text Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoder-decoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks -- sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10267v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"96545\", \"216394\", \"216387\"], \"outgoing_citations\": [\"216396\", \"274665\", \"292371\", \"303593\", \"304481\", \"313435\", \"314240\", \"316803\", \"320332\", \"320814\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"216632","document_content":"# The jamming transition as a paradigm to understand the loss landscape of deep neural networks\n## Categories\n- Disordered Systems and Neural Networks\n- Machine Learning\n## Abstract\nDeep learning has been immensely successful at a variety of tasks, ranging from classification to AI. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understanding under which conditions neural networks do not get stuck in poor minima of the loss, and how the landscape of that loss evolves as depth is increased remains a challenge. Here we predict, and test empirically, an analogy between this landscape and the energy landscape of repulsive ellipses. We argue that in FC networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. In the vicinity of this transition, properties of the curvature of the minima of the loss are critical. This transition shares direct similarities with the jamming transition by which particles form a disordered solid as the density is increased, which also occurs in certain classes of computational optimization and learning problems such as the perceptron. Our analysis gives a simple explanation as to why poor minima of the loss cannot be encountered in the overparametrized regime, and puts forward the surprising result that the ability of fully connected networks to fit random data is independent of their depth. Our observations suggests that this independence also holds for real data. We also study a quantity $\\Delta$ which characterizes how well ($\\Delta<0$) or badly ($\\Delta>0$) a datum is learned. At the critical point it is power-law distributed, $P_+(\\Delta)\\sim\\Delta^\\theta$ for $\\Delta>0$ and $P_-(\\Delta)\\sim(-\\Delta)^{-\\gamma}$ for $\\Delta<0$, with $\\theta\\approx0.3$ and $\\gamma\\approx0.2$. This observation suggests that near the transition the loss landscape has a hierarchical structure and that the learning dynamics is prone to avalanche-like dynamics, with abrupt changes in the set of patterns that are learned.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1103\/PhysRevE.100.012115\", \"primary_category\": \"cond-mat.dis-nn\", \"categories\": [\"cs.LG\", \"cond-mat.dis-nn\"], \"primary_category_human_readable\": \"Disordered Systems and Neural Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Disordered Systems and Neural Networks\"], \"incoming_citations\": [\"4672\", \"4871\", \"14406\", \"16925\", \"74217\", \"85255\", \"91118\", \"99179\", \"20721\", \"33727\", \"34378\", \"38066\", \"44968\", \"47315\", \"64163\", \"65924\", \"69622\", \"70976\", \"72745\", \"75227\", \"108103\", \"111764\", \"124680\", \"138641\", \"77826\", \"84043\", \"87963\", \"100141\", \"105071\", \"106006\", \"111581\", \"118552\", \"119403\", \"120384\", \"120828\", \"122362\", \"122971\", \"123172\", \"135037\", \"139941\", \"140437\", \"146558\", \"152022\", \"152688\", \"160406\", \"163966\", \"201806\", \"203805\", \"150465\", \"150699\", \"151020\", \"153561\", \"158112\", \"180504\", \"194222\", \"199791\", \"201823\", \"202567\"], \"outgoing_citations\": [\"201180\", \"203805\", \"234058\", \"238517\", \"253651\", \"263775\", \"265555\", \"270773\", \"272564\", \"273340\", \"281255\", \"290235\", \"291761\", \"297737\", \"301593\", \"321079\", \"333968\", \"336223\"]}","task_split":"paper_retrieval"}
{"document_id":"216686","document_content":"# From Audio to Semantics: Approaches to end-to-end spoken language understanding\n## Categories\n- Audio and Speech Processing\n- Sound\n- Computation and Language\n## Abstract\nConventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of domains, intents, and arguments. These modules are typically optimized independently. In this paper, we formulate audio to semantic understanding as a sequence-to-sequence problem [1]. We propose and compare various encoder-decoder based approaches that optimize both modules jointly, in an end-to-end manner. Evaluations on a real-world task show that 1) having an intermediate text representation is crucial for the quality of the predicted semantics, especially the intent arguments and 2) jointly optimizing the full system improves overall accuracy of prediction. Compared to independently trained models, our best jointly trained model achieves similar domain and intent prediction F1 scores, but improves argument word error rate by 18% relative.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.09190v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.SD\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Sound\", \"Computation and Language\"], \"incoming_citations\": [\"3823\", \"38538\", \"86254\", \"14400\", \"20622\", \"20718\", \"25153\", \"12476\", \"28852\", \"31268\", \"41573\", \"42707\", \"96502\", \"43635\", \"43650\", \"48244\", \"49488\", \"50076\", \"50569\", \"52178\", \"54462\", \"57429\", \"57637\", \"58416\", \"69349\", \"69680\", \"69895\", \"72161\", \"80300\", \"86592\", \"91161\", \"93316\", \"87723\", \"84544\", \"87977\", \"38791\", \"96244\", \"97729\", \"105483\", \"106068\", \"106269\", \"106294\", \"107179\", \"107416\", \"107737\", \"109851\", \"114312\", \"119809\", \"124588\", \"95623\", \"129355\", \"142302\", \"142276\", \"145190\", \"153406\", \"159820\", \"164326\", \"165233\", \"191769\", \"210645\"], \"outgoing_citations\": [\"241146\", \"244978\", \"248223\", \"248246\", \"257499\", \"264214\", \"272191\", \"285317\", \"303003\", \"309025\"]}","task_split":"paper_retrieval"}
{"document_id":"216743","document_content":"# A Framework towards Domain Specific Video Summarization\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn the light of exponentially increasing video content, video summarization has attracted a lot of attention recently due to its ability to optimize time and storage. Characteristics of a good summary of a video depend on the particular domain under question. We propose a novel framework for domain specific video summarization. Given a video of a particular domain, our system can produce a summary based on what is important for that domain in addition to possessing other desired characteristics like representativeness, coverage, diversity etc. as suitable to that domain. Past related work has focused either on using supervised approaches for ranking the snippets to produce summary or on using unsupervised approaches of generating the summary as a subset of snippets with the above characteristics. We look at the joint problem of learning domain specific importance of segments as well as the desired summary characteristic for that domain. Our studies show that the more efficient way of incorporating domain specific relevances into a summary is by obtaining ratings of shots as opposed to binary inclusion\/exclusion information. We also argue that ratings can be seen as unified representation of all possible ground truth summaries of a video, taking us one step closer in dealing with challenges associated with multiple ground truth summaries of a video. We also propose a novel evaluation measure which is more naturally suited in assessing the quality of video summary for the task at hand than F1 like measures. It leverages the ratings information and is richer in appropriately modeling desirable and undesirable characteristics of a summary. Lastly, we release a gold standard dataset for furthering research in domain specific video summarization, which to our knowledge is the first dataset with long videos across several domains with rating annotations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08854v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"66576\", \"73446\", \"115712\", \"93651\", \"94926\", \"109123\"], \"outgoing_citations\": [\"244440\", \"261096\", \"269799\", \"288206\", \"291817\", \"296780\", \"305469\", \"328015\", \"353402\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"216765","document_content":"# Chargrid: Towards Understanding 2D Documents\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWe introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08799v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"1218\", \"17886\", \"9109\", \"21025\", \"26331\", \"27956\", \"29640\", \"31120\", \"34931\", \"39796\", \"40383\", \"40734\", \"40793\", \"43360\", \"44251\", \"47534\", \"47716\", \"49990\", \"50580\", \"55400\", \"55565\", \"60412\", \"64008\", \"73149\", \"73803\", \"45473\", \"77979\", \"96466\", \"127548\", \"130903\", \"141395\", \"142227\", \"84406\", \"36451\", \"97694\", \"122763\", \"123526\", \"125078\", \"126431\", \"130460\", \"138476\", \"36454\", \"147348\", \"149004\", \"153958\", \"45488\", \"157981\", \"163155\", \"167422\", \"167606\", \"192470\", \"193543\"], \"outgoing_citations\": [\"248223\", \"257785\", \"258854\", \"264315\", \"295707\", \"302247\", \"321736\", \"326774\", \"336231\"]}","task_split":"paper_retrieval"}
{"document_id":"216815","document_content":"# Learning to Read by Spelling: Towards Unsupervised Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work presents a method for visual text recognition without using any paired supervisory data. We formulate the text recognition task as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings sampled from target corpora. This enables fully automated, and unsupervised learning from just line-level text-images, and unpaired text-string samples, obviating the need for large aligned datasets. We present detailed analysis for various aspects of the proposed method, namely - (1) impact of the length of training sequences on convergence, (2) relation between character frequencies and the order in which they are learnt, (3) generalisation ability of our recognition network to inputs of arbitrary lengths, and (4) impact of varying the text corpus on recognition accuracy. Finally, we demonstrate excellent text recognition accuracy on both synthetically generated text images, and scanned images of real printed books, using no labelled training examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08675v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7179\", \"71238\", \"79422\", \"80427\", \"177660\", \"196632\", \"182075\", \"184209\"], \"outgoing_citations\": [\"245038\", \"251816\", \"251974\", \"273121\", \"294023\", \"296685\", \"296828\", \"302902\", \"311196\", \"320148\", \"320837\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"216841","document_content":"# Neural Arithmetic Expression Calculator\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper presents a pure neural solver for arithmetic expression calculation (AEC) problem. Previous work utilizes the powerful capabilities of deep neural networks and attempts to build an end-to-end model to solve this problem. However, most of these methods can only deal with the additive operations. It is still a challenging problem to solve the complex expression calculation problem, which includes the adding, subtracting, multiplying, dividing and bracketing operations. In this work, we regard the arithmetic expression calculation as a hierarchical reinforcement learning problem. An arithmetic operation is decomposed into a series of sub-tasks, and each sub-task is dealt with by a skill module. The skill module could be a basic module performing elementary operations, or interactive module performing complex operations by invoking other skill models. With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models. Experiments show that our model significantly outperforms the previous models for arithmetic expression calculation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08590v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"73887\", \"121473\", \"131065\", \"131801\", \"136392\", \"147159\", \"168198\"], \"outgoing_citations\": [\"262325\", \"272823\", \"281354\", \"293876\", \"302515\", \"302688\", \"302933\", \"310023\", \"350010\"]}","task_split":"paper_retrieval"}
{"document_id":"216859","document_content":"# DT-LET: Deep Transfer Learning by Exploring where to Transfer\n## Categories\n- Machine Learning\n## Abstract\nPrevious transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains. This assumption doesn't always hold true, especially when the data from the two domains are heterogeneous with different resolutions. In such case, the most suitable numbers of layers for the source domain data and the target domain data would differ. As a result, the high level knowledge from the source domain would be transferred to the wrong layer of target domain. Based on this observation, \"where to transfer\" proposed in this paper should be a novel research frontier. We propose a new mathematic model named DT-LET to solve this heterogeneous transfer learning problem. In order to select the best matching of layers to transfer knowledge, we define specific loss function to estimate the corresponding relationship between high-level features of data in the source domain and the target domain. To verify this proposed cross-layer model, experiments for two cross-domain recognition\/classification tasks are conducted, and the achieved superior results demonstrate the necessity of layer correspondence searching.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08541v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"189785\", \"226118\", \"232823\", \"234773\", \"247732\", \"264787\", \"267935\", \"357681\"]}","task_split":"paper_retrieval"}
{"document_id":"216895","document_content":"# Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 \\% in terms of the top-1 metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08440v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3402\", \"47552\", \"74634\", \"89719\", \"6953\", \"14807\", \"20822\", \"24157\", \"33140\", \"52347\", \"76369\", \"99656\", \"125002\", \"141152\"], \"outgoing_citations\": [\"222262\", \"237094\", \"249058\", \"250157\", \"259197\", \"260472\", \"260926\", \"273235\", \"273696\", \"275346\", \"292386\", \"292812\", \"292825\", \"295619\", \"309106\"]}","task_split":"paper_retrieval"}
{"document_id":"216918","document_content":"# A Byte-sized Approach to Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nIn biomedical literature, it is common for entity boundaries to not align with word boundaries. Therefore, effective identification of entity spans requires approaches capable of considering tokens that are smaller than words. We introduce a novel, subword approach for named entity recognition (NER) that uses byte-pair encodings (BPE) in combination with convolutional and recurrent neural networks to produce byte-level tags of entities. We present experimental results on several standard biomedical datasets, namely the BioCreative VI Bio-ID, JNLPBA, and GENETAG datasets. We demonstrate competitive performance while bypassing the specialized domain expertise needed to create biomedical text tokenization rules.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08386v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"220637\", \"240504\", \"259758\", \"302247\"]}","task_split":"paper_retrieval"}
{"document_id":"217039","document_content":"# CollaboNet: collaboration of deep neural networks for biomedical named entity recognition\n## Categories\n- Computation and Language\n- Machine Learning\n- I.2.7; J.3\n## Abstract\nBackground: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1186\/s12859-019-2813-6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"I.2.7; J.3\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"I.2.7; J.3\"], \"incoming_citations\": [\"82136\", \"93607\", \"126134\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"217105","document_content":"# Benchmarking Reinforcement Learning Algorithms on Real-World Robots\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nThrough many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks. To carry forward these successes to real-world applications, it is crucial to withhold utilizing the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots. However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code. In this work, we introduce several reinforcement learning tasks with multiple commercially available robots that present varying levels of learning difficulty, setup, and repeatability. On these tasks, we test the learning performance of off-the-shelf implementations of four reinforcement learning algorithms and analyze sensitivity to their hyper-parameters to determine their readiness for applications in various real-world tasks. Our results show that with a careful setup of the task interface and computations, some of these implementations can be readily applicable to physical robots. We find that state-of-the-art learning algorithms are highly sensitive to their hyper-parameters and their relative ordering does not transfer across tasks, indicating the necessity of re-tuning them for each task for best performance. On the other hand, the best hyper-parameter configuration from one task may often result in effective learning on held-out tasks even with different robots, providing a reasonable default. We make the benchmark tasks publicly available to enhance reproducibility in real-world reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.07731v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"1469\", \"19532\", \"21591\", \"64325\", \"67693\", \"87566\", \"87712\", \"120436\", \"7492\", \"15989\", \"16685\", \"26872\", \"35498\", \"44490\", \"70499\", \"71959\", \"81042\", \"81047\", \"87363\", \"93168\", \"97468\", \"102552\", \"142415\", \"148172\", \"183015\", \"85695\", \"97005\", \"105291\", \"109883\", \"107492\", \"128637\", \"134470\", \"139141\", \"140105\", \"142124\", \"143661\", \"150577\", \"165381\", \"160980\", \"164963\", \"167889\", \"193396\", \"193458\", \"197904\", \"198560\", \"204750\", \"208585\"], \"outgoing_citations\": [\"238489\", \"240506\", \"241884\", \"246118\", \"248841\", \"255613\", \"260284\", \"261780\", \"272987\", \"282684\", \"283394\", \"283410\"]}","task_split":"paper_retrieval"}
{"document_id":"217154","document_content":"# C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nGenerating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited, and may not be able to capture the variety of concepts and behavior that may be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.10238v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3854\", \"738\", \"73658\", \"82744\", \"93826\", \"152402\", \"192646\"], \"outgoing_citations\": [\"227824\", \"240774\", \"240885\", \"241205\", \"249031\", \"283643\", \"285093\", \"292386\", \"298195\", \"323833\"]}","task_split":"paper_retrieval"}
{"document_id":"217158","document_content":"# Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification\n## Categories\n- Computers and Society\n- Information Retrieval\n- Social and Information Networks\n- Computation and Language\n## Abstract\nThe selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Because the data is very varied form and very unstructured, it must be managed and uninformed using data pre-processing techniques into semi-structured data. This semi-structured information is followed by a classification stage to categorize the opinion into negative or positive opinions. The research methodology uses a literature study where the research will examine previous research on a similar topic. The purpose of this study is to find the right architecture to develop it into the application of twitter opinion mining to know public sentiments toward the election of the governor of west java. The result of this research is that Twitter opinion mining is part of text mining where opinions in Twitter if they want to be classified, must go through the preprocessing text stage first. The preprocessing step required from twitter data is cleansing, case folding, POS Tagging and stemming. The resulting text mining architecture is an architecture that can be used for text mining research with different topics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1810.07767v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.IR\", \"cs.CY\", \"cs.SI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Information Retrieval\", \"Computers and Society\", \"Social and Information Networks\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"328690\"]}","task_split":"paper_retrieval"}
{"document_id":"217189","document_content":"# Distribution Networks for Open Set Learning\n## Categories\n- Machine Learning\n## Abstract\nIn open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learning poses a realistic learning scenario that is receiving growing attention. Existing studies on open set learning mainly focused on detecting novel classes, but few studies tried to model them for differentiating novel classes. In this paper, we recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can model different novel classes based on probability distributions. We hypothesize that, through a certain mapping, samples from different classes with the same classification criterion should follow different probability distributions from the same distribution family. A deep neural network is learned to map the samples in the original feature space to a latent space where the distributions of known classes can be jointly learned with the network. We additionally propose a distribution parameter transfer and updating strategy for novel class modeling when a novel class is detected in the latent space. By novel class modeling, the detected novel classes can serve as known classes to the subsequent classification. Our experimental results on image datasets MNIST and CIFAR10 show that the distribution networks can detect novel classes accurately, and model them well for the subsequent classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.08106v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"132167\"], \"outgoing_citations\": [\"242429\", \"249018\", \"260413\", \"267347\", \"267895\", \"280385\", \"302985\", \"320186\", \"350237\", \"357745\"]}","task_split":"paper_retrieval"}
{"document_id":"217214","document_content":"# A Dataset for Document Grounded Conversations\n## Categories\n- Computation and Language\n## Abstract\nThis paper introduces a document grounded dataset for text conversations. We define \"Document Grounded Conversations\" as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.07358v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2633\", \"2913\", \"3822\", \"16245\", \"19499\", \"26386\", \"35167\", \"49689\", \"49804\", \"50552\", \"52433\", \"10508\", \"20842\", \"23711\", \"24229\", \"24593\", \"24852\", \"24847\", \"26644\", \"35073\", \"44917\", \"53489\", \"55799\", \"65469\", \"168075\", \"74350\", \"95425\", \"127566\", \"128718\", \"133099\", \"85976\", \"93520\", \"97474\", \"99635\", \"103430\", \"106904\", \"122331\", \"124397\", \"126246\", \"126748\", \"127048\", \"127748\", \"129128\", \"132227\", \"128037\", \"134250\", \"140423\", \"141639\", \"157654\", \"157654\", \"170205\", \"171288\", \"186662\", \"130674\", \"151897\", \"175250\", \"181458\", \"201552\", \"220723\"], \"outgoing_citations\": [\"244479\", \"270018\", \"301313\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"217330","document_content":"# Interpretable Reinforcement Learning with Ensemble Methods\n## Categories\n- Machine Learning\n## Abstract\nWe propose to use boosted regression trees as a way to compute human-interpretable solutions to reinforcement learning problems. Boosting combines several regression trees to improve their accuracy without significantly reducing their inherent interpretability. Prior work has focused independently on reinforcement learning and on interpretable machine learning, but there has been little progress in interpretable reinforcement learning. Our experimental results show that boosted regression trees compute solutions that are both interpretable and match the quality of leading reinforcement learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06995v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"130732\"], \"outgoing_citations\": [\"290114\", \"296637\"]}","task_split":"paper_retrieval"}
{"document_id":"217400","document_content":"# Multiple Combined Constraints for Image Stitching\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSeveral approaches to image stitching use different constraints to estimate the motion model between image pairs. These constraints can be roughly divided into two categories: geometric constraints and photometric constraints. In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary. On the one hand, geometric constraints (e.g., point and line correspondences) are usually spatially biased and are insufficient in some extreme scenes, while photometric constraints are always evenly and densely distributed. On the other hand, photometric constraints are sensitive to displacements and are not suitable for images with large parallaxes, while geometric constraints are usually imposed by feature matching and are more robust to handle parallaxes. The proposed method therefore combines them together in an efficient mesh-based image warping framework. It achieves better alignment quality than methods only with geometric constraints, and can handle larger parallax than photometric-constraint-based method. Experimental results on various images illustrate that the proposed method outperforms representative state-of-the-art image stitching methods reported in the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06706v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"132862\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"217463","document_content":"# Scene Text Recognition from Two-Dimensional Perspective\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nInspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem. Though achieving excellent performance, these methods usually neglect an important fact that text in images are actually distributed in two-dimensional space. It is a nature quite different from that of speech, which is essentially a one-dimensional signal. In principle, directly compressing features of text into a one-dimensional form may lose useful information and introduce extra noise. In this paper, we approach scene text recognition from a two-dimensional perspective. A simple yet effective model, called Character Attention Fully Convolutional Network (CA-FCN), is devised for recognizing the text of arbitrary shapes. Scene text recognition is realized with a semantic segmentation network, where an attention mechanism for characters is adopted. Combined with a word formation module, CA-FCN can simultaneously recognize the script and predict the position of each character. Experiments demonstrate that the proposed algorithm outperforms previous methods on both regular and irregular text datasets. Moreover, it is proven to be more robust to imprecise localizations in the text detection phase, which are very common in practice.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06508v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1039\", \"50917\", \"80383\", \"191\", \"7580\", \"8109\", \"10204\", \"24824\", \"28270\", \"29640\", \"33358\", \"43037\", \"48901\", \"49805\", \"50591\", \"53750\", \"63761\", \"64528\", \"67749\", \"93345\", \"122764\", \"133054\", \"145945\", \"147463\", \"163092\", \"111991\", \"113191\", \"120420\", \"123550\", \"126244\", \"126431\", \"134664\", \"135021\", \"136218\", \"136843\", \"136831\", \"143280\", \"144173\", \"149329\", \"150060\", \"151319\", \"156958\", \"162477\", \"189663\", \"192671\", \"204309\", \"210634\", \"155455\", \"169411\", \"172884\", \"174894\", \"180931\", \"229426\", \"207786\"], \"outgoing_citations\": [\"232706\", \"239002\", \"245591\", \"250738\", \"256108\", \"256711\", \"279950\", \"294023\", \"296685\", \"296828\", \"311196\", \"320148\", \"320837\", \"322955\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"217506","document_content":"# Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nMany reinforcement-learning researchers treat the reward function as a part of the environment, meaning that the agent can only know the reward of a state if it encounters that state in a trial run. However, we argue that this is an unnecessary limitation and instead, the reward function should be provided to the learning algorithm. The advantage is that the algorithm can then use the reward function to check the reward for states that the agent hasn't even encountered yet. In addition, the algorithm can simultaneously learn policies for multiple reward functions. For each state, the algorithm would calculate the reward using each of the reward functions and add the rewards to its experience replay dataset. The Hindsight Experience Replay algorithm developed by Andrychowicz et al. (2017) does just this, and learns to generalize across a distribution of sparse, goal-based rewards. We extend this algorithm to linearly-weighted, multi-objective rewards and learn a single policy that can generalize across all linear combinations of the multi-objective reward. Whereas other multi-objective algorithms teach the Q-function to generalize across the reward weights, our algorithm enables the policy to generalize, and can thus be used with continuous actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06364v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"101460\", \"124913\", \"163457\", \"168262\"], \"outgoing_citations\": [\"281257\", \"334182\"]}","task_split":"paper_retrieval"}
{"document_id":"217602","document_content":"# Transparency and Explanation in Deep Reinforcement Learning Neural Networks\n## Categories\n- Machine Learning\n## Abstract\nAutonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the system should be transparent. System transparency enables humans to form coherent explanations of the system's decisions and actions. Transparency is important not only for user trust, but also for software debugging and certification. In recent years, Deep Neural Networks have made great advances in multiple application areas. However, deep neural networks are opaque. In this paper, we report on work in transparency in Deep Reinforcement Learning Networks (DRLN). Such networks have been extremely successful in accurately learning action control in image input domains, such as Atari games. In this paper, we propose a novel and general method that (a) incorporates explicit object recognition processing into deep reinforcement learning models, (b) forms the basis for the development of \"object saliency maps\", to provide visualization of internal states of DRLNs, thus enabling the formation of explanations and (c) can be incorporated in any existing deep reinforcement learning framework. We present computational results and human experiments to evaluate our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06061v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"4944\", \"62062\", \"128043\", \"144265\", \"952\", \"20452\", \"4276\", \"31060\", \"46043\", \"49911\", \"57808\", \"58346\", \"66879\", \"71255\", \"90350\", \"95151\", \"101524\", \"103185\", \"116402\", \"120714\", \"123234\", \"124327\", \"89675\", \"92048\", \"107917\", \"145988\", \"147146\", \"148012\", \"149656\", \"149869\", \"152090\", \"150061\", \"150803\", \"151247\", \"151771\", \"164842\", \"183922\", \"205630\", \"217794\", \"231376\", \"297491\"], \"outgoing_citations\": [\"217601\", \"332853\"]}","task_split":"paper_retrieval"}
{"document_id":"217615","document_content":"# Binocular Tone Mapping with Improved Overall Contrast and Local Details\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTone mapping is a commonly used technique that maps the set of colors in high-dynamic-range (HDR) images to another set of colors in low-dynamic-range (LDR) images, to fit the need for print-outs, LCD monitors and projectors. Unfortunately, during the compression of dynamic range, the overall contrast and local details generally cannot be preserved simultaneously. Recently, with the increased use of stereoscopic devices, the notion of binocular tone mapping has been proposed in the existing research study. However, the existing research lacks the binocular perception study and is unable to generate the optimal binocular pair that presents the most visual content. In this paper, we propose a novel perception-based binocular tone mapping method, that can generate an optimal binocular image pair (generating left and right images simultaneously) from an HDR image that presents the most visual content by designing a binocular perception metric. Our method outperforms the existing method in terms of both visual and time performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.06036v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271619\"]}","task_split":"paper_retrieval"}
{"document_id":"217640","document_content":"# Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nResponses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strategy that addresses these two related but distinct problems. To foster response diversity, we leverage adversarial training that allows distributional matching of synthetic and real responses. To improve informativeness, our framework explicitly optimizes a variational lower bound on pairwise mutual information between query and response. Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.05972v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"23614\", \"15998\", \"29341\", \"33712\", \"34351\", \"49689\", \"50552\", \"55305\", \"63954\", \"5954\", \"8702\", \"14362\", \"23733\", \"24004\", \"24229\", \"24400\", \"25574\", \"29406\", \"29612\", \"3168\", \"36066\", \"39618\", \"44204\", \"44892\", \"45169\", \"44997\", \"45214\", \"45967\", \"53218\", \"55479\", \"55960\", \"62527\", \"65915\", \"67539\", \"67799\", \"77482\", \"78355\", \"80354\", \"80747\", \"90310\", \"94230\", \"128115\", \"129680\", \"132550\", \"153393\", \"184591\", \"79302\", \"82594\", \"87810\", \"89861\", \"95201\", \"95992\", \"96643\", \"100365\", \"100574\", \"100503\", \"101699\", \"106711\", \"107200\", \"108688\", \"115081\", \"124736\", \"125694\", \"126112\", \"126748\", \"127592\", \"128874\", \"128060\", \"137731\", \"138581\", \"139131\", \"145384\", \"155080\", \"157124\", \"157654\", \"157654\", \"158493\", \"170205\", \"171288\", \"174210\", \"194015\", \"196340\", \"199839\", \"212028\", \"130674\", \"154886\", \"165396\", \"167399\", \"169010\", \"168855\", \"173810\", \"174596\", \"173889\", \"179444\", \"180534\", \"180695\", \"182950\", \"180801\", \"182107\", \"186426\", \"189798\", \"195238\", \"197057\", \"200698\", \"201405\", \"202498\", \"211946\", \"218185\", \"230013\", \"220073\", \"216953\", \"234062\"], \"outgoing_citations\": [\"216953\", \"228832\", \"230664\", \"243291\", \"250335\", \"255035\", \"255322\", \"255463\", \"256927\", \"258494\", \"262256\", \"263935\", \"269030\", \"271234\", \"271503\", \"275597\", \"279688\", \"281749\", \"287823\", \"291102\", \"291302\", \"292261\", \"310740\", \"316328\", \"334299\"]}","task_split":"paper_retrieval"}
{"document_id":"217756","document_content":"# Incorporating Behavioral Constraints in Online AI Systems\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- Machine Learning\n## Abstract\nAI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and\/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.05720v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Machine Learning\"], \"incoming_citations\": [\"19002\", \"38593\", \"119944\", \"21685\", \"77071\", \"94053\", \"94784\", \"104900\", \"110476\", \"111247\", \"112438\", \"115806\", \"127089\", \"151253\", \"152167\", \"152167\", \"160399\", \"176788\", \"183271\", \"206527\", \"217068\", \"216939\"], \"outgoing_citations\": [\"206527\", \"206854\", \"216939\", \"249185\", \"266779\", \"272509\", \"298539\", \"305154\", \"309082\", \"313858\", \"332265\", \"354490\", \"358661\"]}","task_split":"paper_retrieval"}
{"document_id":"217808","document_content":"# Events Beyond ACE: Curated Training for Events\n## Categories\n- Computation and Language\n## Abstract\nWe explore a human-driven approach to annotation, curated training (CT), in which annotation is framed as teaching the system by using interactive search to identify informative snippets of text to annotate, unlike traditional approaches which either annotate preselected text or use active learning. A trained annotator performed 80 hours of CT for the thirty event types of the NIST TAC KBP Event Argument Extraction evaluation. Combining this annotation with ACE results in a 6% reduction in error and the learning curve of CT plateaus more slowly than for full-document annotation. 3 NLP researchers performed CT for one event type and showed much sharper learning curves with all three exceeding ACE performance in less than ninety minutes, suggesting that CT can provide further benefits when the annotator deeply understands the system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.05576v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"218084","document_content":"# Visual-Quality-Driven Learning for Underwater Vision Enhancement\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nThe image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICIP.2018.8451356\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"40764\", \"177223\"], \"outgoing_citations\": [\"250062\", \"273246\"]}","task_split":"paper_retrieval"}
{"document_id":"218089","document_content":"# Efficient and Privacy-preserving Voice-based Search over mHealth Data\n## Categories\n- Cryptography and Security\n## Abstract\nIn-home IoT devices play a major role in healthcare systems as smart personal assistants. They usually come with a voice-enabled feature to add an extra level of usability and convenience to elderly, disabled people, and patients. In this paper, we propose an efficient and privacy-preserving voice-based search scheme to enhance the efficiency and the privacy of in-home healthcare applications. We consider an application scenario where patients use the devices to record and upload their voice to servers and the caregivers search the interested voices of their patient's based on the voice content, mood, tone and background sound. Our scheme preserves the richness and privacy of voice data and enables accurate and efficient voice-based search, while in current systems that use speech recognition the richness and privacy of voice data are compromised. Specifically, our scheme achieves the privacy by employing a homomorphic encryption; only encrypted voice data is uploaded to the server who is unable to access the original voice data. In addition, our scheme enables the server to selectively and accurately respond to caregiver's queries on the voice data based on voice's feature similarity. We evaluate our scheme through real experiments and show that our scheme even with privacy preservation can successfully match similar voice data at an average accuracy of 80.8%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.04583v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"216870\", \"215027\", \"255326\"], \"outgoing_citations\": [\"255326\", \"283453\", \"283455\", \"283459\", \"296803\"]}","task_split":"paper_retrieval"}
{"document_id":"218124","document_content":"# Multi-task Deep Reinforcement Learning with PopArt\n## Categories\n- Machine Learning\n## Abstract\nThe reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.04474v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"76692\", \"9649\", \"17155\", \"36009\", \"37164\", \"68895\", \"78473\", \"260314\", \"6628\", \"11416\", \"14575\", \"17102\", \"17651\", \"17752\", \"22978\", \"24536\", \"25823\", \"27582\", \"33080\", \"35440\", \"39932\", \"40615\", \"41002\", \"41980\", \"42371\", \"46314\", \"47830\", \"50187\", \"51151\", \"55486\", \"58392\", \"62425\", \"67718\", \"68459\", \"70816\", \"94549\", \"94833\", \"96663\", \"100693\", \"120402\", \"124274\", \"126513\", \"77684\", \"87719\", \"101301\", \"101725\", \"116107\", \"116473\", \"117151\", \"119105\", \"123442\", \"124913\", \"127978\", \"132686\", \"133890\", \"140129\", \"143410\", \"146533\", \"161660\", \"161947\", \"164729\", \"165065\", \"164984\", \"175541\", \"149805\", \"160036\", \"160512\", \"160736\", \"162602\", \"163195\", \"164141\", \"167562\", \"177367\", \"177381\", \"178186\", \"179602\", \"188953\", \"192518\", \"193351\", \"196801\", \"199156\", \"203756\", \"214238\", \"207983\", \"209347\", \"210486\", \"207096\"], \"outgoing_citations\": [\"239419\", \"241185\", \"248225\", \"249196\", \"261316\", \"277472\", \"278195\", \"280296\", \"280302\", \"280681\", \"281169\", \"284468\", \"284567\", \"290378\", \"293995\", \"294694\", \"297667\", \"298281\", \"302952\", \"302967\", \"309498\", \"339851\"]}","task_split":"paper_retrieval"}
{"document_id":"218189","document_content":"# Knowledge Based Machine Reading Comprehension\n## Categories\n- Computation and Language\n## Abstract\nMachine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability. To encourage the progress on knowledge-based reasoning in MRC, we present knowledge-based MRC in this paper, and build a new dataset consisting of 40,047 question-answer pairs. The annotation of this dataset is designed so that successfully answering the questions requires understanding and the knowledge involved in a document. We implement a framework consisting of both a question answering model and a question generation model, both of which take the knowledge extracted from the document as well as relevant facts from an external knowledge base such as Freebase\/ProBase\/Reverb\/NELL. Results show that incorporating side information from external KB improves the accuracy of the baseline question answer system. We compare it with a standard MRC model BiDAF, and also provide the difficulty of the dataset and lay out remaining challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.04267v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"128230\", \"178032\"], \"outgoing_citations\": [\"181742\", \"244439\", \"247060\", \"253068\", \"268570\", \"286317\", \"290747\", \"303827\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"218240","document_content":"# End-to-end Image Captioning Exploits Multimodal Distributional Similarity\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn `distributional similarity' in a multimodal feature space by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus on the `image' side of image captioning, and vary the input image representation but keep the RNN text generation component of a CNN-RNN model constant. Our analysis indicates that image captioning models (i) are capable of separating structure from noisy input representations; (ii) suffer virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space; (iii) cluster images with similar visual and linguistic information together. Our findings indicate that our distributional similarity hypothesis holds. We conclude that regardless of the image representation used image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.04144v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"78975\", \"175035\", \"180146\"], \"outgoing_citations\": [\"234545\", \"260521\", \"281152\", \"284230\", \"311824\", \"312506\", \"313196\", \"317592\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"218291","document_content":"# Assessing Composition in Sentence Vector Representations\n## Categories\n- Computation and Language\n## Abstract\nAn important component of achieving language understanding is mastering the composition of sentence meaning, but an immediate challenge to solving this problem is the opacity of sentence vector representations produced by current neural sentence composition models. We present a method to address this challenge, developing tasks that directly target compositional meaning information in sentence vector representations with a high degree of precision and control. To enable the creation of these controlled tasks, we introduce a specialized sentence generation system that produces large, annotated sentence sets meeting specified syntactic, semantic and lexical constraints. We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models. We find that the method is able to extract useful information about the differing capacities of these models, and we discuss the implications of our results with respect to these systems' capturing of sentence information. We make available for public use the datasets used for these experiments, as well as the generation system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.03992v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9935\", \"14164\", \"24452\", \"44100\", \"46212\", \"54783\", \"73219\", \"91725\", \"94314\", \"95816\", \"96142\", \"126130\", \"127056\", \"131846\", \"128087\", \"137785\", \"153289\", \"154316\", \"158818\", \"167134\", \"173615\", \"167642\", \"168097\", \"168415\", \"180302\", \"181000\", \"186130\", \"192782\", \"195050\", \"200362\", \"203285\", \"205219\", \"217064\", \"218227\", \"229768\", \"218247\", \"220137\"], \"outgoing_citations\": [\"237157\", \"239831\", \"242449\", \"251412\", \"251893\", \"251911\", \"256948\", \"277288\", \"281225\", \"286594\", \"290515\", \"296289\", \"297423\", \"297648\", \"298543\", \"302516\", \"311845\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"218420","document_content":"# VPE: Variational Policy Embedding for Transfer Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nReinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffers from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider this as a problem of transferring knowledge within a family of similar Markov decision processes.   For this purpose we assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.03548v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"93176\", \"5886\", \"46143\", \"113866\", \"161071\", \"161304\", \"168328\", \"153471\", \"176654\"], \"outgoing_citations\": [\"255139\", \"264419\", \"265069\", \"265075\", \"270925\", \"272659\", \"297667\", \"298784\"]}","task_split":"paper_retrieval"}
{"document_id":"218422","document_content":"# Bayesian Patchworks: An Approach to Case-Based Reasoning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nDoctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficient computationally aided diagnostic tool that thinks in the same way might be helpful in locating key past cases of interest that could assist with diagnosis. This article develops a novel mathematical model to mimic the type of logical thinking that physicians use when considering past cases. The proposed model can also provide physicians with explanations that would be similar to the way they would naturally reason about cases. The proposed method is designed to yield predictive accuracy, computational efficiency, and insight into medical data; the key element is the insight into medical data, in some sense we are automating a complicated process that physicians might perform manually. We finally implemented the result of this work on two publicly available healthcare datasets, for heart disease prediction and breast cancer prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.03541v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"192256\"], \"outgoing_citations\": [\"316549\"]}","task_split":"paper_retrieval"}
{"document_id":"218435","document_content":"# ViZDoom Competitions: Playing Doom from Pixels\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nThis paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To play well, the bots needed to understand their surroundings, navigate, explore, and handle the opponents at the same time. These aspects, together with the competitive multi-agent aspect of the game, make the competition a unique platform for evaluating the state of the art reinforcement learning algorithms. The paper discusses the rules, solutions, results, and statistics that give insight into the agents' behaviors. Best-performing agents are described in more detail. The results of the competition lead to the conclusion that, although reinforcement learning can produce capable Doom bots, they still are not yet able to successfully compete against humans in this game. The paper also revisits the ViZDoom environment, which is a flexible, easy to use, and efficient 3D platform for research for vision-based reinforcement learning, based on a well-recognized first-person perspective game Doom.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TG.2018.2877047\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"57263\", \"1414\", \"4687\", \"8634\", \"9463\", \"37438\", \"38103\", \"58958\", \"71387\", \"88041\", \"113327\", \"81211\", \"116592\", \"117151\", \"133480\", \"133479\", \"133632\", \"136185\", \"136916\", \"145405\", \"146919\", \"148111\", \"150680\", \"152524\", \"184833\", \"185834\", \"159298\", \"175289\", \"190634\", \"208058\", \"223268\"], \"outgoing_citations\": [\"222345\", \"237048\", \"244283\", \"244457\", \"245731\", \"247418\", \"248225\", \"248885\", \"263104\", \"272222\", \"272930\", \"278195\", \"278999\", \"280296\", \"281110\", \"284468\", \"285147\", \"285351\", \"293071\", \"302001\", \"309111\"]}","task_split":"paper_retrieval"}
{"document_id":"218658","document_content":"# A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial recognition has always been a challeng- ing task for computer vision scientists and experts. Despite complexities arising due to variations in camera parameters, illumination and face orientations, significant progress has been made in the field with deep learning algorithms now competing with human-level accuracy. But in contrast to the recent advances in face recognition techniques, Disguised Facial Identification continues to be a tougher challenge in the field of computer vision. The modern day scenario, where security is of prime concern, regular face identification techniques do not perform as required when the faces are disguised, which calls for a different approach to handle situations where intruders have their faces masked. Along the same lines, we propose a deep learning architecture for disguised facial recognition (DFR). The algorithm put forward in this paper detects 20 facial key-points in the first stage, using a 14-layered convolutional neural network (CNN). These facial key-points are later utilized by a support vector machine (SVM) for classifying the disguised faces based on the euclidean distance ratios and angles between different facial key-points. This overall architecture imparts a basic intelligence to our system. Our key-point feature prediction accuracy is 65% while the classification rate is 72.4%. Moreover, the architecture works at 19 FPS, thereby performing in almost real-time. The efficiency of our approach is also compared with the state-of-the-art Disguised Facial Identification methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02875v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"56716\"], \"outgoing_citations\": [\"257344\", \"303724\", \"311501\"]}","task_split":"paper_retrieval"}
{"document_id":"218749","document_content":"# Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost existing works of adversarial samples focus on attacking image recognition models, while little attention is paid to the image retrieval task. In this paper, we identify two inherent challenges in applying prevailing image recognition attack methods to image retrieval. First, image retrieval demands discriminative visual features, which is significantly different from the one-hot class prediction in image recognition. Second, due to the disjoint and potentially unrelated classes between the training and test set in image retrieval, predicting the query category from predefined training classes is not accurate and leads to a sub-optimal adversarial gradient. To address these limitations, we propose a new white-box attack approach, Opposite-Direction Feature Attack (ODFA), to generate adversarial queries. Opposite-Direction Feature Attack (ODFA) effectively exploits feature-level adversarial gradients and takes advantage of feature distance in the representation space. To our knowledge, we are among the early attempts to design an attack method specifically for image retrieval. When we deploy an attacked image as the query, the true matches are prone to receive low ranks. We demonstrate through extensive experiments that (1) only crafting adversarial queries is sufficient to fool the state-of-the-art retrieval systems; (2) the proposed attack method, ODFA, leads to a higher attack success rate than classification attack methods, validating the necessity of leveraging characteristics of image retrieval; (3) the adversarial queries generated by our method have good transferability to other retrieval models without accessing their parameters, i.e.,the black-box setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02681v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"22644\", \"174992\"], \"outgoing_citations\": [\"167842\", \"173408\", \"190365\", \"207610\", \"216097\", \"222949\", \"235021\", \"237363\", \"245633\", \"245634\", \"248991\", \"249642\", \"249672\", \"251473\", \"255867\", \"270593\", \"270653\", \"275954\", \"277638\", \"278526\", \"280225\", \"280873\", \"281860\", \"291989\", \"294983\", \"302895\", \"303067\", \"306882\", \"310303\", \"314231\", \"316942\", \"318839\"]}","task_split":"paper_retrieval"}
{"document_id":"218755","document_content":"# Unsupervised Sentence Compression using Denoising Auto-Encoders\n## Categories\n- Computation and Language\n## Abstract\nIn sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences. To remove the need for paired corpora, we emulate a summarization task and add noise to extend sentences and train a denoising auto-encoder to recover the original, constructing an end-to-end training regime without the need for any examples of compressed sentences. We conduct a human evaluation of our model on a standard text summarization dataset and show that it performs comparably to a supervised baseline based on grammatical correctness and retention of meaning. Despite being exposed to no target data, our unsupervised models learn to generate imperfect but reasonably readable sentence summaries. Although we underperform supervised models based on ROUGE scores, our models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02669v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16590\", \"70033\", \"149602\", \"42007\", \"49524\", \"80578\", \"80738\", \"100630\", \"80905\", \"93797\", \"95567\", \"96175\", \"123079\", \"127065\", \"129810\", \"127956\", \"132577\", \"148688\", \"148688\", \"149448\", \"153233\", \"163045\", \"173799\", \"156072\", \"163715\", \"166540\", \"174508\", \"173669\", \"191777\", \"215236\"], \"outgoing_citations\": [\"215243\", \"231863\", \"233400\", \"234748\", \"250505\", \"251816\", \"251974\", \"280557\", \"283618\", \"284092\", \"298543\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"218826","document_content":"# Improving Neural Question Generation using Answer Separation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nNeural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer. By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used. We also propose a new module termed keyword-net, which helps the model better capture the key information in the target answer and generate an appropriate question. Experimental results demonstrate that our answer separation method significantly reduces the number of improper questions which include answers. Consequently, our model significantly outperforms previous state-of-the-art NQG models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02393v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"1409\", \"62307\", \"9998\", \"25124\", \"8702\", \"17962\", \"44098\", \"47520\", \"67278\", \"71422\", \"81248\", \"91785\", \"121591\", \"132550\", \"40920\", \"84597\", \"86824\", \"92389\", \"93144\", \"96800\", \"103568\", \"123267\", \"125364\", \"122474\", \"128743\", \"130408\", \"131826\", \"133009\", \"145498\", \"153174\", \"160290\", \"165514\", \"171886\", \"155568\", \"157770\", \"158977\", \"160887\", \"161500\", \"161855\", \"162809\", \"167769\", \"167916\", \"166959\", \"169350\", \"180808\", \"182144\", \"185278\"], \"outgoing_citations\": [\"228030\", \"234577\", \"240068\", \"264394\", \"267694\", \"269583\", \"274120\", \"292261\", \"295815\", \"296279\"]}","task_split":"paper_retrieval"}
{"document_id":"218828","document_content":"# Improving On-policy Learning with Statistical Reward Accumulation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns. Such reward signals represent the immediate feedback of a particular action performed by an agent. However, tasks with sparse reward signals are still challenging to on-policy methods. In this paper, we introduce an effective characterization of past reward statistics (which can be seen as long-term feedback signals) to supplement this immediate reward feedback. In particular, value functions are learned with multi-critics supervision, enabling complex value functions to be more easily approximated in on-policy learning, even when the reward signals are sparse. We also introduce a novel exploration mechanism called \"hot-wiring\" that can give a boost to seemingly trapped agents. We demonstrate the effectiveness of our advantage actor multi-critic (A2MC) method across the discrete domains in Atari games as well as continuous domains in the MuJoCo environments. A video demo is provided at https:\/\/youtu.be\/zBmpf3Yz8tc.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02387v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"240736\", \"251601\", \"258384\", \"260583\", \"261316\", \"263852\", \"280296\", \"281257\", \"357232\"]}","task_split":"paper_retrieval"}
{"document_id":"218889","document_content":"# Deep Neural Net with Attention for Multi-channel Multi-touch Attribution\n## Categories\n- Machine Learning\n## Abstract\nCustomers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertising channel. The inference about the influence of each channel plays an important role in budget allocation and inventory pricing decisions. Several simplistic rule-based strategies and data-driven algorithmic strategies have been widely used in marketing field, but they do not address the issues, such as channel interaction, time dependency, user characteristics. In this paper, we propose a novel attribution algorithm based on deep learning to assess the impact of each advertising channel. We present Deep Neural Net With Attention multi-touch attribution model (DNAMTA) model in a supervised learning fashion of predicting if a series of events leads to conversion, and it leads us to have a deep understanding of the dynamic interaction effects between media channels. DNAMTA also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects. We used computational experiment of large real world marketing dataset to demonstrate that our proposed model is superior to existing methods in both conversion prediction and media channel influence evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02230v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1697\", \"83059\", \"79223\", \"110821\", \"156704\", \"177734\", \"200669\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"218932","document_content":"# Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies that evaluate over-sensitivity to small and semantics-preserving edits, as well as Should-Change strategies that test if a model is over-stable against subtle yet semantics-changing modifications. We next perform adversarial training with each strategy, employing a max-margin approach for negative generative examples. This not only makes the target dialogue model more robust to the adversarial inputs, but also helps it perform significantly better on the original inputs. Moreover, training on all strategies combined achieves further improvements, achieving a new state-of-the-art performance on the original task (also verified via human evaluation). In addition to adversarial training, we also address the robustness task at the model-level, by feeding it subword units as both inputs and outputs, and show that the resulting model is equally competitive, requires only 1\/4 of the original vocabulary size, and is robust to one of the adversarial strategies (to which the original model is vulnerable) even without adversarial training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.02079v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"35996\", \"42229\", \"33712\", \"21332\", \"23714\", \"24416\", \"36866\", \"82584\", \"89861\", \"98259\", \"106553\", \"122733\", \"125926\", \"127737\", \"131419\", \"142096\", \"146975\", \"158786\", \"166272\", \"202296\", \"152623\", \"164509\", \"168169\", \"205161\"], \"outgoing_citations\": [\"235345\", \"245196\", \"247428\", \"248340\", \"249416\", \"250360\", \"251240\", \"251893\", \"255143\", \"261635\", \"267955\", \"268168\", \"272998\", \"277060\", \"277638\", \"291102\", \"291289\", \"292261\", \"295863\", \"303833\", \"310377\", \"317213\", \"357148\"]}","task_split":"paper_retrieval"}
{"document_id":"218973","document_content":"# Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDiversity is a long-studied topic in information retrieval that usually refers to the requirement that retrieved results should be non-repetitive and cover different aspects. In a conversational setting, an additional dimension of diversity matters: an engaging response generation system should be able to output responses that are diverse and interesting. Sequence-to-sequence (Seq2Seq) models have been shown to be very effective for response generation. However, dialogue responses generated by Seq2Seq models tend to have low diversity. In this paper, we review known sources and existing approaches to this low-diversity problem. We also identify a source of low diversity that has been little studied so far, namely model over-confidence. We sketch several directions for tackling model over-confidence and, hence, the low-diversity problem, including confidence penalties and label smoothing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.01941v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"30826\", \"52428\", \"76485\", \"115707\", \"82594\", \"89861\", \"95566\", \"144206\", \"146975\", \"165308\", \"183857\", \"179567\", \"197727\", \"197016\", \"234062\"], \"outgoing_citations\": [\"247379\", \"266155\", \"270040\", \"275596\", \"275597\", \"276365\", \"278443\", \"279543\", \"281002\", \"283076\", \"292261\", \"302056\", \"310740\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"218997","document_content":"# Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing\n## Categories\n- Computation and Language\n## Abstract\nThe addition of syntax-aware decoding in Neural Machine Translation (NMT) systems requires an effective tree-structured neural network, a syntax-aware attention model and a language generation model that is sensitive to sentence structure. We exploit a top-down tree-structured model called DRNN (Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola (2017) to create an NMT model called Seq2DRNN that combines a sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. Unlike previous approaches to syntax-based NMT which use dependency parsing models our method uses constituency parsing which we argue provides useful information for translation. In addition, we use the syntactic structure of the sentence to add new connections to the tree-structured decoder neural network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of the art syntax-based NMT models and show that our model produces more fluent translations with better reordering. Since our model is capable of doing translation and constituency parsing at the same time we also compare our parsing accuracy against other neural parsing models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.01854v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72647\", \"13221\", \"26064\", \"77667\", \"95403\", \"100437\", \"100441\", \"132440\", \"141406\", \"147812\", \"169436\", \"191740\"], \"outgoing_citations\": [\"265779\", \"268788\", \"268812\", \"274183\", \"276174\", \"290777\", \"292552\", \"296274\", \"297648\", \"300505\", \"303003\", \"310136\", \"312066\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"219151","document_content":"# Localizing Moments in Video with Temporal Language\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nLocalizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.01337v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"66667\", \"82708\", \"10836\", \"21807\", \"22104\", \"23020\", \"33654\", \"39527\", \"45218\", \"40616\", \"48860\", \"49800\", \"59050\", \"59325\", \"63086\", \"64186\", \"74091\", \"86873\", \"96230\", \"105055\", \"152245\", \"86252\", \"90607\", \"105236\", \"117153\", \"132452\", \"128305\", \"145702\", \"146027\", \"146489\", \"152254\", \"155521\", \"167401\", \"170995\", \"188920\", \"152705\", \"172256\", \"169293\", \"173844\", \"182192\", \"192172\", \"191676\", \"207818\"], \"outgoing_citations\": [\"259318\", \"259865\", \"267223\", \"268871\", \"269216\", \"272075\", \"278655\", \"279177\", \"279990\", \"295117\", \"316430\", \"316472\"]}","task_split":"paper_retrieval"}
{"document_id":"219266","document_content":"# MesoNet: a Compact Facial Video Forgery Detection Network\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nThis paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/WIFS.2018.8630761\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"39837\", \"215465\", \"612\", \"773\", \"2413\", \"9516\", \"18330\", \"20381\", \"35846\", \"37500\", \"41408\", \"47857\", \"54715\", \"60771\", \"60820\", \"63076\", \"66529\", \"66875\", \"72009\", \"77244\", \"145492\", \"145520\", \"164990\", \"7353\", \"9582\", \"12806\", \"19100\", \"23326\", \"24087\", \"25341\", \"25286\", \"27035\", \"24432\", \"29047\", \"29565\", \"29571\", \"30118\", \"30201\", \"32451\", \"36043\", \"37327\", \"37490\", \"39955\", \"40777\", \"42085\", \"21980\", \"46740\", \"2004\", \"49958\", \"51332\", \"52186\", \"52363\", \"53983\", \"52366\", \"57170\", \"57796\", \"58340\", \"59679\", \"61299\", \"62706\", \"62977\", \"63176\", \"63594\", \"64721\", \"65522\", \"65781\", \"65715\", \"65736\", \"66221\", \"67673\", \"68373\", \"70034\", \"71804\", \"75236\", \"76305\", \"77059\", \"115685\", \"125841\", \"80000\", \"80623\", \"82529\", \"82761\", \"85879\", \"85961\", \"96813\", \"97391\", \"108852\", \"123788\", \"125647\", \"126637\", \"130817\", \"143374\", \"79537\", \"80226\", \"82116\", \"82025\", \"72239\", \"88918\", \"91029\", \"76932\", \"97332\", \"99721\", \"100971\", \"58197\", \"220660\", \"103668\", \"104013\", \"104302\", \"106581\", \"106664\", \"107186\", \"111179\", \"115971\", \"117104\", \"118896\", \"118954\", \"119147\", \"119223\", \"120014\", \"122287\", \"125600\", \"128070\", \"128776\", \"129101\", \"129682\", \"130875\", \"135994\", \"136775\", \"137305\", \"137786\", \"138674\", \"146273\", \"146630\", \"147899\", \"148895\", \"148972\", \"153552\", \"158431\", \"158434\", \"163461\", \"164494\", \"167088\", \"203644\", \"150051\", \"151524\", \"155881\", \"158415\", \"159421\", \"162125\", \"179581\", \"180443\", \"182585\", \"188099\", \"192313\", \"197338\", \"209356\", \"211893\", \"211897\", \"212878\", \"206993\", \"208044\"], \"outgoing_citations\": [\"237855\", \"260516\", \"306435\"]}","task_split":"paper_retrieval"}
{"document_id":"219311","document_content":"# Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing\n## Categories\n- Computation and Language\n## Abstract\nThis paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.00773v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"112333\", \"98394\", \"921\", \"24345\", \"42869\", \"71934\", \"73363\", \"76915\", \"77198\", \"77653\", \"95359\", \"116633\", \"122607\", \"132440\", \"128387\", \"134017\", \"154813\", \"164113\", \"164529\", \"169144\", \"176753\", \"181153\", \"180362\", \"218187\", \"204820\"], \"outgoing_citations\": [\"203410\", \"258130\", \"268067\", \"274271\", \"290632\", \"299936\", \"300505\", \"300543\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"219419","document_content":"# Data Augmentation for Neural Online Chat Response Selection\n## Categories\n- Computation and Language\n## Abstract\nData augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.00428v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18991\", \"92872\", \"99613\", \"157249\", \"182577\", \"190742\", \"200725\"], \"outgoing_citations\": [\"232355\", \"250586\", \"258130\", \"267601\", \"270018\", \"272068\", \"274513\", \"278671\", \"286594\", \"292443\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"219441","document_content":"# Zero-shot User Intent Detection via Capsule Neural Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nUser intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1809.00385v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"74469\", \"22618\", \"42791\", \"127897\", \"173829\", \"3107\", \"24842\", \"26015\", \"27041\", \"33513\", \"45038\", \"47388\", \"50869\", \"52037\", \"53732\", \"54261\", \"53838\", \"62790\", \"71388\", \"83081\", \"95751\", \"95759\", \"98109\", \"110206\", \"144292\", \"77782\", \"94280\", \"96385\", \"97701\", \"99373\", \"104522\", \"111010\", \"111046\", \"117616\", \"118245\", \"123361\", \"123561\", \"127901\", \"129831\", \"130634\", \"133147\", \"133395\", \"128934\", \"144545\", \"167138\", \"221584\", \"160070\", \"115630\", \"162910\", \"169329\", \"174761\", \"177377\", \"178176\", \"179881\", \"180447\", \"182073\", \"190005\", \"197286\", \"205014\"], \"outgoing_citations\": [\"285317\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"219459","document_content":"# IntentsKB: A Knowledge Base of Entity-Oriented Search Intents\n## Categories\n- Information Retrieval\n- Computation and Language\n- H.3.3\n- Artificial Intelligence\n## Abstract\nWe address the problem of constructing a knowledge base of entity-oriented search intents. Search intents are defined on the level of entity types, each comprising of a high-level intent category (property, website, service, or other), along with a cluster of query terms used to express that intent. These machine-readable statements can be leveraged in various applications, e.g., for generating entity cards or query recommendations. By structuring service-oriented search intents, we take one step towards making entities actionable. The main contribution of this paper is a pipeline of components we develop to construct a knowledge base of entity intents. We evaluate performance both component-wise and end-to-end, and demonstrate that our approach is able to generate high-quality data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3269206.3269257\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"H.3.3\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"H.3.3\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"241254\"]}","task_split":"paper_retrieval"}
{"document_id":"219611","document_content":"# Seeing Colors: Learning Semantic Text Encoding for Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we present a novel text classification method which converts a text document into an encoded image, using word embedding and capabilities of Convolutional Neural Networks (CNNs), successfully employed in image classification. We evaluate our approach by obtaining promising results on some well-known benchmark datasets for text classification. This work allows the application of many of the advanced CNN architectures developed for Computer Vision to Natural Language Processing. We test the proposed approach on a multi-modal dataset, proving that it is possible to use a single deep model to represent text and image in the same feature space.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.10822v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"264628\", \"291045\"]}","task_split":"paper_retrieval"}
{"document_id":"219618","document_content":"# Bottom-Up Abstractive Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nNeural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.10792v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2553\", \"2651\", \"3028\", \"4498\", \"5604\", \"15876\", \"15872\", \"20779\", \"25100\", \"44358\", \"48962\", \"56032\", \"72536\", \"80634\", \"81985\", \"84343\", \"94351\", \"95561\", \"96841\", \"101278\", \"1890\", \"2611\", \"8236\", \"11304\", \"14641\", \"17096\", \"17897\", \"18998\", \"19290\", \"20340\", \"20781\", \"21836\", \"22773\", \"23904\", \"24361\", \"24431\", \"24752\", \"24948\", \"26826\", \"27024\", \"27160\", \"27340\", \"29009\", \"30911\", \"31148\", \"31578\", \"37689\", \"38843\", \"40395\", \"40468\", \"40912\", \"41554\", \"136011\", \"45920\", \"46562\", \"47002\", \"47368\", \"47416\", \"48405\", \"49524\", \"50834\", \"50895\", \"53256\", \"53640\", \"6363\", \"55285\", \"55970\", \"56583\", \"56832\", \"58149\", \"58194\", \"58358\", \"61175\", \"61771\", \"65777\", \"66275\", \"68448\", \"72431\", \"73139\", \"77612\", \"77963\", \"81800\", \"86983\", \"91041\", \"93920\", \"95304\", \"95814\", \"99320\", \"100606\", \"100560\", \"102910\", \"109884\", \"127972\", \"129174\", \"129200\", \"177966\", \"207215\", \"139168\", \"49608\", \"127602\", \"79775\", \"82073\", \"132794\", \"85960\", \"89771\", \"86887\", \"92056\", \"92765\", \"93797\", \"93820\", \"93973\", \"95174\", \"95485\", \"95843\", \"95840\", \"96126\", \"96069\", \"96263\", \"96700\", \"96411\", \"92422\", \"108843\", \"109314\", \"69109\", \"110195\", \"111350\", \"50181\", \"114995\", \"115694\", \"115701\", \"117830\", \"118456\", \"119817\", \"121501\", \"123892\", \"126346\", \"127065\", \"127040\", \"127281\", \"127538\", \"127753\", \"123878\", \"128009\", \"128252\", \"128886\", \"129103\", \"129810\", \"131340\", \"132176\", \"132304\", \"132665\", \"133165\", \"133082\", \"131391\", \"128269\", \"128250\", \"132332\", \"134394\", \"134393\", \"136256\", \"139469\", \"140175\", \"140173\", \"140414\", \"141539\", \"143331\", \"143330\", \"146323\", \"147514\", \"149732\", \"149997\", \"154648\", \"154649\", \"157383\", \"158869\", \"159316\", \"173940\", \"174054\", \"174554\", \"168185\", \"153936\", \"156100\", \"156528\", \"157120\", \"157529\", \"159851\", \"161321\", \"163579\", \"163950\", \"164036\", \"164178\", \"164411\", \"160954\", \"163791\", \"165249\", \"165356\", \"166020\", \"166082\", \"167979\", \"168613\", \"168634\", \"168744\", \"167615\", \"169255\", \"169457\", \"169466\", \"170449\", \"170598\", \"171101\", \"170657\", \"174508\", \"175306\", \"177125\", \"178214\", \"181065\", \"181713\", \"181722\", \"181817\", \"182486\", \"183065\", \"183161\", \"183200\", \"183462\", \"183876\", \"185326\", \"186316\", \"187236\", \"183197\", \"186043\", \"189770\", \"189805\", \"190495\", \"191486\", \"192342\", \"192456\", \"192811\", \"194056\", \"197339\", \"197709\", \"200414\", \"201539\", \"202472\", \"203630\", \"211838\", \"214174\", \"217759\", \"220955\"], \"outgoing_citations\": [\"225556\", \"230212\", \"231869\", \"235232\", \"235463\", \"237477\", \"237494\", \"243803\", \"267692\", \"280552\", \"280557\", \"280735\", \"295665\", \"296029\", \"299541\", \"299936\", \"307547\"]}","task_split":"paper_retrieval"}
{"document_id":"219714","document_content":"# Syntactic Scaffolds for Semantic Structures\n## Categories\n- Computation and Language\n## Abstract\nWe introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.10485v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2946\", \"15974\", \"16648\", \"8854\", \"10566\", \"21139\", \"22204\", \"22254\", \"23153\", \"24388\", \"32301\", \"56426\", \"56614\", \"72812\", \"73291\", \"77573\", \"81532\", \"114947\", \"132345\", \"160655\", \"84541\", \"91125\", \"91829\", \"92475\", \"93973\", \"121009\", \"122609\", \"127858\", \"127832\", \"131846\", \"157178\", \"157212\", \"165653\", \"156847\", \"166000\", \"166223\", \"168581\", \"169008\", \"175042\", \"176848\", \"180252\", \"181869\", \"182093\", \"183096\", \"184091\", \"192607\", \"195017\", \"205074\", \"234729\"], \"outgoing_citations\": [\"232290\", \"233612\", \"235377\", \"235555\", \"248279\", \"260541\", \"262592\", \"268059\", \"268263\", \"281169\", \"283809\", \"289403\", \"291177\", \"294814\", \"296163\", \"303003\", \"303038\", \"309148\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"219859","document_content":"# Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification. For this to be successful, the network must be capable of suppressing irrelevant scene background and extract the representation from the most discriminative part of the video. Our contribution builds on the observation that spatio-temporal patterns characterizing actions in videos are highly correlated with objects and their location in the video. We propose Top-down Attention Action VLAD (TA-VLAD), a deep recurrent architecture with built-in spatial attention that performs temporally aggregated VLAD encoding for action recognition from videos. We adopt a top-down approach of attention, by using class specific activation maps obtained from a deep CNN pre-trained for image classification, to weight appearance features before encoding them into a fixed-length video descriptor using Gated Recurrent Units. Our method achieves state of the art recognition accuracy on HMDB51 and UCF101 benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.09892v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"69031\", \"100429\", \"177965\"], \"outgoing_citations\": [\"222771\", \"224927\", \"251423\", \"255469\", \"255474\", \"267105\", \"269276\", \"281003\", \"288919\", \"303465\", \"312644\"]}","task_split":"paper_retrieval"}
{"document_id":"219885","document_content":"# Notes on Deep Learning for NLP\n## Categories\n- Computation and Language\n## Abstract\nMy notes on Deep Learning for NLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.09772v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"174222\", \"190375\", \"171491\"], \"outgoing_citations\": [\"274597\", \"305094\", \"305591\", \"311845\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"220137","document_content":"# Targeted Syntactic Evaluation of Language Models\n## Categories\n- Computation and Language\n## Abstract\nWe present a dataset for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.09031v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"4879\", \"18764\", \"24928\", \"26824\", \"9935\", \"16463\", \"21263\", \"21274\", \"21669\", \"22041\", \"23257\", \"23484\", \"25228\", \"26083\", \"26685\", \"32301\", \"41709\", \"44100\", \"44434\", \"45348\", \"45420\", \"45432\", \"45684\", \"44710\", \"46118\", \"46654\", \"49051\", \"49471\", \"49530\", \"50044\", \"50449\", \"50969\", \"50979\", \"54472\", \"54783\", \"55359\", \"56222\", \"56526\", \"56568\", \"59454\", \"62299\", \"66215\", \"72160\", \"72660\", \"73143\", \"73219\", \"76678\", \"77629\", \"77760\", \"89750\", \"92478\", \"95252\", \"117453\", \"129558\", \"133976\", \"153444\", \"78655\", \"84115\", \"89632\", \"87179\", \"89022\", \"89900\", \"91141\", \"92151\", \"94152\", \"94886\", \"95004\", \"95436\", \"95690\", \"96658\", \"112305\", \"113544\", \"121405\", \"122609\", \"124609\", \"124598\", \"125586\", \"126370\", \"127376\", \"127295\", \"127739\", \"127056\", \"127101\", \"127806\", \"127822\", \"127922\", \"128059\", \"131846\", \"132871\", \"132773\", \"128087\", \"134662\", \"125976\", \"153289\", \"155134\", \"157386\", \"157547\", \"158818\", \"159706\", \"160751\", \"173615\", \"180511\", \"30949\", \"160283\", \"159742\", \"165362\", \"168415\", \"164192\", \"165928\", \"166310\", \"167553\", \"168951\", \"170102\", \"169266\", \"179201\", \"181468\", \"181575\", \"182271\", \"182508\", \"182552\", \"180658\", \"184006\", \"184687\", \"186250\", \"186130\", \"191740\", \"194731\", \"195050\", \"195989\", \"196722\", \"200362\", \"203285\", \"213362\", \"218227\", \"229768\", \"202773\", \"211421\"], \"outgoing_citations\": [\"218291\", \"229768\", \"234307\", \"237283\", \"239503\", \"260929\", \"262592\", \"263992\", \"267385\", \"278016\", \"281225\", \"290041\", \"297648\"]}","task_split":"paper_retrieval"}
{"document_id":"220147","document_content":"# Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nNeural Machine Translation has achieved state-of-the-art performance for several language pairs using a combination of parallel and synthetic data. Synthetic data is often generated by back-translating sentences randomly sampled from monolingual data using a reverse translation model. While back-translation has been shown to be very effective in many cases, it is not entirely clear why. In this work, we explore different aspects of back-translation, and show that words with high prediction loss during training benefit most from the addition of synthetic data. We introduce several variations of sampling strategies targeting difficult-to-predict words using prediction losses and frequencies of words. In addition, we also target the contexts of difficult words and sample sentences that are similar in context. Experimental results for the WMT news translation task show that our method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.09006v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38855\", \"107968\", \"149936\", \"164336\", \"86936\", \"139450\", \"154259\"], \"outgoing_citations\": [\"229845\", \"235312\", \"249730\", \"259541\", \"261234\", \"267601\"]}","task_split":"paper_retrieval"}
{"document_id":"220420","document_content":"# Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nWe present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-the-loop systems further in the discussion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.08268v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"108698\", \"27731\", \"36744\", \"58227\", \"65096\", \"94130\", \"118091\", \"119110\", \"131830\", \"165708\", \"181109\", \"182283\", \"197022\", \"208994\", \"184478\", \"222363\", \"231267\", \"243197\", \"256839\"], \"outgoing_citations\": [\"256839\", \"257335\", \"315405\"]}","task_split":"paper_retrieval"}
{"document_id":"220466","document_content":"# Role Semantics for Better Models of Implicit Discourse Relations\n## Categories\n- Computation and Language\n## Abstract\nPredicting the structure of a discourse is challenging because relations between discourse segments are often implicit and thus hard to distinguish computationally. I extend previous work to classify implicit discourse relations by introducing a novel set of features on the level of semantic roles. My results demonstrate that such features are helpful, yielding results competitive with other feature-rich approaches on the PDTB. My main contribution is an analysis of improvements that can be traced back to role-based features, providing insights into why and when role semantics is helpful.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.08047v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"160717\"], \"outgoing_citations\": [\"269973\", \"278872\", \"284294\", \"290777\", \"291941\"]}","task_split":"paper_retrieval"}
{"document_id":"220515","document_content":"# Style Transfer as Unsupervised Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nLanguage style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.07894v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"18494\", \"89936\", \"22484\", \"23105\", \"24668\", \"29896\", \"40838\", \"42706\", \"44902\", \"49539\", \"53390\", \"55152\", \"61851\", \"72246\", \"91686\", \"98388\", \"114991\", \"91705\", \"94898\", \"108688\", \"113240\", \"104804\", \"117959\", \"119254\", \"122173\", \"126953\", \"127511\", \"133100\", \"143501\", \"164142\", \"157111\", \"160881\", \"165025\", \"164800\", \"169277\", \"170273\", \"171143\", \"181686\", \"182442\", \"183542\", \"184839\", \"186381\", \"193541\", \"200820\", \"213824\", \"211937\"], \"outgoing_citations\": [\"232164\", \"234391\", \"234807\", \"235238\", \"240377\", \"250035\", \"251816\", \"251974\", \"265309\", \"272531\", \"282462\", \"289589\", \"296250\", \"301840\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"220548","document_content":"# Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.07724v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"52303\", \"56980\", \"132589\", \"80104\", \"94483\", \"96019\", \"97559\", \"150634\", \"171429\", \"178856\", \"192079\", \"192655\", \"170279\", \"232527\"], \"outgoing_citations\": [\"232527\", \"253019\", \"267123\", \"308265\", \"308277\", \"311843\", \"313947\", \"315078\", \"328992\", \"356847\"]}","task_split":"paper_retrieval"}
{"document_id":"220576","document_content":"# Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nExisting neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \\textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.07624v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"131434\", \"230611\", \"921\", \"26402\", \"41042\", \"74933\", \"83087\", \"109697\", \"131931\", \"132440\", \"132725\", \"154813\", \"155432\", \"117093\", \"171886\", \"173570\", \"150834\", \"135521\", \"157009\", \"160887\", \"170618\", \"184117\", \"193731\", \"196314\", \"220105\"], \"outgoing_citations\": [\"212285\", \"232980\", \"236917\", \"240174\", \"242229\", \"251240\", \"268067\", \"268464\", \"274271\", \"290632\", \"297226\", \"300505\", \"313074\", \"321546\"]}","task_split":"paper_retrieval"}
{"document_id":"220637","document_content":"# Neural Named Entity Recognition from Subword Units\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition (NER) is a vital task in spoken language understanding, which aims to identify mentions of named entities in text e.g., from transcribed speech. Existing neural models for NER rely mostly on dedicated word-level representations, which suffer from two main shortcomings. First, the vocabulary size is large, yielding large memory requirements and training time. Second, these models are not able to learn morphological or phonological representations. To remedy the above shortcomings, we adopt a neural solution based on bidirectional LSTMs and conditional random fields, where we rely on subword units, namely characters, phonemes, and bytes. For each word in an utterance, our model learns a representation from each of the subword units. We conducted experiments in a real-world large-scale setting for the use case of a voice-controlled device covering four languages with up to 5.5M utterances per language. Our experiments show that (1) with increasing training data, performance of models trained solely on subword units becomes closer to that of models with dedicated word-level embeddings (91.35 vs 93.92 F1 for English), while using a much smaller vocabulary size (332 vs 74K), (2) subword units enhance models with dedicated word-level embeddings, and (3) combining different subword units improves performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.21437\/Interspeech.2019-1305\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"122283\", \"216918\"], \"outgoing_citations\": [\"159867\", \"296221\", \"302247\", \"312608\"]}","task_split":"paper_retrieval"}
{"document_id":"220704","document_content":"# Deciding the status of controversial phonemes using frequency distributions; an application to semiconsonants in Spanish\n## Categories\n- Computation and Language\n- Physics and Society\n## Abstract\nExploiting the fact that natural languages are complex systems, the present exploratory article proposes a direct method based on frequency distributions that may be useful when making a decision on the status of problematic phonemes, an open problem in linguistics. The main notion is that natural languages, which can be considered from a complex outlook as information processing machines, and which somehow manage to set appropriate levels of redundancy, already \"made the choice\" whether a linguistic unit is a phoneme or not, and this would be reflected in a greater smoothness in a frequency versus rank graph. For the particular case we chose to study, we conclude that it is reasonable to consider the Spanish semiconsonant \/w\/ as a separate phoneme from its vowel counterpart \/u\/, on the one hand, and possibly also the semiconsonant \/j\/ as a separate phoneme from its vowel counterpart \/i\/, on the other. As language has been so central a topic in the study of complexity, this discussion grants us, in addition, an opportunity to gain insight into emerging properties in the broader complex systems debate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.physa.2018.08.031\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Physics and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"220719","document_content":"# The What, the Why, and the How of Artificial Explanations in Automated Decision-Making\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- Human-Computer Interaction\n## Abstract\nThe increasing incorporation of Artificial Intelligence in the form of automated systems into decision-making procedures highlights not only the importance of decision theory for automated systems but also the need for these decision procedures to be explainable to the people involved in them. Traditional realist accounts of explanation, wherein explanation is a relation that holds (or does not hold) eternally between an explanans and an explanandum, are not adequate to account for the notion of explanation required for artificial decision procedures. We offer an alternative account of explanation as used in the context of automated decision-making that makes explanation an epistemic phenomenon, and one that is dependent on context. This account of explanation better accounts for the way that we talk about, and use, explanations and derived concepts, such as `explanatory power', and also allows us to differentiate between reasons or causes on the one hand, which do not need to have an epistemic aspect, and explanations on the other, which do have such an aspect. Against this theoretical backdrop we then review existing approaches to explanation in Artificial Intelligence and Machine Learning, and suggest desiderata which truly explainable decision systems should fulfill.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.07074v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"249204\", \"254281\"]}","task_split":"paper_retrieval"}
{"document_id":"220974","document_content":"# Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts\n## Categories\n- Computation and Language\n## Abstract\nPrevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.06161v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"68290\", \"70000\", \"167517\", \"82967\", \"125714\", \"147887\", \"149817\", \"156575\", \"167795\"], \"outgoing_citations\": [\"253158\", \"277865\", \"280967\", \"283936\", \"291045\", \"292448\", \"296702\", \"309965\"]}","task_split":"paper_retrieval"}
{"document_id":"221086","document_content":"# Read + Verify: Machine Reading Comprehension with Unanswerable Questions\n## Categories\n- Computation and Language\n## Abstract\nMachine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional \"no-answer\" probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as no-answer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2.0 dataset show that our system achieves a score of 74.2 F1 on the test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.05759v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15846\", \"70033\", \"145567\", \"148368\", \"3977\", \"33189\", \"44877\", \"45238\", \"55199\", \"64554\", \"38493\", \"74543\", \"77192\", \"77366\", \"86983\", \"92123\", \"127977\", \"145454\", \"88610\", \"92666\", \"98639\", \"100873\", \"73544\", \"113254\", \"121854\", \"124679\", \"125364\", \"128548\", \"129649\", \"142096\", \"156086\", \"163944\", \"168549\", \"171817\", \"160070\", \"175028\", \"178032\", \"179348\", \"181345\", \"184828\", \"187236\", \"184848\", \"197855\", \"200370\", \"203630\", \"204098\", \"212117\", \"217346\", \"169239\", \"214463\"], \"outgoing_citations\": [\"208172\", \"220570\", \"234577\", \"243803\", \"247060\", \"247824\", \"250304\", \"252059\", \"263874\", \"266850\", \"267031\", \"279213\", \"284357\", \"291019\", \"296289\", \"300867\", \"303827\"]}","task_split":"paper_retrieval"}
{"document_id":"221142","document_content":"# The DALPHI annotation framework & how its pre-annotations can improve annotator efficiency\n## Categories\n- Information Retrieval\n- Human-Computer Interaction\n- H.3.4; H.3.5; D.2.2\n## Abstract\nProducing the required amounts of training data for machine learning and NLP tasks often involves human annotators doing very repetitive and monotonous work. In this paper, we present and evaluate our novel annotation framework DALPHI, which facilitates the annotation process by providing the annotator with suggestions generated by an automated, active-learning based assistance system. In a study with 66 participants, we demonstrate on the exemplary task of annotating named entities in text documents that with this assistance system the annotation processes can be improved with respect to the quality and quantity of produced annotations, even if the pre-annotations provided by the assistance system are at a recall level of only 50%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.05558v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.HC\", \"H.3.4; H.3.5; D.2.2\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Human-Computer Interaction\", \"H.3.4; H.3.5; D.2.2\"], \"incoming_citations\": [\"182163\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"221153","document_content":"# Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition\n## Categories\n- Computation and Language\n## Abstract\nSentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks. In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space. Then, we propose the Paraphrase-Thought (P-thought) model to pursue semantic coherence as much as possible. Experimental results on two paraphrase identification datasets (MS COCO and STS benchmark) show that the P-thought models outperform the benchmarked sentence embedding methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.05505v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38766\", \"202795\", \"149817\"], \"outgoing_citations\": [\"251816\", \"251974\", \"255894\", \"259723\", \"261768\", \"262256\", \"267161\", \"272080\", \"279991\", \"282914\", \"282931\", \"288174\", \"291045\", \"298543\", \"302507\", \"302516\", \"310737\", \"328051\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"221430","document_content":"# Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nText-to-Image translation has been an active area of research in the recent past. The ability for a network to learn the meaning of a sentence and generate an accurate image that depicts the sentence shows ability of the model to think more like humans. Popular methods on text to image translation make use of Generative Adversarial Networks (GANs) to generate high quality images based on text input, but the generated images don't always reflect the meaning of the sentence given to the model as input. We address this issue by using a captioning network to caption on generated images and exploit the distance between ground truth captions and generated captions to improve the network further. We show extensive comparisons between our method and existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.04538v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"82744\", \"93826\", \"193909\"], \"outgoing_citations\": [\"245825\", \"271135\", \"271234\", \"283070\", \"292386\", \"294289\"]}","task_split":"paper_retrieval"}
{"document_id":"221473","document_content":"# Large-Scale Study of Curiosity-Driven Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nReinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https:\/\/pathak22.github.io\/large-scale-curiosity\/","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.04355v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"44400\", \"158780\", \"4300\", \"14643\", \"20579\", \"23585\", \"34577\", \"39352\", \"36251\", \"42781\", \"46165\", \"47245\", \"56916\", \"64409\", \"71572\", \"78473\", \"87712\", \"98489\", \"106428\", \"106764\", \"109434\", \"129693\", \"144508\", \"173042\", \"4583\", \"8098\", \"10167\", \"11087\", \"12959\", \"13435\", \"14283\", \"14839\", \"15680\", \"16270\", \"18761\", \"20625\", \"21606\", \"22025\", \"22761\", \"26464\", \"26551\", \"27991\", \"32373\", \"34605\", \"35616\", \"36807\", \"37526\", \"38714\", \"41008\", \"43040\", \"43557\", \"45108\", \"37600\", \"113199\", \"49900\", \"50487\", \"51764\", \"51976\", \"53826\", \"54181\", \"56227\", \"57194\", \"57919\", \"58945\", \"60263\", \"61341\", \"63020\", \"63088\", \"64051\", \"64836\", \"66140\", \"67153\", \"70161\", \"70703\", \"73448\", \"74451\", \"93557\", \"96180\", \"97096\", \"107635\", \"112104\", \"116987\", \"117087\", \"118638\", \"130627\", \"148172\", \"152892\", \"153376\", \"161444\", \"183481\", \"201315\", \"151613\", \"79180\", \"79829\", \"80273\", \"80396\", \"84272\", \"86362\", \"87109\", \"87748\", \"95099\", \"96390\", \"96517\", \"96714\", \"96500\", \"98069\", \"100235\", \"104828\", \"107705\", \"109956\", \"111868\", \"113565\", \"115852\", \"118362\", \"118534\", \"118830\", \"114889\", \"116357\", \"123654\", \"125475\", \"126444\", \"86650\", \"21672\", \"129546\", \"133890\", \"136074\", \"139653\", \"139807\", \"140582\", \"141480\", \"142272\", \"142617\", \"143663\", \"143954\", \"146919\", \"155617\", \"160073\", \"162119\", \"162100\", \"164883\", \"161717\", \"166489\", \"171190\", \"176698\", \"178420\", \"179967\", \"181005\", \"181163\", \"181382\", \"184260\", \"184535\", \"186749\", \"189573\", \"195849\", \"174708\", \"155376\", \"155630\", \"158110\", \"160673\", \"158905\", \"164505\", \"164842\", \"152047\", \"166981\", \"169881\", \"171112\", \"130592\", \"172746\", \"173531\", \"175995\", \"177292\", \"177959\", \"178125\", \"178186\", \"178620\", \"179115\", \"179957\", \"180089\", \"180118\", \"181724\", \"182334\", \"184374\", \"184649\", \"181532\", \"186631\", \"193458\", \"194738\", \"196393\", \"110822\", \"198217\", \"199131\", \"200264\", \"200574\", \"201736\", \"202620\", \"203834\", \"205643\", \"206945\", \"206800\", \"212556\", \"209469\", \"211558\", \"215425\", \"212299\", \"199330\", \"201682\", \"209551\", \"214636\", \"218770\"], \"outgoing_citations\": [\"234517\", \"240517\", \"241900\", \"262381\", \"264419\", \"264523\", \"271375\", \"272290\", \"272422\", \"272437\", \"279790\", \"280457\", \"291025\", \"291460\", \"298281\", \"305802\", \"310171\", \"319885\"]}","task_split":"paper_retrieval"}
{"document_id":"221612","document_content":"# Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation. However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming. In this paper, we present a novel semi-supervised method for skin lesion segmentation by leveraging both labeled and unlabeled data. The network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. Our method encourages a consistent prediction for unlabeled images using the outputs of the network-in-training under different regularizations, so that it can utilize the unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With only 300 labeled training samples, our method sets a new record on the benchmark of the International Skin Imaging Collaboration (ISIC) 2017 skin lesion segmentation challenge. Such a result clearly surpasses fully-supervised state-of-the-arts that are trained with 2000 labeled data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.03887v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"66061\", \"3108\", \"7352\", \"7872\", \"17762\", \"29764\", \"49492\", \"57179\", \"117593\", \"8122\", \"14238\", \"22284\", \"31916\", \"45212\", \"116472\", \"45660\", \"46403\", \"52422\", \"53300\", \"54164\", \"64454\", \"65010\", \"85147\", \"87658\", \"111845\", \"197166\", \"93280\", \"110564\", \"115562\", \"115991\", \"124972\", \"138612\", \"182405\", \"208003\", \"158179\", \"175869\", \"178129\", \"216827\"], \"outgoing_citations\": [\"225412\", \"241281\", \"254665\", \"271651\", \"271680\", \"272645\", \"278014\", \"290407\", \"298695\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"221642","document_content":"# A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?\n## Categories\n- Computation and Language\n## Abstract\nSemantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.03815v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"78281\", \"200951\", \"159954\", \"162910\", \"173016\", \"175042\"], \"outgoing_citations\": [\"224711\", \"226956\", \"268198\", \"269973\", \"271527\", \"276493\", \"281121\", \"291941\", \"334675\"]}","task_split":"paper_retrieval"}
{"document_id":"221754","document_content":"# A Hassle-Free Machine Learning Method for Cohort Selection of Clinical Trials\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nTraditional text classification techniques in clinical domain have heavily relied on the manually extracted textual cues. This paper proposes a generally supervised machine learning method that is equally hassle-free and does not use clinical knowledge. The employed methods were simple to implement, fast to run and yet effective. This paper proposes a novel named entity recognition (NER) based an ensemble system capable of learning the keyword features in the document. Instead of merely considering the whole sentence\/paragraph for analysis, the NER based keyword features can stress the important clinic relevant phases more. In addition, to capture the semantic information in the documents, the FastText features originating from the document level FastText classification results are exploited.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.04694v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"320084\"]}","task_split":"paper_retrieval"}
{"document_id":"221828","document_content":"# On feature selection and evaluation of transportation mode prediction strategies\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nTransportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasant moments for residents and tourists. Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating. In this work, we explore wrapper and information retrieval methods to find the best subset of trajectory features. After finding the best classifier and the best feature subset, our results were compared with two related papers that applied deep learning methods and the results showed that our framework achieved better performance. Furthermore, two types of cross-validation approaches were investigated, and the performance results show that the random cross-validation method provides optimistic results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.03096v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"236618\", \"240636\", \"250628\"]}","task_split":"paper_retrieval"}
{"document_id":"222042","document_content":"# Automatic Recognition of Student Engagement using Deep Learning and Facial Expression\n## Categories\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nEngagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the model's weights to initialize our deep learning based model to recognize engagement; we term this the engagement model. We train the model on our new engagement recognition dataset with 4627 engaged and disengaged samples. We find that the engagement model outperforms effective deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using histogram of oriented gradients and support vector machines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.02324v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.HC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"54569\", \"172586\", \"166104\"], \"outgoing_citations\": [\"225585\", \"278399\", \"285229\", \"303467\", \"306659\", \"316435\", \"343074\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"222054","document_content":"# ODSQA: Open-domain Spoken Question Answering Dataset\n## Categories\n- Computation and Language\n## Abstract\nReading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem. In this paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with more than three thousand questions. To the best of our knowledge, this is the largest real SQA dataset. On this dataset, we found that ASR errors have catastrophic impact on SQA. To mitigate the effect of ASR errors, subword units are involved, which brings consistent improvements over all the models. We further found that data augmentation on text-based QA training examples can improve SQA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.02280v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"3823\", \"17412\", \"21249\", \"25153\", \"140934\", \"36345\", \"96502\", \"68909\", \"92518\", \"92519\", \"146890\", \"95971\", \"120029\", \"172587\", \"174159\", \"176238\", \"190231\", \"200693\"], \"outgoing_citations\": [\"229429\", \"234577\", \"237064\", \"250304\", \"250474\", \"258130\", \"268809\", \"279213\", \"285849\", \"286110\", \"301767\", \"307547\", \"319169\"]}","task_split":"paper_retrieval"}
{"document_id":"222198","document_content":"# Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and How to Go Forward, Again\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nWe argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between objects of various ontological types. We will then show that accounting for these differences amounts to the integration of lexical and compositional semantics in one coherent framework, and to an embedding in our logical semantics of a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. We will show that in such a framework a number of challenges in natural language semantics can be adequately and systematically treated.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.01741v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"26617\", \"215919\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"222397","document_content":"# Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware. This paper introduces a fast and robust method that ensures both of these goals by obtaining state-of-the-art performance for an image-to-video search scenario. Hence, we present critical enhancements to well-known indexing and visual representation techniques by promoting faster, better and moderate retrieval performance. We also boost the superiority of our method for some visual challenges by exploiting individual decisions of local and global descriptors at query time. For instance, local content descriptors represent copied\/duplicated scenes with large geometric deformations such as scale, orientation and affine transformation. In contrast, the use of global content descriptors is more practical for near-duplicate and semantic searches. Experiments are conducted on a large-scale Stanford I2V dataset. The experimental results show that our method is useful in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. The proposed method is superior and achieves state-of-the-art performance based on the mean average precision (MAP) score of this dataset. Lastly, we report additional MAP scores after updating the ground annotations unveiled by retrieval results of the proposed method, and it shows that the actual performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.01101v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"172238\", \"277635\", \"295204\", \"295615\"]}","task_split":"paper_retrieval"}
{"document_id":"222519","document_content":"# Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects\n## Categories\n- Audio and Speech Processing\n- Machine Learning\n- Sound\n- Computation and Language\n## Abstract\nWe investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder. We compared an ideal system that uses manually corrected linguistic features including phoneme and prosodic information in training and test sets against a few other systems that use corrupted linguistic features. Both subjective and objective results demonstrate that corrupted linguistic features, especially those in the test set, affected the ideal system's performance significantly in a statistical sense due to a mismatched condition between the training and test sets. Interestingly, while an utterance-level Turing test showed that listeners had a difficult time differentiating synthetic speech from natural speech, it further indicated that adding noise to the linguistic features in the training set can partially reduce the effect of the mismatch, regularize the model, and help the system perform better when linguistic features of the test set are noisy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.00665v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"stat.ML\", \"cs.SD\", \"cs.CL\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Machine Learning\", \"Sound\", \"Computation and Language\"], \"incoming_citations\": [\"57348\", \"123776\", \"81878\", \"129560\", \"159462\", \"188745\", \"169512\", \"169953\", \"192871\", \"212546\", \"212631\", \"212620\"], \"outgoing_citations\": [\"236453\"]}","task_split":"paper_retrieval"}
{"document_id":"222526","document_content":"# Two-Layer Lossless HDR Coding using Histogram Packing Technique with Backward Compatibility to JPEG\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAn efficient two-layer coding method using the histogram packing technique with the backward compatibility to the legacy JPEG is proposed in this paper. The JPEG XT, which is the international standard to compress HDR images, adopts two-layer coding scheme for backward compatibility to the legacy JPEG. However, this two-layer coding structure does not give better lossless performance than the other existing methods for HDR image compression with single-layer structure. Moreover, the lossless compression of the JPEG XT has a problem on determination of the coding parameters; The lossless performance is affected by the input images and\/or the parameter values. That is, finding appropriate combination of the values is necessary to achieve good lossless performance. It is firstly pointed out that the histogram packing technique considering the histogram sparseness of HDR images is able to improve the performance of lossless compression. Then, a novel two-layer coding with the histogram packing technique and an additional lossless encoder is proposed. The experimental results demonstrate that not only the proposed method has a better lossless compression performance than that of the JPEG XT, but also there is no need to determine image-dependent parameter values for good compression performance without losing the backward compatibility to the well known legacy JPEG standard.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1587\/transfun.E101.A.1823\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"174501\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"222542","document_content":"# Physics-Based Generative Adversarial Models for Image Restoration and Beyond\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we find that these problems can be solved by generative models with adversarial learning. However, the basic formulation of generative adversarial networks (GANs) does not generate realistic images, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose a physics model constrained learning algorithm so that it can guide the estimation of the specific task in the conventional GAN framework. The proposed algorithm is trained in an end-to-end fashion and can be applied to a variety of image restoration and related low-level vision problems. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.00605v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9007\", \"44522\", \"89484\", \"109306\", \"22580\", \"63607\", \"189778\", \"79092\", \"145247\", \"79005\", \"84398\", \"87587\", \"105393\", \"108188\", \"121600\", \"133159\", \"170255\", \"171291\", \"203644\", \"164038\", \"170337\", \"203792\"], \"outgoing_citations\": [\"238103\", \"239567\", \"269388\", \"271419\", \"275747\", \"277357\", \"277386\", \"278546\", \"282643\", \"283989\", \"285187\", \"287403\", \"299368\", \"303384\", \"313846\", \"316657\"]}","task_split":"paper_retrieval"}
{"document_id":"222549","document_content":"# Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWeather conditions often disrupt the proper functioning of transportation systems. Present systems either deploy an array of sensors or use an in-vehicle camera to predict weather conditions. These solutions have resulted in incremental cost and limited scope. To ensure smooth operation of all transportation services in all-weather conditions, a reliable detection system is necessary to classify weather in wild. The challenges involved in solving this problem is that weather conditions are diverse in nature and there is an absence of discriminate features among various weather conditions. The existing works to solve this problem have been scene specific and have targeted classification of two categories of weather. In this paper, we have created a new open source dataset consisting of images depicting three classes of weather i.e rain, snow and fog called RFS Dataset. A novel algorithm has also been proposed which has used super pixel delimiting masks as a form of data augmentation, leading to reasonable results with respect to ten Convolutional Neural Network architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.00588v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"52952\", \"145397\", \"160413\"], \"outgoing_citations\": [\"271703\"]}","task_split":"paper_retrieval"}
{"document_id":"222572","document_content":"# Low-Latency Neural Speech Translation\n## Categories\n- Computation and Language\n## Abstract\nThrough the development of neural machine translation, the quality of machine translation systems has been improved significantly. By exploiting advancements in deep learning, systems are now able to better approximate the complex mapping from source sentences to target sentences. But with this ability, new challenges also arise. An example is the translation of partial sentences in low-latency speech translation. Since the model has only seen complete sentences in training, it will always try to generate a complete sentence, though the input may only be a partial sentence. We show that NMT systems can be adapted to scenarios where no task-specific training data is available. Furthermore, this is possible without losing performance on the original training data. We achieve this by creating artificial data and by using multi-task learning. After adaptation, we are able to reduce the number of corrections displayed during incremental output construction by 45%, without a decrease in translation quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.00491v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"12420\", \"22290\", \"31466\", \"41419\", \"52755\", \"57545\", \"65196\", \"67594\", \"74038\", \"92493\", \"78000\", \"87997\", \"157457\", \"99814\", \"100492\", \"102384\", \"120884\", \"122053\", \"123177\", \"123464\", \"131318\", \"131513\", \"132420\", \"152334\", \"178633\", \"164154\", \"168771\", \"173791\", \"185533\"], \"outgoing_citations\": [\"259471\", \"269888\", \"277665\", \"283451\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"222871","document_content":"# To learn image super-resolution, use a GAN to learn how to do image degradation first\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper is on image and face super-resolution. The vast majority of prior work for this problem focus on how to increase the resolution of low-resolution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling).We show that such methods fail to produce good results when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a two-stage process which firstly trains a High-to-Low Generative Adversarial Network (GAN) to learn how to degrade and downsample high-resolution images requiring, during training, only unpaired high and low-resolution images. Once this is achieved, the output of this network is used to train a Low-to-High GAN for image super-resolution using this time paired low- and high-resolution images. Our main result is that this network can be now used to efectively increase the quality of real-world low-resolution images. We have applied the proposed pipeline for the problem of face super-resolution where we report large improvement over baselines and prior work although the proposed method is potentially applicable to other object categories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.11458v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37039\", \"76110\", \"100824\", \"215465\", \"25119\", \"31384\", \"31799\", \"57402\", \"62094\", \"65543\", \"71292\", \"1773\", \"2621\", \"3736\", \"8303\", \"9266\", \"12112\", \"14832\", \"18535\", \"20279\", \"22511\", \"27536\", \"36677\", \"46947\", \"47370\", \"49564\", \"62799\", \"65270\", \"65342\", \"68463\", \"73272\", \"75417\", \"113809\", \"84624\", \"96073\", \"99898\", \"133419\", \"137522\", \"143372\", \"148107\", \"166673\", \"198751\", \"78197\", \"89019\", \"94977\", \"96309\", \"103155\", \"108440\", \"111861\", \"117180\", \"126971\", \"131516\", \"133159\", \"135868\", \"137881\", \"137913\", \"138472\", \"139986\", \"142083\", \"149113\", \"154138\", \"163078\", \"164523\", \"171291\", \"193117\", \"230252\", \"150049\", \"155696\", \"155704\", \"165717\", \"173485\", \"173711\", \"181259\", \"190354\", \"191918\", \"191920\", \"192959\", \"193376\", \"201814\", \"202226\", \"205139\", \"197717\", \"223755\", \"206446\", \"249406\"], \"outgoing_citations\": [\"247268\", \"248019\", \"248969\", \"258886\", \"274838\", \"277357\", \"288238\", \"302868\"]}","task_split":"paper_retrieval"}
{"document_id":"223051","document_content":"# Code-Switching Detection with Data-Augmented Acoustic and Language Models\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we investigate the code-switching detection performance of a code-switching (CS) automatic speech recognition (ASR) system with data-augmented acoustic and language models. We focus on the recognition of Frisian-Dutch radio broadcasts where one of the mixed languages, namely Frisian, is under-resourced. Recently, we have explored how the acoustic modeling (AM) can benefit from monolingual speech data belonging to the high-resourced mixed language. For this purpose, we have trained state-of-the-art AMs on a significantly increased amount of CS speech by applying automatic transcription and monolingual Dutch speech. Moreover, we have improved the language model (LM) by creating CS text in various ways including text generation using recurrent LMs trained on existing CS text. Motivated by the significantly improved CS ASR performance, we delve into the CS detection performance of the same ASR system in this work by reporting CS detection accuracies together with a detailed detection error analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1808.00521v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"90761\", \"164559\", \"180025\", \"180203\"], \"outgoing_citations\": [\"213306\", \"223054\"]}","task_split":"paper_retrieval"}
{"document_id":"223054","document_content":"# Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we describe several techniques for improving the acoustic and language model of an automatic speech recognition (ASR) system operating on code-switching (CS) speech. We focus on the recognition of Frisian-Dutch radio broadcasts where one of the mixed languages, namely Frisian, is an under-resourced language. In previous work, we have proposed several automatic transcription strategies for CS speech to increase the amount of available training speech data. In this work, we explore how the acoustic modeling (AM) can benefit from monolingual speech data belonging to the high-resourced mixed language. For this purpose, we train state-of-the-art AMs, which were ineffective due to lack of training data, on a significantly increased amount of CS speech and monolingual Dutch speech. Moreover, we improve the language model (LM) by creating code-switching text, which is in practice almost non-existent, by (1) generating text using recurrent LMs trained on the transcriptions of the training CS speech data, (2) adding the transcriptions of the automatically transcribed CS speech data and (3) translating Dutch text extracted from the transcriptions of a large Dutch speech corpora. We report significantly improved CS ASR performance due to the increase in the acoustic and textual training data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.10945v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"9440\", \"20273\", \"90761\", \"78811\", \"3587\", \"94956\", \"138203\", \"155467\", \"156391\", \"164559\", \"180025\", \"180043\", \"180203\", \"191716\", \"193839\", \"211265\", \"212026\", \"213306\", \"223051\", \"223650\", \"223652\"], \"outgoing_citations\": [\"213306\"]}","task_split":"paper_retrieval"}
{"document_id":"223078","document_content":"# Transportation Modes Classification Using Feature Engineering\n## Categories\n- Machine Learning\n## Abstract\nPredicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism, traffic congestion, smart cities management, animal behaviour analysis, environmental preservation, and traffic dynamics are some of the trajectory mining applications. Transportation modes prediction as one of the tasks in human mobility and vehicle mobility applications plays an important role in resource allocation, traffic management systems, tourism planning and accident detection. In this work, the proposed framework in Etemad et al. is extended to consider other aspects in the task of transportation modes prediction. Wrapper search and information retrieval methods were investigated to find the best subset of trajectory features. Finding the best classifier and the best feature subset, the framework is compared against two related papers that applied deep learning methods. The results show that our framework achieved better performance. Moreover, the ground truth noise removal improved accuracy of transportation modes prediction task; however, the assumption of having access to test set labels in pre-processing task is invalid. Furthermore, the cross validation approaches were investigated and the performance results show that the random cross validation method provides optimistic results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.10876v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"23117\"], \"outgoing_citations\": [\"236618\", \"239218\", \"240636\", \"250628\", \"267084\"]}","task_split":"paper_retrieval"}
{"document_id":"223098","document_content":"# Clustering Prominent People and Organizations in Topic-Specific Text Corpora\n## Categories\n- Computation and Language\n## Abstract\nNamed entities in text documents are the names of people, organization, location or other types of objects in the documents that exist in the real world. A persisting research challenge is to use computational techniques to identify such entities in text documents. Once identified, several text mining tools and algorithms can be utilized to leverage these discovered named entities and improve NLP applications. In this paper, a method that clusters prominent names of people and organizations based on their semantic similarity in a text corpus is proposed. The method relies on common named entity recognition techniques and on recent word embeddings models. The semantic similarity scores generated using the word embeddings models for the named entities are used to cluster similar entities of the people and organizations types. Two human judges evaluated ten variations of the method after it was run on a corpus that consists of 4,821 articles on a specific topic. The performance of the method was measured using three quantitative measures. The results of these three metrics demonstrate that the method is effective in clustering semantically similar named entities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.10800v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"283832\", \"291574\", \"297261\", \"321913\", \"339803\"]}","task_split":"paper_retrieval"}
{"document_id":"223142","document_content":"# X2Face: A network for controlling face generation by using images, audio, and pose codes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing.   We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but the pose and expression of the face in the driving frame. Second, we propose a method for training the network fully self-supervised using a large collection of video data. Third, we show that the generation process can be driven by other modalities, such as audio or pose codes, without any further training of the network.   The generation results for driving a face with another face are compared to state-of-the-art self-supervised\/supervised methods. We show that our approach is more robust than other methods, as it makes fewer assumptions about the input data. We also show examples of using our framework for video face editing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.10550v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2294\", \"21787\", \"37459\", \"54087\", \"78902\", \"4035\", \"5062\", \"5325\", \"25676\", \"46204\", \"57722\", \"66875\", \"77244\", \"87132\", \"90008\", \"109150\", \"119696\", \"147082\", \"174773\", \"193902\", \"7353\", \"10613\", \"2017\", \"12561\", \"16005\", \"17644\", \"17672\", \"21848\", \"22840\", \"22992\", \"26210\", \"26317\", \"27428\", \"28650\", \"29054\", \"30907\", \"32451\", \"33342\", \"29975\", \"41290\", \"40502\", \"43829\", \"47103\", \"51791\", \"52721\", \"54075\", \"54129\", \"54198\", \"54494\", \"55482\", \"55594\", \"57858\", \"59596\", \"61632\", \"61913\", \"62977\", \"63293\", \"70889\", \"66749\", \"80394\", \"81593\", \"82761\", \"83625\", \"83775\", \"85608\", \"88154\", \"107434\", \"117837\", \"123788\", \"147240\", \"34976\", \"80737\", \"37004\", \"88571\", \"89576\", \"90654\", \"91527\", \"99587\", \"104442\", \"105432\", \"106581\", \"108345\", \"108357\", \"111438\", \"111565\", \"114470\", \"80701\", \"123562\", \"125301\", \"126546\", \"126722\", \"127610\", \"128020\", \"128863\", \"129114\", \"129267\", \"132347\", \"132593\", \"134428\", \"130175\", \"137156\", \"140504\", \"141526\", \"155142\", \"155552\", \"170169\", \"184017\", \"188872\", \"188937\", \"192481\", \"139362\", \"156027\", \"168432\", \"171016\", \"180878\", \"182589\", \"183923\", \"184471\", \"184965\", \"185334\", \"185516\", \"187059\", \"187996\", \"188779\", \"197839\", \"205195\", \"223922\", \"208309\", \"220756\"], \"outgoing_citations\": [\"230031\", \"241433\", \"243165\", \"247008\", \"247495\", \"252884\", \"256227\", \"257846\", \"259890\", \"264963\", \"268307\", \"270134\", \"273864\", \"278966\", \"279260\", \"280313\", \"302963\", \"316183\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"223229","document_content":"# Generating 3D faces using Convolutional Mesh Autoencoders\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLearned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http:\/\/github.com\/anuragranj\/coma","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.10267v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2288\", \"6007\", \"66565\", \"73946\", \"82788\", \"110157\", \"72121\", \"3093\", \"5523\", \"5884\", \"7546\", \"14780\", \"15411\", \"21412\", \"30978\", \"31460\", \"37900\", \"40071\", \"42824\", \"44854\", \"49296\", \"53219\", \"56524\", \"70940\", \"104514\", \"106479\", \"120672\", \"3046\", \"5491\", \"6397\", \"9297\", \"10878\", \"11411\", \"14353\", \"15885\", \"16773\", \"19986\", \"23179\", \"25578\", \"25884\", \"26350\", \"27178\", \"27490\", \"27479\", \"28391\", \"28756\", \"28812\", \"34652\", \"38182\", \"39492\", \"41224\", \"44440\", \"40450\", \"48039\", \"51501\", \"53728\", \"54508\", \"55764\", \"55755\", \"54599\", \"57114\", \"57648\", \"57661\", \"59029\", \"59383\", \"59532\", \"60120\", \"60501\", \"61262\", \"221215\", \"75585\", \"77609\", \"79164\", \"79831\", \"80034\", \"81593\", \"84175\", \"84871\", \"87603\", \"88538\", \"91698\", \"97227\", \"101427\", \"107810\", \"115663\", \"129855\", \"139420\", \"155623\", \"80454\", \"90654\", \"91114\", \"91861\", \"92452\", \"93080\", \"75870\", \"97329\", \"101493\", \"101871\", \"103082\", \"104084\", \"104917\", \"105432\", \"106079\", \"108386\", \"109577\", \"110341\", \"110961\", \"112309\", \"116052\", \"118722\", \"120410\", \"120516\", \"121504\", \"123287\", \"127050\", \"132795\", \"133118\", \"133476\", \"133851\", \"134669\", \"136719\", \"136866\", \"137181\", \"138804\", \"140591\", \"154346\", \"160147\", \"163314\", \"166510\", \"167192\", \"168545\", \"168874\", \"172693\", \"173584\", \"174575\", \"184738\", \"186379\", \"188208\", \"156452\", \"163998\", \"174707\", \"185981\", \"187327\", \"187266\", \"187224\", \"187760\", \"188069\", \"191054\", \"191844\", \"191933\", \"192689\", \"193837\", \"195191\", \"195806\", \"196255\", \"196774\", \"197493\", \"199069\", \"199512\", \"202404\", \"205494\", \"206570\", \"206905\", \"244345\", \"217670\"], \"outgoing_citations\": [\"247495\", \"248646\", \"270134\", \"270795\", \"278935\", \"286586\", \"292184\", \"318528\", \"335418\", \"354038\"]}","task_split":"paper_retrieval"}
{"document_id":"223299","document_content":"# Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nUnderstanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09995v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5374\", \"21785\", \"39639\", \"47944\", \"132642\", \"149568\", \"9553\", \"12281\", \"21817\", \"23300\", \"44501\", \"52930\", \"54107\", \"55730\", \"64930\", \"66568\", \"113996\", \"116670\", \"120882\", \"136246\", \"137006\", \"144848\", \"148683\", \"153754\", \"166501\", \"214194\", \"161193\", \"181782\", \"197258\", \"207516\", \"217774\"], \"outgoing_citations\": [\"232095\", \"232096\", \"241801\", \"252398\", \"254931\", \"268891\", \"269126\", \"275566\"]}","task_split":"paper_retrieval"}
{"document_id":"223302","document_content":"# Recurrent Fusion Network for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The existing models counting on this framework merely employ one kind of CNNs, e.g., ResNet or Inception-X, which describe image contents from only one specific view point. Thus, the semantic meaning of an input image cannot be comprehensively understood, which restricts the performance of captioning. In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning. The fusion process in our model can exploit the interactions among the outputs of the image encoders and then generate new compact yet informative representations for the decoder. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets a new state-of-the-art for image captioning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09986v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23109\", \"35427\", \"4080\", \"5625\", \"19710\", \"6500\", \"27871\", \"61394\", \"61663\", \"65077\", \"74508\", \"78606\", \"82892\", \"93826\", \"137828\", \"80061\", \"89648\", \"95771\", \"107896\", \"154779\", \"112250\", \"124665\", \"132390\", \"133970\", \"135506\", \"138218\", \"149491\", \"162678\", \"162959\", \"182940\", \"185622\", \"125246\", \"151345\", \"152816\", \"165896\", \"167838\", \"169417\", \"170020\", \"171057\", \"171096\", \"139529\", \"186189\", \"187005\", \"187017\", \"184018\", \"188386\", \"189964\", \"192659\", \"195170\", \"200501\", \"208555\", \"231619\"], \"outgoing_citations\": [\"236897\", \"237150\", \"237199\", \"237201\", \"240071\", \"267523\", \"269030\", \"277511\", \"277936\", \"278243\", \"279002\", \"281152\", \"284230\", \"290392\", \"291851\", \"300548\", \"303003\", \"303224\", \"310023\", \"311824\", \"313435\", \"320003\", \"321621\", \"345732\"]}","task_split":"paper_retrieval"}
{"document_id":"223392","document_content":"# A Novel ILP Framework for Summarizing Content with High Lexical Variety\n## Categories\n- Computation and Language\n## Abstract\nSummarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09671v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"227983\", \"227985\", \"230234\", \"230458\", \"230459\", \"230466\", \"233710\", \"235463\", \"237494\", \"241070\", \"250690\", \"263243\", \"268186\", \"268334\", \"277028\", \"281847\", \"283618\", \"295412\", \"295665\", \"333603\", \"334217\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"223456","document_content":"# Distinctive-attribute Extraction for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nImage captioning, an open research issue, has been evolved with the progress of deep neural networks. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to compute image features and generate natural language descriptions in the research. In previous works, a caption involving semantic description can be generated by applying additional information into the RNNs. In this approach, we propose a distinctive-attribute extraction (DaE) which explicitly encourages significant meanings to generate an accurate caption describing the overall meaning of the image with their unique situation. Specifically, the captions of training images are analyzed by term frequency-inverse document frequency (TF-IDF), and the analyzed semantic information is trained to extract distinctive-attributes for inferring captions. The proposed scheme is evaluated on a challenge data, and it improves an objective performance while describing images in more detail.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09434v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"89648\"], \"outgoing_citations\": [\"253934\", \"279676\", \"311824\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"223511","document_content":"# Multi-Class Lesion Diagnosis with Pixel-wise Classification Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLesion diagnosis of skin lesions is a very challenging task due to high inter-class similarities and intra-class variations in terms of color, size, site and appearance among different skin lesions. With the emergence of computer vision especially deep learning algorithms, lesion diagnosis is made possible using these algorithms trained on dermoscopic images. Usually, deep classification networks are used for the lesion diagnosis to determine different types of skin lesions. In this work, we used pixel-wise classification network to provide lesion diagnosis rather than classification network. We propose to use DeeplabV3+ for multi-class lesion diagnosis in dermoscopic images of Task 3 of ISIC Challenge 2018. We used various post-processing methods with DeeplabV3+ to determine the lesion diagnosis in this challenge and submitted the test results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09227v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"200441\"], \"outgoing_citations\": [\"249038\", \"259214\"]}","task_split":"paper_retrieval"}
{"document_id":"223523","document_content":"# Skin disease identification from dermoscopy images using deep convolutional neural network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images. The developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.09163v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"223563","document_content":"# Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!\n## Categories\n- Computation and Language\n## Abstract\nArgumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \\url{http:\/\/github.com\/UKPLab\/coling2018-xling_argument_mining}.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08998v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38279\", \"8516\", \"39110\", \"75422\", \"94584\", \"96601\", \"120315\", \"127555\", \"169200\", \"193462\", \"240090\"], \"outgoing_citations\": [\"235953\", \"250586\", \"257375\", \"263680\", \"267232\", \"268140\", \"268162\", \"268433\", \"268968\", \"271158\", \"291073\", \"293849\", \"294248\", \"295407\", \"300302\"]}","task_split":"paper_retrieval"}
{"document_id":"223610","document_content":"# Lesion segmentation using U-Net network\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n- 62P10\n## Abstract\nThis paper explains the method used in the segmentation challenge (Task 1) in the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We have trained a U-Net network to perform the segmentation. The key elements for the training were first to adjust the loss function to incorporate unbalanced proportion of background and second to perform post-processing operation to adjust the contour of the prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08844v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\", \"62P10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\", \"62P10\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"223624","document_content":"# Identity Preserving Face Completion for Large Ocular Region Occlusion\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a novel deep learning approach to synthesize complete face images in the presence of large ocular region occlusions. This is motivated by recent surge of VR\/AR displays that hinder face-to-face communications. Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity. At the core of our method is a novel generative network with dedicated constraints to regularize the synthesis process. To preserve the identity, our network takes an arbitrary occlusion-free image of the target identity to infer the missing content, and its high-level CNN features as an identity prior to regularize the searching space of generator. Since the input reference image may have a different pose, a pose map and a novel pose discriminator are further adopted to supervise the learning of implicit pose transformations. Our method is capable of generating coherent facial inpainting with consistent identity over videos with large variations of head motions. Experiments on both synthesized and real data demonstrate that our method greatly outperforms the state-of-the-art methods in terms of both synthesis quality and robustness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08772v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26240\", \"205461\", \"209731\", \"259579\", \"182059\", \"186197\", \"187325\", \"195721\", \"197539\", \"203459\", \"227145\", \"281841\"], \"outgoing_citations\": [\"268405\", \"268494\", \"268970\", \"279178\", \"281841\", \"282432\", \"282902\", \"287740\"]}","task_split":"paper_retrieval"}
{"document_id":"223640","document_content":"# Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences\n## Categories\n- Machine Learning\n## Abstract\nMachine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development of transparent classification models has received much attention, but there are few developments for achieving transparent Reinforcement Learning (RL) models. In this study we propose a method that enables a RL agent to explain its behavior in terms of the expected consequences of state transitions and outcomes. First, we define a translation of states and actions to a description that is easier to understand for human users. Second, we developed a procedure that enables the agent to obtain the consequences of a single action, as well as its entire policy. The method calculates contrasts between the consequences of a policy derived from a user query, and of the learned policy of the agent. Third, a format for generating explanations was constructed. A pilot survey study was conducted to explore preferences of users for different explanation properties. Results indicate that human users tend to favor explanations about policy rather than about single actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08706v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"28573\", \"51298\", \"57877\", \"136611\", \"144265\", \"952\", \"2258\", \"47013\", \"49476\", \"72722\", \"76818\", \"103185\", \"124327\", \"211032\", \"86275\", \"87895\", \"105972\", \"125366\", \"145283\", \"150447\", \"184390\", \"164491\", \"179578\", \"183421\", \"204226\"], \"outgoing_citations\": [\"236488\", \"239137\", \"247002\", \"247677\", \"259855\", \"279639\", \"279797\"]}","task_split":"paper_retrieval"}
{"document_id":"223643","document_content":"# PS-FCN: A Flexible Learning Framework for Photometric Stereo\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08696v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17221\", \"17233\", \"74967\", \"78362\", \"109661\", \"13586\", \"35304\", \"53332\", \"56332\", \"60277\", \"61443\", \"80979\", \"106554\", \"95673\", \"101039\", \"193877\", \"134658\", \"134561\", \"178720\", \"186960\", \"194752\"], \"outgoing_citations\": [\"249732\", \"270532\", \"315984\", \"321620\"]}","task_split":"paper_retrieval"}
{"document_id":"223719","document_content":"# Deep attention-guided fusion network for lesion segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe participated the Task 1: Lesion Segmentation. The paper describes our algorithm and the final result of validation set for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08471v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"259194\"]}","task_split":"paper_retrieval"}
{"document_id":"223755","document_content":"# SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable. To address this problem, we propose a Siamese GAN (SiGAN) to reconstruct HR faces that visually resemble their corresponding identities. On top of a Siamese network, the proposed SiGAN consists of a pair of two identical generators and one discriminator. We incorporate reconstruction error and identity label information in the loss function of SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the generator pair and discriminator of SiGAN, we cannot only achieve photo-realistic face reconstruction, but also ensures the reconstructed information is useful for identity recognition. Experimental results demonstrate that SiGAN significantly outperforms existing face hallucination GANs in objective face verification performance, while achieving photo-realistic reconstruction. Moreover, for input LR faces from unknown identities who are not included in training, SiGAN can still do a good job.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2019.2924554\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60770\", \"76110\", \"8330\", \"36730\", \"55754\", \"60697\", \"91952\", \"8303\", \"59927\", \"89636\", \"103761\", \"114593\", \"97923\", \"112246\", \"159775\", \"165328\", \"166256\"], \"outgoing_citations\": [\"222871\", \"234204\", \"248019\", \"248969\", \"258886\", \"259660\", \"274838\", \"279655\", \"288238\", \"296033\", \"307292\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"223769","document_content":"# Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis article presents the design, experiments and results of our solution submitted to the 2018 ISIC challenge: Skin Lesion Analysis Towards Melanoma Detection. We design a pipeline using state-of-the-art Convolutional Neural Network (CNN) models for a Lesion Boundary Segmentation task and a Lesion Diagnosis task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08332v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"111482\", \"188540\"], \"outgoing_citations\": [\"247336\"]}","task_split":"paper_retrieval"}
{"document_id":"223810","document_content":"# Asynchronous Advantage Actor-Critic Agent for Starcraft II\n## Categories\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games. Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment. Despite being a target for several AI developers, few have achieved human level performance. In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08217v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"27720\", \"175034\"], \"outgoing_citations\": [\"247242\", \"258472\", \"261427\", \"282684\", \"309498\"]}","task_split":"paper_retrieval"}
{"document_id":"223851","document_content":"# Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma classification is a serious stage to identify the skin disease. It is considered a challenging process due to the intra-class discrepancy of melanomas, skin lesions low contrast, and the artifacts in the dermoscopy images, including noise, existence of hair, air bubbles, and the similarity between melanoma and non-melanoma cases. To solve these problems, we propose a novel multiple convolution neural network model (MCNN) to classify different seven disease types in dermoscopic images, where several models were trained separately using an additive sample learning strategy. The MCNN model is trained and tested using the training and validation sets from the International Skin Imaging Collaboration (ISIC 2018), respectively. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the proposed method. The values of AUC (the area under the ROC curve) were used to evaluate the performance of the MCNN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08114v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271531\", \"271944\", \"272225\"]}","task_split":"paper_retrieval"}
{"document_id":"223862","document_content":"# Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n## Abstract\nWord embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spoken words (signal segments for words) learned from signals within spoken words. This paper proposes a two-stage framework to perform phonetic-and-semantic embedding on spoken words considering the context of the spoken words. Stage 1 performs phonetic embedding with speaker characteristics disentangled. Stage 2 then performs semantic embedding in addition. We further propose to evaluate the phonetic-and-semantic nature of the audio embeddings obtained in Stage 2 by parallelizing with text embeddings. In general, phonetic structure and semantics inevitably disturb each other. For example the words \"brother\" and \"sister\" are close in semantics but very different in phonetic structure, while the words \"brother\" and \"bother\" are in the other way around. But phonetic-and-semantic embedding is attractive, as shown in the initial experiments on spoken document retrieval. Not only spoken documents including the spoken query can be retrieved based on the phonetic structures, but spoken documents semantically related to the query but not including the query can also be retrieved based on the semantics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.08089v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"43506\", \"39836\", \"73120\", \"62043\", \"121507\", \"131775\", \"133239\", \"143880\", \"154048\", \"160160\", \"190511\", \"193250\", \"211976\", \"191602\", \"211141\", \"212429\"], \"outgoing_citations\": [\"222073\", \"237333\", \"237945\", \"244758\", \"249700\", \"251231\", \"255129\", \"259594\", \"263944\", \"264202\", \"264990\", \"267881\", \"268121\", \"269624\", \"270712\", \"280501\", \"280918\", \"286181\", \"289838\", \"294248\", \"297211\", \"305536\", \"307547\", \"308559\", \"313435\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"223921","document_content":"# From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nCurrent face recognition systems robustly recognize identities across a wide variety of imaging conditions. In these systems recognition is performed via classification into known identities obtained from supervised identity annotations. There are two problems with this current paradigm: (1) current systems are unable to benefit from unlabelled data which may be available in large quantities; and (2) current systems equate successful recognition with labelling a given input image. Humans, on the other hand, regularly perform identification of individuals completely unsupervised, recognising the identity of someone they have seen before even without being able to name that individual. How can we go beyond the current classification paradigm towards a more human understanding of identities? We propose an integrated Bayesian model that coherently reasons about the observed images, identities, partial knowledge about names, and the situational context of each observation. While our model achieves good recognition performance against known identities, it can also discover new identities from unsupervised data and learns to associate identities with different contexts depending on which identities tend to be observed together. In addition, the proposed semi-supervised component is able to handle not only acquaintances, whose names are known, but also unlabelled familiar faces and complete strangers in a unified framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-01216-8_46\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"100349\", \"209487\"], \"outgoing_citations\": [\"269766\", \"320262\", \"324933\"]}","task_split":"paper_retrieval"}
{"document_id":"223954","document_content":"# An Efficient End-to-End Neural Model for Handwritten Text Recognition\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nOffline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents\/reports. Deep recurrent models such as Multi-dimensional LSTMs have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss, an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.07965v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"82145\", \"122798\", \"21983\", \"55703\", \"64171\", \"78094\", \"172702\", \"247723\", \"100709\", \"81703\", \"106374\", \"204309\", \"156446\", \"194746\", \"206404\"], \"outgoing_citations\": [\"276462\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"224223","document_content":"# On Evaluation of Embodied Navigation Agents\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nSkillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.06757v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"2138\", \"6637\", \"34264\", \"2048\", \"4053\", \"4499\", \"5373\", \"7377\", \"15160\", \"19526\", \"21900\", \"22304\", \"23428\", \"26795\", \"27350\", \"29801\", \"30806\", \"35753\", \"38664\", \"39108\", \"39368\", \"47577\", \"64409\", \"75621\", \"81244\", \"81992\", \"128249\", \"1414\", \"5824\", \"6704\", \"9686\", \"13414\", \"13764\", \"14495\", \"15383\", \"16502\", \"16975\", \"17160\", \"17795\", \"18840\", \"17137\", \"19911\", \"22720\", \"22936\", \"25684\", \"27520\", \"27522\", \"28168\", \"28187\", \"28518\", \"31155\", \"3896\", \"34913\", \"36974\", \"36874\", \"37932\", \"40343\", \"42082\", \"43678\", \"45665\", \"48390\", \"48758\", \"49226\", \"49746\", \"51976\", \"52954\", \"54096\", \"54330\", \"54802\", \"56049\", \"57305\", \"57326\", \"59216\", \"59470\", \"61178\", \"61747\", \"61841\", \"63124\", \"63557\", \"65095\", \"65953\", \"66007\", \"66896\", \"68278\", \"76846\", \"77756\", \"78420\", \"79162\", \"81574\", \"81899\", \"82453\", \"82597\", \"84618\", \"84889\", \"84938\", \"88546\", \"88552\", \"91301\", \"93649\", \"97665\", \"100277\", \"100401\", \"101182\", \"104690\", \"105429\", \"110042\", \"112104\", \"132558\", \"135815\", \"148172\", \"153902\", \"163326\", \"175031\", \"82126\", \"85467\", \"85766\", \"87242\", \"88444\", \"89221\", \"90871\", \"91773\", \"93277\", \"93950\", \"101886\", \"102971\", \"103484\", \"104828\", \"104772\", \"106567\", \"109133\", \"109092\", \"109438\", \"110525\", \"110941\", \"112949\", \"113236\", \"113074\", \"114824\", \"116592\", \"117896\", \"118832\", \"123032\", \"123031\", \"127113\", \"127852\", \"131180\", \"131891\", \"131814\", \"132720\", \"137075\", \"137384\", \"139037\", \"139227\", \"140318\", \"142371\", \"149565\", \"149647\", \"151367\", \"152871\", \"155676\", \"155831\", \"155908\", \"158547\", \"162959\", \"163867\", \"165218\", \"165819\", \"180340\", \"196909\", \"136757\", \"152372\", \"149759\", \"159154\", \"165129\", \"166667\", \"167688\", \"168651\", \"172416\", \"174221\", \"174444\", \"177351\", \"177674\", \"176445\", \"180898\", \"183378\", \"183823\", \"183699\", \"184119\", \"183562\", \"185747\", \"190556\", \"191671\", \"191570\", \"192738\", \"193972\", \"195772\", \"196148\", \"196218\", \"196525\", \"196393\", \"200941\", \"203398\", \"203854\", \"207499\", \"208042\", \"208773\", \"214195\", \"204289\", \"207509\", \"208585\", \"274102\"], \"outgoing_citations\": [\"219658\", \"234287\", \"237119\", \"240323\", \"245731\", \"246823\", \"247418\", \"247749\", \"248885\", \"272930\", \"274102\", \"275003\", \"279420\", \"280296\", \"280570\", \"280681\", \"281110\", \"284468\", \"284565\", \"291568\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"224233","document_content":"# Generating Levels That Teach Mechanics\n## Categories\n- Artificial Intelligence\n## Abstract\nThe automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3235765.3235820\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"22313\", \"67124\", \"68080\", \"61740\", \"143545\", \"168225\", \"165417\", \"181330\", \"189837\", \"193434\", \"202740\"], \"outgoing_citations\": [\"224955\", \"228285\", \"230005\", \"261387\", \"267630\", \"289709\"]}","task_split":"paper_retrieval"}
{"document_id":"224330","document_content":"# Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper summarizes our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.06466v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"167310\", \"216827\"], \"outgoing_citations\": [\"271680\"]}","task_split":"paper_retrieval"}
{"document_id":"224422","document_content":"# Effective Use of Synthetic Data for Urban Scene Semantic Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTraining a deep network to perform semantic segmentation requires large amounts of labeled data. To alleviate the manual effort of annotating real images, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images during training. In this paper, we introduce a drastically different way to handle synthetic images that does not require seeing any real images at training time. Our approach builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently. In particular, the former should be handled in a detection-based manner to better account for the fact that, while their texture in synthetic images is not photo-realistic, their shape looks natural. Our experiments evidence the effectiveness of our approach on Cityscapes and CamVid with models trained on synthetic data only.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.06132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"44346\", \"44521\", \"60549\", \"74970\", \"94155\", \"120418\", \"5240\", \"18284\", \"19959\", \"44546\", \"44526\", \"44694\", \"109031\", \"49019\", \"56492\", \"59875\", \"65454\", \"65542\", \"72549\", \"142131\", \"57444\", \"86744\", \"88994\", \"93453\", \"99271\", \"98463\", \"109864\", \"165015\", \"47170\", \"95573\", \"99856\", \"108908\", \"111442\", \"114064\", \"119396\", \"123870\", \"124627\", \"133557\", \"135263\", \"135902\", \"136237\", \"164159\", \"176257\", \"204853\", \"246115\", \"171558\", \"189228\", \"190799\", \"193736\", \"202742\", \"206108\", \"214955\", \"207429\", \"207642\", \"210890\", \"213503\", \"259869\"], \"outgoing_citations\": [\"246082\", \"248595\", \"248740\", \"255272\", \"258564\", \"259869\", \"264498\", \"267843\", \"270629\", \"275215\", \"278451\", \"285507\", \"295969\", \"296268\", \"306793\", \"311347\", \"312185\", \"316276\", \"316457\", \"319887\", \"321418\"]}","task_split":"paper_retrieval"}
{"document_id":"224507","document_content":"# MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream\n## Categories\n- Computer Vision and Pattern Recognition\n- Digital Libraries\n- 68T10\n## Abstract\nA lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses.   The main goal of this paper is to present a dataset. However, in addition and as a baseline, we present evaluation results for existing methods for face detection, text line recognition, and document data extraction, using the presented dataset.   (The dataset is available for download at ftp:\/\/smartengines.com\/midv-500\/.)","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18287\/2412-6179-2019-43-5-818-824\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.DL\", \"68T10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Digital Libraries\", \"68T10\"], \"incoming_citations\": [\"24832\", \"38211\", \"101165\", \"5077\", \"7430\", \"11001\", \"41140\", \"76956\", \"100763\", \"104981\", \"107518\", \"107498\", \"152814\", \"154003\", \"162575\", \"162611\", \"160805\", \"157317\", \"167879\"], \"outgoing_citations\": [\"299461\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"224537","document_content":"# Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this report we propose a classification technique for skin lesion images as a part of our submission for ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection. Our data was extracted from the ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge datasets. The features are extracted through a Convolutional Neural Network, in our case ResNet50 and then using these features we train a DeepForest, having cascading layers, to classify our skin lesion images. We know that Convolutional Neural Networks are a state-of-the-art technique in representation learning for images, with the convolutional filters learning to detect features from images through backpropagation. These features are then usually fed to a classifier like a softmax layer or other such classifiers for classification tasks. In our case we do not use the traditional backpropagation method and train a softmax layer for classification. Instead, we use Deep Forest, a novel decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. Thus we use a ResNet50 to extract the features from skin lesion images and then use the Deep Forest to classify these images. This method has been used because Deep Forest has been found to be hugely efficient in areas where there are only small-scale training data available. Also as the Deep Forest network decides its complexity by itself, it also caters to the problem of dataset imbalance we faced in this problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.05711v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"919\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"224833","document_content":"# Practical Obstacles to Deploying Active Learning\n## Categories\n- Machine Learning\n## Abstract\nActive learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.04801v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"5119\", \"15862\", \"15958\", \"35917\", \"44795\", \"55439\", \"55779\", \"183650\", \"1486\", \"24774\", \"24994\", \"28122\", \"37379\", \"45542\", \"53221\", \"35118\", \"73006\", \"74456\", \"77887\", \"162969\", \"82011\", \"83922\", \"93177\", \"95954\", \"105605\", \"115960\", \"130889\", \"155896\", \"163234\", \"183451\", \"199518\"], \"outgoing_citations\": [\"221103\", \"228681\", \"260847\", \"290493\", \"298760\", \"305094\"]}","task_split":"paper_retrieval"}
{"document_id":"224955","document_content":"# AtDelfi: Automatically Designing Legible, Full Instructions For Games\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3235765.3235790\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"22313\", \"68080\", \"183066\", \"120822\", \"163503\", \"168225\", \"176024\", \"178386\", \"181330\", \"189837\", \"240572\", \"224233\"], \"outgoing_citations\": [\"230005\", \"240572\", \"261387\", \"267630\"]}","task_split":"paper_retrieval"}
{"document_id":"225048","document_content":"# CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDocument image quality assessment (DIQA) is an important and challenging problem in real applications. In order to predict the quality scores of document images, this paper proposes a novel no-reference DIQA method based on character gradient, where the OCR accuracy is used as a ground-truth quality metric. Character gradient is computed on character patches detected with the maximally stable extremal regions (MSER) based method. Character patches are essentially significant to character recognition and therefore suitable for use in estimating document image quality. Experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art methods in estimating the quality score of document images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.04047v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"103980\", \"182408\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"225181","document_content":"# Difficulty Controllable Generation of Reading Comprehension Questions\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension paragraph and some of its text fragments (i.e., answers) that we want to ask questions about, a DQG method needs to generate questions each of which has a given text fragment as its answer, and meanwhile the generation is under the control of specified difficulty labels---the output questions should satisfy the specified difficulty as much as possible. To solve this task, we propose an end-to-end framework to generate questions of designated difficulty levels by exploring a few important intuitions. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that the question generated by our framework not only have better quality under the metrics like BLEU, but also comply with the specified difficulty labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.03586v5\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15903\", \"25124\", \"8702\", \"16775\", \"47520\", \"68754\", \"127855\", \"158044\", \"161855\", \"165025\", \"167916\", \"180437\", \"184273\"], \"outgoing_citations\": [\"218708\", \"220295\", \"231981\", \"238422\", \"239777\", \"246411\", \"265309\", \"267246\", \"267694\", \"268809\", \"269583\", \"296122\", \"296279\"]}","task_split":"paper_retrieval"}
{"document_id":"225253","document_content":"# Jointly Embedding Entities and Text with Distant Supervision\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nLearning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.03399v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"121898\", \"34289\", \"54510\", \"55653\", \"60004\", \"61937\", \"76611\", \"84249\", \"127623\", \"189593\", \"127749\", \"86291\", \"164046\", \"169340\"], \"outgoing_citations\": [\"264335\", \"281833\", \"286253\", \"289632\", \"293018\", \"298095\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"225279","document_content":"# Adaptive Adversarial Attack on Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n- Machine Learning\n## Abstract\nRecent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples). We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks. In this work, we speed up adversarial attacks, especially on sequential learning tasks. By leveraging the uncertainty of each task, we directly learn the adaptive multi-task weightings, without manually searching hyper-parameters. A unified architecture is developed and evaluated for both non-sequential tasks and sequential ones. To validate the effectiveness, we take the scene text recognition task as a case study. To our best knowledge, our proposed method is the first attempt to adversarial attack for scene text recognition. Adaptive Attack achieves over 99.9\\% success rate with 3-6X speedup compared to state-of-the-art adversarial attacks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.03326v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [\"202296\", \"210634\", \"173327\"], \"outgoing_citations\": [\"222140\", \"233708\", \"240776\", \"241081\", \"245587\", \"245835\", \"247042\", \"247927\", \"252039\", \"258657\", \"259046\", \"260450\", \"261004\", \"265949\", \"266162\", \"268526\", \"270593\", \"272478\", \"274487\", \"277288\", \"284871\", \"291989\", \"293141\", \"296685\", \"296828\", \"311196\", \"312457\", \"320148\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"225585","document_content":"# Face-Cap: Image Captioning using Facial Expression Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning is the process of generating a natural language description of an image. Most current image captioning models, however, do not take into account the emotional aspect of an image, which is very relevant to activities and interpersonal relationships represented therein. Towards developing a model that can produce human-like captions incorporating these, we use facial expression features extracted from images including human faces, with the aim of improving the descriptive ability of the model. In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions. Using all standard evaluation metrics, our Face-Cap models outperform a state-of-the-art baseline model for generating image captions when applied to an image caption dataset extracted from the standard Flickr 30K dataset, consisting of around 11K images containing faces. An analysis of the captions finds that, perhaps surprisingly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.02250v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23497\", \"119686\", \"145499\", \"172586\", \"154243\", \"192753\", \"222042\", \"208888\"], \"outgoing_citations\": [\"243816\", \"265677\", \"278399\", \"302621\", \"305473\", \"316435\", \"321621\", \"322022\", \"343074\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"225647","document_content":"# Reflection Analysis for Face Morphing Attack Detection\n## Categories\n- Computer Vision and Pattern Recognition\n- Cryptography and Security\n## Abstract\nA facial morph is a synthetically created image of a face that looks similar to two different individuals and can even trick biometric facial recognition systems into recognizing both individuals. This attack is known as face morphing attack. The process of creating such a facial morph is well documented and a lot of tutorials and software to create them are freely available. Therefore, it is mandatory to be able to detect this kind of fraud to ensure the integrity of the face as reliable biometric feature. In this work, we study the effects of face morphing on the physically correctness of the illumination. We estimate the direction to the light sources based on specular highlights in the eyes and use them to generate a synthetic map for highlights on the skin. This map is compared with the highlights in the image that is suspected to be a fraud. Morphing faces with different geometries, a bad alignment of the source images or using images with different illuminations, can lead to inconsistencies in reflections that indicate the existence of a morphing attack.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.02030v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\"], \"incoming_citations\": [\"7690\", \"13646\", \"102490\", \"38646\", \"41772\", \"60321\", \"89191\", \"79552\", \"148504\", \"129348\", \"179854\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"225819","document_content":"# Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation\n## Categories\n- Machine Learning\n## Abstract\nDynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The \"actor\" in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to the lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.01473v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"30438\", \"75234\", \"170695\", \"8161\", \"61460\", \"88305\", \"143228\", \"386\", \"974\", \"14784\", \"21810\", \"22978\", \"25074\", \"34568\", \"51642\", \"64396\", \"66593\", \"68374\", \"68900\", \"92132\", \"90029\", \"90660\", \"106860\", \"117202\", \"117298\", \"125479\", \"126951\", \"127099\", \"140894\", \"145493\", \"149451\", \"156415\", \"169228\", \"175034\", \"176825\", \"185056\", \"201685\", \"209551\", \"260407\"], \"outgoing_citations\": [\"248533\", \"249233\", \"265649\", \"268399\", \"281925\", \"297317\", \"303053\", \"338103\"]}","task_split":"paper_retrieval"}
{"document_id":"225841","document_content":"# Region Growing Curriculum Generation for Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nLearning a policy capable of moving an agent between any two states in the environment is important for many robotics problems involving navigation and manipulation. Due to the sparsity of rewards in such tasks, applying reinforcement learning in these scenarios can be challenging. Common approaches for tackling this problem include reward engineering with auxiliary rewards, requiring domain-specific knowledge or changing the objective.   In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states. Our algorithm first learns how to move between nearby states and then increases the difficulty of the start-goal transitions as the agent's performance improves. This approach creates an efficient curriculum for learning the objective behavior of reaching any goal from any initial state. In addition, we describe a method to adaptively adjust expansion of the growing region that allows automatic adjustment of the key exploration hyperparameter to environments with different requirements. We evaluate our approach on a set of simulated navigation and manipulation tasks, where we demonstrate that our algorithm can efficiently learn a policy in the presence of sparse rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.01425v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"158110\"], \"outgoing_citations\": [\"261017\", \"261780\", \"262325\", \"262926\", \"263957\", \"266182\", \"269235\", \"270105\", \"271375\", \"271952\", \"272422\", \"273608\", \"280296\", \"283427\", \"291025\", \"291460\", \"302558\", \"310171\"]}","task_split":"paper_retrieval"}
{"document_id":"225884","document_content":"# Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations\n## Categories\n- Computation and Language\n## Abstract\nGoal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.01292v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"239849\", \"291916\"]}","task_split":"paper_retrieval"}
{"document_id":"225886","document_content":"# Human-level performance in first-person multiplayer games with population-based deep reinforcement learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRecent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, and environments reflecting this degree of complexity remain an open challenge. In this work, we demonstrate for the first time that an agent can achieve human-level in a popular 3D multiplayer first-person video game, Quake III Arena Capture the Flag, using only pixels and game points as input. These results were achieved by a novel two-tier optimisation process in which a population of independent RL agents are trained concurrently from thousands of parallel matches with agents playing in teams together and against each other on randomly generated environments. Each agent in the population learns its own internal reward signal to complement the sparse delayed reward from winning, and selects actions using a novel temporally hierarchical representation that enables the agent to reason at multiple timescales. During game-play, these agents display human-like behaviours such as navigating, following, and defending based on a rich learned representation that is shown to encode high-level game knowledge. In an extensive tournament-style evaluation the trained agents exceeded the win-rate of strong human players both as teammates and opponents, and proved far stronger than existing state-of-the-art agents. These results demonstrate a significant jump in the capabilities of artificial agents, bringing us closer to the goal of human-level intelligence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1126\/science.aau6249\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"120334\", \"191707\", \"33207\", \"166030\", \"167727\", \"180897\", \"80396\", \"128500\", \"133480\", \"133479\", \"135077\", \"135271\", \"135334\", \"135645\", \"144695\", \"147391\", \"147937\", \"152690\", \"157063\", \"185834\", \"200580\", \"149805\", \"151260\", \"151271\", \"151570\", \"150072\", \"168583\", \"170350\", \"171557\", \"172021\", \"173229\", \"175289\", \"177951\", \"181759\", \"182278\", \"183312\", \"184374\", \"196614\", \"196801\", \"196784\", \"198383\", \"200152\", \"201452\", \"201876\", \"202556\", \"205643\", \"210098\", \"211574\", \"213235\", \"216156\", \"212891\", \"218116\", \"223268\", \"207983\", \"209453\"], \"outgoing_citations\": [\"249196\", \"251601\", \"253638\", \"256103\", \"271497\", \"272464\", \"274315\", \"280296\", \"284468\", \"284567\", \"291892\", \"291936\", \"297186\", \"303459\", \"311643\", \"333638\", \"350227\"]}","task_split":"paper_retrieval"}
{"document_id":"225891","document_content":"# Playing against Nature: causal discovery for decision making under uncertainty\n## Categories\n- Artificial Intelligence\n## Abstract\nWe consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to the decision maker. We ask how a decision maker can learn about this causal mechanism through sequential decision making as well as using current causal knowledge inside each round in order to make better choices had she not considered causal knowledge and propose a decision making procedure in which an agent holds \\textit{beliefs} about her environment which are used to make a choice and are updated using the observed outcome. As proof of concept, we present an implementation of this causal decision making model and apply it in a simple scenario. We show that the model achieves a performance similar to the classic Q-learning while it also acquires a causal model of the environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.01268v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"94710\", \"162163\", \"199975\"], \"outgoing_citations\": [\"245296\", \"276458\", \"284470\", \"290722\", \"291598\", \"297526\", \"303524\", \"326956\", \"334218\", \"347356\"]}","task_split":"paper_retrieval"}
{"document_id":"225949","document_content":"# BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nThe fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system, based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data.   The objective of the system is the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.biosystems.2019.04.006\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"38771\", \"145146\", \"168714\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"225983","document_content":"# Long Activity Video Understanding using Functional Object-Oriented Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nVideo understanding is one of the most challenging topics in computer vision. In this paper, a four-stage video understanding pipeline is presented to simultaneously recognize all atomic actions and the single on-going activity in a video. This pipeline uses objects and motions from the video and a graph-based knowledge representation network as prior reference. Two deep networks are trained to identify objects and motions in each video sequence associated with an action. Low Level image features are then used to identify objects of interest in that video sequence. Confidence scores are assigned to objects of interest based on their involvement in the action and to motion classes based on results from a deep neural network that classifies the on-going action in video into motion classes. Confidence scores are computed for each candidate functional unit associated with an action using a knowledge representation network, object confidences, and motion confidences. Each action is therefore associated with a functional unit and the sequence of actions is further evaluated to identify the single on-going activity in the video. The knowledge representation used in the pipeline is called the functional object-oriented network which is a graph-based network useful for encoding knowledge about manipulation tasks. Experiments are performed on a dataset of cooking videos to test the proposed algorithm with action inference and activity classification. Experiments show that using functional object oriented network improves video understanding significantly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00983v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2116\", \"5206\", \"8100\", \"45614\", \"45886\", \"75431\", \"188144\", \"108649\", \"163912\", \"186602\", \"186523\", \"187688\", \"225607\", \"234726\"], \"outgoing_citations\": [\"200208\", \"225608\", \"231673\", \"283384\", \"302756\", \"303203\", \"305232\", \"307001\", \"311357\", \"319084\"]}","task_split":"paper_retrieval"}
{"document_id":"225995","document_content":"# Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGiven image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestX-ray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00958v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73684\", \"35599\", \"122075\", \"30882\", \"48281\", \"56806\", \"62872\", \"95511\", \"113338\", \"97504\", \"101124\", \"120471\", \"171438\", \"213924\"], \"outgoing_citations\": [\"245206\", \"248406\", \"249639\", \"250194\", \"252131\", \"257884\", \"270629\"]}","task_split":"paper_retrieval"}
{"document_id":"226011","document_content":"# Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing\n## Categories\n- Computation and Language\n## Abstract\nLinguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00914v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"231233\", \"4765\", \"16321\", \"55305\", \"33683\", \"44974\", \"45436\", \"46671\", \"49321\", \"55332\", \"55261\", \"72915\", \"74650\", \"77628\", \"81487\", \"104894\", \"144897\", \"89663\", \"93794\", \"94795\", \"112695\", \"126439\", \"127665\", \"127555\", \"128134\", \"130833\", \"127876\", \"130205\", \"137472\", \"145018\", \"30949\", \"159849\", \"164680\", \"180249\", \"193634\", \"203509\", \"218235\"], \"outgoing_citations\": [\"177515\", \"217957\", \"218235\", \"220331\", \"225432\", \"232765\", \"241113\", \"251974\", \"253612\", \"253623\", \"259840\", \"262139\", \"264790\", \"264791\", \"266180\", \"267321\", \"267605\", \"267740\", \"268968\", \"277444\", \"280447\", \"282869\", \"283655\", \"289309\", \"290777\", \"291073\", \"291209\", \"292691\", \"295407\", \"295867\", \"295953\", \"296214\", \"296221\", \"297115\", \"297242\", \"298944\", \"302247\", \"307168\", \"308446\", \"310117\", \"323451\", \"329561\", \"330643\", \"334040\", \"335264\", \"335268\", \"335600\", \"336196\", \"339424\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"226117","document_content":"# Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data. This can lead to incorrect captions in domains where unbiased captions are desired, or required, due to over reliance on the learned prior and image context. We investigate generation of gender specific caption words (e.g. man, woman) based on the person's appearance or the image context. We introduce a new Equalizer model that ensures equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present. The resulting model is forced to look at a person rather than use contextual cues to make a gender specific prediction. The losses that comprise our model, the Appearance Confusion Loss and the Confident Loss, are general, and can be added to any description model in order to mitigate impacts of unwanted bias in a description dataset. Our proposed model has lower error than prior work when describing images with people and mentioning their gender and more closely matches the ground truth ratio of sentences including women to sentences including men.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00517v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"226156","document_content":"# Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAs the most successful variant and improvement for Trust Region Policy Optimization (TRPO), proximal policy optimization (PPO) has been widely applied across various domains with several advantages: efficient data utilization, easy implementation, and good parallelism. In this paper, a first-order gradient reinforcement learning algorithm called Policy Optimization with Penalized Point Probability Distance (POP3D), which is a lower bound to the square of total variance divergence is proposed as another powerful variant. Firstly, we talk about the shortcomings of several commonly used algorithms, by which our method is partly motivated. Secondly, we address to overcome these shortcomings by applying POP3D. Thirdly, we dive into its mechanism from the perspective of solution manifold. Finally, we make quantitative comparisons among several state-of-the-art algorithms based on common benchmarks. Simulation results show that POP3D is highly competitive compared with PPO. Besides, our code is released in https:\/\/github.com\/paperwithcode\/pop3d.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00442v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"203960\"], \"outgoing_citations\": [\"235227\", \"240233\", \"258384\", \"260583\"]}","task_split":"paper_retrieval"}
{"document_id":"226204","document_content":"# Modeling, comprehending and summarizing textual content by graphs\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nAutomatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the summary will be used. Nonetheless, there are special cases where it is not found enough textual sources, and one possible alternative is to generate a summary from a different domain. One manner to summarize texts consists of using a graph model. This model allows giving more importance to words corresponding to the main concepts from the target domain found in the summarized text. This gives the reader an overview of the main text concepts as well as their relationships. However, this kind of summarization presents a significant number of repeated terms when compared to human-generated summaries. In this paper, we present an approach to produce graph-model extractive summaries of texts, meeting the target domain exigences and treating the terms repetition problem. To evaluate the proposition, we performed a series of experiments showing that the proposed approach statistically improves the performance of a model based on Graph Centrality, achieving better coverage, accuracy, and recall.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00303v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"347359\"]}","task_split":"paper_retrieval"}
{"document_id":"226302","document_content":"# Adversarial Examples in Deep Learning: Characterization and Divergence\n## Categories\n- Machine Learning\n## Abstract\nThe burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learning have emerged as one of the dominating security threat to a range of mission-critical deep learning systems and applications. This paper takes a holistic and principled approach to perform statistical characterization of adversarial examples in deep learning. We provide a general formulation of adversarial examples and elaborate on the basic principle for adversarial attack algorithm design. We introduce easy and hard categorization of adversarial attacks to analyze the effectiveness of adversarial examples in terms of attack success rate, degree of change in adversarial perturbation, average entropy of prediction qualities, and fraction of adversarial examples that lead to successful attacks. We conduct extensive experimental study on adversarial behavior in easy and hard attacks under deep learning models with different hyperparameters and different deep learning frameworks. We show that the same adversarial attack behaves differently under different hyperparameters and across different frameworks due to the different features learned under different deep learning model training process. Our statistical characterization with strong empirical evidence provides a transformative enlightenment on mitigation strategies towards effective countermeasures against present and future adversarial attacks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1807.00051v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"100718\", \"132140\", \"164021\", \"169669\", \"170875\"], \"outgoing_citations\": [\"237711\", \"240808\", \"241282\", \"243238\", \"245632\", \"255738\", \"259984\", \"260636\", \"265456\", \"269129\", \"270593\", \"274033\", \"280659\", \"320493\", \"362730\"]}","task_split":"paper_retrieval"}
{"document_id":"226378","document_content":"# Measuring the quality of Synthetic data for use in competitions\n## Categories\n- Machine Learning\n## Abstract\nMachine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not being realized because it would require sharing data in a way that compromises privacy. In order to overcome this hurdle, several methods have been proposed that generate synthetic data while preserving the privacy of the real data. In this paper we consider a key characteristic that synthetic data should have in order to be useful for machine learning researchers - the relative performance of two algorithms (trained and tested) on the synthetic dataset should be the same as their relative performance (when trained and tested) on the original dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.11345v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"18336\", \"58885\", \"87705\"], \"outgoing_citations\": [\"241651\", \"264243\", \"271122\"]}","task_split":"paper_retrieval"}
{"document_id":"226544","document_content":"# Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels. When RL models overfit, even slight modifications to the environment can result in poor agent performance. This paper explores how procedurally generated levels during training can increase generality. We show that for some games procedural level generation enables generalization to new levels within the same distribution. Additionally, it is possible to achieve better performance with less data by manipulating the difficulty of the levels in response to the performance of the agent. The generality of the learned behaviors is also evaluated on a set of human-designed levels. The results suggest that the ability to generalize to human-designed levels highly depends on the design of the level generators. We apply dimensionality reduction and clustering techniques to visualize the generators' distributions of levels and analyze to what degree they can produce levels similar to those designed by a human.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.10729v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"11995\", \"17457\", \"18344\", \"63862\", \"68895\", \"78473\", \"87663\", \"225257\", \"28370\", \"33080\", \"35657\", \"37488\", \"41291\", \"40969\", \"46143\", \"149224\", \"51175\", \"54347\", \"55611\", \"56433\", \"58225\", \"62425\", \"64439\", \"68034\", \"70713\", \"75606\", \"82750\", \"95739\", \"121024\", \"123091\", \"142583\", \"82289\", \"84805\", \"86659\", \"87182\", \"94475\", \"99829\", \"107165\", \"111576\", \"113915\", \"114433\", \"116687\", \"119084\", \"116357\", \"123032\", \"123442\", \"125326\", \"127100\", \"133632\", \"135309\", \"136046\", \"136936\", \"137663\", \"139653\", \"140395\", \"145405\", \"145664\", \"149777\", \"152918\", \"153614\", \"257674\", \"158047\", \"159298\", \"172062\", \"176932\", \"178706\", \"184374\", \"183690\", \"188651\", \"193434\", \"199895\", \"200264\", \"203756\", \"207051\", \"240572\", \"212511\", \"218770\"], \"outgoing_citations\": [\"229002\", \"233501\", \"235092\", \"238475\", \"240572\", \"253076\", \"257674\", \"257819\", \"261017\", \"262325\", \"263811\", \"269253\", \"271375\", \"280570\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"227036","document_content":"# Towards Practical Visual Search Engine within Elasticsearch\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine. As far as we know, this is the first time such a system has been implemented in eCommerce, and our efforts have turned out to be highly worthwhile. We end up with a novel and exciting visual search solution that is extremely easy to be deployed, distributed, scaled and monitored in a cost-friendly manner. Moreover, our platform is intrinsically flexible in supporting multimodal searches, where visual and textual information can be jointly leveraged in retrieval.   The core idea is to encode image feature vectors into a collection of string tokens in a way such that closer vectors will share more string tokens in common. By doing that, we can utilize Elasticsearch to efficiently retrieve similar images based on similarities within encoded sting tokens. As part of the development, we propose a novel vector to string encoding method, which is shown to substantially outperform the previous ones in terms of both precision and latency.   First-hand experiences in implementing this Elasticsearch-based platform are extensively addressed, which should be valuable to practitioners also interested in building visual search engine on top of Elasticsearch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08896v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"191109\", \"173028\", \"179234\", \"198207\", \"227493\"], \"outgoing_citations\": [\"227493\", \"242865\", \"259070\", \"264639\", \"267351\", \"314568\"]}","task_split":"paper_retrieval"}
{"document_id":"227053","document_content":"# Multi-Task Handwritten Document Layout Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDocument Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08852v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1105\", \"40295\", \"67395\", \"130972\", \"176938\", \"242784\"], \"outgoing_citations\": [\"234009\", \"242784\", \"261807\", \"266895\"]}","task_split":"paper_retrieval"}
{"document_id":"227067","document_content":"# Hierarchical Graph Representation Learning with Differentiable Pooling\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n- Social and Information Networks\n## Abstract\nRecently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08804v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"18076\", \"18977\", \"27897\", \"35089\", \"45478\", \"56502\", \"66165\", \"109417\", \"113308\", \"118201\", \"127269\", \"131434\", \"129270\", \"142936\", \"153143\", \"222\", \"193\", \"404\", \"1480\", \"2094\", \"2760\", \"3222\", \"3205\", \"5536\", \"6449\", \"7929\", \"8738\", \"9648\", \"9898\", \"10337\", \"12375\", \"13009\", \"16311\", \"16432\", \"17349\", \"18117\", \"18640\", \"20006\", \"20281\", \"21372\", \"21553\", \"22163\", \"27697\", \"30460\", \"34972\", \"36946\", \"38282\", \"38677\", \"40269\", \"41310\", \"41773\", \"42873\", \"47654\", \"48005\", \"52615\", \"55646\", \"65306\", \"65941\", \"66249\", \"66588\", \"66586\", \"107485\", \"71250\", \"74969\", \"78838\", \"80211\", \"80990\", \"80912\", \"84414\", \"87880\", \"92953\", \"94914\", \"95561\", \"97457\", \"89373\", \"97503\", \"101759\", \"102897\", \"106919\", \"119164\", \"120682\", \"120789\", \"122212\", \"122185\", \"129924\", \"131467\", \"133777\", \"142993\", \"145413\", \"154755\", \"159879\", \"164881\", \"168353\", \"178142\", \"191904\", \"203910\", \"206470\", \"275\", \"297\", \"742\", \"1222\", \"1807\", \"2057\", \"3105\", \"4237\", \"4410\", \"4572\", \"5304\", \"5495\", \"6029\", \"7168\", \"7679\", \"7776\", \"7914\", \"8285\", \"9515\", \"11318\", \"11491\", \"5916\", \"12111\", \"13074\", \"13814\", \"14249\", \"14239\", \"14706\", \"16374\", \"18592\", \"19370\", \"19609\", \"22486\", \"25600\", \"25701\", \"26197\", \"26710\", \"27230\", \"27710\", \"27860\", \"28136\", \"28343\", \"28824\", \"28671\", \"29194\", \"29397\", \"30489\", \"32079\", \"32275\", \"33300\", \"34643\", \"35218\", \"36145\", \"36208\", \"36731\", \"36807\", \"37805\", \"37859\", \"38575\", \"38705\", \"39216\", \"39605\", \"39747\", \"40357\", \"42236\", \"42876\", \"43872\", \"44064\", \"44307\", \"44422\", \"45725\", \"35825\", \"46284\", \"46384\", \"46443\", \"46795\", \"49201\", \"49388\", \"49956\", \"50876\", \"51501\", \"51896\", \"51899\", \"53381\", \"53447\", \"53672\", \"54110\", \"54670\", \"68556\", \"55175\", \"55650\", \"56089\", \"56458\", \"56609\", \"57033\", \"57223\", \"57492\", \"59866\", \"60996\", \"61570\", \"61780\", \"61869\", \"62147\", \"62447\", \"62486\", \"63618\", \"64696\", \"64837\", \"65047\", \"64968\", \"66264\", \"66279\", \"67519\", \"67672\", \"68664\", \"69652\", \"69656\", \"71008\", \"71873\", \"72028\", \"72345\", \"74440\", \"74632\", \"75053\", \"75269\", \"75522\", \"75955\", \"76434\", \"76408\", \"76825\", \"77498\", \"77517\", \"78629\", \"80220\", \"80977\", \"81304\", \"83107\", \"84773\", \"90277\", \"91606\", \"93054\", \"95034\", \"94952\", \"95412\", \"96703\", \"99317\", \"100975\", \"101265\", \"103545\", \"104059\", \"106490\", \"107706\", \"108548\", \"112773\", \"115890\", \"116093\", \"116977\", \"142362\", \"143141\", \"143456\", \"153457\", \"161678\", \"149742\", \"56175\", \"165434\", \"188686\", \"198305\", \"201866\", \"5387\", \"102725\", \"64686\", \"78173\", \"78180\", \"79045\", \"79521\", \"80776\", \"81711\", \"82471\", \"83834\", \"84386\", \"85317\", \"86320\", \"88420\", \"89480\", \"92363\", \"92075\", \"92176\", \"92294\", \"95023\", \"94864\", \"96691\", \"97082\", \"98392\", \"98488\", \"98833\", \"99240\", \"99187\", \"99318\", \"99711\", \"99873\", \"100999\", \"101383\", \"40535\", \"103085\"], \"outgoing_citations\": [\"231240\", \"238725\", \"242322\", \"249443\", \"256044\", \"265468\", \"269273\", \"291213\", \"296356\", \"302934\"]}","task_split":"paper_retrieval"}
{"document_id":"227103","document_content":"# Variational learning across domains with triplet information\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nThe work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relative constraints or triplets that sampled from the shared latent space across domains. In other words, we combine the deep generative models with a metric learning ideas in order to improve the final objective with the triplets information. The performance of the VBTA model is demonstrated on different tasks: image-to-image translation, bi-directional image generation and cross-lingual document classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08672v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"255463\", \"264994\", \"281066\", \"282853\", \"291857\", \"302516\", \"311052\", \"311643\", \"316183\", \"323451\", \"334040\", \"340226\", \"343159\"]}","task_split":"paper_retrieval"}
{"document_id":"227127","document_content":"# Game AI Research with Fast Planet Wars Variants\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper describes a new implementation of Planet Wars, designed from the outset for Game AI research. The skill-depth of the game makes it a challenge for game-playing agents, and the speed of more than 1 million game ticks per second enables rapid experimentation and prototyping. The parameterised nature of the game together with an interchangeable actuator model make it well suited to automated game tuning. The game is designed to be fun to play for humans, and is directly playable by General Video Game AI agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08544v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"134021\", \"106742\", \"118468\", \"204062\", \"206900\"], \"outgoing_citations\": [\"240572\", \"241932\", \"253402\", \"255613\", \"262106\", \"271176\", \"285147\"]}","task_split":"paper_retrieval"}
{"document_id":"227150","document_content":"# Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose Adversarial Residual Dictionary Learning (ARDL) to supervise facial texture map recovering with only monocular images. Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves high-resolution frontalization appearances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08472v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"42280\", \"146468\", \"5991\", \"1917\", \"33535\", \"34850\", \"77737\", \"87603\", \"90446\", \"37004\", \"105800\", \"130186\", \"136199\", \"141765\", \"188872\", \"185334\", \"190674\", \"193087\", \"209312\"], \"outgoing_citations\": [\"226432\", \"236050\", \"243558\", \"247558\", \"268405\", \"268970\", \"275999\", \"278412\", \"302701\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"227153","document_content":"# Paragraph-based complex networks: application to document classification and authenticity verification\n## Categories\n- Computation and Language\n- Physics and Society\n## Abstract\nWith the increasing number of texts made available on the Internet, many applications have relied on text mining tools to tackle a diversity of problems. A relevant model to represent texts is the so-called word adjacency (co-occurrence) representation, which is known to capture mainly syntactical features of texts.In this study, we introduce a novel network representation that considers the semantic similarity between paragraphs. Two main properties of paragraph networks are considered: (i) their ability to incorporate characteristics that can discriminate real from artificial, shuffled manuscripts and (ii) their ability to capture syntactical and semantic textual features. Our results revealed that real texts are organized into communities, which turned out to be an important feature for discriminating them from artificial texts. Interestingly, we have also found that, differently from traditional co-occurrence networks, the adopted representation is able to capture semantic features. Additionally, the proposed framework was employed to analyze the Voynich manuscript, which was found to be compatible with texts written in natural languages. Taken together, our findings suggest that the proposed methodology can be combined with traditional network models to improve text classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.ipm.2018.12.008\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Physics and Society\"], \"incoming_citations\": [\"136175\"], \"outgoing_citations\": [\"289273\", \"302047\", \"310342\", \"310932\", \"319646\", \"328228\", \"330959\", \"338986\", \"348105\"]}","task_split":"paper_retrieval"}
{"document_id":"227207","document_content":"# Learning Multimodal Representations for Unseen Activities\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constraints on the embedding space using paired text and video data. We also propose a method to improve the joint embedding space using an adversarial formulation, allowing it to benefit from unpaired text and video data. By using unpaired text data, we show the ability to learn a representation that better captures unseen activities.   In addition to testing on publicly available datasets, we introduce a new, large-scale text\/video dataset.   We experimentally confirm that using paired and unpaired data to learn a shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning, outperforming the state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08251v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4579\", \"22807\", \"81067\"], \"outgoing_citations\": [\"236222\", \"236918\", \"240605\", \"240640\", \"248411\", \"250335\", \"259318\", \"259963\", \"267523\", \"267895\", \"269388\", \"286978\", \"291812\", \"295664\", \"302621\", \"303011\", \"303392\", \"303534\", \"306520\", \"310499\", \"313796\", \"315200\", \"317994\", \"323833\", \"328051\", \"336312\", \"350237\"]}","task_split":"paper_retrieval"}
{"document_id":"227262","document_content":"# Finding Original Image Of A Sub Image Using CNNs\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolututional Neural Networks have achieved state of the art in image classification, object detection and other image related tasks. In this paper I present another use of CNNs i.e. if given a set of images and then giving a single test image the network identifies that the test image is part of which image from the images given before. This is a task somehow similar to measuring image similarity and can be done using a simple CNN. Doing this task manually by looping can be quite a time consuming problem and won't be a generalizable solution. The task is quite similar to doing object detection but for that lots training data should be given or in the case of sliding window it takes lot of time and my algorithm can work with much fewer examples, is totally unsupervised and works much efficiently. Also, I explain that how unsupervised algorithm like K-Means or supervised algorithm like K-NN are not good enough to perform this task. The basic idea is that image encodings are collected for each image from a CNN, when a test image comes it is replaced by a part of original image, the encoding is generated using the same network, the frobenius norm is calculated and if it comes under a tolerance level then the test image is said to be the part of the original image.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08078v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"294983\"]}","task_split":"paper_retrieval"}
{"document_id":"227323","document_content":"# The Natural Language Decathlon: Multitask Learning as Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nDeep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.08730v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"32774\", \"33114\", \"14\", \"7112\", \"8144\", \"12087\", \"15733\", \"16002\", \"16442\", \"16569\", \"17843\", \"18305\", \"18787\", \"19378\", \"20119\", \"20283\", \"20995\", \"23273\", \"23643\", \"25863\", \"41963\", \"41924\", \"46096\", \"46540\", \"49099\", \"49190\", \"49550\", \"49918\", \"55171\", \"81985\", \"128476\", \"175321\", \"1631\", \"1890\", \"5696\", \"7019\", \"7309\", \"11600\", \"15564\", \"17102\", \"18278\", \"18414\", \"22996\", \"25040\", \"25356\", \"25730\", \"29830\", \"30714\", \"41554\", \"43695\", \"47697\", \"48369\", \"49274\", \"51133\", \"53960\", \"54748\", \"54915\", \"55146\", \"55131\", \"55486\", \"57095\", \"57374\", \"57670\", \"58839\", \"61113\", \"62937\", \"63290\", \"64707\", \"66630\", \"68388\", \"71449\", \"71888\", \"73521\", \"74389\", \"75427\", \"76870\", \"77670\", \"78303\", \"80297\", \"81315\", \"85061\", \"88786\", \"92112\", \"92168\", \"93230\", \"95883\", \"99765\", \"102310\", \"102549\", \"108527\", \"114588\", \"146195\", \"207215\", \"51468\", \"169651\", \"78191\", \"79805\", \"86099\", \"90357\", \"92420\", \"96142\", \"96221\", \"96333\", \"96376\", \"96556\", \"98073\", \"99357\", \"101301\", \"101376\", \"102649\", \"110315\", \"112684\", \"114607\", \"118766\", \"119804\", \"119852\", \"121303\", \"122375\", \"124449\", \"124569\", \"125241\", \"125364\", \"125919\", \"127513\", \"127479\", \"127283\", \"127557\", \"127383\", \"128152\", \"128398\", \"130744\", \"131629\", \"128932\", \"128057\", \"134405\", \"139804\", \"140815\", \"127547\", \"143508\", \"146533\", \"146930\", \"148271\", \"151809\", \"152680\", \"153138\", \"153825\", \"157131\", \"157211\", \"157587\", \"158605\", \"159862\", \"168120\", \"169684\", \"174054\", \"175013\", \"190642\", \"206151\", \"232331\", \"97908\", \"116488\", \"149348\", \"151278\", \"151842\", \"157143\", \"157965\", \"160242\", \"165103\", \"189707\", \"165546\", \"165935\", \"165970\", \"167338\", \"167432\", \"168143\", \"168855\", \"169760\", \"170241\", \"170370\", \"170592\", \"156520\", \"169266\", \"176158\", \"176642\", \"178300\", \"181730\", \"182261\", \"182764\", \"183319\", \"184455\", \"185347\", \"185680\", \"186405\", \"188123\", \"189699\", \"189821\", \"191589\", \"188933\", \"196187\", \"198870\", \"199847\", \"200340\", \"200804\", \"203630\", \"205074\", \"207395\", \"207273\", \"208689\", \"210078\", \"211456\", \"214174\", \"211433\", \"217346\", \"330325\", \"184455\", \"210276\", \"216731\", \"217202\", \"217956\", \"218693\", \"216953\", \"234895\"], \"outgoing_citations\": [\"230131\", \"231442\", \"232288\", \"233710\", \"234164\", \"234189\", \"234193\", \"234577\", \"234628\", \"235232\", \"240292\", \"242069\", \"243765\", \"247808\", \"247824\", \"248167\", \"248279\", \"250304\", \"250354\", \"251798\", \"252130\", \"257206\", \"259175\", \"259706\", \"259954\", \"261640\", \"263620\", \"263874\", \"267031\", \"269654\", \"270623\", \"271533\", \"274120\", \"274520\", \"275087\", \"276493\", \"277028\", \"279213\", \"280884\", \"281162\", \"281169\", \"285811\", \"288364\", \"288415\", \"290588\", \"294494\", \"295028\", \"295815\", \"297133\", \"303003\", \"310639\"]}","task_split":"paper_retrieval"}
{"document_id":"227355","document_content":"# Categorization of Semantic Roles for Dictionary Definitions\n## Categories\n- Computation and Language\n## Abstract\nUnderstanding the semantic relationships between terms is a fundamental task in natural language processing applications. While structured resources that can express those relationships in a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering dictionary definitions is becoming available, but understanding the semantic structure of natural language definitions is fundamental to make them useful in semantic interpretation tasks. Based on an analysis of a subset of WordNet's glosses, we propose a set of semantic roles that compose the semantic structure of a dictionary definition, and show how they are related to the definition's syntactic configuration, identifying patterns that can be used in the development of information extraction frameworks and semantic models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07711v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"98518\", \"168568\", \"227347\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"227413","document_content":"# Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nSeveral researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07552v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"56408\", \"71823\", \"21216\", \"5816\", \"30090\", \"32783\", \"34794\", \"36448\", \"41904\", \"57595\", \"59930\", \"73733\", \"74201\", \"76176\", \"83369\", \"102357\", \"208204\", \"241330\", \"91912\", \"102965\", \"122242\", \"143595\", \"145662\", \"151752\", \"185674\", \"222721\", \"161356\", \"168124\", \"181241\", \"196744\", \"204055\", \"216048\", \"216191\"], \"outgoing_citations\": [\"238355\", \"243505\", \"251515\", \"261398\", \"264168\", \"267981\", \"271811\", \"281897\", \"283294\", \"289563\", \"295742\", \"301835\"]}","task_split":"paper_retrieval"}
{"document_id":"227460","document_content":"# Improved Image Selection for Stack-Based HDR Imaging\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nStack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exposures. Minimizing the set of images to combine, while ensuring that the resulting HDR image fully captures the scene's irradiance, is important to avoid long image acquisition and post-processing times. The problem of selecting the set of images has received much attention. However, existing methods either are not fully automatic, can be slow, or can fail to fully capture more challenging scenes. In this paper, we propose a fully automatic method for selecting the set of exposures to acquire that is both fast and more accurate. We show on an extensive set of benchmark scenes that our proposed method leads to improved HDR images as measured against ground truth using the mean squared error, a pixel-based metric, and a visible difference predictor and a quality score, both perception-based metrics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07420v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"114390\", \"122944\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"227488","document_content":"# Dynamic Multi-Level Multi-Task Learning for Sentence Simplification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nSentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07304v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"17436\", \"22482\", \"32732\", \"36870\", \"42174\", \"49011\", \"57374\", \"64358\", \"108663\", \"86824\", \"114607\", \"118053\", \"127956\", \"138284\", \"140815\", \"163045\", \"206151\", \"162482\", \"191589\", \"192018\", \"214400\"], \"outgoing_citations\": [\"230234\", \"250360\", \"263620\", \"265708\", \"267622\", \"268083\", \"269127\", \"269253\", \"270048\", \"281169\", \"284357\", \"291460\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"227514","document_content":"# Self-adaptive Privacy Concern Detection for User-generated Content\n## Categories\n- Cryptography and Security\n- Computation and Language\n## Abstract\nTo protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07221v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.CL\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computation and Language\"], \"incoming_citations\": [\"185390\", \"193809\"], \"outgoing_citations\": [\"236999\", \"249198\", \"280659\"]}","task_split":"paper_retrieval"}
{"document_id":"227599","document_content":"# A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nTraditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.07000v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"95561\", \"99486\", \"1816\", \"21665\", \"25282\", \"47718\", \"49583\", \"50414\", \"80358\", \"119586\", \"155361\", \"143254\", \"186034\", \"190169\"], \"outgoing_citations\": [\"268265\", \"269757\", \"289061\", \"289943\", \"292261\", \"295863\", \"301178\"]}","task_split":"paper_retrieval"}
{"document_id":"227695","document_content":"# Nonparametric Topic Modeling with Neural Inference\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nThis work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as trainable parameters and the document-specific topic proportions are obtained by a stick-breaking construction. The inference of iTM-VAE is modeled by neural networks such that it can be computed in a simple feed-forward manner. We also describe how to introduce a hyper-prior into iTM-VAE so as to model the uncertainty of the prior parameter. Actually, the hyper-prior technique is quite general and we show that it can be applied to other AEVB based models to alleviate the {\\it collapse-to-prior} problem elegantly. Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner. HiTM-VAE is even more flexible and can generate topic distributions with better variability. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-art baselines significantly. The advantages of the hyper-prior technique and the hierarchical model construction are also confirmed by experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.06583v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"37444\", \"66383\"], \"outgoing_citations\": [\"264795\", \"265419\", \"274252\", \"280882\", \"284198\", \"298784\", \"303029\", \"334226\", \"334299\", \"335884\"]}","task_split":"paper_retrieval"}
{"document_id":"227746","document_content":"# Measuring Semantic Coherence of a Conversation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nConversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.06411v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"50345\", \"77530\", \"117860\", \"170622\", \"227007\", \"151195\", \"204647\", \"205181\"], \"outgoing_citations\": [\"252768\", \"310377\", \"360990\"]}","task_split":"paper_retrieval"}
{"document_id":"227770","document_content":"# Approximate Submodular Functions and Performance Guarantees\n## Categories\n- Data Structures and Algorithms\n- Machine Learning\n## Abstract\nWe consider the problem of maximizing non-negative non-decreasing set functions. Although most of the recent work focus on exploiting submodularity, it turns out that several objectives we encounter in practice are not submodular. Nonetheless, often we leverage the greedy algorithms used in submodular functions to determine a solution to the non-submodular functions. Hereafter, we propose to address the original problem by \\emph{approximating} the non-submodular function and analyze the incurred error, as well as the performance trade-offs. To quantify the approximation error, we introduce a novel concept of $\\delta$-approximation of a function, which we used to define the space of submodular functions that lie within an approximation error. We provide necessary conditions on the existence of such $\\delta$-approximation functions, which might not be unique. Consequently, we characterize this subspace which we refer to as \\emph{region of submodularity}. Furthermore, submodular functions are known to lead to different sub-optimality guarantees, so we generalize those dependencies upon a $\\delta$-approximation into the notion of \\emph{greedy curvature}. Finally, we used this latter notion to simplify some of the existing results and efficiently (i.e., linear complexity) determine tightened bounds on the sub-optimality guarantees using objective functions commonly used in practical setups and validate them using real data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.06323v1\", \"primary_category\": \"cs.DS\", \"categories\": [\"cs.LG\", \"cs.DS\"], \"primary_category_human_readable\": \"Data Structures and Algorithms\", \"categories_human_readable\": [\"Machine Learning\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"104081\", \"151300\", \"247701\"], \"outgoing_citations\": [\"239410\", \"272200\", \"300217\", \"302702\", \"330340\", \"338097\", \"338099\", \"341683\", \"345855\"]}","task_split":"paper_retrieval"}
{"document_id":"227808","document_content":"# Offline Extraction of Indic Regional Language from Natural Scene Image using Text Segmentation and Deep Convolutional Sequence\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n- Information Retrieval\n## Abstract\nRegional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.06208v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271112\", \"284871\", \"311196\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"227880","document_content":"# Partially-Supervised Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning models are becoming increasingly successful at describing the content of images in restricted domains. However, if these models are to function in the wild - for example, as assistants for people with impaired vision - a much larger number and variety of visual concepts must be understood. To address this problem, we teach image captioning models new visual concepts from labeled images and object detection datasets. Since image labels and object classes can be interpreted as partial captions, we formulate this problem as learning from partially-specified sequence data. We then propose a novel algorithm for training sequence models, such as recurrent neural networks, on partially-specified sequences which we represent using finite state automata. In the context of image captioning, our method lifts the restriction that previously required image captioning models to be trained on paired image-sentence corpora only, or otherwise required specialized model architectures to take advantage of alternative data modalities. Applying our approach to an existing neural captioning model, we achieve state of the art results on the novel object captioning task using the COCO dataset. We further show that we can train a captioning model to describe new visual concepts from the Open Images dataset while maintaining competitive COCO evaluation scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.06004v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"24386\", \"53028\", \"68626\", \"73707\", \"93826\", \"89629\", \"138132\", \"170299\", \"208555\", \"205227\"], \"outgoing_citations\": [\"235175\", \"237655\", \"238518\", \"258345\", \"258992\", \"268166\", \"271625\", \"278948\", \"289627\", \"290520\", \"290766\", \"291851\", \"295639\", \"297534\", \"298543\", \"303209\", \"303997\", \"313196\", \"316430\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"228046","document_content":"# Learning Cross-lingual Distributed Logical Representations for Semantic Parsing\n## Categories\n- Computation and Language\n## Abstract\nWith the development of several multilingual datasets used for semantic parsing, recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup. However, how to improve the performance of a monolingual semantic parser for a specific language by leveraging data annotated in different languages remains a research question that is under-explored. In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser. We extend two existing monolingual semantic parsers to incorporate such cross-lingual distributed logical representations as features. Experiments show that our proposed approach is able to yield improved semantic parsing results on the standard multilingual GeoQuery dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.05461v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"122607\", \"132812\", \"143461\", \"161669\", \"169320\", \"219551\"], \"outgoing_citations\": [\"260320\", \"261734\", \"300505\", \"350301\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"228098","document_content":"# Deep Reinforcement Learning for Dynamic Urban Transportation Problems\n## Categories\n- Machine Learning\n## Abstract\nWe explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation models used to represent dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. Use of deep learning metamodels can produce a lower dimensional representation of those relations and allow to implement optimization and reinforcement learning algorithms in an efficient manner. In particular, we develop deep learning models for calibrating transportation simulators and for reinforcement learning to solve the problem of optimal scheduling of travelers on the network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.05310v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"127389\", \"46459\", \"109449\", \"107474\"], \"outgoing_citations\": [\"235092\", \"238441\", \"253658\", \"275068\", \"281270\", \"337840\"]}","task_split":"paper_retrieval"}
{"document_id":"228216","document_content":"# A Machine-Learning Item Recommendation System for Video Games\n## Categories\n- Machine Learning\n## Abstract\nVideo-game players generate huge amounts of data, as everything they do within a game is recorded. In particular, among all the stored actions and behaviors, there is information on the in-game purchases of virtual products. Such information is of critical importance in modern free-to-play titles, where gamers can select or buy a profusion of items during the game in order to progress and fully enjoy their experience.   To try to maximize these kind of purchases, one can use a recommendation system so as to present players with items that might be interesting for them. Such systems can better achieve their goal by employing machine learning algorithms that are able to predict the rating of an item or product by a particular user. In this paper we evaluate and compare two of these algorithms, an ensemble-based model (extremely randomized trees) and a deep neural network, both of which are promising candidates for operational video-game recommender engines.   Item recommenders can help developers improve the game. But, more importantly, it should be possible to integrate them into the game, so that users automatically get personalized recommendations while playing. The presented models are not only able to meet this challenge, providing accurate predictions of the items that a particular player will find attractive, but also sufficiently fast and robust to be used in operational settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CIG.2018.8490456\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"37283\", \"11919\", \"17112\", \"136898\", \"202815\"], \"outgoing_citations\": [\"349562\"]}","task_split":"paper_retrieval"}
{"document_id":"228285","document_content":"# Talakat: Bullet Hell Generation through Constrained Map-Elites\n## Categories\n- Artificial Intelligence\n## Abstract\nWe describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are represented using a domain-specific description language, and search in the space defined by this language is performed by a novel variant of the Map-Elites algorithm which incorporates a feasible- infeasible approach to constraint satisfaction. Simulation-based evaluation is used to gauge the fitness of levels, using an agent based on best-first search. The performance of the agent can be tuned according to the two dimensions of strategy and dexterity, making it possible to search for level configurations that require a specific combination of both. As far as we know, this paper describes the first generator for this game genre, and includes several algorithmic innovations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.04718v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"133218\", \"138151\", \"37071\", \"36213\", \"41863\", \"50570\", \"55165\", \"55260\", \"67124\", \"112786\", \"94524\", \"104062\", \"124521\", \"124853\", \"143545\", \"176932\", \"181115\", \"183988\", \"186120\", \"189206\", \"189837\", \"193434\", \"202740\", \"224233\"], \"outgoing_citations\": [\"314276\"]}","task_split":"paper_retrieval"}
{"document_id":"228350","document_content":"# Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information. This may make the translation of a sentence ambiguous or even inconsistent with the translations of neighboring sentences. In order to handle this issue, we propose an inter-sentence gate model that uses the same encoder to encode two adjacent sentences and controls the amount of information flowing from the preceding sentence to the translation of the current sentence with an inter-sentence gate. In this way, our proposed model can capture the connection between sentences and fuse recency from neighboring sentences into neural machine translation. On several NIST Chinese-English translation tasks, our experiments demonstrate that the proposed inter-sentence gate model achieves substantial improvements over the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.04466v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"93450\", \"100234\", \"95143\", \"141444\", \"154483\", \"150615\", \"158725\", \"163992\"], \"outgoing_citations\": [\"282509\", \"286173\", \"290996\", \"292261\", \"324965\"]}","task_split":"paper_retrieval"}
{"document_id":"228372","document_content":"# Named Entity Recognition with Extremely Limited Data\n## Categories\n- Information Retrieval\n- H.3.2; H.3.3\n## Abstract\nTraditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans.   However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant \"documents\". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.04411v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"H.3.2; H.3.3\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"H.3.2; H.3.3\", \"Information Retrieval\"], \"incoming_citations\": [\"3397\", \"24562\", \"108633\"], \"outgoing_citations\": [\"245620\", \"276435\"]}","task_split":"paper_retrieval"}
{"document_id":"228574","document_content":"# Are All Languages Equally Hard to Language-Model?\n## Categories\n- Computation and Language\n## Abstract\nFor general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both $n$-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.03743v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1834\", \"35733\", \"38372\", \"44851\", \"45439\", \"49321\", \"49449\", \"81174\", \"104894\", \"93794\", \"97831\", \"124609\", \"125675\", \"126463\", \"126855\", \"114595\", \"128299\", \"126336\", \"158951\", \"194126\", \"30949\", \"162185\", \"162221\", \"164680\", \"167513\", \"168735\", \"171533\", \"179347\", \"180302\", \"181279\", \"183673\", \"191342\", \"195050\", \"225275\", \"226209\"], \"outgoing_citations\": [\"263992\", \"268212\", \"281225\"]}","task_split":"paper_retrieval"}
{"document_id":"228575","document_content":"# Unsupervised Disambiguation of Syncretism in Inflected Lexicons\n## Categories\n- Computation and Language\n## Abstract\nLexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.03740v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"48496\", \"127108\", \"178745\", \"214031\"], \"outgoing_citations\": [\"262705\", \"301264\"]}","task_split":"paper_retrieval"}
{"document_id":"228596","document_content":"# Deconvolution-Based Global Decoding for Neural Machine Translation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nA great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.03692v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"139541\", \"140526\", \"152767\", \"182668\", \"189555\", \"220232\", \"220634\"], \"outgoing_citations\": [\"232260\", \"232543\", \"240068\", \"243170\", \"248149\", \"252163\", \"255322\", \"258494\", \"276439\", \"282530\", \"283997\", \"290996\", \"292831\", \"299936\"]}","task_split":"paper_retrieval"}
{"document_id":"228603","document_content":"# Smart Novel Computer-based Analytical Tool for Image Forgery Authentication\n## Categories\n- Image and Video Processing\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents an integration of image forgery detection with image facial recognition using black propagation neural network (BPNN). We observed that facial image recognition by itself will always give a matching output or closest possible output image for every input image irrespective of the authenticity or otherwise not of the testing input image. Based on this, we are proposing the combination of the blind but powerful automation image forgery detection for entire input images for the BPNN recognition program. Hence, an input image must first be authenticated before being fed into the recognition program. Thus, an image security identification and authentication requirement, any image that fails the authentication\/verification stage are not to be used as an input\/test image. In addition, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of 2% error rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICCircuitsAndSystems.2012.6408276\", \"primary_category\": \"eess.IV\", \"categories\": [\"eess.IV\", \"cs.CV\"], \"primary_category_human_readable\": \"Image and Video Processing\", \"categories_human_readable\": [\"Image and Video Processing\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"228748","document_content":"# DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nJPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at high compression rates, which could greatly affect the Quality of Experience (QoE). Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. Lots of efforts have been made to deepen the CNNs and extract deeper features, while relatively few works pay attention to the receptive field of the network. In this paper, we illustrate that the quality of output images can be significantly improved by enlarging the receptive fields in many cases. One step further, we propose a Dual-domain Multi-scale CNN (DMCNN) to take full advantage of redundancies on both the pixel and DCT domains. Experiments show that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.03275v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"100347\", \"5997\", \"97572\", \"12490\", \"20186\", \"50356\", \"59979\", \"97528\", \"146913\", \"95417\", \"99625\", \"100596\", \"117419\", \"130705\", \"147608\", \"147712\", \"167947\", \"195126\", \"213798\", \"167542\", \"178373\", \"191462\", \"225593\", \"231407\"], \"outgoing_citations\": [\"227595\", \"231407\", \"249153\", \"279844\", \"308182\", \"313846\"]}","task_split":"paper_retrieval"}
{"document_id":"228796","document_content":"# Temporal Difference Variational Auto-Encoder\n## Categories\n- Machine Learning\n## Abstract\nTo act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.03107v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"28573\", \"14631\", \"17165\", \"68339\", \"83505\", \"109960\", \"13649\", \"23573\", \"31195\", \"32963\", \"42692\", \"63434\", \"68395\", \"71651\", \"74889\", \"86305\", \"96527\", \"116577\", \"118140\", \"161991\", \"89697\", \"91106\", \"93571\", \"104237\", \"108787\", \"109270\", \"112983\", \"110142\", \"115366\", \"127882\", \"135309\", \"138272\", \"140780\", \"140910\", \"147522\", \"178094\", \"183564\", \"190955\", \"152148\", \"159160\", \"163811\", \"178186\", \"179471\", \"179594\", \"193172\", \"203753\", \"210517\"], \"outgoing_citations\": [\"220531\", \"221364\", \"229008\", \"235276\", \"236752\", \"242891\", \"249044\", \"250449\", \"254938\", \"260583\", \"263139\", \"269458\", \"273953\", \"281749\", \"283428\", \"285259\", \"291936\", \"292261\", \"293035\", \"293570\", \"308711\", \"309807\", \"311643\", \"321177\", \"333638\"]}","task_split":"paper_retrieval"}
{"document_id":"228842","document_content":"# Representation Learning of Entities and Documents from Knowledge Base Descriptions\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nIn this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.02960v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"47144\", \"55194\", \"205857\", \"168892\", \"181078\"], \"outgoing_citations\": [\"267123\", \"276616\", \"278439\", \"287676\", \"289601\", \"300498\", \"302516\", \"313946\", \"315078\"]}","task_split":"paper_retrieval"}
{"document_id":"228980","document_content":"# Simplifying Reward Design through Divide-and-Conquer\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDesigning a good reward function is essential to robot planning and reinforcement learning, but it can also be challenging and frustrating. The reward needs to work across multiple different environments, and that often requires many iterations of tuning. We introduce a novel divide-and-conquer approach that enables the designer to specify a reward separately for each environment. By treating these separate reward functions as observations about the underlying true reward, we derive an approach to infer a common reward across all environments. We conduct user studies in an abstract grid world domain and in a motion planning domain for a 7-DOF manipulator that measure user effort and solution quality. We show that our method is faster, easier to use, and produces a higher quality solution than the typical method of designing a reward jointly across all environments. We additionally conduct a series of experiments that measure the sensitivity of these results to different properties of the reward design task, such as the number of environments, the number of feasible solutions per environment, and the fraction of the total features that vary within each environment. We find that independent reward design outperforms the standard, joint, reward design process but works best when the design problem can be divided into simpler subproblems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.02501v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"1269\", \"36650\", \"191103\", \"8628\", \"33848\", \"42747\", \"64198\", \"142761\", \"181933\", \"179705\", \"216060\"], \"outgoing_citations\": [\"251082\", \"263957\", \"300596\", \"301305\"]}","task_split":"paper_retrieval"}
{"document_id":"228992","document_content":"# Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nGenerating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.02473v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"30438\", \"42611\", \"112333\", \"118617\", \"131434\", \"183269\", \"52113\", \"70194\", \"6369\", \"6449\", \"12851\", \"14780\", \"16875\", \"21919\", \"191893\", \"38282\", \"42873\", \"43280\", \"43712\", \"43922\", \"44918\", \"51514\", \"60885\", \"73635\", \"78228\", \"80211\", \"93176\", \"99908\", \"119164\", \"168353\", \"206470\", \"178603\", \"1441\", \"1441\", \"4751\", \"6431\", \"6569\", \"6849\", \"8235\", \"12150\", \"15576\", \"13473\", \"17665\", \"19253\", \"19800\", \"20859\", \"26897\", \"27616\", \"27980\", \"33376\", \"34804\", \"36662\", \"37049\", \"39674\", \"39957\", \"40812\", \"40869\", \"42253\", \"42583\", \"45158\", \"46443\", \"48519\", \"49084\", \"49395\", \"50777\", \"50882\", \"50987\", \"51342\", \"54670\", \"62301\", \"64504\", \"64915\", \"65294\", \"65586\", \"66013\", \"67661\", \"68389\", \"70461\", \"70669\", \"71008\", \"71676\", \"72028\", \"72111\", \"72187\", \"72274\", \"72345\", \"73530\", \"75269\", \"77653\", \"79842\", \"82007\", \"84737\", \"89239\", \"89881\", \"91129\", \"96489\", \"97047\", \"97844\", \"106033\", \"115707\", \"117694\", \"120492\", \"144943\", \"166560\", \"201866\", \"51304\", \"4941\", \"68086\", \"79187\", \"79829\", \"80705\", \"83725\", \"86320\", \"87018\", \"88986\", \"89748\", \"90146\", \"92433\", \"93248\", \"94864\", \"95828\", \"99785\", \"111627\", \"112378\", \"112797\", \"114250\", \"115054\", \"115602\", \"115840\", \"117085\", \"117234\", \"117868\", \"118224\", \"119151\", \"121058\", \"121171\", \"121748\", \"124375\", \"128144\", \"128848\", \"130766\", \"131161\", \"132263\", \"134044\", \"134526\", \"135856\", \"139133\", \"141562\", \"142343\", \"142825\", \"143081\", \"143408\", \"143417\", \"144216\", \"145591\", \"145523\", \"146016\", \"146640\", \"146639\", \"154098\", \"154628\", \"156624\", \"164213\", \"164955\", \"165036\", \"173067\", \"175750\", \"183816\", \"184729\", \"200016\", \"205283\", \"138999\", \"159199\", \"163000\", \"163097\", \"163431\", \"163822\", \"150079\", \"181245\", \"164233\", \"164463\", \"165546\", \"166667\", \"166889\", \"169562\", \"170062\", \"176579\", \"181855\", \"181931\", \"182301\", \"182436\", \"182429\", \"181095\", \"183032\", \"183366\", \"183388\", \"184120\", \"184135\", \"181255\", \"188125\", \"189083\", \"189851\", \"190159\", \"194449\", \"195767\", \"197359\", \"197581\", \"205026\", \"209026\", \"209582\", \"213629\", \"207472\", \"218329\", \"218718\", \"225881\", \"242150\", \"186789\", \"201799\", \"208897\"], \"outgoing_citations\": [\"239581\", \"241022\", \"241029\", \"242430\", \"242739\", \"244768\", \"246965\", \"254557\", \"264478\", \"264984\", \"266605\", \"268060\", \"297245\"]}","task_split":"paper_retrieval"}
{"document_id":"229145","document_content":"# Learning to Understand Goal Specifications by Modelling Reward\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nRecent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.01946v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"3136\", \"79803\", \"11352\", \"16824\", \"17680\", \"22124\", \"25558\", \"26076\", \"34439\", \"35938\", \"37989\", \"41627\", \"57391\", \"64051\", \"66817\", \"67594\", \"69616\", \"70713\", \"74858\", \"89636\", \"97596\", \"105761\", \"116304\", \"116987\", \"132041\", \"144521\", \"119120\", \"80268\", \"160608\", \"84300\", \"108770\", \"112980\", \"119169\", \"124122\", \"135746\", \"140901\", \"145594\", \"147391\", \"151618\", \"157664\", \"158515\", \"157435\", \"166082\", \"171822\", \"180270\", \"181620\", \"182138\", \"196377\", \"198373\", \"199210\", \"210613\", \"199330\", \"208351\", \"213745\", \"207523\", \"209453\", \"252307\"], \"outgoing_citations\": [\"198208\", \"234517\", \"236849\", \"245731\", \"248885\", \"254581\", \"261378\", \"263104\", \"263270\", \"263650\", \"263957\", \"267779\", \"272298\", \"280296\", \"303714\", \"307569\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"229186","document_content":"# Contextual Slot Carryover for Disparate Schemas\n## Categories\n- Computation and Language\n## Abstract\nIn the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges - scaling to a very large and potentially unbounded set of slot values, and dealing with diverse schemas. We present a neural network architecture that addresses the slot value scalability challenge by reformulating the contextual interpretation as a decision to carryover a slot from a set of possible candidates. To deal with heterogenous schemas, we introduce a simple data-driven method for trans- forming the candidate slots. Our experiments show that our approach can scale to multiple domains and provides competitive results over a strong baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.21437\/Interspeech.2018-1035\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"118285\", \"121509\", \"142844\", \"195279\", \"170084\", \"174373\", \"180447\", \"182728\", \"193389\", \"195418\", \"208422\"], \"outgoing_citations\": [\"251669\", \"264463\", \"266962\"]}","task_split":"paper_retrieval"}
{"document_id":"229266","document_content":"# SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present \\textit{SoPhie}; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.01482v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5198\", \"64110\", \"113488\", \"216698\", \"2001\", \"7145\", \"10133\", \"12209\", \"14456\", \"15272\", \"16447\", \"16755\", \"20386\", \"23013\", \"25792\", \"27754\", \"30353\", \"39639\", \"53587\", \"59368\", \"60595\", \"64005\", \"66619\", \"68567\", \"74920\", \"100535\", \"105409\", \"113579\", \"144528\", \"145013\", \"182562\", \"191103\", \"226421\", \"6122\", \"6065\", \"7156\", \"7698\", \"13032\", \"15313\", \"16150\", \"17526\", \"20017\", \"23300\", \"24911\", \"25916\", \"28452\", \"28890\", \"28894\", \"28885\", \"29921\", \"30337\", \"32241\", \"32559\", \"32561\", \"34925\", \"36122\", \"36556\", \"38000\", \"38505\", \"41580\", \"40266\", \"45683\", \"44501\", \"47215\", \"139151\", \"51104\", \"53795\", \"54097\", \"54525\", \"54933\", \"55457\", \"56106\", \"57363\", \"57738\", \"58456\", \"58792\", \"59041\", \"59257\", \"59579\", \"60527\", \"60625\", \"61254\", \"61618\", \"61695\", \"63204\", \"64848\", \"65015\", \"65139\", \"72562\", \"73408\", \"75053\", \"75455\", \"76195\", \"76616\", \"79130\", \"81152\", \"81464\", \"82421\", \"83006\", \"82847\", \"82921\", \"83182\", \"85367\", \"87249\", \"87994\", \"88738\", \"88879\", \"89947\", \"90212\", \"93453\", \"93951\", \"98313\", \"109124\", \"115057\", \"118757\", \"121363\", \"122444\", \"122980\", \"124545\", \"126790\", \"134804\", \"136203\", \"136580\", \"137081\", \"153627\", \"154903\", \"86175\", \"87449\", \"87593\", \"173577\", \"87955\", \"92810\", \"93407\", \"94097\", \"94863\", \"94978\", \"95755\", \"97072\", \"99205\", \"99442\", \"101535\", \"104303\", \"105216\", \"106162\", \"108183\", \"109691\", \"110009\", \"111849\", \"112263\", \"112768\", \"113564\", \"114292\", \"115066\", \"116670\", \"118594\", \"119595\", \"120033\", \"120047\", \"120207\", \"120248\", \"120882\", \"121741\", \"122751\", \"124451\", \"127485\", \"127689\", \"128169\", \"128967\", \"129703\", \"129947\", \"131366\", \"131492\", \"131973\", \"132131\", \"133078\", \"133076\", \"133645\", \"134029\", \"134965\", \"136753\", \"136834\", \"137027\", \"137572\", \"137715\", \"138169\", \"139777\", \"141043\", \"142298\", \"142189\", \"144247\", \"147880\", \"148116\", \"148683\", \"150530\", \"150749\", \"151229\", \"151320\", \"153095\", \"154697\", \"158208\", \"161127\", \"162735\", \"165422\", \"166354\", \"166501\", \"173508\", \"179871\", \"186194\", \"194576\", \"220753\", \"153930\", \"152183\", \"155854\", \"155881\", \"161633\", \"161193\", \"165981\", \"170388\", \"172171\", \"174716\", \"174738\", \"176275\", \"177460\", \"184899\", \"185315\", \"186492\", \"187729\", \"189614\", \"191356\", \"193745\", \"196132\", \"196428\", \"196856\", \"199454\", \"216750\", \"217198\", \"227049\", \"205479\", \"217774\", \"219340\", \"231430\", \"239595\", \"249164\"], \"outgoing_citations\": [\"235490\", \"249164\", \"263139\", \"267118\", \"268197\", \"268891\", \"271683\", \"273754\", \"296086\"]}","task_split":"paper_retrieval"}
{"document_id":"229426","document_content":"# NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the encoder-decoder paradigm, where the encoder uses stacked self-attention to extract image features, and the decoder applies stacked self-attention to recognize texts based on encoder output. NRTR relies solely on self-attention mechanism thus could be trained with more parallelization and less complexity. Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features. NRTR achieves state-of-the-art or highly competitive performance on both regular and irregular benchmarks, while requires only a small fraction of training time compared to the best model from the literature (at least 8 times faster).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.00926v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3394\", \"6379\", \"122798\", \"191\", \"8109\", \"9220\", \"21983\", \"29640\", \"42497\", \"48822\", \"49805\", \"63761\", \"163092\", \"100417\", \"111991\", \"126431\", \"135021\", \"152219\", \"162477\"], \"outgoing_citations\": [\"211851\", \"217463\", \"232706\", \"250738\", \"256108\", \"256711\", \"256785\", \"264526\", \"282929\", \"294023\", \"296685\", \"296828\", \"311196\", \"320148\", \"320837\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"229508","document_content":"# TI-CNN: Convolutional Neural Networks for Fake News Detection\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nWith the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.00749v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"26987\", \"63247\", \"7737\", \"20841\", \"21011\", \"24508\", \"27906\", \"30489\", \"30709\", \"34209\", \"53995\", \"63135\", \"66278\", \"76206\", \"79642\", \"40535\", \"112064\", \"113687\", \"129247\", \"141447\", \"164328\", \"230998\", \"167944\", \"181527\", \"189897\", \"212740\", \"219245\"], \"outgoing_citations\": [\"267552\", \"272625\", \"303047\", \"319035\"]}","task_split":"paper_retrieval"}
{"document_id":"229518","document_content":"# Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression\n## Categories\n- Machine Learning\n## Abstract\nIn order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregation process. We first prove the inconsistency of typical aggregations using disjoint or random data partition, and then present a consistent yet efficient aggregation model for large-scale GP. The proposed model inherits the advantages of aggregations, e.g., closed-form inference and aggregation, parallelization and distributed computing. Furthermore, theoretical and empirical analyses reveal that the new aggregation model performs better due to the consistent predictions that converge to the true underlying function when the training size approaches infinity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1806.00720v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"2237\", \"44924\", \"62298\", \"68195\", \"124470\", \"3625\", \"50425\", \"69477\", \"71951\", \"93492\", \"103428\", \"211687\", \"225955\", \"274721\", \"211699\", \"230830\"], \"outgoing_citations\": [\"267085\", \"303849\", \"304230\", \"310034\", \"316569\", \"322597\", \"334045\", \"339837\", \"358415\"]}","task_split":"paper_retrieval"}
{"document_id":"229734","document_content":"# Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning is an ambiguous problem, with many suitable captions for an image. To address ambiguity, beam search is the de facto method for sampling multiple captions. However, beam search is computationally expensive and known to produce generic captions. To address this concern, some variational auto-encoder (VAE) and generative adversarial net (GAN) based methods have been proposed. Though diverse, GAN and VAE are less accurate. In this paper, we first predict a meaningful summary of the image, then generate the caption based on that summary. We use part-of-speech as summaries, since our summary should drive caption generation. We achieve the trifecta: (1) High accuracy for the diverse captions as evaluated by standard captioning metrics and user studies; (2) Faster computation of diverse captions compared to beam search and diverse beam search; and (3) High diversity as evaluated by counting novel sentences, distinct n-grams and mutual overlap (i.e., mBleu-4) scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.12589v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"5842\", \"5789\", \"50907\", \"60281\", \"113677\", \"93187\", \"5837\", \"15902\", \"28921\", \"31502\", \"53992\", \"61394\", \"64945\", \"72906\", \"74508\", \"80867\", \"82846\", \"96190\", \"179282\", \"89629\", \"5781\", \"110219\", \"110233\", \"111062\", \"112250\", \"122297\", \"139263\", \"139825\", \"141978\", \"157078\", \"165396\", \"170604\", \"171902\", \"208615\", \"209504\"], \"outgoing_citations\": [\"230900\", \"234545\", \"236907\", \"237655\", \"239242\", \"249394\", \"249972\", \"269128\", \"270154\", \"271234\", \"279002\", \"281152\", \"284230\", \"312704\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"229768","document_content":"# Neural Network Acceptability Judgments\n## Categories\n- Computation and Language\n## Abstract\nThis paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.'s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.12471v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"60337\", \"64601\", \"142609\", \"306609\", \"677\", \"3076\", \"4345\", \"8144\", \"9230\", \"12671\", \"15068\", \"16162\", \"16029\", \"16516\", \"19030\", \"22148\", \"25863\", \"26977\", \"32617\", \"35211\", \"38427\", \"41029\", \"41286\", \"42372\", \"43658\", \"46110\", \"91712\", \"162304\", \"175321\", \"2412\", \"3868\", \"5834\", \"7703\", \"8145\", \"8645\", \"8740\", \"9935\", \"10091\", \"10734\", \"14057\", \"14858\", \"15921\", \"16185\", \"19023\", \"19932\", \"20438\", \"21263\", \"21572\", \"21450\", \"22453\", \"22484\", \"22603\", \"23280\", \"23370\", \"23466\", \"23553\", \"23566\", \"23937\", \"25483\", \"26875\", \"27454\", \"31590\", \"34931\", \"35313\", \"39474\", \"40307\", \"41637\", \"41709\", \"42398\", \"42939\", \"43020\", \"43213\", \"43329\", \"43695\", \"44070\", \"44805\", \"45169\", \"45358\", \"36274\", \"45684\", \"45901\", \"46308\", \"46255\", \"46329\", \"46666\", \"47386\", \"47906\", \"48369\", \"48682\", \"48659\", \"95217\", \"49299\", \"49649\", \"50354\", \"50979\", \"51146\", \"52671\", \"53442\", \"54069\", \"54207\", \"54472\", \"55134\", \"55146\", \"55131\", \"55143\", \"55182\", \"55472\", \"55647\", \"55549\", \"55743\", \"56222\", \"55997\", \"56355\", \"56403\", \"56557\", \"56995\", \"56832\", \"57095\", \"57645\", \"58488\", \"59454\", \"59821\", \"60818\", \"61046\", \"61157\", \"61113\", \"62299\", \"63626\", \"63801\", \"64910\", \"66943\", \"68183\", \"68851\", \"72007\", \"72914\", \"73219\", \"73266\", \"75526\", \"77001\", \"77588\", \"77501\", \"77560\", \"77760\", \"77800\", \"78250\", \"127841\", \"80727\", \"81532\", \"88463\", \"92007\", \"94881\", \"96678\", \"97716\", \"99765\", \"103816\", \"81704\", \"107584\", \"117082\", \"119643\", \"120172\", \"120762\", \"122192\", \"132345\", \"153444\", \"165028\", \"115471\", \"77833\", \"78131\", \"81778\", \"84837\", \"85710\", \"86646\", \"89422\", \"80296\", \"91141\", \"91724\", \"92238\", \"92151\", \"92747\", \"92779\", \"93607\", \"94758\", \"94898\", \"95089\", \"95283\", \"95758\", \"96095\", \"96377\", \"96316\", \"96445\", \"96454\", \"97443\", \"97807\", \"98135\", \"99641\", \"100151\", \"100647\", \"100957\", \"107547\", \"109043\", \"111890\", \"112305\", \"112684\", \"114145\", \"50181\", \"111759\", \"117368\", \"120497\", \"120922\", \"122609\", \"123204\", \"126370\", \"127479\", \"127056\", \"127590\", \"127558\", \"127739\", \"128044\", \"128766\", \"128882\", \"129131\", \"130129\", \"130252\", \"131151\", \"132354\", \"132223\", \"132687\", \"133508\", \"127850\", \"129087\", \"129891\", \"134316\", \"135296\", \"131569\", \"139463\", \"140175\", \"140425\", \"141445\", \"142153\", \"143564\", \"148662\", \"153289\", \"157147\", \"157349\", \"158683\", \"158818\", \"159095\", \"162714\", \"164841\", \"164923\", \"165098\", \"165441\", \"168495\", \"168549\", \"171585\", \"172108\", \"172001\", \"174612\", \"124694\", \"153933\", \"157482\", \"157874\", \"157917\", \"30949\", \"162397\", \"162693\", \"159742\", \"164628\", \"164768\", \"166964\", \"168229\", \"168415\", \"169008\", \"169895\", \"174235\", \"176642\", \"177550\", \"182104\", \"184691\", \"185889\", \"187236\", \"188123\", \"184576\", \"192756\", \"188933\", \"195157\", \"199847\", \"200586\", \"200764\", \"202709\", \"203285\", \"204598\", \"173958\", \"214654\", \"208560\", \"211737\", \"220137\", \"234895\"], \"outgoing_citations\": [\"196722\", \"218291\", \"219156\", \"219586\", \"220137\", \"233400\", \"281225\", \"286594\"]}","task_split":"paper_retrieval"}
{"document_id":"229839","document_content":"# Evaluating Reinforcement Learning Algorithms in Observational Health Settings\n## Categories\n- Machine Learning\n## Abstract\nMuch attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL) is a sub-field within machine learning that is concerned with learning how to make sequences of decisions so as to optimize long-term effects. Already, RL algorithms have been proposed to identify decision-making strategies for mechanical ventilation, sepsis management and treatment of schizophrenia. However, before implementing treatment policies learned by black-box algorithms in high-stakes clinical decision problems, special care must be taken in the evaluation of these policies.   In this document, our goal is to expose some of the subtleties associated with evaluating RL algorithms in healthcare. We aim to provide a conceptual starting point for clinical and computational researchers to ask the right questions when designing and evaluating algorithms for new ways of treating patients. In the following, we describe how choices about how to summarize a history, variance of statistical estimators, and confounders in more ad-hoc measures can result in unreliable, even misleading estimates of the quality of a treatment policy. We also provide suggestions for mitigating these effects---for while there is much promise for mining observational health data to uncover better treatment policies, evaluation must be performed thoughtfully.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.12298v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"170695\", \"6774\", \"8161\", \"74313\", \"6736\", \"8061\", \"27929\", \"33819\", \"34568\", \"37807\", \"42559\", \"64122\", \"64396\", \"66593\", \"92132\", \"120766\", \"124327\", \"184991\", \"80071\", \"92230\", \"95628\", \"99800\", \"103648\", \"105759\", \"109865\", \"114617\", \"116584\", \"125479\", \"126139\", \"127099\", \"143300\", \"167909\", \"154589\", \"156013\", \"186326\", \"229305\", \"202934\", \"208939\", \"204688\"], \"outgoing_citations\": [\"249233\", \"268399\", \"282366\", \"290547\", \"295281\", \"303539\", \"321467\"]}","task_split":"paper_retrieval"}
{"document_id":"229928","document_content":"# Automatic Large-Scale Data Acquisition via Crowdsourcing for Crosswalk Classification: A Deep Learning Approach\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nCorrectly identifying crosswalks is an essential task for the driving activity and mobility autonomy. Many crosswalk classification, detection and localization systems have been proposed in the literature over the years. These systems use different perspectives to tackle the crosswalk classification problem: satellite imagery, cockpit view (from the top of a car or behind the windshield), and pedestrian perspective. Most of the works in the literature are designed and evaluated using small and local datasets, i.e. datasets that present low diversity. Scaling to large datasets imposes a challenge for the annotation procedure. Moreover, there is still need for cross-database experiments in the literature because it is usually hard to collect the data in the same place and conditions of the final application. In this paper, we present a crosswalk classification system based on deep learning. For that, crowdsourcing platforms, such as OpenStreetMap and Google Street View, are exploited to enable automatic training via automatic acquisition and annotation of a large-scale database. Additionally, this work proposes a comparison study of models trained using fully-automatic data acquisition and annotation against models that were partially annotated. Cross-database experiments were also included in the experimentation to show that the proposed methods enable use with real world applications. Our results show that the model trained on the fully-automatic database achieved high overall accuracy (94.12%), and that a statistically significant improvement (to 96.30%) can be achieved by manually annotating a specific part of the database. Finally, the results of the cross-database experiments show that both models are robust to the many variations of image and scenarios, presenting a consistent behavior.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/J.CAG.2017.08.004\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"78665\", \"108704\", \"88428\", \"112487\", \"202990\", \"174913\", \"175308\", \"182528\", \"233933\"], \"outgoing_citations\": [\"227884\", \"262638\"]}","task_split":"paper_retrieval"}
{"document_id":"229979","document_content":"# Visual Referring Expression Recognition: What Do Systems Actually Learn?\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nWe present an empirical analysis of the state-of-the-art systems for referring expression recognition -- the task of identifying the object in an image referred to by a natural language expression -- with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore the linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image $\\textit{without the input referring expression}$ can achieve a precision of 71.2% in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2% in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning -- and further, how our data is constructed -- is critical as we seek to make substantive progress on grounded language tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11818v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"3059\", \"21837\", \"34439\", \"35181\", \"41011\", \"111075\", \"175031\", \"86950\", \"96106\", \"113587\", \"121487\", \"127083\", \"127093\", \"127447\", \"130336\", \"139250\", \"152418\", \"165254\", \"181794\", \"189699\", \"192156\", \"191676\", \"192382\", \"204038\", \"211921\", \"239831\"], \"outgoing_citations\": [\"230386\", \"239831\", \"279177\", \"279518\", \"287377\", \"287427\", \"289503\", \"289739\", \"290789\", \"301884\", \"303452\", \"303534\", \"303714\", \"303833\", \"312704\"]}","task_split":"paper_retrieval"}
{"document_id":"230005","document_content":"# \"Press Space to Fire\": Automatic Video Game Tutorial Generation\n## Categories\n- Artificial Intelligence\n## Abstract\nWe propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11768v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"68080\", \"120822\", \"168225\", \"165417\", \"181330\", \"189837\", \"240572\", \"224233\", \"224955\"], \"outgoing_citations\": [\"267630\"]}","task_split":"paper_retrieval"}
{"document_id":"230082","document_content":"# The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making\n## Categories\n- Artificial Intelligence\n## Abstract\nOff-policy reinforcement learning enables near-optimal policy from suboptimal experience, thereby provisions opportunity for artificial intelligence applications in healthcare. Previous works have mainly framed patient-clinician interactions as Markov decision processes, while true physiological states are not necessarily fully observable from clinical data. We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories. A Gaussian mixture model is fitted for the observed data. Moreover, we take into account the fact that nuance in pharmaceutical dosage could presumably result in significantly different effect by modelling a continuous policy through a Gaussian approximator directly in the policy space, i.e. the actor. To address the challenge of infinite number of possible belief states which renders exact value iteration intractable, we evaluate and plan for only every encountered belief, through heuristic search tree by tightly maintaining lower and upper bounds of the true value of belief. We further resort to function approximations to update value bounds estimation, i.e. the critic, so that the tree search can be improved through more compact bounds at the fringe nodes that will be back-propagated to the root. Both actor and critic parameters are learned via gradient-based approaches. Our proposed policy trained from real intensive care unit data is capable of dictating dosing on vasopressors and intravenous fluids for sepsis patients that lead to the best patient outcomes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11548v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"170695\", \"31177\", \"39279\", \"64220\", \"98984\", \"85341\", \"109865\", \"147531\", \"196267\"], \"outgoing_citations\": [\"268399\", \"295281\", \"303539\", \"349851\", \"356537\"]}","task_split":"paper_retrieval"}
{"document_id":"230095","document_content":"# On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAlthough face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, nonuniform lighting, and nonfrontal face pose. In this paper, we analyze face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: {\\em (i)} we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; {\\em (ii)} we study face re-identification on various public face datasets including real surveillance and low-resolution subsets of large-scale datasets, present a baseline result for several deep learning based approaches, and improve them by introducing a GAN pre-training approach and fully convolutional architecture; and {\\em (iii)} we explore low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. Evaluations are conducted on challenging portions of the SCFace and UCCSface datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIFS.2018.2890812\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"31799\", \"36730\", \"60697\", \"61366\", \"91952\", \"7540\", \"32695\", \"74756\", \"129059\", \"139239\", \"152527\", \"263345\", \"154031\", \"170043\", \"172947\", \"208805\", \"230097\"], \"outgoing_citations\": [\"209118\", \"239574\", \"244877\", \"248019\", \"248969\", \"267136\", \"267612\", \"267785\", \"270376\", \"278526\", \"285517\", \"287578\", \"287580\", \"290450\", \"296989\", \"297330\", \"299439\", \"300053\", \"301376\", \"301945\", \"308687\", \"314520\", \"316430\", \"322793\", \"326888\"]}","task_split":"paper_retrieval"}
{"document_id":"230097","document_content":"# Face Recognition in Low Quality Images: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLow-resolution face recognition (LRFR) has received increasing attention over the past few years. Its applications lie widely in the real-world environment when high-resolution or high-quality images are hard to capture. One of the biggest demands for LRFR technologies is video surveillance. As the the number of surveillance cameras in the city increases, the videos that captured will need to be processed automatically. However, those videos or images are usually captured with large standoffs, arbitrary illumination condition, and diverse angles of view. Faces in these images are generally small in size. Several studies addressed this problem employed techniques like super resolution, deblurring, or learning a relationship between different resolution domains. In this paper, we provide a comprehensive review of approaches to low-resolution face recognition in the past five years. First, a general problem definition is given. Later, systematically analysis of the works on this topic is presented by catogory. In addition to describing the methods, we also focus on datasets and experiment settings. We further address the related works on unconstrained low-resolution face recognition and compare them with the result that use synthetic low-resolution data. Finally, we summarized the general limitations and speculate a priorities for the future effort.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11519v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"31799\", \"7226\", \"11616\", \"73773\", \"112283\"], \"outgoing_citations\": [\"189937\", \"209118\", \"211273\", \"226546\", \"230095\", \"234204\", \"239574\", \"248019\", \"248969\", \"254116\", \"258667\", \"261657\", \"267136\", \"267612\", \"267785\", \"285517\", \"294611\", \"300053\", \"301276\", \"308687\", \"317441\", \"318091\", \"348561\"]}","task_split":"paper_retrieval"}
{"document_id":"230145","document_content":"# CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing\n## Categories\n- Cryptography and Security\n- Computer Vision and Pattern Recognition\n## Abstract\nDetection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11318v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CV\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Cryptography and Security\"], \"incoming_citations\": [\"17761\", \"38847\", \"73867\", \"93101\", \"161208\", \"98625\", \"125465\", \"128831\", \"159789\", \"197980\", \"211510\"], \"outgoing_citations\": [\"259495\"]}","task_split":"paper_retrieval"}
{"document_id":"230234","document_content":"# Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAn accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN\/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model's learned saliency and entailment skills.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.11004v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"88725\", \"4498\", \"84343\", \"12088\", \"13790\", \"17627\", \"22482\", \"22773\", \"36093\", \"44625\", \"46200\", \"55327\", \"58358\", \"65777\", \"91642\", \"109884\", \"207215\", \"93583\", \"94994\", \"78021\", \"50181\", \"123228\", \"127411\", \"128252\", \"131681\", \"133009\", \"128932\", \"147311\", \"147804\", \"149732\", \"154648\", \"154649\", \"159316\", \"166679\", \"206151\", \"161027\", \"168018\", \"169425\", \"170449\", \"177759\", \"181093\", \"182438\", \"182629\", \"183200\", \"185278\", \"187002\", \"183197\", \"192342\", \"197339\", \"200764\", \"207045\", \"174905\", \"223392\", \"227488\"], \"outgoing_citations\": [\"230456\", \"235232\", \"263620\", \"264467\", \"267694\", \"268083\", \"268885\", \"269127\", \"281169\", \"284357\", \"289673\", \"294694\", \"303003\", \"357176\"]}","task_split":"paper_retrieval"}
{"document_id":"230252","document_content":"# Face hallucination using cascaded super-resolution and identity priors\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors. We approach the problem with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the low-resolution images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from competing super-resolution approaches that typically rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of $2\\times$. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. Our model is able to upscale (very) low-resolution images captured in unconstrained conditions and produce visually convincing results. We rigorously evaluate the proposed model on a large datasets of facial images and report superior performance compared to the state-of-the-art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10938v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"100824\", \"8330\", \"18974\", \"60697\", \"102723\", \"46052\", \"56898\", \"68463\", \"78941\", \"83477\", \"107602\", \"117456\", \"94977\", \"97923\", \"111131\", \"120894\", \"125816\", \"190690\", \"205139\"], \"outgoing_citations\": [\"205139\", \"209098\", \"211273\", \"222871\", \"238021\", \"239574\", \"248019\", \"248969\", \"250250\", \"258886\", \"260755\", \"267093\", \"268494\", \"277357\", \"278546\", \"288238\", \"301276\", \"321122\"]}","task_split":"paper_retrieval"}
{"document_id":"230281","document_content":"# Inducing Grammars with and for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMachine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modeling both types of knowledge. However, requiring parses is expensive and does not explore the question of what syntax a model needs during translation. To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees. In this way, we leverage the benefits of structure while investigating what syntax NMT must induce to maximize performance. We show that our dependency trees are 1. language pair dependent and 2. improve translation quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10850v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"35733\", \"108927\", \"168201\", \"165559\", \"182601\", \"191740\", \"202454\", \"223095\", \"223351\", \"228855\"], \"outgoing_citations\": [\"256740\", \"256948\", \"261640\", \"262535\", \"265431\", \"267460\", \"268788\", \"268812\", \"269127\", \"274183\", \"274499\", \"274817\", \"279384\", \"281205\", \"281225\", \"286225\", \"297648\"]}","task_split":"paper_retrieval"}
{"document_id":"230362","document_content":"# Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator, and the effect of the quality of reward estimates on the overall RL task. Our analysis of cardinal (5-point ratings) and ordinal (pairwise preferences) feedback shows that their intra- and inter-annotator $\\alpha$-agreement is comparable. Best reliability is obtained for standardized cardinal feedback, and cardinal feedback is also easiest to learn and generalize from. Finally, improvements of over 1 BLEU can be obtained by integrating a regression-based reward estimator trained on cardinal feedback for 800 translations into RL for NMT. This shows that RL is possible even from small amounts of fairly reliable human feedback, pointing to a great potential for applications at larger scale.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10627v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"193208\", \"16594\", \"30309\", \"67582\", \"69518\", \"89002\", \"80268\", \"129401\", \"153474\", \"230774\", \"166082\", \"168908\", \"176503\", \"177541\", \"182023\", \"173805\", \"209453\", \"235393\"], \"outgoing_citations\": [\"235393\", \"259945\", \"260420\", \"263957\", \"268354\", \"268454\", \"275728\", \"277537\", \"287823\", \"291294\", \"295281\", \"301840\", \"303539\", \"317842\", \"354620\"]}","task_split":"paper_retrieval"}
{"document_id":"230394","document_content":"# Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). The neural network representation integrates both continuous context signals and game history, using a possession-based LSTM. The learned Q-function is used to value players' actions under different game contexts. To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions. Empirical Evaluation shows GIM is consistent throughout a play season, and correlates highly with standard success measures and future salary.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.24963\/ijcai.2018\/478\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"185428\", \"5799\", \"8217\", \"23652\", \"51498\", \"69258\", \"86182\", \"86091\", \"89516\", \"121162\", \"226177\", \"214125\", \"217931\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"230442","document_content":"# Human Action Generation with Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nInspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions. We propose a framework of an autoencoder and a generative adversarial network (GAN) to produce multiple and consecutive human actions conditioned on the initial state and the given class label. The proposed model is trained in an end-to-end fashion, where the autoencoder is jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and we show that the proposed model can generate different styles of actions. Moreover, the model can successfully generate a sequence of novel actions given different action labels as conditions. The conventional human action prediction and generation models lack those features, which are essential for practical applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10416v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79468\", \"114265\", \"150081\", \"202187\"], \"outgoing_citations\": [\"249248\", \"249515\", \"262110\", \"265604\", \"267134\", \"273208\", \"303203\", \"308617\"]}","task_split":"paper_retrieval"}
{"document_id":"230456","document_content":"# Toward Abstractive Summarization Using Semantic Representations\n## Categories\n- Computation and Language\n## Abstract\nWe present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: https:\/\/github.com\/summarization","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10399v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2701\", \"6900\", \"11304\", \"12098\", \"12821\", \"19590\", \"20965\", \"43545\", \"34349\", \"47368\", \"47485\", \"48161\", \"61496\", \"91749\", \"175031\", \"223084\", \"139168\", \"141507\", \"85960\", \"89451\", \"95485\", \"95491\", \"95843\", \"126431\", \"154648\", \"154649\", \"155821\", \"170717\", \"152664\", \"161045\", \"164036\", \"161027\", \"167691\", \"169457\", \"190077\", \"192891\", \"193497\", \"196244\", \"214407\", \"211433\", \"209398\", \"217759\", \"220083\", \"220955\", \"227985\", \"227983\", \"229798\", \"230234\", \"232138\", \"234840\", \"235232\", \"250239\", \"253480\", \"264467\", \"267881\", \"268885\", \"269890\", \"274271\", \"230458\", \"284943\", \"290048\", \"307651\"], \"outgoing_citations\": [\"345105\"]}","task_split":"paper_retrieval"}
{"document_id":"230522","document_content":"# SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than $100$ images of size 384x384 per second on a recent GPU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10241v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"40211\", \"127366\", \"130639\", \"5510\", \"9153\", \"69054\", \"71829\", \"99192\", \"119297\", \"147593\", \"176840\", \"195957\", \"219050\", \"178129\", \"188702\", \"198873\", \"203182\", \"216014\", \"216827\", \"222227\"], \"outgoing_citations\": [\"254674\", \"271426\", \"271680\", \"272645\"]}","task_split":"paper_retrieval"}
{"document_id":"230556","document_content":"# Context-Aware Neural Machine Translation Learns Anaphora Resolution\n## Categories\n- Computation and Language\n## Abstract\nStandard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.10163v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42896\", \"55467\", \"56086\", \"93450\", \"4719\", \"23267\", \"24057\", \"25285\", \"26247\", \"27657\", \"31764\", \"33741\", \"38189\", \"44069\", \"45301\", \"49525\", \"56051\", \"60463\", \"61132\", \"61113\", \"64743\", \"168075\", \"70057\", \"73265\", \"77460\", \"78142\", \"82324\", \"89537\", \"91646\", \"92589\", \"94951\", \"100234\", \"127874\", \"160655\", \"200289\", \"91705\", \"93198\", \"94075\", \"95020\", \"95079\", \"95143\", \"95187\", \"95287\", \"95605\", \"96698\", \"99771\", \"100182\", \"107632\", \"118358\", \"120233\", \"126458\", \"127163\", \"127997\", \"131513\", \"129890\", \"134314\", \"134662\", \"141444\", \"151289\", \"154483\", \"163902\", \"150615\", \"152767\", \"157472\", \"107763\", \"158182\", \"158725\", \"158899\", \"161372\", \"163992\", \"168854\", \"169164\", \"169146\", \"169237\", \"169344\", \"169440\", \"171491\", \"169401\", \"173791\", \"178214\", \"181468\", \"181964\", \"182508\", \"185112\", \"186239\", \"192046\", \"194334\", \"195512\", \"195909\", \"198836\", \"201546\", \"205161\", \"210165\", \"181283\", \"210454\", \"213126\", \"215003\", \"219083\", \"219460\", \"219773\", \"221522\", \"214554\"], \"outgoing_citations\": [\"251684\", \"258163\", \"259525\", \"268684\", \"268909\", \"294814\"]}","task_split":"paper_retrieval"}
{"document_id":"231055","document_content":"# Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks\n## Categories\n- Machine Learning\n## Abstract\nExtended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017] is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a goal-directed control signal of an agent whose dynamics has previously been learned during a training phase in an unsupervised manner. PFAx hardly requires assumptions or prior knowledge of the agent's sensor or control mechanics, or of the environment. It selects features from a high-dimensional input by intrinsic predictability and organizes them into a reasonably low-dimensional model.   While PFA obtains a well predictable model, PFAx yields a model ideally suited for manipulations with predictable outcome. This allows for goal-directed manipulation of an agent and thus for local navigation, i.e. for reaching states where intermediate actions can be chosen by a permanent descent of distance to the goal. The approach is limited when it comes to global navigation, e.g. involving obstacles or multiple rooms.   In this article, we extend theoretical results from [Sprekeler and Wiskott, 2008], enabling PFAx to perform stable global navigation. So far, the most widely exploited characteristic of Slow Feature Analysis (SFA) was that slowness yields invariances. We focus on another fundamental characteristics of slow signals: They tend to yield monotonicity and one significant property of monotonicity is that local optimization is sufficient to find a global optimum.   We present an SFA-based algorithm that structures an environment such that navigation tasks hierarchically decompose into subgoals. Each of these can be efficiently achieved by PFAx, yielding an overall global solution of the task. The algorithm needs to explore and process an environment only once and can then perform all sorts of navigation tasks efficiently. We support this algorithm by mathematical theory and apply it to different problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.08565v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"248543\", \"299151\"]}","task_split":"paper_retrieval"}
{"document_id":"231187","document_content":"# Learning What Information to Give in Partially Observed Domains\n## Categories\n- Artificial Intelligence\n- Robotics\n## Abstract\nIn many robotic applications, an autonomous agent must act within and explore a partially observed environment that is unobserved by its human teammate. We consider such a setting in which the agent can, while acting, transmit declarative information to the human that helps them understand aspects of this unseen environment. In this work, we address the algorithmic question of how the agent should plan out what actions to take and what information to transmit. Naturally, one would expect the human to have preferences, which we model information-theoretically by scoring transmitted information based on the change it induces in weighted entropy of the human's belief state. We formulate this setting as a belief MDP and give a tractable algorithm for solving it approximately. Then, we give an algorithm that allows the agent to learn the human's preferences online, through exploration. We validate our approach experimentally in simulated discrete and continuous partially observed search-and-recover domains. Visit http:\/\/tinyurl.com\/chitnis-corl-18 for a supplementary video.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.08263v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"25974\", \"76818\"], \"outgoing_citations\": [\"284953\"]}","task_split":"paper_retrieval"}
{"document_id":"231267","document_content":"# Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior\n## Categories\n- Machine Learning\n## Abstract\nInferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this inferred intent to predict, imitate, or assist the human user. However, a central assumption in inverse reinforcement learning is that the demonstrator is close to optimal. While models of suboptimal behavior exist, they typically assume that suboptimal actions are the result of some type of random noise or a known cognitive bias, like temporal inconsistency. In this paper, we take an alternative approach, and model suboptimal behavior as the result of internal model misspecification: the reason that user actions might deviate from near-optimal actions is that the user has an incorrect set of beliefs about the rules -- the dynamics -- governing how actions affect the environment. Our insight is that while demonstrated actions may be suboptimal in the real world, they may actually be near-optimal with respect to the user's internal model of the dynamics. By estimating these internal beliefs from observed behavior, we arrive at a new method for inferring intent. We demonstrate in simulation and in a user study with 12 participants that this approach enables us to more accurately model human intent, and can be used in a variety of applications, including offering assistance in a shared autonomy framework and inferring human preferences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.08010v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"33971\", \"2405\", \"26978\", \"35649\", \"37110\", \"222967\", \"9814\", \"14671\", \"39686\", \"54303\", \"59140\", \"64631\", \"87472\", \"114208\", \"114596\", \"74639\", \"85487\", \"86815\", \"87679\", \"107425\", \"108216\", \"109829\", \"115941\", \"122106\", \"126543\", \"137960\", \"145205\", \"162995\", \"184304\", \"166909\", \"179418\", \"194802\", \"195041\", \"230659\"], \"outgoing_citations\": [\"220420\", \"236393\", \"243197\", \"247344\", \"251924\", \"253426\", \"270913\", \"272987\", \"294621\", \"297317\", \"301305\", \"309396\", \"315405\", \"330073\", \"357569\"]}","task_split":"paper_retrieval"}
{"document_id":"231398","document_content":"# Human-guided data exploration using randomisation\n## Categories\n- Machine Learning\n## Abstract\nAn explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with the most informative and useful views of the data. We propose a principled way to do exploratory data analysis, where the user's background knowledge is modeled by a distribution parametrised by subsets of rows and columns in the data, called tiles. The user can also use tiles to describe his or her interests concerning relations in the data. We provide a computationally efficient implementation of this concept based on constrained randomisation. The implementation is used to model both the background knowledge and the user's information request and is a necessary prerequisite for any interactive system. Furthermore, we describe a novel linear projection pursuit method to find and show the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that our method is robust under noise and fast enough for interactive use. We also show that the method gives understandable and useful results when analysing real-world data sets. We will release an open source library implementing the idea, including the experiments presented in this paper. We show that our method can outperform standard projection pursuit visualisation methods in exploration tasks. Our framework makes it possible to construct human-guided data exploration systems which are fast, powerful, and give results that are easy to comprehend.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.07725v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"187431\"], \"outgoing_citations\": [\"118133\", \"252672\", \"282175\", \"302399\"]}","task_split":"paper_retrieval"}
{"document_id":"231469","document_content":"# Robust Optimization over Multiple Domains\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding model, multiple domains (i.e., applications\/users) may share the same machine learning model due to maintenance loads in cloud computing services. For example, a digit-recognition model should be applicable to hand-written digits, house numbers, car plates, etc. Therefore, an ideal model for cloud computing has to perform well at each applicable domain. To address this new challenge from cloud computing, we develop a framework of robust optimization over multiple domains. In lieu of minimizing the empirical risk, we aim to learn a model optimized to the adversarial distribution over multiple domains. Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency. Theoretically, we analyze the convergence rate for convex and non-convex models. To our best knowledge, we first study the convergence rate of learning a robust non-convex model with a practical algorithm. Furthermore, we demonstrate that the robustness of the framework and the convergence rate can be further enhanced by appropriate regularizers over the adversarial distribution. The empirical study on real-world fine-grained visual categorization and digits recognition tasks verifies the effectiveness and efficiency of the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.07588v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"121351\", \"1206\", \"2215\", \"215485\", \"18035\", \"28560\", \"59789\", \"68602\", \"181793\", \"117885\", \"140767\", \"142796\", \"164151\", \"181525\"], \"outgoing_citations\": [\"262111\", \"298923\", \"340028\"]}","task_split":"paper_retrieval"}
{"document_id":"231551","document_content":"# Learning to Collaborate for User-Controlled Privacy\n## Categories\n- Machine Learning\n## Abstract\nIt is becoming increasingly clear that users should own and control their data. Utility providers are also becoming more interested in guaranteeing data privacy. As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community. We introduce this concept and present explicit architectures where the user controls what characteristics of the data she\/he wants to share and what she\/he wants to keep private. This is achieved by collaborative learning a sensitization function, either a deterministic or a stochastic one, that retains valuable information for the utility tasks but it also eliminates necessary information for the privacy ones. As illustration examples, we implement them using a plug-and-play approach, where no algorithm is changed at the system provider end, and an adversarial approach, where minor re-training of the privacy inferring engine is allowed. In both cases the learned sanitization function keeps the data in the original domain, thereby allowing the system to use the same algorithms it was using before for both original and privatized data. We show how we can maintain utility while fully protecting private information if the user chooses to do so, even when the first is harder than the second, as in the case here illustrated of identity detection while hiding gender.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.07410v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"237152\", \"240981\", \"244799\", \"256057\", \"264781\", \"264914\", \"273221\", \"281291\", \"287568\", \"333991\"]}","task_split":"paper_retrieval"}
{"document_id":"231619","document_content":"# Improving Image Captioning with Conditional Generative Adversarial Nets\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- 68T45\n## Abstract\nIn this paper, we propose a novel conditional-generative-adversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture. To deal with the inconsistent evaluation problem among different objective language metrics, we are motivated to design some \"discriminator\" networks to automatically and progressively determine whether generated caption is human described or machine generated. Two kinds of discriminator architectures (CNN and RNN-based structures) are introduced since each has its own advantages. The proposed algorithm is generic so that it can enhance any existing RL-based image captioning framework and we show that the conventional RL training method is just a special case of our approach. Empirically, we show consistent improvements over all language evaluation metrics for different state-of-the-art image captioning models. In addition, the well-trained discriminators can also be viewed as objective image captioning evaluators","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1609\/aaai.v33i01.33018142\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"68T45\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"68T45\"], \"incoming_citations\": [\"5286\", \"6500\", \"15902\", \"29271\", \"32961\", \"98018\", \"74508\", \"80867\", \"93826\", \"103591\", \"105346\", \"88096\", \"97743\", \"120590\", \"135154\", \"135506\", \"155278\", \"174277\", \"206058\"], \"outgoing_citations\": [\"221169\", \"223302\", \"256347\", \"262564\", \"269030\", \"270154\", \"271234\", \"278604\", \"279002\", \"281152\", \"302815\", \"313465\", \"317966\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"231851","document_content":"# Object detection at 200 Frames Per Second\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose an efficient and fast object detector which can process hundreds of frames per second. To achieve this goal we investigate three main aspects of the object detection framework: network architecture, loss function and training data (labeled and unlabeled). In order to obtain compact network architecture, we introduce various improvements, based on recent work, to develop an architecture which is computationally light-weight and achieves a reasonable performance. To further improve the performance, while keeping the complexity same, we utilize distillation loss function. Using distillation loss we transfer the knowledge of a more accurate teacher network to proposed light-weight student network. We propose various innovations to make distillation efficient for the proposed one stage detector pipeline: objectness scaled distillation loss, feature map non-maximal suppression and a single unified distillation loss function for detection. Finally, building upon the distillation loss, we explore how much can we push the performance by utilizing the unlabeled data. We train our model with unlabeled data using the soft labels of the teacher network. Our final network consists of 10x fewer parameters than the VGG based object detection network and it achieves a speed of more than 200 FPS and proposed changes improve the detection accuracy by 14 mAP over the baseline on Pascal dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.06361v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"47972\", \"134182\", \"196548\", \"192453\", \"193405\"], \"outgoing_citations\": [\"247616\", \"259417\", \"285788\", \"302867\", \"316486\", \"320898\", \"344133\"]}","task_split":"paper_retrieval"}
{"document_id":"231898","document_content":"# Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.06201v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"36981\", \"42229\", \"6449\", \"6596\", \"15842\", \"16508\", \"18991\", \"19858\", \"26658\", \"29718\", \"42896\", \"55305\", \"55926\", \"60419\", \"64046\", \"66487\", \"93564\", \"13866\", \"2024\", \"8723\", \"10937\", \"17070\", \"21352\", \"21738\", \"22194\", \"22443\", \"23762\", \"24008\", \"24125\", \"24549\", \"24637\", \"25244\", \"25264\", \"26384\", \"26455\", \"26808\", \"40477\", \"42082\", \"43140\", \"43328\", \"43377\", \"45164\", \"46724\", \"46744\", \"47759\", \"51130\", \"52161\", \"58285\", \"59303\", \"61077\", \"65915\", \"67367\", \"67553\", \"68928\", \"74463\", \"72973\", \"75533\", \"77389\", \"77353\", \"80863\", \"84839\", \"91922\", \"105831\", \"128551\", \"133905\", \"165015\", \"82583\", \"84390\", \"86408\", \"89639\", \"85637\", \"82584\", \"89415\", \"90279\", \"92217\", \"95171\", \"95549\", \"95359\", \"95947\", \"96422\", \"96487\", \"96721\", \"96695\", \"96765\", \"96881\", \"99332\", \"101765\", \"114239\", \"114713\", \"114883\", \"122165\", \"129289\", \"131952\", \"127924\", \"132808\", \"134870\", \"134932\", \"138517\", \"141614\", \"143521\", \"144757\", \"157726\", \"158044\", \"159153\", \"164142\", \"170825\", \"177196\", \"153837\", \"157467\", \"157965\", \"159333\", \"167726\", \"175870\", \"185283\", \"184635\", \"193137\", \"200867\", \"205715\", \"207216\", \"214997\"], \"outgoing_citations\": [\"244368\", \"256804\", \"262256\", \"267601\", \"272531\", \"290632\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"231925","document_content":"# FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning\n## Categories\n- Robotics\n- Machine Learning\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nUnderstanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural architecture for learning multi-modal navigation policies. FollowNet maps natural language instructions as well as visual and depth inputs to locomotion primitives. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.06150v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"683\", \"27350\", \"77439\", \"19503\", \"980\", \"50249\", \"54330\", \"92592\", \"107458\", \"86950\", \"86813\", \"104479\", \"130190\", \"142610\", \"188484\", \"196909\", \"158642\", \"160530\", \"161338\", \"181081\", \"183823\", \"185705\", \"189110\", \"198208\", \"205085\", \"196994\", \"213171\", \"210666\"], \"outgoing_citations\": [\"243782\", \"248742\", \"249906\", \"253600\", \"263104\", \"263270\", \"267779\", \"268335\", \"279420\", \"298651\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"231985","document_content":"# CLINIQA: A Machine Intelligence Based Clinical Question Answering System\n## Categories\n- Computation and Language\n- 68T50\n## Abstract\nThe recent developments in the field of biomedicine have made large volumes of biomedical literature available to the medical practitioners. Due to the large size and lack of efficient searching strategies, medical practitioners struggle to obtain necessary information available in the biomedical literature. Moreover, the most sophisticated search engines of age are not intelligent enough to interpret the clinicians' questions. These facts reflect the urgent need of an information retrieval system that accepts the queries from medical practitioners' in natural language and returns the answers quickly and efficiently. In this paper, we present an implementation of a machine intelligence based CLINIcal Question Answering system (CLINIQA) to answer medical practitioner's questions. The system was rigorously evaluated on different text mining algorithms and the best components for the system were selected. The system makes use of Unified Medical Language System for semantic analysis of both questions and medical documents. In addition, the system employs supervised machine learning algorithms for classification of the documents, identifying the focus of the question and answer selection. Effective domain-specific heuristics are designed for answer ranking. The performance evaluation on hundred clinical questions shows the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.05927v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\"], \"incoming_citations\": [\"15289\", \"21951\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"232068","document_content":"# Facial Landmark Detection: a Literature Survey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression-based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning-based algorithms.   The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection \"in-the-wild\".","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11263-018-1097-z\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5015\", \"15630\", \"16351\", \"27096\", \"75911\", \"7001\", \"8828\", \"28483\", \"45256\", \"52699\", \"76524\", \"78565\", \"80581\", \"70135\", \"103727\", \"104009\", \"104748\", \"114478\", \"126182\", \"130603\", \"131257\", \"133029\", \"175564\", \"204754\", \"154555\", \"154889\", \"155849\", \"174020\", \"189156\", \"208315\", \"218516\"], \"outgoing_citations\": [\"255056\", \"255058\", \"255689\", \"255690\", \"296292\", \"297167\", \"302701\", \"311317\", \"318091\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"232069","document_content":"# Simplifying Sentences with Sequence to Sequence Models\n## Categories\n- Computation and Language\n## Abstract\nWe simplify sentences with an attentive neural network sequence to sequence model, dubbed S4. The model includes a novel word-copy mechanism and loss function to exploit linguistic similarities between the original and simplified sentences. It also jointly uses pre-trained and fine-tuned word embeddings to capture the semantics of complex sentences and to mitigate the effects of limited data. When trained and evaluated on pairs of sentences from thousands of news articles, we observe a 8.8 point improvement in BLEU score over a sequence to sequence baseline; however, learning word substitutions remains difficult. Such sequence to sequence models are promising for other text generation tasks such as style transfer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.05557v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96175\", \"202338\"], \"outgoing_citations\": [\"322537\", \"335302\"]}","task_split":"paper_retrieval"}
{"document_id":"232074","document_content":"# A Dynamic Neural Network Approach to Generating Robot's Novel Actions: A Simulation Experiment\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nIn this study, we investigate how a robot can generate novel and creative actions from its own experience of learning basic actions. Inspired by a machine learning approach to computational creativity, we propose a dynamic neural network model that can learn and generate robot's actions. We conducted a set of simulation experiments with a humanoid robot. The results showed that the proposed model was able to learn the basic actions and also to generate novel actions by modulating and combining those learned actions. The analysis on the neural activities illustrated that the ability to generate creative actions emerged from the model's nonlinear memory structure self-organized during training. The results also showed that the different way of learning the basic actions induced the self-organization of the memory structure with the different characteristics, resulting in the generation of different levels of creative actions. Our approach can be utilized in human-robot interaction in which a user can interactively explore the robot's memory to control its behavior and also discover other novel actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.05537v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"112261\"], \"outgoing_citations\": [\"261984\", \"264286\"]}","task_split":"paper_retrieval"}
{"document_id":"232097","document_content":"# Did the Model Understand the Question?\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of \\emph{attribution} (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from $61.1\\%$ to $19\\%$, and that of a tabular question answering model from $33.5\\%$ to $3.3\\%$. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.05492v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15922\", \"30305\", \"9635\", \"15928\", \"26061\", \"55177\", \"70033\", \"78288\", \"119288\", \"143523\", \"93187\", \"6153\", \"16218\", \"21407\", \"25420\", \"26502\", \"26881\", \"27097\", \"43919\", \"45449\", \"54502\", \"56568\", \"57857\", \"60179\", \"61915\", \"63597\", \"71336\", \"76192\", \"99715\", \"106905\", \"127977\", \"129406\", \"129726\", \"11060\", \"208373\", \"133131\", \"78305\", \"79159\", \"85584\", \"93442\", \"96276\", \"99775\", \"110587\", \"113587\", \"119364\", \"122176\", \"127678\", \"127737\", \"132471\", \"138164\", \"153296\", \"156086\", \"162419\", \"168549\", \"171817\", \"175309\", \"176612\", \"183641\", \"198892\", \"222721\", \"156990\", \"164178\", \"169254\", \"171131\", \"171991\", \"177649\", \"176822\", \"179945\", \"192195\", \"198161\", \"199319\", \"205161\", \"209479\", \"211921\", \"215230\", \"216292\", \"218741\", \"207103\", \"218350\", \"218594\", \"218693\"], \"outgoing_citations\": [\"239189\", \"250228\", \"259007\", \"266120\", \"269203\", \"270343\", \"279428\", \"280191\", \"289739\", \"289848\", \"293152\", \"295302\", \"303251\", \"303306\", \"303603\", \"308610\"]}","task_split":"paper_retrieval"}
{"document_id":"232159","document_content":"# A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing\n## Categories\n- Computation and Language\n- 68T50\n- I.2.7\n## Abstract\nWe propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99% coverage on non-projective syntactic corpora. This novel approach outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the SWAP transition, which can handle unrestricted non-projectivity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.05202v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\", \"I.2.7\"], \"incoming_citations\": [\"39718\", \"89826\", \"117465\"], \"outgoing_citations\": [\"296583\"]}","task_split":"paper_retrieval"}
{"document_id":"232224","document_content":"# Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a novel framework for automatic learning of complex strategies in human decision making. The task that we are interested in is to better facilitate long term planning for complex, multi-step events. We observe temporal relationships at the subtask level of expert demonstrations, and determine the different strategies employed in order to successfully complete a task. To capture the relationship between the subtasks and the overall goal, we utilise two external memory modules, one for capturing dependencies within a single expert demonstration, such as the sequential relationship among different sub tasks, and a global memory module for modelling task level characteristics such as best practice employed by different humans based on their domain expertise. Furthermore, we demonstrate how the hidden state representation of the memory can be used as a reward signal to smooth the state transitions, eradicating subtle changes. We evaluate the effectiveness of the proposed model for an autonomous highway driving application, where we demonstrate its capability to learn different expert policies and outperform state-of-the-art methods. The scope in industrial applications extends to any robotics and automation application which requires learning from complex demonstrations containing series of subtasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.04969v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"82834\", \"111774\", \"87753\", \"202825\", \"155881\", \"162131\", \"205479\", \"223749\", \"232207\"], \"outgoing_citations\": [\"239570\", \"239572\", \"268573\", \"270529\", \"270925\", \"271683\", \"272298\", \"272930\", \"273754\", \"275566\", \"280655\", \"293071\", \"297317\", \"310639\", \"321263\"]}","task_split":"paper_retrieval"}
{"document_id":"232235","document_content":"# Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nThis paper discusses and demonstrates the outcomes from our experimentation on Image Captioning. Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects\/concepts in the image and the creation of a succinct sentential narration. Experiments on several labeled datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. As a toy application, we apply image captioning to create video captions, and we advance a few hypotheses on the challenges we encountered.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.09137v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"232260","document_content":"# Bag-of-Words as Target for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nA sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage. Since most of the correct translations for one sentence share the similar bag-of-words, it is possible to distinguish the correct translations from the incorrect ones by the bag-of-words. In this paper, we propose an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in the training set. We evaluate our model on a Chinese-English translation dataset, and experiments show our model outperforms the strong baselines by the BLEU score of 4.55.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.04871v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"43408\", \"41949\", \"43528\", \"44904\", \"48954\", \"108602\", \"87465\", \"143496\", \"143869\", \"153474\", \"155086\", \"169704\", \"250140\", \"148240\", \"155957\", \"164256\", \"165853\", \"167028\", \"179505\", \"186407\", \"192272\", \"192328\", \"201190\", \"240027\", \"218543\", \"219466\", \"219519\", \"220209\", \"220740\", \"221551\", \"220634\", \"228250\", \"228596\", \"243291\"], \"outgoing_citations\": [\"211732\", \"232164\", \"240068\", \"243170\", \"243291\", \"248149\", \"252163\", \"260977\", \"263362\", \"267460\", \"270040\", \"274729\", \"282530\", \"283997\", \"287474\", \"296029\", \"299936\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"232472","document_content":"# Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and\/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext. We empirically evaluate and show the effectiveness of our multi-task learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.04237v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96371\", \"147804\", \"159947\", \"193228\", \"209217\", \"219491\"], \"outgoing_citations\": [\"244357\", \"259471\", \"263927\", \"265708\", \"267881\", \"268059\", \"268083\", \"268263\", \"268518\", \"269127\", \"269899\", \"276174\", \"281169\", \"300543\", \"303003\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"232479","document_content":"# Deep RNNs Encode Soft Hierarchical Syntax\n## Categories\n- Computation and Language\n## Abstract\nWe present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.04218v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16621\", \"23438\", \"94453\", \"24464\", \"38189\", \"42133\", \"43790\", \"45930\", \"56330\", \"62299\", \"75179\", \"77789\", \"78444\", \"133976\", \"175565\", \"96277\", \"96273\", \"96298\", \"96658\", \"97095\", \"97403\", \"98333\", \"98535\", \"115421\", \"121685\", \"124609\", \"127295\", \"128059\", \"128214\", \"128575\", \"128087\", \"160751\", \"167168\", \"168856\", \"169687\", \"169901\", \"169266\", \"174411\", \"177550\", \"181468\", \"181964\", \"182116\", \"182390\", \"182935\", \"180658\", \"186130\", \"186250\", \"183680\", \"192402\", \"192491\", \"194308\", \"198134\", \"205161\", \"212036\", \"219156\", \"219933\", \"220159\", \"239503\"], \"outgoing_citations\": [\"237283\", \"244357\", \"260541\", \"268788\", \"269127\", \"281121\", \"281225\"]}","task_split":"paper_retrieval"}
{"document_id":"232557","document_content":"# Improv Chat: Second Response Generation for Chatbot\n## Categories\n- Computation and Language\n## Abstract\nExisting research on response generation for chatbot focuses on \\textbf{First Response Generation} which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user's query). In this paper, we introduce a new task \\textbf{Second Response Generation}, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users' query and the chatbot's first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.03900v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"267551\", \"325200\"]}","task_split":"paper_retrieval"}
{"document_id":"232723","document_content":"# Reward Estimation for Variance Reduction in Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the reward with external factors that are not known to the agent. Corruption or stochasticity of the reward signal can be especially problematic in robotics, where goal specification can be particularly difficult for complex tasks. While many variance reduction techniques have been studied to improve the robustness of the RL process, handling such stochastic or corrupted reward structures remains difficult. As an alternative for handling this scenario in model-free RL methods, we suggest using an estimator for both rewards and value functions. We demonstrate that this improves performance under corrupted stochastic rewards in both the tabular and non-linear function approximation settings for a variety of noise types and environments. The use of reward estimation is a robust and easy-to-implement improvement for handling corrupted reward signals in model-free RL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.03359v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"109820\", \"115575\", \"69751\", \"79257\", \"96719\", \"74948\", \"105972\", \"117210\", \"139719\", \"215780\", \"151746\", \"185050\", \"185544\", \"227313\"], \"outgoing_citations\": [\"218114\", \"230042\", \"240471\", \"243940\", \"248170\", \"251082\", \"251792\", \"255427\", \"257130\", \"260583\", \"260770\", \"263658\", \"263957\", \"265652\", \"265985\", \"277193\", \"277564\", \"280296\", \"360762\"]}","task_split":"paper_retrieval"}
{"document_id":"232808","document_content":"# Active Object Reconstruction Using a Guided View Planner\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nInspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.03081v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"83073\", \"93153\", \"103523\", \"160895\", \"195721\"], \"outgoing_citations\": [\"268404\", \"270389\", \"279050\", \"279075\", \"287549\", \"293595\", \"295716\", \"321369\", \"321448\"]}","task_split":"paper_retrieval"}
{"document_id":"232896","document_content":"# A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the current era, biometric based access control is becoming more popular due to its simplicity and ease to use by the users. It reduces the manual work of identity recognition and facilitates the automatic processing. The face is one of the most important biometric visual information that can be easily captured without user cooperation in an uncontrolled environment. Precise detection of spoofed faces should be on the high priority to make face based identity recognition and access control robust against possible attacks. The recently evolved Convolutional Neural Network (CNN) based deep learning technique has proven as one of the excellent method to deal with the visual information very effectively. The CNN learns the hierarchical features at intermediate layers automatically from the data. Several CNN based methods such as Inception and ResNet have shown outstanding performance for image classification problem. This paper does a performance evaluation of CNNs for face anti-spoofing. The Inception and ResNet CNN architectures are used in this study. The results are computed over benchmark MSU Mobile Face Spoofing Database. The experiments are done by considering the different aspects such as the depth of the model, random weight initialization vs weight transfer, fine tuning vs training from scratch and different learning rate. The favorable results are obtained using these CNN architectures for face anti-spoofing in different settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.04176v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38932\", \"9939\", \"30596\", \"48122\", \"69610\", \"121069\", \"79412\", \"173966\", \"129091\", \"136226\", \"143707\", \"167274\", \"198533\", \"152655\", \"193492\", \"204139\", \"204268\", \"235504\", \"210332\"], \"outgoing_citations\": [\"204267\", \"215992\", \"235504\", \"302772\", \"323518\"]}","task_split":"paper_retrieval"}
{"document_id":"233038","document_content":"# DocFace: Matching ID Document Photos to Selfies\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nNumerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e.g. passports and driver licenses. An automatic system for matching ID document photos to live face images in real time with high accuracy would speedup the verification process and remove the burden on human operators. In this paper, by employing the transfer learning technique, we propose a new method, DocFace, to train a domain-specific network for ID document photo matching without a large dataset. Compared with the baseline of applying existing methods for general face recognition to this problem, our method achieves considerable improvement. A cross validation on an ID-Selfie dataset shows that DocFace improves the TAR from 61.14% to 92.77% at FAR=0.1%. Experimental results also indicate that given more training data, a viable system for automatic ID document photo matching can be developed and deployed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.02283v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"62160\", \"139709\", \"150130\", \"157317\", \"217798\"], \"outgoing_citations\": [\"244877\", \"268379\", \"269914\", \"270376\", \"270642\", \"294284\", \"320918\"]}","task_split":"paper_retrieval"}
{"document_id":"233164","document_content":"# Causal programming: inference with structural causal models as finding instances of a relation\n## Categories\n- Methodology\n- Artificial Intelligence\n## Abstract\nThis paper proposes a causal inference relation and causal programming as general frameworks for causal inference with structural causal models. A tuple, $\\langle M, I, Q, F \\rangle$, is an instance of the relation if a formula, $F$, computes a causal query, $Q$, as a function of known population probabilities, $I$, in every model entailed by a set of model assumptions, $M$. Many problems in causal inference can be viewed as the problem of enumerating instances of the relation that satisfy given criteria. This unifies a number of previously studied problems, including causal effect identification, causal discovery and recovery from selection bias. In addition, the relation supports formalizing new problems in causal inference with structural causal models, such as the problem of research design. Causal programming is proposed as a further generalization of causal inference as the problem of finding optimal instances of the relation, with respect to a cost function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.01960v1\", \"primary_category\": \"stat.ME\", \"categories\": [\"stat.ME\", \"cs.AI\"], \"primary_category_human_readable\": \"Methodology\", \"categories_human_readable\": [\"Methodology\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"357257\", \"358911\"]}","task_split":"paper_retrieval"}
{"document_id":"233196","document_content":"# Towards a Spectrum of Graph Convolutional Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nWe present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where the aggregation operations are (a) determined by structural properties of the local neighborhood graphs and (b) not restricted to weighted averages. We show that the proposed approach is strictly more expressive while requiring only a modest increase in the number of parameters and computations. We also show that the proposed generalization is identical to standard convolutional layers when applied to regular grid graphs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.01837v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"253780\", \"350606\"]}","task_split":"paper_retrieval"}
{"document_id":"233206","document_content":"# Extreme Adaptation for Personalized Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nEvery person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin.   When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models.   In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation.   Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.01817v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"29715\", \"41029\", \"56086\", \"63205\", \"77590\", \"32358\", \"42880\", \"18097\", \"51961\", \"60768\", \"72713\", \"95425\", \"100234\", \"94688\", \"95078\", \"132058\", \"132761\", \"133899\", \"140754\", \"155356\", \"157763\", \"158725\", \"160221\", \"166114\", \"167062\", \"170180\", \"170263\", \"171733\", \"177541\", \"180034\", \"193185\", \"195512\", \"211944\", \"211405\", \"211710\", \"211937\", \"213954\", \"217917\", \"219439\", \"220330\", \"228393\", \"228855\"], \"outgoing_citations\": [\"259746\", \"262535\", \"276174\", \"282462\", \"284263\", \"286225\", \"295028\"]}","task_split":"paper_retrieval"}
{"document_id":"233342","document_content":"# Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nCounterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.01252v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"21737\", \"24345\", \"30309\", \"40308\", \"40489\", \"43704\", \"89002\", \"112487\", \"94820\", \"94946\", \"102649\", \"122607\", \"126793\", \"132918\", \"142033\", \"154734\", \"167789\", \"170083\", \"177296\", \"207494\", \"208079\", \"235393\"], \"outgoing_citations\": [\"249528\", \"259945\", \"267790\", \"271637\", \"278430\", \"279428\", \"290632\", \"295281\", \"300505\", \"303539\", \"354620\"]}","task_split":"paper_retrieval"}
{"document_id":"233650","document_content":"# Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nTraditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch. In reality, some sentences are well-learned during the initial few epochs; however, using this approach, the well-learned sentences would continue to be trained along with those sentences that were not well learned for 10-30 epochs, which results in a wastage of time. Here, we propose an efficient method to dynamically sample the sentences in order to accelerate the NMT training. In this approach, a weight is assigned to each sentence based on the measured difference between the training costs of two iterations. Further, in each epoch, a certain percentage of sentences are dynamically sampled according to their weights. Empirical results based on the NIST Chinese-to-English and the WMT English-to-German tasks depict that the proposed method can significantly accelerate the NMT training and improve the NMT performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.00178v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56086\", \"27278\", \"83965\", \"169731\", \"165343\", \"177443\", \"182662\", \"182740\", \"194737\", \"210594\", \"211859\"], \"outgoing_citations\": [\"259541\", \"259851\", \"271637\", \"281508\", \"282440\", \"289336\"]}","task_split":"paper_retrieval"}
{"document_id":"233661","document_content":"# Dialog-based Interactive Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nExisting methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results. However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact. In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effective interaction. We formulate the task of dialog-based interactive image retrieval as a reinforcement learning problem, and reward the dialog system for improving the rank of the target image during each dialog turn. To mitigate the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train our system with a user simulator, which is itself trained to describe the differences between target and candidate images. The efficacy of our approach is demonstrated in a footwear retrieval application. Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.00145v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"39309\", \"63954\", \"102580\", \"93187\", \"30600\", \"43864\", \"53680\", \"56271\", \"57769\", \"58028\", \"65855\", \"68658\", \"70460\", \"71883\", \"75813\", \"106682\", \"117426\", \"119906\", \"183623\", \"108729\", \"114022\", \"115043\", \"121544\", \"126198\", \"127112\", \"127994\", \"131626\", \"151265\", \"157078\", \"157302\", \"205306\", \"157175\", \"168379\", \"169444\", \"189714\", \"196779\", \"200553\", \"205616\", \"245667\"], \"outgoing_citations\": [\"255095\", \"263960\", \"271085\", \"271367\", \"273696\", \"276436\", \"277773\", \"279518\", \"279678\", \"281953\", \"287427\", \"291916\", \"295204\", \"301565\", \"301767\", \"303011\", \"303452\", \"303534\", \"312090\", \"312643\", \"330936\"]}","task_split":"paper_retrieval"}
{"document_id":"233751","document_content":"# Competitive Training of Mixtures of Independent Deep Generative Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervised learning, however, is often concerned with training a single model to capture the overall distribution or aspects thereof. Inspired by clustering approaches, we consider mixtures of implicit generative models that ``disentangle'' the independent generative mechanisms underlying the data. Relying on an additional set of discriminators, we propose a competitive training procedure in which the models only need to capture the portion of the data distribution from which they can produce realistic samples. As a by-product, each model is simpler and faster to train. We empirically show that our approach splits the training distribution in a sensible way and increases the quality of the generated samples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.11130v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"38128\", \"48542\", \"91004\", \"115856\", \"128671\", \"143581\", \"187863\", \"154401\", \"154589\", \"183257\", \"183732\", \"208041\", \"229064\"], \"outgoing_citations\": [\"208041\", \"229068\", \"241933\", \"242239\", \"248417\", \"249069\", \"251597\", \"256720\", \"259052\", \"259283\", \"264374\", \"265075\", \"269271\", \"272623\", \"274247\", \"280109\", \"280365\", \"280893\", \"349217\"]}","task_split":"paper_retrieval"}
{"document_id":"233761","document_content":"# Demand-Weighted Completeness Prediction for a Knowledge Base\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.11109v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"7052\", \"98792\", \"116467\", \"138193\"], \"outgoing_citations\": [\"277749\"]}","task_split":"paper_retrieval"}
{"document_id":"234004","document_content":"# An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n- Robotics\n## Abstract\nRehabilitation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISIE.2017.8001435\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"234010\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"234141","document_content":"# Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nBuilding intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1805.00462v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"34439\", \"202748\"], \"outgoing_citations\": [\"243782\", \"265222\", \"271085\", \"271367\", \"271497\", \"272298\", \"277491\", \"277941\", \"281257\", \"287823\", \"290891\", \"291102\", \"291892\", \"294144\", \"303349\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"234204","document_content":"# Surveillance Face Recognition Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition (FR) is one of the most extensively investigated problems in computer vision. Significant progress in FR has been made due to the recent introduction of the larger scale FR challenges, particularly with constrained social media web images, e.g. high-resolution photos of celebrity faces taken by professional photo-journalists. However, the more challenging FR in unconstrained and low-resolution surveillance images remains largely under-studied. To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly the only true surveillance FR benchmark to our best knowledge, where low-resolution images are not synthesised by artificial down-sampling of native high-resolution images. This challenge contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. As a consequence, it presents an extremely challenging FR benchmark. We benchmark the FR performance on this challenge using five representative deep learning face recognition models, in comparison to existing benchmarks. We show that the current state of the arts are still far from being satisfactory to tackle the under-investigated surveillance FR problem in practical forensic scenarios. Face recognition is generally more difficult in an open-set setting which is typical for surveillance scenarios, owing to a large number of non-target people (distractors) appearing open spaced scenes. This is evidently so that on the new Surveillance FR Challenge, the top-performing CentreFace deep learning FR model on the MegaFace benchmark can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09691v6\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"50673\", \"79880\", \"7226\", \"12349\", \"143810\", \"111622\", \"149113\", \"152527\", \"154138\", \"223755\", \"230097\"], \"outgoing_citations\": [\"233856\", \"247921\", \"253395\", \"259108\", \"267612\", \"272314\", \"278071\", \"281201\", \"288238\", \"288545\", \"300053\", \"302157\", \"302985\", \"305359\", \"310635\", \"320186\", \"320918\", \"328120\", \"331074\", \"346265\"]}","task_split":"paper_retrieval"}
{"document_id":"234287","document_content":"# Driving Policy Transfer via Modularity and Abstraction\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nEnd-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches. The key idea is to encapsulate the driving policy such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. We evaluate the presented approach in simulated urban environments and in the real world. In particular, we transfer a driving policy trained in simulation to a 1\/5-scale robotic truck that is deployed in a variety of conditions, with no finetuning, on two continents. The supplementary video can be viewed at https:\/\/youtu.be\/BrMDJqI6H5U","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09364v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"20946\", \"23360\", \"24124\", \"31846\", \"60310\", \"75603\", \"76756\", \"131263\", \"136922\", \"181938\", \"10008\", \"13581\", \"14495\", \"17417\", \"24698\", \"28886\", \"37143\", \"39672\", \"52145\", \"52785\", \"58889\", \"64677\", \"74992\", \"74982\", \"75104\", \"75102\", \"57444\", \"95409\", \"104690\", \"105429\", \"106863\", \"128789\", \"135780\", \"142303\", \"149829\", \"163665\", \"77336\", \"85695\", \"87242\", \"90871\", \"103411\", \"103790\", \"104828\", \"106121\", \"106160\", \"106604\", \"112808\", \"115020\", \"117151\", \"117327\", \"119743\", \"123870\", \"127001\", \"131180\", \"136143\", \"139098\", \"142021\", \"148111\", \"150096\", \"151367\", \"152470\", \"153475\", \"153686\", \"165833\", \"175919\", \"185463\", \"149380\", \"149759\", \"161338\", \"162423\", \"165689\", \"170410\", \"174444\", \"177490\", \"75043\", \"185747\", \"185951\", \"186524\", \"189835\", \"192533\", \"193618\", \"194653\", \"196226\", \"196770\", \"199443\", \"201482\", \"205912\", \"206571\", \"206814\", \"210234\", \"216952\", \"226824\", \"240173\", \"203355\", \"224223\", \"227720\", \"247776\"], \"outgoing_citations\": [\"243591\", \"246921\", \"252062\", \"253042\", \"253112\", \"253177\", \"253953\", \"254494\", \"255139\", \"260284\", \"261786\", \"263733\", \"267832\", \"269012\", \"270419\", \"271975\", \"277452\", \"278822\", \"280570\", \"282684\", \"283150\", \"284134\", \"292178\"]}","task_split":"paper_retrieval"}
{"document_id":"234325","document_content":"# On the effectiveness of task granularity for transfer learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning. For solving the new task domain in transfer learning, we freeze the trained encoder and fine-tune a neural net on the target domain. We train on the Something-Something dataset with over 220, 000 videos, and multiple levels of target granularity, including 50 action groups, 174 fine-grained action categories and captions. Classification and captioning with Something-Something are challenging because of the subtle differences between actions, applied to thousands of different object classes, and the diversity of captions penned by crowd actors. Our model performs better than existing classification baselines for SomethingSomething, with impressive fine-grained results. And it yields a strong baseline on the new Something-Something captioning task. Experiments reveal that training with more fine-grained tasks tends to produce better features for transfer learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09235v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"59457\", \"2783\", \"116568\", \"60281\", \"17062\", \"28877\", \"70109\", \"131873\", \"111954\", \"133390\", \"138944\", \"170057\", \"164410\", \"167525\", \"169997\", \"172394\", \"190229\", \"206057\", \"209553\", \"249567\"], \"outgoing_citations\": [\"245503\", \"249567\", \"259012\", \"263838\", \"277530\", \"282800\", \"288919\", \"303359\", \"312704\", \"320332\"]}","task_split":"paper_retrieval"}
{"document_id":"234346","document_content":"# An Integrated Framework for AI Assisted Level Design in 2D Platformers\n## Categories\n- Artificial Intelligence\n## Abstract\nThe design of video game levels is a complex and critical task. Levels need to elicit fun and challenge while avoiding frustration at all costs. In this paper, we present a framework to assist designers in the creation of levels for 2D platformers. Our framework provides designers with a toolbox (i) to create 2D platformer levels, (ii) to estimate the difficulty and probability of success of single jump actions (the main mechanics of platformer games), and (iii) a set of metrics to evaluate the difficulty and probability of completion of entire levels. At the end, we present the results of a set of experiments we carried out with human players to validate the metrics included in our framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09153v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"234368","document_content":"# Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings\n## Categories\n- Machine Learning\n- Social and Information Networks\n- Applications\n## Abstract\nFake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles. In contrast, we focus on content-based detection of fake news articles, while assuming that we have a small amount of labels, made available by manual fact-checkers or automated sources. We argue this is a more realistic setting in the presence of massive amounts of content, most of which cannot be easily factchecked. To that end, we represent collections of news articles as multi-dimensional tensors, leverage tensor decomposition to derive concise article embeddings that capture spatial\/contextual information about each news article, and use those embeddings to create an article-by-article graph on which we propagate limited labels. Results on three real-world datasets show that our method performs on par or better than existing models that are fully supervised, in that we achieve better detection accuracy using fewer labels. In particular, our proposed method achieves 75.43% of accuracy using only 30% of labels of a public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our method achieves 70.92% of accuracy in a large dataset using only 2% of labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09088v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"63247\", \"96562\", \"20626\", \"42217\", \"56773\", \"66278\", \"68638\", \"69141\", \"69136\", \"75222\", \"112064\", \"121634\", \"126132\", \"128448\", \"129853\", \"194215\", \"219169\", \"156462\", \"148166\", \"167944\", \"168707\", \"171063\", \"172209\"], \"outgoing_citations\": [\"155561\", \"270399\", \"271021\", \"323227\"]}","task_split":"paper_retrieval"}
{"document_id":"234377","document_content":"# Unsupervised Neural Machine Translation with Weight Sharing\n## Categories\n- Computation and Language\n## Abstract\nUnsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09057v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25308\", \"38855\", \"34638\", \"43451\", \"93412\", \"132055\", \"158975\", \"77667\", \"139549\", \"94039\", \"100144\", \"113957\", \"113988\", \"116404\", \"129632\", \"131513\", \"131659\", \"132255\", \"132587\", \"132406\", \"129799\", \"157095\", \"164256\", \"168976\", \"169317\", \"170174\", \"178909\", \"181277\", \"182181\", \"182946\", \"183037\", \"182210\", \"187452\", \"192337\", \"194844\", \"200292\", \"203054\", \"210331\", \"212367\", \"219175\", \"220351\", \"234807\"], \"outgoing_citations\": [\"211732\", \"248149\", \"249315\", \"251816\", \"251974\", \"255999\", \"267489\", \"271503\", \"280421\", \"290356\", \"290500\", \"298543\", \"301840\"]}","task_split":"paper_retrieval"}
{"document_id":"234384","document_content":"# Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.09021v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"12087\", \"37082\", \"205022\", \"12251\", \"24770\", \"74878\", \"81948\", \"91976\", \"102739\", \"135800\", \"142316\", \"206151\", \"205899\", \"214221\", \"249726\"], \"outgoing_citations\": [\"262064\", \"266198\", \"271158\", \"279053\", \"286922\", \"289232\", \"296192\", \"297261\"]}","task_split":"paper_retrieval"}
{"document_id":"234434","document_content":"# Assessing Language Models with Scaling Properties\n## Categories\n- Computation and Language\n## Abstract\nLanguage models have primarily been evaluated with perplexity. While perplexity quantifies the most comprehensible prediction performance, it does not provide qualitative information on the success or failure of models. Another approach for evaluating language models is thus proposed, using the scaling properties of natural language. Five such tests are considered, with the first two accounting for the vocabulary population and the other three for the long memory of natural language. The following models were evaluated with these tests: n-grams, probabilistic context-free grammar (PCFG), Simon and Pitman-Yor (PY) processes, hierarchical PY, and neural language models. Only the neural language models exhibit the long memory properties of natural language, but to a limited degree. The effectiveness of every test of these models is also discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08881v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"195623\", \"234750\"], \"outgoing_citations\": [\"234750\", \"250815\", \"259145\", \"260929\", \"261140\", \"278065\", \"298750\", \"317675\", \"328234\", \"356974\"]}","task_split":"paper_retrieval"}
{"document_id":"234436","document_content":"# Data-driven Summarization of Scientific Articles\n## Categories\n- Computation and Language\n## Abstract\nData-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08875v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24431\", \"46562\", \"80738\", \"138936\", \"177966\", \"96769\", \"127832\", \"142835\", \"173799\", \"153936\", \"174508\", \"182634\", \"192018\", \"252381\"], \"outgoing_citations\": [\"263905\", \"277028\", \"281508\", \"282931\", \"286677\", \"289107\", \"310136\"]}","task_split":"paper_retrieval"}
{"document_id":"234523","document_content":"# Large Scale Scene Text Verification with Guided Attention\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMany tasks are related to determining if a particular text string exists in an image. In this work, we propose a new framework that learns this task in an end-to-end way. The framework takes an image and a text string as input and then outputs the probability of the text string being present in the image. This is the first end-to-end framework that learns such relationships between text and images in scene text area. The framework does not require explicit scene text detection or recognition and thus no bounding box annotations are needed for it. It is also the first work in scene text area that tackles suh a weakly labeled problem. Based on this framework, we developed a model called Guided Attention. Our designed model achieves much better results than several state-of-the-art scene text reading based solutions for a challenging Street View Business Matching task. The task tries to find correct business names for storefront images and the dataset we collected for it is substantially larger, and more challenging than existing scene text dataset. This new real-world task provides a new perspective for studying scene text related problems. We also demonstrate the uniqueness of our task via a comparison between our problem and a typical Visual Question Answering problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08588v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"269108\", \"271112\", \"274093\", \"284871\", \"294606\", \"296685\", \"301360\", \"311196\", \"319833\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"234554","document_content":"# Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories\n## Categories\n- Computation and Language\n## Abstract\nThe first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity. We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8% improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08460v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"121898\", \"5406\", \"7966\", \"21873\", \"23992\", \"96431\", \"103097\", \"189763\", \"219367\"], \"outgoing_citations\": [\"248226\", \"268414\", \"274533\", \"283927\", \"295323\"]}","task_split":"paper_retrieval"}
{"document_id":"234556","document_content":"# Attention Based Natural Language Grounding by Navigating Virtual Environment\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this work, we focus on the problem of grounding language by training an agent to follow a set of natural language instructions and navigate to a target object in an environment. The agent receives visual information through raw pixels and a natural language instruction telling what task needs to be achieved and is trained in an end-to-end way. We develop an attention mechanism for multi-modal fusion of visual and textual modalities that allows the agent to learn to complete the task and achieve language grounding. Our experimental results show that our attention mechanism outperforms the existing multi-modal fusion mechanisms proposed for both 2D and 3D environments in order to solve the above-mentioned task in terms of both speed and success rate. We show that the learnt textual representations are semantically meaningful as they follow vector arithmetic in the embedding space. The effectiveness of our attention approach over the contemporary fusion mechanisms is also highlighted from the textual embeddings learnt by the different approaches. We also show that our model generalizes effectively to unseen scenarios and exhibit zero-shot generalization capabilities both in 2D and 3D environments. The code for our 2D environment as well as the models that we developed for both 2D and 3D are available at https:\/\/github.com\/rl-lang-grounding\/rl-lang-ground.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08454v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"77439\", \"102580\", \"36454\", \"188484\"], \"outgoing_citations\": [\"243782\", \"254920\", \"263104\", \"284468\", \"293071\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"234587","document_content":"# Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks\n## Categories\n- Computation and Language\n## Abstract\nSemantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08313v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"30612\", \"4956\", \"16621\", \"70222\", \"72647\", \"79026\", \"80211\", \"5837\", \"26492\", \"48161\", \"50517\", \"53366\", \"61562\", \"89376\", \"104906\", \"113226\", \"84541\", \"91829\", \"5843\", \"96323\", \"108324\", \"154779\", \"117465\", \"122331\", \"124756\", \"125291\", \"125542\", \"127692\", \"132503\", \"133091\", \"133899\", \"136153\", \"138622\", \"149230\", \"205283\", \"152767\", \"160177\", \"162910\", \"163327\", \"168146\", \"169587\", \"171238\", \"176190\", \"182540\", \"185805\", \"198349\", \"212092\", \"213229\", \"228855\", \"249990\"], \"outgoing_citations\": [\"268098\", \"268788\", \"268812\", \"271527\", \"274776\", \"276493\", \"280973\", \"281225\", \"290777\", \"296274\", \"296283\", \"317974\"]}","task_split":"paper_retrieval"}
{"document_id":"234617","document_content":"# PeRView: A Framework for Personalized Review Selection Using Micro-Reviews\n## Categories\n- Information Retrieval\n- Computation and Language\n- Social and Information Networks\n## Abstract\nIn the contemporary era, social media has its influence on people in making decisions. The proliferation of online reviews with diversified and verbose content often causes problems inaccurate decision making. Since online reviews have an impact on people of all walks of life while taking decisions, choosing appropriate reviews based on the podsolization consisting is very important since it relies on using such micro-reviews consistency to evaluate the review set section. Micro-reviews are very concise and directly talk about product or service instead of having unnecessary verbose content. Thus, micro-reviews can help in choosing reviews based on their personalized consistency that is related to directly or indirectly to the main profile of the reviews. Personalized reviews selection that is highly relevant with high personalized coverage in terms of matching with micro-reviews is the main problem that is considered in this paper. Furthermore, personalization with user preferences while making review selection is also considered based on the personalized users' profile. Towards this end, we proposed a framework known as PeRView for personalized review selection using micro-reviews based on the proposed evaluation metric approach which considering two main factors (personalized matching score and subset size). Personalized Review Selection Algorithm (PRSA) is proposed which makes use of multiple similarity measures merged to have highly efficient personalized reviews matching function for selection. The experimental results based on using reviews dataset which is collected from YELP.COM while micro-reviews dataset is obtained from Foursqure.COM. show that the personalized reviews selection is a very empirical case of study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08234v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.SI\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"234623","document_content":"# Adaptive Performance Assessment For Drivers Through Behavioral Advantage\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nThe potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method that compares the performance of drivers or autonomous driving agents even if the environmental conditions are different, by using the driver behavioral advantage instead of absolute metrics, which efficiently removes the environmental factors. A concrete application of the method is also presented, where the performance of more than 100 truck drivers was evaluated and ranked in terms of fuel efficiency, covering more than 90,000 trips spanning an average of 300 miles in a variety of driving conditions and environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08219v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"234631","document_content":"# Linguistically-Informed Self-Attention for Semantic Role Labeling\n## Categories\n- Computation and Language\n## Abstract\nCurrent state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08199v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"14960\", \"56964\", \"2946\", \"4018\", \"4956\", \"6019\", \"16648\", \"16621\", \"23643\", \"59368\", \"72647\", \"78288\", \"127897\", \"157592\", \"177581\", \"12821\", \"29188\", \"32301\", \"35572\", \"44947\", \"48161\", \"48862\", \"49525\", \"53168\", \"56614\", \"57466\", \"58701\", \"72660\", \"73062\", \"73291\", \"77042\", \"77047\", \"77364\", \"77812\", \"79142\", \"81532\", \"82324\", \"83446\", \"94951\", \"97791\", \"101065\", \"104905\", \"128048\", \"200289\", \"81940\", \"84032\", \"84541\", \"78769\", \"88056\", \"91829\", \"92475\", \"94934\", \"95183\", \"95433\", \"95581\", \"95840\", \"96156\", \"96251\", \"96298\", \"97592\", \"100437\", \"100582\", \"104406\", \"108201\", \"117465\", \"122609\", \"125260\", \"126026\", \"127356\", \"127612\", \"129838\", \"131352\", \"131627\", \"135265\", \"138930\", \"140456\", \"144813\", \"150636\", \"151257\", \"154867\", \"155080\", \"156931\", \"157137\", \"157178\", \"158797\", \"165653\", \"168201\", \"169251\", \"169520\", \"171484\", \"171817\", \"174289\", \"157513\", \"156847\", \"159954\", \"164010\", \"159947\", \"165559\", \"166223\", \"166853\", \"167062\", \"167691\", \"168146\", \"168539\", \"168581\", \"168604\", \"163752\", \"169230\", \"169254\", \"169587\", \"169687\", \"169799\", \"170070\", \"170543\", \"171238\", \"171944\", \"173016\", \"169264\", \"173476\", \"174756\", \"175042\", \"176158\", \"177231\", \"178587\", \"181468\", \"182093\", \"182313\", \"182601\", \"182935\", \"183080\", \"183096\", \"183110\", \"183578\", \"186043\", \"191177\", \"191740\", \"192043\", \"192046\", \"192040\", \"192372\", \"192418\", \"193908\", \"195017\", \"195384\", \"164377\", \"197226\", \"200277\", \"202775\", \"212092\", \"218179\", \"233448\", \"210435\", \"215434\", \"205419\", \"227981\", \"232290\"], \"outgoing_citations\": [\"232290\", \"248279\", \"260541\", \"262592\", \"265431\", \"268263\", \"271527\", \"272944\", \"276493\", \"278531\", \"281121\", \"281169\", \"291941\", \"296163\", \"296583\", \"296712\", \"303003\", \"317874\"]}","task_split":"paper_retrieval"}
{"document_id":"234646","document_content":"# Word Embedding Perturbation for Sentence Classification\n## Categories\n- Computation and Language\n## Abstract\nIn this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods. Specifically, we attempt several types of noise to perturb the input word embedding, such as Gaussian noise, Bernoulli noise, and adversarial noise, etc. We also apply several constraints on different types of noise. By implementing these proposed data augmentation methods, the baseline models can gain improvements on several sentence classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08166v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"50860\", \"27012\", \"28119\", \"13501\", \"100560\", \"123947\", \"114239\", \"117228\", \"157503\", \"220648\", \"211411\", \"214997\"], \"outgoing_citations\": [\"154631\", \"261446\", \"300141\", \"308505\", \"317966\"]}","task_split":"paper_retrieval"}
{"document_id":"234655","document_content":"# Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDistributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08139v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"55352\", \"22482\", \"33423\", \"50824\", \"61705\", \"140480\", \"171295\", \"179900\", \"195097\", \"207045\"], \"outgoing_citations\": [\"268518\", \"276805\", \"283697\", \"292448\", \"300548\", \"309965\"]}","task_split":"paper_retrieval"}
{"document_id":"234679","document_content":"# Inducing and Embedding Senses with Scaled Gumbel Softmax\n## Categories\n- Computation and Language\n## Abstract\nMethods for learning word sense embeddings represent a single word with multiple sense-specific vectors. These methods should not only produce interpretable sense embeddings, but should also learn how to select which sense to use in a given context. We propose an unsupervised model that learns sense embeddings using a modified Gumbel softmax function, which allows for differentiable discrete sense selection. Our model produces sense embeddings that are competitive (and sometimes state of the art) on multiple similarity based downstream evaluations. However, performance on these downstream evaluations tasks does not correlate with interpretability of sense embeddings, as we discover through an interpretability comparison with competing multi-sense embeddings. While many previous approaches perform well on downstream evaluations, they do not produce interpretable embeddings and learn duplicated sense groups; our method achieves the best of both worlds.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.08077v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"191007\"], \"outgoing_citations\": [\"227792\", \"228875\", \"232527\", \"258826\", \"263629\", \"268833\", \"278993\", \"284357\", \"287071\", \"291574\", \"295613\", \"311843\", \"313947\", \"316936\", \"325742\", \"333603\", \"357169\", \"360209\"]}","task_split":"paper_retrieval"}
{"document_id":"234835","document_content":"# Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we apply neural networks into digital marketing world for the purpose of better targeting the potential customers. To do so, we model the customer online behaviours using dedicated neural network architectures. Starting from user searched keywords in a search engine to the landing page and different following pages, until the user left the site, we model the whole visited journey with a Recurrent Neural Network (RNN), together with Convolution Neural Networks (CNN) that can take into account of the semantic meaning of user searched keywords and different visited page names. With such model, we use Monte Carlo simulation to estimate the conversion rates of each potential customer in the future visiting. We believe our concept and the preliminary promising results in this paper enable the use of largely available customer online behaviours data for advanced digital marketing analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.07669v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"156704\", \"159163\", \"169598\", \"179165\"], \"outgoing_citations\": [\"258393\", \"263008\", \"276513\", \"278997\", \"290033\", \"313320\"]}","task_split":"paper_retrieval"}
{"document_id":"234907","document_content":"# Sentence Simplification with Memory-Augmented Neural Networks\n## Categories\n- Computation and Language\n## Abstract\nSentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.07445v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"105099\", \"127956\", \"154650\", \"163045\", \"162482\", \"191112\", \"214400\"], \"outgoing_citations\": [\"270048\", \"288415\"]}","task_split":"paper_retrieval"}
{"document_id":"234937","document_content":"# Survey of Face Detection on Low-quality Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.07362v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"110480\", \"94389\", \"105448\", \"121567\", \"172445\", \"191865\", \"205542\"], \"outgoing_citations\": [\"246907\", \"256007\", \"258352\", \"258667\", \"259852\", \"264188\", \"264615\", \"275209\", \"276692\", \"278071\", \"285059\", \"287166\", \"290219\", \"290682\", \"294611\", \"300053\", \"326112\", \"327014\", \"344630\"]}","task_split":"paper_retrieval"}
{"document_id":"235028","document_content":"# Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning\n## Categories\n- Computation and Language\n## Abstract\nIn formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize a similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.07068v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"94730\"], \"outgoing_citations\": [\"257324\", \"268228\", \"281121\", \"297648\"]}","task_split":"paper_retrieval"}
{"document_id":"235228","document_content":"# On Learning Intrinsic Rewards for Policy Gradient Methods\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good by the agent designer. A number of different formulations of the reward-design problem, or close variants thereof, have been proposed in the literature. In this paper we build on the Optimal Rewards Framework of Singh et.al. that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the task-specifying or extrinsic reward function. Previous work in this framework has shown how good intrinsic reward functions can be learned for lookahead search based planning agents. Whether it is possible to learn intrinsic reward functions for learning agents remains an open problem. In this paper we derive a novel algorithm for learning intrinsic rewards for policy-gradient based learning agents. We compare the performance of an augmented agent that uses our algorithm to provide additive intrinsic rewards to an A2C-based policy learner (for Atari games) and a PPO-based policy learner (for Mujoco domains) with a baseline agent that uses the same policy learners but with only extrinsic rewards. Our results show improved performance on most but not all of the domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.06459v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"23640\", \"1730\", \"5117\", \"7093\", \"24689\", \"29108\", \"35959\", \"40273\", \"70453\", \"78473\", \"95854\", \"131702\", \"76\", \"6332\", \"12560\", \"12912\", \"40884\", \"43689\", \"45631\", \"46858\", \"57302\", \"60357\", \"62688\", \"67224\", \"67924\", \"69685\", \"73786\", \"74451\", \"93557\", \"94620\", \"96784\", \"101609\", \"116987\", \"121570\", \"150868\", \"153376\", \"208707\", \"88922\", \"88976\", \"96390\", \"111431\", \"111603\", \"114282\", \"113549\", \"119620\", \"132104\", \"133857\", \"137294\", \"137299\", \"139426\", \"143361\", \"147937\", \"151100\", \"151618\", \"162581\", \"159333\", \"160736\", \"167562\", \"179901\", \"183337\", \"184126\", \"173805\", \"198373\", \"206945\", \"211558\", \"230616\", \"207506\", \"230669\"], \"outgoing_citations\": [\"230669\", \"270925\", \"272043\", \"272422\", \"272464\", \"280202\", \"280296\", \"280457\", \"280870\", \"291025\", \"293922\", \"310171\"]}","task_split":"paper_retrieval"}
{"document_id":"235635","document_content":"# A comparison of methods for model selection when estimating individual treatment effects\n## Categories\n- Machine Learning\n## Abstract\nPractitioners in medicine, business, political science, and other fields are increasingly aware that decisions should be personalized to each patient, customer, or voter. A given treatment (e.g. a drug or advertisement) should be administered only to those who will respond most positively, and certainly not to those who will be harmed by it. Individual-level treatment effects can be estimated with tools adapted from machine learning, but different models can yield contradictory estimates. Unlike risk prediction models, however, treatment effect models cannot be easily evaluated against each other using a held-out test set because the true treatment effect itself is never directly observed. Besides outcome prediction accuracy, several metrics that can leverage held-out data to evaluate treatment effects models have been proposed, but they are not widely used. We provide a didactic framework that elucidates the relationships between the different approaches and compare them all using a variety of simulations of both randomized and observational data. Our results show that researchers estimating heterogenous treatment effects need not limit themselves to a single model-fitting algorithm. Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set. The model minimizing that objective should be used for estimating the individual treatment effect for future individuals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.05146v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"69907\", \"57334\", \"69758\", \"100767\", \"129296\", \"23622\", \"27240\", \"104468\", \"167329\", \"200374\", \"215882\"], \"outgoing_citations\": [\"256278\", \"265629\"]}","task_split":"paper_retrieval"}
{"document_id":"235729","document_content":"# Learning Warped Guidance for Blind Face Restoration\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet). However, the degraded observation and guided image generally are different in pose, illumination and expression, thereby making plain CNNs (e.g., U-Net) fail to recover fine and identity-aware facial details. To tackle this issue, our GFRNet model includes both a warping subnetwork (WarpNet) and a reconstruction subnetwork (RecNet). The WarpNet is introduced to predict flow field for warping the guided image to correct pose and expression (i.e., warped guidance), while the RecNet takes the degraded observation and warped guidance as input to produce the restoration result. Due to that the ground-truth flow field is unavailable, landmark loss together with total variation regularization are incorporated to guide the learning of WarpNet. Furthermore, to make the model applicable to blind restoration, our GFRNet is trained on the synthetic data with versatile settings on blur kernel, noise level, downsampling scale factor, and JPEG quality factor. Experiments show that our GFRNet not only performs favorably against the state-of-the-art image and face restoration methods, but also generates visually photo-realistic results on real degraded facial images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.04829v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5231\", \"26240\", \"37459\", \"76110\", \"109960\", \"19343\", \"28953\", \"49874\", \"75333\", \"76015\", \"107094\", \"83295\", \"99898\", \"131136\", \"196750\", \"94977\", \"100258\", \"105800\", \"108335\", \"125816\", \"132093\", \"149056\", \"205461\", \"180372\", \"189528\", \"197058\"], \"outgoing_citations\": [\"248969\", \"258886\", \"267785\", \"268494\", \"269384\", \"271984\", \"274441\", \"276749\", \"279182\", \"287801\", \"288238\", \"292758\", \"296033\", \"296510\", \"313846\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"235857","document_content":"# Image Correction via Deep Reciprocating HDR Transformation\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nImage correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under\/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.04371v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"586\", \"14984\", \"15203\", \"19090\", \"30441\", \"36087\", \"49720\", \"62559\", \"72450\", \"92970\", \"125614\", \"134931\", \"136704\", \"174456\", \"85785\", \"94732\", \"122944\", \"124998\", \"133418\", \"149650\", \"160206\", \"146519\", \"187468\", \"202671\", \"209098\", \"205608\"], \"outgoing_citations\": [\"251150\", \"252829\", \"255403\", \"257567\", \"258815\", \"259658\", \"259659\", \"270221\", \"277607\", \"307791\", \"319785\"]}","task_split":"paper_retrieval"}
{"document_id":"235909","document_content":"# Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nModern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data at an unprecedented scale and depth. Patients, doctors, healthcare providers, and researchers depend on analytical models derived from such data sources to remotely monitor patients, early-diagnose diseases, and find personalized treatments and medications. However, without appropriate privacy protection, conducting data analytics becomes a source of a privacy nightmare. In this article, we present the research challenges in developing practical privacy-preserving analytics in healthcare information systems. The study is based on kHealth - a personalized digital healthcare information system that is being developed and tested for disease monitoring. We analyze the data and analytic requirements for the involved parties, identify the privacy assets, analyze existing privacy substrates, and discuss the potential tradeoff among privacy, efficiency, and model quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MIC.2018.112102519\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CR\", \"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computers and Society\"], \"incoming_citations\": [\"36531\", \"33446\", \"72756\", \"80446\", \"53708\", \"82838\", \"86155\", \"207072\", \"246209\"], \"outgoing_citations\": [\"327473\", \"351718\"]}","task_split":"paper_retrieval"}
{"document_id":"235953","document_content":"# Multi-Task Learning for Argumentation Mining in Low-Resource Settings\n## Categories\n- Computation and Language\n## Abstract\nWe investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.04083v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38279\", \"70000\", \"20783\", \"64589\", \"148371\", \"94618\", \"136366\", \"177565\", \"185224\", \"208636\", \"207670\", \"223563\"], \"outgoing_citations\": [\"247830\", \"259758\", \"260462\", \"268140\", \"268433\", \"268968\", \"269899\", \"271158\", \"272944\", \"273558\", \"278531\", \"281169\", \"281971\", \"293849\", \"294248\", \"300302\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"235975","document_content":"# Emergent Communication through Negotiation\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n- Multiagent Systems\n## Abstract\nMulti-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -- one grounded in the semantics of the game, and one which is \\textit{a priori} ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.03980v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [\"3136\", \"38642\", \"6801\", \"6960\", \"12902\", \"21525\", \"23839\", \"35913\", \"42161\", \"43439\", \"44981\", \"200130\", \"47080\", \"50249\", \"50758\", \"54210\", \"59827\", \"63104\", \"73543\", \"73552\", \"80268\", \"81676\", \"86099\", \"89663\", \"90372\", \"92818\", \"95323\", \"95657\", \"96571\", \"102464\", \"120519\", \"121221\", \"127495\", \"131992\", \"132745\", \"132309\", \"136313\", \"139142\", \"139735\", \"144255\", \"144206\", \"146176\", \"154477\", \"198468\", \"200580\", \"217297\", \"151570\", \"156563\", \"157226\", \"157663\", \"158217\", \"162187\", \"168583\", \"164276\", \"170240\", \"182237\", \"183158\", \"183991\", \"184148\", \"186337\", \"195416\", \"201667\", \"213637\", \"209920\", \"213513\", \"219388\", \"219937\"], \"outgoing_citations\": [\"251601\", \"253638\", \"256103\", \"256504\", \"263622\", \"264897\", \"271197\", \"274315\", \"277491\", \"291892\"]}","task_split":"paper_retrieval"}
{"document_id":"236071","document_content":"# Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma is a curable aggressive skin cancer if detected early. Typically, the diagnosis involves initial screening with subsequent biopsy and histopathological examination if necessary. Computer aided diagnosis offers an objective score that is independent of clinical experience and the potential to lower the workload of a dermatologist. In the recent past, success of deep learning algorithms in the field of general computer vision has motivated successful application of supervised deep learning methods in computer aided melanoma recognition. However, large quantities of labeled images are required to make further improvements on the supervised method. A good annotation generally requires clinical and histological confirmation, which requires significant effort. In an attempt to alleviate this constraint, we propose to use categorical generative adversarial network to automatically learn the feature representation of dermoscopy images in an unsupervised and semi-supervised manner. Thorough experiments on ISIC 2016 skin lesion chal- lenge demonstrate that the proposed feature learning method has achieved an average precision score of 0.424 with only 140 labeled images. Moreover, the proposed method is also capable of generating real-world like dermoscopy images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.03700v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"43384\", \"45212\", \"87658\", \"72537\", \"190228\", \"217232\", \"208337\"], \"outgoing_citations\": [\"258792\", \"265273\", \"272139\", \"281718\", \"282613\", \"290407\", \"291089\", \"294604\", \"298118\", \"299339\", \"309807\"]}","task_split":"paper_retrieval"}
{"document_id":"236241","document_content":"# Human-Guided Data Exploration\n## Categories\n- Machine Learning\n- Human-Computer Interaction\n## Abstract\nThe outcome of the explorative data analysis (EDA) phase is vital for successful data analysis. EDA is more effective when the user interacts with the system used to carry out the exploration. In the recently proposed paradigm of iterative data mining the user controls the exploration by inputting knowledge in the form of patterns observed during the process. The system then shows the user views of the data that are maximally informative given the user's current knowledge. Although this scheme is good at showing surprising views of the data to the user, there is a clear shortcoming: the user cannot steer the process. In many real cases we want to focus on investigating specific questions concerning the data. This paper presents the Human Guided Data Exploration framework, generalising previous research. This framework allows the user to incorporate existing knowledge into the exploration process, focus on exploring a subset of the data, and compare different complex hypotheses concerning relations in the data. The framework utilises a computationally efficient constrained randomisation scheme. To showcase the framework, we developed a free open-source tool, using which the empirical evaluation on real-world datasets was carried out. Our evaluation shows that the ability to focus on particular subsets and being able to compare hypotheses are important additions to the interactive iterative data mining process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.03194v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.HC\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"187431\"], \"outgoing_citations\": [\"118133\", \"252672\"]}","task_split":"paper_retrieval"}
{"document_id":"236404","document_content":"# Detecting Multi-Oriented Text with Corner-based Region Proposals\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPrevious approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https:\/\/github.com\/xhzdeng\/crpn","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.neucom.2019.01.013\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"80383\", \"101165\", \"155848\", \"166479\"], \"outgoing_citations\": [\"249405\", \"256364\", \"257197\", \"258992\", \"262570\", \"263976\", \"270197\", \"270668\", \"271112\", \"272390\", \"272481\", \"276664\", \"279687\", \"279950\", \"284871\", \"285788\", \"291983\", \"293979\", \"294023\", \"294087\", \"294606\", \"295358\", \"295557\", \"299461\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"236435","document_content":"# Simple Models for Word Formation in English Slang\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe propose generative models for three types of extra-grammatical word formation phenomena abounding in English slang: Blends, Clippings, and Reduplicatives. Adopting a data-driven approach coupled with linguistic knowledge, we propose simple models with state of the art performance on human annotated gold standard datasets. Overall, our models reveal insights into the generative processes of word formation in slang -- insights which are increasingly relevant in the context of the rising prevalence of slang and non-standard varieties on the Internet.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02596v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"236449","document_content":"# Guiding Neural Machine Translation with Retrieved Translation Pieces\n## Categories\n- Computation and Language\n## Abstract\nOne of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect $n$-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call \"translation pieces\". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02559v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54897\", \"4364\", \"9883\", \"15735\", \"21633\", \"37336\", \"46280\", \"56086\", \"83039\", \"89936\", \"94631\", \"11572\", \"15894\", \"22116\", \"23662\", \"46950\", \"46972\", \"47693\", \"50900\", \"55178\", \"56596\", \"74412\", \"97283\", \"277667\", \"113982\", \"154483\", \"160143\", \"163041\", \"171969\", \"197016\", \"219825\", \"220083\"], \"outgoing_citations\": [\"211732\", \"254185\", \"258858\", \"263927\", \"265947\", \"271935\", \"278199\", \"284263\", \"290778\", \"290995\", \"291043\"]}","task_split":"paper_retrieval"}
{"document_id":"236468","document_content":"# Learning a Text-Video Embedding from Incomplete and Heterogeneous Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nJoint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks. Code is available at: https:\/\/github.com\/antoine77340\/Mixture-of-Embedding-Experts","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02516v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1081\", \"2284\", \"2212\", \"4348\", \"5478\", \"5960\", \"8438\", \"12103\", \"33472\", \"43252\", \"49014\", \"60281\", \"101354\", \"5726\", \"10271\", \"12778\", \"13933\", \"15699\", \"20039\", \"22104\", \"22807\", \"32325\", \"33374\", \"39527\", \"40597\", \"46080\", \"49800\", \"51492\", \"54595\", \"55460\", \"57216\", \"58876\", \"59465\", \"60135\", \"60436\", \"62053\", \"62053\", \"62318\", \"64186\", \"67133\", \"67375\", \"72354\", \"81390\", \"82713\", \"85426\", \"89020\", \"118223\", \"138177\", \"81067\", \"89825\", \"108197\", \"110610\", \"114022\", \"113808\", \"115367\", \"87013\", \"124223\", \"126167\", \"144120\", \"145702\", \"146888\", \"151327\", \"187454\", \"180920\", \"170617\", \"172394\", \"173627\", \"173844\", \"177847\", \"181897\", \"206058\"], \"outgoing_citations\": [\"221984\", \"259318\", \"259963\", \"263034\", \"263185\", \"264645\", \"265701\", \"269128\", \"269276\", \"279990\", \"282943\", \"283914\", \"285178\", \"287740\", \"294503\", \"301767\", \"302621\", \"303011\", \"303593\", \"304481\", \"310322\", \"312423\", \"312643\", \"313189\", \"313320\", \"316430\", \"319169\", \"328051\", \"351151\", \"351732\"]}","task_split":"paper_retrieval"}
{"document_id":"236477","document_content":"# Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis\n## Categories\n- Computation and Language\n## Abstract\nConventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model. We also develop two evaluation metrics to estimate if the responses are reasonable given the input. These metrics together with other two popularly used metrics were used to analyze the performance of the five proposed models on different aspects, and reinforcement learning and cycleGAN were shown to be very attractive. The evaluation metrics were also found to be well correlated with human evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02504v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"34066\", \"59514\", \"75687\", \"97875\", \"112729\", \"169928\", \"217027\", \"220353\"], \"outgoing_citations\": [\"265309\", \"276160\", \"279091\", \"280606\", \"283076\", \"291102\", \"294494\", \"296250\", \"316328\", \"320032\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"236510","document_content":"# Image Segmentation Using Subspace Representation and Sparse Decomposition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage foreground extraction is a classical problem in image processing and vision, with a large range of applications. In this dissertation, we focus on the extraction of text and graphics in mixed-content images, and design novel approaches for various aspects of this problem.   We first propose a sparse decomposition framework, which models the background by a subspace containing smooth basis vectors, and foreground as a sparse and connected component. We then formulate an optimization framework to solve this problem, by adding suitable regularizations to the cost function to promote the desired characteristics of each component. We present two techniques to solve the proposed optimization problem, one based on alternating direction method of multipliers (ADMM), and the other one based on robust regression. Promising results are obtained for screen content image segmentation using the proposed algorithm.   We then propose a robust subspace learning algorithm for the representation of the background component using training images that could contain both background and foreground components, as well as noise. With the learnt subspace for the background, we can further improve the segmentation results, compared to using a fixed subspace. Lastly, we investigate a different class of signal\/image decomposition problem, where only one signal component is active at each signal element. In this case, besides estimating each component, we need to find their supports, which can be specified by a binary mask. We propose a mixed-integer programming problem, that jointly estimates the two components and their supports through an alternating optimization scheme. We show the application of this algorithm on various problems, including image segmentation, video motion segmentation, and also separation of text from textured images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02419v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"242913\", \"252990\", \"257609\", \"259293\", \"264037\", \"271585\", \"274715\", \"279686\", \"288762\", \"295635\", \"298740\", \"302775\", \"306739\", \"309899\", \"318996\", \"324283\", \"325690\", \"361307\", \"361654\"]}","task_split":"paper_retrieval"}
{"document_id":"236568","document_content":"# On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks\n## Categories\n- Robotics\n- Computation and Language\n- Audio and Speech Processing\n- Sound\n- Human-Computer Interaction\n## Abstract\nSpeech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural network-based architectures were proposed recently and pushed the performance to a new level. However, the applicability of such neural SER models trained only on in-domain data to noisy conditions is currently under-researched. In this work, we evaluate the robustness of state-of-the-art neural acoustic emotion recognition models in human-robot interaction scenarios. We hypothesize that a robot's ego noise, room conditions, and various acoustic events that can occur in a home environment can significantly affect the performance of a model. We conduct several experiments on the iCub robot platform and propose several novel ways to reduce the gap between the model's performance during training and testing in real-world conditions. Furthermore, we observe large improvements in the model performance on the robot and demonstrate the necessity of introducing several data augmentation techniques like overlaying background noise and loudness variations to improve the robustness of the neural approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02173v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CL\", \"eess.AS\", \"cs.RO\", \"cs.SD\", \"cs.HC\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computation and Language\", \"Audio and Speech Processing\", \"Robotics\", \"Sound\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"89855\", \"90838\", \"197045\"], \"outgoing_citations\": [\"237187\", \"247041\", \"264080\", \"264732\", \"283391\", \"287247\", \"295636\"]}","task_split":"paper_retrieval"}
{"document_id":"236595","document_content":"# Structured Disentangled Representations\n## Categories\n- Machine Learning\n## Abstract\nDeep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation by introducing modifications to the standard objective function. These approaches generally assume a simple diagonal Gaussian prior and as a result are not able to reliably disentangle discrete factors of variation. We propose a two-level hierarchical objective to control relative degree of statistical independence between blocks of variables and individual variables within blocks. We derive this objective as a generalization of the evidence lower bound, which allows us to explicitly represent the trade-offs between mutual information between data and representation, KL divergence between representation and prior, and coverage of the support of the empirical data distribution. Experiments on a variety of datasets demonstrate that our objective can not only disentangle discrete variables, but that doing so also improves disentanglement of other variables and, importantly, generalization even to unseen combinations of factors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.02086v4\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7546\", \"27322\", \"38128\", \"76023\", \"80015\", \"161683\", \"169936\", \"17072\", \"27026\", \"35918\", \"39760\", \"43335\", \"60603\", \"61327\", \"69932\", \"70382\", \"72199\", \"80149\", \"84768\", \"87413\", \"90047\", \"92381\", \"95962\", \"141540\", \"183113\", \"39444\", \"78660\", \"81601\", \"81997\", \"88259\", \"100165\", \"105333\", \"112398\", \"110621\", \"113870\", \"116650\", \"117401\", \"119003\", \"119937\", \"123374\", \"132208\", \"132719\", \"136839\", \"139944\", \"141636\", \"143831\", \"145743\", \"147237\", \"160471\", \"176649\", \"184464\", \"215956\", \"156867\", \"165021\", \"165584\", \"171077\", \"171324\", \"179803\", \"183257\", \"183732\", \"184269\", \"182154\", \"201416\", \"206228\", \"184464\", \"206218\", \"207094\", \"229064\"], \"outgoing_citations\": [\"234979\", \"236093\", \"237146\", \"241933\", \"241998\", \"242002\", \"242239\", \"250353\", \"251597\", \"251693\", \"264327\", \"264782\", \"265526\", \"272675\", \"278987\", \"280398\", \"280882\", \"281227\", \"291302\", \"295909\", \"303029\", \"311052\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"236907","document_content":"# Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe study how to generate captions that are not only accurate in describing an image but also discriminative across different images. The problem is both fundamental and interesting, as most machine-generated captions, despite phenomenal research progresses in the past several years, are expressed in a very monotonic and featureless format. While such captions are normally accurate, they often lack important characteristics in human languages - distinctiveness for each caption and diversity for different images. To address this problem, we propose a novel conditional generative adversarial network for generating diverse captions across images. Instead of estimating the quality of a caption solely on one image, the proposed comparative adversarial learning framework better assesses the quality of captions by comparing a set of captions within the image-caption joint space. By contrasting with human-written captions and image-mismatched captions, the caption generator effectively exploits the inherent characteristics of human languages, and generates more discriminative captions. We show that our proposed network is capable of producing accurate and diverse captions across images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.00861v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"16718\", \"113677\", \"6978\", \"61394\", \"167495\", \"93065\", \"110219\", \"110233\", \"130657\", \"141978\", \"152126\", \"157078\", \"174277\", \"170299\", \"170604\", \"191486\", \"199530\", \"217881\", \"221127\", \"229734\", \"213954\", \"223823\", \"224307\"], \"outgoing_citations\": [\"191486\", \"219342\", \"220026\", \"238125\", \"239242\", \"243764\", \"249972\", \"253934\", \"255035\", \"264937\", \"269030\", \"269122\", \"270154\", \"271234\", \"273062\", \"279002\", \"279676\", \"283076\", \"313196\", \"317410\", \"320003\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"237064","document_content":"# Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension\n## Categories\n- Computation and Language\n## Abstract\nReading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task - Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.00320v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"3823\", \"24775\", \"21249\", \"25153\", \"140934\", \"36345\", \"96502\", \"43919\", \"68909\", \"91476\", \"92518\", \"92519\", \"146890\", \"93464\", \"95971\", \"120029\", \"159820\", \"174159\", \"176238\", \"190231\", \"200693\", \"209770\", \"222054\", \"284548\"], \"outgoing_citations\": [\"250304\", \"265347\", \"277259\", \"285849\", \"286110\", \"289624\", \"307547\", \"329561\"]}","task_split":"paper_retrieval"}
{"document_id":"237092","document_content":"# Modeling Individual Differences in Game Behavior using HMM\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nPlayer modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms per- formed on aggregated game actions. However, players' individual differences may be better manifested through sequential patterns of the in-game player's actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In par- ticular, we developed a modeling approach using data col- lected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players' characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.00245v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"15730\", \"33421\", \"106958\", \"117588\", \"189204\"], \"outgoing_citations\": [\"39462\"]}","task_split":"paper_retrieval"}
{"document_id":"237119","document_content":"# Learning to Navigate in Cities Without a Map\n## Categories\n- Artificial Intelligence\n## Abstract\nNavigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation (\"I am here\") and a representation of the goal (\"I am going there\"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage http:\/\/streetlearn.cc contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at https:\/\/github.com\/deepmind\/streetlearn","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1804.00168v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"23640\", \"5373\", \"7237\", \"10987\", \"39368\", \"47175\", \"48063\", \"59461\", \"75621\", \"148134\", \"186811\", \"1414\", \"15584\", \"15383\", \"17412\", \"22193\", \"26678\", \"27522\", \"36974\", \"40343\", \"41518\", \"51175\", \"57729\", \"59690\", \"63867\", \"64141\", \"64369\", \"65698\", \"66404\", \"69040\", \"69178\", \"57444\", \"77756\", \"84961\", \"85572\", \"92592\", \"114991\", \"120758\", \"132558\", \"144521\", \"161638\", \"162996\", \"163326\", \"163665\", \"169612\", \"172223\", \"82126\", \"88444\", \"86813\", \"98224\", \"112798\", \"112949\", \"113074\", \"116835\", \"118312\", \"123031\", \"126377\", \"126597\", \"124069\", \"131013\", \"131180\", \"131891\", \"132020\", \"134432\", \"136760\", \"139112\", \"139227\", \"144339\", \"145753\", \"147685\", \"162505\", \"164699\", \"161717\", \"165819\", \"180340\", \"189381\", \"196909\", \"211706\", \"156121\", \"159154\", \"166667\", \"167688\", \"170410\", \"171332\", \"172416\", \"173034\", \"175840\", \"180851\", \"183049\", \"190556\", \"193972\", \"193993\", \"196598\", \"203398\", \"208042\", \"211558\", \"231955\", \"207509\", \"210077\", \"216221\", \"216647\", \"224223\", \"225036\", \"225266\", \"231155\", \"231487\"], \"outgoing_citations\": [\"237386\", \"238273\", \"240323\", \"241006\", \"243090\", \"244266\", \"245731\", \"246823\", \"247418\", \"249137\", \"249876\", \"249906\", \"255699\", \"262593\", \"263104\", \"263270\", \"269253\", \"272930\", \"274102\", \"275003\", \"278505\", \"279689\", \"280296\", \"280681\", \"281110\", \"284134\", \"284468\", \"292920\", \"293071\", \"293876\", \"298153\"]}","task_split":"paper_retrieval"}
{"document_id":"237222","document_content":"# Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.11366v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5306\", \"160715\", \"57572\", \"168403\", \"12174\", \"16773\", \"33535\", \"44440\", \"62160\", \"62385\", \"76752\", \"84347\", \"87603\", \"129855\", \"219961\", \"81610\", \"93194\", \"109928\", \"111223\", \"112307\", \"132772\", \"155582\", \"163461\", \"188872\", \"191312\", \"194402\", \"197493\", \"199069\", \"233545\", \"210976\", \"233591\"], \"outgoing_citations\": [\"204221\", \"260755\", \"264387\", \"268405\", \"268703\", \"270134\", \"270235\", \"270795\", \"274313\", \"277948\", \"280394\", \"299047\", \"302701\", \"311317\"]}","task_split":"paper_retrieval"}
{"document_id":"237272","document_content":"# Towards Open-Set Identity Preserving Face Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a framework based on Generative Adversarial Networks to disentangle the identity and attributes of faces, such that we can conveniently recombine different identities and attributes for identity preserving face synthesis in open domains. Previous identity preserving face synthesis processes are largely confined to synthesizing faces with known identities that are already in the training dataset. To synthesize a face with identity outside the training dataset, our framework requires one input image of that subject to produce an identity vector, and any other input face image to extract an attribute vector capturing, e.g., pose, emotion, illumination, and even the background. We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute. Our proposed framework does not need to annotate the attributes of faces in any way. It is trained with an asymmetric loss function to better preserve the identity and stabilize the training process. It can also effectively leverage large amounts of unlabeled training face images to further improve the fidelity of the synthesized faces for subjects that are not presented in the labeled training face dataset. Our experiments demonstrate the efficacy of the proposed framework. We also present its usage in a much broader set of applications including face frontalization, face attribute morphing, and face adversarial example detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.11182v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3792\", \"60820\", \"99623\", \"124303\", \"5511\", \"19117\", \"19513\", \"24052\", \"29040\", \"28899\", \"29571\", \"32451\", \"35668\", \"39344\", \"29975\", \"43812\", \"2004\", \"50329\", \"59128\", \"59449\", \"59800\", \"64721\", \"76574\", \"96887\", \"123800\", \"193335\", \"235160\", \"82116\", \"208378\", \"42813\", \"104140\", \"113345\", \"114470\", \"118810\", \"119309\", \"124611\", \"124845\", \"128863\", \"129267\", \"129244\", \"147604\", \"153025\", \"154108\", \"154107\", \"155552\", \"158820\", \"168545\", \"172656\", \"174537\", \"188872\", \"203644\", \"209731\", \"148973\", \"159654\", \"171016\", \"174732\", \"182649\", \"184596\", \"184713\", \"190994\", \"192689\", \"196529\", \"198503\", \"205003\", \"209503\", \"217027\", \"244375\", \"245824\", \"207377\", \"207935\", \"225025\", \"240496\"], \"outgoing_citations\": [\"260588\", \"268405\", \"268970\", \"270105\", \"270229\", \"270435\", \"270436\", \"274033\", \"274550\", \"286096\", \"302162\", \"316183\", \"321117\", \"321461\"]}","task_split":"paper_retrieval"}
{"document_id":"237347","document_content":"# Transport-domain applications of widely used data sources in the smart transportation: A survey\n## Categories\n- Applications\n- Computers and Society\n## Abstract\nThe rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. Smart transportation is a framework that leverages the power of Information and Communication Technology for acquisition, management, and mining of traffic-related data sources, which, in this study, are categorized into: 1) traffic flow sensors, 2) video image processors, 3) probe people and vehicles based on Global Positioning Systems (GPS), mobile phone cellular networks, and Bluetooth, 4) location-based social networks, 5) transit data with the focus on smart cards, and 6) environmental data. For each data source, first, the operational mechanism of the technology for capturing the data is succinctly demonstrated. Secondly, as the most salient feature of this study, the transport-domain applications of each data source that have been conducted by the previous studies are reviewed and classified into the main groups. Thirdly, a number of possible future research directions are provided for all types of data sources. Moreover, in order to alleviate the shortcomings pertaining to each single data source and acquire a better understanding of mobility behavior in transportation systems, the data fusion architectures are introduced to fuse the knowledge learned from a set of heterogeneous but complementary data sources. Finally, we briefly mention the current challenges and their corresponding solutions in the smart transportation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.10902v3\", \"primary_category\": \"stat.AP\", \"categories\": [\"stat.AP\", \"cs.CY\"], \"primary_category_human_readable\": \"Applications\", \"categories_human_readable\": [\"Applications\", \"Computers and Society\"], \"incoming_citations\": [\"207422\", \"234930\"], \"outgoing_citations\": [\"234930\", \"236618\", \"263641\", \"268589\", \"290912\", \"302425\", \"320193\", \"320887\", \"339603\"]}","task_split":"paper_retrieval"}
{"document_id":"237426","document_content":"# Image Generation and Translation with Disentangled Representations\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nGenerative models have made significant progress in the tasks of modeling complex data distributions such as natural images. The introduction of Generative Adversarial Networks (GANs) and auto-encoders lead to the possibility of training on big data sets in an unsupervised manner. However, for many generative models it is not possible to specify what kind of image should be generated and it is not possible to translate existing images into new images of similar domains. Furthermore, models that can perform image-to-image translation often need distinct models for each domain, making it hard to scale these systems to multiple domain image-to-image translation. We introduce a model that can do both, controllable image generation and image-to-image translation between multiple domains. We split our image representation into two parts encoding unstructured and structured information respectively. The latter is designed in a disentangled manner, so that different parts encode different image characteristics. We train an encoder to encode images into these representations and use a small amount of labeled data to specify what kind of information should be encoded in the disentangled part. A generator is trained to generate images from these representations using the characteristics provided by the disentangled part of the representation. Through this we can control what kind of images the generator generates, translate images between different domains, and even learn unknown data-generating factors while only using one single model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.10567v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"104445\"], \"outgoing_citations\": [\"239750\", \"272139\", \"277837\", \"280070\", \"291302\"]}","task_split":"paper_retrieval"}
{"document_id":"237665","document_content":"# Women also Snowboard: Overcoming Bias in Captioning Models\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data (e.g., if a word is present in 60% of training sentences, it might be predicted in 70% of sentences at test time). This can lead to incorrect captions in domains where unbiased captions are desired, or required, due to over-reliance on the learned prior and image context. In this work we investigate generation of gender-specific caption words (e.g. man, woman) based on the person's appearance or the image context. We introduce a new Equalizer model that ensures equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present. The resulting model is forced to look at a person rather than use contextual cues to make a gender-specific predictions. The losses that comprise our model, the Appearance Confusion Loss and the Confident Loss, are general, and can be added to any description model in order to mitigate impacts of unwanted bias in a description dataset. Our proposed model has lower error than prior work when describing images with people and mentioning their gender and more closely matches the ground truth ratio of sentences including women to sentences including men. We also show that unlike other approaches, our model is indeed more often looking at people when predicting their gender.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.09797v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"237887","document_content":"# Machine Learning and Applied Linguistics\n## Categories\n- Computation and Language\n## Abstract\nThis entry introduces the topic of machine learning and provides an overview of its relevance for applied linguistics and language learning. The discussion will focus on giving an introduction to the methods and applications of machine learning in applied linguistics, and will provide references for further study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1002\/9781405198431.wbeal1486\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"230458\"]}","task_split":"paper_retrieval"}
{"document_id":"237968","document_content":"# On the difficulty of a distributional semantics of spoken language\n## Categories\n- Computation and Language\n- Machine Learning\n- Sound\n- Audio and Speech Processing\n## Abstract\nIn the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units. In contrast, for written language, where there is a large body of work on unsupervised induction of semantic representations of words, whole sentences and longer texts. In this study we examine the challenges of adapting these approaches from written to spoken language. We conjecture that unsupervised learning of the semantics of spoken language becomes feasible if we abstract from the surface variability. We simulate this setting with a dataset of utterances spoken by a realistic but uniform synthetic voice. We evaluate two simple unsupervised models which, to varying degrees of success, learn semantic representations of speech fragments. Finally we present inconclusive results on human speech, and discuss the challenges inherent in learning distributional semantic representations on unrestricted natural spoken language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.08869v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.SD\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Sound\", \"Audio and Speech Processing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"222073\", \"237333\", \"237945\", \"239686\", \"242192\", \"251409\", \"263945\", \"268243\", \"270711\", \"274549\", \"275409\", \"288551\", \"303546\", \"314240\"]}","task_split":"paper_retrieval"}
{"document_id":"238125","document_content":"# Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe aim of image captioning is to generate captions by machine to describe image contents. Despite many efforts, generating discriminative captions for images remains non-trivial. Most traditional approaches imitate the language structure patterns, thus tend to fall into a stereotype of replicating frequent phrases or sentences and neglect unique aspects of each image. In this work, we propose an image captioning framework with a self-retrieval module as training guidance, which encourages generating discriminative captions. It brings unique advantages: (1) the self-retrieval guidance can act as a metric and an evaluator of caption discriminativeness to assure the quality of generated captions. (2) The correspondence between generated captions and images are naturally incorporated in the generation process without human annotations, and hence our approach could utilize a large amount of unlabeled images to boost captioning performance with no additional laborious annotations. We demonstrate the effectiveness of the proposed retrieval-guided method on COCO and Flickr30k captioning datasets, and show its superior captioning performance with more discriminative captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.08314v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"548\", \"5625\", \"67099\", \"14760\", \"28498\", \"40810\", \"53028\", \"55207\", \"61394\", \"93826\", \"110600\", \"117326\", \"142094\", \"145513\", \"142464\", \"88217\", \"86447\", \"90867\", \"101699\", \"107901\", \"112250\", \"132390\", \"133695\", \"134565\", \"135506\", \"137915\", \"141957\", \"154752\", \"158839\", \"161738\", \"174277\", \"167256\", \"168553\", \"171726\", \"172805\", \"178885\", \"181622\", \"182982\", \"192659\", \"193328\", \"196763\", \"206164\", \"220232\", \"222262\", \"223306\", \"236907\"], \"outgoing_citations\": [\"226318\", \"239242\", \"249972\", \"253934\", \"256347\", \"259807\", \"260926\", \"268218\", \"269030\", \"270154\", \"271234\", \"276436\", \"277511\", \"278846\", \"279002\", \"279676\", \"281152\", \"283076\", \"283732\", \"311824\", \"313196\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"238297","document_content":"# Inference in Probabilistic Graphical Models by Graph Neural Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nA fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops. Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then demonstrate the efficacy of this inference approach by training GNNs on a collection of graphical models and showing that they substantially outperform belief propagation on loopy graphs. Our message-passing algorithms generalize out of the training set to larger graphs and graphs with different structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.07710v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"17756\", \"80211\", \"120912\", \"12022\", \"20328\", \"35897\", \"44432\", \"47035\", \"54775\", \"59743\", \"76616\", \"119303\", \"138587\", \"112772\", \"113063\", \"114959\", \"115834\", \"117942\", \"129836\", \"160169\", \"192186\", \"151284\", \"170460\", \"182965\", \"180683\", \"182170\", \"186164\", \"196388\", \"203857\", \"237455\", \"229324\", \"235833\"], \"outgoing_citations\": [\"258592\", \"277968\", \"296356\", \"303436\", \"311670\", \"350478\", \"351385\"]}","task_split":"paper_retrieval"}
{"document_id":"238489","document_content":"# Setting up a Reinforcement Learning Task with a Real-World Robot\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nReinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots. In this work, we develop a learning task with a UR5 robotic arm to bring to light some key elements of a task setup and study their contributions to the challenges with robots. We find that learning performance can be highly sensitive to the setup, and thus oversights and omissions in setup details can make effective learning, reproducibility, and fair comparison hard. Our study suggests some mitigating steps to help future experimenters avoid difficulties and pitfalls. We show that highly reliable and repeatable experiments can be performed in our setup, indicating the possibility of reinforcement learning research extensively based on real-world robots.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.07067v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"6914\", \"24538\", \"77939\", \"87566\", \"11658\", \"86785\", \"96184\", \"183015\", \"81352\", \"87588\", \"90910\", \"107492\", \"113926\", \"175521\", \"175488\", \"165829\", \"167889\", \"193458\", \"215643\", \"217974\", \"217105\"], \"outgoing_citations\": [\"282684\", \"283394\"]}","task_split":"paper_retrieval"}
{"document_id":"238610","document_content":"# The Web as a Knowledge-base for Answering Complex Questions\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAnswering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.06643v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"33114\", \"131434\", \"2641\", \"8356\", \"12316\", \"13090\", \"13997\", \"15847\", \"15964\", \"16640\", \"17151\", \"16963\", \"26287\", \"29573\", \"30857\", \"32083\", \"35275\", \"113490\", \"92\", \"1631\", \"2084\", \"2185\", \"2762\", \"3695\", \"10139\", \"13687\", \"21684\", \"21843\", \"22590\", \"23843\", \"24368\", \"26483\", \"28745\", \"32476\", \"40892\", \"45225\", \"45421\", \"45863\", \"47520\", \"47547\", \"47641\", \"49206\", \"50364\", \"55178\", \"55174\", \"55255\", \"55916\", \"56248\", \"56463\", \"56538\", \"61450\", \"63241\", \"64146\", \"69510\", \"71004\", \"76113\", \"76684\", \"80858\", \"157136\", \"91699\", \"91823\", \"98369\", \"102310\", \"127977\", \"3264\", \"102856\", \"39243\", \"89596\", \"88767\", \"95954\", \"101369\", \"109697\", \"111343\", \"92667\", \"119381\", \"123658\", \"127152\", \"127214\", \"127499\", \"127667\", \"129128\", \"129168\", \"130183\", \"130961\", \"131193\", \"131681\", \"132614\", \"132441\", \"132611\", \"133009\", \"133400\", \"134017\", \"137359\", \"138091\", \"140317\", \"140689\", \"144766\", \"149018\", \"150226\", \"157277\", \"159864\", \"186970\", \"130674\", \"152664\", \"157041\", \"160485\", \"160649\", \"161554\", \"162857\", \"166447\", \"166449\", \"169683\", \"169742\", \"173415\", \"176190\", \"180575\", \"181212\", \"182036\", \"182026\", \"183319\", \"185952\", \"186432\", \"189600\", \"196980\", \"209976\", \"215164\", \"216577\", \"220723\", \"216563\", \"217165\", \"218944\", \"219306\", \"220724\", \"223404\", \"216953\"], \"outgoing_citations\": [\"252059\", \"253068\", \"257206\", \"261257\", \"265786\", \"267980\", \"268671\", \"268809\", \"270519\", \"281251\", \"286317\", \"286757\", \"288978\", \"290632\", \"290789\", \"298789\", \"303827\", \"308610\", \"340201\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"238732","document_content":"# Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nWhen extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.06252v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"4468\", \"150132\", \"197056\", \"224379\"], \"outgoing_citations\": [\"290450\", \"293655\", \"294753\"]}","task_split":"paper_retrieval"}
{"document_id":"238777","document_content":"# A Kernel Theory of Modern Data Augmentation\n## Categories\n- Machine Learning\n## Abstract\nData augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.06084v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"15796\", \"16281\", \"18852\", \"18991\", \"42222\", \"43168\", \"43871\", \"57352\", \"75313\", \"11817\", \"15912\", \"23531\", \"40729\", \"42068\", \"43651\", \"43847\", \"47747\", \"50278\", \"51130\", \"60379\", \"67623\", \"82756\", \"92474\", \"105831\", \"114031\", \"119954\", \"139389\", \"143513\", \"151114\", \"84625\", \"92137\", \"93935\", \"96997\", \"101369\", \"127518\", \"127744\", \"138520\", \"141037\", \"142778\", \"174490\", \"158352\", \"165237\", \"165972\", \"182110\", \"185555\", \"187243\", \"202156\", \"218853\", \"221138\"], \"outgoing_citations\": [\"201551\", \"214546\", \"221138\", \"252583\", \"256558\", \"256813\", \"278583\", \"290407\", \"291491\", \"311577\", \"317433\", \"320129\", \"327865\", \"361296\"]}","task_split":"paper_retrieval"}
{"document_id":"238960","document_content":"# Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ITSC.2018.8569568\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"48121\", \"93179\", \"800\", \"2286\", \"2227\", \"10057\", \"76756\", \"127389\", \"144965\", \"1048\", \"18826\", \"24339\", \"46459\", \"49723\", \"53142\", \"64795\", \"69408\", \"74475\", \"82441\", \"84598\", \"88048\", \"98587\", \"103594\", \"77954\", \"84599\", \"91318\", \"94703\", \"98700\", \"117991\", \"119728\", \"129687\", \"136332\", \"143673\", \"148658\", \"187603\", \"166239\", \"169767\", \"173527\", \"182305\", \"186589\", \"190820\", \"193066\", \"196584\"], \"outgoing_citations\": [\"269382\", \"274823\", \"282884\"]}","task_split":"paper_retrieval"}
{"document_id":"239002","document_content":"# Rotation-Sensitive Regression for Oriented Scene Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText in natural images is of arbitrary orientations, requiring detection in terms of oriented bounding boxes. Normally, a multi-oriented text detector often involves two key tasks: 1) text presence detection, which is a classification problem disregarding text orientation; 2) oriented bounding box regression, which concerns about text orientation. Previous methods rely on shared features for both tasks, resulting in degraded performance due to the incompatibility of the two tasks. To address this issue, we propose to perform classification and regression on features of different characteristics, extracted by two network branches of different designs. Concretely, the regression branch extracts rotation-sensitive features by actively rotating the convolutional filters, while the classification branch extracts rotation-invariant features by pooling the rotation-sensitive features. The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on three oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17 and COCO-Text. Furthermore, RRD achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.05265v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"128508\", \"155242\", \"142099\", \"6741\", \"13927\", \"14016\", \"14003\", \"22020\", \"31017\", \"39902\", \"47752\", \"51497\", \"52248\", \"54017\", \"61613\", \"61729\", \"66255\", \"67237\", \"74974\", \"83916\", \"104773\", \"109996\", \"126408\", \"137208\", \"3406\", \"8735\", \"9921\", \"11650\", \"25132\", \"26802\", \"33142\", \"35713\", \"35782\", \"47089\", \"50136\", \"58673\", \"62678\", \"63261\", \"72891\", \"43191\", \"82049\", \"85992\", \"93577\", \"105700\", \"113025\", \"136651\", \"150258\", \"155066\", \"171645\", \"207887\", \"59016\", \"78601\", \"84406\", \"90549\", \"102479\", \"105122\", \"106579\", \"111035\", \"111059\", \"111116\", \"122949\", \"123921\", \"129484\", \"131896\", \"136466\", \"140006\", \"156029\", \"174062\", \"176257\", \"210634\", \"150452\", \"166479\", \"166353\", \"169067\", \"170332\", \"171543\", \"171686\", \"171807\", \"173837\", \"174928\", \"182255\", \"186238\", \"193513\", \"194004\", \"195963\", \"203525\", \"207348\", \"209227\", \"217463\", \"234390\", \"207786\", \"218516\"], \"outgoing_citations\": [\"245591\", \"253398\", \"257197\", \"257286\", \"257954\", \"261365\", \"270668\", \"271112\", \"272390\", \"276664\", \"279950\", \"284871\", \"293979\", \"294012\", \"294023\", \"294606\", \"294931\", \"299461\", \"315484\", \"320837\", \"321465\"]}","task_split":"paper_retrieval"}
{"document_id":"239049","document_content":"# DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Performance\n## Abstract\nAs one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification. DNN achieves such performance, to a large extent, by performing expensive training over huge volumes of training data. To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a \"must-have\" feature before transferring real-time produced dataset for training or classification. While there have been many well-known image compression approaches (such as JPEG), we for the first time find that a human-visual based image compression approach such as JPEG compression is not an optimized solution for DNN systems, especially with high compression ratios. To this end, we develop an image compression framework tailored for DNN applications, named \"DeepN-JPEG\", to embrace the nature of deep cascaded information process mechanism of DNN architecture. Extensive experiments, based on \"ImageNet\" dataset with various state-of-the-art DNNs, show that \"DeepN-JPEG\" can achieve ~3.5x higher compression rate over the popular JPEG solution while maintaining the same accuracy level for image recognition, demonstrating its great potential of storage and power efficiency in DNN-based smart IoT system design.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.05788v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.PF\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Performance\"], \"incoming_citations\": [\"6\", \"16972\", \"3197\", \"21815\", \"23164\", \"26859\", \"39622\", \"40729\", \"51564\", \"108257\", \"158506\", \"100681\", \"134242\", \"138011\", \"138313\", \"140744\", \"141894\", \"162788\", \"166254\"], \"outgoing_citations\": [\"239045\", \"239046\", \"257085\"]}","task_split":"paper_retrieval"}
{"document_id":"239053","document_content":"# Algorithmic Social Intervention\n## Categories\n- Artificial Intelligence\n- Computers and Society\n- Social and Information Networks\n## Abstract\nSocial and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty. However, real-world interventions are almost always plagued by limited resources and limited data, which creates a computational challenge: how can we use algorithmic techniques to enhance the targeting and delivery of social and behavioral interventions? The goal of my thesis is to provide a unified study of such questions, collectively considered under the name \"algorithmic social intervention\". This proposal introduces algorithmic social intervention as a distinct area with characteristic technical challenges, presents my published research in the context of these challenges, and outlines open problems for future work. A common technical theme is decision making under uncertainty: how can we find actions which will impact a social system in desirable ways under limitations of knowledge and resources? The primary application area for my work thus far is public health, e.g. HIV or tuberculosis prevention. For instance, I have developed a series of algorithms which optimize social network interventions for HIV prevention. Two of these algorithms have been pilot-tested in collaboration with LA-area service providers for homeless youth, with preliminary results showing substantial improvement over status-quo approaches. My work also spans other topics in infectious disease prevention and underlying algorithmic questions in robust and risk-aware submodular optimization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.05098v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CY\", \"cs.SI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computers and Society\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254233\", \"262111\", \"272807\", \"290175\", \"304166\", \"318477\", \"331590\", \"343291\", \"356018\"]}","task_split":"paper_retrieval"}
{"document_id":"239242","document_content":"# Discriminability objective for training descriptive captions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOne property that remains lacking in image captions generated by contemporary methods is discriminability: being able to tell two images apart given the caption for one of them. We propose a way to improve this aspect of caption generation. By incorporating into the captioning training objective a loss component directly related to ability (by a machine) to disambiguate image\/caption matches, we obtain systems that produce much more discriminative caption, according to human evaluation. Remarkably, our approach leads to improvement in other aspects of generated captions, reflected by a battery of standard scores such as BLEU, SPICE etc. Our approach is modular and can be applied to a variety of model\/loss combinations commonly proposed for image captioning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.04376v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19259\", \"3492\", \"4080\", \"5625\", \"24945\", \"29678\", \"38766\", \"89494\", \"28498\", \"39327\", \"41801\", \"43997\", \"55207\", \"57659\", \"60148\", \"61394\", \"65077\", \"110600\", \"163326\", \"72115\", \"80728\", \"81724\", \"86135\", \"88217\", \"86447\", \"90867\", \"92747\", \"101699\", \"102772\", \"105896\", \"112250\", \"126112\", \"132390\", \"132717\", \"128086\", \"133695\", \"134565\", \"135506\", \"137915\", \"147001\", \"149063\", \"204751\", \"158839\", \"161738\", \"174277\", \"171726\", \"171902\", \"172668\", \"172811\", \"176591\", \"180149\", \"182062\", \"182982\", \"189565\", \"189964\", \"191058\", \"192659\", \"193328\", \"193909\", \"202386\", \"203562\", \"204730\", \"207034\", \"229734\", \"218912\", \"224307\", \"236907\", \"238125\"], \"outgoing_citations\": [\"248855\", \"249972\", \"253934\", \"256347\", \"258987\", \"260926\", \"262564\", \"264514\", \"264856\", \"268218\", \"269030\", \"269128\", \"270154\", \"271085\", \"271234\", \"276299\", \"276436\", \"277060\", \"279002\", \"279678\", \"281152\", \"281407\", \"287427\", \"291851\", \"295368\", \"295742\", \"302943\", \"303011\", \"303833\", \"314050\", \"320003\", \"350457\"]}","task_split":"paper_retrieval"}
{"document_id":"239257","document_content":"# Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention\n## Categories\n- Computation and Language\n## Abstract\nSemantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our approach is based on vector space model and neural networks. The proposed model is based in two learning steps. The first step generates a vector representation for the sentence in natural language and SPARQL query. The second step uses this vector representation as input to a neural network (LSTM with attention mechanism) to generate a model able to encode natural language and decode SPARQL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.04329v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"11442\", \"37119\", \"41131\", \"179570\", \"73933\"], \"outgoing_citations\": [\"297648\", \"300505\", \"308433\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"239535","document_content":"# An end-to-end TextSpotter with Explicit Alignment and Attention\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText detection and recognition in natural images have long been considered as two separate tasks that are processed sequentially. Training of two tasks in a unified framework is non-trivial due to significant dif- ferences in optimisation difficulties. In this work, we present a conceptually simple yet efficient framework that simultaneously processes the two tasks in one shot. Our main contributions are three-fold: 1) we propose a novel text-alignment layer that allows it to precisely compute convolutional features of a text instance in ar- bitrary orientation, which is the key to boost the per- formance; 2) a character attention mechanism is introduced by using character spatial information as explicit supervision, leading to large improvements in recognition; 3) two technologies, together with a new RNN branch for word recognition, are integrated seamlessly into a single model which is end-to-end trainable. This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances. Our model achieves impressive results in end-to-end recognition on the ICDAR2015 dataset, significantly advancing most recent results, with improvements of F-measure from (0.54, 0.51, 0.47) to (0.82, 0.77, 0.63), by using a strong, weak and generic lexicon respectively. Thanks to joint training, our method can also serve as a good detec- tor by achieving a new state-of-the-art detection performance on two datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.03474v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2918\", \"13927\", \"50917\", \"52248\", \"59368\", \"101165\", \"118842\", \"121818\", \"180823\", \"4188\", \"14976\", \"29950\", \"43153\", \"58438\", \"81960\", \"81968\", \"93345\", \"99456\", \"104170\", \"150258\", \"84406\", \"98220\", \"105122\", \"108674\", \"111035\", \"111116\", \"120420\", \"122763\", \"126431\", \"134013\", \"135255\", \"135263\", \"139816\", \"140478\", \"141887\", \"147253\", \"154995\", \"210634\", \"161220\", \"166353\", \"168306\", \"166370\", \"170332\", \"173837\", \"174894\", \"178256\", \"180931\", \"183268\", \"192471\", \"192680\", \"193382\", \"194314\", \"195963\", \"205988\", \"207348\", \"208801\", \"218907\", \"204872\", \"218516\", \"220129\", \"225195\"], \"outgoing_citations\": [\"245899\", \"253398\", \"254870\", \"257197\", \"261365\", \"269108\", \"270668\", \"279687\", \"279950\", \"284871\", \"287166\", \"289389\", \"293979\", \"294023\", \"294606\", \"295564\", \"296685\", \"296828\", \"305162\", \"311196\", \"320837\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"239574","document_content":"# Deep Semantic Face Deblurring\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs). As face images are highly structured and share several key semantic components (e.g., eyes and mouths), the semantic information of a face provides a strong prior for restoration. As such, we propose to incorporate global semantic priors as input and impose local structure losses to regularize the output within a multi-scale deep CNN. We train the network with perceptual and adversarial losses to generate photo-realistic results and develop an incremental training strategy to handle random blur kernels in the wild. Quantitative and qualitative evaluations demonstrate that the proposed face deblurring algorithm restores sharp images with more facial details and performs favorably against state-of-the-art methods in terms of restoration quality, face recognition and execution speed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.03345v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37459\", \"60637\", \"100824\", \"50155\", \"50816\", \"106071\", \"2120\", \"3046\", \"3668\", \"6156\", \"7354\", \"12551\", \"46947\", \"49720\", \"49874\", \"59002\", \"59917\", \"60545\", \"66205\", \"76015\", \"78941\", \"88827\", \"107434\", \"125614\", \"196750\", \"235160\", \"108335\", \"111131\", \"111619\", \"133159\", \"135332\", \"145440\", \"146517\", \"162917\", \"173743\", \"230252\", \"159596\", \"146519\", \"172491\", \"177223\", \"180906\", \"185273\", \"196529\", \"196712\", \"209098\", \"230095\", \"230097\"], \"outgoing_citations\": [\"239567\", \"239580\", \"243188\", \"259890\", \"280029\", \"296510\", \"312178\", \"313846\", \"316657\"]}","task_split":"paper_retrieval"}
{"document_id":"239686","document_content":"# An efficient framework for learning sentence representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.02893v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"19259\", \"66904\", \"103740\", \"228746\", \"383\", \"1511\", \"4896\", \"15842\", \"17564\", \"18712\", \"20786\", \"21990\", \"24755\", \"24730\", \"28627\", \"40913\", \"49207\", \"69933\", \"114308\", \"119540\", \"4464\", \"6090\", \"11375\", \"11894\", \"12093\", \"12200\", \"12629\", \"13961\", \"14592\", \"18913\", \"19536\", \"20988\", \"22507\", \"23573\", \"25232\", \"25523\", \"26961\", \"27215\", \"37901\", \"37931\", \"38188\", \"38603\", \"39322\", \"41609\", \"43563\", \"43651\", \"45179\", \"45499\", \"45556\", \"45876\", \"49042\", \"50859\", \"55047\", \"55137\", \"55574\", \"56187\", \"56361\", \"58897\", \"60381\", \"68669\", \"69609\", \"69742\", \"70993\", \"77002\", \"78113\", \"79130\", \"80747\", \"80856\", \"81557\", \"95455\", \"95883\", \"102092\", \"104452\", \"108090\", \"120760\", \"122635\", \"128695\", \"127818\", \"138523\", \"158023\", \"188856\", \"77689\", \"78131\", \"83346\", \"86262\", \"89829\", \"91201\", \"92405\", \"93444\", \"94197\", \"95191\", \"96752\", \"98636\", \"98933\", \"99769\", \"100517\", \"101455\", \"102772\", \"102924\", \"105398\", \"115939\", \"117912\", \"118253\", \"119745\", \"119190\", \"123248\", \"123414\", \"123530\", \"123820\", \"123821\", \"125753\", \"127882\", \"128295\", \"128542\", \"133855\", \"137785\", \"139378\", \"141073\", \"142174\", \"142446\", \"143117\", \"148943\", \"149374\", \"159720\", \"161070\", \"162452\", \"168076\", \"174612\", \"199839\", \"214542\", \"151717\", \"156528\", \"157321\", \"143643\", \"162397\", \"167214\", \"169008\", \"169539\", \"170278\", \"171673\", \"169337\", \"173884\", \"178889\", \"181398\", \"181936\", \"181902\", \"182465\", \"182813\", \"181154\", \"183076\", \"179924\", \"186386\", \"186418\", \"187452\", \"192271\", \"192381\", \"193367\", \"195157\", \"197714\", \"198614\", \"200986\", \"201539\", \"211249\", \"212322\", \"214025\", \"215953\", \"218724\", \"220130\", \"214654\", \"215765\", \"216212\", \"217429\", \"221739\", \"231537\", \"231566\", \"232120\", \"234743\", \"234778\", \"237968\"], \"outgoing_citations\": [\"254855\", \"264427\", \"267622\", \"268243\", \"279688\", \"286594\", \"290387\", \"298543\", \"302516\", \"302943\", \"314240\", \"317841\", \"320003\", \"320814\", \"336196\"]}","task_split":"paper_retrieval"}
{"document_id":"239738","document_content":"# Generating Contradictory, Neutral, and Entailing Sentences\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nLearning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to learn a model that approximates the conditional latent space over the representations of a logical antecedent of the given statement. In our paper, we propose an approach to generating sentences, conditioned on an input sentence and a logical inference label. We do this by modeling the different possibilities for the output sentence as a distribution over the latent representation, which we train using an adversarial objective. We evaluate the model using two state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and measure the BLEU scores against the actual sentences as a probe for the diversity of sentences produced by our model. The experiment results show that, given our framework, we have clear ways to improve the quality and diversity of generated sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.02710v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"243594\", \"254855\", \"255894\", \"256098\", \"259870\", \"272531\", \"272912\", \"284357\", \"291145\", \"296264\"]}","task_split":"paper_retrieval"}
{"document_id":"239855","document_content":"# ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nHigh dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion\/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.02266v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"105191\", \"4084\", \"9660\", \"14984\", \"10165\", \"14388\", \"17760\", \"19090\", \"21880\", \"22686\", \"23633\", \"28699\", \"35011\", \"41356\", \"49980\", \"54876\", \"60199\", \"61088\", \"61885\", \"72450\", \"115507\", \"153567\", \"79005\", \"85785\", \"88550\", \"80896\", \"94732\", \"124998\", \"129600\", \"133418\", \"139663\", \"172113\", \"149650\", \"187468\", \"197135\", \"205608\"], \"outgoing_citations\": [\"252829\", \"259863\", \"260981\", \"270221\", \"280057\", \"284157\"]}","task_split":"paper_retrieval"}
{"document_id":"240027","document_content":"# Automatic Translating between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora\n## Categories\n- Computation and Language\n## Abstract\nThe Chinese language has evolved a lot during the long-term development. Therefore, native speakers now have trouble in reading sentences written in ancient Chinese. In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese. However, the existing ancient-contemporary Chinese parallel corpora are not aligned at the sentence level and sentence-aligned corpora are limited, which makes it difficult to train the model. To build the sentence level parallel training data for the model, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs by using the fact that the aligned sentence pair shares many of the tokens. Based on the aligned corpus, we propose an end-to-end neural model with copying mechanism and local attention to translate between ancient and contemporary Chinese. Experiments show that the proposed unsupervised algorithm achieves 99.4% F1 score for sentence alignment, and the translation model achieves 26.95 BLEU from ancient to contemporary, and 36.34 BLEU from contemporary to ancient.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-32236-6_13\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56500\", \"221664\"], \"outgoing_citations\": [\"232260\", \"232543\", \"274729\", \"282530\", \"283997\", \"287474\", \"299936\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"240068","document_content":"# Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nMost recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.01465v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"25308\", \"26367\", \"55589\", \"61157\", \"69343\", \"91705\", \"133752\", \"128932\", \"179361\", \"182958\", \"189555\", \"217992\", \"218826\", \"232526\", \"217988\", \"218710\", \"221551\", \"228596\", \"232261\", \"232543\", \"233391\", \"232164\", \"232260\"], \"outgoing_citations\": [\"243170\", \"243291\", \"249213\", \"250140\", \"252163\", \"253965\", \"259563\", \"263362\", \"264283\", \"270048\", \"279352\", \"282914\", \"296029\", \"301996\", \"310904\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"240078","document_content":"# Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming\n## Categories\n- Machine Learning\n## Abstract\nAn important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking.   In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees and naturally incorporates flexible parametric\/nonparametric models on the covariates, including models robust to outliers. Our proposal is found to outperform state-of- the-art estimators in terms of efficiency and predictive performance on real world public and private datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.01440v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"259695\"]}","task_split":"paper_retrieval"}
{"document_id":"240129","document_content":"# An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nFor most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http:\/\/github.com\/locuslab\/TCN .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.01271v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"5198\", \"8622\", \"12672\", \"13318\", \"18457\", \"18443\", \"24905\", \"114427\", \"124073\", \"129784\", \"100045\", \"139\", \"1489\", \"3396\", \"4726\", \"4981\", \"9550\", \"11078\", \"11324\", \"12479\", \"15749\", \"16091\", \"16910\", \"17624\", \"17674\", \"17935\", \"19933\", \"20386\", \"24655\", \"26218\", \"21517\", \"27148\", \"28695\", \"29766\", \"30654\", \"32531\", \"32909\", \"32909\", \"34030\", \"35108\", \"36383\", \"37137\", \"37265\", \"37836\", \"38019\", \"39372\", \"39678\", \"40913\", \"44924\", \"46610\", \"47548\", \"50794\", \"52633\", \"54396\", \"56682\", \"58303\", \"59174\", \"59939\", \"59874\", \"69673\", \"71503\", \"72565\", \"79821\", \"46373\", \"87826\", \"100006\", \"103942\", \"123254\", \"130806\", \"138239\", \"159879\", \"154375\", \"175082\", \"218928\", \"228736\", \"93187\", \"1653\", \"2825\", \"3097\", \"3627\", \"3898\", \"3882\", \"5070\"], \"outgoing_citations\": [\"250815\", \"253999\", \"257927\", \"259145\", \"260929\", \"268605\", \"274569\", \"277869\", \"278065\", \"280328\", \"280973\", \"281176\", \"281504\", \"281508\", \"285259\", \"286225\", \"289897\", \"290041\", \"291045\", \"291061\", \"291183\", \"295636\", \"297566\", \"302481\", \"302888\", \"315023\", \"321035\", \"333638\", \"336246\", \"357151\"]}","task_split":"paper_retrieval"}
{"document_id":"240209","document_content":"# TACO: Learning Task Decomposition via Temporal Alignment for Control\n## Categories\n- Machine Learning\n## Abstract\nMany advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks. By reusing the corresponding sub-policies within and between tasks, they provide training data for each policy from different high-level tasks and compose them to perform novel ones. Existing approaches to modular LfD focus either on learning a single high-level task or depend on domain knowledge and temporal segmentation. In contrast, we propose a weakly supervised, domain-agnostic approach based on task sketches, which include only the sequence of sub-tasks performed in each demonstration. Our approach simultaneously aligns the sketches with the observed demonstrations and learns the required sub-policies. This improves generalisation in comparison to separate optimisation procedures. We evaluate the approach on multiple domains, including a simulated 3D robot arm control task using purely image-based observations. The results show that our approach performs commensurately with fully supervised approaches, while requiring significantly less annotation effort.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.01840v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"4134\", \"19140\", \"20494\", \"35649\", \"8621\", \"22124\", \"41955\", \"66882\", \"96102\", \"108757\", \"88081\", \"115366\", \"116841\", \"130677\", \"145658\", \"178846\", \"161338\", \"171477\", \"190013\", \"207327\", \"216027\", \"217069\", \"209570\", \"212935\", \"217523\", \"219728\"], \"outgoing_citations\": [\"234517\", \"253256\", \"254096\", \"255427\", \"255939\", \"263104\", \"270414\", \"270667\", \"270925\", \"281106\", \"287541\", \"303038\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"240263","document_content":"# Towards a Question Answering System over the Semantic Web\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nThanks to the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of Question Answering (QA) over KBs. Most current QA systems query one KB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provided a conceptional solution for multilingual, KB-agnostic Question Answering over the Semantic Web. The provided first approximation validates this concept.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.00832v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"2943\", \"3977\", \"25488\", \"28745\", \"53084\", \"63653\", \"83087\", \"117272\", \"123658\", \"126381\", \"129168\", \"216454\", \"151195\", \"171110\", \"175028\", \"175374\"], \"outgoing_citations\": [\"240854\", \"290690\", \"290997\", \"291251\", \"295325\", \"304934\", \"311684\"]}","task_split":"paper_retrieval"}
{"document_id":"240323","document_content":"# Semi-parametric Topological Memory for Navigation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nWe introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph stores no metric information, only connectivity of locations corresponding to the nodes. We use SPTM as a planning module in a navigation system. Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals. The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three. A video of the agent is available at https:\/\/youtu.be\/vRF7f4lhswo","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.00653v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"12555\", \"23640\", \"44400\", \"65465\", \"5373\", \"11364\", \"14284\", \"15877\", \"16494\", \"17155\", \"30806\", \"31846\", \"34572\", \"35753\", \"60623\", \"64409\", \"75621\", \"99608\", \"128249\", \"186811\", \"1414\", \"2239\", \"5820\", \"6704\", \"8634\", \"13615\", \"13764\", \"15383\", \"16502\", \"16976\", \"18827\", \"21975\", \"22025\", \"22936\", \"25684\", \"27630\", \"35616\", \"39279\", \"39688\", \"41883\", \"43678\", \"49451\", \"51764\", \"48479\", \"54096\", \"56552\", \"57729\", \"61081\", \"65095\", \"66007\", \"68278\", \"69303\", \"71624\", \"77686\", \"77736\", \"79162\", \"79811\", \"81574\", \"84844\", \"85068\", \"86305\", \"94561\", \"101182\", \"104690\", \"120758\", \"135780\", \"135815\", \"142415\", \"148172\", \"151443\", \"152072\", \"161638\", \"44089\", \"82126\", \"85766\", \"102971\", \"104828\", \"109133\", \"110466\", \"110941\", \"112798\", \"112949\", \"113074\", \"113236\", \"114824\", \"117297\", \"117896\", \"119375\", \"123031\", \"125870\", \"126377\", \"129360\", \"131180\", \"131891\", \"131814\", \"132719\", \"136039\", \"136916\", \"138485\", \"139633\", \"140039\", \"140089\", \"140129\", \"147314\", \"148111\", \"149565\", \"149647\", \"152712\", \"152732\", \"164699\", \"175488\", \"180340\", \"211706\", \"104852\", \"157901\", \"166667\", \"167688\", \"171702\", \"173526\", \"177674\", \"181081\", \"178792\", \"190556\", \"193993\", \"195772\", \"196148\", \"196226\", \"196393\", \"196898\", \"200941\", \"207499\", \"215425\", \"214195\", \"231593\", \"207509\", \"210666\", \"213768\", \"216221\", \"216647\", \"218445\", \"223487\", \"224223\", \"225320\", \"225768\", \"230068\", \"237119\", \"248742\", \"274102\", \"277504\"], \"outgoing_citations\": [\"247749\", \"272222\", \"272930\", \"274102\", \"278999\", \"280296\", \"280681\", \"281110\", \"290433\", \"291568\", \"293071\", \"298651\", \"314520\"]}","task_split":"paper_retrieval"}
{"document_id":"240377","document_content":"# Joint Training for Neural Machine Translation Models with Monolingual Data\n## Categories\n- Computation and Language\n## Abstract\nMonolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.00353v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21633\", \"38855\", \"21594\", \"23120\", \"23662\", \"34638\", \"45496\", \"72246\", \"149936\", \"82572\", \"90756\", \"93832\", \"94719\", \"95549\", \"95979\", \"96391\", \"125199\", \"131513\", \"152860\", \"154259\", \"152767\", \"157095\", \"167086\", \"168553\", \"169317\", \"170863\", \"180299\", \"191352\", \"203054\", \"211724\", \"223138\", \"212018\", \"217312\", \"220515\", \"225890\", \"231614\", \"232282\", \"230181\", \"238926\"], \"outgoing_citations\": [\"280884\", \"281472\", \"290402\", \"299936\", \"301840\", \"316102\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"240477","document_content":"# Chinese Text in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, detection and recognition of text in natural images is still a challenging problem, especially for more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data.   In this paper we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters annotated by experts in over 30 thousand street view images. This is a challenging dataset with good diversity. It contains planar text, raised text, text in cities, text in rural areas, text under poor illumination, distant text, partially occluded text, etc. For each character in the dataset, the annotation includes its underlying character, its bounding box, and 6 attributes. The attributes indicate whether it has complex background, whether it is raised, whether it is handwritten or printed, etc. The large size and diversity of this dataset make it suitable for training robust neural networks for various tasks, particularly detection and recognition. We give baseline results using several state-of-the-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.00085v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"58132\", \"150852\", \"102479\", \"113191\", \"126431\", \"210634\", \"166353\", \"150251\", \"166370\", \"193824\"], \"outgoing_citations\": [\"274093\", \"286320\", \"294606\", \"295564\", \"299461\", \"301389\", \"328595\"]}","task_split":"paper_retrieval"}
{"document_id":"240486","document_content":"# Analyzing Uncertainty in Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMachine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. As part of this study, we release multiple human reference translations for two popular benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1803.00047v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"6441\", \"9883\", \"25242\", \"26304\", \"30346\", \"38234\", \"38804\", \"43408\", \"49908\", \"55234\", \"56086\", \"5116\", \"19556\", \"21679\", \"22712\", \"23790\", \"25034\", \"25949\", \"34029\", \"34890\", \"38562\", \"45496\", \"45519\", \"45984\", \"47036\", \"48848\", \"50542\", \"141592\", \"170642\", \"55960\", \"60148\", \"68880\", \"69615\", \"77330\", \"77556\", \"78041\", \"83393\", \"87412\", \"91672\", \"92589\", \"100606\", \"120964\", \"129680\", \"164402\", \"223084\", \"78488\", \"84437\", \"91148\", \"93839\", \"93975\", \"94484\", \"95549\", \"96296\", \"99008\", \"99469\", \"107936\", \"123670\", \"123818\", \"126380\", \"127369\", \"127445\", \"128024\", \"140414\", \"141764\", \"153185\", \"153825\", \"155080\", \"157383\", \"157641\", \"159153\", \"171020\", \"171380\", \"181300\", \"211205\", \"152767\", \"165853\", \"168448\", \"168634\", \"170013\", \"169331\", \"180299\", \"182142\", \"186239\", \"192811\", \"196775\", \"198185\", \"200263\", \"204947\", \"211724\", \"216217\", \"219049\", \"210197\", \"217402\", \"217548\", \"219952\", \"220017\", \"229674\", \"229845\", \"235175\"], \"outgoing_citations\": [\"250577\", \"259586\", \"263927\", \"268098\", \"276427\", \"283076\", \"286498\", \"291858\", \"292690\", \"300652\", \"307776\", \"310717\"]}","task_split":"paper_retrieval"}
{"document_id":"240506","document_content":"# Learning by Playing - Solving Sparse Reward Tasks from Scratch\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nWe propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.10567v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"357157\", \"343094\", \"4134\", \"11414\", \"11446\", \"17854\", \"18466\", \"22458\", \"30074\", \"47322\", \"52813\", \"63621\", \"65969\", \"79803\", \"109508\", \"2483\", \"8628\", \"9356\", \"9593\", \"10167\", \"11876\", \"14806\", \"15055\", \"16958\", \"22761\", \"27991\", \"31471\", \"35616\", \"35932\", \"37058\", \"43616\", \"44418\", \"46463\", \"50187\", \"54100\", \"55486\", \"56286\", \"57326\", \"62673\", \"63319\", \"63876\", \"64583\", \"66832\", \"66864\", \"69685\", \"71397\", \"73918\", \"68241\", \"74451\", \"75117\", \"75738\", \"76491\", \"89316\", \"92122\", \"93939\", \"108757\", \"114931\", \"142989\", \"161444\", \"177272\", \"208707\", \"79679\", \"90484\", \"90966\", \"162503\", \"96714\", \"99059\", \"101153\", \"101382\", \"107539\", \"108295\", \"109549\", \"116978\", \"116853\", \"118123\", \"114889\", \"118301\", \"124909\", \"124913\", \"311352\", \"86650\", \"129238\", \"129185\", \"129546\", \"129794\", \"130999\", \"134149\", \"136513\", \"137441\", \"137546\", \"140129\", \"141277\", \"146533\", \"158911\", \"160599\", \"161213\", \"162100\", \"173318\", \"178846\", \"180204\", \"160598\", \"160827\", \"162568\", \"164235\", \"164644\", \"164842\", \"154419\", \"150072\", \"154139\", \"167562\", \"167688\", \"171112\", \"172305\", \"176551\", \"179966\", \"180089\", \"180885\", \"180928\", \"181724\", \"182535\", \"181498\", \"182305\", \"184214\", \"185008\", \"184948\", \"186101\", \"181405\", \"187871\", \"192016\", \"192383\", \"194653\", \"194729\", \"194738\", \"196859\", \"198383\", \"198524\", \"199156\", \"200772\", \"200843\", \"207730\", \"209393\", \"210938\", \"214238\", \"208351\", \"227313\", \"239153\", \"207082\", \"207983\", \"210486\", \"214592\", \"216221\", \"217105\", \"218936\", \"56246\", \"223487\", \"227065\", \"241185\", \"259143\", \"277504\"], \"outgoing_citations\": [\"108017\", \"240233\", \"246921\", \"249187\", \"254596\", \"259143\", \"260018\", \"261535\", \"261780\", \"266109\", \"270925\", \"277564\", \"280296\", \"280681\", \"281110\", \"282684\", \"284468\", \"284567\", \"288338\", \"290243\", \"290825\", \"290904\", \"294142\", \"304250\"]}","task_split":"paper_retrieval"}
{"document_id":"240524","document_content":"# Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream\n## Categories\n- Machine Learning\n- Computation\n## Abstract\nIn marketing we are often confronted with a continuous stream of responses to marketing messages. Such streaming data provide invaluable information regarding message effectiveness and segmentation. However, streaming data are hard to analyze using conventional methods: their high volume and the fact that they are continuously augmented means that it takes considerable time to analyze them. We propose a method for estimating a finite mixture of logistic regression models which can be used to cluster customers based on a continuous stream of responses. This method, which we coin oFMLR, allows segments to be identified in data streams or extremely large static datasets. Contrary to black box algorithms, oFMLR provides model estimates that are directly interpretable. We first introduce oFMLR, explaining in passing general topics such as online estimation and the EM algorithm, making this paper a high level overview of possible methods of dealing with large data streams in marketing practice. Next, we discuss model convergence, identifiability, and relations to alternative, Bayesian, methods; we also identify more general issues that arise from dealing with continuously augmented data sets. Finally, we introduce the oFMLR [R] package and evaluate the method by numerical simulation and by analyzing a large customer clickstream dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.10529v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.CO\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"349895\", \"357213\", \"357612\"]}","task_split":"paper_retrieval"}
{"document_id":"240572","document_content":"# General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms\n## Categories\n- Artificial Intelligence\n## Abstract\nGeneral Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.10363v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"8658\", \"32730\", \"33420\", \"86180\", \"87663\", \"20726\", \"22313\", \"33228\", \"38564\", \"46143\", \"48146\", \"51175\", \"54418\", \"55260\", \"68080\", \"87353\", \"95477\", \"93487\", \"142583\", \"183066\", \"94524\", \"99829\", \"101112\", \"103301\", \"106742\", \"111576\", \"119692\", \"123417\", \"123442\", \"124521\", \"125173\", \"125326\", \"127100\", \"128885\", \"133632\", \"135309\", \"138106\", \"145664\", \"147140\", \"152918\", \"153614\", \"163503\", \"168225\", \"186608\", \"218647\", \"149805\", \"155146\", \"158047\", \"166184\", \"170002\", \"178386\", \"181590\", \"181592\", \"183302\", \"184489\", \"192488\", \"193172\", \"204062\", \"224955\", \"226544\", \"227127\", \"229002\", \"229359\", \"239003\"], \"outgoing_citations\": [\"181122\", \"218647\", \"224955\", \"226544\", \"229359\", \"230005\", \"239003\", \"241932\", \"243671\", \"268185\", \"268226\", \"268227\", \"271176\", \"271177\", \"285147\", \"301569\"]}","task_split":"paper_retrieval"}
{"document_id":"240608","document_content":"# Neural Aesthetic Image Reviewer\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high- or low-aesthetic score should be assigned to an image. From such a point of view, we propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, we propose two multi-task architectures based on shared aesthetically semantic layers and task-specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, we collect the AVA-Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA-Reviews dataset. Moreover, we demonstrate experimentally that our model can generate textual reviews related to aesthetics, which are consistent with human perception.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.10240v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32273\", \"39078\", \"57715\", \"48798\", \"176591\"], \"outgoing_citations\": [\"268518\", \"269963\", \"281152\", \"283372\", \"291072\", \"292199\", \"294372\", \"294694\", \"295307\", \"302621\", \"305473\", \"311824\", \"312704\", \"313196\"]}","task_split":"paper_retrieval"}
{"document_id":"240687","document_content":"# Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions\n## Categories\n- Computation and Language\n## Abstract\nWe describe an algorithm for automatic classification of idiomatic and literal expressions. Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression. Our additional hypothesis is that contexts in which idioms occur, typically, are more affective and therefore, we incorporate a simple analysis of the intensity of the emotions expressed by the contexts. We investigate the bag of words topic representation of one to three paragraphs containing an expression that should be classified as idiomatic or literal (a target phrase). We extract topics from paragraphs containing idioms and from paragraphs containing literals using an unsupervised clustering method, Latent Dirichlet Allocation (LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property of non-compositionality, we assume that they usually present different semantics than the words used in the local topic. We treat idioms as semantic outliers, and the identification of a semantic shift as outlier detection. Thus, this topic representation allows us to differentiate idioms from literals using local semantic contexts. Our results are encouraging.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.09961v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"55244\", \"15122\", \"57015\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"240812","document_content":"# Disentangling the independently controllable factors of variation by interacting with the world\n## Categories\n- Machine Learning\n## Abstract\nIt has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.09484v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"240823","document_content":"# Tone Biased MMR Text Summarization\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nText summarization is an interesting area for researchers to develop new techniques to provide human like summaries for vast amounts of information. Summarization techniques tend to focus on providing accurate representation of content, and often the tone of the content is ignored. Tone of the content sets a baseline for how a reader perceives the content. As such being able to generate summary with tone that is appropriate for the reader is important. In our work we implement Maximal Marginal Relevance [MMR] based multi-document text summarization and propose a naive model to change tone of the summarization by setting a bias to specific set of words and restricting other words in the summarization output. This bias towards a specified set of words produces a summary whose tone is same as tone of specified words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.09426v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"82334\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"240965","document_content":"# Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPrevious deep learning based state-of-the-art scene text detection methods can be roughly classified into two categories. The first category treats scene text as a type of general objects and follows general object detection paradigm to localize scene text by regressing the text box locations, but troubled by the arbitrary-orientation and large aspect ratios of scene text. The second one segments text regions directly, but mostly needs complex post processing. In this paper, we present a method that combines the ideas of the two types of methods while avoiding their shortcomings. We propose to detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions. In inference stage, candidate boxes are generated by sampling and grouping corner points, which are further scored by segmentation maps and suppressed by NMS. Compared with previous methods, our method can handle long oriented text naturally and doesn't need complex post processing. The experiments on ICDAR2013, ICDAR2015, MSRA-TD500, MLT and COCO-Text demonstrate that the proposed algorithm achieves better or comparable results in both accuracy and efficiency. Based on VGG16, it achieves an F-measure of 84.3% on ICDAR2015 and 81.5% on MSRA-TD500.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08948v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155242\", \"186590\", \"77004\", \"6741\", \"7829\", \"13927\", \"14003\", \"39902\", \"51497\", \"52248\", \"54017\", \"66255\", \"80383\", \"83916\", \"101165\", \"121818\", \"180823\", \"7179\", \"10580\", \"11650\", \"17176\", \"25132\", \"33142\", \"35003\", \"35666\", \"35713\", \"50136\", \"58673\", \"59727\", \"74776\", \"99340\", \"113025\", \"136651\", \"150258\", \"155066\", \"207887\", \"59016\", \"81667\", \"92285\", \"105122\", \"106579\", \"111116\", \"120420\", \"122981\", \"129484\", \"131896\", \"136466\", \"139566\", \"140006\", \"142949\", \"147908\", \"154995\", \"156029\", \"176257\", \"179503\", \"196632\", \"210634\", \"166479\", \"169067\", \"171543\", \"171686\", \"172884\", \"174928\", \"176583\", \"182255\", \"186238\", \"186748\", \"190708\", \"192471\", \"193382\", \"194314\", \"203525\", \"207348\", \"209227\", \"209860\", \"234390\", \"204166\", \"209603\", \"216006\", \"225795\"], \"outgoing_citations\": [\"253398\", \"257197\", \"257954\", \"261365\", \"263976\", \"268829\", \"270197\", \"270668\", \"271112\", \"272481\", \"279732\", \"279950\", \"284871\", \"289389\", \"293979\", \"294023\", \"294606\", \"294931\", \"295708\", \"296685\", \"299461\", \"306502\", \"314541\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"241029","document_content":"# GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Social and Information Networks\n- I.2.6\n## Abstract\nModeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far.   In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08773v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.SI\", \"I.2.6\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Social and Information Networks\", \"I.2.6\"], \"incoming_citations\": [\"17850\", \"19259\", \"24612\", \"45478\", \"47526\", \"67813\", \"99827\", \"112333\", \"113308\", \"118617\", \"52113\", \"3595\", \"7739\", \"12644\", \"14780\", \"16862\", \"17471\", \"18117\", \"35373\", \"38315\", \"42873\", \"43280\", \"44918\", \"45404\", \"45929\", \"67885\", \"80211\", \"82942\", \"89373\", \"97503\", \"206470\", \"230611\", \"882\", \"1883\", \"4696\", \"7397\", \"12651\", \"12855\", \"17665\", \"19370\", \"23907\", \"26897\", \"28338\", \"28682\", \"29888\", \"33601\", \"34122\", \"34800\", \"35062\", \"36476\", \"37535\", \"38790\", \"40364\", \"40812\", \"41070\", \"42886\", \"44307\", \"38945\", \"46926\", \"47566\", \"47793\", \"48281\", \"48633\", \"49956\", \"50351\", \"53345\", \"54522\", \"54670\", \"55765\", \"56351\", \"59978\", \"65460\", \"68244\", \"70439\", \"70858\", \"71008\", \"71676\", \"72028\", \"73530\", \"73851\", \"74530\", \"74737\", \"75109\", \"77151\", \"77653\", \"84463\", \"92938\", \"94262\", \"95412\", \"101265\", \"112773\", \"114324\", \"115707\", \"116977\", \"117694\", \"117724\", \"117989\", \"120492\", \"127626\", \"133326\", \"144943\", \"151824\", \"183316\", \"201866\", \"262834\", \"79177\", \"79187\", \"80705\", \"88103\", \"62816\", \"96048\", \"96329\", \"93982\", \"99665\", \"102772\", \"103494\", \"104902\", \"111570\", \"112378\", \"113589\", \"115669\", \"115700\", \"115840\", \"116959\", \"117776\", \"119937\", \"119997\", \"120702\", \"121058\", \"116533\", \"124541\", \"126374\", \"128568\", \"130278\", \"131161\", \"132208\", \"134526\", \"138547\", \"138811\", \"139045\", \"139133\", \"139344\", \"139460\", \"140591\", \"142343\", \"143081\", \"143408\", \"143417\", \"140826\", \"144656\", \"145224\", \"145591\", \"146016\", \"146106\", \"149230\", \"149610\", \"154277\", \"157339\", \"158180\", \"160169\", \"117093\", \"170459\", \"175750\", \"182087\", \"188682\", \"191070\", \"200067\", \"204003\", \"205283\", \"208130\", \"215529\", \"218399\", \"150857\", \"151660\", \"153215\", \"149364\", \"155422\", \"155789\", \"135521\", \"158762\", \"159199\", \"161001\", \"161189\", \"163822\", \"163823\", \"163318\", \"164676\", \"150079\", \"172725\", \"165220\", \"165546\", \"172260\", \"173391\", \"174891\", \"175473\", \"176336\", \"177838\", \"180600\", \"181931\", \"182429\", \"183366\", \"183498\", \"184120\", \"184135\", \"181255\", \"186184\", \"183456\", \"189083\", \"191502\", \"194771\", \"196193\", \"197581\", \"199832\", \"200527\", \"202588\", \"204425\", \"183685\", \"218329\", \"237455\", \"186789\", \"213215\", \"213457\", \"208897\", \"219138\", \"220678\", \"228992\", \"230880\"], \"outgoing_citations\": [\"237455\", \"239581\", \"241022\", \"242739\", \"268060\", \"276771\", \"303643\", \"303913\"]}","task_split":"paper_retrieval"}
{"document_id":"241074","document_content":"# An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTrajectory and intention prediction of traffic participants is an important task in automated driving and crucial for safe interaction with the environment. In this paper, we present a new approach to vehicle trajectory prediction based on automatically generated maps containing statistical information about the behavior of traffic participants in a given area. These maps are generated based on trajectory observations using image processing and map matching techniques and contain all typical vehicle movements and probabilities in the considered area. Our prediction approach matches an observed trajectory to a behavior contained in the map and uses this information to generate a prediction. We evaluated our approach on a dataset containing over 14000 trajectories and found that it produces significantly more precise mid-term predictions compared to motion model-based prediction approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08632v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"241146","document_content":"# Towards end-to-end spoken language understanding\n## Categories\n- Computation and Language\n## Abstract\nSpoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for down-streaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08395v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"3823\", \"55947\", \"86254\", \"11520\", \"20622\", \"25153\", \"25366\", \"12476\", \"26863\", \"28852\", \"31268\", \"37727\", \"41735\", \"96502\", \"43635\", \"43650\", \"49488\", \"50569\", \"54462\", \"57429\", \"57637\", \"58181\", \"58416\", \"58582\", \"59421\", \"69349\", \"69680\", \"72161\", \"72237\", \"86592\", \"88529\", \"91161\", \"91476\", \"91935\", \"92519\", \"93316\", \"124378\", \"87723\", \"84544\", \"87977\", \"38791\", \"93464\", \"97729\", \"105483\", \"106068\", \"106269\", \"106294\", \"107179\", \"107416\", \"107737\", \"111463\", \"114312\", \"119809\", \"95623\", \"129355\", \"142302\", \"142446\", \"144381\", \"142276\", \"159820\", \"152874\", \"164326\", \"165042\", \"165233\", \"177269\", \"180179\", \"180891\", \"190687\", \"191769\", \"211863\", \"216686\", \"229907\", \"232847\"], \"outgoing_citations\": [\"260414\", \"281534\", \"307902\"]}","task_split":"paper_retrieval"}
{"document_id":"241237","document_content":"# MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEnhancing low resolution images via super-resolution or image synthesis for cross-resolution face recognition has been well studied. Several image processing and machine learning paradigms have been explored for addressing the same. In this research, we propose Synthesis via Deep Sparse Representation algorithm for synthesizing a high resolution face image from a low resolution input image. The proposed algorithm learns multi-level sparse representation for both high and low resolution gallery images, along with an identity aware dictionary and a transformation function between the two representations for face identification scenarios. With low resolution test data as input, the high resolution test image is synthesized using the identity aware dictionary and transformation which is then used for face recognition. The performance of the proposed SDSR algorithm is evaluated on four databases, including one real world dataset. Experimental results and comparison with existing seven algorithms demonstrate the efficacy of the proposed algorithm in terms of both face identification and image quality measures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08057v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"36730\"], \"outgoing_citations\": [\"274838\", \"300053\", \"354269\"]}","task_split":"paper_retrieval"}
{"document_id":"241252","document_content":"# Learning Topic Models by Neighborhood Aggregation\n## Categories\n- Machine Learning\n## Abstract\nTopic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.08012v6\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"71722\", \"214235\", \"269202\", \"296356\", \"349845\", \"363064\"]}","task_split":"paper_retrieval"}
{"document_id":"241443","document_content":"# Active Learning with Partial Feedback\n## Categories\n- Machine Learning\n## Abstract\nWhile many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes\/no binary feedback). To annotate examples corpora for multiclass classification, we might need to ask multiple yes\/no questions, exploiting a label hierarchy if one is available. To address this more realistic setting, we propose active learning with partial feedback (ALPF), where the learner must actively choose both which example to label and which binary question to ask. At each step, the learner selects an example, asking if it belongs to a chosen (possibly composite) class. Each answer eliminates some classes, leaving the learner with a partial label. The learner may then either ask more questions about the same example (until an exact label is uncovered) or move on immediately, leaving the first example partially labeled. Active learning with partial labels requires (i) a sampling strategy to choose (example, class) pairs, and (ii) learning from partial labels between rounds. Experiments on Tiny ImageNet demonstrate that our most effective method improves 26% (relative) in top-1 classification accuracy compared to i.i.d. baselines and standard active learners given 30% of the annotation budget that would be required (naively) to annotate the dataset. Moreover, ALPF-learners fully annotate TinyImageNet at 42% lower cost. Surprisingly, we observe that accounting for per-example annotation costs can alter the conventional wisdom that active learners should solicit labels for hard examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.07427v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"17769\", \"2543\", \"20156\", \"21570\", \"24502\", \"40699\", \"53221\", \"170636\", \"73006\", \"84387\", \"88924\", \"104153\", \"107796\", \"116271\", \"123370\", \"135893\", \"221103\"], \"outgoing_citations\": [\"259604\", \"260847\", \"276281\", \"290493\", \"303726\"]}","task_split":"paper_retrieval"}
{"document_id":"241605","document_content":"# Simplicial Closure and higher-order link prediction\n## Categories\n- Social and Information Networks\n- Statistical Mechanics\n- Algebraic Topology\n- Physics and Society\n- Machine Learning\n## Abstract\nNetworks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once; for example, communication within a group rather than person-to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental differences from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1073\/pnas.1800683115\", \"primary_category\": \"cs.SI\", \"categories\": [\"cond-mat.stat-mech\", \"math.AT\", \"physics.soc-ph\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Statistical Mechanics\", \"Algebraic Topology\", \"Physics and Society\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"61541\", \"3348\", \"3897\", \"10305\", \"21881\", \"30989\", \"31821\", \"40137\", \"42979\", \"46332\", \"47106\", \"52805\", \"69637\", \"74616\", \"76560\", \"77410\", \"93679\", \"123393\", \"147971\", \"252051\", \"1979\", \"4764\", \"12532\", \"12526\", \"12536\", \"22861\", \"24437\", \"22474\", \"29112\", \"31491\", \"34086\", \"43770\", \"51254\", \"54509\", \"57467\", \"59492\", \"60235\", \"61763\", \"63423\", \"63916\", \"67557\", \"68661\", \"69252\", \"71068\", \"71136\", \"72674\", \"73845\", \"74737\", \"75258\", \"75354\", \"75512\", \"74607\", \"101850\", \"120210\", \"121060\", \"129265\", \"169322\", \"186308\", \"199957\", \"95635\", \"103085\", \"103494\", \"176757\", \"114778\", \"117911\", \"119161\", \"121453\", \"173322\", \"131708\", \"131999\", \"133384\", \"137725\", \"139678\", \"140849\", \"141516\", \"142691\", \"144992\", \"151995\", \"158141\", \"159366\", \"160395\", \"163864\", \"165907\", \"197694\", \"309302\", \"138663\", \"224744\", \"200478\", \"168523\", \"179648\", \"200923\", \"218440\", \"223399\", \"210708\", \"222113\"], \"outgoing_citations\": [\"240500\", \"272490\", \"277109\", \"277250\", \"281667\", \"308149\", \"311305\", \"311349\", \"344587\"]}","task_split":"paper_retrieval"}
{"document_id":"241719","document_content":"# Heron Inference for Bayesian Graphical Models\n## Categories\n- Machine Learning\n## Abstract\nBayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields. Current inference methods primarily follow different kinds of trade-offs between computational complexity and predictive accuracy. At one end of the spectrum, variational inference approaches perform well in computational efficiency, while at the other end, Gibbs sampling approaches are known to be relatively accurate for prediction in practice. In this paper, we extend an existing Gibbs sampling method, and propose a new deterministic Heron inference (Heron) for a family of Bayesian graphical models. In addition to the support for nontrivial distributability, one more benefit of Heron is that it is able to not only allow us to easily assess the convergence status but also largely improve the running efficiency. We evaluate Heron against the standard collapsed Gibbs sampler and state-of-the-art state augmentation method in inference for well-known graphical models. Experimental results using publicly available real-life data have demonstrated that Heron significantly outperforms the baseline methods for inferring Bayesian graphical models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.06526v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"283057\", \"304335\", \"320292\", \"320877\", \"324330\", \"351402\", \"357194\", \"363064\"]}","task_split":"paper_retrieval"}
{"document_id":"241893","document_content":"# Modeling the Formation of Social Conventions from Embodied Real-Time Interactions\n## Categories\n- Multiagent Systems\n- Machine Learning\n- Neurons and Cognition\n- Artificial Intelligence\n- Computer Science and Game Theory\n## Abstract\nWhat is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent's reactive behaviors (pre-wired reflexes), along with an adaptive layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.06108v3\", \"primary_category\": \"cs.MA\", \"categories\": [\"stat.ML\", \"q-bio.NC\", \"cs.MA\", \"cs.AI\", \"cs.GT\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Machine Learning\", \"Neurons and Cognition\", \"Multiagent Systems\", \"Artificial Intelligence\", \"Computer Science and Game Theory\"], \"incoming_citations\": [\"264898\", \"318\", \"78338\", \"149262\", \"149258\"], \"outgoing_citations\": [\"149258\", \"166489\", \"252952\", \"256504\", \"260667\", \"262105\", \"269664\", \"274315\", \"275334\", \"302395\"]}","task_split":"paper_retrieval"}
{"document_id":"241933","document_content":"# Disentangling by Factorising\n## Categories\n- Machine Learning\n## Abstract\nWe define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon $\\beta$-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.05983v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"7181\", \"8357\", \"11935\", \"28381\", \"191885\", \"118799\", \"146184\", \"315\", \"1785\", \"2522\", \"2900\", \"3489\", \"3852\", \"5274\", \"5405\", \"6975\", \"7149\", \"7546\", \"14588\", \"15670\", \"15748\", \"16600\", \"16722\", \"17295\", \"17675\", \"19772\", \"22548\", \"23287\", \"26622\", \"27417\", \"27322\", \"34887\", \"38128\", \"38677\", \"39463\", \"36346\", \"39959\", \"42408\", \"45970\", \"59823\", \"67913\", \"70887\", \"72458\", \"74251\", \"76023\", \"80015\", \"83792\", \"87490\", \"89067\", \"90014\", \"94090\", \"97544\", \"100302\", \"103983\", \"119167\", \"144278\", \"148742\", \"184092\", \"193553\", \"483\", \"738\", \"4575\", \"6825\", \"7221\", \"10004\", \"10442\", \"12936\", \"14234\", \"14278\", \"16855\", \"17072\", \"17445\", \"18244\", \"18189\", \"19486\", \"18493\", \"19599\", \"20747\", \"20955\", \"22992\", \"23400\", \"24977\", \"26673\", \"26892\", \"27458\", \"29142\", \"29613\", \"29931\", \"32174\", \"32216\", \"32963\", \"32953\", \"33116\", \"33537\", \"35918\", \"36339\", \"36425\", \"36764\", \"37276\", \"38469\", \"38668\", \"39228\", \"39178\", \"42085\", \"42895\", \"43049\", \"43333\", \"43335\", \"43651\", \"44223\", \"44100\", \"122823\", \"44506\", \"44531\", \"45681\", \"46469\", \"47197\", \"47792\", \"48654\", \"52722\", \"45942\", \"55764\", \"56003\", \"57749\", \"58469\", \"59449\", \"60113\", \"60102\", \"61327\", \"61452\", \"61817\", \"61995\", \"62588\", \"62794\", \"65007\", \"65861\", \"67912\", \"68053\", \"69932\", \"70211\", \"70382\", \"71306\", \"72074\", \"72199\", \"74357\", \"74520\", \"74730\", \"75502\", \"76760\", \"80149\", \"79090\", \"107094\", \"83066\", \"83499\", \"84768\", \"85085\", \"85847\", \"86412\", \"86397\", \"87413\", \"90921\", \"93022\", \"95962\", \"96114\", \"149306\", \"100719\", \"103627\", \"110493\", \"111236\", \"117883\", \"118768\", \"119484\", \"120747\", \"127020\", \"127928\", \"129567\", \"133318\", \"141540\", \"145349\", \"161344\", \"139836\", \"173519\", \"183113\", \"162060\", \"93244\", \"69639\", \"39444\", \"34278\", \"9733\", \"78660\", \"79911\", \"80538\", \"81062\", \"81997\", \"83493\", \"85723\", \"87061\", \"88027\", \"88259\", \"91004\", \"91259\", \"91429\", \"92004\", \"93568\", \"93672\", \"94225\", \"95026\", \"96305\", \"96496\", \"97865\", \"98006\", \"99542\", \"99665\", \"100165\", \"100660\", \"100782\", \"101063\", \"101955\", \"103111\", \"103462\", \"103173\", \"104295\", \"104568\", \"105034\", \"105383\", \"105668\", \"106195\", \"107008\", \"107535\", \"107530\", \"108233\", \"108960\", \"108991\", \"108962\", \"109667\", \"109786\", \"110341\", \"110924\", \"110784\", \"111712\", \"112398\", \"101658\", \"110621\", \"113541\", \"113578\", \"106407\", \"114524\", \"114820\", \"116297\", \"116650\", \"117038\", \"117959\", \"117945\", \"118821\", \"119003\", \"119136\", \"119691\", \"119708\", \"119721\", \"119937\", \"120483\", \"116132\", \"121542\", \"121349\", \"122438\", \"122771\", \"123374\", \"124845\", \"125666\", \"126318\", \"127307\", \"128671\", \"129114\", \"129244\", \"130407\", \"131445\", \"131678\", \"131672\", \"132073\", \"132208\", \"132429\", \"132719\", \"109266\", \"133310\", \"133398\", \"130199\", \"135485\", \"135698\", \"136839\", \"137670\", \"138086\", \"138664\", \"139756\", \"140081\", \"140360\", \"140423\", \"140573\", \"140938\", \"141054\", \"141826\", \"143360\", \"143581\", \"143633\", \"143831\", \"146600\", \"147237\", \"147421\", \"147394\", \"148091\", \"148419\", \"150018\", \"151922\", \"152119\", \"154215\", \"154412\", \"154584\", \"155582\", \"155562\", \"155868\", \"156282\", \"157130\", \"160471\", \"163466\", \"163661\", \"164600\", \"164876\", \"166672\", \"167627\", \"168518\", \"170071\", \"171190\", \"173673\", \"176649\", \"178648\", \"180811\", \"182775\", \"183696\", \"184045\", \"184464\", \"187863\", \"196964\", \"201588\", \"201881\", \"215021\", \"223224\", \"265249\", \"266719\", \"163896\", \"144684\", \"149182\", \"151710\", \"152148\", \"152670\", \"153486\", \"154101\", \"154401\", \"156867\", \"158823\", \"161911\", \"162032\", \"164167\", \"165021\", \"184658\", \"164905\", \"168312\", \"168885\", \"169000\", \"170805\", \"171077\", \"171324\", \"174976\", \"176141\", \"178668\", \"181762\", \"181909\", \"181916\", \"182658\", \"183257\", \"183732\", \"184269\", \"182154\", \"184867\", \"185109\", \"185954\", \"186707\", \"187768\", \"187976\", \"188590\", \"190015\", \"190475\", \"190926\", \"193014\", \"193391\", \"193900\", \"195128\", \"196163\", \"196894\", \"199202\", \"200197\", \"201416\", \"201724\", \"202065\", \"202216\", \"205697\", \"206228\", \"206907\", \"208041\", \"210916\", \"210596\", \"212169\", \"198022\", \"202024\", \"216250\", \"218117\", \"218975\", \"236595\", \"241998\", \"184464\", \"194116\", \"204662\", \"205518\", \"205684\", \"206218\", \"207094\", \"207247\", \"207443\", \"207627\", \"210846\", \"210434\", \"217774\", \"220865\", \"227322\", \"227890\", \"229184\", \"230159\", \"230949\", \"230375\", \"231018\", \"233751\", \"235070\", \"237146\", \"238548\", \"238499\", \"242239\", \"242142\"], \"outgoing_citations\": [\"241627\", \"242239\", \"251597\", \"255129\", \"264750\", \"264782\", \"264963\", \"276018\", \"280734\", \"281227\", \"282643\", \"282844\", \"297855\", \"316183\", \"320131\", \"321465\", \"333469\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"241989","document_content":"# A Unified View of Causal and Non-causal Feature Selection\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we are able to interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-word data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.05844v4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"29464\", \"35811\", \"61279\", \"76970\", \"87234\", \"99114\", \"113943\"], \"outgoing_citations\": [\"240081\", \"264992\", \"306040\", \"314180\", \"346695\"]}","task_split":"paper_retrieval"}
{"document_id":"242062","document_content":"# Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nDuring sleep and awake rest, the hippocampus replays sequences of place cells that have been activated during prior experiences. These have been interpreted as a memory consolidation process, but recent results suggest a possible interpretation in terms of reinforcement learning. The Dyna reinforcement learning algorithms use off-line replays to improve learning. Under limited replay budget, a prioritized sweeping approach, which requires a model of the transitions to the predecessors, can be used to improve performance. We investigate whether such algorithms can explain the experimentally observed replays. We propose a neural network version of prioritized sweeping Q-learning, for which we developed a growing multiple expert algorithm, able to cope with multiple predecessors. The resulting architecture is able to improve the learning of simulated agents confronted to a navigation task. We predict that, in animals, learning the world model should occur during rest periods, and that the corresponding replays should be shuffled.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-95972-6_4\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"125378\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"242072","document_content":"# Semi-Supervised Learning on Graphs Based on Local Label Distributions\n## Categories\n- Machine Learning\n## Abstract\nMost approaches that tackle the problem of node classification consider nodes to be similar, if they have shared neighbors or are close to each other in the graph. Recent methods for attributed graphs additionally take attributes of neighboring nodes into account. We argue that the class labels of the neighbors bear important information and considering them helps to improve classification quality. Two nodes which are similar based on class labels in their neighborhood do not need to be close-by in the graph and may even belong to different connected components. In this work, we propose a novel approach for the semi-supervised node classification. Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node. We show that this is a different setting from attribute-based embeddings and thus, we propose a new method to learn label-based node embeddings which can mirror a variety of relations between the class labels of neighboring nodes. Our experimental evaluation demonstrates that our new methods can significantly improve the prediction quality on real world data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.05563v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"138526\"], \"outgoing_citations\": [\"253052\", \"253504\", \"253807\", \"261412\", \"262989\", \"265114\", \"265828\", \"269202\", \"269273\", \"318072\"]}","task_split":"paper_retrieval"}
{"document_id":"242092","document_content":"# Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nMachine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing such effects is structured prediction, which optimizes over complex labels, while modeling within-label interactions. However, it is unclear what principles should guide the design of a structured prediction model that utilizes the power of deep learning components. Here we propose a design principle for such architectures that follows from a natural requirement of permutation invariance. We prove a necessary and sufficient characterization for architectures that follow this invariance, and discuss its implication on model design. Finally, we show that the resulting model achieves new state of the art results on the Visual Genome scene graph labeling benchmark, outperforming all recent approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.05451v4\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CV\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"62580\", \"16624\", \"53813\", \"2056\", \"14338\", \"26827\", \"28264\", \"28638\", \"28753\", \"37464\", \"24263\", \"82091\", \"99043\", \"101349\", \"115753\", \"195321\", \"77336\", \"81305\", \"91503\", \"97645\", \"98147\", \"106459\", \"132272\", \"132403\", \"133742\", \"134425\", \"147250\", \"148137\", \"149147\", \"150966\", \"151123\", \"191895\", \"197406\", \"161189\", \"159494\", \"165842\", \"179784\", \"181226\", \"183719\", \"194032\", \"200636\", \"201584\", \"203180\", \"207049\", \"207400\", \"208598\", \"242322\"], \"outgoing_citations\": [\"222661\", \"236736\", \"250108\", \"263084\", \"269617\", \"271301\", \"271635\", \"273869\", \"276530\", \"279191\", \"279990\", \"280289\", \"284398\", \"287454\", \"311670\", \"316332\", \"327259\"]}","task_split":"paper_retrieval"}
{"document_id":"242239","document_content":"# Isolating Sources of Disentanglement in Variational Autoencoders\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.04942v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2288\", \"8357\", \"11935\", \"40706\", \"83187\", \"161291\", \"118799\", \"315\", \"1785\", \"2522\", \"2900\", \"3852\", \"5405\", \"6975\", \"7149\", \"8908\", \"10486\", \"14588\", \"14646\", \"15670\", \"16115\", \"16600\", \"16722\", \"17675\", \"26622\", \"27417\", \"27322\", \"34887\", \"38128\", \"38677\", \"36346\", \"42408\", \"43433\", \"45970\", \"46100\", \"49605\", \"59089\", \"59823\", \"67913\", \"70887\", \"72458\", \"73900\", \"76023\", \"80015\", \"83792\", \"84086\", \"87490\", \"90014\", \"97544\", \"103983\", \"108191\", \"119167\", \"146022\", \"148742\", \"153555\", \"184092\", \"227779\", \"483\", \"4575\", \"6825\", \"7221\", \"9992\", \"11424\", \"12936\", \"13277\", \"14234\", \"14278\", \"17072\", \"17445\", \"17237\", \"17988\", \"18189\", \"18244\", \"19486\", \"17446\", \"19599\", \"20149\", \"20747\", \"20955\", \"22992\", \"28999\", \"30287\", \"31874\", \"32107\", \"32807\", \"32953\", \"33116\", \"11676\", \"34438\", \"35918\", \"36764\", \"36887\", \"37276\", \"38455\", \"38652\", \"38668\", \"39228\", \"39178\", \"40695\", \"42085\", \"43049\", \"43335\", \"43333\", \"44100\", \"44223\", \"122823\", \"47792\", \"48654\", \"52004\", \"52722\", \"45942\", \"56003\", \"56475\", \"57749\", \"59449\", \"60603\", \"61452\", \"61817\", \"61995\", \"62256\", \"62588\", \"63935\", \"64176\", \"64576\", \"64660\", \"65007\", \"65861\", \"65740\", \"67560\", \"67912\", \"68053\", \"69932\", \"70211\", \"70382\", \"71306\", \"72074\", \"72199\", \"74357\", \"74520\", \"74730\", \"76760\", \"80149\", \"81320\", \"81807\", \"83066\", \"84068\", \"84768\", \"85847\", \"86412\", \"86397\", \"89349\", \"90921\", \"92381\", \"179978\", \"94262\", \"96040\", \"95962\", \"96114\", \"100719\", \"110493\", \"111236\", \"112073\", \"118255\", \"118768\", \"119484\", \"120747\", \"123800\", \"133318\", \"140109\", \"141540\", \"149360\", \"165238\", \"183113\", \"69639\", \"162060\", \"93244\", \"39444\", \"34278\", \"78660\", \"81062\", \"81997\", \"83493\", \"86628\", \"87061\", \"88027\", \"88024\", \"90658\", \"91004\", \"91259\", \"91429\", \"92004\", \"93568\", \"93724\", \"94225\", \"96305\", \"96496\", \"97865\", \"99542\", \"99665\", \"100165\", \"100059\", \"100660\", \"101063\", \"101955\", \"103430\", \"103173\", \"104568\", \"105034\", \"105383\", \"107530\", \"107776\", \"108960\", \"109667\", \"109786\", \"110341\", \"110784\", \"111712\", \"112145\", \"112398\", \"101658\", \"110621\", \"113541\", \"114586\", \"116297\", \"116650\", \"117038\", \"117671\", \"117799\", \"118821\", \"119003\", \"119136\", \"119691\", \"119937\", \"120425\", \"121542\", \"116023\", \"121349\", \"121844\", \"122438\", \"122771\", \"123374\", \"123706\", \"124279\", \"126318\", \"127307\", \"128671\", \"129244\", \"130407\", \"131445\", \"131678\", \"131672\", \"131969\", \"132073\", \"132208\", \"132719\", \"133034\", \"109266\", \"133398\", \"130199\", \"136839\", \"137157\", \"137670\", \"138086\", \"138664\", \"139756\", \"139944\", \"140100\", \"140423\", \"140360\", \"140573\", \"140582\", \"141054\", \"143360\", \"143581\", \"143633\", \"144211\", \"145743\", \"145963\", \"145883\", \"146137\", \"147210\", \"147237\", \"147421\", \"148091\", \"149748\", \"151922\", \"154215\", \"154584\", \"154928\", \"155562\", \"155868\", \"156282\", \"157130\", \"159228\", \"160471\", \"163661\", \"164600\", \"164876\", \"145113\", \"168518\", \"170071\", \"171190\", \"176649\", \"178648\", \"180811\", \"182775\", \"183223\", \"183696\", \"184045\", \"184464\", \"187863\", \"190842\", \"201588\", \"206756\", \"208520\", \"144684\", \"149182\", \"151710\", \"152148\", \"153486\", \"154101\", \"154401\", \"155597\", \"156867\", \"157145\", \"157732\", \"158823\", \"159601\", \"161911\", \"162032\", \"163994\", \"164167\", \"184658\", \"165021\", \"165584\", \"168312\", \"168885\", \"171324\", \"174976\", \"176254\", \"173579\", \"179803\", \"181388\", \"181762\", \"181909\", \"182121\", \"182658\", \"182854\", \"183257\", \"183732\", \"183941\", \"184269\", \"184508\", \"182154\", \"185109\", \"185954\", \"187768\", \"187976\", \"184302\", \"188590\", \"190015\", \"190475\", \"193900\", \"195128\", \"195230\", \"195310\", \"195427\", \"196615\", \"197371\", \"199202\", \"200197\", \"201235\", \"201416\", \"201724\", \"202216\", \"202378\", \"202883\", \"205697\", \"206228\", \"206907\", \"208041\", \"210596\", \"212169\", \"198022\", \"216250\", \"218117\", \"220828\", \"236595\", \"241998\", \"184464\", \"205518\", \"205684\", \"206218\", \"207094\", \"207443\", \"224293\", \"227322\", \"227890\", \"228049\", \"230159\", \"230949\", \"230375\", \"231018\", \"233751\", \"235070\", \"236863\", \"237146\", \"241933\"], \"outgoing_citations\": [\"227890\", \"241933\", \"241998\", \"245283\", \"251597\", \"264327\", \"264782\", \"264994\", \"270823\", \"277856\", \"278060\", \"281227\", \"282643\", \"304094\", \"311052\", \"311482\", \"316183\", \"320031\", \"322661\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"242478","document_content":"# State Representation Learning for Control: An Overview\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nRepresentation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning.   This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.neunet.2018.07.006\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3379\", \"169536\", \"4713\", \"14036\", \"14157\", \"17165\", \"18199\", \"19488\", \"20579\", \"51298\", \"63075\", \"115404\", \"144508\", \"148742\", \"14495\", \"30956\", \"37671\", \"37677\", \"42315\", \"42563\", \"42946\", \"43557\", \"45072\", \"47287\", \"49658\", \"64677\", \"64647\", \"66036\", \"69108\", \"76745\", \"76846\", \"83695\", \"84694\", \"100538\", \"105871\", \"109030\", \"112665\", \"121387\", \"197666\", \"79679\", \"94892\", \"110974\", \"114586\", \"118444\", \"114889\", \"123128\", \"125378\", \"126338\", \"123596\", \"131018\", \"133455\", \"135913\", \"117372\", \"138758\", \"138752\", \"139653\", \"143420\", \"144836\", \"151978\", \"155474\", \"166672\", \"166762\", \"174114\", \"178420\", \"184811\", \"225557\", \"156845\", \"164842\", \"171112\", \"176551\", \"176654\", \"176421\", \"178620\", \"179957\", \"180319\", \"181909\", \"185001\", \"186100\", \"181405\", \"190134\", \"195713\", \"196853\", \"198915\", \"207798\", \"213161\", \"201682\", \"209469\", \"220103\", \"231360\", \"239153\", \"214902\", \"208453\", \"216625\", \"225626\", \"231522\", \"231877\", \"232420\", \"238049\"], \"outgoing_citations\": [\"233926\", \"240282\", \"244430\", \"247163\", \"250973\", \"255139\", \"255861\", \"260136\", \"261391\", \"261493\", \"265263\", \"269206\", \"272530\", \"277472\", \"278195\", \"289719\", \"290371\", \"290378\", \"291302\", \"292009\", \"292185\", \"294660\", \"295194\", \"305367\", \"306266\", \"310624\", \"311482\", \"313480\", \"317863\", \"342875\"]}","task_split":"paper_retrieval"}
{"document_id":"242801","document_content":"# Slice Sampling Particle Belief Propagation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nInference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings Markov chain Monte Carlo methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.03275v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"62217\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"242940","document_content":"# Action Anticipation: Reading the Intentions of Humans and Robots\n## Categories\n- Robotics\n## Abstract\nHumans have the fascinating capacity of processing non-verbal visual cues to understand and anticipate the actions of other humans. This \"intention reading\" ability is underpinned by shared motor-repertoires and action-models, which we use to interpret the intentions of others as if they were our own.   We investigate how the different cues contribute to the legibility of human actions during interpersonal interactions. Our first contribution is a publicly available dataset with recordings of human body-motion and eye-gaze, acquired in an experimental scenario with an actor interacting with three subjects. From these data, we conducted a human study to analyse the importance of the different non-verbal cues for action perception. As our second contribution, we used the motion\/gaze recordings to build a computational model describing the interaction between two persons. As a third contribution, we embedded this model in the controller of an iCub humanoid robot and conducted a second human study, in the same scenario with the robot as an actor, to validate the model's \"intention reading\" capability.   Our results show that it is possible to model (non-verbal) signals exchanged by humans during interaction, and how to incorporate such a mechanism in robotic systems with the twin goal of : (i) being able to \"read\" human action intentions, and (ii) acting in a way that is legible by humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LRA.2018.2861569\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"56869\", \"85228\", \"163851\", \"186969\", \"98251\", \"200322\"], \"outgoing_citations\": [\"240531\", \"299830\", \"330318\"]}","task_split":"paper_retrieval"}
{"document_id":"243056","document_content":"# From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations. Regarding the former requirement, Convolutional Neural Network (CNN) is proven to be a very powerful tool to extract highly discriminative local descriptors for effective image search. Additionally, in order to further improve the discriminative power of the descriptors, recent works adopt fine-tuned strategies. In this paper, taking a different approach, we propose a novel, computationally efficient, and competitive framework. Specifically, we firstly propose various strategies to compute masks, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and eliminate redundant features. Our in-depth analyses demonstrate that proposed masking schemes are effective to address the burstiness drawback and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods which can significantly boost the feature discriminability. Regarding the computation and storage efficiency, we include a hashing module to produce very compact binary image representations. Extensive experiments on six image retrieval benchmarks demonstrate that our proposed framework achieves the state-of-the-art retrieval performances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.02899v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73146\", \"85376\", \"211067\", \"247969\", \"178605\", \"189084\"], \"outgoing_citations\": [\"241439\", \"241679\", \"243080\", \"251473\", \"255271\", \"259070\", \"262169\", \"267404\", \"269814\", \"275310\", \"275954\", \"277635\", \"281953\", \"283210\", \"285231\", \"287103\", \"288169\", \"288218\", \"291400\", \"292069\", \"294367\", \"294983\", \"295204\", \"301572\", \"303067\", \"315007\", \"316430\", \"319454\", \"321486\", \"328917\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"243090","document_content":"# A Critical Investigation of Deep Reinforcement Learning for Navigation\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nThe navigation problem is classically approached in two steps: an exploration step, where map-information about the environment is gathered; and an exploitation step, where this information is used to navigate efficiently. Deep reinforcement learning (DRL) algorithms, alternatively, approach the problem of navigation in an end-to-end fashion. Inspired by the classical approach, we ask whether DRL algorithms are able to inherently explore, gather and exploit map-information over the course of navigation. We build upon Mirowski et al. [2017] work and introduce a systematic suite of experiments that vary three parameters: the agent's starting location, the agent's target location, and the maze structure. We choose evaluation metrics that explicitly measure the algorithm's ability to gather and exploit map-information. Our experiments show that when trained and tested on the same maps, the algorithm successfully gathers and exploits map-information. However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps. Furthermore, we find that when the goal location is randomized and the map is kept static, the algorithm is able to gather and exploit map-information but the exploitation is far from optimal. We open-source our experimental suite in the hopes that it serves as a framework for the comparison of future algorithms and leads to the discovery of robust alternatives to classical navigation methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.02274v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\"], \"incoming_citations\": [\"26795\", \"128249\", \"60938\", \"91773\", \"132020\", \"162505\", \"196909\", \"187845\", \"216647\", \"237119\"], \"outgoing_citations\": [\"263811\", \"272930\", \"274102\", \"280296\", \"280681\", \"290904\", \"291568\"]}","task_split":"paper_retrieval"}
{"document_id":"243184","document_content":"# Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election\n## Categories\n- Social and Information Networks\n- Applications\n- Machine Learning\n- Information Retrieval\n- Computation and Language\n## Abstract\nOpinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4018\/IJSDS.2018010102\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"stat.AP\", \"stat.ML\", \"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Applications\", \"Machine Learning\", \"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [\"28399\", \"20059\", \"54968\", \"128294\", \"151730\", \"196165\", \"178243\", \"184957\", \"206754\", \"206755\", \"221024\", \"221025\", \"226805\", \"237874\", \"237875\"], \"outgoing_citations\": [\"154394\", \"255123\", \"255124\", \"267465\", \"327628\", \"340127\"]}","task_split":"paper_retrieval"}
{"document_id":"243196","document_content":"# ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nCross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts risk of re-admission for a particular set of patients will be more generalizable if developed with data from multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each participating site contributes to model parameter estimation without revealing any patient health information (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-preserving online machine learning with a private Blockchain network, apply transaction metadata to disseminate partial models, and design a new proof-of-information algorithm to determine the order of the online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities such as Patient-Centered Outcomes Research (PCOR).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.01746v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CR\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Cryptography and Security\"], \"incoming_citations\": [\"47538\", \"111170\", \"175785\", \"182721\", \"169270\", \"206924\", \"238839\", \"245803\"], \"outgoing_citations\": [\"328045\"]}","task_split":"paper_retrieval"}
{"document_id":"243197","document_content":"# Shared Autonomy via Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Human-Computer Interaction\n- Robotics\n## Abstract\nIn shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user's policy given their goal, and the set of possible goals the user might target, which limits their application to real-world scenarios. We propose a deep reinforcement learning framework for model-free shared autonomy that lifts these assumptions. We use human-in-the-loop reinforcement learning with neural network function approximation to learn an end-to-end mapping from environmental observation and user input to agent action values, with task reward as the only form of supervision. This approach poses the challenge of following user commands closely enough to provide the user with real-time action feedback and thereby ensure high-quality user input, but also deviating from the user's actions when they are suboptimal. We balance these two needs by discarding actions whose values fall below some threshold, then selecting the remaining action closest to the user's input. Controlled studies with users (n = 12) and synthetic pilots playing a video game, and a pilot study with users (n = 4) flying a real quadrotor, demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user's private information through observations, but receives a reward signal and user input that both depend on the user's intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user's input. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.01744v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"2405\", \"22668\", \"37110\", \"39368\", \"73900\", \"222967\", \"4113\", \"11352\", \"17175\", \"21558\", \"22916\", \"27840\", \"34317\", \"56269\", \"59140\", \"65096\", \"81984\", \"84575\", \"136258\", \"139253\", \"107239\", \"108840\", \"115941\", \"119110\", \"126543\", \"129377\", \"131830\", \"103314\", \"137402\", \"137960\", \"138947\", \"152542\", \"165608\", \"165708\", \"153228\", \"157336\", \"178503\", \"182138\", \"182283\", \"182976\", \"184168\", \"196851\", \"203923\", \"208994\", \"209452\", \"184478\", \"219954\", \"231267\"], \"outgoing_citations\": [\"220420\", \"254581\", \"256193\", \"275319\", \"283365\", \"290891\", \"315405\"]}","task_split":"paper_retrieval"}
{"document_id":"243242","document_content":"# The Matrix Calculus You Need For Deep Learning\n## Categories\n- Machine Learning\n## Abstract\nThis paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way---just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at forums.fast.ai. Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here. See related articles at http:\/\/explained.ai","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.01528v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"27977\", \"77854\", \"171809\", \"233216\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"243326","document_content":"# Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings\n## Categories\n- Computation and Language\n## Abstract\nThe words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces - called \"word embeddings\" - can be learned from patterns of lexical co-occurrences in natural language. Despite their popularity, a fundamental concern about word embeddings is that they appear to be semantically \"rigid\": inter-word proximity captures only overall similarity, yet human judgments about object similarities are highly context-dependent and involve multiple, distinct semantic features. For example, dolphins and alligators appear similar in size, but differ in intelligence and aggressiveness. Could such context-dependent relationships be recovered from word embeddings? To address this issue, we introduce a powerful, domain-general solution: \"semantic projection\" of word-vectors onto lines that represent various object features, like size (the line extending from the word \"small\" to \"big\"), intelligence (from \"dumb\" to \"smart\"), or danger (from \"safe\" to \"dangerous\"). This method, which is intuitively analogous to placing objects \"on a mental scale\" between two extremes, recovers human judgments across a range of object categories and properties. We thus show that word embeddings inherit a wealth of common knowledge from word co-occurrence statistics and can be flexibly manipulated to express context-dependent meanings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.01241v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"161567\", \"34034\", \"107839\", \"109410\", \"113263\", \"133239\", \"135274\", \"143724\"], \"outgoing_citations\": [\"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"243401","document_content":"# Plan Explanations as Model Reconciliation -- An Empirical Study\n## Categories\n- Artificial Intelligence\n## Abstract\nRecent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models. Existing algorithms in such settings, while having been built on contrastive, selective and social properties of explanations as studied extensively in the psychology literature, have not, to the best of our knowledge, been evaluated in settings with actual humans in the loop. As such, the applicability of such explanations to human-AI and human-robot interactions remains suspect. In this paper, we set out to evaluate these explanation generation algorithms in a series of studies in a mock search and rescue scenario with an internal semi-autonomous robot and an external human commander. We demonstrate to what extent the properties of these algorithms hold as they are evaluated by humans, and how the dynamics of trust between the human and the robot evolve during the process of these interactions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.01013v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"64321\", \"17928\", \"59886\", \"84933\", \"102965\", \"139877\", \"161527\", \"194783\", \"196464\", \"208919\", \"259593\"], \"outgoing_citations\": [\"208919\", \"213766\", \"241614\", \"243879\", \"248571\", \"259593\", \"261163\", \"275194\", \"302521\"]}","task_split":"paper_retrieval"}
{"document_id":"243416","document_content":"# Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records\n## Categories\n- Neural and Evolutionary Computing\n## Abstract\nModern healthcare is ripe for disruption by AI. A game changer would be automatic understanding the latent processes from electronic medical records, which are being collected for billions of people worldwide. However, these healthcare processes are complicated by the interaction between at least three dynamic components: the illness which involves multiple diseases, the care which involves multiple treatments, and the recording practice which is biased and erroneous. Existing methods are inadequate in capturing the dynamic structure of care. We propose Resset, an end-to-end recurrent model that reads medical record and predicts future risk. The model adopts the algebraic view in that discrete medical objects are embedded into continuous vectors lying in the same space. We formulate the problem as modeling sequences of sets, a novel setting that have rarely, if not, been addressed. Within Resset, the bag of diseases recorded at each clinic visit is modeled as function of sets. The same hold for the bag of treatments. The interaction between the disease bag and the treatment bag at a visit is modeled in several, one of which as residual of diseases minus the treatments. Finally, the health trajectory, which is a sequence of visits, is modeled using a recurrent neural network. We report results on over a hundred thousand hospital visits by patients suffered from two costly chronic diseases -- diabetes and mental health. Resset shows promises in multiple predictive tasks such as readmission prediction, treatments recommendation and diseases progression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.00948v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"34202\", \"181285\", \"185414\", \"213345\"], \"outgoing_citations\": [\"264024\", \"273342\", \"279410\", \"286253\", \"287747\", \"298095\", \"302481\", \"302934\"]}","task_split":"paper_retrieval"}
{"document_id":"243717","document_content":"# An efficient algorithm for global interval solution of nonlinear algebraic equations and its GPGPU implementation\n## Categories\n- Numerical Analysis\n- Symbolic Computation\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nSolving nonlinear algebraic equations is a classic mathematics problem, and common in scientific researches and engineering applications. There are many numeric, symbolic and numeric-symbolic methods of solving (real) solutions. Unlucky, these methods are constrained by some factors, e.g., high complexity, slow serial calculation, and the notorious intermediate expression expansion. Especially when the count of variables is larger than six, the efficiency is decreasing drastically. In this paper, according to the property of physical world, we pay attention to nonlinear algebraic equations whose variables are in fixed constraints, and get meaningful real solutions. Combining with parallelism of GPGPU, we present an efficient algorithm, by searching the solution space globally and solving the nonlinear algebraic equations with real interval solutions. Furthermore, we realize the Hansen-Sengupta method on GPGPU. The experiments show that our method can solve many nonlinear algebraic equations, and the results are accurate and more efficient compared to traditional serial methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1802.00330v1\", \"primary_category\": \"cs.NA\", \"categories\": [\"cs.SC\", \"cs.DC\", \"cs.NA\"], \"primary_category_human_readable\": \"Numerical Analysis\", \"categories_human_readable\": [\"Symbolic Computation\", \"Distributed, Parallel, and Cluster Computing\", \"Numerical Analysis\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"243816","document_content":"# Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic image captioning has recently approached human-level performance due to the latest advances in computer vision and natural language understanding. However, most of the current models can only generate plain factual descriptions about the content of a given image. However, for human beings, image caption writing is quite flexible and diverse, where additional language dimensions, such as emotion, humor and language styles, are often incorporated to produce diverse, emotional, or appealing captions. In particular, we are interested in generating sentiment-conveying image descriptions, which has received little attention. The main challenge is how to effectively inject sentiments into the generated captions without altering the semantic matching between the visual content and the generated descriptions. In this work, we propose two different models, which employ different schemes for injecting sentiments into image captions. Compared with the few existing approaches, the proposed models are much simpler and yet more effective. The experimental results show that our model outperform the state-of-the-art models in generating sentimental (i.e., sentiment-bearing) image captions. In addition, we can also easily manipulate the model by assigning different sentiments to the testing image to generate captions with the corresponding sentiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.10121v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5789\", \"113677\", \"27365\", \"74768\", \"142094\", \"145513\", \"23530\", \"5781\", \"141957\", \"154752\", \"172586\", \"78600\", \"213042\", \"208888\", \"225585\"], \"outgoing_citations\": [\"269654\", \"272531\", \"274461\", \"305473\", \"306291\", \"313934\", \"317592\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"243855","document_content":"# COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nClustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms select the most useful constraints to query, aiming to produce a good clustering using as few constraints as possible. We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints. In its merging step, COBRA is able to keep the number of pairwise queries low by maximally exploiting constraint transitivity and entailment. We experimentally show that COBRA outperforms the state of the art in terms of clustering quality and runtime, without requiring the number of clusters in advance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09955v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"129440\", \"10647\", \"83874\", \"80168\", \"233484\", \"237302\"], \"outgoing_citations\": [\"312337\", \"362508\"]}","task_split":"paper_retrieval"}
{"document_id":"243880","document_content":"# Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n## Abstract\nMotivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results: We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09851v4\", \"primary_category\": \"cs.IR\", \"categories\": [\"stat.ML\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"66846\", \"77478\", \"205022\", \"7555\", \"31131\", \"39953\", \"73310\", \"97054\", \"102549\", \"109085\", \"114853\", \"129792\", \"87371\", \"88174\", \"89428\", \"79310\", \"89930\", \"91571\", \"107711\", \"116629\", \"126134\", \"126679\", \"127709\", \"120925\", \"145761\", \"162527\", \"201647\", \"157143\", \"161962\", \"165005\", \"198222\", \"205914\", \"212106\", \"218407\", \"249726\"], \"outgoing_citations\": [\"256158\", \"268518\", \"317907\"]}","task_split":"paper_retrieval"}
{"document_id":"243894","document_content":"# Personalized Survival Prediction with Contextual Explanation Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAccurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific survival distributions and to explain its predictions in terms of patient attributes such as clinical tests or assessments. Our model is flexible and based on a recurrent network, can handle various modalities of data including temporal measurements, and yet constructs and uses simple explanations in the form of patient- and time-specific linear regression. For analysis, we use two publicly available datasets and show that our networks outperform a number of baselines in prediction while providing a way to inspect the reasons behind each prediction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09810v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"161574\"], \"outgoing_citations\": [\"265119\"]}","task_split":"paper_retrieval"}
{"document_id":"243940","document_content":"# Learning the Reward Function for a Misspecified Model\n## Categories\n- Machine Learning\n- I.2.6; I.2.8\n## Abstract\nIn model-based reinforcement learning it is typical to decouple the problems of learning the dynamics model and learning the reward function. However, when the dynamics model is flawed, it may generate erroneous states that would never occur in the true environment. It is not clear a priori what value the reward function should assign to such states. This paper presents a novel error bound that accounts for the reward model's behavior in states sampled from the model. This bound is used to extend the existing Hallucinated DAgger-MC algorithm, which offers theoretical performance guarantees in deterministic MDPs that do not assume a perfect model can be learned. Empirically, this approach to reward learning can yield dramatic improvements in control performance when the dynamics model is flawed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09624v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"I.2.6; I.2.8\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"I.2.6; I.2.8\"], \"incoming_citations\": [\"200791\", \"232723\"], \"outgoing_citations\": [\"253291\", \"261493\", \"269458\", \"277193\", \"277696\", \"293922\", \"298651\", \"308711\", \"361126\"]}","task_split":"paper_retrieval"}
{"document_id":"244011","document_content":"# Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning. The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification. A primary level of `inter-domain' transfer learning is used by exporting weights from a pre-trained VGG16 architecture on the ImageNet dataset to train a document classifier on whole document images. Exploiting the nature of region based influence modelling, a secondary level of `intra-domain' transfer learning is used for rapid training of deep learning models for image segments. Finally, stacked generalization based ensembling is utilized for combining the predictions of the base deep neural network models. The proposed method achieves state-of-the-art accuracy of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks set by existing algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09321v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"9109\", \"27805\", \"39440\", \"40383\", \"47716\", \"52829\", \"68062\", \"77979\", \"97694\", \"118235\", \"128712\", \"130735\", \"147689\", \"149004\", \"170885\", \"176090\", \"195138\"], \"outgoing_citations\": [\"251500\", \"258857\", \"269105\", \"297196\", \"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"244024","document_content":"# Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctor and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict the most possible expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks.   As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.09271v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"170695\", \"172206\", \"34568\", \"20450\", \"49895\", \"61817\", \"70806\", \"87242\", \"90103\", \"90660\", \"130732\", \"156421\", \"192739\"], \"outgoing_citations\": [\"271671\", \"290520\", \"299198\"]}","task_split":"paper_retrieval"}
{"document_id":"244266","document_content":"# Active Neural Localization\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nLocalization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose \"Active Neural Localizer\", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.08214v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"75621\", \"81992\", \"15584\", \"24652\", \"40489\", \"51975\", \"160136\", \"87690\", \"95828\", \"109438\", \"114824\", \"118153\", \"123031\", \"131891\", \"131814\", \"139112\", \"162505\", \"157901\", \"187714\", \"187715\", \"195772\", \"196237\", \"196501\", \"200941\", \"208058\", \"218003\", \"207509\", \"225266\", \"227747\", \"230918\", \"237119\", \"241625\", \"274102\", \"277504\"], \"outgoing_citations\": [\"254966\", \"271062\", \"272930\", \"273466\", \"273649\", \"274102\", \"275169\", \"280681\", \"284468\", \"293071\", \"298651\", \"346732\"]}","task_split":"paper_retrieval"}
{"document_id":"244392","document_content":"# High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n## Abstract\nWe present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in \"holes\" whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of \"holes\" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes.   We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.07632v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\"], \"incoming_citations\": [\"14789\", \"74893\", \"139440\", \"152836\", \"209731\", \"171016\", \"174732\", \"182585\", \"182059\", \"195652\", \"197539\", \"207935\", \"236135\"], \"outgoing_citations\": [\"268494\", \"269002\", \"279178\", \"287740\", \"302935\"]}","task_split":"paper_retrieval"}
{"document_id":"244409","document_content":"# Analyzing Language Learned by an Active Question Answering Agent\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to reformulate the user's questions to elicit the optimal answers. It probes the system with many versions of a question that are generated via a sequence-to-sequence question reformulation model, then aggregates the returned evidence to find the best answer. This process is an instance of \\emph{machine-machine} communication. The question reformulation model must adapt its language to increase the quality of the answers returned, matching the language of the question answering system. We find that the agent does not learn transformations that align with semantic intuitions but discovers through learning classical information retrieval techniques such as tf-idf re-weighting and stemming.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.07537v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"79729\", \"171447\", \"216280\"], \"outgoing_citations\": [\"263622\", \"265786\", \"268671\", \"268845\"]}","task_split":"paper_retrieval"}
{"document_id":"244554","document_content":"# Cross-Domain Transfer in Reinforcement Learning using Target Apprentice\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly in related domains. We show the optimal policy from a related source task can be near optimal in target domain provided an adaptive policy accounts for the model error between target and source. The main benefit of this policy augmentation is generalizing policies across multiple related domains without having to re-learn the new tasks. Our results show that this architecture leads to better sample efficiency in the transfer, reducing sample complexity of target task learning to target apprentice learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.06920v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"46143\", \"50445\", \"79812\", \"178651\", \"163324\", \"171239\", \"171778\", \"202907\", \"259693\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"244664","document_content":"# How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIV.2018.2804159\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"119790\", \"20916\", \"68567\", \"83956\", \"139067\", \"191103\", \"4263\", \"7156\", \"9553\", \"21817\", \"29921\", \"38000\", \"52254\", \"57738\", \"59422\", \"60527\", \"61254\", \"61453\", \"67309\", \"75053\", \"75983\", \"118757\", \"161282\", \"91801\", \"66246\", \"95755\", \"104303\", \"116265\", \"118382\", \"119341\", \"120248\", \"126686\", \"128169\", \"128967\", \"130520\", \"135586\", \"137006\", \"138073\", \"142189\", \"161436\", \"191895\", \"197149\", \"209873\", \"158269\", \"166383\", \"167345\", \"175291\", \"175629\", \"175830\", \"179170\", \"188132\", \"191356\", \"191613\", \"192305\", \"196757\", \"196856\", \"202834\", \"206321\", \"217198\", \"232095\", \"232096\"], \"outgoing_citations\": [\"241039\", \"255242\"]}","task_split":"paper_retrieval"}
{"document_id":"244729","document_content":"# Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image\n## Categories\n- Computer Vision and Pattern Recognition\n- Image and Video Processing\n## Abstract\nIn this paper, we propose a novel deep neural network model that reconstructs a high dynamic range (HDR) image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively easy for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range, but also has the advantage of restoring the light information of the actual physical world. For the HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is the most popular evaluation metric for HDR images, was 56.36 for a display with a 1920$\\times$1200 resolution, which is an improvement of 6 compared with the scores of conventional algorithms. In addition, when comparing the peak signal-to-noise ratio values for tone mapped HDR images generated by the proposed and conventional algorithms, the average value obtained by the proposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by the conventional algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACCESS.2018.2868246\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"eess.IV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Image and Video Processing\"], \"incoming_citations\": [\"14984\", \"19090\", \"23633\", \"41356\", \"54876\", \"61088\", \"115507\", \"156796\", \"85785\", \"124998\", \"129600\", \"133418\", \"137099\", \"172113\", \"149650\", \"197145\", \"205608\"], \"outgoing_citations\": [\"252829\", \"260981\", \"270221\", \"289412\"]}","task_split":"paper_retrieval"}
{"document_id":"244738","document_content":"# Investigating the Working of Text Classifiers\n## Categories\n- Computation and Language\n## Abstract\nText classification is one of the most widely studied tasks in natural language processing. Motivated by the principle of compositionality, large multilayer neural network models have been employed for this task in an attempt to effectively utilize the constituent expressions. Almost all of the reported work train large networks using discriminative approaches, which come with a caveat of no proper capacity control, as they tend to latch on to any signal that may not generalize. Using various recent state-of-the-art approaches for text classification, we explore whether these models actually learn to compose the meaning of the sentences or still just focus on some keywords or lexicons for classifying the document. To test our hypothesis, we carefully construct datasets where the training and test splits have no direct overlap of such lexicons, but overall language structure would be similar. We study various text classifiers and observe that there is a big performance drop on these datasets. Finally, we show that even simple models with our proposed regularization techniques, which disincentivize focusing on key lexicons, can substantially improve classification accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.06261v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"101677\", \"124468\"], \"outgoing_citations\": [\"251816\", \"277007\", \"298543\", \"298760\", \"303997\", \"314946\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"244883","document_content":"# Image Captioning using Deep Neural Architectures\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically creating the description of an image using any natural languages sentence like English is a very challenging task. It requires expertise of both image processing as well as natural language processing. This paper discuss about different available models for image captioning task. We have also discussed about how the advancement in the task of object recognition and machine translation has greatly improved the performance of image captioning model in recent years. In addition to that we have discussed how this model can be implemented. In the end, we have also evaluated the performance of model using standard evaluation matrices.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICIIECS.2017.8276124\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"284230\"]}","task_split":"paper_retrieval"}
{"document_id":"244981","document_content":"# Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nImage denoising is always a challenging task in the field of computer vision and image processing. In this paper, we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model consists of an encoder and a decoder, where the encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memory network. In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image. We have trained our model on MNIST handwritten digit database after making lower half of every image as black as well as adding noise top of that. After a massive destruction of the images where it is hard for a human to understand the content of those images, our model can retrieve that image with minimal error. Our proposed model has been compared with convolutional encoder-decoder, where our model has performed better at generating missing part of the images than convolutional autoencoder.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.05141v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"121768\", \"138404\", \"191119\"], \"outgoing_citations\": [\"302621\"]}","task_split":"paper_retrieval"}
{"document_id":"245113","document_content":"# Deep Reinforcement Fuzzing\n## Categories\n- Artificial Intelligence\n- Cryptography and Security\n## Abstract\nFuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.04589v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Cryptography and Security\"], \"incoming_citations\": [\"4755\", \"869\", \"63965\", \"123008\", \"123339\", \"151554\", \"173138\", \"204848\", \"180861\", \"183836\", \"203950\", \"224563\", \"222988\", \"224040\", \"228554\", \"220423\"], \"outgoing_citations\": [\"275460\", \"287122\"]}","task_split":"paper_retrieval"}
{"document_id":"245122","document_content":"# DCDistance: A Supervised Text Document Feature extraction based on class labels\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n## Abstract\nText Mining is a field that aims at extracting information from textual data. One of the challenges of such field of study comes from the pre-processing stage in which a vector (and structured) representation should be extracted from unstructured data. The common extraction creates large and sparse vectors representing the importance of each term to a document. As such, this usually leads to the curse-of-dimensionality that plagues most machine learning algorithms. To cope with this issue, in this paper we propose a new supervised feature extraction and reduction algorithm, named DCDistance, that creates features based on the distance between a document to a representative of each class label. As such, the proposed technique can reduce the features set in more than 99% of the original set. Additionally, this algorithm was also capable of improving the classification accuracy over a set of benchmark datasets when compared to traditional and state-of-the-art features selection algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.04554v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"302496\"]}","task_split":"paper_retrieval"}
{"document_id":"245227","document_content":"# Conditional Probability Models for Deep Image Compression\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDeep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.04260v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"47239\", \"125962\", \"3535\", \"3584\", \"4434\", \"7260\", \"9247\", \"25262\", \"27638\", \"39792\", \"49272\", \"79886\", \"82132\", \"85979\", \"97572\", \"97830\", \"116171\", \"123186\", \"143178\", \"2018\", \"11354\", \"20061\", \"22411\", \"28322\", \"28395\", \"30766\", \"36887\", \"38208\", \"40998\", \"44585\", \"46569\", \"56971\", \"57667\", \"138021\", \"59267\", \"59412\", \"59824\", \"59979\", \"60017\", \"65334\", \"83013\", \"143298\", \"76720\", \"83920\", \"93754\", \"104397\", \"113228\", \"113821\", \"116426\", \"138585\", \"142696\", \"146619\", \"151708\", \"192903\", \"86844\", \"98307\", \"104348\", \"104873\", \"111470\", \"117925\", \"73831\", \"122829\", \"125050\", \"126210\", \"127209\", \"121609\", \"130143\", \"135025\", \"135412\", \"141734\", \"141894\", \"143347\", \"143912\", \"145566\", \"146623\", \"147245\", \"147608\", \"151353\", \"151604\", \"160928\", \"162788\", \"179246\", \"193985\", \"225233\", \"244779\", \"149165\", \"167493\", \"170337\", \"171806\", \"172377\", \"172350\", \"175457\", \"177909\", \"79389\", \"182691\", \"188251\", \"189501\", \"192107\", \"199006\", \"201968\", \"202159\", \"204814\", \"205024\", \"207812\", \"207875\", \"211551\", \"217431\", \"236298\", \"198311\", \"216677\", \"218722\", \"230221\", \"231842\", \"235085\"], \"outgoing_citations\": [\"266304\", \"269868\", \"270142\", \"270241\", \"272675\", \"281135\", \"286385\", \"288248\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"245238","document_content":"# Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions\n## Categories\n- Machine Learning\n## Abstract\nAccurate noise modelling is important for training of deep learning reconstruction algorithms. While noise models are well known for traditional imaging techniques, the noise distribution of a novel sensor may be difficult to determine a priori. Therefore, we propose learning arbitrary noise distributions. To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function. During the training, the Jensen-Shannon divergence between the distribution of the model's output and the target distribution is minimized. We experimentally demonstrate that our model converges towards the desired state. It provides an alternative to existing sampling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.04211v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"214491\"], \"outgoing_citations\": [\"263035\"]}","task_split":"paper_retrieval"}
{"document_id":"245323","document_content":"# Stochastic Learning of Nonstationary Kernels for Natural Language Modeling\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nNatural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships. While convolution kernels provide a powerful tool for comparing graph structure based on node (word) level relationships, they are difficult to customize and can be computationally expensive. We propose a generalization of convolution kernels, with a nonstationary model, for better expressibility of natural languages in supervised settings. For a scalable learning of the parameters introduced with our model, we propose a novel algorithm that leverages stochastic sampling on k-nearest neighbor graphs, along with approximations based on locality-sensitive hashing. We demonstrate the advantages of our approach on a challenging real-world (structured inference) problem of automatically extracting biological models from the text of scientific papers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.03911v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"167855\"], \"outgoing_citations\": [\"154631\", \"268252\", \"300593\", \"302024\", \"308043\", \"308304\", \"310572\", \"310639\", \"313962\", \"316741\", \"316763\", \"319782\", \"322697\"]}","task_split":"paper_retrieval"}
{"document_id":"245393","document_content":"# Using probabilistic programs as proposals\n## Categories\n- Artificial Intelligence\n- Computation\n## Abstract\nMonte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals. Handcrafted proposals that rely on user knowledge about the posterior distribution can be efficient, but are difficult to derive and implement. This paper proposes to let users express their posterior knowledge in the form of proposal programs, which are samplers written in probabilistic programming languages. One strategy for writing good proposal programs is to combine domain-specific heuristic algorithms with neural network models. The heuristics identify high probability regions, and the neural networks model the posterior uncertainty around the outputs of the algorithm. Proposal programs can be used as proposal distributions in importance sampling and Metropolis-Hastings samplers without sacrificing asymptotic consistency, and can be optimized offline using inference compilation. Support for optimizing and using proposal programs is easily implemented in a sampling-based probabilistic programming runtime. The paper illustrates the proposed technique with a proposal program that combines RANSAC and neural networks to accelerate inference in a Bayesian linear regression with outliers model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.03612v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.CO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation\"], \"incoming_citations\": [\"118955\", \"211364\", \"238712\", \"242926\"], \"outgoing_citations\": [\"268714\", \"277987\", \"281551\", \"282411\", \"296252\", \"297855\", \"311008\", \"331740\", \"334299\"]}","task_split":"paper_retrieval"}
{"document_id":"245667","document_content":"# DeepStyle: Multimodal Search Engine for Fashion and Interior Design\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21% on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.03002v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1560\", \"13035\", \"25608\", \"68658\", \"125244\", \"128239\", \"178188\", \"187630\", \"208472\", \"226313\"], \"outgoing_citations\": [\"70590\", \"208472\", \"213577\", \"213578\", \"233661\", \"242250\", \"260577\", \"260908\", \"266120\", \"286865\", \"295204\", \"303067\", \"312133\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"245731","document_content":"# Building Generalizable Agents with a Realistic and Rich 3D Environment\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTeaching an agent to navigate in an unseen 3D environment is a challenging task, even in the event of simulated environments. To generalize to unseen environments, an agent needs to be robust to low-level variations (e.g. color, texture, object changes), and also high-level variations (e.g. layout changes of the environment). To improve overall generalization, all types of variations in the environment have to be taken under consideration via different level of data augmentation steps. To this end, we propose House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of visually realistic houses, ranging from single-room studios to multi-storied houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et.al.). The diversity in House3D opens the door towards scene-level augmentation, while the label-rich nature of House3D enables us to inject pixel- & task-level augmentations such as domain randomization (Toubin et. al.) and multi-task training. Using a subset of houses in House3D, we show that reinforcement learning agents trained with an enhancement of different levels of augmentations perform much better in unseen environments than our baselines with raw RGB input by over 8% in terms of navigation success rate. House3D is publicly available at http:\/\/github.com\/facebookresearch\/House3D.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.02209v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"13212\", \"29801\", \"30710\", \"33721\", \"35707\", \"36587\", \"38664\", \"39368\", \"60623\", \"128249\", \"172206\", \"6778\", \"13877\", \"15829\", \"17198\", \"17412\", \"21525\", \"25559\", \"27522\", \"27520\", \"28168\", \"29854\", \"31155\", \"32449\", \"36974\", \"37932\", \"42082\", \"44012\", \"45665\", \"52082\", \"54330\", \"55346\", \"59216\", \"61081\", \"64195\", \"64556\", \"66007\", \"70725\", \"87597\", \"88994\", \"91301\", \"92592\", \"93035\", \"93649\", \"97665\", \"100277\", \"113084\", \"143997\", \"152072\", \"165015\", \"86813\", \"90871\", \"102971\", \"103523\", \"104902\", \"109794\", \"110941\", \"110426\", \"116022\", \"124122\", \"126297\", \"130190\", \"131013\", \"131180\", \"135334\", \"136058\", \"136760\", \"87612\", \"139227\", \"130231\", \"139348\", \"140039\", \"31330\", \"147391\", \"148111\", \"155676\", \"155831\", \"162100\", \"162372\", \"162505\", \"166672\", \"180340\", \"181795\", \"185834\", \"189381\", \"190940\", \"191583\", \"196909\", \"200261\", \"221758\", \"155641\", \"157901\", \"167688\", \"170354\", \"171280\", \"171629\", \"171822\", \"171882\", \"172352\", \"172538\", \"173039\", \"175055\", \"178379\", \"179610\", \"178792\", \"188899\", \"190948\", \"191096\", \"191493\", \"191394\", \"191671\", \"191570\", \"191878\", \"192564\", \"192738\", \"193993\", \"195240\", \"195772\", \"196393\", \"196525\", \"196598\", \"196049\", \"196898\", \"198524\", \"200941\", \"203854\", \"206489\", \"206941\", \"208018\", \"209152\", \"211921\", \"212891\", \"213745\", \"216952\", \"218182\", \"218435\", \"229145\", \"231955\", \"208585\", \"208683\", \"210374\", \"212893\", \"202419\", \"216221\", \"218357\", \"223477\", \"224223\", \"219658\", \"227594\", \"230918\", \"231155\", \"237119\", \"237357\", \"244457\", \"248742\", \"248885\", \"249906\", \"258121\", \"247418\", \"247749\", \"274102\", \"277504\"], \"outgoing_citations\": [\"247749\", \"248742\", \"248885\", \"258472\", \"259693\", \"260136\", \"261316\", \"262106\", \"263104\", \"263270\", \"263650\", \"266155\", \"268563\", \"271085\", \"272930\", \"274102\", \"278984\", \"279420\", \"280296\", \"280570\", \"280681\", \"280870\", \"281106\", \"281484\", \"282684\", \"282841\", \"283200\", \"290904\", \"291099\", \"293071\", \"293876\", \"298651\", \"304250\"]}","task_split":"paper_retrieval"}
{"document_id":"245735","document_content":"# Cross-modal Embeddings for Video and Audio Retrieval\n## Categories\n- Information Retrieval\n- Sound\n- Computer Vision and Pattern Recognition\n- Audio and Speech Processing\n## Abstract\nThe increasing amount of online videos brings several opportunities for training self-supervised neural networks. The creation of large scale datasets of videos such as the YouTube-8M allows us to deal with this large amount of data in manageable way. In this work, we find new ways of exploiting this dataset by taking advantage of the multi-modal information it provides. By means of a neural network, we are able to create links between audio and visual documents, by projecting them into a common region of the feature space, obtaining joint audio-visual embeddings. These links are used to retrieve audio samples that fit well to a given silent video, and also to retrieve images that match a given a query audio. The results in terms of Recall@K obtained over a subset of YouTube-8M videos show the potential of this unsupervised approach for cross-modal feature learning. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.02200v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.SD\", \"cs.CV\", \"eess.AS\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Sound\", \"Computer Vision and Pattern Recognition\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"35271\", \"2284\", \"6331\", \"13715\", \"58946\", \"62353\", \"90307\", \"147240\", \"121808\", \"172394\", \"184285\", \"237974\"], \"outgoing_citations\": [\"264645\", \"283807\", \"303011\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"245829","document_content":"# Visual Text Correction\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nVideos, images, and sentences are mediums that can express the same semantics. One can imagine a picture by reading a sentence or can describe a scene with some words. However, even small changes in a sentence can cause a significant semantic inconsistency with the corresponding video\/image. For example, by changing the verb of a sentence, the meaning may drastically change. There have been many efforts to encode a video\/sentence and decode it as a sentence\/video. In this research, we study a new scenario in which both the sentence and the video are given, but the sentence is inaccurate. A semantic inconsistency between the sentence and the video or between the words of a sentence can result in an inaccurate description. This paper introduces a new problem, called Visual Text Correction (VTC), i.e., finding and replacing an inaccurate word in the textual description of a video. We propose a deep network that can simultaneously detect an inaccuracy in a sentence, and fix it by replacing the inaccurate word(s). Our method leverages the semantic interdependence of videos and words, as well as the short-term and long-term relations of the words in a sentence. In our formulation, part of a visual feature vector for every single word is dynamically selected through a gating process. Furthermore, to train and evaluate our model, we propose an approach to automatically construct a large dataset for VTC problem. Our experiments and performance analysis demonstrates that the proposed method provides very good results and also highlights the general challenges in solving the VTC problem. To the best of our knowledge, this work is the first of its kind for the Visual Text Correction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-030-01261-8_10\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"93187\", \"175031\", \"91048\", \"108246\", \"143565\"], \"outgoing_citations\": [\"268805\", \"279703\", \"282943\", \"297133\", \"301767\", \"302621\", \"303251\", \"308857\", \"310639\", \"311684\", \"313143\", \"313320\", \"319169\", \"323775\"]}","task_split":"paper_retrieval"}
{"document_id":"245903","document_content":"# A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition\n## Categories\n- Cryptography and Security\n- Computers and Society\n- Quantitative Methods\n## Abstract\nA novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis) and K-NN (K-Nearest Neighbor). It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition. with bags of paper votes. The central station compiles and publishes the names of winners and losers through television and radio stations. This method is useful only if the whole process is completed in a transparent way. However, there are some drawbacks to this system. These include higher expenses, longer time to complete the voting process, fraudulent practices by the authorities administering elections as well as malpractices by the voters [1]. These challenges result in manipulated election results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.02430v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CY\", \"cs.CR\", \"q-bio.QM\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Computers and Society\", \"Cryptography and Security\", \"Quantitative Methods\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"245969","document_content":"# Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n- Digital Libraries\n- Multimedia\n## Abstract\nDaily engagement in life experiences is increasingly interwoven with mobile device use. Screen capture at the scale of seconds is being used in behavioral studies and to implement \"just-in-time\" health interventions. The increasing psychological breadth of digital information will continue to make the actual screens that people view a preferred if not required source of data about life experiences. Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data. In this paper, we present the experimental workflow we exploited to: (i) pre-process a unique collection of screen captures, (ii) extract unstructured text embedded in the images, (iii) organize image text and metadata based on a structured schema, (iv) index the resulting document collection, and (v) allow for Image Retrieval through a dedicated vertical search engine application. The adopted procedure integrates different open source libraries for traditional image processing, Optical Character Recognition (OCR), and Image Retrieval. Our aim is to assess whether and how state-of-the-art methodologies can be applied to this novel data set. We show how combining OpenCV-based pre-processing modules with a Long short-term memory (LSTM) based release of Tesseract OCR, without ad hoc training, led to a 74% character-level accuracy of the extracted text. Further, we used the processed repository as baseline for a dedicated Image Retrieval system, for the immediate use and application for behavioral and prevention scientists. We discuss issues of Text Information Extraction and Retrieval that are particular to the screenshot image case and suggest important future work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1801.01316v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CV\", \"cs.DL\", \"cs.MM\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computer Vision and Pattern Recognition\", \"Digital Libraries\", \"Multimedia\"], \"incoming_citations\": [\"147659\", \"203493\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"246180","document_content":"# Facial emotion recognition using min-max similarity classifier\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICACCI.2017.8125932\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"97959\"], \"outgoing_citations\": [\"294778\"]}","task_split":"paper_retrieval"}
{"document_id":"246202","document_content":"# Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Social and Information Networks\n## Abstract\nText representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts\/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10791-018-9340-3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Social and Information Networks\"], \"incoming_citations\": [\"44780\", \"66795\", \"61808\", \"114807\", \"141563\", \"148927\", \"171420\"], \"outgoing_citations\": [\"274243\", \"278439\", \"287676\", \"300498\", \"300505\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"246333","document_content":"# Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOne of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets which outperforms state-of-the-art algorithms in the skin lesion segmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.10207v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"461\", \"44185\", \"103021\", \"110938\"], \"outgoing_citations\": [\"241335\", \"268279\", \"282063\"]}","task_split":"paper_retrieval"}
{"document_id":"246446","document_content":"# Combining Representation Learning with Logic for Language Processing\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Logic in Computer Science\n- Computation and Language\n## Abstract\nThe current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.09687v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.LG\", \"cs.LO\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Logic in Computer Science\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"229275\"], \"outgoing_citations\": [\"264931\", \"272667\", \"274120\", \"274997\", \"275513\", \"281214\", \"284528\", \"288364\", \"288415\", \"288590\", \"288699\", \"288979\", \"289508\", \"290208\", \"290446\", \"291575\", \"292141\", \"292167\", \"296214\", \"296289\", \"298627\", \"300421\", \"300867\", \"302930\", \"302943\", \"302970\", \"303306\", \"304901\", \"306182\", \"311585\", \"312264\", \"313944\", \"316165\", \"316576\", \"319833\", \"336189\", \"344981\", \"345692\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"246516","document_content":"# Mapping to Declarative Knowledge for Word Problem Solving\n## Categories\n- Computation and Language\n## Abstract\nMath word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.09391v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96833\", \"12817\", \"21843\", \"101113\", \"37796\", \"45468\", \"50647\", \"56186\", \"63475\", \"102856\", \"80713\", \"94434\", \"124449\", \"220655\", \"161669\", \"183403\", \"211865\", \"210184\"], \"outgoing_citations\": [\"266702\", \"278863\", \"283758\", \"287181\", \"294137\"]}","task_split":"paper_retrieval"}
{"document_id":"246590","document_content":"# Stochastic Multi-armed Bandits in Constant Space\n## Categories\n- Data Structures and Algorithms\n- Machine Learning\n- Machine Learning\n## Abstract\nWe consider the stochastic bandit problem in the sublinear space setting, where one cannot record the win-loss record for all $K$ arms. We give an algorithm using $O(1)$ words of space with regret \\[   \\sum_{i=1}^{K}\\frac{1}{\\Delta_i}\\log \\frac{\\Delta_i}{\\Delta}\\log T \\] where $\\Delta_i$ is the gap between the best arm and arm $i$ and $\\Delta$ is the gap between the best and the second-best arms. If the rewards are bounded away from $0$ and $1$, this is within an $O(\\log 1\/\\Delta)$ factor of the optimum regret possible without space constraints.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.09007v2\", \"primary_category\": \"cs.DS\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.DS\"], \"primary_category_human_readable\": \"Data Structures and Algorithms\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"65403\", \"6360\", \"208177\", \"81626\", \"86799\", \"132010\", \"139849\", \"201764\", \"188312\", \"238516\"], \"outgoing_citations\": [\"291603\", \"298811\", \"326956\", \"335987\"]}","task_split":"paper_retrieval"}
{"document_id":"246608","document_content":"# Comparative Opinion Mining: A Review\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nOpinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyse the public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form (e.g.~\"paper X is better than the Y\"). Comparative opinion mining plays a very important role when ones tries to evaluate something, as it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from perspective of techniques and the other from perspective of comparative opinion elements. It also incorporates preprocessing tools as well as dataset that were used by the past researchers that can be useful to the future researchers in the field of comparative opinion mining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1002\/asi.23716\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"48565\", \"172961\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"246734","document_content":"# Contour: A Practical System for Binary Transparency\n## Categories\n- Cryptography and Security\n## Abstract\nTransparency is crucial in security-critical applications that rely on authoritative information, as it provides a robust mechanism for holding these authorities accountable for their actions. A number of solutions have emerged in recent years that provide transparency in the setting of certificate issuance, and Bitcoin provides an example of how to enforce transparency in a financial setting. In this work we shift to a new setting, the distribution of software package binaries, and present a system for so-called \"binary transparency.\" Our solution, Contour, uses proactive methods for providing transparency, privacy, and availability, even in the face of persistent man-in-the-middle attacks. We also demonstrate, via benchmarks and a test deployment for the Debian software repository, that Contour is the only system for binary transparency that satisfies the efficiency and coordination requirements that would make it possible to deploy today.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.08427v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"56421\", \"160469\", \"88114\", \"195741\"], \"outgoing_citations\": [\"267939\", \"272163\", \"297801\", \"303985\", \"315220\"]}","task_split":"paper_retrieval"}
{"document_id":"246946","document_content":"# Ethical Questions in NLP Research: The (Mis)-Use of Forensic Linguistics\n## Categories\n- Computation and Language\n- Computers and Society\n## Abstract\nIdeas from forensic linguistics are now being used frequently in Natural Language Processing (NLP), using machine learning techniques. While the role of forensic linguistics was more benign earlier, it is now being used for purposes which are questionable. Certain methods from forensic linguistics are employed, without considering their scientific limitations and ethical concerns. While we take the specific case of forensic linguistics as an example of such trends in NLP and machine learning, the issue is a larger one and present in many other scientific and data-driven domains. We suggest that such trends indicate that some of the applied sciences are exceeding their legal and scientific briefs. We highlight how carelessly implemented practices are serving to short-circuit the due processes of law as well breach ethical codes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.07512v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"246957","document_content":"# Differentially Private Distributed Learning for Language Modeling Tasks\n## Categories\n- Computation and Language\n- Cryptography and Security\n- Machine Learning\n## Abstract\nOne of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm. In language modeling, users' language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data. At the same time, public data can be used for obtaining general knowledge (i.e. general model of English). We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs. We propose a novel technique that significantly improves prediction quality on users' language compared to a general model and outperforms gradient compression methods in terms of communication efficiency. The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts. We also show that the range of tasks our approach is applicable to is not limited by language modeling only. Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.07473v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Cryptography and Security\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"248216\", \"273221\", \"276270\", \"281205\", \"282410\", \"286225\", \"286631\", \"312670\", \"336181\"]}","task_split":"paper_retrieval"}
{"document_id":"247002","document_content":"# Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nLearning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.07294v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"214\", \"19140\", \"73578\", \"78473\", \"260314\", \"952\", \"2495\", \"4271\", \"14182\", \"16789\", \"22124\", \"26664\", \"46897\", \"55486\", \"55964\", \"61817\", \"69948\", \"89875\", \"93035\", \"116402\", \"44049\", \"86275\", \"113946\", \"117936\", \"121716\", \"125366\", \"180894\", \"162456\", \"173914\", \"175055\", \"175344\", \"180270\", \"181620\", \"189416\", \"190023\", \"195240\", \"212772\", \"212893\", \"215924\", \"218770\", \"223640\", \"231155\", \"231847\"], \"outgoing_citations\": [\"261316\", \"262342\", \"263104\", \"263270\", \"280681\", \"281106\", \"281257\", \"284567\", \"290371\", \"293071\", \"293876\", \"294142\", \"302952\", \"302967\", \"306156\", \"310370\"]}","task_split":"paper_retrieval"}
{"document_id":"247016","document_content":"# Attentive Memory Networks: Efficient Machine Reading for Conversational Search\n## Categories\n- Computation and Language\n## Abstract\nRecent advances in conversational systems have changed the search paradigm. Traditionally, a user poses a query to a search engine that returns an answer based on its index, possibly leveraging external knowledge bases and conditioning the response on earlier interactions in the search session. In a natural conversation, there is an additional source of information to take into account: utterances produced earlier in a conversation can also be referred to and a conversational IR system has to keep track of information conveyed by the user during the conversation, even if it is implicit.   We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions. The questions should be answered solely by referring to the statements provided, without consulting external knowledge. The time is right for the information retrieval community to embrace this task, both as a stand-alone task and integrated in a broader conversational search setting.   In this paper, we focus on machine reading as a stand-alone task and present the Attentive Memory Network (AMN), an end-to-end trainable machine reading algorithm. Its key contribution is in efficiency, achieved by having an hierarchical input encoder, iterating over the input only once. Speed is an important requirement in the setting of conversational search, as gaps between conversational turns have a detrimental effect on naturalness. On 20 datasets commonly used for evaluating machine reading algorithms we show that the AMN achieves performance comparable to the state-of-the-art models, while using considerably fewer computations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.07229v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"164662\", \"89675\", \"91092\", \"151897\", \"156164\", \"168606\", \"185858\", \"205181\", \"233646\"], \"outgoing_citations\": [\"263761\", \"278163\", \"286757\", \"290747\", \"291916\", \"292261\", \"297133\", \"300508\", \"309886\", \"310639\", \"317213\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"247041","document_content":"# Improved Regularization Techniques for End-to-End Speech Recognition\n## Categories\n- Computation and Language\n- Sound\n- Audio and Speech Processing\n- Machine Learning\n## Abstract\nRegularization is important for end-to-end speech models, since the models are highly flexible and easy to overfit. Data augmentation and dropout has been important for improving end-to-end models in other domains. However, they are relatively under explored for end-to-end speech models. Therefore, we investigate the effectiveness of both methods for end-to-end trainable, deep speech recognition models. We augment audio data through random perturbations of tempo, pitch, volume, temporal alignment, and adding random noise.We further investigate the effect of dropout when applied to the inputs of all layers of the network. We show that the combination of data augmentation and dropout give a relative performance improvement on both Wall Street Journal (WSJ) and LibriSpeech dataset of over 20%. Our model performance is also competitive with other end-to-end speech models on both datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.07108v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\", \"eess.AS\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\", \"Audio and Speech Processing\", \"Machine Learning\"], \"incoming_citations\": [\"11959\", \"36353\", \"145660\", \"145691\", \"180293\", \"197045\", \"210400\", \"226023\", \"236568\"], \"outgoing_citations\": [\"282929\", \"284965\", \"307902\", \"312457\"]}","task_split":"paper_retrieval"}
{"document_id":"247136","document_content":"# Multilingual Topic Models\n## Categories\n- Machine Learning\n- Information Retrieval\n- Computation and Language\n## Abstract\nScientific publications have evolved several features for mitigating vocabulary mismatch when indexing, retrieving, and computing similarity between articles. These mitigation strategies range from simply focusing on high-value article sections, such as titles and abstracts, to assigning keywords, often from controlled vocabularies, either manually or through automatic annotation. Various document representation schemes possess different cost-benefit tradeoffs. In this paper, we propose to model different representations of the same article as translations of each other, all generated from a common latent representation in a multilingual topic model. We start with a methodological overview on latent variable models for parallel document representations that could be used across many information science tasks. We then show how solving the inference problem of mapping diverse representations into a shared topic space allows us to evaluate representations based on how topically similar they are to the original article. In addition, our proposed approach provides means to discover where different concept vocabularies require improvement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.06704v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.IR\", \"stat.ML\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"340353\"]}","task_split":"paper_retrieval"}
{"document_id":"247242","document_content":"# Towards a Deep Reinforcement Learning Approach for Tower Line Wars\n## Categories\n- Artificial Intelligence\n## Abstract\nThere have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II from Blizzard Entertainment. We propose a game environment in between Atari 2600 and Starcraft II, particularly targeting Deep Reinforcement Learning algorithm research. The environment is a variant of Tower Line Wars from Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the environment can harbor Deep Reinforcement algorithms, we propose and apply a Deep Q-Reinforcement architecture. The architecture simplifies the state space so that it is applicable to Q-learning, and in turn improves performance compared to current state-of-the-art methods. Our experiments show that the proposed architecture can learn to play the environment well, and score 33% better than standard Deep Q-learning which in turn proves the usefulness of the game environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-71078-5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"160827\", \"174126\", \"215743\", \"223810\", \"243943\"], \"outgoing_citations\": [\"258472\", \"263852\", \"280681\"]}","task_split":"paper_retrieval"}
{"document_id":"247262","document_content":"# Query-Based Abstractive Summarization Using Neural Networks\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present a model for generating summaries of text documents with respect to a query. This is known as query-based summarization. We adapt an existing dataset of news article summaries for the task and train a pointer-generator model using this dataset. The generated summaries are evaluated by measuring similarity to reference summaries. Our results show that a neural network summarization model, similar to existing neural network models for abstractive summarization, can be constructed to make use of queries to produce targeted summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.06100v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"51199\", \"86983\", \"91732\", \"95832\", \"103764\", \"104180\", \"140236\", \"141721\", \"202015\", \"203630\", \"243272\"], \"outgoing_citations\": [\"267899\", \"286876\", \"289673\", \"295815\", \"303469\", \"310639\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"247591","document_content":"# Learning From Noisy Singly-labeled Data\n## Categories\n- Machine Learning\n## Abstract\nSupervised learning depends on annotated examples, which are taken to be the \\emph{ground truth}. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem). Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples. This raises two fundamental questions: (1) How can we best learn from noisy workers? (2) How should we allocate our labeling budget to maximize the performance of a classifier? We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data. The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality. Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality. We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality is above a threshold. Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.04577v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"14588\", \"18248\", \"33669\", \"181825\", \"11685\", \"14568\", \"31976\", \"34297\", \"37428\", \"37441\", \"38840\", \"40311\", \"47894\", \"56665\", \"82029\", \"100471\", \"114597\", \"120031\", \"151719\", \"158665\", \"147647\", \"116849\", \"88163\", \"164636\", \"94725\", \"99256\", \"142851\", \"108601\", \"115819\", \"128941\", \"129116\", \"131432\", \"132478\", \"47633\", \"135327\", \"139761\", \"141237\", \"142051\", \"152500\", \"155896\", \"184847\", \"192606\", \"193433\", \"215780\", \"221824\", \"237209\", \"152637\", \"160584\", \"163626\", \"166783\", \"169215\", \"171215\", \"183330\", \"195622\", \"199491\", \"206710\", \"212663\", \"215247\", \"207416\", \"211010\", \"228742\", \"231347\"], \"outgoing_citations\": [\"266873\", \"270545\", \"291900\", \"298544\", \"302806\", \"303844\", \"315454\", \"328627\"]}","task_split":"paper_retrieval"}
{"document_id":"247597","document_content":"# IT Risk Assessment for Group6 Healthcare Clinic Report\n## Categories\n- Cryptography and Security\n## Abstract\nInformation security and privacy in the healthcare sector is an issue of growing importance. The adoption of digital patient records, increased regulation, provider consolidation and the increasing need for information exchange between patients, providers and payers, all point towards the need for better information security. We critically survey the literature on information security and privacy in healthcare, published in information systems journals as well as many other related disciplines including health informatics, public health, law, medicine, and the trade press and industry reports","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.04560v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"247713","document_content":"# PacGAN: The power of two samples in generative adversarial networks\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n## Abstract\nGenerative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples. Despite remarkable recent improvements in generating realistic images, one of their major shortcomings is the fact that in practice, they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the main focus of several recent advances in GANs. Yet there is little understanding of why mode collapse happens and why existing approaches are able to mitigate mode collapse. We propose a principled approach to handling mode collapse, which we call packing. The main idea is to modify the discriminator to make decisions based on multiple samples from the same class, either real or artificially generated. We borrow analysis tools from binary hypothesis testing---in particular the seminal result of Blackwell [Bla53]---to prove a fundamental connection between packing and mode collapse. We show that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process. Numerical experiments on benchmark datasets suggests that packing provides significant improvements in practice as well.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.04086v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [\"45478\", \"117785\", \"146468\", \"315\", \"2983\", \"8848\", \"9940\", \"25661\", \"34103\", \"34155\", \"184659\", \"422\", \"4174\", \"17755\", \"21384\", \"24411\", \"31381\", \"33799\", \"35276\", \"41754\", \"41821\", \"42184\", \"51963\", \"53879\", \"58058\", \"58792\", \"66829\", \"67326\", \"68896\", \"75457\", \"78218\", \"78355\", \"80387\", \"87695\", \"97300\", \"101173\", \"109264\", \"235848\", \"118261\", \"81891\", \"86789\", \"87963\", \"93156\", \"95450\", \"98696\", \"106100\", \"107008\", \"109389\", \"117128\", \"117330\", \"117768\", \"120303\", \"117607\", \"122731\", \"127805\", \"132506\", \"132929\", \"135851\", \"136109\", \"138406\", \"140488\", \"140938\", \"141167\", \"142526\", \"144171\", \"144227\", \"147676\", \"147766\", \"147883\", \"149307\", \"160810\", \"164287\", \"169723\", \"180811\", \"195141\", \"230425\", \"152297\", \"152639\", \"158679\", \"159220\", \"162407\", \"162520\", \"164584\", \"177440\", \"177520\", \"178249\", \"178446\", \"179388\", \"183098\", \"184718\", \"186444\", \"190533\", \"190946\", \"191795\", \"199163\", \"199766\", \"199563\", \"201674\", \"201731\", \"201857\", \"203956\", \"207829\", \"214144\", \"226061\", \"226571\", \"242751\", \"211010\", \"225102\", \"231428\", \"245283\", \"252221\"], \"outgoing_citations\": [\"218106\", \"229773\", \"239456\", \"245963\", \"246541\", \"251130\", \"252028\", \"256229\", \"261264\", \"262885\", \"263355\", \"263859\", \"265075\", \"265398\", \"265482\", \"265604\", \"271912\", \"272623\", \"276542\", \"277021\", \"278560\", \"280505\", \"280545\", \"280655\", \"281002\", \"285093\", \"291301\", \"291302\", \"291440\", \"303913\", \"310922\", \"327473\", \"338290\"]}","task_split":"paper_retrieval"}
{"document_id":"247723","document_content":"# Character-Based Handwritten Text Transcription with Attention Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n## Abstract\nThe paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment on lines of text from popular handwriting datasets and compare different activation functions for the attention mechanism used for aligning image pixels and target characters. We find that softmax attention focuses heavily on individual characters, while sigmoid attention focuses on multiple characters at each step of the decoding. When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise. When a linear function is used to obtain attention weights, the model predicts a character by looking at the entire sequence of characters and performs poorly because it lacks a precise alignment between the source and target. Future research may explore HTR in natural scene images, since the model is capable of transcribing handwritten text without the need for producing segmentations or bounding boxes of text in images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s00521-021-05813-1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"59368\"], \"outgoing_citations\": [\"81709\", \"82145\", \"102888\", \"105674\", \"118987\", \"122798\", \"135295\", \"150014\", \"166115\", \"176625\", \"194746\", \"201913\", \"204309\", \"223954\", \"266955\", \"274817\", \"277434\", \"284605\", \"293655\", \"294753\", \"296685\", \"296828\", \"299461\", \"310136\", \"310601\", \"319833\", \"320814\", \"320837\", \"336973\", \"338253\", \"357416\"]}","task_split":"paper_retrieval"}
{"document_id":"247749","document_content":"# MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Graphics\n- Robotics\n## Abstract\nWe present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at http:\/\/minosworld.org . A video that shows MINOS can be found at https:\/\/youtu.be\/c0mL9K64q84","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.03931v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.GR\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Graphics\", \"Robotics\"], \"incoming_citations\": [\"148617\", \"2048\", \"3664\", \"13212\", \"15121\", \"39368\", \"81244\", \"172206\", \"186811\", \"8473\", \"22792\", \"25559\", \"27520\", \"28168\", \"31155\", \"34913\", \"36974\", \"53885\", \"54220\", \"59216\", \"61747\", \"63557\", \"75107\", \"79152\", \"79903\", \"81899\", \"88552\", \"88994\", \"91301\", \"101182\", \"148172\", \"165015\", \"192512\", \"82126\", \"89221\", \"86813\", \"90871\", \"102971\", \"104828\", \"104772\", \"106127\", \"109794\", \"110426\", \"113074\", \"114760\", \"114824\", \"116592\", \"118153\", \"127444\", \"130190\", \"131180\", \"134207\", \"136058\", \"139037\", \"87612\", \"130231\", \"140318\", \"148111\", \"151367\", \"155831\", \"156897\", \"162505\", \"161717\", \"165819\", \"185834\", \"189381\", \"190940\", \"196909\", \"200261\", \"208545\", \"157901\", \"160036\", \"167688\", \"169788\", \"174221\", \"175055\", \"176543\", \"177674\", \"190948\", \"191394\", \"191493\", \"191671\", \"191878\", \"192738\", \"192564\", \"193993\", \"195240\", \"196525\", \"196598\", \"196049\", \"196898\", \"198524\", \"200941\", \"206489\", \"208018\", \"208773\", \"209152\", \"214078\", \"231955\", \"243597\", \"208585\", \"203355\", \"202419\", \"224223\", \"225768\", \"219658\", \"227594\", \"231155\", \"240323\", \"244457\", \"245731\", \"249906\", \"247418\", \"274102\", \"277504\"], \"outgoing_citations\": [\"245731\", \"248742\", \"248885\", \"249906\", \"262106\", \"274102\", \"278195\", \"279420\", \"280296\", \"280570\", \"280681\", \"281110\", \"293071\"]}","task_split":"paper_retrieval"}
{"document_id":"247753","document_content":"# Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome. However, synthetic images do not resemble real images exactly, and using them for training can result in suboptimal performance. It was recently shown that for exemplar-based approaches, it is possible to learn a mapping from the exemplar representations of real images to the exemplar representations of synthetic images. In this paper, we show that this approach is more general, and that a network can also be applied after the mapping to infer a 3D pose: At run time, given a real image of the target object, we first compute the features for the image, map them to the feature space of synthetic images, and finally use the resulting features as input to another network which predicts the 3D pose. Since this network can be trained very effectively by using synthetic images, it performs very well in practice, and inference is faster and more accurate than with an exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for 3D object pose estimation from color images, and the NYU dataset for 3D hand pose estimation from depth maps. We show that it allows us to outperform the state-of-the-art on both datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.03904v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"110069\", \"11991\", \"37900\", \"26859\", \"50043\", \"63537\", \"83912\", \"165015\", \"169955\", \"90205\", \"98407\", \"119671\", \"133265\", \"134642\", \"135105\", \"136423\", \"137238\", \"148698\", \"150984\", \"167541\", \"174575\", \"163636\", \"167491\", \"170940\", \"172607\", \"186621\", \"191569\", \"192173\", \"192187\", \"188537\", \"206984\", \"206922\", \"207729\", \"208964\", \"214976\", \"220068\", \"224640\", \"235982\", \"262222\"], \"outgoing_citations\": [\"249175\", \"252062\", \"253077\", \"257553\", \"269467\", \"269476\", \"270057\", \"274447\", \"278441\", \"283579\", \"284827\", \"289858\", \"292494\", \"292787\", \"294023\", \"296192\", \"301832\", \"302817\", \"316998\", \"350529\"]}","task_split":"paper_retrieval"}
{"document_id":"247777","document_content":"# Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nComputation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/DAS.2018.14\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19089\"], \"outgoing_citations\": [\"256775\"]}","task_split":"paper_retrieval"}
{"document_id":"248005","document_content":"# Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe reconstruction of dense 3D models of face geometry and appearance from a single image is highly challenging and ill-posed. To constrain the problem, many approaches rely on strong priors, such as parametric face models learned from limited 3D scan data. However, prior models restrict generalization of the true diversity in facial geometry, skin reflectance and illumination. To alleviate this problem, we present the first approach that jointly learns 1) a regressor for face shape, expression, reflectance and illumination on the basis of 2) a concurrently learned parametric face model. Our multi-level face model combines the advantage of 3D Morphable Models for regularization with the out-of-space generalization of a learned corrective space. We train end-to-end on in-the-wild images without dense annotations by fusing a convolutional encoder with a differentiable expert-designed renderer and a self-supervised training loss, both defined at multiple detail levels. Our approach compares favorably to the state-of-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02859v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5306\", \"76110\", \"3093\", \"4603\", \"5264\", \"17613\", \"40071\", \"49287\", \"57572\", \"94953\", \"431\", \"3046\", \"9232\", \"3767\", \"12174\", \"15251\", \"17589\", \"30067\", \"32133\", \"32365\", \"41321\", \"42098\", \"50204\", \"51399\", \"51844\", \"57586\", \"59948\", \"59791\", \"61585\", \"221215\", \"65734\", \"71608\", \"72586\", \"76752\", \"79555\", \"79767\", \"81253\", \"82106\", \"84690\", \"84871\", \"87603\", \"96763\", \"104420\", \"117837\", \"161844\", \"219961\", \"80521\", \"84680\", \"95563\", \"97010\", \"99587\", \"104989\", \"107596\", \"107540\", \"108463\", \"109928\", \"112307\", \"112309\", \"110207\", \"133193\", \"133828\", \"134002\", \"134076\", \"135637\", \"150974\", \"159733\", \"168874\", \"170169\", \"180084\", \"188208\", \"207001\", \"152310\", \"158372\", \"169032\", \"171045\", \"171500\", \"169837\", \"182589\", \"182972\", \"174524\", \"185981\", \"189945\", \"191310\", \"191463\", \"191844\", \"192520\", \"192754\", \"193656\", \"194402\", \"197693\", \"198793\", \"199512\", \"199586\", \"200905\", \"202404\", \"202710\", \"205494\", \"205998\", \"249049\", \"257958\", \"206284\", \"216762\", \"235754\", \"247495\"], \"outgoing_citations\": [\"257846\", \"268963\", \"270134\", \"270235\", \"275879\", \"277948\", \"279050\", \"280394\", \"281332\", \"283622\", \"284716\", \"286096\", \"286598\", \"287769\", \"292103\", \"294926\", \"323646\"]}","task_split":"paper_retrieval"}
{"document_id":"248019","document_content":"# Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper addresses 2 challenging tasks: improving the quality of low resolution facial images and accurately locating the facial landmarks on such poor resolution images. To this end, we make the following 5 contributions: (a) we propose Super-FAN: the very first end-to-end system that addresses both tasks simultaneously, i.e. both improves face resolution and detects the facial landmarks. The novelty or Super-FAN lies in incorporating structural information in a GAN-based super-resolution algorithm via integrating a sub-network for face alignment through heatmap regression and optimizing a novel heatmap loss. (b) We illustrate the benefit of training the two networks jointly by reporting good results not only on frontal images (as in prior work) but on the whole spectrum of facial poses, and not only on synthetic low resolution images (as in prior work) but also on real-world images. (c) We improve upon the state-of-the-art in face super-resolution by proposing a new residual-based architecture. (d) Quantitatively, we show large improvement over the state-of-the-art for both face super-resolution and alignment. (e) Qualitatively, we show for the first time good results on real-world low resolution images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02765v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37039\", \"76110\", \"82174\", \"100824\", \"71292\", \"188704\", \"1229\", \"7883\", \"9772\", \"11664\", \"27306\", \"49720\", \"49874\", \"56280\", \"62799\", \"64297\", \"68463\", \"76015\", \"78941\", \"82066\", \"83295\", \"83625\", \"85019\", \"92268\", \"99898\", \"103761\", \"118989\", \"143372\", \"198751\", \"84183\", \"89019\", \"94977\", \"100258\", \"117180\", \"121349\", \"123243\", \"123548\", \"124611\", \"125816\", \"126182\", \"127710\", \"127680\", \"130603\", \"130730\", \"131219\", \"132093\", \"137913\", \"140312\", \"149113\", \"154701\", \"155416\", \"159858\", \"160915\", \"164523\", \"173743\", \"223543\", \"230252\", \"150049\", \"159596\", \"166256\", \"170700\", \"173711\", \"189584\", \"191918\", \"206561\", \"208664\", \"208834\", \"223755\", \"230095\", \"210909\", \"211834\", \"220609\", \"222871\", \"230097\", \"233298\"], \"outgoing_citations\": [\"258886\", \"272547\", \"277357\", \"285257\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"248119","document_content":"# Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nThis paper addresses the question: Why do neural dialog systems generate short and meaningless replies? We conjecture that, in a dialog system, an utterance may have multiple equally plausible replies, causing the deficiency of neural networks in the dialog application. We propose a systematic way to mimic the dialog scenario in a machine translation system, and manage to reproduce the phenomenon of generating short and less meaningful sentences in the translation setting, showing evidence of our conjecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02250v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"33507\", \"122948\", \"157180\", \"167399\", \"177387\", \"186426\", \"217366\"], \"outgoing_citations\": [\"260364\", \"271637\", \"283076\", \"289061\", \"289943\", \"292261\", \"295863\", \"311683\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"248152","document_content":"# Separating Reflection and Transmission Images in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02099v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"83400\", \"30921\", \"174146\", \"64716\", \"109228\", \"134499\", \"139663\", \"152237\"], \"outgoing_citations\": [\"258612\", \"258798\", \"288380\"]}","task_split":"paper_retrieval"}
{"document_id":"248171","document_content":"# Learning Semantic Concepts and Order for Image and Sentence Matching\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage and sentence matching has made great progress recently, but it remains challenging due to the large visual-semantic discrepancy. This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence. In this work, we propose a semantic-enhanced image and sentence matching model, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its semantic concepts, including objects, properties, actions, etc. Then, considering that different orders of semantic concepts lead to diverse semantic meanings, we use a context-gated sentence generation scheme for semantic order learning. It simultaneously uses the image global context containing concept relations as reference and the groundtruth semantic order in the matched sentence as supervision. After obtaining the improved image representation, we learn the sentence representation with a conventional LSTM, and then jointly perform image and sentence matching and sentence generation for model learning. Extensive experiments demonstrate the effectiveness of our learned semantic concepts and order, by achieving the state-of-the-art results on two public benchmark datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02036v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"179\", \"18648\", \"72957\", \"10271\", \"31347\", \"33140\", \"44781\", \"49326\", \"54435\", \"55539\", \"59840\", \"61785\", \"62836\", \"66734\", \"76974\", \"77428\", \"88202\", \"92023\", \"106415\", \"130188\", \"161696\", \"96188\", \"105922\", \"111384\", \"115975\", \"95983\", \"129073\", \"133742\", \"137906\", \"139259\", \"140566\", \"141225\", \"147671\", \"150513\", \"162227\", \"168031\", \"171465\", \"179422\", \"250405\", \"156399\", \"154606\", \"165046\", \"165588\", \"166854\", \"167256\", \"168374\", \"169858\", \"171357\", \"172180\", \"174872\", \"180438\", \"181437\", \"189628\", \"195097\", \"207637\", \"223749\", \"238205\"], \"outgoing_citations\": [\"280249\", \"281407\", \"284230\", \"285796\", \"293220\", \"294491\", \"301594\", \"302943\", \"303011\", \"311824\", \"312643\", \"313189\", \"314050\", \"321621\", \"322022\", \"323634\", \"328038\", \"336408\"]}","task_split":"paper_retrieval"}
{"document_id":"248184","document_content":"# An analysis of incorporating an external language model into a sequence-to-sequence model\n## Categories\n- Audio and Speech Processing\n- Computation and Language\n- Artificial Intelligence\n- Sound\n## Abstract\nAttention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with a neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.01996v1\", \"primary_category\": \"eess.AS\", \"categories\": [\"eess.AS\", \"cs.CL\", \"cs.AI\", \"cs.SD\"], \"primary_category_human_readable\": \"Audio and Speech Processing\", \"categories_human_readable\": [\"Audio and Speech Processing\", \"Computation and Language\", \"Artificial Intelligence\", \"Sound\"], \"incoming_citations\": [\"16298\", \"20763\", \"218928\", \"3171\", \"11325\", \"16176\", \"18986\", \"20664\", \"26270\", \"54125\", \"57148\", \"57166\", \"58152\", \"58416\", \"59826\", \"69188\", \"86762\", \"88030\", \"88751\", \"89215\", \"90949\", \"91138\", \"92003\", \"101935\", \"105930\", \"106052\", \"152859\", \"80535\", \"90510\", \"104345\", \"117729\", \"117722\", \"125937\", \"124010\", \"131863\", \"129786\", \"136281\", \"137065\", \"139932\", \"152345\", \"153750\", \"153854\", \"140063\", \"148312\", \"148838\", \"159831\", \"161022\", \"161477\", \"166036\", \"164451\", \"169714\", \"176248\", \"177078\", \"179579\", \"190164\", \"193516\", \"193547\", \"198037\", \"198379\", \"207115\", \"209901\", \"211349\", \"188332\", \"165575\", \"216084\", \"221967\", \"221985\", \"223083\", \"227765\", \"227867\", \"228405\", \"231881\", \"233779\", \"233894\", \"248246\", \"248220\", \"248229\", \"234309\"], \"outgoing_citations\": [\"246088\", \"258039\", \"264220\", \"278443\", \"298750\", \"299936\", \"307902\", \"309025\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"248431","document_content":"# Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe propose a Label Propagation based algorithm for weakly supervised text classification. We construct a graph where each document is represented by a node and edge weights represent similarities among the documents. Additionally, we discover underlying topics using Latent Dirichlet Allocation (LDA) and enrich the document graph by including the topics in the form of additional nodes. The edge weights between a topic and a text document represent level of \"affinity\" between them. Our approach does not require document level labelling, instead it expects manual labels only for topic nodes. This significantly minimizes the level of supervision needed as only a few topics are observed to be enough for achieving sufficiently high accuracy. The Label Propagation Algorithm is employed on this enriched graph to propagate labels among the nodes. Our approach combines the advantages of Label Propagation (through document-document similarities) and Topic Modelling (for minimal but smart supervision). We demonstrate the effectiveness of our approach on various datasets and compare with state-of-the-art weakly supervised text classification approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.02767v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"248593","document_content":"# Visual Features for Context-Aware Speech Recognition\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Audio and Speech Processing\n## Abstract\nAutomatic transcriptions of consumer-generated multi-media content such as \"Youtube\" videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus on the visual modality, and may have been post-processed or edited. In this paper, we extend our earlier work on adapting the acoustic model of a DNN-based speech recognition system to an RNN language model and show how both can be adapted to the objects and scenes that can be automatically detected in the video. We are working on a corpus of \"how-to\" videos from the web, and the idea is that an object that can be seen (\"car\"), or a scene that is being detected (\"kitchen\") can be used to condition both models on the \"context\" of the recording, thereby reducing perplexity and improving transcription. We achieve good improvements in both cases and compare and analyze the respective reductions in word error rate. We expect that our results can be used for any type of speech processing in which \"context\" information is available, for example in robotics, man-machine interaction, or when indexing large audio-visual archives, and should ultimately help to bring together the \"video-to-text\" and \"speech-to-text\" communities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICASSP.2017.7953112\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.LG\", \"eess.AS\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Audio and Speech Processing\"], \"incoming_citations\": [\"22300\", \"88317\", \"96450\", \"142433\", \"157324\", \"178267\", \"210772\", \"254061\", \"211986\", \"221600\", \"235120\", \"234172\", \"270711\"], \"outgoing_citations\": [\"281534\", \"293418\", \"320332\", \"334501\"]}","task_split":"paper_retrieval"}
{"document_id":"248675","document_content":"# 3D Facial Action Units Recognition for Emotional Expression\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe muscular activities caused the activation of certain AUs for every facial expression at the certain duration of time throughout the facial expression. This paper presents the methods to recognise facial Action Unit (AU) using facial distance of the facial features which activates the muscles. The seven facial action units involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterises happy and sad expression. The recognition is performed on each AU according to rules defined based on the distance of each facial points. The facial distances chosen are extracted from twelve facial features. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.00195v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254439\"]}","task_split":"paper_retrieval"}
{"document_id":"248705","document_content":"# Semantic Photometric Bundle Adjustment on Natural Sequences\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe problem of obtaining dense reconstruction of an object in a natural sequence of images has been long studied in computer vision. Classically this problem has been solved through the application of bundle adjustment (BA). More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames. A fundamental drawback to BA & PBA, however, is: (i) their reliance on having to view all points on the object, and (ii) for the object surface to be well textured. To circumvent these limitations we propose semantic PBA which incorporates a 3D object prior, obtained through deep learning, within the photometric bundle adjustment problem. We demonstrate state of the art performance in comparison to leading methods for object reconstruction across numerous natural sequences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.00110v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"174909\", \"214622\", \"224621\"], \"outgoing_citations\": [\"251024\", \"251421\", \"258323\", \"259277\", \"261184\", \"263147\", \"264966\", \"268404\", \"269123\", \"279050\", \"279732\", \"287053\", \"295716\", \"324227\"]}","task_split":"paper_retrieval"}
{"document_id":"248821","document_content":"# Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches\n## Categories\n- Computation and Language\n## Abstract\nSentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies. This may lead to generate an incoherent target text for a coherent source text. In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document. Particularly, we explore two types of caches: a dynamic cache, which stores words from the best translation hypotheses of preceding sentences, and a topic cache, which maintains a set of target-side topical words that are semantically related to the document to be translated. On this basis, we build a new layer to score target words in these two caches with a cache-based neural model. Here the estimated probabilities from the cache-based neural model are combined with NMT probabilities into the final word prediction probabilities via a gating mechanism. Finally, the proposed cache-based neural model is trained jointly with NMT system in an end-to-end manner. Experiments and analysis presented in this paper demonstrate that the proposed cache-based model achieves substantial improvements over several state-of-the-art SMT and NMT baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.11221v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"93450\", \"26247\", \"33741\", \"42880\", \"70057\", \"82324\", \"100234\", \"137310\", \"223084\", \"95143\", \"95605\", \"95836\", \"118358\", \"120233\", \"127512\", \"131513\", \"141444\", \"151289\", \"152860\", \"150615\", \"157472\", \"158725\", \"163992\", \"168854\", \"169146\", \"173791\", \"186239\", \"194334\", \"181283\", \"219460\"], \"outgoing_citations\": [\"268684\", \"268909\", \"278065\", \"301840\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"248835","document_content":"# Neural Response Generation with Dynamic Vocabularies\n## Categories\n- Computation and Language\n## Abstract\nWe study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of their inputs, which not only makes them vulnerable to generic patterns and irrelevant noise, but also causes a high cost in decoding. We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows each input to possess their own vocabulary in decoding. In training, vocabulary construction and response generation are jointly learned by maximizing a lower bound of the true objective with a Monte Carlo sampling method. In inference, the model dynamically allocates a small vocabulary for an input with the word prediction model, and conducts decoding only with the small vocabulary. Because of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and irrelevant words in generation, and enjoys efficient decoding at the same time. Experimental results on both automatic metrics and human annotations show that DVS2S can significantly outperform state-of-the-art methods in terms of response quality, but only requires 60% decoding time compared to the most efficient baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.11191v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"27088\", \"46949\", \"65336\", \"98467\", \"95282\", \"159960\", \"163173\", \"163891\", \"160954\", \"128007\", \"180801\", \"183329\", \"189235\", \"188746\", \"210594\", \"219049\", \"227586\", \"251892\", \"210831\", \"217366\", \"220073\", \"226959\"], \"outgoing_citations\": [\"259268\", \"269757\", \"270040\", \"275597\", \"276365\", \"278671\", \"283527\", \"289061\", \"289943\", \"291102\", \"292261\", \"292821\", \"295863\", \"296250\", \"310740\", \"316328\", \"320814\", \"350457\"]}","task_split":"paper_retrieval"}
{"document_id":"248849","document_content":"# Structured learning and detailed interpretation of minimal object images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe model the process of human full interpretation of object images, namely the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is approached by dividing the interpretation of the complete object to the interpretation of multiple reduced but interpretable local regions. We model interpretation by a structured learning framework, in which there are primitive components and relations that play a useful role in local interpretation by humans. To identify useful components and relations used in the interpretation process, we consider the interpretation of minimal configurations, namely reduced local regions that are minimal in the sense that further reduction will turn them unrecognizable and uninterpretable. We show experimental results of our model, and results of predicting and testing relations that were useful to the model via transformed minimal images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.11151v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268000\", \"302528\", \"302783\", \"315007\"]}","task_split":"paper_retrieval"}
{"document_id":"248855","document_content":"# Video Captioning via Hierarchical Reinforcement Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nVideo captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.11135v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"60281\", \"114091\", \"186958\", \"163074\", \"8514\", \"9706\", \"19039\", \"46309\", \"47764\", \"59050\", \"62051\", \"67594\", \"80826\", \"85254\", \"86873\", \"110042\", \"175031\", \"201800\", \"83901\", \"86053\", \"86824\", \"5843\", \"5781\", \"102731\", \"104154\", \"104610\", \"108787\", \"115643\", \"124561\", \"128167\", \"134565\", \"136352\", \"143679\", \"146462\", \"146748\", \"146928\", \"70437\", \"158601\", \"170020\", \"172416\", \"173531\", \"173652\", \"174419\", \"175214\", \"128955\", \"182442\", \"183823\", \"187005\", \"191861\", \"201023\", \"202277\", \"206058\", \"207818\", \"208773\", \"211144\", \"220801\", \"231203\", \"206888\", \"203277\", \"225315\", \"229142\", \"231173\", \"234342\", \"235531\", \"238287\", \"239242\"], \"outgoing_citations\": [\"235531\", \"259121\", \"262564\", \"264568\", \"267523\", \"268083\", \"269030\", \"269232\", \"269646\", \"272464\", \"279002\", \"279337\", \"279676\", \"282943\", \"286427\", \"287823\", \"294142\", \"295117\", \"303593\", \"304481\", \"310717\", \"313189\", \"313427\", \"313435\", \"316803\", \"320332\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"248865","document_content":"# Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands\n## Categories\n- Computation and Language\n- Robotics\n## Abstract\nMany task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot's physical surroundings. Such interpretation is known variously as the symbol grounding problem, grounded semantics and grounded language acquisition. This problem is challenging because people employ diverse vocabulary and grammar, and because robots have substantial uncertainty about the nature and contents of their surroundings, making it difficult to associate the constitutive language elements (principally noun phrases and spatial relations) of the command text to elements of those surroundings. Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users. Existing statistical approaches can better handle diversity, but have not to date modeled complex linguistic structure, limiting achievable accuracy. Recent hybrid approaches have addressed limitations in scaling and complexity, but have not effectively associated linguistic and perceptual features. Our framework, called Generalized Grounding Graphs (G^3), addresses these issues by defining a probabilistic graphical model dynamically according to the linguistic parse structure of a natural language command. This approach scales effectively, handles linguistic diversity, and enables the system to associate parts of a command with the specific objects, places, and events in the external world to which they refer. We show that robots can learn word meanings and use those learned meanings to robustly follow natural language commands produced by untrained users. We demonstrate our approach for both mobility commands and mobile manipulation commands involving a variety of semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle, a forklift, and the Willow Garage PR2.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1712.01097v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.RO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Robotics\"], \"incoming_citations\": [\"33224\", \"56221\", \"74954\", \"142162\", \"165254\", \"188341\", \"218182\", \"261764\", \"275213\"], \"outgoing_citations\": [\"261378\", \"267779\", \"268335\", \"285657\", \"300421\", \"303224\", \"307569\", \"334986\", \"356833\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"248918","document_content":"# Sparse Photometric 3D Face Reconstruction Guided by Morphable Models\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration\/modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust optimization technique that can calibrate per-pixel lighting direction and illumination at a very high precision without assuming uniform surface albedos. Next, we apply semantic segmentation on input images and the geometry proxy to refine hairy vs. bare skin regions using tailored filters. Experiments on synthetic and real data show that by using a very small set of images, our technique is able to reconstruct fine geometric details such as wrinkles, eyebrows, whelks, pores, etc, comparable to and sometimes surpassing movie quality productions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.10870v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"25419\", \"120672\", \"82106\", \"87603\", \"134658\", \"136024\", \"192217\", \"322992\", \"193656\"], \"outgoing_citations\": [\"257118\", \"268703\", \"270795\", \"271712\", \"275999\", \"277948\", \"280394\", \"284716\", \"288971\"]}","task_split":"paper_retrieval"}
{"document_id":"248969","document_content":"# FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps\/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.10703v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17463\", \"37039\", \"37459\", \"38757\", \"55768\", \"60770\", \"76110\", \"103684\", \"18974\", \"26693\", \"32066\", \"50155\", \"60697\", \"62107\", \"67863\", \"71292\", \"139332\", \"168403\", \"174773\", \"188704\", \"996\", \"1773\", \"1229\", \"9857\", \"12012\", \"14246\", \"22967\", \"25618\", \"33719\", \"49874\", \"56280\", \"62799\", \"65678\", \"68463\", \"72268\", \"74756\", \"76015\", \"79696\", \"83295\", \"83625\", \"92268\", \"99898\", \"103761\", \"117456\", \"143372\", \"149919\", \"196750\", \"198751\", \"89019\", \"94977\", \"97923\", \"100258\", \"108233\", \"108335\", \"111131\", \"117180\", \"124310\", \"125816\", \"127680\", \"132093\", \"134378\", \"134386\", \"135332\", \"137881\", \"137913\", \"139986\", \"140312\", \"142045\", \"143198\", \"149113\", \"149962\", \"154138\", \"154701\", \"158246\", \"160255\", \"160915\", \"162917\", \"164523\", \"187775\", \"219346\", \"223543\", \"230252\", \"150049\", \"170700\", \"184495\", \"187955\", \"189415\", \"191293\", \"197365\", \"209098\", \"211990\", \"213155\", \"223755\", \"230095\", \"264911\", \"210477\", \"222871\", \"223310\", \"226546\", \"230097\", \"235729\"], \"outgoing_citations\": [\"251746\", \"258886\", \"259660\", \"260755\", \"262491\", \"264439\", \"267614\", \"268494\", \"296033\", \"330081\"]}","task_split":"paper_retrieval"}
{"document_id":"248975","document_content":"# Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHuman face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. The motion of a facial local region in facial expression is related to the motion of other facial local regions. In this paper, a novel deep learning approach, named facial dynamics interpreter network, has been proposed to interpret the important relations between local dynamics for estimating facial traits from expression sequence. The facial dynamics interpreter network is designed to be able to encode a relational importance, which is used for interpreting the relation between facial local dynamics and estimating facial traits. By comparative experiments, the effectiveness of the proposed method has been verified. The important relations between facial local dynamics are investigated by the proposed facial dynamics interpreter network in gender classification and age estimation. Moreover, experimental results show that the proposed method outperforms the state-of-the-art methods in gender classification and age estimation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.10688v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"230926\"], \"outgoing_citations\": [\"298629\"]}","task_split":"paper_retrieval"}
{"document_id":"249012","document_content":"# Intent-Aware Contextual Recommendation System\n## Categories\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nRecommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve.   Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations.   We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICDMW.2017.8\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"87405\", \"179680\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"249038","document_content":"# Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma is clinically difficult to distinguish from common benign skin lesions, particularly melanocytic naevus and seborrhoeic keratosis. The dermoscopic appearance of these lesions has huge intra-class variations and high inter-class visual similarities. Most current research is focusing on single-class segmentation irrespective of classes of skin lesions. In this work, we evaluate the performance of deep learning on multi-class segmentation of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We propose an end-to-end solution using fully convolutional networks (FCNs) for multi-class semantic segmentation to automatically segment the melanoma, seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer learning and a hybrid loss function are used. We evaluate the performance of the deep learning segmentation methods for multi-class segmentation and lesion diagnosis (with post-processing method) on the testing set of the ISIC-2017 challenge dataset. The results showed that the two-tier level transfer learning FCN-8s achieved the overall best result with \\textit{Dice} score of 78.5% in a naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in lesion diagnosis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.10449v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"81232\", \"87658\", \"97324\", \"111851\", \"249039\", \"200441\", \"223113\", \"223511\", \"246192\"], \"outgoing_citations\": [\"259214\", \"268261\"]}","task_split":"paper_retrieval"}
{"document_id":"249049","document_content":"# An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSeveral factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data, while the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as \"in-the-wild\") and label information is not available.   In this paper, we propose the first unsupervised deep learning method (with pseudo-supervision) for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where the multiplicative interactions of multiple latent factors of variation are explicitly modelled by means of multilinear (tensor) structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11263-019-01163-7\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7821\", \"33535\", \"82277\", \"39754\", \"50653\", \"36876\", \"177660\", \"134076\", \"154346\", \"168874\", \"165436\", \"182561\"], \"outgoing_citations\": [\"18227\", \"236050\", \"238254\", \"248005\", \"248320\", \"252884\", \"268963\", \"270105\", \"270134\", \"270795\", \"275879\", \"280734\", \"281624\", \"316183\", \"320031\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"249114","document_content":"# Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nIn this work we propose a system for visual question answering. Our architecture is composed of two parts, the first part creates the logical knowledge base given the image. The second part evaluates questions against the knowledge base. Differently from previous work, the knowledge base is represented using hyper-dimensional computing. This choice has the advantage that all the operations in the system, namely creating the knowledge base and evaluating the questions against it, are differentiable, thereby making the system easily trainable in an end-to-end fashion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.10185v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"9851\", \"130255\", \"206044\"], \"outgoing_citations\": [\"312506\", \"319904\"]}","task_split":"paper_retrieval"}
{"document_id":"249215","document_content":"# Language Bootstrapping: Learning Word Meanings From Perception-Action Association\n## Categories\n- Robotics\n- Machine Learning\n- Human-Computer Interaction\n- I.2.9; I.2.10; I.2.7; I.2.6\n- Computation and Language\n## Abstract\nWe address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TSMCB.2011.2172420\", \"primary_category\": \"cs.RO\", \"categories\": [\"stat.ML\", \"cs.HC\", \"I.2.9; I.2.10; I.2.7; I.2.6\", \"cs.RO\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\", \"I.2.9; I.2.10; I.2.7; I.2.6\", \"Robotics\", \"Computation and Language\"], \"incoming_citations\": [\"197577\", \"249430\", \"179314\", \"275080\", \"249413\", \"275213\", \"275623\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"249219","document_content":"# Production Ready Chatbots: Generate if not Retrieve\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09684v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"18036\", \"59514\"], \"outgoing_citations\": [\"295815\", \"295863\", \"304343\", \"310377\", \"310740\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"249233","document_content":"# Deep Reinforcement Learning for Sepsis Treatment\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nSepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09602v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"75234\", \"170695\", \"74313\", \"88305\", \"31177\", \"33819\", \"51328\", \"52180\", \"54518\", \"64122\", \"115731\", \"184991\", \"80071\", \"85341\", \"90660\", \"99839\", \"109865\", \"114617\", \"126139\", \"127099\", \"142892\", \"151069\", \"169228\", \"179811\", \"184671\", \"189070\", \"193925\", \"200688\", \"208939\", \"204688\", \"225819\", \"229839\"], \"outgoing_citations\": [\"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"249252","document_content":"# Accessible Melanoma Detection using Smartphones and Mobile Image Analysis\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resource-constrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human-computer interface (HCI) design to understand the usability and acceptance issues of the proposed system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09553v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"99927\", \"217707\"], \"outgoing_citations\": [\"299193\", \"319420\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"249394","document_content":"# Convolutional Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning is an important but challenging task, applicable to virtual assistants, editing tools, image indexing, and support of the disabled. Its challenges are due to the variability and ambiguity of possible image descriptions. In recent years significant progress has been made in image captioning, using Recurrent Neural Networks powered by long-short-term-memory (LSTM) units. Despite mitigating the vanishing gradient problem, and despite their compelling ability to memorize dependencies, LSTM units are complex and inherently sequential across time. To address this issue, recent work has shown benefits of convolutional networks for machine translation and conditional image generation. Inspired by their success, in this paper, we develop a convolutional image captioning technique. We demonstrate its efficacy on the challenging MSCOCO dataset and demonstrate performance on par with the baseline, while having a faster training time per number of parameters. We also perform a detailed analysis, providing compelling reasons in favor of convolutional language generation approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09151v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"6697\", \"22827\", \"32570\", \"89494\", \"159645\", \"6500\", \"24321\", \"25348\", \"28498\", \"28908\", \"30600\", \"49033\", \"54435\", \"57659\", \"64411\", \"67637\", \"59671\", \"96190\", \"163326\", \"175031\", \"67532\", \"80728\", \"82482\", \"89629\", \"94863\", \"102563\", \"107473\", \"112250\", \"113721\", \"114016\", \"108389\", \"117173\", \"124818\", \"129776\", \"138132\", \"139269\", \"139805\", \"141978\", \"147784\", \"162959\", \"179422\", \"190771\", \"225421\", \"124792\", \"150464\", \"161715\", \"166024\", \"169599\", \"170604\", \"171904\", \"174419\", \"176591\", \"176583\", \"176355\", \"182877\", \"185556\", \"153293\", \"190945\", \"190949\", \"192659\", \"193328\", \"202386\", \"208566\", \"215181\", \"229734\", \"244059\", \"208798\", \"212441\", \"214856\", \"219340\", \"219342\"], \"outgoing_citations\": [\"237265\", \"237268\", \"237271\", \"249972\", \"250712\", \"269122\", \"271189\", \"279518\", \"281152\", \"281749\", \"284230\", \"296279\", \"302662\", \"320003\", \"320997\"]}","task_split":"paper_retrieval"}
{"document_id":"249419","document_content":"# Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nProgress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09026v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"12209\", \"34959\", \"55531\", \"60595\", \"63800\", \"78757\", \"96543\", \"131804\", \"166253\", \"7482\", \"7605\", \"13516\", \"17780\", \"26134\", \"27616\", \"28890\", \"32241\", \"3430\", \"35545\", \"48354\", \"52846\", \"61254\", \"64854\", \"70170\", \"82421\", \"82921\", \"85367\", \"86182\", \"87412\", \"109124\", \"130800\", \"154879\", \"203762\", \"92849\", \"94978\", \"105399\", \"115066\", \"120047\", \"120248\", \"123636\", \"132689\", \"133645\", \"134054\", \"136458\", \"138111\", \"153095\", \"164839\", \"221071\", \"125246\", \"173383\", \"175291\", \"175629\", \"182990\", \"179692\", \"187083\", \"187115\", \"194269\", \"196856\", \"215860\", \"217198\", \"227621\"], \"outgoing_citations\": [\"268891\", \"270832\", \"273711\", \"278822\", \"286426\", \"287219\", \"311660\"]}","task_split":"paper_retrieval"}
{"document_id":"249522","document_content":"# A blockchain-based Decentralized System for proper handling of temporary Employment contracts\n## Categories\n- Computers and Society\n- Cryptography and Security\n## Abstract\nTemporary work is an employment situation useful and suitable in all occasions in which business needs to adjust more easily and quickly to workload fluctuations or maintain staffing flexibility. Temporary workers play therefore an important role in many companies, but this kind of activity is subject to a special form of legal protections and many aspects and risks must be taken into account both employers and employees. In this work we propose a blockchain-based system that aims to ensure respect for the rights for all actors involved in a temporary employment, in order to provide employees with the fair and legal remuneration (including taxes) of work performances and a protection in the case employer becomes insolvent. At the same time, our system wants to assist the employer in processing contracts with a fully automated and fast procedure. To resolve these problems we propose the D-ES (Decentralized Employment System). We first model the employment relationship as a state system. Then we describe the enabling technology that makes us able to realize the D-ES. In facts, we propose the implementation of a DLT (Decentralized Ledger Technology) based system, consisting in a blockchain system and of a web-based environment. Thanks the decentralized application platforms that makes us able to develop smart contracts, we define a discrete event control system that works inside the blockchain. In addition, we discuss the temporary work in agriculture as a interesting case of study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.09758v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CR\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Cryptography and Security\"], \"incoming_citations\": [\"154193\", \"171279\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"249726","document_content":"# Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition\n## Categories\n- Computation and Language\n## Abstract\nBiomedical named entity recognition (NER) is a fundamental task in text mining of medical documents and has many applications. Deep learning based approaches to this task have been gaining increasing attention in recent years as their parameters can be learned end-to-end without the need for hand-engineered features. However, these approaches rely on high-quality labeled data, which is expensive to obtain. To address this issue, we investigate how to use unlabeled text data to improve the performance of NER models. Specifically, we train a bidirectional language model (BiLM) on unlabeled data and transfer its weights to \"pretrain\" an NER model with the same architecture as the BiLM, which results in a better parameter initialization of the NER model. We evaluate our approach on four benchmark datasets for biomedical NER and show that it leads to a substantial improvement in the F1 scores compared with the state-of-the-art approaches. We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.07908v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"17477\", \"46463\", \"49866\", \"64595\", \"73411\", \"81948\", \"99717\", \"102549\", \"89428\", \"102214\", \"126134\", \"145761\", \"201647\", \"206151\", \"198222\", \"214174\"], \"outgoing_citations\": [\"234384\", \"235275\", \"243880\", \"266198\", \"274520\", \"280884\", \"281546\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"249830","document_content":"# Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device\n## Categories\n- Computers and Society\n## Abstract\nRecent years have witnessed the increasing popularity of mobile devices (such as iphone) due to the convenience that it brings to human lives. On one hand, rich user profiling and behavior data (including per-app level, app-interaction level and system-interaction level) from heterogeneous information sources make it possible to provide much better services (such as recommendation, advertisement targeting) to customers, which further drives revenue from understanding users' behaviors and improving user' engagement. In order to delight the customers, intelligent personal assistants (such as Amazon Alexa, Google Home and Google Now) are highly desirable to provide real-time audio, video and image recognition, natural language understanding, comfortable user interaction interface, satisfactory recommendation and effective advertisement targeting.   This paper presents the research efforts we have conducted on mobile devices which aim to provide much smarter and more convenient services by leveraging statistics and big data science, machine learning and deep learning, user modeling and marketing techniques to bring in significant user growth and user engagement and satisfactions (and happiness) on mobile devices. The developed new features are built at either cloud side or device side, harmonically working together to enhance the current service with the purpose of increasing users' happiness. We illustrate how we design these new features from system and algorithm perspective using different case studies, through which one can easily understand how science driven innovations help to provide much better service in technology and bring more revenue liftup in business. In the meantime, these research efforts have clear scientific contributions and published in top venues, which are playing more and more important roles for mobile AI products.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.07580v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\"], \"incoming_citations\": [\"134725\"], \"outgoing_citations\": [\"256632\", \"258495\", \"285761\", \"310377\"]}","task_split":"paper_retrieval"}
{"document_id":"249870","document_content":"# Modular Continual Learning in a Unified Visual Environment\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neurons and Cognition\n## Abstract\nA core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previously-seen tasks to substantially improve their own learning efficiency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.07425v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"q-bio.NC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Neurons and Cognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268586\", \"272422\", \"275087\", \"280202\", \"280870\", \"284134\", \"296432\", \"301005\", \"303141\"]}","task_split":"paper_retrieval"}
{"document_id":"249990","document_content":"# Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model\n## Categories\n- Computation and Language\n## Abstract\nIncorporating syntactic information in Neural Machine Translation models is a method to compensate their requirement for a large amount of parallel training text, especially for low-resource language pairs. Previous works on using syntactic information provided by (inevitably error-prone) parsers has been promising. In this paper, we propose a forest-to-sequence Attentional Neural Machine Translation model to make use of exponentially many parse trees of the source sentence to compensate for the parser errors. Our method represents the collection of parse trees as a packed forest, and learns a neural attentional transduction model from the forest to the target sentence. Experiments on English to German, Chinese and Persian translation show the superiority of our method over the tree-to-sequence and vanilla sequence-to-sequence neural translation models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.07019v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"48157\", \"95549\", \"152767\", \"157009\", \"166853\", \"171238\", \"192407\"], \"outgoing_citations\": [\"234587\", \"250737\", \"260977\", \"267460\", \"268812\", \"276174\", \"296274\", \"300543\", \"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"250183","document_content":"# Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTextual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.06420v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19259\", \"5125\", \"14958\", \"72957\", \"137813\", \"242837\", \"10271\", \"24771\", \"33140\", \"49326\", \"50398\", \"39748\", \"53385\", \"53451\", \"54435\", \"61014\", \"61785\", \"62836\", \"66734\", \"74508\", \"76974\", \"77995\", \"82892\", \"88202\", \"92023\", \"92282\", \"93826\", \"97088\", \"106415\", \"118644\", \"130188\", \"130722\", \"128625\", \"161696\", \"191408\", \"83242\", \"90654\", \"96188\", \"105922\", \"108947\", \"115975\", \"119789\", \"95983\", \"119213\", \"124223\", \"133740\", \"133742\", \"136245\", \"139259\", \"139577\", \"140566\", \"141225\", \"141152\", \"141978\", \"147001\", \"147671\", \"150513\", \"157078\", \"162227\", \"192325\", \"211784\", \"250405\", \"257108\", \"156399\", \"164107\", \"165046\", \"165588\", \"166854\", \"167256\", \"168374\", \"168605\", \"169858\", \"172180\", \"174872\", \"176772\", \"180438\", \"181437\", \"183392\", \"189628\", \"191552\", \"192948\", \"193738\", \"196332\", \"213042\", \"238205\", \"238941\"], \"outgoing_citations\": [\"256347\", \"277511\", \"280249\", \"281407\", \"288709\", \"301594\", \"302731\", \"302943\", \"303011\", \"312643\", \"314050\", \"320003\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"250220","document_content":"# Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Populations and Evolution\n## Abstract\nPandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.06299v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"q-bio.PE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Populations and Evolution\"], \"incoming_citations\": [\"61397\", \"74592\", \"92271\", \"121761\", \"134142\", \"154759\", \"213964\"], \"outgoing_citations\": [\"297526\", \"298287\", \"326956\", \"334182\", \"335987\"]}","task_split":"paper_retrieval"}
{"document_id":"250292","document_content":"# Learning from Millions of 3D Scans for Large-scale 3D Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2018.00203\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"59300\", \"1425\", \"8965\", \"22235\", \"23017\", \"32156\", \"45848\", \"54967\", \"62160\", \"76752\", \"117456\", \"161844\", \"235160\", \"89101\", \"97576\", \"133204\", \"134658\", \"208867\", \"156806\", \"194402\", \"213320\", \"27680\", \"227499\", \"250290\"], \"outgoing_citations\": [\"267347\", \"267612\", \"268703\", \"270106\", \"284716\", \"296061\", \"302157\"]}","task_split":"paper_retrieval"}
{"document_id":"250302","document_content":"# How Generative Adversarial Networks and Their Variants Work: An Overview\n## Categories\n- Machine Learning\n## Abstract\nGenerative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property leads GAN to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation and other academic fields. In this paper, we aim to discuss the details of GAN for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives. In addition, we explain how GAN operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GAN for their research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3301282\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"208787\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"250362","document_content":"# Markov Decision Processes with Continuous Side Information\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-information or context that determines the dynamics of the MDP for that episode. Our setting is motivated by applications in healthcare where baseline measurements of a patient at the start of a treatment episode form the context that may provide information about how the patient might respond to treatment decisions. We propose algorithms for learning in such Contextual Markov Decision Processes (CMDPs) under an assumption that the unobserved MDP parameters vary smoothly with the observed context. We also give lower and upper PAC bounds under the smoothness assumption. Because our lower bound has an exponential dependence on the dimension, we consider a tractable linear setting where the context is used to create linear combinations of a finite set of MDPs. For the linear setting, we give a PAC learning algorithm based on KWIK learning techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.05726v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"76692\", \"16608\", \"18892\", \"29694\", \"43324\", \"43396\", \"56639\", \"66832\", \"70473\", \"141979\", \"184991\", \"114800\", \"119473\", \"125165\", \"129185\", \"160166\", \"195113\", \"162994\", \"159038\", \"211058\", \"212480\"], \"outgoing_citations\": [\"278978\", \"304303\", \"317861\", \"327880\", \"337793\", \"339851\", \"358902\"]}","task_split":"paper_retrieval"}
{"document_id":"250460","document_content":"# Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings\n## Categories\n- Computation and Language\n## Abstract\nIn neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language.   In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others.   Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.05380v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"24309\", \"170615\", \"154382\", \"154534\", \"161049\", \"164070\", \"175499\", \"178500\", \"181966\", \"197286\", \"210304\"], \"outgoing_citations\": [\"267460\", \"284752\", \"287474\", \"290995\", \"299936\", \"300543\", \"323501\", \"340226\"]}","task_split":"paper_retrieval"}
{"document_id":"250549","document_content":"# Learning an Executable Neural Semantic Parser\n## Categories\n- Computation and Language\n## Abstract\nThis paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.05066v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15945\", \"45539\", \"24345\", \"122205\", \"73363\", \"91880\", \"130838\", \"175028\", \"198784\", \"220575\", \"238518\"], \"outgoing_citations\": [\"235098\", \"250496\", \"257206\", \"258130\", \"259618\", \"263808\", \"265431\", \"267790\", \"267877\", \"267881\", \"268067\", \"268336\", \"274271\", \"274658\", \"274817\", \"279428\", \"280201\", \"283652\", \"283665\", \"290632\", \"296150\", \"297226\", \"297648\", \"299541\", \"300505\", \"302970\", \"303306\", \"308381\", \"308610\", \"312066\", \"321546\", \"328352\", \"340201\", \"356833\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"250605","document_content":"# Denoising Imaging Polarimetry by an Adapted BM3D Method\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImaging polarimetry allows more information to be extracted from a scene than conventional intensity or colour imaging. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D. This algorithm, PBM3D, gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it to spectroscopy methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1364\/JOSAA.35.000690\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"198247\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"250738","document_content":"# AON: Towards Arbitrarily-Oriented Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.04226v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38757\", \"1039\", \"5857\", \"38506\", \"50917\", \"52248\", \"44535\", \"121818\", \"180823\", \"191\", \"7580\", \"8109\", \"28270\", \"33363\", \"33360\", \"33358\", \"48901\", \"49128\", \"50591\", \"53750\", \"58277\", \"63761\", \"67749\", \"93345\", \"102326\", \"116951\", \"122764\", \"133054\", \"145945\", \"147463\", \"163092\", \"99077\", \"100417\", \"100709\", \"108209\", \"111035\", \"111991\", \"120420\", \"123550\", \"126244\", \"126431\", \"134664\", \"135021\", \"136843\", \"136831\", \"144173\", \"145587\", \"149329\", \"150060\", \"151319\", \"158242\", \"162477\", \"170229\", \"189663\", \"210634\", \"151110\", \"155455\", \"167606\", \"169711\", \"170332\", \"172884\", \"173837\", \"174894\", \"180931\", \"176394\", \"192483\", \"195963\", \"203406\", \"205988\", \"205980\", \"211851\", \"217463\", \"222513\", \"229426\"], \"outgoing_citations\": [\"256711\", \"262570\", \"271112\", \"272481\", \"294023\", \"294606\", \"296685\", \"296828\", \"311196\", \"320148\", \"320837\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"250782","document_content":"# Enabling Reasoning with LegalRuleML\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n- F.4.2; F.1.1\n## Abstract\nIn order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.06128v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\", \"F.4.2; F.1.1\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\", \"F.4.2; F.1.1\"], \"incoming_citations\": [\"16573\", \"144959\", \"145754\"], \"outgoing_citations\": [\"235124\"]}","task_split":"paper_retrieval"}
{"document_id":"250811","document_content":"# Applications of Deep Learning and Reinforcement Learning to Biological Data\n## Categories\n- Machine Learning\n- A.1, I.2, I.5, J.3\n## Abstract\nRapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain\/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. The growth in computational power accompanied by faster and increased data storage and declining computing costs have already allowed scientists in various fields to apply these techniques on datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TNNLS.2018.2790388\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\", \"A.1, I.2, I.5, J.3\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"A.1, I.2, I.5, J.3\"], \"incoming_citations\": [\"3502\", \"12923\", \"170695\", \"257\", \"21722\", \"46421\", \"109508\", \"148134\", \"3531\", \"5509\", \"31177\", \"56252\", \"58000\", \"60884\", \"62678\", \"64141\", \"65118\", \"66405\", \"70797\", \"75694\", \"77316\", \"117449\", \"139401\", \"145294\", \"180897\", \"197166\", \"108815\", \"135502\", \"145046\", \"146836\", \"175196\", \"186944\", \"195227\", \"196165\", \"228034\", \"162131\", \"197840\", \"182863\", \"213472\", \"217559\", \"217557\", \"217558\", \"219746\", \"225389\", \"245309\"], \"outgoing_citations\": [\"144568\", \"281257\", \"286488\", \"288189\", \"289151\", \"293533\", \"293693\", \"295614\", \"296193\", \"297268\", \"297341\", \"301231\", \"302169\", \"302897\", \"303208\", \"310611\", \"312963\", \"336246\", \"336275\", \"360567\"]}","task_split":"paper_retrieval"}
{"document_id":"250820","document_content":"# Bayesian Paragraph Vectors\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWord2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences. In this work, we propose a novel interpretation for neural-network-based paragraph vectors by developing an unsupervised generative model whose maximum likelihood solution corresponds to traditional paragraph vectors. This probabilistic formulation allows us to go beyond point estimates of parameters and to perform Bayesian posterior inference. We find that the entropy of paragraph vectors decreases with the length of documents, and that information about posterior uncertainty improves performance in supervised learning tasks such as sentiment analysis and paraphrase detection.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.03946v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"158564\", \"178054\", \"192601\"], \"outgoing_citations\": [\"272080\", \"281570\", \"296180\", \"311384\", \"316977\", \"335884\"]}","task_split":"paper_retrieval"}
{"document_id":"250831","document_content":"# Neural-Symbolic Learning and Reasoning: A Survey and Interpretation\n## Categories\n- Artificial Intelligence\n## Abstract\nThe study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.03902v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"39731\", \"122909\", \"174304\", \"32042\", \"34078\", \"50898\", \"53820\", \"142247\", \"10372\", \"19478\", \"21518\", \"23025\", \"27558\", \"32567\", \"37570\", \"41484\", \"41823\", \"41826\", \"42232\", \"47935\", \"56343\", \"60961\", \"63886\", \"66546\", \"67522\", \"73865\", \"115685\", \"78844\", \"91828\", \"98285\", \"117902\", \"124633\", \"95011\", \"79220\", \"80104\", \"82588\", \"85025\", \"90930\", \"95452\", \"96406\", \"97251\", \"100901\", \"114729\", \"119357\", \"121044\", \"136139\", \"151631\", \"151773\", \"166549\", \"213764\", \"151284\", \"168932\", \"180712\", \"180754\", \"186726\", \"188375\", \"194343\", \"226092\", \"231542\", \"239558\"], \"outgoing_citations\": [\"298691\", \"311683\", \"322930\", \"332494\", \"357895\", \"361803\"]}","task_split":"paper_retrieval"}
{"document_id":"250863","document_content":"# Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension\n## Categories\n- Computation and Language\n## Abstract\nReading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model.   We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.03754v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"148368\", \"178587\", \"236958\"], \"outgoing_citations\": [\"155336\", \"253507\", \"259706\", \"259954\", \"263620\", \"268243\", \"271533\", \"279213\", \"281162\", \"281169\", \"282278\", \"283345\", \"284528\", \"286317\", \"290789\", \"291099\", \"297106\", \"303550\", \"303827\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"251473","document_content":"# Fine-tuning CNN Image Retrieval with No Human Annotation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide the selection of the training data. We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method to the VGG network achieves state-of-the-art performance on the standard benchmarks: Oxford Buildings, Paris, and Holidays datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.02512v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2482\", \"7327\", \"7342\", \"18013\", \"47564\", \"51004\", \"59656\", \"145777\", \"94405\", \"458\", \"1850\", \"3265\", \"3426\", \"5294\", \"5681\", \"7251\", \"7819\", \"14097\", \"22567\", \"22863\", \"25585\", \"26995\", \"27638\", \"28253\", \"34971\", \"36437\", \"51814\", \"52389\", \"60541\", \"61655\", \"64479\", \"70613\", \"73146\", \"85376\", \"100302\", \"102580\", \"112364\", \"118203\", \"144051\", \"147460\", \"210664\", \"1407\", \"3468\", \"3807\", \"5101\", \"5942\", \"6925\", \"9732\", \"9748\", \"10554\", \"11937\", \"13707\", \"15391\", \"16566\", \"17603\", \"18012\", \"18123\", \"18447\", \"18593\", \"19928\", \"21131\", \"21970\", \"22193\", \"24332\", \"25890\", \"26378\", \"28877\", \"28959\", \"29248\", \"29310\", \"30179\", \"31118\", \"34003\", \"35095\", \"36782\", \"37306\", \"40425\", \"40853\", \"41294\", \"41288\", \"41988\", \"42853\", \"183974\", \"28791\", \"46590\", \"49887\", \"50046\", \"50940\", \"52816\", \"55977\", \"56767\", \"59149\", \"59564\", \"60685\", \"61377\", \"62045\", \"62667\", \"63700\", \"64949\", \"65943\", \"66717\", \"70156\", \"70460\", \"81677\", \"84765\", \"85033\", \"85480\", \"88119\", \"88202\", \"89448\", \"90913\", \"96911\", \"100926\", \"106043\", \"110152\", \"114441\", \"115346\", \"124630\", \"125777\", \"176317\", \"35418\", \"79969\", \"80633\", \"87306\", \"88683\", \"85055\", \"91958\", \"218749\", \"99702\", \"100788\", \"103466\", \"104349\", \"104398\", \"105747\", \"109113\", \"109398\", \"109645\", \"111143\", \"111811\", \"112934\", \"96926\", \"117835\", \"119112\", \"122581\", \"122880\", \"127476\", \"129986\", \"103218\", \"130179\", \"133188\", \"133586\", \"134085\", \"135052\", \"136732\", \"139112\", \"139135\", \"139321\", \"139689\", \"145646\", \"145738\", \"146346\", \"146703\", \"147155\", \"161543\", \"173124\", \"182747\", \"190577\", \"155102\", \"160531\", \"165966\", \"165769\", \"169975\", \"170359\", \"170436\", \"170948\", \"172435\", \"174176\", \"176318\", \"176978\", \"177110\", \"180184\", \"180670\", \"181186\", \"181202\", \"181568\", \"181615\", \"180562\", \"186126\", \"187122\", \"188194\", \"190477\", \"191383\", \"193230\", \"193646\", \"193735\", \"194713\", \"196177\", \"198922\", \"200203\", \"201111\", \"207283\", \"207598\", \"207610\", \"211595\", \"236908\", \"239587\", \"241439\", \"243056\", \"206586\", \"208829\", \"209520\", \"212048\", \"134853\", \"223359\", \"223644\", \"237248\", \"250094\"], \"outgoing_citations\": [\"274926\", \"277635\", \"281953\", \"294470\", \"294983\", \"295204\", \"295468\", \"296534\", \"301572\", \"303067\", \"304501\", \"305764\", \"306293\", \"311472\", \"314584\", \"320036\", \"320052\", \"327036\", \"327312\", \"330936\", \"331429\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"251565","document_content":"# Neural Language Modeling by Jointly Learning Syntax and Lexicon\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWe propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word\/character-level language model tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.02013v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"19259\", \"2946\", \"18821\", \"78288\", \"81322\", \"94453\", \"6883\", \"6880\", \"12673\", \"14944\", \"17205\", \"24388\", \"42980\", \"45199\", \"46018\", \"48157\", \"52996\", \"53094\", \"55669\", \"57216\", \"61064\", \"63172\", \"64678\", \"83446\", \"92478\", \"132336\", \"115471\", \"89757\", \"93189\", \"93975\", \"95283\", \"95403\", \"96095\", \"96422\", \"98535\", \"100441\", \"102772\", \"93887\", \"117216\", \"122618\", \"124030\", \"125514\", \"127083\", \"127512\", \"127922\", \"132978\", \"138515\", \"144956\", \"153150\", \"165559\", \"165766\", \"166853\", \"168581\", \"174503\", \"175234\", \"179201\", \"179274\", \"182042\", \"182304\", \"182271\", \"182506\", \"182552\", \"191740\", \"192036\", \"192407\", \"209006\", \"213362\", \"214875\", \"219836\", \"220511\", \"219789\", \"228464\"], \"outgoing_citations\": [\"256948\", \"260929\", \"278065\", \"280201\", \"281205\", \"285259\", \"286225\", \"288551\", \"289897\", \"291183\", \"295636\", \"296289\", \"297648\", \"299541\", \"302481\", \"304229\", \"319782\", \"333638\", \"350314\"]}","task_split":"paper_retrieval"}
{"document_id":"251585","document_content":"# Hi, how can I help you?: Automating enterprise IT support help desks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nQuestion answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.02012v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"43050\", \"95602\", \"173096\"], \"outgoing_citations\": [\"272191\", \"284216\", \"311684\", \"319543\"]}","task_split":"paper_retrieval"}
{"document_id":"251597","document_content":"# Variational Inference of Disentangled Latent Concepts from Unlabeled Observations\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nDisentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors. We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement. We also propose a new disentanglement metric which is better aligned with the qualitative disentanglement observed in the decoder's output. We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00848v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"65457\", \"66565\", \"118799\", \"315\", \"1785\", \"3852\", \"7546\", \"15670\", \"21216\", \"22548\", \"23036\", \"27322\", \"34887\", \"38128\", \"39463\", \"45970\", \"59823\", \"67913\", \"70887\", \"72458\", \"76023\", \"80015\", \"87490\", \"103983\", \"119167\", \"146022\", \"227779\", \"16855\", \"17072\", \"17237\", \"18189\", \"18244\", \"19486\", \"19599\", \"20747\", \"20955\", \"22992\", \"23783\", \"23881\", \"24977\", \"29142\", \"32107\", \"35918\", \"38668\", \"43651\", \"44223\", \"44100\", \"45831\", \"47505\", \"52722\", \"45942\", \"57749\", \"61327\", \"61452\", \"65861\", \"68053\", \"69932\", \"70211\", \"70382\", \"74357\", \"80149\", \"84768\", \"87413\", \"90921\", \"92381\", \"95962\", \"103627\", \"110493\", \"118768\", \"173519\", \"177711\", \"183668\", \"255820\", \"162060\", \"93244\", \"69639\", \"34278\", \"80538\", \"83493\", \"87061\", \"82026\", \"91004\", \"91259\", \"91429\", \"93568\", \"96305\", \"96496\", \"97865\", \"99542\", \"100165\", \"100660\", \"101063\", \"101955\", \"104568\", \"105383\", \"108960\", \"109667\", \"109786\", \"110784\", \"111712\", \"112398\", \"116650\", \"117959\", \"118821\", \"119937\", \"121542\", \"123008\", \"123287\", \"124375\", \"128574\", \"131678\", \"132208\", \"109266\", \"133398\", \"133874\", \"136839\", \"138086\", \"139756\", \"139925\", \"140573\", \"141054\", \"143360\", \"143581\", \"143831\", \"147237\", \"154584\", \"156282\", \"160471\", \"164600\", \"167627\", \"168518\", \"170071\", \"171190\", \"174336\", \"180811\", \"183696\", \"183943\", \"184045\", \"187863\", \"201588\", \"144684\", \"149182\", \"149569\", \"151710\", \"153300\", \"153486\", \"154101\", \"154401\", \"164240\", \"146104\", \"167893\", \"168243\", \"168312\", \"168885\", \"171324\", \"178668\", \"179803\", \"179975\", \"181388\", \"181909\", \"183257\", \"183732\", \"185954\", \"186784\", \"190353\", \"200197\", \"201416\", \"201724\", \"201739\", \"206907\", \"208041\", \"212169\", \"216250\", \"218117\", \"225984\", \"236595\", \"272447\", \"204709\", \"206218\", \"207094\", \"207627\", \"220865\", \"233751\", \"234246\", \"241933\", \"242239\", \"246808\", \"249874\"], \"outgoing_citations\": [\"253197\", \"264782\", \"264963\", \"272848\", \"277856\", \"278583\", \"280734\", \"283481\", \"291302\", \"297838\", \"298695\", \"300606\", \"303029\", \"311052\", \"311482\", \"311577\", \"312814\", \"315703\", \"316183\", \"317771\", \"319797\", \"320031\", \"333469\", \"337753\", \"353174\", \"357986\", \"361129\"]}","task_split":"paper_retrieval"}
{"document_id":"251638","document_content":"# Extracting an English-Persian Parallel Corpus from Comparable Corpora\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nParallel data are an important part of a reliable Statistical Machine Translation (SMT) system. The more of these data are available, the better the quality of the SMT system. However, for some language pairs such as Persian-English, parallel sources of this kind are scarce. In this paper, a bidirectional method is proposed to extract parallel sentences from English and Persian document aligned Wikipedia. Two machine translation systems are employed to translate from Persian to English and the reverse after which an IR system is used to measure the similarity of the translated sentences. Adding the extracted sentences to the training data of the existing SMT systems is shown to improve the quality of the translation. Furthermore, the proposed method slightly outperforms the one-directional approach. The extracted corpus consists of about 200,000 sentences which have been sorted by their degree of similarity calculated by the IR system and is freely available for public access on the Web.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00681v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"81219\", \"136878\"], \"outgoing_citations\": [\"275185\"]}","task_split":"paper_retrieval"}
{"document_id":"251691","document_content":"# Learning with Latent Language\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nThe named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter's loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00482v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"4639\", \"19140\", \"55519\", \"71663\", \"176302\", \"11920\", \"41041\", \"42116\", \"44473\", \"47080\", \"53492\", \"55157\", \"55964\", \"64053\", \"64268\", \"66630\", \"67296\", \"69518\", \"89875\", \"96214\", \"96764\", \"84300\", \"99291\", \"108770\", \"123088\", \"127002\", \"127517\", \"127537\", \"157664\", \"161143\", \"176990\", \"174916\", \"180270\", \"181620\", \"184501\", \"199210\", \"209452\", \"209453\", \"231132\"], \"outgoing_citations\": [\"259693\", \"260107\", \"261378\", \"263306\", \"263650\", \"266702\", \"268866\", \"270893\", \"280870\", \"281106\", \"284092\", \"290632\", \"334986\", \"359341\"]}","task_split":"paper_retrieval"}
{"document_id":"251721","document_content":"# Semantic Structure and Interpretability of Word Embeddings\n## Categories\n- Computation and Language\n## Abstract\nDense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions, which makes interpretation a big challenge. In this study, we propose a statistical method to uncover the latent semantic structure in the dense word embeddings. To perform our analysis we introduce a new dataset (SEMCAT) that contains more than 6500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings; the proposed method is a practical alternative to the classical word intrusion test that requires human intervention.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TASLP.2018.2837384\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15609\", \"57027\", \"9702\", \"14944\", \"42495\", \"56302\", \"56526\", \"65594\", \"77745\", \"95620\", \"125793\", \"196679\", \"224091\", \"162928\", \"169553\", \"181770\", \"189942\", \"192641\", \"201297\", \"203421\", \"205161\", \"218789\", \"227645\", \"244586\"], \"outgoing_citations\": [\"261193\", \"287036\", \"300128\", \"311696\", \"315542\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"251741","document_content":"# Learning to Represent Programs with Graphs\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Programming Languages\n- Software Engineering\n## Abstract\nLearning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.   In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00740v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.PL\", \"cs.SE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Programming Languages\", \"Software Engineering\"], \"incoming_citations\": [\"11546\", \"29883\", \"33594\", \"41703\", \"54897\", \"109417\", \"112333\", \"1716\", \"3660\", \"5718\", \"12167\", \"13215\", \"16684\", \"17856\", \"18085\", \"8369\", \"46302\", \"49038\", \"67549\", \"69811\", \"80211\", \"85345\", \"86663\", \"82031\", \"100745\", \"102498\", \"152471\", \"206470\", \"216001\", \"1897\", \"5956\", \"6203\", \"9015\", \"9453\", \"15337\", \"17210\", \"23397\", \"27313\", \"27588\", \"27719\", \"27633\", \"29470\", \"32835\", \"35045\", \"35510\", \"40664\", \"42715\", \"42828\", \"42938\", \"44239\", \"45588\", \"46571\", \"47091\", \"47749\", \"48521\", \"50588\", \"51106\", \"51223\", \"162216\", \"53426\", \"54320\", \"54360\", \"56550\", \"58486\", \"59985\", \"61156\", \"61634\", \"61780\", \"62551\", \"63207\", \"64191\", \"65233\", \"67249\", \"67139\", \"69291\", \"69616\", \"70622\", \"70595\", \"71008\", \"72846\", \"76485\", \"77990\", \"78680\", \"80856\", \"83359\", \"84643\", \"88548\", \"91584\", \"93938\", \"100035\", \"102092\", \"102195\", \"106528\", \"108510\", \"112216\", \"112483\", \"115483\", \"116977\", \"120001\", \"126197\", \"128391\", \"134074\", \"138159\", \"140442\", \"140850\", \"142497\", \"79224\", \"80247\", \"82445\", \"85593\", \"88230\", \"197749\", \"59071\", \"100961\", \"103366\", \"104274\", \"104957\", \"107322\", \"109611\", \"113099\", \"118510\", \"120746\", \"122716\", \"123956\", \"124491\", \"126609\", \"127525\", \"284979\", \"128110\", \"128566\", \"130974\", \"131931\", \"132217\", \"132826\", \"132725\", \"133704\", \"134864\", \"135097\", \"135305\", \"135350\", \"136938\", \"141901\", \"142833\", \"142894\", \"141151\", \"148080\", \"150065\", \"158180\", \"161545\", \"164110\", \"177022\", \"178533\", \"180324\", \"205283\", \"206367\", \"223878\", \"167006\", \"151755\", \"153709\", \"159273\", \"159502\", \"161926\", \"165910\", \"167535\", \"168029\", \"170326\", \"175051\", \"176393\", \"177631\", \"178650\", \"179788\", \"181212\", \"183073\", \"183498\", \"184201\", \"184240\", \"185331\", \"186000\", \"186538\", \"187063\", \"188743\", \"188801\", \"192058\", \"192555\", \"192491\", \"195131\", \"195801\", \"204631\", \"205787\", \"213735\", \"211433\", \"220798\", \"222320\", \"231088\", \"237770\", \"242150\", \"210154\", \"211833\", \"218674\", \"221764\", \"224027\", \"229324\", \"227479\", \"230880\", \"233739\", \"238600\", \"238725\", \"255562\"], \"outgoing_citations\": [\"254619\", \"255562\", \"271527\", \"279606\", \"298627\", \"333520\", \"335819\"]}","task_split":"paper_retrieval"}
{"document_id":"251753","document_content":"# Re-DPoctor: Real-time health data releasing with w-day differential privacy\n## Categories\n- Cryptography and Security\n## Abstract\nWearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data release. However, they have limitations in healthcare scenarios because they do not consider the unique features of health data being collected from wearables, such as continuous real-time collection and pattern preservation. In this paper, we propose Re-DPoctor, a real-time health data releasing scheme with $w$-day differential privacy where the privacy of health data collected from any consecutive $w$ days is preserved. We improve utility by using a specially-designed partition algorithm to protect the health data patterns. Meanwhile, we improve privacy preservation by applying newly proposed adaptive sampling technique and budget allocation method. We prove that Re-DPoctor satisfies $w$-day differential privacy. Experiments on real health data demonstrate that our method achieves better utility with strong privacy guarantee than existing state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00232v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"87428\", \"207072\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"251816","document_content":"# Unsupervised Machine Translation Using Monolingual Corpora Only\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMachine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1711.00043v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"42229\", \"87792\", \"90924\", \"3461\", \"16590\", \"18494\", \"25308\", \"26304\", \"27012\", \"38588\", \"38855\", \"55203\", \"55766\", \"55875\", \"81858\", \"89936\", \"149081\", \"202795\", \"4181\", \"4711\", \"10275\", \"12784\", \"13799\", \"14943\", \"15355\", \"15844\", \"16207\", \"17281\", \"18065\", \"21620\", \"23566\", \"25034\", \"25886\", \"26968\", \"27349\", \"34638\", \"34723\", \"35580\", \"39916\", \"40810\", \"42594\", \"42553\", \"42706\", \"43140\", \"43451\", \"43901\", \"44753\", \"44902\", \"44951\", \"91643\", \"45996\", \"46080\", \"47198\", \"47578\", \"47624\", \"48267\", \"48458\", \"49011\", \"50311\", \"50756\", \"55413\", \"55793\", \"59359\", \"59110\", \"59790\", \"60936\", \"60928\", \"61486\", \"62138\", \"63546\", \"63678\", \"63988\", \"63831\", \"64743\", \"66981\", \"68490\", \"69818\", \"71089\", \"71092\", \"73135\", \"73360\", \"77576\", \"77611\", \"80699\", \"81948\", \"81987\", \"82744\", \"87143\", \"90826\", \"91783\", \"92414\", \"92527\", \"93412\", \"98388\", \"98946\", \"114991\", \"118745\", \"120213\", \"121306\", \"122048\", \"124574\", \"127930\", \"132055\", \"132595\", \"132972\", \"133976\", \"142497\", \"145005\", \"158975\", \"77667\", \"81356\", \"81571\", \"82994\", \"80215\", \"82146\", \"139549\", \"84886\", \"85242\", \"87908\", \"89663\", \"90856\", \"91763\", \"92203\", \"92958\", \"94039\", \"94613\", \"94898\", \"95366\", \"95392\", \"95549\", \"95674\", \"95821\", \"96097\", \"95979\", \"96371\", \"97559\", \"84547\", \"98135\", \"98221\", \"98508\", \"100144\", \"100341\", \"101854\", \"103151\", \"103430\", \"103738\", \"104783\", \"108392\", \"111128\", \"113240\", \"113957\", \"94338\", \"48054\", \"456\", \"115867\", \"116404\", \"118104\", \"120247\", \"120302\", \"120826\", \"122607\", \"123457\", \"123655\", \"125399\", \"125894\", \"126112\", \"126586\", \"126651\", \"127512\", \"126802\", \"87746\", \"127861\", \"127839\", \"127765\", \"128185\", \"128181\", \"129340\", \"129632\", \"129921\", \"131513\", \"131659\", \"132255\", \"132587\", \"133020\", \"127956\", \"133462\", \"129799\", \"131913\", \"132577\", \"133873\", \"134539\", \"136152\", \"139450\", \"140689\", \"142316\", \"143126\", \"143551\", \"144686\", \"146003\", \"148542\", \"148688\", \"148688\", \"149448\", \"151106\", \"151289\", \"151737\", \"153162\", \"155016\", \"157171\", \"157121\", \"157660\", \"159153\", \"161902\", \"162387\", \"163902\", \"141733\", \"183558\", \"183634\", \"189638\", \"133053\", \"204970\", \"221153\", \"152314\", \"152767\", \"156078\", \"157095\", \"157111\", \"162846\", \"163715\", \"161599\", \"168553\", \"168753\", \"168976\", \"164800\", \"168735\", \"169277\", \"169317\", \"169332\", \"169686\", \"169893\", \"170174\", \"170299\", \"171143\", \"171398\", \"171399\", \"171582\", \"171533\", \"169366\", \"174325\", \"176571\", \"177293\", \"177562\", \"177974\", \"93228\", \"172535\", \"178555\", \"179347\", \"179401\", \"180654\", \"180871\", \"181185\", \"181277\", \"181324\", \"181686\", \"182075\", \"182425\", \"182402\", \"182656\", \"182716\", \"182728\", \"182943\", \"182946\", \"183037\", \"182210\", \"183646\", \"183976\", \"184209\", \"184839\", \"184909\", \"185813\", \"186172\", \"186425\", \"186603\", \"186874\", \"187452\", \"184635\", \"189530\", \"191113\", \"191373\", \"191352\", \"192245\", \"192335\", \"192337\", \"191777\", \"193738\", \"193867\", \"194844\", \"197656\", \"200263\", \"200307\", \"200292\", \"200815\", \"200820\", \"201626\", \"202124\", \"203054\", \"208350\", \"208223\", \"208555\", \"211724\", \"212661\", \"214407\", \"147158\", \"204590\", \"204963\", \"218436\", \"221389\", \"221478\", \"263680\", \"191602\", \"194580\", \"203812\", \"208422\", \"208944\", \"211293\", \"211710\", \"211937\", \"211712\", \"212008\", \"193488\", \"212367\", \"213533\", \"214224\", \"215478\", \"214197\", \"216815\", \"217402\", \"217957\", \"218755\", \"218851\", \"219175\", \"219543\", \"219573\", \"219595\", \"219864\", \"220215\", \"220351\", \"220515\", \"220678\", \"220951\", \"221514\", \"220017\", \"220165\", \"220330\", \"223071\", \"224032\", \"224307\", \"226845\", \"227876\", \"230014\", \"230490\", \"231519\", \"231526\", \"231863\", \"232631\", \"231420\", \"231932\", \"233817\", \"234377\", \"234391\", \"234728\", \"234748\", \"234807\", \"234855\", \"235238\", \"237053\", \"237066\", \"237333\", \"238926\", \"238473\", \"240640\", \"241611\", \"242125\", \"242133\", \"244738\", \"249276\", \"250620\", \"251486\"], \"outgoing_citations\": [\"253508\", \"264781\", \"264914\", \"265309\", \"267524\", \"272531\", \"280498\", \"280996\", \"281472\", \"281749\", \"290402\", \"290510\", \"293418\", \"298543\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"251855","document_content":"# Deep Hashing with Triplet Quantization Loss\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the explosive growth of image databases, deep hashing, which learns compact binary descriptors for images, has become critical for fast image retrieval. Many existing deep hashing methods leverage quantization loss, defined as distance between the features before and after quantization, to reduce the error from binarizing features. While minimizing the quantization loss guarantees that quantization has minimal effect on retrieval accuracy, it unfortunately significantly reduces the expressiveness of features even before the quantization. In this paper, we show that the above definition of quantization loss is too restricted and in fact not necessary for maintaining high retrieval accuracy. We therefore propose a new form of quantization loss measured in triplets. The core idea of the triplet quantization loss is to learn discriminative real-valued descriptors which lead to minimal loss on retrieval accuracy after quantization. Extensive experiments on two widely used benchmark data sets of different scales, CIFAR-10 and In-shop, demonstrate that the proposed method outperforms the state-of-the-art deep hashing methods. Moreover, we show that the compact binary descriptors obtained with triplet quantization loss lead to very small performance drop after quantization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.11445v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85376\", \"215790\"], \"outgoing_citations\": [\"314568\"]}","task_split":"paper_retrieval"}
{"document_id":"251924","document_content":"# Learning Robust Rewards with Adversarial Inverse Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nReinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. Deep reinforcement learning methods can remove the need for explicit engineering of policy or value features, but still require a manually specified reward function. Inverse reinforcement learning holds the promise of automatic reward acquisition, but has proven exceptionally difficult to apply to large, high-dimensional problems with unknown dynamics. In this work, we propose adverserial inverse reinforcement learning (AIRL), a practical and scalable inverse reinforcement learning algorithm based on an adversarial reward learning formulation. We demonstrate that AIRL is able to recover reward functions that are robust to changes in dynamics, enabling us to learn policies even under significant variation in the environment seen during training. Our experiments show that AIRL greatly outperforms prior methods in these transfer settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.11248v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"43417\", \"53008\", \"353397\", \"163\", \"1269\", \"1752\", \"1769\", \"3212\", \"3959\", \"7093\", \"8426\", \"10486\", \"11670\", \"13755\", \"17854\", \"19571\", \"22458\", \"24764\", \"29891\", \"34331\", \"35649\", \"36644\", \"38950\", \"52057\", \"64019\", \"73192\", \"76756\", \"89420\", \"96439\", \"116594\", \"86\", \"2483\", \"7833\", \"9485\", \"9356\", \"9593\", \"9814\", \"10822\", \"11802\", \"13156\", \"14557\", \"14671\", \"15614\", \"17353\", \"17658\", \"17859\", \"21975\", \"31582\", \"31581\", \"34081\", \"34863\", \"35136\", \"35306\", \"36315\", \"36692\", \"37058\", \"37526\", \"38356\", \"40149\", \"35842\", \"40961\", \"40995\", \"41463\", \"41518\", \"42404\", \"42747\", \"43475\", \"43961\", \"44161\", \"44473\", \"45095\", \"45285\", \"45629\", \"155602\", \"46858\", \"47465\", \"48429\", \"49426\", \"51162\", \"52687\", \"53903\", \"54109\", \"54347\", \"56203\", \"57391\", \"59140\", \"59341\", \"61181\", \"61195\", \"61430\", \"62278\", \"64007\", \"64482\", \"65194\", \"66915\", \"67095\", \"67374\", \"69627\", \"69620\", \"69799\", \"74900\", \"75338\", \"77464\", \"82750\", \"86496\", \"89189\", \"93063\", \"95856\", \"100401\", \"105601\", \"105761\", \"114596\", \"116304\", \"116450\", \"116575\", \"116668\", \"120259\", \"121387\", \"128759\", \"130096\", \"132434\", \"139572\", \"177272\", \"183561\", \"227635\", \"66826\", \"80924\", \"82779\", \"82620\", \"83235\", \"83427\", \"84270\", \"84723\", \"85766\", \"86028\", \"86153\", \"86448\", \"107895\", \"87299\", \"87679\", \"88065\", \"89945\", \"92140\", \"93584\", \"97031\", \"99595\", \"104878\", \"105419\", \"107124\", \"108553\", \"110002\", \"112041\", \"112521\", \"113646\", \"114983\", \"115004\", \"115852\", \"117587\", \"118690\", \"120829\", \"121530\", \"116566\", \"116206\", \"121735\", \"121973\", \"122334\", \"122601\", \"126089\", \"126508\", \"127289\", \"131375\", \"132719\", \"133647\", \"135154\", \"135919\", \"137443\", \"139811\", \"140373\", \"140983\", \"141010\", \"141096\", \"142074\", \"144191\", \"145857\", \"146158\", \"146523\", \"148129\", \"148275\", \"148768\", \"151786\", \"154928\", \"158515\", \"159984\", \"164743\", \"163689\", \"174008\", \"176370\", \"177629\", \"184304\", \"193240\", \"200791\", \"215780\", \"117347\", \"149096\", \"152033\", \"153521\", \"156039\", \"156096\", \"157804\", \"158583\", \"162466\", \"163730\", \"165811\", \"166783\", \"169800\", \"179988\", \"180256\", \"180885\", \"181066\", \"181256\", \"183581\", \"173723\", \"185142\", \"165031\", \"190237\", \"190766\", \"192228\", \"192776\", \"194601\", \"193925\", \"198208\", \"200843\", \"201429\", \"206655\", \"216058\", \"215836\", \"199330\", \"209452\", \"217499\", \"218050\", \"218636\", \"218939\", \"227313\", \"229737\", \"231418\", \"213070\", \"224411\", \"226794\", \"230962\", \"231267\", \"233450\", \"230042\", \"234342\", \"241813\"], \"outgoing_citations\": [\"266406\", \"272987\", \"280655\", \"291568\", \"297317\", \"309396\"]}","task_split":"paper_retrieval"}
{"document_id":"252008","document_content":"# Learning to solve inverse problems using Wasserstein loss\n## Categories\n- Computer Vision and Pattern Recognition\n- Functional Analysis\n- Optimization and Control\n## Abstract\nWe propose using the Wasserstein loss for training in inverse problems. In particular, we consider a learned primal-dual reconstruction scheme for ill-posed inverse problems using the Wasserstein distance as loss function in the learning. This is motivated by miss-alignments in training data, which when using standard mean squared error loss could severely degrade reconstruction quality. We prove that training with the Wasserstein loss gives a reconstruction operator that correctly compensates for miss-alignments in certain cases, whereas training with the mean squared error gives a smeared reconstruction. Moreover, we demonstrate these effects by training a reconstruction algorithm using both mean squared error and optimal transport loss for a problem in computerized tomography.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.10898v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"math.FA\", \"math.OC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Functional Analysis\", \"Optimization and Control\"], \"incoming_citations\": [\"34716\", \"59293\", \"62942\", \"200855\", \"220210\", \"119712\", \"170255\", \"184323\", \"207502\", \"210106\", \"249170\"], \"outgoing_citations\": [\"260692\", \"263890\", \"264885\", \"268975\", \"310979\", \"362595\"]}","task_split":"paper_retrieval"}
{"document_id":"252034","document_content":"# Transfer Learning to Learn with Multitask Neural Model Search\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nDeep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design. NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures. However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures. This procedure needs to be executed from scratch for each new task. The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks. In this paper, we present the Multitask Neural Model Search (MNMS) controller. Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks. We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task. We then show that pre-trained MNMS controllers can transfer learning to new tasks. By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.10776v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"101301\", \"265708\", \"220126\"], \"outgoing_citations\": [\"255255\", \"261128\", \"261316\", \"310237\"]}","task_split":"paper_retrieval"}
{"document_id":"252059","document_content":"# Simple and Effective Multi-Paragraph Reading Comprehension\n## Categories\n- Computation and Language\n## Abstract\nWe consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.10723v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"2594\", \"66667\", \"77794\", \"148368\", \"4719\", \"12198\", \"17547\", \"19742\", \"22104\", \"25089\", \"28745\", \"29018\", \"31147\", \"36194\", \"37467\", \"38229\", \"38481\", \"42374\", \"43298\", \"44947\", \"46524\", \"47558\", \"49102\", \"50614\", \"53619\", \"55188\", \"61378\", \"61610\", \"64554\", \"64932\", \"65473\", \"67840\", \"69873\", \"72141\", \"75085\", \"76788\", \"77192\", \"77366\", \"77468\", \"77604\", \"91823\", \"93096\", \"98266\", \"98238\", \"100459\", \"103478\", \"114545\", \"127466\", \"132805\", \"127818\", \"158022\", \"216683\", \"104940\", \"77808\", \"83692\", \"87755\", \"91506\", \"94644\", \"95904\", \"96031\", \"95987\", \"97100\", \"92550\", \"98639\", \"99775\", \"109314\", \"110925\", \"119983\", \"123404\", \"123394\", \"124679\", \"125364\", \"125542\", \"127016\", \"128027\", \"128088\", \"129128\", \"129112\", \"130183\", \"61111\", \"131151\", \"131414\", \"131817\", \"132687\", \"128049\", \"140689\", \"125555\", \"125976\", \"145873\", \"146059\", \"150222\", \"154010\", \"154601\", \"156086\", \"156931\", \"158501\", \"170371\", \"130674\", \"157144\", \"157513\", \"157600\", \"157836\", \"160070\", \"161045\", \"161554\", \"163996\", \"164411\", \"167467\", \"168003\", \"166959\", \"166283\", \"166449\", \"166781\", \"167159\", \"167930\", \"170238\", \"170728\", \"171447\", \"171683\", \"175583\", \"178032\", \"180575\", \"181185\", \"181101\", \"181345\", \"182026\", \"182036\", \"180353\", \"180808\", \"183319\", \"184455\", \"186432\", \"186337\", \"185429\", \"190664\", \"192756\", \"188736\", \"193228\", \"193369\", \"195152\", \"196980\", \"200155\", \"201539\", \"201689\", \"202866\", \"203064\", \"203138\", \"203630\", \"204098\", \"208346\", \"211259\", \"213566\", \"217754\", \"218179\", \"218444\", \"220723\", \"221086\", \"171595\", \"184455\", \"211788\", \"214654\", \"207084\", \"208172\", \"211371\", \"212304\", \"214463\", \"215928\", \"216563\", \"217522\", \"218485\", \"218693\", \"218944\", \"220570\", \"220724\", \"220858\", \"223404\", \"227615\", \"227981\", \"228665\", \"229630\", \"231239\", \"231667\", \"234814\", \"236958\", \"238321\", \"238610\", \"234577\", \"242126\", \"213353\", \"250703\", \"279343\"], \"outgoing_citations\": [\"250529\", \"251589\", \"253866\", \"257223\", \"259954\", \"261389\", \"263691\", \"264250\", \"271533\", \"274520\", \"285811\", \"286757\", \"297106\", \"299541\", \"303827\"]}","task_split":"paper_retrieval"}
{"document_id":"252089","document_content":"# Finding Dominant User Utterances And System Responses in Conversations\n## Categories\n- Computation and Language\n## Abstract\nThere are several dialog frameworks which allow manual specification of intents and rule based dialog flow. The rule based framework provides good control to dialog designers at the expense of being more time consuming and laborious. The job of a dialog designer can be reduced if we could identify pairs of user intents and corresponding responses automatically from prior conversations between users and agents. In this paper we propose an approach to find these frequent user utterances (which serve as examples for intents) and corresponding agent responses. We propose a novel SimCluster algorithm that extends standard K-means algorithm to simultaneously cluster user utterances and agent utterances by taking their adjacency information into account. The method also aligns these clusters to provide pairs of intents and response groups. We compare our results with those produced by using simple Kmeans clustering on a real dataset and observe upto 10% absolute improvement in F1-scores. Through our experiments on synthetic dataset, we show that our algorithm gains more advantage over K-means algorithm when the data has large variance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.10609v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"144445\"], \"outgoing_citations\": [\"263622\", \"291916\"]}","task_split":"paper_retrieval"}
{"document_id":"252159","document_content":"# Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text community. On top of the conventional horizontal and multi-oriented texts, it features curved-oriented text. Total-Text is highly diversified in orientations, more than half of its images have a combination of more than two orientations. Recently, a new breed of solutions that casted text detection as a segmentation problem has demonstrated their effectiveness against multi-oriented text. In order to evaluate its robustness against curved text, we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its annotation is available at https:\/\/github.com\/cs-chan\/Total-Text-Dataset","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.10400v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155242\", \"2918\", \"6741\", \"7829\", \"13927\", \"31775\", \"39902\", \"52248\", \"66255\", \"80383\", \"83916\", \"84505\", \"121818\", \"180823\", \"4188\", \"8735\", \"11650\", \"14976\", \"25132\", \"31607\", \"35713\", \"35782\", \"35666\", \"3080\", \"40057\", \"47089\", \"48901\", \"50136\", \"54412\", \"55703\", \"56713\", \"59727\", \"62678\", \"43191\", \"81960\", \"84238\", \"136651\", \"150258\", \"155848\", \"59016\", \"194005\", \"81667\", \"83582\", \"84406\", \"106579\", \"111035\", \"111116\", \"120420\", \"119526\", \"123078\", \"128870\", \"131896\", \"132856\", \"135263\", \"140478\", \"141887\", \"143280\", \"152806\", \"155530\", \"156029\", \"210634\", \"150452\", \"161220\", \"166353\", \"166606\", \"166370\", \"169411\", \"169711\", \"169679\", \"170061\", \"170332\", \"171543\", \"171686\", \"174894\", \"174928\", \"186238\", \"189470\", \"190708\", \"192471\", \"192870\", \"193382\", \"194314\", \"201515\", \"203406\", \"203525\", \"207348\", \"209227\", \"205602\", \"204166\", \"204872\", \"225795\", \"243851\", \"256364\"], \"outgoing_citations\": [\"271112\", \"289389\", \"294606\", \"295564\", \"299461\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"252290","document_content":"# Image Compression: Sparse Coding vs. Bottleneck Autoencoders\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nBottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality reconstructed images. In this work, we explore the ability of sparse coding to improve reconstructed image quality for the same degree of compression. We observe that sparse image compression produces visually superior reconstructed images and yields higher values of pixel-wise measures of reconstruction quality (PSNR and SSIM) compared to bottleneck autoencoders. % In addition, we find that using alternative metrics that correlate better with human perception, such as feature perceptual loss and the classification accuracy, sparse image compression scores up to 18.06\\% and 2.7\\% higher, respectively, compared to bottleneck autoencoders. Although computationally much more intensive, we find that sparse coding is otherwise superior to bottleneck autoencoders for the same degree of compression.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.09926v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"197104\"], \"outgoing_citations\": [\"198138\", \"277357\", \"298700\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"252516","document_content":"# Robust Photometric Stereo via Dictionary Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nPhotometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance patterns, and it is sensitive to non-idealities in the images. In this work, we propose a novel approach to photometric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Specifically, we develop two formulations for applying dictionary learning to photometric stereo. We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model. We then generalize this model to explicitly model non-Lambertian objects. We investigate both approaches through extensive experimentation on synthetic and real benchmark datasets and observe state-of-the-art performance compared to existing robust photometric stereo methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.08873v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"254424\", \"280602\", \"284788\", \"302388\", \"302956\", \"315984\", \"339203\"]}","task_split":"paper_retrieval"}
{"document_id":"252607","document_content":"# Convolutional Neural Knowledge Graph Learning\n## Categories\n- Machine Learning\n## Abstract\nPrevious models for learning entity and relationship embeddings of knowledge graphs such as TransE, TransH, and TransR aim to explore new links based on learned representations. However, these models interpret relationships as simple translations on entity embeddings. In this paper, we try to learn more complex connections between entities and relationships. In particular, we use a Convolutional Neural Network (CNN) to learn entity and relationship representations in knowledge graphs. In our model, we treat entities and relationships as one-dimensional numerical sequences with the same length. After that, we combine each triplet of head, relationship, and tail together as a matrix with height 3. CNN is applied to the triplets to get confidence scores. Positive and manually corrupted negative triplets are used to train the embeddings and the CNN model simultaneously. Experimental results on public benchmark datasets show that the proposed model outperforms state-of-the-art models on exploring unseen relationships, which proves that CNN is effective to learn complex interactive patterns between entities and relationships.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.08502v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"82567\", \"147659\"], \"outgoing_citations\": [\"280288\", \"294402\", \"301467\", \"302050\", \"311970\"]}","task_split":"paper_retrieval"}
{"document_id":"252829","document_content":"# HDR image reconstruction from a single exposure using deep CNNs\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Machine Learning\n## Abstract\nCamera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3130800.3130816\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Machine Learning\"], \"incoming_citations\": [\"26246\", \"105191\", \"4084\", \"6693\", \"7025\", \"14984\", \"15203\", \"35686\", \"116761\", \"976\", \"14388\", \"17760\", \"19090\", \"23633\", \"28758\", \"28699\", \"29027\", \"35011\", \"41356\", \"45270\", \"46943\", \"47942\", \"49980\", \"54876\", \"57063\", \"60199\", \"61088\", \"61885\", \"72450\", \"101380\", \"114138\", \"114390\", \"115507\", \"125614\", \"134931\", \"156796\", \"164444\", \"173040\", \"79005\", \"79185\", \"85785\", \"88550\", \"39610\", \"122944\", \"124705\", \"124998\", \"127310\", \"129600\", \"129672\", \"132588\", \"133124\", \"133418\", \"133686\", \"136121\", \"136350\", \"137099\", \"139663\", \"167645\", \"172113\", \"173375\", \"149650\", \"159006\", \"180260\", \"186128\", \"186790\", \"187027\", \"187468\", \"187984\", \"189044\", \"189323\", \"197135\", \"197247\", \"197307\", \"202671\", \"203022\", \"207312\", \"173313\", \"239853\", \"205608\", \"234473\", \"235857\", \"239855\", \"244729\", \"249439\"], \"outgoing_citations\": [\"228199\", \"270221\", \"279178\", \"279598\", \"293446\", \"313846\", \"341153\"]}","task_split":"paper_retrieval"}
{"document_id":"252993","document_content":"# Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDespite the appeal of deep neural networks that largely replace the traditional handmade filters, they still suffer from isolated cases that cannot be properly handled only by the training of convolutional filters. Abnormal factors, including real-world noise, blur, or other quality degradations, ruin the output of a neural network. These unexpected problems can produce critical complications, and it is surprising that there has only been minimal research into the effects of noise in the deep neural network model. Therefore, we present an exhaustive investigation into the effect of noise in image classification and suggest a generalized architecture of a dual-channel model to treat quality degraded input images. We compare the proposed dual-channel model with a simple single model and show it improves the overall performance of neural networks on various types of quality degraded input datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06805v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"62385\", \"174286\", \"201385\", \"171866\", \"193500\", \"210131\", \"208472\", \"217558\"], \"outgoing_citations\": [\"270717\", \"276646\", \"285059\", \"294549\", \"294611\"]}","task_split":"paper_retrieval"}
{"document_id":"253051","document_content":"# Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF\n## Categories\n- Computation and Language\n## Abstract\nSentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency--Inverse Document Frequency (TF--IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06524v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"1962\", \"7006\", \"55573\", \"148449\", \"133601\", \"143643\", \"162609\"], \"outgoing_citations\": [\"234808\", \"260470\", \"267622\", \"269677\", \"272080\", \"286677\", \"288697\", \"289148\", \"295383\", \"298543\", \"301397\", \"302478\", \"302516\", \"321994\"]}","task_split":"paper_retrieval"}
{"document_id":"253098","document_content":"# Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nImages in the wild encapsulate rich knowledge about varied abstract concepts and cannot be sufficiently described with models built only using image-caption pairs containing selected objects. We propose to handle such a task with the guidance of a knowledge base that incorporate many abstract concepts. Our method is a two-step process where we first build a multi-entity-label image recognition model to predict abstract concepts as image labels and then leverage them in the second step as an external semantic attention and constrained inference in the caption generation model for describing images that depict unseen\/novel objects. Evaluations show that our models outperform most of the prior work for out-of-domain captioning on MSCOCO and are useful for integration of knowledge and vision in general.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06303v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"138132\", \"189018\"], \"outgoing_citations\": [\"258345\", \"274595\", \"278948\", \"284230\", \"287413\", \"289627\", \"296122\", \"303209\", \"310816\", \"311336\", \"319159\", \"320332\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"253108","document_content":"# Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions\n## Categories\n- Robotics\n- Computation and Language\n## Abstract\nComprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06280v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CL\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computation and Language\"], \"incoming_citations\": [\"4865\", \"8070\", \"9368\", \"41765\", \"80120\", \"15076\", \"21233\", \"27120\", \"34290\", \"35947\", \"27675\", \"36362\", \"38068\", \"38056\", \"45735\", \"58923\", \"60327\", \"143193\", \"69014\", \"113238\", \"116569\", \"135461\", \"164626\", \"138944\", \"139265\", \"145906\", \"149861\", \"164625\", \"163752\", \"167730\", \"180468\", \"183468\", \"190487\", \"244131\", \"214445\", \"228542\"], \"outgoing_citations\": [\"257595\", \"260900\", \"277060\", \"287377\", \"287427\", \"287454\", \"302621\", \"303833\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"253145","document_content":"# Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodily changes. Deep reinforcement learning has been shown to be successful in training robot control policies for operation in complex environments. However, existing methods typically employ only a single policy. This can limit the adaptability since a large environmental modification might require a completely different behavior compared to the learning environment. To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change. Thanks to these policies, which are stored into a multi-dimensional discrete map according to its behavioral feature, adaptation can be performed within reasonable time without retraining the robot. An appropriate pre-trained policy from the map can be recalled using Bayesian optimization. Our experiments show that MMPRL enables robots to quickly adapt to large changes without requiring any prior knowledge on the type of injuries that could occur. A highlight of the learned behaviors can be found here: https:\/\/youtu.be\/QwInbilXNOE .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06117v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8582\", \"138675\", \"163637\", \"236005\"], \"outgoing_citations\": [\"258834\", \"261391\", \"272030\", \"272464\", \"272636\", \"274445\", \"282696\", \"283216\", \"283289\", \"301502\", \"327091\", \"327144\", \"349397\"]}","task_split":"paper_retrieval"}
{"document_id":"253174","document_content":"# SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n## Abstract\nMost of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.05980v3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [\"5030\", \"55459\", \"61482\", \"121488\", \"133685\"], \"outgoing_citations\": [\"261013\", \"281925\", \"350523\"]}","task_split":"paper_retrieval"}
{"document_id":"253255","document_content":"# A General Framework for Robust Interactive Learning\n## Categories\n- Data Structures and Algorithms\n- Machine Learning\n## Abstract\nWe propose a general framework for interactively learning models, such as (binary or non-binary) classifiers, orderings\/rankings of items, or clusterings of data points. Our framework is based on a generalization of Angluin's equivalence query model and Littlestone's online learning model: in each iteration, the algorithm proposes a model, and the user either accepts it or reveals a specific mistake in the proposal. The feedback is correct only with probability $p > 1\/2$ (and adversarially incorrect with probability $1 - p$), i.e., the algorithm must be able to learn in the presence of arbitrary noise. The algorithm's goal is to learn the ground truth model using few iterations.   Our general framework is based on a graph representation of the models and user feedback. To be able to learn efficiently, it is sufficient that there be a graph $G$ whose nodes are the models and (weighted) edges capture the user feedback, with the property that if $s, s^*$ are the proposed and target models, respectively, then any (correct) user feedback $s'$ must lie on a shortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural algorithm reminiscent of the Multiplicative Weights Update algorithm, which will efficiently learn $s^*$ even in the presence of noise in the user's feedback.   From this general result, we rederive with barely any extra effort classic results on learning of classifiers and a recent result on interactive clustering; in addition, we easily obtain new interactive learning algorithms for ordering\/ranking.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.05422v1\", \"primary_category\": \"cs.DS\", \"categories\": [\"cs.LG\", \"cs.DS\"], \"primary_category_human_readable\": \"Data Structures and Algorithms\", \"categories_human_readable\": [\"Machine Learning\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"35889\", \"75056\", \"127763\", \"215925\", \"141708\", \"182344\", \"236601\", \"272781\", \"265724\", \"281219\"], \"outgoing_citations\": [\"316613\", \"336096\"]}","task_split":"paper_retrieval"}
{"document_id":"253382","document_content":"# User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Machine Learning\n- H.1.2; I.2.6; H.3.3\n## Abstract\nIn human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed the potential defects the designs can cause. Effective interaction often requires exposing the user to the training data or its statistics. The design of the system is then critical, as this can lead to double use of data and overfitting, if the user reinforces noisy patterns in the data. We propose a user modelling methodology, by assuming simple rational behaviour, to correct the problem. We show, in a user study with 48 participants, that the method improves predictive performance in a sparse linear regression sentiment analysis task, where graded user knowledge on feature relevance is elicited. We believe that the key idea of inferring user knowledge with probabilistic user models has general applicability in guarding against overfitting and improving interactive machine learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3172944.3172989\", \"primary_category\": \"cs.HC\", \"categories\": [\"stat.ML\", \"cs.LG\", \"H.1.2; I.2.6; H.3.3\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"H.1.2; I.2.6; H.3.3\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"153339\", \"218661\", \"158479\"], \"outgoing_citations\": [\"266898\", \"278295\", \"278418\", \"278496\"]}","task_split":"paper_retrieval"}
{"document_id":"253450","document_content":"# Auto Analysis of Customer Feedback using CNN and GRU Network\n## Categories\n- Computation and Language\n## Abstract\nAnalyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences in four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75% and third ranks for all the other languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.04600v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"211980\"], \"outgoing_citations\": [\"299195\", \"305094\"]}","task_split":"paper_retrieval"}
{"document_id":"253508","document_content":"# Emergent Translation in Multi-Agent Communication\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWhile most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monolingual, independent text and corresponding images. Our proposed translation model achieves this by grounding the source and target languages into a shared visual modality, and outperforms several baselines on both word-level and sentence-level translation tasks. Furthermore, we show that agents in a multilingual community learn to translate better and faster than in a bilingual communication setting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.06922v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"43939\", \"10004\", \"11920\", \"31598\", \"73552\", \"79670\", \"89663\", \"90658\", \"104577\", \"120519\", \"136313\", \"144255\", \"159883\", \"156535\", \"157257\", \"174277\", \"167598\", \"182237\", \"182716\", \"183327\", \"183718\", \"186337\", \"183787\", \"189799\", \"201667\", \"204914\", \"219639\", \"220351\", \"234993\", \"251816\", \"260746\", \"248549\"], \"outgoing_citations\": [\"252903\", \"260373\", \"260746\", \"262814\", \"263622\", \"264897\", \"265111\", \"266669\", \"267500\", \"268223\", \"271085\", \"271497\", \"274729\", \"274784\", \"277491\", \"280498\", \"280774\", \"281472\", \"284800\", \"290510\", \"291892\", \"300096\", \"302943\", \"303844\", \"305132\", \"311336\", \"313796\", \"314050\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"253622","document_content":"# Disentangled Representations via Synergy Minimization\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n## Abstract\nScientists often seek simplified representations of complex systems to facilitate prediction and understanding. If the factors comprising a representation allow us to make accurate predictions about our system, but obscuring any subset of the factors destroys our ability to make predictions, we say that the representation exhibits informational synergy. We argue that synergy is an undesirable feature in learned representations and that explicitly minimizing synergy can help disentangle the true factors of variation underlying data. We explore different ways of quantifying synergy, deriving new closed-form expressions in some cases, and then show how to modify learning to produce representations that are minimally synergistic. We introduce a benchmark task to disentangle separate characters from images of words. We demonstrate that Minimally Synergistic (MinSyn) representations correctly disentangle characters while methods relying on statistical independence fail.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.03839v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\"], \"incoming_citations\": [\"94373\", \"190475\", \"224731\", \"255246\"], \"outgoing_citations\": [\"255444\", \"273803\", \"281227\", \"322661\", \"328805\", \"358657\"]}","task_split":"paper_retrieval"}
{"document_id":"253638","document_content":"# Emergent Complexity via Multi-Agent Competition\n## Categories\n- Artificial Intelligence\n## Abstract\nReinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training. In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself. We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty. This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https:\/\/goo.gl\/eR7fbX","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.03748v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"146468\", \"4274\", \"6719\", \"23702\", \"23578\", \"47322\", \"83792\", \"89512\", \"120334\", \"131263\", \"4616\", \"11237\", \"13655\", \"17572\", \"19231\", \"26149\", \"35142\", \"35440\", \"37932\", \"41813\", \"44449\", \"52174\", \"63794\", \"75738\", \"76811\", \"84405\", \"85256\", \"97685\", \"100658\", \"120463\", \"132256\", \"158022\", \"166030\", \"166165\", \"151613\", \"68258\", \"81211\", \"81276\", \"84678\", \"85449\", \"99829\", \"100902\", \"108258\", \"109829\", \"117151\", \"118051\", \"119280\", \"120284\", \"122106\", \"126508\", \"128500\", \"133859\", \"135334\", \"136046\", \"136239\", \"137441\", \"137546\", \"137838\", \"141067\", \"141799\", \"142161\", \"143397\", \"143799\", \"144695\", \"146684\", \"150220\", \"161717\", \"166040\", \"166489\", \"177064\", \"184592\", \"185834\", \"217927\", \"183135\", \"151271\", \"152679\", \"151570\", \"153810\", \"160827\", \"145308\", \"214440\", \"175289\", \"175324\", \"179202\", \"179610\", \"181042\", \"181403\", \"184374\", \"184897\", \"164500\", \"192966\", \"196801\", \"196784\", \"198383\", \"198914\", \"200656\", \"201876\", \"203756\", \"205643\", \"206266\", \"206896\", \"204294\", \"208234\", \"215786\", \"216156\", \"218583\", \"223268\", \"205557\", \"207082\", \"213513\", \"210887\", \"218770\", \"219768\", \"223460\", \"224770\", \"225763\", \"236969\", \"228028\", \"235974\", \"235975\", \"245557\", \"253658\", \"225886\", \"251601\"], \"outgoing_citations\": [\"256103\", \"261780\", \"271375\", \"283216\", \"284459\", \"286531\", \"297186\", \"302395\"]}","task_split":"paper_retrieval"}
{"document_id":"253704","document_content":"# Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhen we say a person is texting, can you tell the person is walking or sitting? Emphatically, no. In order to solve this incomplete representation problem, this paper presents a sub-action descriptor for detailed action detection. The sub-action descriptor consists of three levels: the posture, the locomotion, and the gesture level. The three levels give three sub-action categories for one action to address the representation problem. The proposed action detection model simultaneously localizes and recognizes the actions of multiple individuals in video surveillance using appearance-based temporal features with multi-CNN. The proposed approach achieved a mean average precision (mAP) of 76.6% at the frame-based and 83.5% at the video-based measurement on the new large-scale ICVL video surveillance dataset that the authors introduce and make available to the community with this paper. Extensive experiments on the benchmark KTH dataset demonstrate that the proposed approach achieved better performance, which in turn boosts the action recognition performance over the state-of-the-art. The action detection model can run at around 25 fps on the ICVL and more than 80 fps on the KTH dataset, which is suitable for real-time surveillance applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.03383v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"174386\", \"193493\", \"212570\"], \"outgoing_citations\": [\"293790\", \"302753\", \"321340\", \"321453\"]}","task_split":"paper_retrieval"}
{"document_id":"253934","document_content":"# Contrastive Learning for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage captioning, a popular topic in computer vision, has achieved substantial progress in recent years. However, the distinctiveness of natural descriptions is often overlooked in previous work. It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects. In this work, we propose a new learning method, Contrastive Learning (CL), for image captioning. Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the overall quality of the generated captions. We tested our method on two challenging datasets, where it improves the baseline model by significant margins. We also showed in our studies that the proposed method is generic and can be used for models with various structures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.02534v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"36716\", \"42093\", \"93187\", \"19513\", \"21178\", \"28498\", \"40702\", \"42494\", \"44393\", \"55196\", \"55207\", \"56209\", \"61394\", \"71008\", \"77697\", \"110600\", \"175031\", \"80728\", \"88217\", \"90867\", \"94613\", \"95771\", \"100365\", \"110105\", \"111668\", \"112250\", \"125091\", \"132390\", \"135154\", \"167333\", \"153518\", \"174277\", \"189565\", \"195170\", \"208315\", \"218332\", \"205514\", \"211302\", \"213330\", \"223293\", \"223314\", \"223456\", \"232719\", \"236907\", \"238125\", \"239242\"], \"outgoing_citations\": [\"268356\", \"271234\", \"276436\", \"279002\", \"281152\", \"303833\", \"312704\", \"317410\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"254377","document_content":"# Mathematical foundations of matrix syntax\n## Categories\n- Computation and Language\n- Quantum Physics\n## Abstract\nMatrix syntax is a formal model of syntactic relations in language. The purpose of this paper is to explain its mathematical foundations, for an audience with some formal background. We make an axiomatic presentation, motivating each axiom on linguistic and practical grounds. The resulting mathematical structure resembles some aspects of quantum mechanics. Matrix syntax allows us to describe a number of language phenomena that are otherwise very difficult to explain, such as linguistic chains, and is arguably a more economical theory of language than most of the theories proposed in the context of the minimalist program in linguistics. In particular, sentences are naturally modelled as vectors in a Hilbert space with a tensor product structure, built from 2x2 matrices belonging to some specific group.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.00372v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"quant-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Quantum Physics\"], \"incoming_citations\": [], \"outgoing_citations\": [\"311199\"]}","task_split":"paper_retrieval"}
{"document_id":"254572","document_content":"# A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nThis paper proposes a novel neural machine reading model for open-domain question answering at scale. Existing machine comprehension models typically assume that a short piece of relevant text containing answers is already identified and given to the models, from which the models are designed to extract answers. This assumption, however, is not realistic for building a large-scale open-domain question answering system which requires both deep text understanding and identifying relevant text from corpus simultaneously.   In this paper, we introduce Neural Comprehensive Ranker (NCR) that integrates both passage ranking and answer extraction in one single framework. A Q&A system based on this framework allows users to issue an open-domain question without needing to provide a piece of text that must contain the answer. Experiments show that the unified NCR model is able to outperform the states-of-the-art in both retrieval of relevant text and answer extraction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.10204v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"254581","document_content":"# Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nWhile recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER's success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling - a task that has proven difficult for even state-of-the-art reinforcement learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.10163v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"193208\", \"163\", \"4773\", \"18315\", \"31773\", \"5616\", \"11431\", \"17680\", \"30094\", \"30309\", \"37526\", \"35842\", \"43409\", \"114456\", \"115937\", \"178146\", \"82620\", \"84429\", \"86153\", \"86323\", \"98034\", \"106567\", \"110680\", \"47007\", \"115266\", \"120473\", \"103314\", \"138947\", \"145848\", \"146535\", \"152542\", \"162530\", \"163867\", \"179214\", \"158642\", \"164292\", \"165614\", \"176970\", \"171783\", \"189110\", \"189959\", \"193829\", \"194175\", \"196851\", \"199302\", \"212788\", \"214004\", \"199330\", \"209452\", \"213745\", \"229145\", \"229347\", \"207983\", \"209453\", \"209926\", \"212704\", \"215939\", \"218582\", \"219954\", \"221550\", \"56246\", \"239105\", \"243197\"], \"outgoing_citations\": [\"263957\", \"309396\"]}","task_split":"paper_retrieval"}
{"document_id":"254641","document_content":"# Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network\n## Categories\n- Computation and Language\n## Abstract\nIn order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive\/negative) of a given document but also to identify the main words that contribute to the classification. However, most datasets for sentiment analysis only have the sentiment label for each document or sentence. In other words, there is no information about which words play an important role in sentiment classification. In this paper, we propose a method for identifying key words discriminating positive and negative sentences by using a weakly supervised learning method based on a convolutional neural network (CNN). In our model, each word is represented as a continuous-valued vector and each sentence is represented as a matrix whose rows correspond to the word vector used in the sentence. Then, the CNN model is trained using these sentence matrices as inputs and the sentiment labels as the output. Once the CNN model is trained, we implement the word attention mechanism that identifies high-contributing words to classification results with a class activation map, using the weights from the fully connected layer at the end of the learned CNN model. In order to verify the proposed methodology, we evaluated the classification accuracy and inclusion rate of polarity words using two movie review datasets. Experimental result show that the proposed model can not only correctly classify the sentence polarity but also successfully identify the corresponding words with high polarity scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.09885v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"95229\", \"223666\"], \"outgoing_citations\": [\"259865\", \"283115\", \"316584\", \"319781\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"254665","document_content":"# Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index (JA) of 0.765 on the 600 challenge testing images, which ranked itself in the first place in the challenge","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.09780v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"37102\", \"130639\", \"5510\", \"24040\", \"44185\", \"69054\", \"87658\", \"197166\", \"78152\", \"88708\", \"90077\", \"147593\", \"141137\", \"176840\", \"195957\", \"201512\", \"271595\", \"216827\", \"221612\", \"225412\", \"253463\"], \"outgoing_citations\": [\"271531\", \"271680\", \"272645\", \"292922\"]}","task_split":"paper_retrieval"}
{"document_id":"254674","document_content":"# Skin Lesion Segmentation: U-Nets versus Clustering\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMany automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. This paper presents, examines and compares two different approaches to skin lesion segmentation. The first approach uses U-Nets and introduces a histogram equalization based preprocessing step. The second approach is a C-Means clustering based approach that is much simpler to implement and faster to execute. The Jaccard Index between the algorithm output and hand segmented images by dermatologists is used to evaluate the proposed algorithms. While many recently proposed deep neural networks to segment skin lesions require a significant amount of computational power for training (i.e., computer with GPUs), the main objective of this paper is to present methods that can be used with only a CPU. This severely limits, for example, the number of training instances that can be presented to the U-Net. Comparing the two proposed algorithms, U-Nets achieved a significantly higher Jaccard Index compared to the clustering approach. Moreover, using the histogram equalization for preprocessing step significantly improved the U-Net segmentation results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1710.01248v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9090\", \"87658\", \"77847\", \"148569\", \"141137\", \"199366\", \"192055\", \"216014\", \"230522\", \"233665\"], \"outgoing_citations\": [\"271426\", \"272645\", \"273257\"]}","task_split":"paper_retrieval"}
{"document_id":"254777","document_content":"# SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine\n## Categories\n- Machine Learning\n## Abstract\nTraditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICMLA.2017.00-68\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"202298\"], \"outgoing_citations\": [\"268469\"]}","task_split":"paper_retrieval"}
{"document_id":"254850","document_content":"# Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages\n## Categories\n- Computation and Language\n- 68U15, 68M20\n## Abstract\nIn machine translation, we often try to collect resources to improve performance. However, most of the language pairs, such as Korean-Arabic and Korean-Vietnamese, do not have enough resources to train machine translation systems. In this paper, we propose the use of synthetic methods for extending a low-resource corpus and apply it to a multi-source neural machine translation model. We showed the improvement of machine translation performance through corpus extension using the synthetic method. We specifically focused on how to create source sentences that can make better target sentences, including the use of synthetic methods. We found that the corpus extension could also improve the performance of multi-source neural machine translation. We showed the corpus extension and multi-source model to be efficient methods for a low-resource language pair. Furthermore, when both methods were used together, we found better machine translation performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.08898v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68U15, 68M20\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68U15, 68M20\"], \"incoming_citations\": [\"22712\", \"48458\", \"148542\", \"186464\"], \"outgoing_citations\": [\"273586\", \"290510\", \"300548\"]}","task_split":"paper_retrieval"}
{"document_id":"254861","document_content":"# Object-oriented Neural Programming (OONP) for Document Understanding\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nWe propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.08853v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"22596\", \"210613\", \"213735\", \"219306\", \"219749\", \"225552\"], \"outgoing_citations\": [\"225552\", \"254241\", \"259525\", \"268177\", \"274658\", \"278163\", \"278430\", \"281158\", \"282530\", \"300505\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"255016","document_content":"# HDLTex: Hierarchical Deep Learning for Text Classification\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nThe continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of these traditional classifiers has degraded as the number of documents has increased. This is because along with this growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICMLA.2017.0-134\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\", \"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"146205\", \"1335\", \"8067\", \"19873\", \"22767\", \"56831\", \"58386\", \"74957\", \"86357\", \"122762\", \"36990\", \"132906\", \"71114\", \"87114\", \"90648\", \"92309\", \"96528\", \"101124\", \"103530\", \"103548\", \"108355\", \"119371\", \"119086\", \"121899\", \"126815\", \"131442\", \"132830\", \"140771\", \"141784\", \"144260\", \"145666\", \"191186\", \"210644\", \"158255\", \"165410\", \"190165\", \"203438\", \"232937\", \"213957\", \"214701\", \"211411\", \"220585\", \"233279\", \"254781\", \"255024\"], \"outgoing_citations\": [\"268089\", \"286735\", \"296702\", \"315605\", \"321035\"]}","task_split":"paper_retrieval"}
{"document_id":"255112","document_content":"# Real time text localization for Indoor Mobile Robot Navigation\n## Categories\n- Robotics\n- Image and Video Processing\n## Abstract\nScene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. In this paper, a novel two-step text localization method for Indoor Mobile Robot Navigation is introduced. This method is based on morphological operators and machine learning techniques and can be used in real time environments. The proposed method has two steps. At First, a new set of morphological operators is applied with a particular sequence to extract high contrast areas that have high probability of text existence. Using of morphological operators has many advantages such as: high computation speed, being invariant to several geometrical transformations like translation, rotations, and scaling, and being able to extract all areas containing text. After extracting text candidate regions, a set of nine features are extracted for accurate detection and deletion of the regions that don't have text. These features are descriptors for texture properties and are computed in real time. Then, we use a SVM classifier to detect the existence of text in the region. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.09634v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"eess.IV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Image and Video Processing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"255363","document_content":"# EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning\n## Categories\n- Artificial Intelligence\n## Abstract\nObjective: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support (CDS). Our objective is a general system that can extract and represent these knowledge contained in EMRs to support three CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation, with the given condition of one patient. Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a single record. Three bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each task. One part of the bi-graph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bi-graph was regarded as a Markov random field to support the inference. Three lazy energy functions and one parameter-based energy function were proposed, as well as two knowledge representation learning-based energy functions, which can provide a distributed representation of medical entities. Three measures were utilized for performance evaluation. Results: On the initial diagnosis task, 80.11% of the test records identified at least one correct disease from top 10 candidates. Test and treatment recommendation results were 87.88% and 92.55%, respectively. These results altogether indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships in regards to knowledge level. Conclusion: Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require designing their energy functions individually.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.06908v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"75089\", \"169111\", \"216902\"], \"outgoing_citations\": [\"279891\", \"281015\", \"298095\", \"303053\", \"327713\", \"350523\", \"353236\"]}","task_split":"paper_retrieval"}
{"document_id":"255492","document_content":"# Neural Networks for Text Correction and Completion in Keyboard Decoding\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDespite the ubiquity of mobile and wearable text messaging applications, the problem of keyboard text decoding is not tackled sufficiently in the light of the enormous success of the deep learning Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) for natural language understanding. In particular, considering that the keyboard decoders should operate on devices with memory and processor resource constraints, makes it challenging to deploy industrial scale deep neural network (DNN) models. This paper proposes a sequence-to-sequence neural attention network system for automatic text correction and completion. Given an erroneous sequence, our model encodes character level hidden representations and then decodes the revised sequence thus enabling auto-correction and completion. We achieve this by a combination of character level CNN and gated recurrent unit (GRU) encoder along with and a word level gated recurrent unit (GRU) attention decoder. Unlike traditional language models that learn from billions of words, our corpus size is only 12 million words; an order of magnitude smaller. The memory footprint of our learnt model for inference and prediction is also an order of magnitude smaller than the conventional language model based text decoders. We report baseline performance for neural keyboard decoders in such limited domain. Our models achieve a word level accuracy of $90\\%$ and a character error rate CER of $2.4\\%$ over the Twitter typo dataset. We present a novel dataset of noisy to corrected mappings by inducing the noise distribution from the Twitter data over the OpenSubtitles 2009 dataset; on which our model predicts with a word level accuracy of $98\\%$ and sequence accuracy of $68.9\\%$. In our user study, our model achieved an average CER of $2.6\\%$ with the state-of-the-art non-neural touch-screen keyboard decoder at CER of $1.6\\%$.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.06429v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"28559\", \"29490\", \"49927\", \"67194\", \"100291\", \"127282\", \"163875\", \"173076\", \"174564\", \"202018\", \"210868\", \"225284\"], \"outgoing_citations\": [\"259966\", \"261850\", \"295499\", \"302515\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"255689","document_content":"# A Hierarchical Probabilistic Model for Facial Feature Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial feature detection from facial images has attracted great attention in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose. The hierarchical model implicitly captures the lower level shape variations of facial components using the mixture model. Furthermore, in the higher level, it also learns the joint relationship among facial components, the facial expression, and the pose information through automatic structure learning and parameter estimation of the probabilistic model. Experimental results on benchmark databases demonstrate the effectiveness of the proposed hierarchical probabilistic model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.05732v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"133029\", \"232068\", \"323646\", \"303264\"], \"outgoing_citations\": [\"255690\"]}","task_split":"paper_retrieval"}
{"document_id":"255761","document_content":"# Role of Morphology Injection in Statistical Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nPhrase-based Statistical models are more commonly used as they perform optimally in terms of both, translation quality and complexity of the system. Hindi and in general all Indian languages are morphologically richer than English. Hence, even though Phrase-based systems perform very well for the less divergent language pairs, for English to Indian language translation, we need more linguistic information (such as morphology, parse tree, parts of speech tags, etc.) on the source side. Factored models seem to be useful in this case, as Factored models consider word as a vector of factors. These factors can contain any information about the surface word and use it while translating. Hence, the objective of this work is to handle morphological inflections in Hindi and Marathi using Factored translation models while translating from English. SMT approaches face the problem of data sparsity while translating into a morphologically rich language. It is very unlikely for a parallel corpus to contain all morphological forms of words. We propose a solution to generate these unseen morphological forms and inject them into original training corpora. In this paper, we study factored models and the problem of sparseness in context of translation to morphologically rich languages. We propose a simple and effective solution which is based on enriching the input with various morphological forms of words. We observe that morphology injection improves the quality of translation in terms of both adequacy and fluency. We verify this with the experiments on two morphologically rich languages: Hindi and Marathi, while translating from English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3129208\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271820\", \"272369\", \"272970\", \"276097\", \"334734\"]}","task_split":"paper_retrieval"}
{"document_id":"255792","document_content":"# \"How May I Help You?\": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts\n## Categories\n- Computation and Language\n## Abstract\nGiven the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained \"dialogue acts\" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3025171.3025191\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"50439\", \"81869\", \"98483\", \"169991\", \"224428\", \"239670\"], \"outgoing_citations\": [\"292429\", \"292436\"]}","task_split":"paper_retrieval"}
{"document_id":"255794","document_content":"# Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue\n## Categories\n- Computation and Language\n## Abstract\nThe greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. In order to make coherent conversational contributions in this context, a conversational agent must be able to track the types and attributes of the entities under discussion in the conversation and know how they are related. In some cases, the agent can rely on structured information sources to help identify the relevant semantic relations and produce a turn, but in other cases, the only content available comes from search, and it may be unclear which semantic relations hold between the search results and the discourse context. A further constraint is that the system must produce its contribution to the ongoing conversation in real-time. This paper describes our experience building SlugBot for the 2017 Alexa Prize, and discusses how we leveraged search and structured data from different sources to help SlugBot produce dialogic turns and carry on conversations whose length over the semi-finals user evaluation period averaged 8:17 minutes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.05411v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"99497\", \"164277\", \"174962\", \"176973\", \"207388\", \"232596\", \"251100\", \"256391\"], \"outgoing_citations\": [\"257773\", \"266411\", \"291100\"]}","task_split":"paper_retrieval"}
{"document_id":"255810","document_content":"# A Rule-Based Approach to Analyzing Database Schema Objects with Datalog\n## Categories\n- Programming Languages\n- Databases\n- Logic in Computer Science\n## Abstract\nDatabase schema elements such as tables, views, triggers and functions are typically defined with many interrelationships. In order to support database users in understanding a given schema, a rule-based approach for analyzing the respective dependencies is proposed using Datalog expressions. We show that many interesting properties of schema elements can be systematically determined this way. The expressiveness of the proposed analysis is exemplarily shown with the problem of computing induced functional dependencies for derived relations. The propagation of functional dependencies plays an important role in data integration and query optimization but represents an undecidable problem in general. And yet, our rule-based analysis covers all relational operators as well as linear recursive expressions in a systematic way showing the depth of analysis possible by our proposal. The analysis of functional dependencies is well-integrated in a uniform approach to analyzing dependencies between schema elements in general.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.05376v1\", \"primary_category\": \"cs.PL\", \"categories\": [\"cs.PL\", \"cs.DB\", \"cs.LO\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"Programming Languages\", \"Databases\", \"Logic in Computer Science\"], \"incoming_citations\": [\"169392\"], \"outgoing_citations\": [\"286288\"]}","task_split":"paper_retrieval"}
{"document_id":"255898","document_content":"# Deep Reinforcement Learning for Conversational AI\n## Categories\n- Artificial Intelligence\n## Abstract\nDeep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems which offer a better knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels in playing video games. In this paper, key concepts of deep reinforcement learning including reward function, differences between reinforcement learning and supervised learning and models for implementation of reinforcement are discussed. Key challenges related to the implementation of reinforcement learning in conversational AI domain are identified as well as discussed in detail. Various conversational models which are based on deep reinforcement learning (as well as deep learning) are also discussed. In summary, this paper discusses key aspects of deep reinforcement learning which are crucial for designing an efficient conversational AI.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.05067v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"190169\"], \"outgoing_citations\": [\"296250\", \"302534\", \"304343\"]}","task_split":"paper_retrieval"}
{"document_id":"256038","document_content":"# Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest\n## Categories\n- Human-Computer Interaction\n- Systems and Control\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nReinforcement Learning AI commonly uses reward\/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective -- e.g. human preferences for certain AI behavior -- in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions. Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual's level of interest in objects\/events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novelly, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20\\% viewing time for subjectively interesting objects. This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.04574v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.SY\", \"stat.ML\", \"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Systems and Control\", \"Machine Learning\", \"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"256171","document_content":"# Variational Reasoning for Question Answering with Knowledge Graph\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nKnowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.04071v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"10264\", \"12316\", \"13997\", \"17151\", \"18380\", \"18706\", \"22719\", \"29168\", \"29573\", \"29824\", \"35818\", \"97239\", \"97239\", \"98406\", \"110411\", \"113490\", \"144530\", \"92\", \"1631\", \"2359\", \"2987\", \"4719\", \"8268\", \"9487\", \"10232\", \"12774\", \"13340\", \"22624\", \"24844\", \"28745\", \"36194\", \"37727\", \"37915\", \"45225\", \"47547\", \"50364\", \"53317\", \"55392\", \"55916\", \"55682\", \"56538\", \"58427\", \"63245\", \"69510\", \"74933\", \"75751\", \"76113\", \"78444\", \"79328\", \"80138\", \"80091\", \"94087\", \"98369\", \"100407\", \"110400\", \"121488\", \"104940\", \"95011\", \"79529\", \"84203\", \"85809\", \"89596\", \"88358\", \"92228\", \"92389\", \"96728\", \"98162\", \"98561\", \"104775\", \"115387\", \"130961\", \"132413\", \"133009\", \"137043\", \"140317\", \"142243\", \"150226\", \"156419\", \"161119\", \"165809\", \"170070\", \"175493\", \"184694\", \"185104\", \"170709\", \"188009\", \"191253\", \"192346\", \"189600\", \"192722\", \"197395\", \"219849\", \"256639\", \"212085\", \"209647\", \"229275\", \"229792\", \"238635\", \"246808\"], \"outgoing_citations\": [\"279428\", \"281472\", \"283927\", \"290747\", \"296356\", \"302771\", \"303863\", \"308610\", \"310639\", \"311834\", \"313944\", \"324358\", \"328352\", \"334299\"]}","task_split":"paper_retrieval"}
{"document_id":"256193","document_content":"# Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds\n## Categories\n- Artificial Intelligence\n## Abstract\nWe describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.03969v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"54834\", \"173229\", \"175055\", \"186139\", \"187845\", \"196851\", \"198521\", \"209926\", \"243197\"], \"outgoing_citations\": [\"210901\", \"263957\", \"291568\", \"297268\"]}","task_split":"paper_retrieval"}
{"document_id":"256204","document_content":"# Human Associations Help to Detect Conventionalized Multiword Expressions\n## Categories\n- Computation and Language\n## Abstract\nIn this paper we show that if we want to obtain human evidence about conventionalization of some phrases, we should ask native speakers about associations they have to a given phrase and its component words. We have shown that if component words of a phrase have each other as frequent associations, then this phrase can be considered as conventionalized. Another type of conventionalized phrases can be revealed using two factors: low entropy of phrase associations and low intersection of component word and phrase associations. The association experiments were performed for the Russian language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.03925v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"256323","document_content":"# Recovering Homography from Camera Captured Documents using Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRemoving perspective distortion from hand held camera captured document images is one of the primitive tasks in document analysis, but unfortunately, no such method exists that can reliably remove the perspective distortion from document images automatically. In this paper, we propose a convolutional neural network based method for recovering homography from hand-held camera captured documents.   Our proposed method works independent of document's underlying content and is trained end-to-end in a fully automatic way. Specifically, this paper makes following three contributions: Firstly, we introduce a large scale synthetic dataset for recovering homography from documents images captured under different geometric and photometric transformations; secondly, we show that a generic convolutional neural network based architecture can be successfully used for regressing the corners positions of documents captured under wild settings; thirdly, we show that L1 loss can be reliably used for corners regression. Our proposed method gives state-of-the-art performance on the tested datasets, and has potential to become an integral part of document analysis pipeline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.03524v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"90469\"], \"outgoing_citations\": [\"290584\", \"319888\"]}","task_split":"paper_retrieval"}
{"document_id":"256347","document_content":"# Stack-Captioning: Coarse-to-Fine Learning for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe existing image captioning approaches typically train a one-stage sentence decoder, which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem. In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions. Our proposed learning approach addresses the difficulty of vanishing gradients during training by providing a learning objective function that enforces intermediate supervisions. Particularly, we optimize our model with a reinforcement learning approach which utilizes the output of each intermediate decoder's test-time inference algorithm as well as the output of its preceding decoder to normalize the rewards, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem. We extensively evaluate the proposed approach on MSCOCO and show that our approach can achieve the state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.03376v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23109\", \"35427\", \"6697\", \"182259\", \"20379\", \"36154\", \"38068\", \"71008\", \"110600\", \"175031\", \"80728\", \"97743\", \"112250\", \"133740\", \"136245\", \"138218\", \"147001\", \"168443\", \"158839\", \"160664\", \"172811\", \"175214\", \"176583\", \"178885\", \"188386\", \"189964\", \"191680\", \"192659\", \"192948\", \"193738\", \"207034\", \"231619\", \"208118\", \"217551\", \"221169\", \"222670\", \"238125\", \"238372\", \"238941\", \"239242\", \"250183\", \"301087\"], \"outgoing_citations\": [\"273718\", \"274450\", \"277511\", \"279002\", \"281152\", \"281749\", \"283732\", \"284230\", \"287823\", \"289906\", \"290766\", \"291851\", \"311824\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"256389","document_content":"# Deep multi-frame face super-resolution\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace verification and recognition problems have seen rapid progress in recent years, however recognition from small size images remains a challenging task that is inherently intertwined with the task of face super-resolution. Tackling this problem using multiple frames is an attractive idea, yet requires solving the alignment problem that is also challenging for low-resolution faces. Here we present a holistic system for multi-frame recognition, alignment, and superresolution of faces. Our neural network architecture restores the central frame of each input sequence additionally taking into account a number of adjacent frames and making use of sub-pixel movements. We present our results using the popular dataset for video face recognition (YouTube Faces). We show a notable improvement of identification score compared to several baselines including the one based on single-image super-resolution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.03196v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"76110\", \"18166\", \"73272\"], \"outgoing_citations\": [\"269321\", \"275999\", \"288238\", \"288883\", \"296033\"]}","task_split":"paper_retrieval"}
{"document_id":"256422","document_content":"# A New Semantic Theory of Natural Language\n## Categories\n- Computation and Language\n- Logic in Computer Science\n## Abstract\nFormal Semantics and Distributional Semantics are two important semantic frameworks in Natural Language Processing (NLP). Cognitive Semantics belongs to the movement of Cognitive Linguistics, which is based on contemporary cognitive science. Each framework could deal with some meaning phenomena, but none of them fulfills all requirements proposed by applications. A unified semantic theory characterizing all important language phenomena has both theoretical and practical significance; however, although many attempts have been made in recent years, no existing theory has achieved this goal yet.   This article introduces a new semantic theory that has the potential to characterize most of the important meaning phenomena of natural language and to fulfill most of the necessary requirements for philosophical analysis and for NLP applications. The theory is based on a unified representation of information, and constructs a kind of mathematical model called cognitive model to interpret natural language expressions in a compositional manner. It accepts the empirical assumption of Cognitive Semantics, and overcomes most shortcomings of Formal Semantics and of Distributional Semantics. The theory, however, is not a simple combination of existing theories, but an extensive generalization of classic logic and Formal Semantics. It inherits nearly all advantages of Formal Semantics, and also provides descriptive contents for objects and events as fine-gram as possible, descriptive contents which represent the results of human cognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.04857v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"308446\", \"312264\"]}","task_split":"paper_retrieval"}
{"document_id":"256711","document_content":"# Focusing Attention: Towards Accurate Text Recognition in Natural Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and\/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon \"attention drift\". To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICCV.2017.543\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38757\", \"41858\", \"1039\", \"3394\", \"37135\", \"38506\", \"50775\", \"59368\", \"44535\", \"180823\", \"191\", \"7580\", \"8109\", \"9220\", \"10204\", \"24824\", \"28123\", \"28270\", \"29428\", \"29777\", \"33358\", \"33360\", \"33363\", \"33798\", \"35666\", \"39759\", \"42497\", \"43153\", \"48901\", \"49128\", \"49805\", \"50591\", \"51408\", \"53750\", \"58277\", \"63761\", \"67749\", \"78142\", \"79422\", \"81960\", \"93345\", \"113794\", \"116951\", \"122764\", \"133054\", \"147463\", \"163092\", \"93470\", \"99077\", \"100417\", \"100709\", \"108209\", \"110820\", \"111035\", \"111991\", \"115073\", \"115614\", \"120420\", \"122765\", \"122763\", \"123550\", \"126244\", \"126431\", \"126484\", \"134664\", \"135021\", \"136218\", \"136843\", \"136831\", \"140021\", \"141887\", \"144173\", \"145587\", \"149329\", \"150060\", \"152219\", \"158242\", \"162477\", \"170229\", \"189663\", \"192671\", \"202305\", \"210634\", \"161220\", \"162650\", \"166353\", \"166621\", \"169411\", \"169711\", \"170332\", \"172884\", \"173837\", \"174894\", \"174928\", \"180931\", \"176394\", \"190056\", \"192483\", \"195963\", \"201515\", \"203406\", \"205988\", \"211851\", \"217463\", \"222513\", \"229426\", \"248491\", \"205602\", \"202645\", \"207071\", \"229565\", \"232706\", \"250738\"], \"outgoing_citations\": [\"284201\", \"296685\", \"296828\", \"320148\", \"320837\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"256720","document_content":"# CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Information Theory\n- Information Theory\n## Abstract\nWe propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph. We consider the application of generating faces based on given binary labels where the dependency structure between the labels is preserved with a causal graph. This problem can be seen as learning a causal implicit generative model for the image and labels. We devise a two-stage procedure for this problem. First we train a causal implicit generative model over binary labels using a neural network consistent with a causal graph as the generator. We empirically show that WassersteinGAN can be used to output discrete labels. Later, we propose two new conditional GAN architectures, which we call CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained causal implicit generative model for the labels is then a causal implicit generative model over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.02023v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.IT\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [\"25753\", \"45506\", \"146468\", \"3852\", \"7525\", \"13518\", \"24683\", \"27322\", \"34155\", \"34142\", \"100066\", \"103338\", \"13184\", \"13915\", \"14297\", \"28568\", \"28691\", \"38067\", \"38520\", \"38686\", \"39620\", \"47930\", \"54329\", \"60882\", \"65007\", \"65077\", \"75048\", \"75323\", \"85085\", \"112751\", \"133796\", \"183668\", \"78162\", \"86777\", \"95048\", \"96305\", \"104007\", \"105780\", \"110678\", \"118559\", \"118936\", \"119435\", \"116023\", \"130407\", \"139684\", \"143390\", \"164814\", \"251849\", \"192419\", \"163450\", \"165956\", \"175837\", \"177520\", \"179193\", \"186062\", \"196971\", \"199971\", \"211010\", \"213949\", \"231181\", \"232872\", \"233751\", \"238468\", \"133817\", \"246873\"], \"outgoing_citations\": [\"265767\", \"267196\", \"268454\", \"270105\", \"271912\", \"272047\", \"274550\", \"282844\", \"291302\", \"291800\", \"304236\", \"310089\", \"358665\"]}","task_split":"paper_retrieval"}
{"document_id":"256775","document_content":"# Automatic Document Image Binarization using Bayesian Optimization\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n## Abstract\nDocument image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3151509.3151520\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CV\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"256772\", \"247777\", \"249617\"], \"outgoing_citations\": [\"344874\"]}","task_split":"paper_retrieval"}
{"document_id":"256823","document_content":"# PageNet: Page Boundary Extraction in Historical Handwritten Documents\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhen digitizing a document into an image, it is common to include a surrounding border region to visually indicate that the entire document is present in the image. However, this border should be removed prior to automated processing. In this work, we present a deep learning based system, PageNet, which identifies the main page region in an image in order to segment content from both textual and non-textual border noise. In PageNet, a Fully Convolutional Network obtains a pixel-wise segmentation which is post-processed into the output quadrilateral region. We evaluate PageNet on 4 collections of historical handwritten documents and obtain over 94% mean intersection over union on all datasets and approach human performance on 2 of these collections. Additionally, we show that PageNet can segment documents that are overlayed on top of other documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.01618v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"127125\", \"242784\", \"234009\", \"243851\"], \"outgoing_citations\": [\"258854\", \"266895\", \"269649\"]}","task_split":"paper_retrieval"}
{"document_id":"256883","document_content":"# Predicting Visual Features from Text for Image and Video Caption Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute \\emph{Word2VisualVec}, a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multi-scale sentence vectorization and further transferred into a deep visual feature of choice via a simple multi-layer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both 3-D convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition and its state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TMM.2018.2832602\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2284\", \"5478\", \"60281\", \"101354\", \"99220\", \"12778\", \"33374\", \"45218\", \"54595\", \"56947\", \"59840\", \"62053\", \"62053\", \"62318\", \"64186\", \"67133\", \"72065\", \"84992\", \"105055\", \"110600\", \"111075\", \"132341\", \"154664\", \"173243\", \"191408\", \"81724\", \"87175\", \"107573\", \"107961\", \"114022\", \"118354\", \"95983\", \"133926\", \"139259\", \"70437\", \"149248\", \"223463\", \"150578\", \"172394\", \"173627\", \"193503\", \"217562\", \"231022\"], \"outgoing_citations\": [\"282943\", \"286978\", \"290549\", \"295307\", \"297769\", \"301594\", \"302943\", \"303011\", \"303715\", \"304481\", \"304596\", \"310136\", \"311619\", \"311945\", \"312643\", \"312704\", \"313189\", \"314050\", \"320003\", \"320332\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"256935","document_content":"# Learning Neural Word Salience Scores\n## Categories\n- Computation and Language\n## Abstract\nMeasuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. We propose \\emph{Neural Word Salience} (NWS) scores, unlike heuristics, are learnt from a corpus. Specifically, we learn word salience scores such that, using pre-trained word embeddings as the input, can accurately predict the words that appear in a sentence, given the words that appear in the sentences preceding or succeeding that sentence. Experimental results on sentence similarity prediction show that the learnt word salience scores perform comparably or better than some of the state-of-the-art approaches for representing sentences on benchmark datasets for sentence similarity, while using only a fraction of the training and prediction times required by prior methods. Moreover, our NWS scores positively correlate with psycholinguistic measures such as concreteness, and imageability implying a close connection to the salience as perceived by humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.01186v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"93972\", \"132161\"], \"outgoing_citations\": [\"286282\", \"286594\", \"290387\", \"298543\", \"301397\", \"302516\"]}","task_split":"paper_retrieval"}
{"document_id":"257115","document_content":"# Patterns versus Characters in Subword-aware Neural Language Modeling\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWords in some natural languages can have a composite structure. Elements of this structure include the root (that could also be composite), prefixes and suffixes with which various nuances and relations to other words can be expressed. Thus, in order to build a proper word representation one must take into account its internal structure. From a corpus of texts we extract a set of frequent subwords and from the latter set we select patterns, i.e. subwords which encapsulate information on character $n$-gram regularities. The selection is made using the pattern-based Conditional Random Field model with $l_1$ regularization. Further, for every word we construct a new sequence over an alphabet of patterns. The new alphabet's symbols confine a local statistical context stronger than the characters, therefore they allow better representations in ${\\mathbb{R}}^n$ and are better building blocks for word representation. In the task of subword-aware language modeling, pattern-based models outperform character-based analogues by 2-20 perplexity points. Also, a recurrent neural network in which a word is represented as a sum of embeddings of its patterns is on par with a competitive and significantly more sophisticated character-based convolutional architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.00541v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"330774\"], \"outgoing_citations\": [\"269294\", \"278312\", \"280556\", \"288551\", \"288697\", \"308310\", \"329561\", \"353946\"]}","task_split":"paper_retrieval"}
{"document_id":"257150","document_content":"# Query-by-example Spoken Term Detection using Attention-based Multi-hop Networks\n## Categories\n- Computation and Language\n- Multimedia\n## Abstract\nRetrieving spoken content with spoken queries, or query-by- example spoken term detection (STD), is attractive because it makes possible the matching of signals directly on the acoustic level without transcribing them into text. Here, we propose an end-to-end query-by-example STD model based on an attention-based multi-hop network, whose input is a spoken query and an audio segment containing several utterances; the output states whether the audio segment includes the query. The model can be trained in either a supervised scenario using labeled data, or in an unsupervised fashion. In the supervised scenario, we find that the attention mechanism and multiple hops improve performance, and that the attention weights indicate the time span of the detected terms. In the unsupervised setting, the model mimics the behavior of the existing query-by-example STD system, yielding performance comparable to the existing system but with a lower search time complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.00354v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"19029\", \"69502\", \"82887\", \"155489\", \"245459\"], \"outgoing_citations\": [\"263944\", \"276243\", \"280501\", \"281162\", \"285849\", \"297133\", \"297211\", \"302662\", \"303047\", \"303224\", \"305536\", \"310639\", \"311684\"]}","task_split":"paper_retrieval"}
{"document_id":"257192","document_content":"# Learning what to read: Focused machine reading\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- H.3.3; I.2.6; I.2.7\n## Abstract\nRecent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today's scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.00149v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.IR\", \"cs.LG\", \"H.3.3; I.2.6; I.2.7\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"H.3.3; I.2.6; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": [\"283809\", \"295884\"]}","task_split":"paper_retrieval"}
{"document_id":"257197","document_content":"# Single Shot Text Detector with Regional Attention\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN- based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allow- ing the detector to work reliably on multi-scale and multi- orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http:\/\/sstd.whuang.org\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1709.00138v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155242\", \"186590\", \"142099\", \"9503\", \"14003\", \"51497\", \"52248\", \"59368\", \"61613\", \"66255\", \"83916\", \"101165\", \"180823\", \"25132\", \"35003\", \"35666\", \"43153\", \"50136\", \"81960\", \"113025\", \"150258\", \"155848\", \"184587\", \"59016\", \"80437\", \"105122\", \"120420\", \"122763\", \"122981\", \"126431\", \"139566\", \"140006\", \"144964\", \"154248\", \"155530\", \"174062\", \"209980\", \"210634\", \"150452\", \"161220\", \"166479\", \"167606\", \"169067\", \"170332\", \"171543\", \"171686\", \"174928\", \"181035\", \"186748\", \"190708\", \"192471\", \"193382\", \"199370\", \"187750\", \"201515\", \"203525\", \"203946\", \"202755\", \"207348\", \"209227\", \"209860\", \"218589\", \"236404\", \"247042\", \"204166\", \"209603\", \"219278\", \"221419\", \"223824\", \"225195\", \"225336\", \"225795\", \"232509\", \"239002\", \"239535\", \"240965\", \"243851\", \"245899\", \"250729\"], \"outgoing_citations\": [\"271112\", \"272390\", \"279950\", \"284871\", \"289389\", \"293979\", \"294606\", \"295564\", \"299461\", \"305162\", \"311196\", \"314541\", \"320837\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"257267","document_content":"# Generating Video Descriptions with Topic Guidance\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nGenerating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.09666v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"126319\", \"221655\", \"227052\", \"237150\", \"257266\"], \"outgoing_citations\": [\"279676\", \"283145\", \"303593\", \"311824\", \"313189\", \"316803\", \"317592\", \"320332\"]}","task_split":"paper_retrieval"}
{"document_id":"257316","document_content":"# Inference of Fine-Grained Event Causality from Blogs and Films\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nHuman understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.09453v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"45157\", \"45154\", \"81499\", \"210155\", \"236516\"], \"outgoing_citations\": [\"257307\", \"257317\"]}","task_split":"paper_retrieval"}
{"document_id":"257375","document_content":"# Cross-lingual, Character-Level Neural Morphological Tagging\n## Categories\n- Computation and Language\n## Abstract\nEven for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30%","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.09157v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42229\", \"38425\", \"44997\", \"48496\", \"62806\", \"89736\", \"144897\", \"103442\", \"115664\", \"123585\", \"145018\", \"138850\", \"204970\", \"164680\", \"164657\", \"174756\", \"159372\", \"181569\", \"182560\", \"183673\", \"210680\", \"231291\", \"209217\", \"212472\", \"214151\", \"214875\", \"228573\", \"223563\", \"232377\"], \"outgoing_citations\": [\"260929\", \"262705\", \"270019\", \"289895\", \"290475\", \"294248\", \"300548\", \"300604\", \"301264\", \"308310\", \"308559\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"257653","document_content":"# Machine Translation in Indian Languages: Challenges and Resolution\n## Categories\n- Computation and Language\n## Abstract\nEnglish to Indian language machine translation poses the challenge of structural and morphological divergence. This paper describes English to Indian language statistical machine translation using pre-ordering and suffix separation. The pre-ordering uses rules to transfer the structure of the source sentences prior to training and translation. This syntactic restructuring helps statistical machine translation to tackle the structural divergence and hence better translation quality. The suffix separation is used to tackle the morphological divergence between English and highly agglutinative Indian languages. We demonstrate that the use of pre-ordering and suffix separation helps in improving the quality of English to Indian Language machine translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.07950v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"81356\", \"130283\"], \"outgoing_citations\": [\"281942\", \"282068\", \"282070\", \"334924\", \"348982\"]}","task_split":"paper_retrieval"}
{"document_id":"257674","document_content":"# Deep Learning for Video Game Playing\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.07902v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"34882\", \"109960\", \"216474\", \"6787\", \"22613\", \"35498\", \"52723\", \"53818\", \"55110\", \"64820\", \"83991\", \"87353\", \"103481\", \"126018\", \"142583\", \"165015\", \"173946\", \"99903\", \"100245\", \"103422\", \"106665\", \"111576\", \"114433\", \"115425\", \"126621\", \"140395\", \"145405\", \"145664\", \"168254\", \"177329\", \"149805\", \"160827\", \"166184\", \"172062\", \"188651\", \"195631\", \"229302\", \"226544\", \"238475\", \"239033\", \"246042\"], \"outgoing_citations\": [\"217261\", \"218923\", \"219180\", \"226321\", \"226544\", \"229002\", \"229302\", \"230067\", \"233501\", \"240233\", \"243235\", \"247158\", \"247163\", \"255613\", \"256330\", \"258384\", \"258472\", \"260583\", \"261316\", \"261391\", \"261427\", \"262106\", \"262325\", \"262381\", \"263811\", \"263852\", \"263957\", \"265505\", \"266342\", \"268226\", \"268584\", \"270254\", \"270315\", \"271783\", \"271868\", \"272422\", \"272788\", \"274315\", \"274886\", \"275087\", \"277472\", \"278195\", \"278999\", \"280296\", \"280681\", \"280995\", \"281110\", \"281257\", \"281484\", \"284468\", \"285018\", \"286522\", \"290378\", \"291025\", \"291568\", \"293071\", \"293876\", \"293995\", \"294142\", \"297162\", \"298281\", \"298691\", \"302395\", \"302659\", \"302660\", \"302952\", \"302967\", \"303349\", \"303459\", \"308711\", \"309111\", \"309498\", \"310370\", \"313499\", \"322672\", \"322930\"]}","task_split":"paper_retrieval"}
{"document_id":"257782","document_content":"# Differentially Private Regression for Discrete-Time Survival Analysis\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Databases\n## Abstract\nIn survival analysis, regression models are used to understand the effects of explanatory variables (e.g., age, sex, weight, etc.) to the survival probability. However, for sensitive survival data such as medical data, there are serious concerns about the privacy of individuals in the data set when medical data is used to fit the regression models. The closest work addressing such privacy concerns is the work on Cox regression which linearly projects the original data to a lower dimensional space. However, the weakness of this approach is that there is no formal privacy guarantee for such projection. In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research. To this end, we extend the Output Perturbation and Objective Perturbation approaches which are originally proposed to protect differential privacy for the Empirical Risk Minimization (ERM) problems. In addition, we also propose a novel sampling approach based on the Markov Chain Monte Carlo (MCMC) method to practically guarantee differential privacy with better accuracy. We show that our proposed approaches achieve good accuracy as compared to the non-private results while guaranteeing differential privacy for individuals in the private data set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.07436v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DB\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Databases\"], \"incoming_citations\": [\"119079\", \"163352\", \"194154\", \"196881\", \"219058\"], \"outgoing_citations\": [\"310836\", \"316862\", \"329116\", \"333529\"]}","task_split":"paper_retrieval"}
{"document_id":"257863","document_content":"# Automatic Detection of Fake News\n## Categories\n- Computation and Language\n## Abstract\nThe proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.07104v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23929\", \"96167\", \"12996\", \"23900\", \"27370\", \"29754\", \"77332\", \"96562\", \"146205\", \"3290\", \"8211\", \"9054\", \"11039\", \"11296\", \"12331\", \"20841\", \"22245\", \"24508\", \"25588\", \"27087\", \"27906\", \"30084\", \"30489\", \"30746\", \"31108\", \"35460\", \"37995\", \"41467\", \"98128\", \"47955\", \"49171\", \"49745\", \"53415\", \"54565\", \"48118\", \"57020\", \"57242\", \"59882\", \"63135\", \"66278\", \"69882\", \"71581\", \"73741\", \"75850\", \"81800\", \"92760\", \"127795\", \"143957\", \"144292\", \"196472\", \"207715\", \"71626\", \"86946\", \"88412\", \"88762\", \"73181\", \"94175\", \"97922\", \"98134\", \"40535\", \"103491\", \"105795\", \"106017\", \"80065\", \"110452\", \"113687\", \"116558\", \"117359\", \"118008\", \"120949\", \"121634\", \"125952\", \"127881\", \"130351\", \"134932\", \"136816\", \"139429\", \"141447\", \"148297\", \"149300\", \"158439\", \"160668\", \"170123\", \"211902\", \"125843\", \"163237\", \"168163\", \"168778\", \"170134\", \"181527\", \"183694\", \"184582\", \"184696\", \"187975\", \"188875\", \"192954\", \"194688\", \"197826\", \"198258\", \"199511\", \"202453\", \"209764\", \"211869\", \"205046\", \"210466\", \"212192\", \"215771\", \"212740\", \"234529\", \"241503\", \"253082\"], \"outgoing_citations\": [\"238986\", \"267552\", \"273723\"]}","task_split":"paper_retrieval"}
{"document_id":"257989","document_content":"# Long-Short Range Context Neural Networks for Language Modeling\n## Categories\n- Computation and Language\n- Machine Learning\n- 97K50\n- I.2.7\n## Abstract\nThe goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and\/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.06555v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"97K50\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"97K50\", \"I.2.7\"], \"incoming_citations\": [\"258145\"], \"outgoing_citations\": [\"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"258190","document_content":"# Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient on-board processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.05884v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"152072\", \"108608\", \"185463\", \"207119\", \"189895\", \"205445\", \"214253\", \"240173\", \"227125\", \"229726\"], \"outgoing_citations\": [\"267104\", \"280570\", \"281110\", \"292178\", \"297162\", \"303342\", \"308535\"]}","task_split":"paper_retrieval"}
{"document_id":"258199","document_content":"# A Brief Survey of Deep Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MSP.2017.2743240\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"1846\", \"18851\", \"28573\", \"63392\", \"1694\", \"8805\", \"13681\", \"18199\", \"18980\", \"202768\", \"25651\", \"35360\", \"36251\", \"41898\", \"43280\", \"46421\", \"46728\", \"52813\", \"90723\", \"97395\", \"115868\", \"150685\", \"7499\", \"7811\", \"8460\", \"9067\", \"11406\", \"17665\", \"17809\", \"31177\", \"651\", \"32459\", \"35719\", \"36970\", \"37786\", \"38337\", \"40343\", \"42423\", \"51300\", \"53434\", \"55470\", \"236721\", \"77117\", \"93649\", \"99493\", \"109449\", \"121493\", \"123903\", \"124182\", \"131268\", \"132483\", \"156559\", \"229300\", \"84442\", \"94703\", \"94892\", \"89277\", \"98682\", \"99649\", \"100245\", \"105127\", \"105397\", \"107467\", \"109226\", \"109567\", \"109874\", \"110649\", \"113915\", \"47007\", \"119067\", \"123726\", \"123679\", \"125378\", \"127174\", \"129043\", \"130732\", \"134261\", \"134432\", \"137302\", \"140619\", \"143049\", \"148085\", \"148308\", \"152524\", \"164869\", \"175196\", \"204248\", \"210375\", \"209307\", \"230774\", \"148123\", \"159934\", \"160827\", \"138185\", \"166532\", \"214440\", \"170265\", \"170300\", \"174225\", \"175034\", \"176825\", \"178004\", \"180861\", \"186850\", \"187147\", \"188362\", \"190141\", \"192015\", \"193342\", \"194168\", \"195095\", \"195772\", \"196361\", \"213852\", \"216414\", \"217794\", \"218471\", \"235799\", \"239264\", \"241762\", \"206900\", \"207205\", \"212950\", \"213308\", \"214866\", \"215511\", \"219167\", \"220513\", \"221364\", \"228465\", \"230637\", \"234850\", \"235227\", \"236885\", \"240158\", \"240484\", \"244859\", \"245828\", \"247900\", \"248619\", \"255357\", \"246975\", \"248816\", \"255332\"], \"outgoing_citations\": [\"258472\", \"259045\", \"260583\", \"260770\", \"260780\", \"261316\", \"263811\", \"264786\", \"266490\", \"268361\", \"269458\", \"270254\", \"272222\", \"272298\", \"272464\", \"272785\", \"272930\", \"274315\", \"278545\", \"280202\", \"280296\", \"280457\", \"280681\", \"280870\", \"280992\", \"281091\", \"281257\", \"281484\", \"282684\", \"282841\", \"284470\", \"284567\", \"285018\", \"287823\", \"290371\", \"290825\", \"290904\", \"291460\", \"291568\", \"291892\", \"293071\", \"293702\", \"293876\", \"294142\", \"297186\", \"297268\", \"298281\", \"298651\", \"298691\", \"301502\", \"302001\", \"302952\", \"302967\", \"304250\", \"305802\", \"306266\", \"306812\", \"308711\", \"309396\", \"309498\", \"310171\", \"310624\", \"311008\", \"317863\", \"322636\"]}","task_split":"paper_retrieval"}
{"document_id":"258271","document_content":"# Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLine separators are used to segregate text-lines from one another in document image analysis. Finding the separator points at every line terminal in a document image would enable text-line segmentation. In particular, identifying the separators in handwritten text could be a thrilling exercise. Obviously it would be challenging to perform this in the compressed version of a document image and that is the proposed objective in this research. Such an effort would prevent the computational burden of decompressing a document for text-line segmentation. Since document images are generally compressed using run length encoding (RLE) technique as per the CCITT standards, the first column in the RLE will be a white column. The value (depth) in the white column is very low when a particular line is a text line and the depth could be larger at the point of text line separation. A longer consecutive sequence of such larger depth should indicate the gap between the text lines, which provides the separator region. In case of over separation and under separation issues, corrective actions such as deletion and insertion are suggested respectively. An extensive experimentation is conducted on the compressed images of the benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the efficacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5120\/ijca2017915133\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"171308\", \"204095\"], \"outgoing_citations\": [\"331837\", \"333944\"]}","task_split":"paper_retrieval"}
{"document_id":"258388","document_content":"# Deep Scene Text Detection with Connected Component Proposals\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nA growing demand for natural-scene text detection has been witnessed by the computer vision community since text information plays a significant role in scene understanding and image indexing. Deep neural networks are being used due to their strong capabilities of pixel-wise classification or word localization, similar to being used in common vision problems. In this paper, we present a novel two-task network with integrating bottom and top cues. The first task aims to predict a pixel-by-pixel labeling and based on which, word proposals are generated with a canonical connected component analysis. The second task aims to output a bundle of character candidates used later to verify the word proposals. The two sub-networks share base convolutional features and moreover, we present a new loss to strengthen the interaction between them. We evaluate the proposed network on public benchmark datasets and show it can detect arbitrary-orientation scene text with a finer output boundary. In ICDAR 2013 text localization task, we achieve the state-of-the-art performance with an F-score of 0.919 and a much better recall of 0.915.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.05133v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"232509\", \"245899\"], \"outgoing_citations\": [\"269821\", \"279950\", \"282387\", \"284871\", \"291983\", \"293979\", \"294606\", \"296685\", \"311148\", \"311449\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"258472","document_content":"# StarCraft II: A New Challenge for Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.04782v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"28573\", \"34882\", \"48760\", \"6615\", \"7483\", \"9774\", \"14597\", \"15121\", \"36650\", \"66068\", \"69661\", \"72538\", \"75833\", \"77210\", \"84802\", \"116197\", \"131867\", \"191707\", \"196815\", \"952\", \"5981\", \"5918\", \"8307\", \"9333\", \"12675\", \"14585\", \"15227\", \"16044\", \"15989\", \"17326\", \"17989\", \"20726\", \"20948\", \"21032\", \"27720\", \"27973\", \"28370\", \"48003\", \"29467\", \"30101\", \"30892\", \"35142\", \"38678\", \"41813\", \"43212\", \"44584\", \"44607\", \"45698\", \"47373\", \"48146\", \"48978\", \"49770\", \"50415\", \"52395\", \"55378\", \"55920\", \"56115\", \"58840\", \"63330\", \"64556\", \"64631\", \"67195\", \"68983\", \"70142\", \"77834\", \"78586\", \"79669\", \"79920\", \"81816\", \"83991\", \"85115\", \"88854\", \"89503\", \"97037\", \"97383\", \"105016\", \"116402\", \"117326\", \"118777\", \"155930\", \"158022\", \"165015\", \"167067\", \"167727\", \"180897\", \"185006\", \"68258\", \"78652\", \"81331\", \"82020\", \"82456\", \"84383\", \"84317\", \"84752\", \"85449\", \"86052\", \"86146\", \"85131\", \"89337\", \"89833\", \"90660\", \"92636\", \"93079\", \"94904\", \"96291\", \"96714\", \"98504\", \"99829\", \"100235\", \"101112\", \"103422\", \"104122\", \"106665\", \"107165\", \"109880\", \"112387\", \"112521\", \"112226\", \"119194\", \"119084\", \"116357\", \"123726\", \"131425\", \"133480\", \"133479\", \"136238\", \"103314\", \"136332\", \"139735\", \"142161\", \"145053\", \"145283\", \"148867\", \"152469\", \"160043\", \"161660\", \"162155\", \"174408\", \"177329\", \"177952\", \"178509\", \"180285\", \"180496\", \"184390\", \"185834\", \"188042\", \"82999\", \"257674\", \"149805\", \"154533\", \"158047\", \"158642\", \"159589\", \"159580\", \"159934\", \"159881\", \"160673\", \"160827\", \"162489\", \"166746\", \"167238\", \"168262\", \"168651\", \"214440\", \"173355\", \"175055\", \"175289\", \"178736\", \"179073\", \"182870\", \"182273\", \"183690\", \"185050\", \"186927\", \"187973\", \"189110\", \"190254\", \"193458\", \"194168\", \"194877\", \"195142\", \"196025\", \"196593\", \"145506\", \"198383\", \"198781\", \"199369\", \"200052\", \"200132\", \"200321\", \"200772\", \"205179\", \"205409\", \"206717\", \"212722\", \"216837\", \"218741\", \"223268\", \"228150\", \"193790\", \"191970\", \"206900\", \"207618\", \"207983\", \"207852\", \"207953\", \"209249\", \"213321\", \"214960\", \"210887\", \"217261\", \"221283\", \"223460\", \"223810\", \"229169\", \"229559\", \"235092\", \"237193\", \"237517\", \"238475\", \"238773\", \"240158\", \"241774\", \"237494\", \"238845\", \"242141\", \"243943\", \"245731\", \"248552\", \"249696\", \"250010\", \"255531\", \"233117\", \"246504\", \"247242\", \"248882\", \"249196\", \"253764\", \"256169\", \"258199\", \"270254\"], \"outgoing_citations\": [\"261427\", \"262106\", \"269235\", \"270254\", \"280995\", \"281484\", \"282684\", \"293071\", \"320042\"]}","task_split":"paper_retrieval"}
{"document_id":"258570","document_content":"# Fluency-Guided Cross-Lingual Image Captioning\n## Categories\n- Computation and Language\n## Abstract\nImage captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual setting. Different from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of fluency in the translated sentences, we propose in this paper a fluency-guided learning framework. The framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both fluency and relevance of the generated captions in Chinese, but without using any manually written sentences from the target language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3123266.3123366\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"35427\", \"5842\", \"101277\", \"24321\", \"32325\", \"81724\", \"115939\", \"144758\", \"168031\", \"171726\", \"170816\", \"179816\", \"191861\", \"231022\"], \"outgoing_citations\": [\"281152\", \"290500\", \"295307\", \"300099\", \"304945\", \"311824\", \"312506\", \"316430\", \"320003\", \"321621\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"258664","document_content":"# AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomated affective computing in the wild setting is a challenging problem in computer vision. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited annotated facial databases for affective computing in the continuous dimensional model (e.g., valence and arousal). To meet this need, we collected, annotated, and prepared for public distribution a new database of facial emotions in the wild (called AffectNet). AffectNet contains more than 1,000,000 facial images from the Internet by querying three major search engines using 1250 emotion related keywords in six different languages. About half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. Two baseline deep neural networks are used to classify images in the categorical model and predict the intensity of valence and arousal. Various evaluation metrics show that our deep neural network baselines can perform better than conventional machine learning methods and off-the-shelf facial expression recognition systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TAFFC.2017.2740923\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"49755\", \"124073\", \"128935\", \"4313\", \"5659\", \"5768\", \"10549\", \"13081\", \"20801\", \"32560\", \"57945\", \"59464\", \"59874\", \"63076\", \"94090\", \"115083\", \"130511\", \"174806\", \"189054\", \"208202\", \"209204\", \"234576\", \"996\", \"5182\", \"6374\", \"8152\", \"8213\", \"8192\", \"8342\", \"10925\", \"15500\", \"17768\", \"17772\", \"21597\", \"22235\", \"25800\", \"27664\", \"28796\", \"33782\", \"34194\", \"36318\", \"36773\", \"36811\", \"36590\", \"36675\", \"36678\", \"36758\", \"36860\", \"37005\", \"38733\", \"40688\", \"42121\", \"43683\", \"44166\", \"45113\", \"45256\", \"49283\", \"50378\", \"50841\", \"54841\", \"55427\", \"55880\", \"55713\", \"57575\", \"59052\", \"59321\", \"59810\", \"60097\", \"61713\", \"62271\", \"62690\", \"63856\", \"64722\", \"64916\", \"65346\", \"68551\", \"70435\", \"73710\", \"75980\", \"78038\", \"83760\", \"86333\", \"93050\", \"97555\", \"99238\", \"108114\", \"109921\", \"110752\", \"110831\", \"128536\", \"131136\", \"142590\", \"154847\", \"163555\", \"203419\", \"79705\", \"80249\", \"80202\", \"89137\", \"90147\", \"90654\", \"97878\", \"99739\", \"100587\", \"13976\", \"105720\", \"106165\", \"106311\", \"106436\", \"108657\", \"111342\", \"112664\", \"120059\", \"118970\", \"125458\", \"126187\", \"127610\", \"128734\", \"130600\", \"132599\", \"135637\", \"136787\", \"137100\", \"140405\", \"142494\", \"143550\", \"144743\", \"146691\", \"146744\", \"147605\", \"154616\", \"167541\", \"180749\", \"186967\", \"210368\", \"233424\", \"152553\", \"156772\", \"157434\", \"159958\", \"161591\", \"161673\", \"78289\", \"164248\", \"164909\", \"167464\", \"171536\", \"172012\", \"185166\", \"116950\", \"188370\", \"189350\", \"191791\", \"193656\", \"196350\", \"197876\", \"199370\", \"201724\", \"203740\", \"208097\", \"208382\", \"213225\", \"233838\", \"239077\", \"64888\", \"212198\", \"217844\", \"221950\", \"220756\", \"222983\", \"223350\", \"223623\", \"222947\", \"233310\", \"234650\", \"243346\"], \"outgoing_citations\": [\"292743\", \"293179\", \"302875\", \"303467\", \"343074\", \"344173\"]}","task_split":"paper_retrieval"}
{"document_id":"258857","document_content":"# Analysis of Convolutional Neural Networks for Document Image Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze the learned features and find evidence that CNNs trained on RVL-CDIP learn region-specific layout features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.03273v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9109\", \"40383\", \"44124\", \"118235\", \"130460\", \"152003\", \"176090\", \"244011\"], \"outgoing_citations\": [\"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"258886","document_content":"# Attention-Aware Face Hallucination via Deep Reinforcement Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.03132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17463\", \"37459\", \"76110\", \"26693\", \"32066\", \"59368\", \"139332\", \"20148\", \"46052\", \"68463\", \"76015\", \"83295\", \"104519\", \"143372\", \"198751\", \"69556\", \"97182\", \"108335\", \"111131\", \"112547\", \"117180\", \"132302\", \"134378\", \"140312\", \"140134\", \"142045\", \"150833\", \"151046\", \"158246\", \"158253\", \"160915\", \"187775\", \"219346\", \"230252\", \"166256\", \"180372\", \"190979\", \"193626\", \"197058\", \"209098\", \"210611\", \"223755\", \"239580\", \"246889\", \"208565\", \"210477\", \"219553\", \"222290\", \"222871\", \"225121\", \"235729\", \"236185\", \"236193\", \"240158\", \"248019\", \"248969\", \"260175\"], \"outgoing_citations\": [\"271958\", \"272029\", \"288238\", \"296033\", \"297133\", \"303040\", \"318091\", \"318826\", \"325660\"]}","task_split":"paper_retrieval"}
{"document_id":"259004","document_content":"# Optimal Identity Testing with High Probability\n## Categories\n- Data Structures and Algorithms\n- Information Theory\n- Information Theory\n- Statistics Theory\n- Machine Learning\n- Statistics Theory\n## Abstract\nWe study the problem of testing identity against a given distribution with a focus on the high confidence regime. More precisely, given samples from an unknown distribution $p$ over $n$ elements, an explicitly given distribution $q$, and parameters $0< \\epsilon, \\delta < 1$, we wish to distinguish, {\\em with probability at least $1-\\delta$}, whether the distributions are identical versus $\\varepsilon$-far in total variation distance. Most prior work focused on the case that $\\delta = \\Omega(1)$, for which the sample complexity of identity testing is known to be $\\Theta(\\sqrt{n}\/\\epsilon^2)$. Given such an algorithm, one can achieve arbitrarily small values of $\\delta$ via black-box amplification, which multiplies the required number of samples by $\\Theta(\\log(1\/\\delta))$.   We show that black-box amplification is suboptimal for any $\\delta = o(1)$, and give a new identity tester that achieves the optimal sample complexity. Our new upper and lower bounds show that the optimal sample complexity of identity testing is \\[   \\Theta\\left( \\frac{1}{\\epsilon^2}\\left(\\sqrt{n \\log(1\/\\delta)} + \\log(1\/\\delta) \\right)\\right) \\] for any $n, \\varepsilon$, and $\\delta$. For the special case of uniformity testing, where the given distribution is the uniform distribution $U_n$ over the domain, our new tester is surprisingly simple: to test whether $p = U_n$ versus $d_{\\mathrm TV}(p, U_n) \\geq \\varepsilon$, we simply threshold $d_{\\mathrm TV}(\\widehat{p}, U_n)$, where $\\widehat{p}$ is the empirical probability distribution. The fact that this simple \"plug-in\" estimator is sample-optimal is surprising, even in the constant $\\delta$ case. Indeed, it was believed that such a tester would not attain sublinear sample complexity even for constant values of $\\varepsilon$ and $\\delta$.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.02728v2\", \"primary_category\": \"cs.DS\", \"categories\": [\"cs.IT\", \"math.IT\", \"math.ST\", \"cs.LG\", \"stat.TH\", \"cs.DS\"], \"primary_category_human_readable\": \"Data Structures and Algorithms\", \"categories_human_readable\": [\"Information Theory\", \"Information Theory\", \"Statistics Theory\", \"Machine Learning\", \"Statistics Theory\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"200638\", \"261048\"], \"outgoing_citations\": [\"280703\", \"299822\", \"307727\", \"309194\", \"323479\", \"327620\", \"341269\", \"360033\"]}","task_split":"paper_retrieval"}
{"document_id":"259005","document_content":"# Deep Face Feature for Face Alignment\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we present a deep learning based image feature extraction method designed specifically for face images. To train the feature extraction model, we construct a large scale photo-realistic face image dataset with ground-truth correspondence between multi-view face images, which are synthesized from real photographs via an inverse rendering procedure. The deep face feature (DFF) is trained using correspondence between face images rendered from different views. Using the trained DFF model, we can extract a feature vector for each pixel of a face image, which distinguishes different facial regions and is shown to be more effective than general-purpose feature descriptors for face-related tasks such as matching and alignment. Based on the DFF, we develop a robust face alignment method, which iteratively updates landmarks, pose and 3D shape. Extensive experiments demonstrate that our method can achieve state-of-the-art results for face alignment under highly unconstrained face images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.02721v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"197211\"], \"outgoing_citations\": [\"257118\", \"302701\", \"303061\", \"311317\"]}","task_split":"paper_retrieval"}
{"document_id":"259011","document_content":"# Neural Vector Spaces for Unsupervised Information Retrieval\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nWe propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed.   NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3196826\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"64447\", \"27349\", \"90897\", \"92854\", \"128063\", \"90737\", \"97036\", \"100287\", \"105278\", \"107381\", \"131025\", \"194919\", \"189744\", \"204060\", \"245847\", \"243681\", \"250274\"], \"outgoing_citations\": [\"249530\", \"253162\", \"261768\", \"263952\", \"276882\", \"280923\", \"281524\", \"281915\", \"285950\", \"286057\", \"290387\", \"297328\", \"300508\"]}","task_split":"paper_retrieval"}
{"document_id":"259124","document_content":"# Corpus-level Fine-grained Entity Typing\n## Categories\n- Computation and Language\n## Abstract\nThis paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as \"food\" or \"artist\". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding- based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. Each of the two proposed models has some specific properties. For the global model, learning high quality entity representations is crucial because it is the only source used for the predictions. Therefore, we introduce representations using name and contexts of entities on the three levels of entity, word, and character. We show each has complementary information and a multi-level representation is the best. For the context model, we need to use distant supervision since the context-level labels are not available for entities. Distant supervised labels are noisy and this harms the performance of models. Therefore, we introduce and apply new algorithms for noise mitigation using multi-instance learning. We show the effectiveness of our models in a large entity typing dataset, built from Freebase.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.02275v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54221\", \"56031\", \"62455\", \"110515\", \"147649\", \"188381\", \"213085\", \"224111\", \"224723\"], \"outgoing_citations\": [\"268014\", \"276616\", \"277440\", \"289600\", \"289601\", \"291175\", \"294249\", \"296440\", \"298157\", \"313946\", \"341939\", \"345467\"]}","task_split":"paper_retrieval"}
{"document_id":"259202","document_content":"# Fake News Detection on Social Media: A Data Mining Perspective\n## Categories\n- Social and Information Networks\n- Artificial Intelligence\n- H.2.8\n## Abstract\nSocial media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \"fake news\", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.01967v3\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.AI\", \"H.2.8\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Artificial Intelligence\", \"H.2.8\", \"Social and Information Networks\"], \"incoming_citations\": [\"5757\", \"23929\", \"41599\", \"87792\", \"4211\", \"2894\", \"9429\", \"14947\", \"19763\", \"20408\", \"22060\", \"22303\", \"23631\", \"23894\", \"27086\", \"27370\", \"40560\", \"48590\", \"55862\", \"63247\", \"77332\", \"78835\", \"96562\", \"125031\", \"737\", \"2717\", \"3289\", \"3480\", \"4126\", \"5978\", \"8211\", \"8259\", \"9054\", \"9135\", \"10334\", \"11296\", \"13501\", \"14033\", \"14184\", \"16832\", \"17451\", \"20841\", \"20709\", \"20910\", \"21100\", \"21518\", \"21555\", \"21630\", \"21779\", \"24508\", \"24863\", \"25782\", \"26572\", \"27743\", \"27906\", \"23586\", \"30489\", \"30746\", \"30843\", \"31108\", \"31403\", \"33288\", \"34962\", \"37995\", \"40187\", \"40534\", \"42117\", \"186698\", \"43272\", \"21624\", \"45966\", \"47955\", \"50496\", \"51100\", \"53754\", \"53672\", \"53995\", \"109315\", \"54565\", \"55551\", \"48118\", \"56239\", \"56085\", \"56629\", \"57527\", \"59105\", \"61961\", \"62745\", \"62658\", \"63243\", \"63992\", \"65009\", \"66278\", \"21022\", \"67365\", \"68638\", \"69141\", \"69136\", \"70497\", \"70658\", \"72389\", \"72369\", \"72637\", \"72944\", \"73815\", \"75518\", \"75689\", \"75850\", \"76033\", \"78165\", \"79024\", \"81800\", \"82909\", \"90393\", \"91328\", \"92760\", \"94401\", \"97356\", \"98730\", \"101392\", \"111821\", \"118048\", \"121815\", \"127820\", \"132615\", \"29954\", \"176789\", \"188248\", \"196472\", \"207715\", \"53889\"], \"outgoing_citations\": [\"267080\", \"267552\", \"268073\", \"270799\", \"271015\", \"271021\", \"273723\", \"274781\", \"281571\", \"288699\", \"289356\", \"293170\", \"297117\", \"313061\", \"319035\", \"346166\"]}","task_split":"paper_retrieval"}
{"document_id":"259293","document_content":"# Automatic Question-Answering Using A Deep Similarity Neural Network\n## Categories\n- Computation and Language\n## Abstract\nAutomatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score. We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data. We have also tested our framework on a public question-answering database and achieved very good performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.01713v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"12048\", \"132906\", \"101700\", \"169991\", \"174056\", \"175023\", \"191564\", \"209630\", \"227114\", \"236510\"], \"outgoing_citations\": [\"303469\", \"308414\", \"314240\", \"316165\", \"330994\"]}","task_split":"paper_retrieval"}
{"document_id":"259476","document_content":"# CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.00980v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"54087\", \"36512\", \"49287\", \"106479\", \"431\", \"3767\", \"12174\", \"12561\", \"221215\", \"53101\", \"82106\", \"102774\", \"130163\", \"235160\", \"106246\", \"109928\", \"129267\", \"131474\", \"134002\", \"134658\", \"140504\", \"159733\", \"192217\", \"208867\", \"163559\", \"171500\", \"182589\", \"182972\", \"193656\", \"194402\", \"197211\", \"198793\", \"199069\", \"241448\", \"202313\", \"216085\", \"227144\", \"238562\"], \"outgoing_citations\": [\"270042\", \"270134\", \"270795\", \"273729\", \"277948\", \"280394\", \"284716\", \"294926\", \"299047\", \"302701\"]}","task_split":"paper_retrieval"}
{"document_id":"259652","document_content":"# Improving Part-of-Speech Tagging for NLP Pipelines\n## Categories\n- Computation and Language\n## Abstract\nThis paper outlines the results of sentence level linguistics based rules for improving part-of-speech tagging. It is well known that the performance of complex NLP systems is negatively affected if one of the preliminary stages is less than perfect. Errors in the initial stages in the pipeline have a snowballing effect on the pipeline's end performance. We have created a set of linguistics based rules at the sentence level which adjust part-of-speech tags from state-of-the-art taggers. Comparison with state-of-the-art taggers on widely used benchmarks demonstrate significant improvements in tagging accuracy and consequently in the quality and accuracy of NLP systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.00241v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"259708","document_content":"# Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nOne question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Successor Features approach this problem by learning a feature representation that satisfies a temporal constraint. We present an implementation of an approach that decouples the feature representation from the reward function, making it suitable for transferring knowledge between domains. We then assess the advantages and limitations of using Successor Features for transfer.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1708.00102v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"76687\", \"96180\", \"100245\", \"200791\", \"160289\", \"180158\", \"212102\", \"225748\", \"199779\", \"237929\", \"255032\"], \"outgoing_citations\": [\"277797\", \"290243\"]}","task_split":"paper_retrieval"}
{"document_id":"259840","document_content":"# Learning Language Representations for Typology Prediction\n## Categories\n- Computation and Language\n## Abstract\nOne central mystery of neural NLP is what neural models \"know\" about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one neural machine translation (NMT) system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages' geographic and phylogenetic neighbors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.09569v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"44334\", \"23802\", \"40395\", \"44974\", \"60936\", \"72915\", \"92489\", \"94724\", \"144897\", \"118167\", \"89177\", \"89348\", \"92100\", \"93794\", \"127787\", \"128134\", \"131195\", \"127876\", \"137472\", \"157502\", \"170295\", \"180249\", \"193634\", \"197007\", \"203509\", \"204715\", \"208560\", \"217125\", \"218115\", \"221514\", \"226011\", \"241113\", \"244590\", \"250429\", \"251523\", \"267740\"], \"outgoing_citations\": [\"269127\", \"269654\", \"276174\", \"277444\", \"280201\", \"280447\", \"282869\", \"292691\", \"295867\", \"298944\", \"311683\", \"330643\"]}","task_split":"paper_retrieval"}
{"document_id":"259884","document_content":"# FontCode: Embedding Information in Text Documents using Glyph Perturbation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce FontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text by perturbing the glyphs of text characters while preserving the text content. We devise an algorithm to chooses unobtrusive yet machine-recognizable glyph perturbations, leveraging a recently developed generative model that alters the glyphs of each character continuously on a font manifold. We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document. We also present a glyph recognition method that recovers the embedded information from an encoded document stored as a vector graphic or pixel image, or even on a printed paper. In addition, we introduce a new error-correction coding scheme that rectifies a certain number of recognition errors. Lastly, we demonstrate that our technique enables a wide array of applications, using it as a text document metadata holder, an unobtrusive optical barcode, a cryptographic message embedding scheme, and a text document signature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3152823\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2947\", \"101902\", \"85934\", \"222833\"], \"outgoing_citations\": [\"85773\", \"309702\", \"349018\"]}","task_split":"paper_retrieval"}
{"document_id":"260092","document_content":"# A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nRobots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.08668v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"11352\", \"64051\", \"95145\", \"112518\", \"126793\"], \"outgoing_citations\": [\"268335\", \"302970\", \"307569\", \"311272\", \"315839\"]}","task_split":"paper_retrieval"}
{"document_id":"260107","document_content":"# Guiding Reinforcement Learning Exploration Using Natural Language\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nIn this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.08616v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"58471\", \"84300\", \"108770\", \"175563\", \"198011\", \"251691\", \"259693\"], \"outgoing_citations\": [\"273118\"]}","task_split":"paper_retrieval"}
{"document_id":"260137","document_content":"# Implicit Entity Linking in Tweets\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nOver the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to corresponding entries in knowledge bases in order to provide unambiguous interpretation and additional con- text. State-of-the-art techniques have focused on linking explicitly mentioned entities in tweets with reasonable success. However, we argue that in addition to explicit mentions i.e. The movie Gravity was more ex- pensive than the mars orbiter mission entities (movie Gravity) can also be mentioned implicitly i.e. This new space movie is crazy. you must watch it!. This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1) the importance of linking implicit entities and its value addition to the standard entity linking task, and 2) the importance of exploiting contextual knowledge associated with an entity for linking their implicit mentions. We also make the ground truth dataset publicly available to foster the research in this new research area.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-34129-3_8\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"48823\", \"153328\", \"216454\", \"222210\", \"246916\"], \"outgoing_citations\": [\"178230\", \"322694\"]}","task_split":"paper_retrieval"}
{"document_id":"260163","document_content":"# Deep Interactive Region Segmentation and Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n- 68T45\n## Abstract\nWith recent innovations in dense image captioning, it is now possible to describe every object of the scene with a caption while objects are determined by bounding boxes. However, interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes. Furthermore, in current captioning frameworks, the user is not able to involve personal preferences to exclude out of interest areas. In this paper, we propose a novel hybrid deep learning architecture for interactive region segmentation and captioning where the user is able to specify an arbitrary region of the image that should be processed. To this end, a dedicated Fully Convolutional Network (FCN) named Lyncean FCN (LFCN) is trained using our special training data to isolate the User Intention Region (UIR) as the output of an efficient segmentation. In parallel, a dense image captioning model is utilized to provide a wide variety of captions for that region. Then, the UIR will be explained with the caption of the best match bounding box. To the best of our knowledge, this is the first work that provides such a comprehensive output. Our experiments show the superiority of the proposed approach over state-of-the-art interactive segmentation methods on several well-known datasets. In addition, replacement of the bounding boxes with the result of the interactive segmentation leads to a better understanding of the dense image captioning output as well as accuracy enhancement for the object detection in terms of Intersection over Union (IoU).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.08364v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68T45\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68T45\"], \"incoming_citations\": [\"141994\", \"225514\"], \"outgoing_citations\": [\"269094\", \"286617\", \"294327\", \"296656\", \"302621\", \"306883\", \"320003\", \"320898\", \"321621\", \"322022\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"260280","document_content":"# Question Dependent Recurrent Entity Network for Question Answering\n## Categories\n- Computation and Language\n## Abstract\nQuestion Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of $Memory\\ Network$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $Question\\ Dependent\\ Recurrent\\ Entity\\ Network$ and extends $Recurrent\\ Entity\\ Network$ by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the $bAbI$ question answering dataset and the $CNN\\ \\&\\ Daily\\ News$ $reading\\ comprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07922v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"148368\", \"185680\", \"220418\"], \"outgoing_citations\": [\"20996\", \"153217\", \"155336\", \"271610\", \"278163\", \"286317\", \"288385\", \"290789\", \"290934\", \"296068\", \"297106\", \"297133\", \"303827\", \"310639\", \"317213\", \"336326\"]}","task_split":"paper_retrieval"}
{"document_id":"260284","document_content":"# Mutual Alignment Transfer Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nTraining robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can adjust to optimize its behaviour for states commonly visited by the real-world agent.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07907v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"94891\", \"109082\", \"13872\", \"43379\", \"50445\", \"121024\", \"84383\", \"92727\", \"139807\", \"155474\", \"166612\", \"178846\", \"180204\", \"185463\", \"168512\", \"189168\", \"190427\", \"205521\", \"214419\", \"214403\", \"230498\", \"217105\", \"227881\", \"234287\", \"253045\", \"247776\", \"253256\"], \"outgoing_citations\": [\"263852\", \"265053\", \"265069\", \"271975\", \"272298\", \"272441\", \"274762\", \"280570\", \"282684\", \"282841\", \"283289\"]}","task_split":"paper_retrieval"}
{"document_id":"260338","document_content":"# Bellman Gradient Iteration for Inverse Reinforcement Learning\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nThis paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly handle different types of actions with two approximations of the Bellman Optimality Equation, and a Bellman Gradient Iteration method to compute the gradient of the Q-value with respect to the reward function. These methods allow us to build a differentiable relation between the Q-value and the reward function and learn an approximately optimal reward function with gradient methods. We test the proposed method in two simulated environments by evaluating the accuracy of different approximations and comparing the proposed method with existing solutions. The results show that even with a linear reward function, the proposed method has a comparable accuracy with the state-of-the-art method adopting a non-linear reward function, and the proposed method is more flexible because it is defined on observed actions instead of trajectories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07767v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"253876\", \"257850\", \"259894\", \"259895\"], \"outgoing_citations\": [\"297317\", \"357569\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"260371","document_content":"# Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking\n## Categories\n- Information Retrieval\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nDeep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive information from users. Privacy and confidentiality concerns prevent many data owners from sharing the data, thus today the research community can only benefit from research on large-scale datasets in a limited manner. In this paper, we discuss privacy preserving mimic learning, i.e., using predictions from a privacy preserving trained model instead of labels from the original sensitive training data as a supervision signal. We present the results of preliminary experiments in which we apply the idea of mimic learning and privacy preserving mimic learning for the task of document re-ranking as one of the core IR tasks. This research is a step toward laying the ground for enabling researchers from data-rich environments to share knowledge learned from actual users' data, which should facilitate research collaborations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07605v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.AI\", \"cs.IR\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"149723\", \"187990\", \"228254\"], \"outgoing_citations\": [\"267775\", \"278099\", \"280923\", \"282410\"]}","task_split":"paper_retrieval"}
{"document_id":"260373","document_content":"# Image Pivoting for Learning Multilingual Multimodal Representations\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07601v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"55305\", \"61497\", \"81858\", \"23204\", \"24382\", \"58992\", \"62806\", \"72354\", \"98055\", \"121147\", \"84886\", \"96244\", \"126586\", \"127093\", \"129340\", \"132146\", \"153733\", \"168031\", \"157257\", \"162846\", \"164107\", \"167598\", \"183820\", \"191237\", \"199724\", \"205074\", \"231022\", \"263680\", \"218710\", \"217125\", \"220421\", \"236276\", \"242857\", \"253508\", \"260746\"], \"outgoing_citations\": [\"264427\", \"267500\", \"274784\", \"290510\", \"293220\", \"300096\", \"300099\", \"302943\", \"305132\", \"313796\", \"314050\", \"320003\", \"321621\", \"322022\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"260505","document_content":"# Attention-Based End-to-End Speech Recognition on Voice Search\n## Categories\n- Computation and Language\n- Sound\n## Abstract\nRecently, there has been a growing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition on a voice search task. Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model. In this paper, we use character embedding to deal with the large vocabulary. Several tricks are used for effective model training, including L2 regularization, Gaussian weight noise and frame skipping. We compare two attention mechanisms and use attention smoothing to cover long context in the attention model. Taken together, these tricks allow us to finally achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on the MiTV voice search dataset. While together with a trigram language model, CER and SER reach 2.81% and 5.77%, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.07167v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SD\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Sound\"], \"incoming_citations\": [\"36353\", \"200343\", \"49799\", \"104971\", \"107605\", \"210342\", \"216854\", \"231881\", \"232580\", \"233894\", \"237342\", \"248287\"], \"outgoing_citations\": [\"264220\", \"271544\", \"278443\", \"282929\", \"284201\", \"284885\", \"307902\", \"308796\", \"309025\", \"319833\", \"320874\"]}","task_split":"paper_retrieval"}
{"document_id":"260559","document_content":"# Split and Rephrase\n## Categories\n- Computation and Language\n## Abstract\nWe propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.06971v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"260577","document_content":"# What Looks Good with my Sofa: Multimodal Search Engine for Interior Design\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a multi-modal search engine for interior design that combines visual and textual queries. The goal of our engine is to retrieve interior objects, e.g. furniture or wall clocks, that share visual and aesthetic similarities with the query. Our search engine allows the user to take a photo of a room and retrieve with a high recall a list of items identical or visually similar to those present in the photo. Additionally, it allows to return other items that aesthetically and stylistically fit well together. To achieve this goal, our system blends the results obtained using textual and visual modalities. Thanks to this blending strategy, we increase the average style similarity score of the retrieved items by 11%. Our work is implemented as a Web-based application and it is planned to be opened to the public.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.15439\/2017F56\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"68658\", \"108364\", \"245667\"], \"outgoing_citations\": [\"260612\", \"283807\", \"295204\", \"298750\", \"303067\", \"312133\"]}","task_split":"paper_retrieval"}
{"document_id":"260588","document_content":"# Semantic Image Synthesis via Adversarial Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.06873v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3854\", \"146468\", \"128493\", \"2709\", \"6331\", \"7149\", \"112829\", \"120045\", \"142596\", \"738\", \"6179\", \"14659\", \"29899\", \"31771\", \"33188\", \"39604\", \"82418\", \"39717\", \"45727\", \"45876\", \"46465\", \"55096\", \"56489\", \"59166\", \"59873\", \"60753\", \"73658\", \"116018\", \"144675\", \"150941\", \"56028\", \"92015\", \"98696\", \"103781\", \"107901\", \"110487\", \"136219\", \"139312\", \"150966\", \"151411\", \"160631\", \"172954\", \"208605\", \"214493\", \"150586\", \"154833\", \"166331\", \"166632\", \"169403\", \"171580\", \"177878\", \"182585\", \"190115\", \"190994\", \"191229\", \"191545\", \"192697\", \"194714\", \"208787\", \"231456\", \"212643\", \"221573\", \"228152\", \"233382\", \"237272\", \"240885\", \"245478\", \"248731\", \"264831\", \"260119\"], \"outgoing_citations\": [\"280070\", \"280996\", \"283070\", \"284132\", \"292386\", \"296355\", \"300606\", \"302162\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"260730","document_content":"# Video Question Answering via Attribute-Augmented Attention Network Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nVideo Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3077136.3080655\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"12898\", \"13723\", \"17412\", \"21807\", \"36974\", \"39599\", \"48946\", \"54820\", \"56858\", \"59458\", \"63086\", \"83575\", \"93476\", \"75366\", \"79633\", \"79655\", \"108281\", \"107363\", \"136840\", \"140281\", \"146725\", \"173698\", \"191515\", \"207049\", \"242943\", \"248204\", \"250058\"], \"outgoing_citations\": [\"258568\", \"302621\"]}","task_split":"paper_retrieval"}
{"document_id":"260746","document_content":"# Learning Visually Grounded Sentence Representations\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding. We train a grounded sentence encoder that achieves good performance on COCO caption and image retrieval and subsequently show that this encoder can successfully be transferred to various NLP tasks, with improved performance over text-only models. Lastly, we analyze the contribution of grounding, and show that word embeddings learned by this system outperform non-grounded ones.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.06320v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"21561\", \"37183\", \"41730\", \"55830\", \"60658\", \"67805\", \"88824\", \"90607\", \"93223\", \"93817\", \"94460\", \"98535\", \"110241\", \"168031\", \"153408\", \"157257\", \"143643\", \"167882\", \"167598\", \"171851\", \"140986\", \"181154\", \"188933\", \"193503\", \"201070\", \"204598\", \"217125\", \"225266\", \"253508\", \"248549\", \"267161\"], \"outgoing_citations\": [\"238963\", \"253508\", \"260373\", \"260926\", \"266669\", \"271085\", \"277126\", \"292386\", \"298543\", \"300099\", \"302516\", \"302943\", \"303715\", \"303844\", \"311336\", \"320003\", \"321621\", \"325742\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"260841","document_content":"# Face Alignment Robust to Pose, Expressions and Occlusions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose an Ensemble of Robust Constrained Local Models for alignment of faces in the presence of significant occlusions and of any unknown pose and expression. To account for partial occlusions we introduce, Robust Constrained Local Models, that comprises of a deformable shape and local landmark appearance model and reasons over binary occlusion labels. Our occlusion reasoning proceeds by a hypothesize-and-test search over occlusion labels. Hypotheses are generated by Constrained Local Model based shape fitting over randomly sampled subsets of landmark detector responses and are evaluated by the quality of face alignment. To span the entire range of facial pose and expression variations we adopt an ensemble of independent Robust Constrained Local Models to search over a discretized representation of pose and expression. We perform extensive evaluation on a large number of face images, both occluded and unoccluded. We find that our face alignment system trained entirely on facial images captured \"in-the-lab\" exhibits a high degree of generalization to facial images captured \"in-the-wild\". Our results are accurate and stable over a wide spectrum of occlusions, pose and expression variations resulting in excellent performance on many real-world face datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.05938v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"177221\", \"241654\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"261027","document_content":"# Learning to select data for transfer learning with Bayesian Optimization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDomain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. Inspired by work on curriculum learning, we propose to \\emph{learn} data selection measures using Bayesian Optimization and evaluate them across models, domains and tasks. Our learned measures outperform existing domain similarity measures significantly on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We show the importance of complementing similarity with diversity, and that learned measures are -- to some degree -- transferable across models, domains, and even tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.05246v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"8144\", \"14310\", \"18650\", \"23901\", \"4165\", \"23733\", \"24035\", \"29086\", \"35786\", \"36460\", \"49686\", \"53542\", \"55546\", \"56550\", \"61113\", \"68711\", \"86075\", \"91976\", \"101387\", \"118601\", \"148371\", \"89930\", \"91426\", \"94211\", \"96725\", \"97062\", \"112296\", \"114268\", \"119108\", \"121866\", \"129260\", \"129503\", \"131753\", \"133033\", \"133719\", \"147401\", \"154734\", \"169731\", \"170885\", \"151852\", \"167681\", \"170362\", \"171444\", \"173202\", \"181093\", \"183673\", \"191589\", \"195157\", \"204401\", \"209925\", \"211979\", \"214511\", \"218874\", \"240692\", \"256646\", \"259541\"], \"outgoing_citations\": [\"259541\", \"272858\", \"274451\", \"292033\", \"292687\", \"294248\", \"296583\"]}","task_split":"paper_retrieval"}
{"document_id":"261098","document_content":"# Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning\n## Categories\n- Artificial Intelligence\n## Abstract\nManagement of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing HF using answer set programming(ASP). In this paper we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.04957v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"257131\", \"281925\", \"329643\"]}","task_split":"paper_retrieval"}
{"document_id":"261522","document_content":"# Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n- Robotics\n## Abstract\nImitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning \"imitation-from-observation,\" and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.03374v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.NE\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\", \"Robotics\"], \"incoming_citations\": [\"11414\", \"18163\", \"20414\", \"20668\", \"23944\", \"29891\", \"40128\", \"48512\", \"59512\", \"16270\", \"31874\", \"36403\", \"35842\", \"41463\", \"43971\", \"48247\", \"53903\", \"59091\", \"59341\", \"62732\", \"64355\", \"67095\", \"79812\", \"80435\", \"94658\", \"132434\", \"177272\", \"213928\", \"151613\", \"66826\", \"107895\", \"87181\", \"87299\", \"88920\", \"91844\", \"93419\", \"95178\", \"103321\", \"106273\", \"108289\", \"117587\", \"117896\", \"121530\", \"122822\", \"121911\", \"128429\", \"128488\", \"128799\", \"136663\", \"139265\", \"139811\", \"140833\", \"139601\", \"144836\", \"151978\", \"149198\", \"154928\", \"156448\", \"164490\", \"165096\", \"170995\", \"180258\", \"195266\", \"150902\", \"162466\", \"162902\", \"164222\", \"164292\", \"166326\", \"165614\", \"174203\", \"177151\", \"180256\", \"183581\", \"184398\", \"185610\", \"185860\", \"190013\", \"190766\", \"191888\", \"195692\", \"201429\", \"202162\", \"210901\", \"215747\", \"216058\", \"209452\", \"209469\", \"218636\", \"225224\", \"226419\", \"227517\", \"231307\", \"207983\", \"80181\", \"212935\", \"213070\", \"214592\", \"220415\", \"224411\", \"213113\", \"230068\", \"233167\", \"209859\", \"240087\", \"240800\", \"234517\", \"243236\", \"253448\", \"268248\", \"249790\", \"260770\"], \"outgoing_citations\": [\"268248\", \"270529\", \"270925\", \"271975\", \"272298\", \"277564\", \"283026\", \"286689\", \"290911\", \"297317\", \"300267\", \"302720\", \"352572\"]}","task_split":"paper_retrieval"}
{"document_id":"261577","document_content":"# A Simple Neural Attentive Meta-Learner\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nDeep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.03141v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"13743\", \"24950\", \"72920\", \"76208\", \"102138\", \"136747\", \"138431\", \"357157\", \"114484\", \"178\", \"2687\", \"2822\", \"4353\", \"9247\", \"10877\", \"12599\", \"13398\", \"15749\", \"16608\", \"16558\", \"17043\", \"18413\", \"18695\", \"18771\", \"19580\", \"22537\", \"23428\", \"32352\", \"38393\", \"43312\", \"44643\", \"49986\", \"50845\", \"59368\", \"59716\", \"73051\", \"78473\", \"83134\", \"84021\", \"84828\", \"85840\", \"107932\", \"112029\", \"115681\", \"130651\", \"131702\", \"133016\", \"136367\", \"163067\", \"170022\", \"184092\", \"602\", \"2391\", \"3186\", \"3790\", \"4718\", \"4976\", \"5873\", \"7354\", \"10443\", \"11237\", \"11416\", \"13513\", \"13817\", \"14156\", \"15911\", \"16345\", \"16844\", \"18629\", \"19209\", \"6479\", \"18054\", \"20111\", \"20392\", \"19527\", \"21751\", \"23207\", \"24842\", \"25068\", \"25823\", \"28582\", \"28931\", \"30588\", \"30750\", \"30923\", \"31164\", \"32601\", \"33304\", \"33415\", \"33264\", \"33641\", \"33992\", \"34852\", \"35949\", \"36403\", \"38729\", \"39279\", \"40091\", \"40821\", \"41607\", \"42481\", \"42570\", \"42820\", \"43648\", \"43753\", \"44582\", \"44780\", \"45498\", \"41793\", \"153684\", \"20450\", \"46752\", \"47996\", \"49107\", \"37221\", \"50005\", \"50200\", \"50304\", \"51092\", \"53275\", \"55486\", \"55674\", \"55946\", \"60104\", \"60809\", \"61742\", \"62302\", \"62728\", \"63815\", \"64825\", \"65595\", \"66024\", \"67245\", \"67803\", \"67820\", \"69420\", \"70890\", \"70924\", \"70997\", \"71100\", \"71387\", \"74846\", \"76452\", \"76927\", \"76980\", \"77916\", \"78324\", \"79437\", \"80258\", \"80540\", \"83418\", \"84445\", \"85090\", \"85594\", \"86722\", \"87479\", \"89958\", \"92009\", \"95935\", \"96040\", \"97074\", \"99728\", \"107285\", \"107437\", \"109713\", \"109806\", \"110544\", \"111435\", \"111956\", \"112785\", \"115473\", \"117816\", \"117992\", \"118383\", \"118638\", \"121163\", \"123563\", \"124463\", \"136733\", \"137669\", \"139506\", \"141632\", \"142637\", \"142989\", \"153371\", \"156384\", \"165038\", \"169955\", \"191522\", \"206433\", \"213721\", \"77807\", \"79220\", \"79889\", \"78278\", \"80071\", \"80589\", \"80796\", \"81326\", \"81654\", \"87182\", \"89599\", \"90042\", \"90289\", \"90966\", \"91523\", \"91729\", \"92287\", \"92418\", \"92827\", \"93933\", \"162209\", \"94557\", \"96385\", \"97251\", \"97411\", \"99551\", \"100158\", \"101041\", \"101382\", \"101805\", \"102271\", \"102466\", \"102883\", \"103292\", \"103606\", \"104047\", \"104561\", \"106866\", \"107562\", \"110289\", \"111473\", \"111745\", \"112266\", \"112294\", \"103730\", \"113201\", \"113732\", \"114088\", \"114084\", \"114507\", \"115674\", \"116748\", \"117623\", \"118572\", \"119084\", \"118999\", \"119017\", \"119108\", \"119122\", \"119342\", \"120422\", \"122567\", \"124228\", \"125413\", \"125454\", \"125569\", \"126067\", \"128699\", \"128937\", \"128931\", \"130677\", \"131013\", \"132365\", \"132572\", \"127249\", \"129574\", \"133719\", \"133800\", \"133923\", \"134297\", \"134322\", \"134828\", \"134763\", \"134943\", \"136035\", \"136173\", \"138141\", \"138836\", \"138908\", \"139680\", \"141455\", \"141913\", \"142973\", \"142906\", \"143394\", \"143486\", \"143919\", \"144444\", \"144630\", \"148680\", \"135837\", \"148867\", \"149517\", \"150432\", \"150397\", \"151757\", \"151800\", \"152554\", \"152616\", \"152425\", \"154289\", \"154496\", \"154316\", \"155767\", \"155831\", \"155986\", \"161071\", \"161924\", \"161947\", \"162581\", \"163656\", \"164201\", \"164719\", \"165273\", \"167235\", \"169398\", \"169709\", \"174172\", \"175541\", \"177289\", \"180867\", \"181883\", \"184463\", \"189825\", \"190020\", \"192376\", \"196047\", \"204765\", \"213723\", \"223826\", \"102715\", \"120063\", \"150652\", \"152041\", \"152946\", \"153576\", \"156858\", \"156819\", \"157350\", \"159036\", \"158874\", \"159276\", \"160036\", \"161314\", \"163588\", \"158555\", \"164217\", \"164216\", \"164454\", \"150176\", \"150188\", \"151378\", \"165072\", \"164945\", \"166305\", \"167551\", \"167562\", \"167671\", \"167688\", \"167745\", \"166416\", \"171778\", \"171924\", \"172795\", \"174888\", \"175792\", \"176210\", \"179283\", \"181066\", \"181113\", \"181248\", \"181649\", \"182295\", \"182338\", \"182451\", \"182961\", \"180536\", \"183141\", \"183550\", \"184098\", \"184299\", \"184734\", \"186048\", \"186101\", \"186261\", \"186514\", \"186857\", \"186981\", \"187278\", \"187797\", \"187908\", \"184854\", \"188473\", \"190018\", \"191258\", \"191323\", \"191732\", \"192090\", \"192198\", \"192827\", \"193257\", \"193870\", \"194494\", \"195439\", \"196393\", \"196657\", \"196671\", \"197286\", \"198400\", \"199635\", \"199890\", \"200648\", \"201534\", \"201842\", \"202646\", \"205208\", \"205915\", \"207031\", \"207499\", \"208207\", \"210764\", \"214013\", \"214993\", \"215835\", \"207480\", \"140345\", \"214135\", \"224464\", \"224873\", \"230604\", \"230640\", \"230919\", \"231222\", \"231401\", \"235579\", \"237229\", \"168796\", \"205447\", \"205592\", \"207736\", \"208558\", \"209542\", \"209733\", \"213179\", \"213232\", \"215015\", \"212511\", \"217919\", \"221419\", \"225395\", \"227419\", \"228212\", \"228299\", \"228556\", \"228896\", \"229169\"], \"outgoing_citations\": [\"280202\", \"280870\", \"289229\"]}","task_split":"paper_retrieval"}
{"document_id":"261618","document_content":"# A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nThe amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.02919v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"50728\", \"5849\", \"48879\", \"73841\", \"81887\", \"156792\", \"98432\", \"50271\", \"144834\", \"146405\", \"173344\", \"167210\", \"168224\", \"173053\", \"177522\", \"180573\", \"184740\", \"185970\", \"192681\", \"195810\", \"220648\", \"213956\", \"229196\", \"238630\", \"238889\", \"259616\", \"260201\", \"260872\", \"261785\"], \"outgoing_citations\": [\"261785\", \"334417\"]}","task_split":"paper_retrieval"}
{"document_id":"261707","document_content":"# Class-Weighted Convolutional Features for Visual Instance Search\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Multimedia\n## Abstract\nImage retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at http:\/\/imatge-upc.github.io\/retrieval-2017-cam\/.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.02581v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [\"34971\", \"73146\", \"144051\", \"144613\", \"211153\", \"36193\", \"48708\", \"55955\", \"90913\", \"176317\", \"103466\", \"134085\", \"180562\", \"194751\", \"256887\", \"222623\", \"227120\", \"232119\", \"255989\", \"248935\"], \"outgoing_citations\": [\"280378\", \"281953\", \"285773\", \"290216\", \"293545\", \"294470\", \"294983\", \"301572\", \"303067\", \"314584\", \"320036\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"261742","document_content":"# Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n## Abstract\nThe state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. However, such a model could still make mistakes if its features favor a wrong entity type. In this paper, we utilize Wikipedia as an open knowledge base to improve multilingual NER systems. Central to our approach is the construction of high-accuracy, high-coverage multilingual Wikipedia entity type mappings. These mappings are built from weakly annotated data and can be extended to new languages with no human annotation or language-dependent knowledge involved. Based on these mappings, we develop several approaches to improve an NER system. We evaluate the performance of the approaches via experiments on NER systems trained for 6 languages. Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18.3 F1 score improvement).","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/D16-1135\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\"], \"incoming_citations\": [\"169324\", \"38872\", \"143768\", \"171862\", \"261734\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"261768","document_content":"# Efficient Vector Representation for Documents through Corruption\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.02377v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"28479\", \"2919\", \"71813\", \"11171\", \"12629\", \"25232\", \"44220\", \"45556\", \"60381\", \"70974\", \"84916\", \"93783\", \"124270\", \"155664\", \"221153\", \"149364\", \"168950\", \"179896\", \"181749\", \"181749\", \"205190\", \"208066\", \"211694\", \"212285\", \"224272\", \"229460\", \"231144\", \"231635\", \"259011\", \"248549\", \"250274\", \"250760\"], \"outgoing_citations\": [\"303997\", \"308857\", \"317966\", \"320239\", \"342934\", \"349607\", \"357658\"]}","task_split":"paper_retrieval"}
{"document_id":"262034","document_content":"# Learning to Design Games: Strategic Environments in Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nIn typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. This extension is motivated by environment design scenarios in the real-world, including game design, shopping space design and traffic signal design. Theoretically, we find a dual Markov decision process (MDP) w.r.t. the environment to that w.r.t. the agent, and derive a policy gradient solution to optimizing the parametrized environment. Furthermore, discontinuous environments are addressed by a proposed general generative framework. Our experiments on a Maze game design task show the effectiveness of the proposed algorithms in generating diverse and challenging Mazes against various agent settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.01310v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"228064\", \"183807\", \"210164\", \"250576\"], \"outgoing_citations\": [\"173070\", \"272464\", \"276513\", \"343216\"]}","task_split":"paper_retrieval"}
{"document_id":"262169","document_content":"# Selective Deep Convolutional Features for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.00809v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85376\", \"211153\", \"98517\", \"208928\", \"241679\", \"247969\", \"167650\", \"193735\", \"231046\", \"241439\", \"242722\", \"243056\", \"209652\", \"222623\", \"237228\", \"237962\", \"246847\", \"248393\"], \"outgoing_citations\": [\"269814\", \"288218\", \"292069\", \"294983\", \"295204\", \"301572\", \"303067\", \"316430\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"262300","document_content":"# Where to Play: Retrieval of Video Segments using Natural-Language Queries\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a new approach for retrieval of video segments using natural language queries. Unlike most previous approaches such as concept-based methods or rule-based structured models, the proposed method uses image captioning model to construct sentential queries for visual information. In detail, our approach exploits multiple captions generated by visual features in each image with `Densecap'. Then, the similarities between captions of adjacent images are calculated, which is used to track semantically similar captions over multiple frames. Besides introducing this novel idea of 'tracking by captioning', the proposed method is one of the first approaches that uses a language generation model learned by neural networks to construct semantic query describing the relations and properties of visual information. To evaluate the effectiveness of our approach, we have created a new evaluation dataset, which contains about 348 segments of scenes in 20 movie-trailers. Through quantitative and qualitative evaluation, we show that our method is effective for retrieval of video segments using natural language queries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.00251v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"174227\"], \"outgoing_citations\": [\"299352\", \"302621\", \"303452\"]}","task_split":"paper_retrieval"}
{"document_id":"262361","document_content":"# Synthetic Data for Neural Machine Translation of Spoken-Dialects\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach transforms a given parallel corpus between a written language and a target language to a parallel corpus between a spoken dialect variant and the target language. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language.   The proposed approach is based on local embedding projection of distributed representations which utilizes monolingual embeddings to transform parallel data across language variants. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the generated data can improve a very large scale system by more than 2.8 Bleu points using synthetic spoken data which shows that it can be used to provide a reliable translation system for a spoken dialect that does not have sufficient parallel data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1707.00079v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"45996\", \"51130\", \"181686\", \"197837\", \"205517\", \"211745\", \"238926\"], \"outgoing_citations\": [\"267252\", \"277537\", \"290131\", \"300548\"]}","task_split":"paper_retrieval"}
{"document_id":"262390","document_content":"# Lifelong Learning in Costly Feature Spaces\n## Categories\n- Machine Learning\n## Abstract\nAn important long-term goal in machine learning systems is to build learning agents that, like humans, can learn many tasks over their lifetime, and moreover use information from these tasks to improve their ability to do so efficiently. In this work, our goal is to provide new theoretical insights into the potential of this paradigm. In particular, we propose a lifelong learning framework that adheres to a novel notion of resource efficiency that is critical in many real-world domains where feature evaluations are costly. That is, our learner aims to reuse information from previously learned related tasks to learn future tasks in a feature-efficient manner. Furthermore, we consider novel combinatorial ways in which learning tasks can relate. Specifically, we design lifelong learning algorithms for two structurally different and widely used families of target functions: decision trees\/lists and monomials\/polynomials. We also provide strong feature-efficiency guarantees for these algorithms; in fact, we show that in order to learn future targets, we need only slightly more feature evaluations per training example than what is needed to predict on an arbitrary example using those targets. We also provide algorithms with guarantees in an agnostic model where not all the targets are related to each other. Finally, we also provide lower bounds on the performance of a lifelong learner in these models, which are in fact tight under some conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.10271v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"259648\", \"322195\", \"340961\", \"351617\", \"353208\", \"357176\", \"360780\"]}","task_split":"paper_retrieval"}
{"document_id":"262564","document_content":"# Actor-Critic Sequence Training for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGenerating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing. Such image captioning methods are typically trained by maximising the likelihood of ground-truth annotated caption given the image. While simple and easy to implement, this approach does not directly maximise the language quality metrics we care about such as CIDEr. In this paper we investigate training image captioning methods based on actor-critic reinforcement learning in order to directly optimise non-differentiable quality metrics of interest. By formulating a per-token advantage and value computation strategy in this novel reinforcement learning based captioning model, we show that it is possible to achieve the state of the art performance on the widely used MSCOCO benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.09601v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23109\", \"35427\", \"22827\", \"97719\", \"113677\", \"182259\", \"5837\", \"8679\", \"69490\", \"96600\", \"110600\", \"142094\", \"145513\", \"92983\", \"107473\", \"141957\", \"125555\", \"149063\", \"154752\", \"162598\", \"230774\", \"167962\", \"173531\", \"190585\", \"206058\", \"207049\", \"209504\", \"215181\", \"231619\", \"219687\", \"221169\", \"220509\", \"225315\", \"189653\", \"239242\", \"248855\", \"280385\"], \"outgoing_citations\": [\"279002\", \"280277\", \"284230\", \"287823\", \"311824\", \"313196\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"262592","document_content":"# Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold\n## Categories\n- Computation and Language\n## Abstract\nWe present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates. Built using an extension to the segmental RNN that emphasizes recall, our basic system achieves competitive performance without any calls to a syntactic parser. We then introduce a method that uses phrase-syntactic annotations from the Penn Treebank during training only, through a multitask objective; no parsing is required at training or test time. This \"syntactic scaffold\" offers a cheaper alternative to traditional syntactic pipelining, and achieves state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.09528v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15575\", \"21139\", \"23153\", \"30010\", \"45337\", \"49537\", \"49925\", \"56614\", \"92751\", \"104906\", \"84541\", \"92551\", \"127831\", \"128409\", \"126135\", \"152948\", \"158094\", \"158715\", \"190862\", \"196299\", \"202086\", \"215434\", \"219714\", \"219946\", \"220137\", \"232123\", \"232339\", \"233612\", \"234631\", \"235377\"], \"outgoing_citations\": [\"268059\", \"268263\", \"274817\", \"276174\", \"289403\", \"291941\", \"296283\", \"297349\", \"297648\", \"303003\", \"303038\"]}","task_split":"paper_retrieval"}
{"document_id":"262673","document_content":"# Named Entity Disambiguation for Noisy Text\n## Categories\n- Computation and Language\n## Abstract\nWe address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.09147v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"42791\", \"121898\", \"56931\", \"76611\", \"98430\", \"98792\", \"122283\", \"86291\", \"128110\", \"132445\", \"127760\", \"57677\", \"116263\", \"157168\", \"157181\", \"167165\", \"205857\", \"242296\", \"168892\", \"165378\", \"213451\", \"209228\", \"232527\"], \"outgoing_citations\": [\"295323\", \"296264\", \"300498\"]}","task_split":"paper_retrieval"}
{"document_id":"262904","document_content":"# Photometric Stereo by Hemispherical Metric Embedding\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPhotometric Stereo methods seek to reconstruct the 3d shape of an object from motionless images obtained with varying illumination. Most existing methods solve a restricted problem where the physical reflectance model, such as Lambertian reflectance, is known in advance. In contrast, we do not restrict ourselves to a specific reflectance model. Instead, we offer a method that works on a wide variety of reflectances. Our approach uses a simple yet uncommonly used property of the problem - the sought after normals are points on a unit hemisphere. We present a novel embedding method that maps pixels to normals on the unit hemisphere. Our experiments demonstrate that this approach outperforms existing manifold learning methods for the task of hemisphere embedding. We further show successful reconstructions of objects from a wide variety of reflectances including smooth, rough, diffuse and specular surfaces, even in the presence of significant attached shadows. Finally, we empirically prove that under these challenging settings we obtain more accurate shape reconstructions than existing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10851-017-0748-y\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73779\", \"155918\", \"254510\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"263102","document_content":"# A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nExisting action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions should be naturally one of observation and refinement: observe the current window and refine the span of attended window to cover true action regions. In this paper, we propose an active action proposal model that learns to find actions through continuously adjusting the temporal bounds in a self-adaptive way. The whole process can be deemed as an agent, which is firstly placed at a position in the video at random, adopts a sequence of transformations on the current attended region to discover actions according to a learned policy. We utilize reinforcement learning, especially the Deep Q-learning algorithm to learn the agent's decision policy. In addition, we use temporal pooling operation to extract more effective feature representation for the long temporal window, and design a regression network to adjust the position offsets between predicted results and the ground truth. Experiment results on THUMOS 2014 validate the effectiveness of the proposed approach, which can achieve competitive performance with current action detection algorithms via much fewer proposals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.07251v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"186958\", \"190385\", \"209268\"], \"outgoing_citations\": [\"271198\", \"272028\", \"272029\", \"277459\", \"280674\", \"300351\", \"302753\", \"303040\"]}","task_split":"paper_retrieval"}
{"document_id":"263235","document_content":"# Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions\n## Categories\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nIn this work a system for recognizing grasp points in RGB-D images is proposed. This system is intended to be used by a domestic robot when deploying clothes lying at a random position on a table. By taking into consideration that the grasp points are usually near key parts of clothing, such as the waist of pants or the neck of a shirt. The proposed system attempts to detect these key parts first, using a local multivariate contour that adapts its shape accordingly. Then, the proposed system applies the Vessel Enhancement filter to identify wrinkles in the clothes, allowing to compute a roughness index for the clothes. Finally, by mixing (i) the key part contours and (ii) the roughness information obtained by the vessel filter, the system is able to recognize grasp points for unfolding a piece of clothing. The recognition system is validated using realistic RGB-D images of different cloth types.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.06694v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"18519\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"263270","document_content":"# Grounded Language Learning in a Simulated 3D World\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nWe are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.06551v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"4639\", \"4865\", \"16536\", \"66794\", \"77439\", \"79803\", \"83792\", \"11352\", \"14861\", \"16824\", \"17680\", \"20397\", \"21177\", \"24604\", \"25068\", \"25366\", \"26076\", \"28168\", \"30714\", \"31155\", \"33356\", \"35938\", \"37989\", \"41627\", \"52082\", \"60327\", \"64051\", \"66630\", \"69518\", \"69616\", \"69948\", \"71164\", \"71679\", \"74771\", \"74858\", \"100277\", \"102494\", \"107458\", \"124867\", \"144521\", \"163326\", \"119120\", \"79462\", \"81402\", \"82588\", \"82624\", \"160608\", \"86099\", \"86580\", \"86813\", \"90871\", \"94460\", \"95145\", \"102464\", \"110974\", \"112980\", \"114824\", \"118153\", \"119169\", \"121716\", \"124122\", \"126128\", \"131013\", \"131061\", \"140901\", \"145594\", \"147391\", \"147685\", \"161143\", \"165875\", \"184811\", \"196909\", \"200261\", \"151495\", \"153357\", \"157901\", \"158217\", \"160530\", \"162187\", \"162602\", \"165254\", \"170410\", \"171822\", \"175699\", \"181620\", \"182138\", \"182237\", \"182870\", \"184898\", \"186101\", \"191878\", \"196218\", \"198208\", \"198373\", \"199210\", \"205488\", \"206489\", \"207499\", \"208454\", \"209152\", \"213241\", \"213745\", \"214195\", \"229145\", \"244131\", \"205800\", \"207523\", \"209052\", \"213171\", \"212893\", \"210666\", \"219388\", \"219305\", \"221774\", \"225266\", \"226321\", \"227757\", \"228916\", \"229726\", \"231155\", \"231289\", \"231925\", \"237119\", \"237386\", \"241185\", \"235974\", \"243235\", \"243782\", \"244283\", \"244457\", \"244854\", \"245731\", \"247002\", \"248742\", \"248885\", \"249717\", \"248926\", \"252202\", \"252307\", \"261535\"], \"outgoing_citations\": [\"268335\", \"270315\", \"280296\", \"290891\", \"302943\", \"308711\", \"310370\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"263333","document_content":"# Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a sin- gle language, requiring multiple language-specific models to build a multilingual captioning system. We propose a very simple technique to build a single unified model across languages, using artificial tokens to control the language, making the captioning system more compact. We evaluate our approach on generating English and Japanese captions, and show that a typical neural captioning architecture is capable of learning a single model that can switch between two different languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.06275v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"101277\", \"77165\", \"171726\", \"191861\", \"231022\"], \"outgoing_citations\": [\"300096\", \"300548\", \"300604\", \"302621\", \"304945\"]}","task_split":"paper_retrieval"}
{"document_id":"263345","document_content":"# Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.06247v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"36730\", \"91952\", \"6742\", \"27986\", \"139096\", \"235160\", \"152527\", \"152655\", \"208825\"], \"outgoing_citations\": [\"174767\", \"209118\", \"230095\", \"307292\"]}","task_split":"paper_retrieval"}
{"document_id":"263650","document_content":"# Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nAs a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of generalizations: to previously unseen instructions and to longer sequences of instructions. For generalization over unseen instructions, we propose a new objective which encourages learning correspondences between similar subtasks by making analogies. For generalization over sequential instructions, we present a hierarchical architecture where a meta controller learns to use the acquired skills for executing the instructions. To deal with delayed reward, we propose a new neural architecture in the meta controller that learns when to update the subtask, which makes learning more efficient. Experimental results on a stochastic 3D domain show that the proposed ideas are crucial for generalization to longer instructions as well as unseen instructions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.05064v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"4713\", \"15387\", \"44916\", \"74891\", \"78473\", \"79803\", \"128249\", \"138130\", \"9356\", \"13792\", \"14182\", \"14861\", \"22124\", \"26076\", \"26664\", \"33187\", \"36403\", \"91688\", \"161168\", \"50758\", \"250273\", \"55964\", \"66817\", \"67803\", \"69616\", \"74771\", \"75606\", \"89875\", \"90622\", \"155930\", \"81676\", \"110974\", \"89234\", \"112798\", \"113866\", \"115004\", \"118123\", \"119188\", \"121716\", \"124122\", \"125969\", \"126128\", \"131013\", \"139573\", \"148867\", \"143589\", \"189595\", \"191583\", \"196909\", \"197465\", \"200261\", \"174708\", \"151495\", \"151617\", \"154953\", \"158775\", \"158907\", \"159481\", \"161767\", \"162602\", \"164433\", \"171778\", \"173039\", \"175055\", \"176597\", \"179602\", \"181093\", \"181620\", \"182870\", \"181498\", \"183652\", \"184898\", \"188867\", \"188953\", \"189739\", \"190023\", \"196657\", \"196049\", \"205488\", \"207327\", \"209452\", \"214195\", \"224873\", \"229145\", \"229795\", \"239264\", \"191970\", \"212893\", \"216221\", \"219305\", \"223990\", \"225373\", \"200927\", \"227301\", \"231155\", \"231657\", \"232285\", \"239160\", \"240348\", \"241185\", \"218778\", \"242963\", \"243782\", \"245731\", \"248742\", \"248885\", \"251691\", \"255134\", \"259693\", \"263104\", \"248805\", \"251789\", \"252307\", \"269664\"], \"outgoing_citations\": [\"269251\", \"281106\", \"282503\", \"284134\", \"284567\", \"285259\", \"290261\", \"291568\", \"293876\", \"294142\", \"302660\", \"302952\", \"302967\", \"308711\", \"311272\", \"311945\", \"313427\", \"333638\"]}","task_split":"paper_retrieval"}
{"document_id":"263724","document_content":"# Planning with Verbal Communication for Human-Robot Collaboration\n## Categories\n- Robotics\n## Abstract\nHuman collaborators coordinate effectively their actions through both verbal and non-verbal communication. We believe that the the same should hold for human-robot teams. We propose a formalism that enables a robot to decide optimally between doing a task and issuing an utterance. We focus on two types of utterances: verbal commands, where the robot expresses how it wants its human teammate to behave, and state-conveying actions, where the robot explains why it is behaving this way. Human subject experiments show that enabling the robot to issue verbal commands is the most effective form of communicating objectives, while retaining user trust in the robot. Communicating why information should be done judiciously, since many participants questioned the truthfulness of the robot statements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.04694v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\"], \"incoming_citations\": [\"15027\", \"25974\", \"89488\", \"109829\", \"261706\"], \"outgoing_citations\": [\"176721\", \"334905\"]}","task_split":"paper_retrieval"}
{"document_id":"263743","document_content":"# Provable benefits of representation learning\n## Categories\n- Machine Learning\n## Abstract\nThere is general consensus that learning representations is useful for a variety of reasons, e.g. efficient use of labeled data (semi-supervised learning), transfer learning and understanding hidden structure of data. Popular techniques for representation learning include clustering, manifold learning, kernel-learning, autoencoders, Boltzmann machines, etc.   To study the relative merits of these techniques, it's essential to formalize the definition and goals of representation learning, so that they are all become instances of the same definition. This paper introduces such a formal framework that also formalizes the utility of learning the representation. It is related to previous Bayesian notions, but with some new twists. We show the usefulness of our framework by exhibiting simple and natural settings -- linear mixture models and loglinear models, where the power of representation learning can be formally shown. In these examples, representation learning can be performed provably and efficiently under plausible assumptions (despite being NP-hard), and furthermore: (i) it greatly reduces the need for labeled data (semi-supervised learning) and (ii) it allows solving classification tasks when simpler approaches like nearest neighbors require too much data (iii) it is more powerful than manifold learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.04601v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"222517\", \"119554\", \"142425\", \"159285\", \"197714\"], \"outgoing_citations\": [\"283315\", \"284455\", \"291730\", \"298788\", \"300495\", \"317627\", \"327665\", \"352900\", \"359251\", \"359942\"]}","task_split":"paper_retrieval"}
{"document_id":"263830","document_content":"# Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art temporal action detectors inefficiently search the entire video for specific actions. Despite the encouraging progress these methods achieve, it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched for. To address this need, we propose the new problem of action spotting in video, which we define as finding a specific action in a video while observing a small portion of that video. Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in video, we propose Action Search, a novel Recurrent Neural Network approach that mimics the way humans spot actions. Moreover, to address the absence of data recording the behavior of human annotators, we put forward the Human Searches dataset, which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets. We consider temporal action localization as an application of the action spotting problem. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently (observing on average 17.3% of the video) but it also accurately finds human activities with 30.8% mAP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.04269v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"105457\", \"16624\", \"25656\", \"6118\", \"70490\", \"73447\", \"79928\", \"85254\", \"86396\", \"114403\", \"119193\", \"131105\", \"158237\", \"161563\", \"193069\", \"72063\", \"131988\", \"133806\", \"134624\", \"136718\", \"151953\", \"152989\", \"154204\", \"173326\", \"181520\", \"189453\", \"154824\", \"171242\", \"172734\", \"191546\", \"209268\", \"209587\", \"223115\", \"237132\", \"247497\"], \"outgoing_citations\": [\"258412\", \"259012\", \"259105\", \"265650\", \"267432\", \"268408\", \"270802\", \"271198\", \"272360\", \"287156\", \"293801\", \"295117\", \"300351\", \"302753\", \"321453\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"263853","document_content":"# Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThis paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experiment with several supervised machine learning techniques to classify sentences as to whether they express conditions and actions. We show the limitations and possible extensions of this work on text mining of medical guidelines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.04206v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"58873\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"264144","document_content":"# Decoupling Learning Rules from Representations\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nIn the artificial intelligence field, learning often corresponds to changing the parameters of a parameterized function. A learning rule is an algorithm or mathematical expression that specifies precisely how the parameters should be changed. When creating an artificial intelligence system, we must make two decisions: what representation should be used (i.e., what parameterized function should be used) and what learning rule should be used to search through the resulting set of representable functions. Using most learning rules, these two decisions are coupled in a subtle (and often unintentional) way. That is, using the same learning rule with two different representations that can represent the same sets of functions can result in two different outcomes. After arguing that this coupling is undesirable, particularly when using artificial neural networks, we present a method for partially decoupling these two decisions for a broad class of learning rules that span unsupervised learning, reinforcement learning, and supervised learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.03100v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"207323\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"264189","document_content":"# Assigning personality\/identity to a chatting machine for coherent conversation generation\n## Categories\n- Computation and Language\n## Abstract\nEndowing a chatbot with personality or an identity is quite challenging but critical to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified agent profile. We design a model consisting of three modules: a profile detector to decide whether a post should be responded using the profile and which key should be addressed, a bidirectional decoder to generate responses forward and backward starting from a selected profile value, and a position detector that predicts a word position from which decoding should start given a selected profile value. We show that general conversation data from social media can be used to generate profile-coherent responses. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.02861v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"22624\", \"35073\", \"37028\", \"48093\", \"86922\", \"90943\", \"103765\", \"130818\", \"156814\", \"159262\", \"44423\", \"165745\", \"182985\", \"183270\", \"183857\", \"195238\", \"201322\", \"210493\", \"232268\", \"251348\"], \"outgoing_citations\": [\"289061\", \"289943\", \"291363\", \"291916\", \"294494\", \"295815\", \"296250\", \"302056\", \"310740\", \"316328\"]}","task_split":"paper_retrieval"}
{"document_id":"264212","document_content":"# Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDigital games are one of the major and most important fields on the entertainment domain, which also involves cinema and music. Numerous attempts have been done to improve the quality of the games including more realistic artistic production and computer science. Assessing the player's behavior, a task known as player modeling, is currently the need of the hour which leads to possible improvements in terms of: (i) better game interaction experience, (ii) better exploitation of the relationship between players, and (iii) increasing\/maintaining the number of players interested in the game. In this paper we model players using the basic four behaviors proposed in \\cite{BartleArtigo}, namely: achiever, explorer, socializer and killer. Our analysis is carried out using data obtained from the game \"World of Warcraft\" over 3 years (2006 $-$ 2009). We employ a semi-supervised learning technique in order to find out characteristics that possibly impact player's behavior.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.02780v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"264265","document_content":"# Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)\n## Categories\n- Digital Libraries\n- Information Retrieval\n## Abstract\nThe large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.02509v1\", \"primary_category\": \"cs.DL\", \"categories\": [\"cs.IR\", \"cs.DL\"], \"primary_category_human_readable\": \"Digital Libraries\", \"categories_human_readable\": [\"Information Retrieval\", \"Digital Libraries\"], \"incoming_citations\": [\"236070\", \"304869\", \"319150\"], \"outgoing_citations\": [\"169077\", \"226514\", \"304869\", \"322174\"]}","task_split":"paper_retrieval"}
{"document_id":"264289","document_content":"# Image Captioning with Object Detection and Localization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperforms previous benchmark models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.02430v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"200295\", \"102192\", \"110589\", \"121569\", \"183485\"], \"outgoing_citations\": [\"310816\", \"319781\", \"320003\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"264358","document_content":"# How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- 68T50, 97R40\n- I.2.7\n## Abstract\nSyntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful.   In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources.   The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10462-017-9584-0\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"68T50, 97R40\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"68T50, 97R40\", \"I.2.7\"], \"incoming_citations\": [\"29188\", \"290193\", \"191897\", \"234729\", \"213527\", \"213525\", \"225069\"], \"outgoing_citations\": [\"292570\", \"296283\", \"312066\", \"315546\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"264409","document_content":"# Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity\n## Categories\n- Computation and Language\n## Abstract\nSemantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within specific domains. Therefore in this study we introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology out performs word embedding methods trained on generic corpus and methods trained on domain specific corpus but do not use lexical semantic similarity methods to augment the results. Further, we prove that text lemmatization can improve the performance of word embedding methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICIINFS.2017.8300343\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"5943\", \"10302\", \"87367\", \"87635\", \"89992\", \"95509\", \"122222\", \"181159\", \"182224\", \"165268\", \"215532\", \"218451\", \"230344\", \"257034\", \"256489\"], \"outgoing_citations\": [\"256489\", \"264287\", \"284236\"]}","task_split":"paper_retrieval"}
{"document_id":"264427","document_content":"# Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext\n## Categories\n- Computation and Language\n## Abstract\nWe consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.01847v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23152\", \"25308\", \"41185\", \"52464\", \"13799\", \"15317\", \"94740\", \"21060\", \"26460\", \"26367\", \"51288\", \"53240\", \"56312\", \"73415\", \"158023\", \"89861\", \"98435\", \"106553\", \"126962\", \"128024\", \"128053\", \"140236\", \"152623\", \"164107\", \"164127\", \"181269\", \"197253\", \"208526\", \"207216\", \"210551\", \"212132\", \"234189\", \"234391\", \"234778\", \"235345\", \"234577\", \"239686\", \"250360\", \"251736\", \"260373\"], \"outgoing_citations\": [\"267622\", \"288697\", \"298543\", \"302516\", \"311384\", \"320072\", \"320814\", \"323696\"]}","task_split":"paper_retrieval"}
{"document_id":"264711","document_content":"# Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAction detection and temporal segmentation of actions in videos are topics of increasing interest. While fully supervised systems have gained much attention lately, full annotation of each action within the video is costly and impractical for large amounts of video data. Thus, weakly supervised action detection and temporal segmentation methods are of great importance. While most works in this area assume an ordered sequence of occurring actions to be given, our approach only uses a set of actions. Such action sets provide much less supervision since neither action ordering nor the number of action occurrences are known. In exchange, they can be easily obtained, for instance, from meta-tags, while ordered sequences still require human annotation. We introduce a system that automatically learns to temporally segment and label actions in a video, where the only supervision that is used are action sets. An evaluation on three datasets shows that our method still achieves good results although the amount of supervision is significantly smaller than for other related methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.00699v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17037\", \"17178\", \"46493\", \"46854\", \"24718\", \"29561\", \"39008\", \"52435\", \"58193\", \"58191\", \"61811\", \"63687\", \"73447\", \"85803\", \"135766\", \"74275\", \"123824\", \"126373\", \"127506\", \"133920\", \"139778\", \"144125\", \"151281\", \"190266\", \"208628\", \"164400\", \"181897\", \"194508\", \"196110\", \"231686\"], \"outgoing_citations\": [\"231686\", \"270713\", \"271891\", \"280328\", \"283115\", \"287541\", \"293801\", \"298629\", \"300351\", \"302753\", \"307001\", \"310322\", \"316472\", \"317032\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"264760","document_content":"# A Vision System for Multi-View Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMultimodal biometric identification has been grown a great attention in the most interests in the security fields. In the real world there exist modern system devices that are able to detect, recognize, and classify the human identities with reliable and fast recognition rates. Unfortunately most of these systems rely on one modality, and the reliability for two or more modalities are further decreased. The variations of face images with respect to different poses are considered as one of the important challenges in face recognition systems. In this paper, we propose a multimodal biometric system that able to detect the human face images that are not only one view face image, but also multi-view face images. Each subject entered to the system adjusted their face at front of the three cameras, and then the features of the face images are extracted based on Speeded Up Robust Features (SURF) algorithm. We utilize Multi-Layer Perceptron (MLP) and combined classifiers based on both Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) for classification purposes. The proposed system has been tested using SDUMLA-HMT, and CASIA datasets. Furthermore, we collected a database of multi-view face images by which we take the additive white Gaussian noise into considerations. The results indicated the reliability, robustness of the proposed system with different poses and variations including noise images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.00510v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"307292\", \"317768\", \"334830\"]}","task_split":"paper_retrieval"}
{"document_id":"264814","document_content":"# Learning to Compute Word Embeddings On the Fly\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nWords in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the \"long tail\" of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.00286v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"21391\", \"36127\", \"47162\", \"56692\", \"88130\", \"94483\", \"97505\", \"98815\", \"103442\", \"107936\", \"181704\", \"126763\", \"128088\", \"133388\", \"150634\", \"161104\", \"170862\", \"160242\", \"164657\", \"190863\", \"191718\", \"193228\", \"210384\", \"210276\", \"210690\", \"218475\", \"218515\", \"220041\", \"226692\", \"239699\", \"264250\", \"213353\", \"256804\"], \"outgoing_citations\": [\"264250\", \"272516\", \"281162\", \"283527\", \"292377\", \"292821\", \"308310\", \"311926\", \"315078\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"264846","document_content":"# Cross-modal Common Representation Learning by Hybrid Transfer Network\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nDNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image\/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.00153v2\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.CV\", \"cs.MM\", \"cs.LG\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\", \"Machine Learning\"], \"incoming_citations\": [\"71008\", \"82645\", \"89112\", \"187947\", \"259090\", \"211129\", \"214976\", \"239436\", \"240094\"], \"outgoing_citations\": [\"310233\"]}","task_split":"paper_retrieval"}
{"document_id":"264856","document_content":"# Teaching Machines to Describe Images via Natural Language Feedback\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n## Abstract\nRobots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. We propose a hierarchical phrase-based captioning model trained with policy gradients, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1706.00130v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"69518\", \"97596\", \"181399\", \"80053\", \"91704\", \"94268\", \"102649\", \"126793\", \"134565\", \"135506\", \"158701\", \"170299\", \"175055\", \"182578\", \"156062\", \"199210\", \"207755\", \"213330\", \"218363\", \"218760\", \"227757\", \"232584\", \"239242\", \"258592\"], \"outgoing_citations\": [\"254514\", \"254515\", \"268584\", \"271234\", \"279002\", \"279215\", \"279518\", \"280004\", \"286237\", \"294144\", \"317592\", \"322022\", \"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"264940","document_content":"# The Atari Grand Challenge Dataset\n## Categories\n- Artificial Intelligence\n## Abstract\nRecent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is to augment RL with learning from human demonstrations. However, human demonstration data is not yet readily available. This hinders progress in this direction. The present work addresses this problem as follows. We (i) collect and describe a large dataset of human Atari 2600 replays -- the largest and most diverse such data set publicly released to date, (ii) illustrate an example use of this dataset by analyzing the relation between demonstration quality and imitation learning performance, and (iii) outline possible research directions that are opened up by our work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.10998v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"18254\", \"77210\", \"21558\", \"64631\", \"106471\", \"123726\", \"133479\", \"163661\", \"194964\", \"159934\", \"173914\", \"187248\", \"190766\", \"196377\", \"205179\", \"213070\", \"204925\", \"231364\", \"256169\"], \"outgoing_citations\": [\"273607\", \"278545\", \"279883\", \"288231\", \"292392\"]}","task_split":"paper_retrieval"}
{"document_id":"265097","document_content":"# Learning to Generate Chairs with Generative Adversarial Nets\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nGenerative adversarial networks (GANs) has gained tremendous popularity lately due to an ability to reinforce quality of its predictive model with generated objects and the quality of the generative model with and supervised feedback. GANs allow to synthesize images with a high degree of realism. However, the learning process of such models is a very complicated optimization problem and certain limitation for such models were found. It affects the choice of certain layers and nonlinearities when designing architectures. In particular, it does not allow to train convolutional GAN models with fully-connected hidden layers. In our work, we propose a modification of the previously described set of rules, as well as new approaches to designing architectures that will allow us to train more powerful GAN models. We show the effectiveness of our methods on the problem of synthesizing projections of 3D objects with the possibility of interpolation by class and view point.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.10413v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"270436\", \"282030\", \"298700\"]}","task_split":"paper_retrieval"}
{"document_id":"265144","document_content":"# On Multilingual Training of Neural Dependency Parsers\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWe show that a recently proposed neural dependency parser can be improved by joint training on multiple languages from the same family. The parser is implemented as a deep neural network whose only input is orthographic representations of words. In order to successfully parse, the network has to discover how linguistically relevant concepts can be inferred from word spellings. We analyze the representations of characters and words that are learned by the network to establish which properties of languages were accounted for. In particular we show that the parser has approximately learned to associate Latin characters with their Cyrillic counterparts and that it can group Polish and Russian words that have a similar grammatical function. Finally, we evaluate the parser on selected languages from the Universal Dependencies dataset and show that it is competitive with other recently proposed state-of-the art methods, while having a simple structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.10209v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"271489\", \"281121\", \"284904\", \"291192\", \"296283\", \"296583\", \"298750\", \"298944\", \"308559\", \"311926\", \"312066\", \"319833\", \"320874\"]}","task_split":"paper_retrieval"}
{"document_id":"265222","document_content":"# Listen, Interact and Talk: Learning to Speak via Interaction\n## Categories\n- Computation and Language\n## Abstract\nOne of the long-term goals of artificial intelligence is to build an agent that can communicate intelligently with human in natural language. Most existing work on natural language learning relies heavily on training over a pre-collected dataset with annotated labels, leading to an agent that essentially captures the statistics of the fixed external training data. As the training data is essentially a static snapshot representation of the knowledge from the annotator, the agent trained this way is limited in adaptiveness and generalization of its behavior. Moreover, this is very different from the language learning process of humans, where language is acquired during communication by taking speaking action and learning from the consequences of speaking action in an interactive manner. This paper presents an interactive setting for grounded natural language learning, where an agent learns natural language by interacting with a teacher and learning from feedback, thus learning and improving language skills while taking part in the conversation. To achieve this goal, we propose a model which incorporates both imitation and reinforcement by leveraging jointly sentence and reward feedbacks from the teacher. Experiments are conducted to validate the effectiveness of the proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.09906v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"85921\", \"173688\", \"202748\", \"234141\", \"241921\"], \"outgoing_citations\": [\"271085\", \"271367\", \"271497\", \"275597\", \"277491\", \"287823\", \"291892\", \"294144\", \"303349\", \"304481\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"265431","document_content":"# Learning Structured Text Representations\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.09207v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"16648\", \"26223\", \"50552\", \"22837\", \"24348\", \"42980\", \"45305\", \"56047\", \"61917\", \"79923\", \"91749\", \"92431\", \"94147\", \"94951\", \"95439\", \"103402\", \"128048\", \"139168\", \"82764\", \"87924\", \"88699\", \"93783\", \"95287\", \"96467\", \"123453\", \"125340\", \"127048\", \"129959\", \"147382\", \"153403\", \"154008\", \"158855\", \"165141\", \"168506\", \"171295\", \"169337\", \"179274\", \"180935\", \"182601\", \"181717\", \"183462\", \"191740\", \"197381\", \"210331\", \"223351\", \"250549\", \"268654\", \"212708\", \"208658\", \"219022\", \"219352\", \"225130\", \"232339\", \"234631\", \"230281\", \"242461\", \"255999\", \"256948\", \"255954\"], \"outgoing_citations\": [\"273977\", \"274591\", \"274817\", \"276415\", \"281225\", \"284357\", \"290747\", \"296289\", \"299541\", \"300803\", \"306182\", \"307076\", \"339803\"]}","task_split":"paper_retrieval"}
{"document_id":"265645","document_content":"# Personalized and Private Peer-to-Peer Machine Learning\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Distributed, Parallel, and Cluster Computing\n- Systems and Control\n## Abstract\nThe rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and asynchronous fashion, with provable convergence rate. We show how to make the algorithm differentially private to protect against the disclosure of information about the personal datasets, and formally analyze the trade-off between utility and privacy. Our experiments show that our approach dramatically outperforms previous work in the non-private case, and that under privacy constraints, we can significantly improve over models learned in isolation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.08435v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.DC\", \"cs.SY\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Distributed, Parallel, and Cluster Computing\", \"Systems and Control\", \"Machine Learning\"], \"incoming_citations\": [\"316453\", \"2324\", \"8843\", \"14434\", \"35312\", \"81561\", \"104850\", \"118784\", \"10455\", \"21113\", \"28004\", \"33423\", \"41882\", \"42689\", \"45331\", \"48498\", \"70442\", \"70515\", \"90071\", \"151798\", \"86123\", \"91963\", \"118055\", \"134225\", \"138126\", \"138770\", \"140296\", \"142802\", \"144978\", \"169981\", \"201741\", \"219797\", \"162301\", \"198386\", \"208435\", \"237677\"], \"outgoing_citations\": [\"265459\", \"273221\", \"282501\", \"290900\", \"298533\", \"327473\", \"329116\", \"338290\", \"353750\"]}","task_split":"paper_retrieval"}
{"document_id":"265649","document_content":"# Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach\n## Categories\n- Machine Learning\n## Abstract\nSepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. Understanding more about a patient's physiological state at a given time could hold the key to effective treatment policies. In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Learning treatment policies over continuous spaces is important, because we retain more of the patient's physiological information. Our model is able to learn clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. Evaluating our algorithm on past ICU patient data, we find that our model could reduce patient mortality in the hospital by up to 3.6% over observed clinical policies, from a baseline mortality of 13.7%. The learned treatment policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.08422v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"170695\", \"4944\", \"27893\", \"100714\", \"145459\", \"2340\", \"17932\", \"31177\", \"33819\", \"34568\", \"43996\", \"49476\", \"64122\", \"68217\", \"98984\", \"100217\", \"113545\", \"157159\", \"95628\", \"109865\", \"116584\", \"126139\", \"134870\", \"137194\", \"147386\", \"147531\", \"147841\", \"165849\", \"175452\", \"165848\", \"173339\", \"175034\", \"181862\", \"182128\", \"184671\", \"189070\", \"179461\", \"201685\", \"221387\", \"202934\", \"207739\", \"208650\", \"208939\", \"210057\", \"213308\", \"204688\", \"225819\", \"225954\", \"242890\", \"244088\", \"250234\", \"248533\"], \"outgoing_citations\": [\"268399\", \"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"265650","document_content":"# AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly.   AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.08421v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4419\", \"19821\", \"49481\", \"67492\", \"79061\", \"90690\", \"2414\", \"3517\", \"3652\", \"4099\", \"5630\", \"7907\", \"13040\", \"116568\", \"16624\", \"17028\", \"20134\", \"22148\", \"24139\", \"24485\", \"33472\", \"41275\", \"41644\", \"47754\", \"48098\", \"58711\", \"60854\", \"75666\", \"79797\", \"87901\", \"92440\", \"93849\", \"97623\", \"226421\", \"2022\", \"2561\", \"2915\", \"4349\", \"5844\", \"9408\", \"10218\", \"15226\", \"8624\", \"16669\", \"20116\", \"21483\", \"23190\", \"27514\", \"30524\", \"30800\", \"31452\", \"33635\", \"38830\", \"40825\", \"41975\", \"40498\", \"41279\", \"153684\", \"36087\", \"47504\", \"47571\", \"47899\", \"48333\", \"49317\", \"50219\", \"50518\", \"52720\", \"53361\", \"54088\", \"54106\", \"54820\", \"55636\", \"57069\", \"57699\", \"58024\", \"58625\", \"58701\", \"58878\", \"59021\", \"59852\", \"61783\", \"62270\", \"65743\", \"66688\", \"73557\", \"69231\", \"77297\", \"78805\", \"79928\", \"115595\", \"82059\", \"83415\", \"84482\", \"85752\", \"89561\", \"92660\", \"94012\", \"94658\", \"100429\", \"101255\", \"104700\", \"106968\", \"110741\", \"112079\", \"118725\", \"129330\", \"131105\", \"135766\", \"147297\", \"147240\", \"175441\", \"176502\", \"74275\", \"81067\", \"82333\", \"82802\", \"82939\", \"84898\", \"90224\", \"93326\", \"93737\", \"93736\", \"84904\", \"103139\", \"103606\", \"104065\", \"105199\", \"86729\", \"107487\", \"108108\", \"108687\", \"108870\", \"97125\", \"108985\", \"110491\", \"110579\", \"110933\", \"112860\", \"113174\", \"113259\", \"113971\", \"115066\", \"115673\", \"116387\", \"118249\", \"119114\", \"119124\", \"112778\", \"115709\", \"119347\", \"122131\", \"122308\", \"123789\", \"123824\", \"125604\", \"126038\", \"126167\", \"126816\", \"127718\", \"122069\", \"129582\", \"129759\", \"130892\", \"131199\", \"131988\", \"132787\", \"133291\", \"103320\", \"133489\", \"133798\", \"128128\", \"134054\", \"134177\", \"134341\", \"134333\", \"137405\", \"138320\", \"139657\", \"139906\", \"142846\", \"143370\", \"145835\", \"146162\", \"146250\", \"146748\", \"147305\", \"150149\", \"151123\", \"151967\", \"154471\", \"154392\", \"154328\", \"162372\", \"188955\", \"188920\", \"191491\", \"204381\", \"205340\", \"245503\", \"151277\", \"151970\", \"154511\", \"155418\", \"156194\", \"158587\", \"133973\", \"162083\", \"159991\", \"180920\", \"165768\", \"74267\", \"164433\", \"169744\", \"170724\", \"171242\", \"172023\", \"172394\", \"174419\", \"174519\", \"175178\", \"176053\", \"177740\", \"182790\", \"181514\", \"184136\", \"186561\", \"186839\", \"187017\", \"188453\", \"189706\", \"190706\", \"190746\", \"191556\", \"192531\", \"192586\", \"191676\", \"192886\", \"196110\", \"197481\", \"199466\", \"202187\", \"203911\", \"205750\", \"206227\", \"206525\", \"206643\", \"206940\", \"207386\", \"208075\", \"208819\", \"209268\", \"219956\", \"222726\", \"236777\", \"238717\", \"246521\", \"247238\", \"206016\", \"206483\", \"207720\", \"220204\", \"220550\", \"221655\", \"222205\", \"222332\", \"222541\", \"222621\", \"222907\", \"223041\", \"223115\", \"223280\", \"225414\", \"226384\", \"226467\", \"227837\", \"228532\", \"226232\", \"231215\", \"235813\", \"235880\", \"237175\", \"242865\", \"244447\", \"247497\", \"247535\", \"248108\", \"248198\", \"263830\", \"268231\", \"268408\", \"278574\"], \"outgoing_citations\": [\"245503\", \"256866\", \"263838\", \"267280\", \"268721\", \"269881\", \"270117\", \"279542\", \"283807\", \"287156\", \"287766\", \"292421\", \"293801\", \"293871\", \"294114\", \"295117\", \"299439\", \"309231\", \"311710\", \"312703\", \"321453\"]}","task_split":"paper_retrieval"}
{"document_id":"265656","document_content":"# Continual Learning in Generative Adversarial Nets\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nDevelopments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.08395v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"19213\", \"2779\", \"4852\", \"14590\", \"31665\", \"36346\", \"43988\", \"68785\", \"138200\", \"5080\", \"7741\", \"16452\", \"5064\", \"55852\", \"56145\", \"59177\", \"59918\", \"77673\", \"80101\", \"89278\", \"102779\", \"137850\", \"2046\", \"2044\", \"53811\", \"82657\", \"111246\", \"115945\", \"118950\", \"114889\", \"128729\", \"145011\", \"178420\", \"148291\", \"90537\", \"174766\", \"192027\", \"205108\", \"208408\", \"208413\", \"212766\", \"208446\", \"218940\", \"220865\", \"225054\", \"252086\", \"264073\", \"267527\"], \"outgoing_citations\": [\"282410\"]}","task_split":"paper_retrieval"}
{"document_id":"265774","document_content":"# Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPRW.2017.282\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"49755\", \"116784\", \"108136\", \"130511\", \"132969\", \"133104\", \"189054\", \"234576\", \"8342\", \"36860\", \"43754\", \"51376\", \"61713\", \"64085\", \"73710\", \"91329\", \"108114\", \"79705\", \"92136\", \"108157\", \"115542\", \"136246\", \"138282\", \"138696\", \"147257\", \"101047\", \"156772\", \"55798\", \"173813\", \"170058\", \"192309\", \"196350\", \"213225\", \"233838\", \"230630\", \"244342\"], \"outgoing_citations\": [\"271011\", \"287271\", \"292743\", \"303467\", \"303828\", \"343074\"]}","task_split":"paper_retrieval"}
{"document_id":"265915","document_content":"# Ensemble Sampling\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of complex models such as neural networks. Ensemble sampling dramatically expands on the range of applications for which Thompson sampling is viable. We establish a theoretical basis that supports the approach and present computational results that offer further insight.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.07347v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"8626\", \"14643\", \"17694\", \"21031\", \"34477\", \"90380\", \"119001\", \"186742\", \"194224\", \"198367\", \"210320\", \"227402\", \"228732\", \"243308\", \"251775\", \"256399\", \"270851\", \"297526\", \"298281\", \"316868\", \"323209\", \"323662\", \"327781\", \"332227\", \"344075\"]}","task_split":"paper_retrieval"}
{"document_id":"265966","document_content":"# AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nApproximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate inference algorithm on a specific data set. This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms. AIDE is based on the observation that inference algorithms can be treated as probabilistic models and the random variables used within the inference algorithm can be viewed as auxiliary variables. This view leads to a new estimator for the symmetric KL divergence between the approximating distributions of two inference algorithms. The paper illustrates application of AIDE to algorithms for inference in regression, hidden Markov, and Dirichlet process mixture models. The experiments show that AIDE captures the qualitative behavior of a broad class of inference algorithms and can detect failure modes of inference algorithms that are missed by standard heuristics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.07224v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"141332\", \"115820\", \"347563\", \"195487\"], \"outgoing_citations\": [\"265422\", \"290933\", \"303764\", \"311475\"]}","task_split":"paper_retrieval"}
{"document_id":"266182","document_content":"# Automatic Goal Generation for Reinforcement Learning Agents\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nReinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.06366v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"12555\", \"28573\", \"44400\", \"7709\", \"14284\", \"18466\", \"37758\", \"46165\", \"47401\", \"91442\", \"78473\", \"79803\", \"104784\", \"106428\", \"109508\", \"115868\", \"5000\", \"5873\", \"10167\", \"10740\", \"10927\", \"13020\", \"13348\", \"13366\", \"13877\", \"15365\", \"18748\", \"18892\", \"21298\", \"22025\", \"22613\", \"23294\", \"24821\", \"27630\", \"31333\", \"33080\", \"33029\", \"38947\", \"39374\", \"41008\", \"41076\", \"41518\", \"42488\", \"43396\", \"43616\", \"45153\", \"45630\", \"46780\", \"49771\", \"53903\", \"58792\", \"62425\", \"62673\", \"63319\", \"63604\", \"66832\", \"68436\", \"68670\", \"70713\", \"75537\", \"75738\", \"89875\", \"93791\", \"179978\", \"97223\", \"101182\", \"105761\", \"112415\", \"114931\", \"116987\", \"123568\", \"145980\", \"151443\", \"161444\", \"173946\", \"77975\", \"84279\", \"84272\", \"86659\", \"92451\", \"96956\", \"107705\", \"109549\", \"113883\", \"116978\", \"116853\", \"117297\", \"117376\", \"118051\", \"87362\", \"119169\", \"121762\", \"125315\", \"123596\", \"128167\", \"129185\", \"132581\", \"132719\", \"136046\", \"137299\", \"137441\", \"137546\", \"137900\", \"140833\", \"140901\", \"141480\", \"141813\", \"143299\", \"143889\", \"147597\", \"149777\", \"151100\", \"161213\", \"175488\", \"180894\", \"189573\", \"195849\", \"207119\", \"262325\", \"38653\", \"152041\", \"155441\", \"158110\", \"158349\", \"161468\", \"162994\", \"158905\", \"164505\", \"167562\", \"171112\", \"177345\", \"179966\", \"180118\", \"180259\", \"180885\", \"180928\", \"181066\", \"182625\", \"181498\", \"185410\", \"185860\", \"187391\", \"200574\", \"200772\", \"200754\", \"203756\", \"207730\", \"214238\", \"208351\", \"215425\", \"217577\", \"237044\", \"239091\", \"207983\", \"215688\", \"225841\", \"227065\", \"228299\", \"231175\", \"241436\", \"244294\", \"250066\", \"242202\", \"271375\", \"261017\", \"261983\"], \"outgoing_citations\": [\"247194\", \"261017\", \"263139\", \"269251\", \"269253\", \"271375\", \"272290\", \"273608\", \"280457\", \"282503\", \"290371\", \"291025\", \"291460\", \"293922\", \"298651\", \"343050\", \"350010\"]}","task_split":"paper_retrieval"}
{"document_id":"266198","document_content":"# Transfer Learning for Named-Entity Recognition with Neural Networks\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nRecent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.06273v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"2566\", \"70000\", \"205022\", \"9456\", \"10012\", \"49866\", \"68826\", \"79090\", \"81948\", \"99542\", \"121872\", \"129260\", \"142316\", \"143366\", \"146876\", \"146051\", \"186296\", \"192906\", \"201404\", \"177599\", \"181177\", \"185216\", \"198134\", \"212221\", \"213175\", \"214140\", \"214221\", \"220797\", \"225793\", \"234384\", \"249726\"], \"outgoing_citations\": [\"266390\", \"290680\", \"296264\", \"303019\", \"306859\"]}","task_split":"paper_retrieval"}
{"document_id":"266207","document_content":"# Learning to Represent Haptic Feedback for Partially-Observable Tasks\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.06243v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"115340\", \"67399\", \"86496\", \"86562\", \"177272\", \"93344\", \"174114\", \"153438\", \"166873\", \"210843\", \"213161\", \"232484\", \"235276\", \"252592\"], \"outgoing_citations\": [\"303020\", \"303245\", \"306266\", \"310624\", \"314620\", \"317863\", \"336241\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"266317","document_content":"# All-relevant feature selection using multidimensional filters with exhaustive search\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper describes a method for identification of the informative variables in the information system with discrete decision variables. It is targeted specifically towards discovery of the variables that are non-informative when considered alone, but are informative when the synergistic interactions between multiple variables are considered. To this end, the mutual entropy of all possible k-tuples of variables with decision variable is computed. Then, for each variable the maximal information gain due to interactions with other variables is obtained. For non-informative variables this quantity conforms to the well known statistical distributions. This allows for discerning truly informative variables from non-informative ones. For demonstration of the approach, the method is applied to several synthetic datasets that involve complex multidimensional interactions between variables. It is capable of identifying most important informative variables, even in the case when the dimensionality of the analysis is smaller than the true dimensionality of the problem. What is more, the high sensitivity of the algorithm allows for detection of the influence of nuisance variables on the response variable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.05756v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"128352\", \"212190\"], \"outgoing_citations\": [\"306040\"]}","task_split":"paper_retrieval"}
{"document_id":"266439","document_content":"# Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis\n## Categories\n- Machine Learning\n## Abstract\nCritically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuous-time probabilistic model for a monitored patient's temporal sequence of physiological data. Our model captures \"informatively sampled\" patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process. In addition, our model captures \"informatively censored\" patient episodes by representing the patient's latent clinical states as an absorbing semi-Markov jump process. The model parameters are learned from offline patient episodes in the electronic health records via an EM-based algorithm. Experiments conducted on a cohort of patients admitted to a major medical center over a 3-year period show that risk prognosis based on our model significantly outperforms the currently deployed medical risk scores and other baseline machine learning algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.05267v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"44525\", \"139571\", \"188692\", \"249498\", \"213089\", \"239123\", \"241511\", \"277700\"], \"outgoing_citations\": [\"284188\"]}","task_split":"paper_retrieval"}
{"document_id":"266688","document_content":"# Efficient Dynamic Programming Solution to a Platoon Coordination Merge Problem With Stochastic Travel Times\n## Categories\n- Systems and Control\n## Abstract\nThe problem of maximizing the probability of two trucks being coordinated to merge into a platoon on a highway is considered. Truck platooning is a promising technology that allows heavy vehicles to save fuel by driving with small automatically controlled inter-vehicle distances. In order to leverage the full potential of platooning, platoons can be formed dynamically en route by small adjustments to their speeds. However, in heavily used parts of the road network, travel times are subject to random disturbances originating from traffic, weather and other sources. We formulate this problem as a stochastic dynamic programming problem over a finite horizon, for which solutions can be computed using a backwards recursion. By exploiting the characteristics of the problem, we derive bounds on the set of states that have to be explored at every stage, which in turn reduces the complexity of computing the solution. Simulations suggest that the approach is applicable to realistic problem instances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.04222v1\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [], \"outgoing_citations\": [\"304663\", \"309656\"]}","task_split":"paper_retrieval"}
{"document_id":"266702","document_content":"# Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nSolving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.04146v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3645\", \"9230\", \"21653\", \"32042\", \"39414\", \"40217\", \"48678\", \"96833\", \"12125\", \"12817\", \"13400\", \"13374\", \"16185\", \"18243\", \"21843\", \"101113\", \"37796\", \"42763\", \"43967\", \"49206\", \"118856\", \"55131\", \"56248\", \"61074\", \"61915\", \"64908\", \"67313\", \"78252\", \"79536\", \"92714\", \"94145\", \"94434\", \"95680\", \"97282\", \"98424\", \"98821\", \"101855\", \"103442\", \"38612\", \"123088\", \"124449\", \"132440\", \"129004\", \"162933\", \"220655\", \"265119\", \"161625\", \"161669\", \"169738\", \"179990\", \"183403\", \"192616\", \"196980\", \"197855\", \"203186\", \"209413\", \"211865\", \"198011\", \"207428\", \"210184\", \"210215\", \"218723\", \"220041\", \"224981\", \"227367\", \"229524\", \"234382\", \"238788\", \"241110\", \"246516\", \"251691\", \"260144\", \"263306\", \"265674\"], \"outgoing_citations\": [\"273118\", \"283758\", \"287181\", \"290515\", \"295742\", \"296138\", \"302970\", \"303306\"]}","task_split":"paper_retrieval"}
{"document_id":"266848","document_content":"# Relevance-based Word Embedding\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nLearning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.03556v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"27696\", \"64260\", \"64447\", \"61211\", \"47090\", \"78994\", \"145260\", \"148015\", \"150095\", \"186694\", \"194919\", \"154856\", \"167766\", \"168682\", \"174566\", \"190352\", \"190190\", \"190658\", \"201803\", \"205190\", \"210902\", \"228254\", \"249381\", \"262420\", \"260327\", \"260668\", \"267775\"], \"outgoing_citations\": [\"267775\", \"291855\"]}","task_split":"paper_retrieval"}
{"document_id":"267123","document_content":"# Learning Distributed Representations of Texts and Entities from Knowledge Base\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nWe describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.02494v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"121898\", \"24055\", \"54221\", \"55194\", \"66795\", \"84249\", \"98792\", \"127749\", \"88658\", \"86291\", \"97100\", \"132445\", \"122351\", \"147649\", \"138850\", \"157168\", \"167165\", \"205857\", \"156729\", \"122498\", \"137257\", \"164387\", \"168892\", \"165378\", \"171884\", \"169340\", \"175482\", \"182409\", \"185861\", \"191350\", \"181078\", \"200370\", \"208559\", \"208638\", \"220548\", \"209228\", \"228842\", \"228030\", \"232329\", \"233941\", \"238024\", \"243397\"], \"outgoing_citations\": [\"178230\", \"287676\", \"290102\", \"290387\", \"298543\", \"300498\", \"302516\", \"302930\", \"311849\", \"315078\"]}","task_split":"paper_retrieval"}
{"document_id":"267226","document_content":"# Learning to see people like people\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nHumans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc) to subjective judgments (facial attractiveness, trustworthiness, sociability, friendliness, etc). While the objective aspects of face perception have been extensively studied, relatively fewer computational models have been developed for the social impressions of faces. Bridging this gap, we develop a method to predict human impressions of faces in 40 subjective social dimensions, using deep representations from state-of-the-art neural networks. We find that model performance grows as the human consensus on a face trait increases, and that model predictions outperform human groups in correlation with human averages. This illustrates the learnability of subjective social perception of faces, especially when there is high human consensus. Our system can be used to decide which photographs from a personal collection will make the best impression. The results are significant for the field of social robotics, demonstrating that robots can learn the subjective judgments defining the underlying fabric of human interaction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.04282v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"281922\", \"302701\", \"304452\", \"311084\"]}","task_split":"paper_retrieval"}
{"document_id":"267423","document_content":"# A Rule-Based Computational Model of Cognitive Arithmetic\n## Categories\n- Artificial Intelligence\n- Neurons and Cognition\n## Abstract\nCognitive arithmetic studies the mental processes used in solving math problems. This area of research explores the retrieval mechanisms and strategies used by people during a common cognitive task. Past research has shown that human performance in arithmetic operations is correlated to the numerical size of the problem. Past research on cognitive arithmetic has pinpointed this trend to either retrieval strength, error checking, or strategy-based approaches when solving equations. This paper describes a rule-based computational model that performs the four major arithmetic operations (addition, subtraction, multiplication and division) on two operands. We then evaluated our model to probe its validity in representing the prevailing concepts observed in psychology experiments from the related works. The experiments specifically explore the problem size effect, an activation-based model for fact retrieval, backup strategies when retrieval fails, and finally optimization strategies when faced with large operands. From our experimental results, we concluded that our model's response times were comparable to results observed when people performed similar tasks during psychology experiments. The fit of our model in reproducing these results and incorporating accuracy into our model are discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.01208v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neurons and Cognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"267500","document_content":"# STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nIn recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.00823v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38226\", \"53303\", \"101277\", \"20485\", \"23204\", \"24321\", \"24382\", \"44917\", \"50311\", \"58992\", \"62836\", \"69434\", \"80826\", \"121147\", \"175031\", \"84886\", \"91237\", \"93574\", \"141196\", \"144619\", \"153733\", \"157257\", \"162846\", \"183035\", \"199724\", \"231022\", \"211986\", \"229967\", \"253508\", \"252903\", \"260373\", \"261917\"], \"outgoing_citations\": [\"293418\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"267692","document_content":"# Semi-supervised sequence tagging with bidirectional language models\n## Categories\n- Computation and Language\n## Abstract\nPre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.00108v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"10617\", \"11588\", \"23273\", \"32042\", \"66358\", \"140362\", \"169593\", \"205022\", \"11894\", \"13669\", \"22549\", \"22655\", \"23847\", \"24562\", \"24713\", \"38212\", \"43485\", \"45529\", \"55948\", \"56248\", \"56550\", \"62068\", \"67375\", \"73607\", \"68987\", \"76043\", \"77800\", \"79090\", \"91742\", \"102439\", \"107653\", \"130712\", \"141780\", \"142002\", \"142497\", \"156620\", \"170375\", \"2949\", \"82594\", \"86408\", \"87226\", \"88739\", \"88989\", \"238071\", \"89900\", \"91761\", \"91766\", \"94175\", \"94619\", \"95863\", \"96800\", \"97725\", \"103442\", \"107184\", \"159902\", \"117679\", \"121494\", \"121872\", \"123503\", \"123472\", \"127344\", \"127340\", \"127555\", \"127635\", \"127705\", \"128082\", \"128357\", \"128868\", \"132251\", \"128934\", \"136153\", \"148520\", \"149817\", \"151106\", \"154109\", \"154642\", \"154905\", \"156364\", \"164303\", \"196679\", \"220673\", \"152767\", \"157092\", \"156640\", \"158750\", \"159672\", \"161396\", \"164657\", \"158405\", \"166780\", \"167100\", \"168008\", \"168399\", \"168832\", \"166025\", \"169362\", \"169511\", \"169687\", \"170053\", \"170606\", \"170712\", \"171165\", \"171515\", \"171754\", \"172844\", \"174411\", \"176377\", \"176605\", \"177114\", \"177599\", \"178587\", \"178889\", \"179635\", \"179881\", \"180181\", \"180390\", \"180997\", \"181741\", \"182220\", \"182316\", \"182715\", \"180582\", \"184694\", \"195727\", \"185514\", \"185889\", \"187987\", \"189601\", \"191471\", \"192408\", \"192937\", \"193560\", \"164377\", \"197273\", \"197584\", \"197921\", \"199309\", \"201539\", \"203064\", \"208668\", \"211603\", \"211979\", \"214387\", \"214654\", \"93297\", \"179458\", \"205710\", \"206185\", \"211710\", \"210435\", \"212472\", \"214121\", \"214221\", \"215070\", \"215986\", \"216212\", \"216925\", \"218063\", \"219946\", \"220159\", \"221566\", \"221599\", \"219618\", \"219866\", \"223096\", \"224699\", \"228348\", \"227993\", \"228681\", \"231803\", \"232527\", \"232576\", \"232980\", \"233047\", \"233136\", \"233064\", \"234783\", \"238321\", \"242126\", \"244755\", \"247808\", \"250586\", \"255092\", \"256158\", \"259009\", \"263027\", \"264202\"], \"outgoing_citations\": [\"271158\", \"281169\", \"281225\", \"298543\", \"298750\", \"311683\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"267763","document_content":"# Fuel-Efficient En Route Formation of Truck Platoons\n## Categories\n- Systems and Control\n## Abstract\nThe problem of how to coordinate a large fleet of trucks with given itinerary to enable fuel-efficient platooning is considered. Platooning is a promising technology that enables trucks to save significant amounts of fuel by driving close together and thus reducing air drag. A setting is considered in which each truck in a fleet is provided with a start location, a destination, a departure time, and an arrival deadline from a higher planning level. Fuel-efficient plans should be computed. The plans consist of routes and speed profiles that allow trucks to arrive by their arrival deadlines. Hereby, trucks can meet on common parts of their routes and form platoons, resulting in decreased fuel consumption.   We formulate a combinatorial optimization problem that combines plans involving only two vehicles. We show that this problem is hard to solve for large problem instances. Hence a heuristic algorithm is proposed. The resulting plans are further optimized using convex optimization techniques. The method is evaluated with Monte Carlo simulations in a realistic setting. We demonstrate that the proposed algorithm can compute plans for thousands of trucks and that significant fuel savings can be achieved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.08836v1\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [\"59037\", \"134376\", \"219171\", \"172740\", \"194965\", \"218662\", \"224083\", \"313444\"], \"outgoing_citations\": [\"304647\", \"304663\", \"309656\"]}","task_split":"paper_retrieval"}
{"document_id":"267769","document_content":"# A new image compression by gradient Haar wavelet\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nWith the development of human communications the usage of Visual Communications has also increased. The advancement of image compression methods is one of the main reasons for the enhancement. This paper first presents main modes of image compression methods such as JPEG and JPEG2000 without mathematical details. Also, the paper describes gradient Haar wavelet transforms in order to construct a preliminary image compression algorithm. Then, a new image compression method is proposed based on the preliminary image compression algorithm that can improve standards of image compression. The new method is compared with original modes of JPEG and JPEG2000 (based on Haar wavelet) by image quality measures such as MAE, PSNAR, and SSIM. The image quality and statistical results confirm that can boost image compression standards. It is suggested that the new method is used in a part or all of an image compression standard.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.08822v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"295023\", \"319563\"]}","task_split":"paper_retrieval"}
{"document_id":"267838","document_content":"# A Survey of Neural Network Techniques for Feature Extraction from Text\n## Categories\n- Computation and Language\n- 68T50\n## Abstract\nThis paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.08531v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\"], \"incoming_citations\": [\"189371\", \"229196\", \"242451\"], \"outgoing_citations\": [\"305591\", \"311849\"]}","task_split":"paper_retrieval"}
{"document_id":"267898","document_content":"# Limits of End-to-End Learning\n## Categories\n- Machine Learning\n## Abstract\nEnd-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning system is specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all \"peripheral\" modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex.   In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of scaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.08305v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3823\", \"16130\", \"20579\", \"39284\", \"71187\", \"87442\", \"5820\", \"25508\", \"30264\", \"55970\", \"81250\", \"95939\", \"112756\", \"161638\", \"89969\", \"103312\", \"115345\", \"115802\", \"143956\", \"148152\", \"183422\", \"207712\", \"199241\", \"155854\", \"164254\", \"165333\", \"176706\", \"209378\", \"215174\", \"216734\", \"248939\"], \"outgoing_citations\": [\"280681\", \"298651\", \"340484\"]}","task_split":"paper_retrieval"}
{"document_id":"267953","document_content":"# Topically Driven Neural Language Model\n## Categories\n- Computation and Language\n## Abstract\nLanguage models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.08012v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"63787\", \"3168\", \"40095\", \"66383\", \"92063\", \"87405\", \"103442\", \"114439\", \"117654\", \"133508\", \"149997\", \"185717\", \"164334\", \"179680\", \"188850\", \"194831\", \"198770\", \"214884\", \"232355\", \"246422\", \"154737\", \"265419\"], \"outgoing_citations\": [\"300508\", \"303493\", \"308027\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"268072","document_content":"# Scalable Planning with Tensorflow for Hybrid Nonlinear Domains\n## Categories\n- Machine Learning\n## Abstract\nGiven recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in this paper whether symbolic gradient optimization tools such as Tensorflow can be effective for planning in hybrid (mixed discrete and continuous) nonlinear domains with high dimensional state and action spaces? To this end, we demonstrate that hybrid planning with Tensorflow and RMSProp gradient descent is competitive with mixed integer linear program (MILP) based optimization on piecewise linear planning domains (where we can compute optimal solutions) and substantially outperforms state-of-the-art interior point methods for nonlinear planning domains. Furthermore, we remark that Tensorflow is highly scalable, converging to a strong plan on a large-scale concurrent domain with a total of 576,000 continuous action parameters distributed over a horizon of 96 time steps and 100 parallel instances in only 4 minutes. We provide a number of insights that clarify such strong performance including observations that despite long horizons, RMSProp avoids both the vanishing and exploding gradient problems. Together these results suggest a new frontier for highly scalable planning in nonlinear hybrid domains by leveraging GPUs and the power of recent advances in gradient descent with highly optimized toolkits like Tensorflow.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.07511v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"15151\", \"115868\", \"42325\", \"125575\", \"164108\", \"192119\", \"209307\"], \"outgoing_citations\": [\"280972\", \"334209\"]}","task_split":"paper_retrieval"}
{"document_id":"268162","document_content":"# Found in Translation: Reconstructing Phylogenetic Language Trees from Translations\n## Categories\n- Computation and Language\n## Abstract\nTranslation has played an important role in trade, law, commerce, politics, and literature for thousands of years. Translators have always tried to be invisible; ideal translations should look as if they were written originally in the target language. We show that traces of the source language remain in the translation product to the extent that it is possible to uncover the history of the source language by looking only at the translation. Specifically, we automatically reconstruct phylogenetic language trees from monolingual texts (translated from several source languages). The signal of the source language is so powerful that it is retained even after two phases of translation. This strongly indicates that source language interference is the most dominant characteristic of translated texts, overshadowing the more subtle signals of universal properties of translation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.07146v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"23210\", \"44974\", \"89177\", \"92100\", \"111308\", \"127263\", \"127876\", \"142033\", \"189148\", \"203509\", \"223563\", \"230729\"], \"outgoing_citations\": [\"284962\", \"330643\", \"341961\"]}","task_split":"paper_retrieval"}
{"document_id":"268197","document_content":"# Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network\n## Categories\n- Machine Learning\n## Abstract\nIn this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.07049v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"4862\", \"119790\", \"1878\", \"15731\", \"16447\", \"23013\", \"50791\", \"159714\", \"3012\", \"23300\", \"24181\", \"27953\", \"31296\", \"45469\", \"52676\", \"52930\", \"54107\", \"60527\", \"89947\", \"136258\", \"151537\", \"165295\", \"87593\", \"84507\", \"106162\", \"108183\", \"110101\", \"118382\", \"126578\", \"126686\", \"128169\", \"128967\", \"129760\", \"133076\", \"135586\", \"138169\", \"139520\", \"148116\", \"153754\", \"154891\", \"173508\", \"183641\", \"186194\", \"220753\", \"239504\", \"150814\", \"158609\", \"166383\", \"167345\", \"174738\", \"174742\", \"178932\", \"187729\", \"156864\", \"188613\", \"193745\", \"194147\", \"196758\", \"199604\", \"202834\", \"211345\", \"217456\", \"229266\", \"182082\", \"212451\", \"232095\", \"232096\", \"240158\", \"241801\"], \"outgoing_citations\": [\"299074\"]}","task_split":"paper_retrieval"}
{"document_id":"268204","document_content":"# Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.07019v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"268226","document_content":"# General Video Game AI: Learning from Screen Capture\n## Categories\n- Artificial Intelligence\n## Abstract\nGeneral Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.06945v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"87663\", \"123442\", \"257674\", \"181592\", \"240572\", \"229002\", \"239003\"], \"outgoing_citations\": [\"293071\", \"302029\"]}","task_split":"paper_retrieval"}
{"document_id":"268281","document_content":"# Deep Learning based Isolated Arabic Scene Character Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 x 3 and 5 x 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.06821v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23004\", \"216241\", \"176625\"], \"outgoing_citations\": [\"295088\", \"295089\", \"297700\", \"300538\", \"305470\", \"314541\", \"341393\"]}","task_split":"paper_retrieval"}
{"document_id":"268339","document_content":"# A Semantic QA-Based Approach for Text Summarization Evaluation\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMany Natural Language Processing and Computational Linguistics applications involves the generation of new texts based on some existing texts, such as summarization, text simplification and machine translation. However, there has been a serious problem haunting these applications for decades, that is, how to automatically and accurately assess quality of these applications. In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books). Our idea is intuitive and very different from existing approaches. We treat one text passage as a small knowledge base, and ask it a large number of questions to exhaustively identify all content points in it. By comparing the correctly answered questions from two text passages, we will be able to compare their content precisely. The experiment using 2007 DUC summarization corpus clearly shows promising results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.06259v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"61976\", \"26826\", \"132431\", \"55802\", \"55804\", \"56583\", \"61182\", \"86055\", \"92056\", \"96702\", \"116558\", \"126346\", \"140642\", \"286244\", \"168744\"], \"outgoing_citations\": [\"307651\"]}","task_split":"paper_retrieval"}
{"document_id":"268356","document_content":"# Attend to You: Personalized Image Captioning with Context Sequence Memory Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nWe address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.06485v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"35427\", \"1081\", \"6224\", \"61694\", \"67993\", \"5837\", \"19343\", \"29982\", \"33619\", \"38692\", \"61394\", \"72999\", \"82846\", \"93826\", \"109073\", \"142094\", \"145513\", \"183829\", \"86447\", \"89648\", \"92422\", \"112250\", \"127403\", \"137944\", \"140504\", \"141957\", \"154752\", \"170062\", \"171057\", \"178885\", \"181781\", \"189821\", \"191058\", \"192932\", \"194255\", \"211838\", \"213042\", \"215181\", \"243808\", \"255907\", \"253934\", \"254760\"], \"outgoing_citations\": [\"284230\", \"290747\", \"310639\", \"319833\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"268433","document_content":"# Neural End-to-End Learning for Computational Argumentation Mining\n## Categories\n- Computation and Language\n## Abstract\nWe investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.06104v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38279\", \"6188\", \"20783\", \"49238\", \"53927\", \"64589\", \"67231\", \"68060\", \"94618\", \"122406\", \"146768\", \"154465\", \"127963\", \"170664\", \"179244\", \"179321\", \"182163\", \"184514\", \"185224\", \"185940\", \"189371\", \"203504\", \"219382\", \"223563\", \"230516\", \"235953\", \"242021\", \"246230\", \"260462\", \"268140\"], \"outgoing_citations\": [\"278531\", \"291192\", \"293849\", \"296221\", \"296583\", \"300302\", \"300593\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"268494","document_content":"# Generative Face Completion\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.05838v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"2870\", \"26240\", \"60770\", \"206876\", \"75309\", \"1796\", \"3666\", \"13484\", \"14983\", \"19746\", \"30486\", \"30927\", \"32066\", \"34450\", \"58511\", \"68499\", \"74717\", \"74893\", \"78939\", \"125270\", \"139332\", \"4513\", \"6863\", \"84706\", \"91415\", \"29676\", \"39363\", \"41612\", \"44824\", \"50359\", \"58340\", \"60618\", \"61168\", \"71471\", \"189778\", \"74894\", \"76015\", \"77059\", \"77253\", \"77737\", \"78355\", \"78565\", \"106645\", \"110698\", \"112716\", \"117348\", \"117456\", \"143526\", \"147666\", \"149556\", \"196750\", \"84398\", \"86038\", \"87587\", \"89590\", \"90654\", \"91841\", \"94911\", \"84557\", \"104828\", \"105119\", \"112219\", \"122768\", \"124611\", \"127805\", \"134043\", \"136708\", \"136725\", \"137100\", \"138674\", \"139312\", \"139578\", \"141253\", \"141967\", \"143696\", \"143970\", \"144985\", \"147906\", \"149962\", \"158246\", \"158286\", \"160255\", \"165999\", \"167579\", \"180053\", \"187632\", \"188208\", \"188385\", \"192540\", \"203644\", \"205461\", \"207609\", \"209731\", \"220538\", \"220626\", \"230252\", \"247571\", \"150560\", \"154233\", \"155715\", \"162866\", \"161496\", \"166974\", \"167589\", \"171564\", \"171875\", \"174856\", \"157815\", \"182585\", \"182641\", \"182059\", \"183766\", \"186454\", \"187384\", \"189011\", \"190170\", \"190636\", \"190860\", \"193255\", \"194960\", \"195652\", \"196712\", \"197539\", \"198455\", \"198493\", \"194943\", \"203467\", \"204867\", \"205139\", \"207471\", \"207922\", \"208225\", \"214181\", \"197717\", \"218619\", \"228631\", \"229023\", \"239264\", \"244649\", \"246573\", \"249251\", \"253243\", \"254116\", \"207047\", \"206757\", \"202959\", \"208232\", \"210477\", \"217644\", \"220668\", \"221342\", \"220605\", \"223624\", \"227890\", \"228152\", \"230126\", \"232726\", \"234403\", \"234815\", \"235729\", \"237954\", \"238379\", \"238649\", \"239351\", \"241281\", \"243990\", \"244327\", \"244392\", \"247558\", \"247737\", \"248969\", \"249153\", \"262675\", \"248413\", \"249323\", \"259219\", \"263714\"], \"outgoing_citations\": [\"296355\", \"296552\", \"298700\", \"311530\"]}","task_split":"paper_retrieval"}
{"document_id":"268703","document_content":"# End-to-end 3D face reconstruction with deep neural networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMonocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.05020v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5179\", \"17613\", \"22082\", \"57572\", \"59300\", \"63313\", \"12174\", \"16773\", \"30067\", \"40450\", \"42098\", \"65250\", \"53101\", \"76752\", \"82106\", \"87603\", \"142963\", \"194156\", \"219961\", \"235160\", \"80521\", \"93153\", \"93727\", \"95563\", \"96970\", \"105432\", \"105652\", \"109928\", \"116130\", \"121943\", \"127007\", \"133204\", \"105662\", \"134002\", \"159733\", \"188208\", \"201184\", \"152310\", \"156806\", \"163559\", \"168953\", \"169032\", \"182589\", \"187576\", \"190402\", \"190690\", \"191463\", \"193656\", \"194402\", \"195501\", \"199069\", \"243564\", \"216762\", \"221177\", \"235754\", \"237222\", \"237536\", \"238254\", \"247495\", \"248918\", \"250081\", \"250290\", \"250292\", \"270042\", \"250754\", \"255468\", \"257649\"], \"outgoing_citations\": [\"284716\", \"289767\", \"291277\", \"301832\", \"302701\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"268728","document_content":"# Deep Joint Entity Disambiguation with Local Neural Attention\n## Categories\n- Computation and Language\n## Abstract\nWe propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.04920v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"2756\", \"2937\", \"18787\", \"26715\", \"45294\", \"59368\", \"61237\", \"64775\", \"80770\", \"121898\", \"144530\", \"169211\", \"4514\", \"5406\", \"23626\", \"24055\", \"24980\", \"26026\", \"37401\", \"41241\", \"49595\", \"55194\", \"56380\", \"73669\", \"77042\", \"84249\", \"92449\", \"92823\", \"97185\", \"190821\", \"85538\", \"86291\", \"94753\", \"97100\", \"97423\", \"100131\", \"21536\", \"106440\", \"41806\", \"121381\", \"124889\", \"131012\", \"116263\", \"144604\", \"146640\", \"148404\", \"138850\", \"151408\", \"153119\", \"157168\", \"157181\", \"167165\", \"170862\", \"189763\", \"205857\", \"164387\", \"166447\", \"167753\", \"168589\", \"168892\", \"164781\", \"165378\", \"171151\", \"171884\", \"169340\", \"175482\", \"179831\", \"180267\", \"180937\", \"182677\", \"185861\", \"181078\", \"199273\", \"200632\", \"208559\", \"208638\", \"214173\", \"217117\", \"209228\", \"220553\", \"227437\", \"233941\", \"236843\", \"240614\", \"264636\"], \"outgoing_citations\": [\"178230\", \"295323\", \"300498\", \"306949\", \"313713\", \"349845\", \"350321\"]}","task_split":"paper_retrieval"}
{"document_id":"268864","document_content":"# Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger vocabulary because training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. In NMTs, words that are out of vocabulary are represented by a single unknown token. In this paper, we propose a method that enables NMT to translate patent sentences comprising a large vocabulary of technical terms. We train an NMT system on bilingual data wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except technical terms. Further, we use it as a decoder to translate source sentences with technical term tokens and replace the tokens with technical term translations using SMT. We also use it to rerank the 1,000-best SMT translations on the basis of the average of the SMT score and that of the NMT rescoring of the translated sentences with technical term tokens. Our experiments on Japanese-Chinese patent sentences show that the proposed NMT system achieves a substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over traditional SMT systems and an improvement of approximately 0.6 BLEU points and 0.8 RIBES points over an equivalent NMT system without our proposed technique.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.04521v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38251\", \"152767\", \"181283\", \"247968\", \"256004\", \"268865\"], \"outgoing_citations\": [\"295308\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"268865","document_content":"# Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. In this paper, we propose to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. This allows the proposed NMT system to be applied to a translation task of any language pair without any language-specific knowledge about technical term identification. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the proposed NMT model achieves a substantial improvement over a baseline NMT model without our proposed technique. Moreover, the number of translation errors of under-translation by the baseline NMT model without our proposed technique reduces to around half by the proposed NMT model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.04520v6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"268864\", \"295308\", \"299936\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"268886","document_content":"# Optimizing Differentiable Relaxations of Coreference Evaluation Metrics\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nCoreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions. Consequently, most approaches optimize objectives only indirectly related to the end goal, resulting in suboptimal performance. Instead, we propose a differentiable relaxation that lends itself to gradient-based optimisation, thus bypassing the need for reinforcement learning or heuristic modification of cross-entropy. We show that by modifying the training objective of a competitive neural coreference system, we obtain a substantial gain in performance. This suggests that our approach can be regarded as a viable alternative to using reinforcement learning or more computationally expensive imitation learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.04451v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"160655\"], \"outgoing_citations\": [\"274450\", \"283809\", \"291177\", \"294814\"]}","task_split":"paper_retrieval"}
{"document_id":"268957","document_content":"# Learning Joint Multilingual Sentence Representations with Neural Machine Translation\n## Categories\n- Computation and Language\n- 68T50\n## Abstract\nIn this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new cross-lingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.04154v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"68T50\"], \"incoming_citations\": [\"23308\", \"52464\", \"23576\", \"28356\", \"35406\", \"42612\", \"43765\", \"46639\", \"51180\", \"52650\", \"63578\", \"63831\", \"90826\", \"86571\", \"93229\", \"101925\", \"105105\", \"106553\", \"106707\", \"114522\", \"48054\", \"124609\", \"128024\", \"128054\", \"145029\", \"157122\", \"159720\", \"166802\", \"167364\", \"165748\", \"166664\", \"164127\", \"178555\", \"182394\", \"179820\", \"181154\", \"186172\", \"189148\", \"191063\", \"204715\", \"209132\", \"211711\", \"212322\", \"216873\", \"220771\", \"208852\", \"214224\", \"217957\", \"223010\", \"230709\", \"231811\", \"232405\", \"230727\", \"234189\", \"234760\", \"237294\", \"240090\", \"268607\"], \"outgoing_citations\": [\"280498\", \"283320\", \"290356\", \"290500\", \"290510\", \"291575\", \"300283\", \"300548\", \"300604\", \"303003\", \"314240\", \"323451\", \"330123\", \"330941\"]}","task_split":"paper_retrieval"}
{"document_id":"269205","document_content":"# EAST: An Efficient and Accurate Scene Text Detector\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nPrevious approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.03155v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"128508\", \"155242\", \"186590\", \"77004\", \"1766\", \"2918\", \"6741\", \"7829\", \"13927\", \"14003\", \"22020\", \"31775\", \"37135\", \"39902\", \"47108\", \"50917\", \"51497\", \"52248\", \"54017\", \"56985\", \"66255\", \"83916\", \"84505\", \"101165\", \"109996\", \"118842\", \"137208\", \"180823\", \"236\", \"4188\", \"6085\", \"7179\", \"8735\", \"9921\", \"10580\", \"11650\", \"3699\", \"17176\", \"18970\", \"24824\", \"25116\", \"25132\", \"25406\", \"29640\", \"29901\", \"29950\", \"31120\", \"31607\", \"33142\", \"33498\", \"35003\", \"35782\", \"35666\", \"35713\", \"36493\", \"43153\", \"46658\", \"47089\", \"49808\", \"50136\", \"51102\", \"51790\", \"52288\", \"53987\", \"54412\", \"55220\", \"56713\", \"58673\", \"59917\", \"62678\", \"71481\", \"72891\", \"74776\", \"43191\", \"78649\", \"81968\", \"81960\", \"85941\", \"85992\", \"93345\", \"103980\", \"113025\", \"114853\", \"123348\", \"136651\", \"150258\", \"155066\", \"155495\", \"155848\", \"171645\", \"184587\", \"207887\", \"59016\", \"80437\", \"194005\", \"81667\", \"83582\", \"85935\", \"89959\", \"90549\", \"92285\", \"98220\", \"101629\", \"102479\", \"105122\", \"105295\", \"106470\", \"107390\", \"107443\", \"107649\", \"108209\", \"108674\", \"111035\", \"111116\", \"112241\", \"5561\", \"114628\", \"115934\", \"120420\", \"119526\", \"122763\", \"122949\", \"122981\", \"123078\", \"123550\", \"125956\", \"126244\", \"124083\", \"128870\", \"129484\", \"130491\", \"130632\", \"131896\", \"134752\", \"135263\", \"136190\", \"136466\", \"139566\", \"140006\", \"141887\", \"144964\", \"147253\", \"147476\", \"149580\", \"149858\", \"152806\", \"154248\", \"156029\", \"165092\", \"172941\", \"174062\", \"176257\", \"179503\", \"196632\", \"209980\", \"210634\", \"150452\", \"151924\", \"156689\", \"161220\", \"164845\", \"166353\", \"166479\", \"167606\", \"168306\", \"169067\", \"165784\", \"169411\", \"169679\", \"170061\", \"170332\", \"171036\", \"171543\", \"171686\", \"171807\", \"172540\", \"172884\", \"173837\", \"174928\", \"177579\", \"177984\", \"179182\", \"181340\", \"182255\", \"182603\", \"183268\"], \"outgoing_citations\": [\"284871\", \"285788\", \"287166\", \"289389\", \"291983\", \"293979\", \"294606\", \"295358\", \"299461\", \"306502\", \"320837\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"269208","document_content":"# Federated Tensor Factorization for Computational Phenotyping\n## Categories\n- Machine Learning\n## Abstract\nTensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3097983.3098118\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"51766\", \"71429\", \"80685\", \"102039\", \"135674\", \"139157\", \"25848\", \"27798\", \"33171\", \"68872\", \"77033\", \"83423\", \"106508\", \"151798\", \"96241\", \"103427\", \"136201\", \"156643\", \"170105\", \"182128\", \"186312\", \"205620\", \"239191\"], \"outgoing_citations\": [\"359490\"]}","task_split":"paper_retrieval"}
{"document_id":"269251","document_content":"# Stochastic Neural Networks for Hierarchical Reinforcement Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n- Robotics\n## Abstract\nDeep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.03012v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Robotics\"], \"incoming_citations\": [\"12555\", \"44400\", \"4134\", \"9804\", \"11364\", \"13252\", \"14864\", \"17854\", \"18599\", \"35834\", \"63523\", \"119682\", \"4919\", \"13615\", \"14806\", \"17658\", \"17989\", \"24821\", \"25823\", \"26894\", \"27991\", \"34799\", \"35179\", \"36767\", \"39178\", \"39374\", \"40695\", \"43409\", \"44490\", \"45285\", \"45638\", \"46314\", \"50316\", \"50954\", \"58158\", \"61166\", \"61399\", \"63319\", \"67153\", \"69225\", \"71015\", \"75117\", \"84017\", \"84266\", \"85855\", \"86550\", \"89875\", \"142303\", \"85164\", \"87820\", \"88947\", \"90966\", \"93928\", \"96517\", \"96956\", \"101153\", \"102470\", \"105682\", \"109958\", \"116671\", \"117038\", \"117297\", \"119375\", \"123654\", \"125378\", \"123596\", \"128167\", \"128637\", \"137034\", \"137109\", \"137299\", \"138724\", \"143204\", \"143898\", \"139874\", \"155831\", \"162100\", \"173318\", \"177830\", \"179050\", \"180894\", \"184557\", \"186749\", \"192812\", \"195849\", \"201197\", \"152662\", \"158349\", \"162456\", \"162568\", \"161370\", \"164644\", \"165350\", \"171112\", \"171139\", \"173935\", \"176830\", \"180270\", \"180885\", \"181789\", \"184214\", \"184951\", \"186059\", \"185860\", \"187399\", \"187768\", \"188527\", \"188515\", \"194729\", \"197462\", \"203901\", \"204013\", \"207327\", \"210609\", \"205132\", \"208351\", \"217276\", \"228150\", \"230498\", \"241900\", \"207983\", \"207843\", \"56246\", \"223225\", \"224127\", \"224840\", \"225023\", \"228899\", \"231175\", \"236352\", \"241496\", \"228028\", \"242487\", \"249119\", \"251957\", \"252334\", \"266182\", \"247716\", \"248449\", \"253256\", \"255100\", \"263650\", \"265069\", \"266490\", \"270667\", \"272437\", \"272987\", \"279941\"], \"outgoing_citations\": [\"282503\", \"284134\", \"284567\", \"289897\", \"290371\", \"291460\", \"304250\", \"310624\", \"334172\"]}","task_split":"paper_retrieval"}
{"document_id":"269358","document_content":"# Mixed Graphical Models for Causal Analysis of Multi-modal Variables\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGraphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.02621v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"352356\", \"357289\", \"358544\", \"361836\"]}","task_split":"paper_retrieval"}
{"document_id":"269440","document_content":"# A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nSentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentence-level studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the two-step framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.02312v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"64358\", \"179990\", \"196978\", \"214312\", \"214570\"], \"outgoing_citations\": [\"289061\", \"301178\"]}","task_split":"paper_retrieval"}
{"document_id":"269500","document_content":"# Improving content marketing processes with the approaches by artificial intelligence\n## Categories\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nContent marketing is todays one of the most remarkable approaches in the context of marketing processes of companies. Value of this kind of marketing has improved in time, thanks to the latest developments regarding to computer and communication technologies. Nowadays, especially social media based platforms have a great importance on enabling companies to design multimedia oriented, interactive content. But on the other hand, there is still something more to do for improved content marketing approaches. In this context, objective of this study is to focus on intelligent content marketing, which can be done by using artificial intelligence. Artificial Intelligence is todays one of the most remarkable research fields and it can be used easily as multidisciplinary. So, this study has aimed to discuss about its potential on improving content marketing. In detail, the study has enabled readers to improve their awareness about the intersection point of content marketing and artificial intelligence. Furthermore, the authors have introduced some example models of intelligent content marketing, which can be achieved by using current Web technologies and artificial intelligence techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.02114v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"269814","document_content":"# Simultaneous Feature Aggregating and Hashing for Large-scale Image Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization produces aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, we also propose a fast version of the recently-proposed Binary Autoencoder to be used in our proposed framework. We perform extensive retrieval experiments on several benchmark datasets with both SIFT and convolutional features. Our results suggest that the proposed framework achieves significant improvements over the state of the art.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.00860v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3489\", \"24333\", \"73146\", \"85376\", \"78925\", \"147197\", \"241679\", \"189084\", \"241439\", \"242722\", \"243056\", \"249448\", \"262169\"], \"outgoing_citations\": [\"288169\", \"288218\", \"303067\", \"306406\", \"307882\", \"311835\", \"314568\", \"316473\", \"318535\", \"319454\", \"325820\", \"328917\", \"331485\"]}","task_split":"paper_retrieval"}
{"document_id":"270008","document_content":"# Learning to Predict Indoor Illumination from a Single Image\n## Categories\n- Computer Vision and Pattern Recognition\n- Graphics\n- Machine Learning\n## Abstract\nWe propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and\/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, we can achieve results that are photo-realistic, which is validated via a perceptual user study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.00090v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.GR\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Graphics\", \"Machine Learning\"], \"incoming_citations\": [\"41700\", \"172644\", \"15203\", \"29071\", \"32279\", \"38102\", \"49588\", \"54475\", \"68083\", \"109661\", \"12597\", \"13293\", \"23633\", \"23897\", \"35277\", \"39078\", \"39363\", \"39737\", \"39663\", \"46275\", \"54654\", \"57309\", \"58928\", \"58977\", \"59618\", \"61088\", \"63293\", \"72450\", \"79320\", \"82123\", \"82894\", \"85609\", \"92970\", \"104420\", \"111705\", \"124630\", \"79910\", \"96704\", \"99825\", \"100072\", \"100685\", \"107596\", \"107540\", \"108463\", \"109794\", \"112637\", \"124705\", \"129529\", \"134114\", \"136121\", \"147293\", \"156346\", \"181882\", \"187386\", \"216437\", \"160894\", \"161172\", \"180260\", \"181679\", \"189945\", \"192754\", \"194752\", \"203596\", \"208009\", \"206284\", \"216987\", \"228499\", \"236898\", \"237380\", \"238709\", \"246024\", \"248581\", \"268575\"], \"outgoing_citations\": [\"95913\", \"149624\", \"149627\", \"280057\", \"295199\", \"295795\", \"303409\", \"305321\", \"321017\"]}","task_split":"paper_retrieval"}
{"document_id":"270028","document_content":"# Opinion Mining on Non-English Short Text\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nAs the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1704.00016v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"270042","document_content":"# InverseFaceNet: Deep Monocular Inverse Face Rendering\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce InverseFaceNet, a deep convolutional inverse rendering framework for faces that jointly estimates facial pose, shape, expression, reflectance and illumination from a single input image. By estimating all parameters from just a single image, advanced editing possibilities on a single face image, such as appearance editing and relighting, become feasible in real time. Most previous learning-based face reconstruction approaches do not jointly recover all dimensions, or are severely limited in terms of visual quality. In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created training corpus. Our approach builds on a novel loss function that measures model-space similarity directly in parameter space and significantly improves reconstruction accuracy. We further propose a self-supervised bootstrapping process in the network training loop, which iteratively updates the synthetic training corpus to better reflect the distribution of real-world imagery. We demonstrate that this strategy outperforms completely synthetically trained networks. Finally, we show high-quality reconstructions and compare our approach to several state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.10956v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"206876\", \"5179\", \"5884\", \"12184\", \"3046\", \"17589\", \"30067\", \"221215\", \"82106\", \"87603\", \"80521\", \"95563\", \"112307\", \"113436\", \"135637\", \"159733\", \"168874\", \"185746\", \"185981\", \"191463\", \"193656\", \"194402\", \"198571\", \"205998\", \"206905\", \"259476\", \"204694\", \"209438\", \"209443\", \"235754\", \"236852\", \"247348\", \"247558\", \"250754\", \"257846\", \"263714\"], \"outgoing_citations\": [\"219267\", \"257428\", \"260916\", \"268703\", \"268963\", \"270134\", \"270235\", \"270795\", \"273729\", \"275879\", \"277232\", \"277948\", \"278967\", \"280394\", \"284248\", \"284716\", \"286094\", \"286311\", \"286598\", \"287875\", \"296292\", \"300688\", \"302701\", \"309106\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"270048","document_content":"# Sentence Simplification with Deep Reinforcement Learning\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call {\\sc Dress} (as shorthand for {\\bf D}eep {\\bf RE}inforcement {\\bf S}entence {\\bf S}implification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.10931v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"3392\", \"12701\", \"15974\", \"127662\", \"17436\", \"24637\", \"26460\", \"29406\", \"32732\", \"36870\", \"40203\", \"44703\", \"56059\", \"56593\", \"64358\", \"92547\", \"92961\", \"107933\", \"108663\", \"207215\", \"78207\", \"78896\", \"82994\", \"86824\", \"92761\", \"95567\", \"96175\", \"98424\", \"98965\", \"105099\", \"110974\", \"118053\", \"123457\", \"127511\", \"127613\", \"126870\", \"127956\", \"135231\", \"140815\", \"151619\", \"154650\", \"163045\", \"214542\", \"151479\", \"158340\", \"162482\", \"164682\", \"166796\", \"168922\", \"172036\", \"172006\", \"181822\", \"182778\", \"182943\", \"183012\", \"179990\", \"173805\", \"192018\", \"192166\", \"193731\", \"219049\", \"227251\", \"237227\", \"213790\", \"214312\", \"214753\", \"214400\", \"209678\", \"203277\", \"221537\", \"212887\", \"214570\", \"226870\", \"227488\", \"227983\", \"228393\", \"230464\", \"233722\", \"235248\", \"235452\", \"235951\", \"234907\", \"238482\", \"240068\", \"241070\", \"232600\", \"243765\", \"251736\", \"253965\", \"267246\"], \"outgoing_citations\": [\"295884\", \"296138\", \"303997\"]}","task_split":"paper_retrieval"}
{"document_id":"270154","document_content":"# Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nWhile strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct. A closer look reveals that this is due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans -- rightfully so -- generate multiple, diverse captions, due to the inherent ambiguity in the captioning task which is not considered in today's systems.   To address these challenges, we change the training objective of the caption generator from reproducing groundtruth captions to generating a set of captions that is indistinguishable from human generated captions. Instead of handcrafting such a learning target, we employ adversarial training in combination with an approximate Gumbel sampler to implicitly match the generated distribution to the human one. While our method achieves comparable performance to the state-of-the-art in terms of the correctness of the captions, we generate a set of diverse captions, that are significantly less biased and match the word statistics better in several aspects.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.10476v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"19141\", \"35427\", \"146468\", \"16718\", \"19075\", \"38766\", \"67993\", \"79138\", \"113677\", \"120045\", \"15263\", \"28498\", \"30775\", \"45169\", \"46309\", \"61394\", \"64255\", \"79155\", \"158811\", \"93826\", \"101963\", \"110600\", \"175031\", \"89629\", \"95771\", \"101699\", \"104882\", \"110219\", \"110233\", \"111062\", \"112250\", \"129573\", \"132390\", \"135154\", \"137915\", \"139263\", \"139825\", \"141978\", \"162598\", \"177775\", \"183087\", \"195851\", \"206142\", \"174277\", \"167642\", \"170299\", \"170604\", \"171902\", \"180567\", \"193328\", \"193573\", \"193909\", \"195170\", \"197542\", \"206058\", \"208615\", \"209504\", \"215181\", \"217232\", \"221127\", \"229734\", \"231619\", \"233685\", \"245161\", \"210290\", \"213954\", \"216351\", \"217955\", \"218912\", \"223751\", \"223815\", \"228859\", \"227741\", \"236907\", \"238125\", \"239242\", \"248388\", \"249820\", \"250098\", \"265760\", \"249972\", \"251296\", \"262828\", \"264539\", \"264514\"], \"outgoing_citations\": [\"269122\", \"271234\", \"275209\", \"275597\", \"279002\", \"279574\", \"281152\", \"283076\", \"287568\", \"288720\", \"295368\", \"295742\", \"298309\", \"302182\", \"303250\", \"304338\", \"313196\", \"318646\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"270235","document_content":"# Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIt has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative, we propose an Image-to-Image translation network that jointly maps the input image to a depth image and a facial correspondence map. This explicit pixel-based mapping can then be utilized to provide high quality reconstructions of diverse faces under extreme expressions, using a purely geometric refinement process. In the spirit of recent approaches, the network is trained only with synthetic data, and is then evaluated on in-the-wild facial images. Both qualitative and quantitative analyses demonstrate the accuracy and the robustness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.10131v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17613\", \"49287\", \"74251\", \"94953\", \"431\", \"3767\", \"12174\", \"16773\", \"17589\", \"19959\", \"30067\", \"33225\", \"40200\", \"44440\", \"42098\", \"57586\", \"59791\", \"63042\", \"53101\", \"82106\", \"84175\", \"87603\", \"96763\", \"180606\", \"219961\", \"80521\", \"97329\", \"108463\", \"109928\", \"120569\", \"121943\", \"133529\", \"133673\", \"133828\", \"134002\", \"134076\", \"134658\", \"134769\", \"135163\", \"135637\", \"136024\", \"140504\", \"145468\", \"159733\", \"168545\", \"168874\", \"192217\", \"216437\", \"151397\", \"152310\", \"163559\", \"163998\", \"169032\", \"171045\", \"182589\", \"185981\", \"189945\", \"191310\", \"191463\", \"191844\", \"193656\", \"194402\", \"194387\", \"198793\", \"202404\", \"205494\", \"206905\", \"220765\", \"257958\", \"161864\", \"208644\", \"216762\", \"220416\", \"224621\", \"227856\", \"230031\", \"235754\", \"237222\", \"237536\", \"238254\", \"238344\", \"238709\", \"247495\", \"248005\", \"248572\", \"250290\", \"270042\", \"249208\", \"257846\"], \"outgoing_citations\": [\"273729\", \"277948\", \"278966\", \"280394\", \"280996\", \"284145\", \"284716\", \"294914\", \"302701\", \"306261\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"270315","document_content":"# A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe tackle a task where an agent learns to navigate in a 2D maze-like environment called XWORLD. In each session, the agent perceives a sequence of raw-pixel frames, a natural language command issued by a teacher, and a set of rewards. The agent learns the teacher's language from scratch in a grounded and compositional manner, such that after training it is able to correctly execute zero-shot commands: 1) the combination of words in the command never appeared before, and\/or 2) the command contains new object concepts that are learned from another task but never learned from navigation. Our deep framework for the agent is trained end to end: it learns simultaneously the visual representations of the environment, the syntax and semantics of the language, and the action module that outputs actions. The zero-shot learning capability of our framework results from its compositionality and modularity with parameter tying. We visualize the intermediate outputs of the framework, demonstrating that the agent truly understands how to solve the problem. We believe that our results provide some preliminary insights on how to train an agent with similar abilities in a 3D environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.09831v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"27522\", \"64051\", \"148867\", \"257674\", \"159481\", \"161767\", \"223990\", \"225266\", \"231289\", \"248742\", \"249717\", \"263104\", \"261535\", \"263270\", \"263306\", \"272851\"], \"outgoing_citations\": [\"268586\", \"280264\", \"280296\", \"282065\", \"293071\", \"298651\", \"300421\", \"302660\", \"303306\", \"303534\", \"303714\", \"307630\", \"312506\", \"313143\", \"313934\"]}","task_split":"paper_retrieval"}
{"document_id":"270350","document_content":"# Linguistic Matrix Theory\n## Categories\n- Computation and Language\n- High Energy Physics - Theory\n- Combinatorics\n## Abstract\nRecent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.10252v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"hep-th\", \"math.CO\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"High Energy Physics - Theory\", \"Combinatorics\"], \"incoming_citations\": [\"150356\", \"14866\", \"217141\"], \"outgoing_citations\": [\"282656\", \"301504\", \"318101\", \"349396\", \"349607\"]}","task_split":"paper_retrieval"}
{"document_id":"270388","document_content":"# A practical approach to dialogue response generation in closed domains\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nWe describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover $>$70\\% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.09439v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"82594\", \"162378\", \"174659\", \"181936\", \"211776\", \"216351\", \"245376\"], \"outgoing_citations\": [\"290337\", \"302516\", \"310377\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"270696","document_content":"# Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMajority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/AIPR.2017.8457967\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"321117\", \"343979\"]}","task_split":"paper_retrieval"}
{"document_id":"270711","document_content":"# Visually grounded learning of keyword prediction from untranscribed speech\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nDuring language acquisition, infants have the benefit of visual cues to ground spoken language. Robots similarly have access to audio and visual sensors. Recent work has shown that images and spoken captions can be mapped into a meaningful common space, allowing images to be retrieved using speech and vice versa. In this setting of images paired with untranscribed spoken captions, we consider whether computer vision systems can be used to obtain textual labels for the speech. Concretely, we use an image-to-words multi-label visual classifier to tag images with soft textual labels, and then train a neural network to map from the speech to these soft targets. We show that the resulting speech system is able to predict which words occur in an utterance---acting as a spoken bag-of-words classifier---without seeing any parallel speech and text. We find that the model often confuses semantically related words, e.g. \"man\" and \"person\", making it even more effective as a semantic keyword spotter.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.08136v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"16891\", \"17037\", \"22962\", \"53303\", \"41626\", \"58270\", \"77697\", \"90581\", \"118555\", \"124782\", \"154976\", \"159472\", \"157113\", \"166011\", \"167882\", \"181008\", \"189109\", \"190511\", \"190639\", \"198064\", \"199724\", \"205074\", \"236772\", \"254061\", \"221600\", \"223105\", \"228175\", \"230159\", \"237968\", \"251231\", \"260141\"], \"outgoing_citations\": [\"248593\", \"272294\", \"274112\", \"274549\", \"275409\", \"281762\", \"282874\", \"285985\", \"296861\", \"300099\", \"303546\", \"305536\", \"305766\", \"321621\"]}","task_split":"paper_retrieval"}
{"document_id":"270743","document_content":"# Content-based similar document image retrieval using fusion of CNN features\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n## Abstract\nRapid increase of digitized document give birth to high demand of document image retrieval. While conventional document image retrieval approaches depend on complex OCR-based text recognition and text similarity detection, this paper proposes a new content-based approach, in which more attention is paid to features extraction and fusion. In the proposed approach, multiple features of document images are extracted by different CNN models. After that, the extracted CNN features are reduced and fused into weighted average feature. Finally, the document images are ranked based on feature similarity to a provided query image. Experimental procedure is performed on a group of document images that transformed from academic papers, which contain both English and Chinese document, the results show that the proposed approach has good ability to retrieve document images with similar text content, and the fusion of CNN features can effectively improve the retrieval accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.08013v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"312178\", \"318743\", \"336231\"]}","task_split":"paper_retrieval"}
{"document_id":"270925","document_content":"# One-Shot Imitation Learning\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n- Robotics\n## Abstract\nImitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning.   Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration. At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task. The use of soft attention allows the model to generalize to conditions and tasks unseen in the training data. We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks.   Videos available at https:\/\/bit.ly\/nips2017-oneshot .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.07326v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Robotics\"], \"incoming_citations\": [\"102138\", \"353397\", \"164087\", \"5280\", \"5643\", \"8426\", \"9312\", \"13710\", \"17155\", \"20494\", \"20668\", \"34545\", \"34544\", \"34572\", \"38393\", \"40128\", \"46302\", \"59368\", \"97832\", \"98936\", \"115575\", \"115744\", \"131702\", \"186708\", \"9593\", \"13798\", \"16345\", \"16958\", \"24946\", \"28672\", \"30956\", \"36605\", \"41883\", \"43996\", \"49757\", \"50445\", \"50523\", \"55486\", \"60681\", \"61513\", \"62732\", \"64984\", \"71397\", \"72558\", \"75738\", \"85855\", \"96663\", \"107913\", \"114384\", \"117531\", \"118383\", \"122991\", \"135951\", \"155930\", \"161444\", \"177272\", \"211326\", \"213928\", \"82779\", \"86028\", \"87518\", \"89945\", \"93121\", \"101153\", \"107705\", \"107896\", \"112732\", \"113227\", \"115941\", \"116841\", \"116807\", \"117587\", \"117936\", \"118561\", \"119271\", \"121911\", \"128488\", \"130677\", \"130999\", \"132719\", \"134432\", \"135334\", \"136608\", \"137034\", \"137930\", \"139265\", \"140124\", \"140363\", \"142957\", \"143083\", \"144444\", \"139601\", \"146721\", \"149777\", \"154289\", \"154340\", \"156448\", \"161071\", \"163722\", \"163689\", \"166489\", \"173318\", \"174008\", \"179214\", \"181883\", \"190780\", \"202825\", \"104527\", \"149096\", \"149894\", \"150902\", \"152517\", \"154681\", \"158907\", \"159949\", \"160156\", \"160310\", \"160827\", \"160980\", \"162466\", \"162860\", \"164032\", \"164217\", \"165811\", \"167562\", \"171477\", \"172146\", \"173229\", \"179705\", \"181724\", \"182115\", \"182426\", \"178792\", \"186229\", \"186659\", \"191258\", \"191119\", \"191888\", \"192827\", \"193849\", \"194601\", \"194653\", \"195093\", \"195692\", \"196387\", \"196532\", \"206918\", \"210901\", \"214174\", \"208234\", \"216060\", \"218420\", \"225224\", \"226419\", \"229737\", \"230605\", \"230673\", \"232332\", \"205447\", \"207983\", \"207843\", \"208570\", \"209570\", \"211255\", \"212935\", \"213647\", \"215931\", \"214592\", \"215924\", \"216438\", \"217034\", \"206231\", \"215537\", \"219728\", \"221441\", \"223033\", \"229554\", \"230068\", \"230962\", \"231587\", \"232224\", \"205457\", \"231633\", \"232285\", \"233924\", \"235228\", \"237232\", \"237386\", \"209859\", \"238323\", \"240209\", \"240506\", \"240800\", \"241354\", \"239456\", \"234517\", \"242322\", \"218778\", \"243236\", \"244669\", \"248734\", \"252006\", \"253348\", \"254096\", \"254596\", \"256051\", \"257819\", \"261522\", \"268248\", \"249398\", \"251098\", \"253541\", \"253876\", \"254739\", \"255939\", \"258331\", \"261653\", \"261786\", \"263306\", \"265069\", \"277504\"], \"outgoing_citations\": [\"271975\", \"272298\", \"279009\", \"280202\", \"280570\", \"280870\", \"282318\", \"290952\", \"291183\", \"296448\", \"297317\", \"302720\", \"304250\", \"350315\", \"357681\"]}","task_split":"paper_retrieval"}
{"document_id":"271055","document_content":"# On the effect of pooling on the geometry of representations\n## Categories\n- Machine Learning\n## Abstract\nIn machine learning and neuroscience, certain computational structures and algorithms are known to yield disentangled representations without us understanding why, the most striking examples being perhaps convolutional neural networks and the ventral stream of the visual cortex in humans and primates. As for the latter, it was conjectured that representations may be disentangled by being flattened progressively and at a local scale. An attempt at a formalization of the role of invariance in learning representations was made recently, being referred to as I-theory. In this framework and using the language of differential geometry, we show that pooling over a group of transformations of the input contracts the metric and reduces its curvature, and provide quantitative bounds, in the aim of moving towards a theoretical understanding on how to disentangle representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.06726v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"186260\", \"86892\"], \"outgoing_citations\": [\"278583\", \"298695\", \"299917\", \"301231\", \"311577\", \"311822\", \"315703\", \"319609\", \"319797\", \"321465\", \"337753\", \"345235\", \"350268\", \"361129\"]}","task_split":"paper_retrieval"}
{"document_id":"271176","document_content":"# The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple algorithm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1705.01080v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"99829\", \"131732\", \"135352\", \"181592\", \"204062\", \"240572\", \"227127\", \"241932\", \"259177\", \"263895\", \"271177\", \"290036\"], \"outgoing_citations\": [\"288925\", \"290036\"]}","task_split":"paper_retrieval"}
{"document_id":"271292","document_content":"# Improving Document Clustering by Eliminating Unnatural Language\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nTechnical documents contain a fair amount of unnatural language, such as tables, formulas, pseudo-codes, etc. Unnatural language can be an important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of unnatural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various formats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that removing unnatural language components gives an absolute improvement in document clustering up to 15%. Our corpus and tool are publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.05706v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271351","document_content":"# Global and Local Information Based Deep Network for Skin Lesion Segmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith a large influx of dermoscopy images and a growing shortage of dermatologists, automatic dermoscopic image analysis plays an essential role in skin cancer diagnosis. In this paper, a new deep fully convolutional neural network (FCNN) is proposed to automatically segment melanoma out of skin images by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN is capable of using both local and global information to segment melanoma by adopting skipping layers. The public benchmark database consisting of 150 validation images, 600 test images and 2000 training images in the melanoma detection challenge 2017 at International Symposium Biomedical Imaging 2017 is used to test the performance of our algorithm. All large size images (for example, $4000\\times 6000$ pixels) are reduced to much smaller images with $384\\times 384$ pixels (more than 10 times smaller). We got and submitted preliminary results to the challenge without any pre or post processing. The performance of our proposed method could be further improved by data augmentation and by avoiding image size reduction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.05467v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271375","document_content":"# Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play\n## Categories\n- Machine Learning\n## Abstract\nWe describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will \"propose\" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.05407v5\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"23640\", \"940\", \"18344\", \"18466\", \"56916\", \"68876\", \"78473\", \"79803\", \"109508\", \"10740\", \"15365\", \"18748\", \"22761\", \"23294\", \"33029\", \"33080\", \"35440\", \"35932\", \"36807\", \"39178\", \"40489\", \"41076\", \"41518\", \"43396\", \"43616\", \"43897\", \"250273\", \"54100\", \"62673\", \"64937\", \"65728\", \"74451\", \"75738\", \"77205\", \"93939\", \"95739\", \"101182\", \"114931\", \"116987\", \"161444\", \"151613\", \"82943\", \"87820\", \"95754\", \"99829\", \"107705\", \"116853\", \"118051\", \"128799\", \"130999\", \"135334\", \"137441\", \"137546\", \"137900\", \"138839\", \"139142\", \"141480\", \"141813\", \"143204\", \"146684\", \"149777\", \"155831\", \"160734\", \"166489\", \"178509\", \"262325\", \"180846\", \"151271\", \"152041\", \"157025\", \"158110\", \"164505\", \"179966\", \"180928\", \"181498\", \"181532\", \"185860\", \"196387\", \"198915\", \"200574\", \"200772\", \"202620\", \"207730\", \"212832\", \"212299\", \"217577\", \"221473\", \"226713\", \"237044\", \"241900\", \"208771\", \"209076\", \"215688\", \"215979\", \"217435\", \"225841\", \"226544\", \"227065\", \"228299\", \"228916\", \"230227\", \"236969\", \"240348\", \"241185\", \"243063\", \"246066\", \"250066\", \"253638\", \"264419\", \"266182\", \"261017\", \"261983\", \"264323\", \"266419\", \"277504\"], \"outgoing_citations\": [\"261017\", \"266182\", \"272030\", \"275597\", \"276018\", \"280457\", \"281484\", \"291025\", \"302660\", \"350010\"]}","task_split":"paper_retrieval"}
{"document_id":"271406","document_content":"# Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis manuscript describes our participation in the International Skin Imaging Collaboration's 2017 Skin Lesion Analysis Towards Melanoma Detection competition. We participated in Part 3: Lesion Classification. The two stated goals of this binary image classification challenge were to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis, followed by distinguishing between (a) seborrheic keratosis and (b) nevus and melanoma. We chose a deep neural network approach with a transfer learning strategy, using a pre-trained Inception V3 network as both a feature extractor to provide input for a multi-layer perceptron as well as fine-tuning an augmented Inception network. This approach yielded validation set AUC's of 0.84 on the second task and 0.76 on the first task, for an average AUC of 0.80. We joined the competition unfortunately late, and we look forward to improving on these results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.05235v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"88594\", \"224554\", \"235260\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271426","document_content":"# Automatic skin lesion segmentation with fully convolutional-deconvolutional networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper summarizes our method and validation results for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part I: Lesion Segmentation","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JBHI.2017.2787487\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9153\", \"47342\", \"71829\", \"78152\", \"88708\", \"103536\", \"128346\", \"135443\", \"161488\", \"176840\", \"170812\", \"178129\", \"180883\", \"188702\", \"271920\", \"198754\", \"198873\", \"271595\", \"225412\", \"227163\", \"230522\", \"233665\", \"235399\", \"254674\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271431","document_content":"# Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET and Random Forests, and calculation of a final score.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.05148v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271506","document_content":"# Sparse Named Entity Classification using Factorization Machines\n## Categories\n- Computation and Language\n## Abstract\nNamed entity classification is the task of classifying text-based elements into various categories, including places, names, dates, times, and monetary values. A bottleneck in named entity classification, however, is the data problem of sparseness, because new named entities continually emerge, making it rather difficult to maintain a dictionary for named entity classification. Thus, in this paper, we address the problem of named entity classification using matrix factorization to overcome the problem of feature sparsity. Experimental results show that our proposed model, with fewer features and a smaller size, achieves competitive accuracy to state-of-the-art models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04879v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271516","document_content":"# Face Recognition using Multi-Modal Low-Rank Dictionary Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04853v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"278681\", \"312865\", \"318065\"]}","task_split":"paper_retrieval"}
{"document_id":"271527","document_content":"# Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nSemantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04826v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"64244\", \"97060\", \"109417\", \"56964\", \"3461\", \"6657\", \"8053\", \"9648\", \"9898\", \"12701\", \"16648\", \"18706\", \"18924\", \"26661\", \"36313\", \"47708\", \"68030\", \"68290\", \"70874\", \"80211\", \"81380\", \"120789\", \"127897\", \"154900\", \"157592\", \"172456\", \"206470\", \"5837\", \"5787\", \"8354\", \"10837\", \"17011\", \"25188\", \"25973\", \"27097\", \"27805\", \"28285\", \"29896\", \"38841\", \"39268\", \"43574\", \"44245\", \"45046\", \"45130\", \"45781\", \"46818\", \"47436\", \"48087\", \"48604\", \"49201\", \"50020\", \"51357\", \"53366\", \"119055\", \"55024\", \"55744\", \"56392\", \"56387\", \"62631\", \"71008\", \"73138\", \"75356\", \"75311\", \"77364\", \"77947\", \"78281\", \"80656\", \"80977\", \"81532\", \"82324\", \"82309\", \"83446\", \"85822\", \"96163\", \"97322\", \"101065\", \"104905\", \"130903\", \"134352\", \"150282\", \"161598\", \"184856\", \"82764\", \"84032\", \"84541\", \"89757\", \"88056\", \"90494\", \"91031\", \"91334\", \"91645\", \"91571\", \"91829\", \"92475\", \"93869\", \"94730\", \"5843\", \"96156\", \"98760\", \"99089\", \"99273\", \"100437\", \"100582\", \"104406\", \"108355\", \"108324\", \"111018\", \"117131\", \"117616\", \"119358\", \"122331\", \"123526\", \"125340\", \"125291\", \"125610\", \"126620\", \"126614\", \"127635\", \"127692\", \"127914\", \"127844\", \"136010\", \"136256\", \"138930\", \"139118\", \"140252\", \"146677\", \"149230\", \"152600\", \"155997\", \"157487\", \"152742\", \"165653\", \"167880\", \"169251\", \"169520\", \"171817\", \"172594\", \"181377\", \"205283\", \"230774\", \"138999\", \"157144\", \"157513\", \"157009\", \"156847\", \"159954\", \"160491\", \"160717\", \"162910\", \"163327\", \"165141\", \"165520\", \"165809\", \"168038\", \"168575\", \"166227\", \"169587\", \"171238\", \"173016\", \"169264\", \"151027\", \"174421\", \"175042\", \"168708\", \"176190\", \"176336\", \"178587\", \"179274\", \"180210\", \"181307\", \"182192\", \"182690\", \"171516\", \"183096\", \"183270\", \"183661\", \"185312\", \"185475\", \"185679\", \"170709\", \"191177\", \"191735\", \"193497\", \"195384\", \"195815\", \"164377\", \"196605\", \"197226\", \"197770\", \"200461\", \"201190\", \"201818\", \"202232\", \"202454\", \"202775\", \"207034\", \"209230\", \"212253\", \"183685\", \"211433\", \"217064\", \"217775\", \"218179\", \"218779\", \"219849\", \"223351\", \"230130\", \"201065\", \"206411\", \"210019\", \"210174\", \"202289\", \"210435\", \"213229\", \"213451\", \"213697\", \"215434\", \"215455\", \"208658\", \"216419\", \"216701\", \"217310\", \"218692\", \"218944\", \"220105\", \"221642\", \"221631\", \"225872\", \"200687\", \"225340\", \"226756\", \"221540\", \"228530\", \"232138\", \"232981\", \"232980\", \"230064\", \"234631\", \"234587\", \"242235\", \"251622\", \"251741\", \"254610\", \"249839\", \"254170\", \"268812\"], \"outgoing_citations\": [\"274183\", \"276493\", \"281225\", \"284701\", \"289403\", \"290777\", \"291941\", \"296583\", \"300593\", \"303003\", \"312066\", \"314976\"]}","task_split":"paper_retrieval"}
{"document_id":"271532","document_content":"# Neural Graph Machines: Learning Neural Networks Using Graphs\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nLabel propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely \"Neural Graph Machines\", that can combine the power of neural networks and label propagation. This work generalises previous literature on graph-augmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a) allowing the network to train using labeled data as in the supervised setting, (b) biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04818v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"64686\", \"86611\", \"167907\", \"196858\", \"231516\", \"259562\"], \"outgoing_citations\": [\"290337\", \"301984\"]}","task_split":"paper_retrieval"}
{"document_id":"271568","document_content":"# A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labelling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve real-time semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICAR.2017.8023499\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"128073\", \"168489\", \"226009\", \"163301\", \"238933\", \"239844\", \"254457\", \"259426\"], \"outgoing_citations\": [\"271148\", \"271704\", \"279321\", \"284570\", \"285994\", \"288181\", \"321017\"]}","task_split":"paper_retrieval"}
{"document_id":"271595","document_content":"# Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead to more quantitative and reproducible diagnoses. We reformulate the task of classifying clinical dermoscopic features within superpixels as a segmentation problem, and propose a fully convolutional neural network to detect clinical dermoscopic features from dermoscopy skin lesion images. Our neural network architecture uses interpolated feature maps from several intermediate network layers, and addresses imbalanced labels by minimizing a negative multi-label Dice-F$_1$ score, where the score is computed across the mini-batch for each label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2: Dermoscopic Feature Classification Task challenge over both the provided validation and test datasets, achieving a 0.895% area under the receiver operator characteristic curve score. We show how simple baseline models can outrank state-of-the-art approaches when using the official metrics of the challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set (i.e., masks devoid of positive pixels) when ranking models. Our results suggest that (i) the classification of clinical dermoscopic features can be effectively approached as a segmentation problem, and (ii) the current metrics used to rank models may not well capture the efficacy of the model. We plan to make our trained model and code publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JBHI.2018.2831680\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"17043\", \"58401\", \"8071\", \"14198\", \"65859\", \"84564\", \"152363\", \"182674\", \"189887\", \"198754\", \"216827\"], \"outgoing_citations\": [\"254665\", \"271426\", \"272621\", \"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"271606","document_content":"# Reinforcement Learning for Transition-Based Mention Detection\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThis paper describes an application of reinforcement learning to the mention detection task. We define a novel action-based formulation for the mention detection task, in which a model can flexibly revise past labeling decisions by grouping together tokens and assigning partial mention labels. We devise a method to create mention-level episodes and we train a model by rewarding correctly labeled complete mentions, irrespective of the inner structure created. The model yields results which are on par with a competitive supervised counterpart while being more flexible in terms of achieving targeted behavior through reward modeling and generating internal mention structure, especially on longer mentions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04489v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"296283\"]}","task_split":"paper_retrieval"}
{"document_id":"271626","document_content":"# Bayesian Optimization with Gradients\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Optimization and Control\n## Abstract\nBayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good performance. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for which we show one-step Bayes-optimality, asymptotic consistency, and greater one-step value of information than is possible in the derivative-free setting. Our procedure accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04389v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\", \"math.OC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\", \"Optimization and Control\"], \"incoming_citations\": [\"8201\", \"160685\", \"23615\", \"36619\", \"40249\", \"45390\", \"68621\", \"69260\", \"72206\", \"100136\", \"144357\", \"112847\", \"122216\", \"124944\", \"126219\", \"138368\", \"140104\", \"140598\", \"142947\", \"155172\", \"161750\", \"172269\", \"195971\", \"160736\", \"163453\", \"168662\", \"170776\", \"173739\", \"178449\", \"182664\", \"194238\", \"195524\", \"199598\", \"200385\", \"200601\", \"200760\", \"214785\", \"242288\", \"205514\", \"210954\", \"211066\", \"211626\", \"175152\", \"212510\", \"225410\", \"230538\", \"231041\", \"260680\"], \"outgoing_citations\": [\"158252\", \"280597\", \"290447\", \"298766\", \"302716\", \"302818\", \"303849\", \"303905\", \"316569\", \"317642\", \"328549\", \"332181\", \"345786\", \"348742\", \"357161\"]}","task_split":"paper_retrieval"}
{"document_id":"271634","document_content":"# Deep Learning for Skin Lesion Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns the representation of the image using Google's pretrained CNN model known as Inception-v3 \\cite{cnn}. After obtaining the representation vector for our input dermoscopic images we have trained two layer feed forward neural network to classify the images as malignant or benign. The system also classifies the images based on the cause of the cancer either due to melanocytic or non-melanocytic cells using a different neural network. These classification tasks are part of the challenge organized by International Skin Imaging Collaboration (ISIC) 2017. Our system learns to classify the images based on the model built using the training images given in the challenge and the experimental results were evaluated using validation and test sets. Our system has achieved an overall accuracy of 65.8\\% for the validation set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04364v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"272909\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271651","document_content":"# Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented based on clustering technique. K-means clustering algorithm is used to cluster the preprocessed images and skin lesions are filtered from these clusters based on the color feature. Color of the skin lesions are learned from the training images using histograms calculations in RGB color space. The training images were downloaded from the ISIC 2017 challenge website and the experimental results were evaluated using validation and test sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04301v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"87658\", \"197166\", \"148569\", \"221612\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271680","document_content":"# Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMalignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04197v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"28601\", \"129076\", \"5510\", \"9153\", \"197166\", \"103536\", \"128346\", \"135443\", \"176840\", \"195957\", \"251699\", \"168493\", \"172832\", \"180883\", \"188540\", \"224554\", \"254665\", \"216827\", \"219115\", \"221612\", \"224330\", \"225412\", \"227163\", \"230522\", \"233665\", \"246117\"], \"outgoing_citations\": [\"279147\", \"292186\"]}","task_split":"paper_retrieval"}
{"document_id":"271842","document_content":"# Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network\n## Categories\n- Artificial Intelligence\n## Abstract\nCommunication is not only an action of choosing a signal, but needs to consider the context and sensor signals. It also needs to decide what information is communicated and how it is represented in or understood from signals. Therefore, communication should be realized comprehensively together with its purpose and other functions.   The recent successful results in end-to-end reinforcement learning (RL) show the importance of comprehensive learning and the usefulness of end-to-end RL. Although little is known, we have shown that a variety of communications emerge through RL using a (recurrent) neural network (NN). Here, three of them are introduced.   In the 1st one, negotiation to avoid conflicts among 4 randomly-picked agents was learned. Each agent generates a binary signal from the output of its recurrent NN (RNN), and receives 4 signals from the agents three times. After learning, each agent made an appropriate final decision after negotiation for any combination of 4 agents. Differentiation of individuality among the agents also could be seen.   The 2nd one focused on discretization of communication signal. A sender agent perceives the receiver's location and generates a continuous signal twice by its RNN. A receiver agent receives them sequentially, and moves according to its RNN's output to reach the sender's location. When noises were added to the signal, it was binarized through learning and 2-bit communication was established.   The 3rd one focused on end-to-end comprehensive communication. A sender receives 1,785-pixel real camera image on which a real robot can be seen, and sends two sounds whose frequencies are computed by its NN. A receiver receives them, and two motion commands for the robot are generated by its NN. After learning, though some preliminary learning was necessary for the sender, the robot could reach the goal from any initial location.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.03543v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271871","document_content":"# Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nWe propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.03400v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"271920","document_content":"# Segmenting Dermoscopic Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentation. We present extensive experimental results achieved by the SDI algorithm on the lesion segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection, highlighting its advantages and disadvantages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.03186v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"84559\", \"135443\"], \"outgoing_citations\": [\"271426\", \"299339\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"271944","document_content":"# Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis short paper reports the method and the evaluation results of Casio and Shinshu University joint team for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part 3: Lesion Classification hosted by ISIC. Our online validation score was 0.958 with melanoma classifier AUC 0.924 and seborrheic keratosis classifier AUC 0.993.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.03108v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"72064\", \"144051\", \"5510\", \"25413\", \"31957\", \"34639\", \"51446\", \"85380\", \"115984\", \"159671\", \"103536\", \"131171\", \"195957\", \"251699\", \"188540\", \"198754\", \"224069\", \"224554\", \"207062\", \"213846\", \"218784\", \"219130\", \"219115\", \"223851\", \"224606\", \"235877\", \"237115\", \"246117\", \"265723\"], \"outgoing_citations\": [\"272645\", \"282613\"]}","task_split":"paper_retrieval"}
{"document_id":"271973","document_content":"# A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nInternet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JIOT.2020.2967734\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"11205\", \"18026\", \"41053\", \"51684\", \"79975\", \"86564\", \"89067\", \"153811\", \"123162\", \"234576\", \"5580\", \"35424\", \"40703\", \"47365\", \"134736\", \"79358\", \"167768\", \"95115\", \"117160\", \"123353\", \"125196\", \"128968\", \"129081\", \"131573\", \"131625\", \"134725\", \"136122\", \"137341\", \"146756\", \"150179\", \"157188\", \"172559\", \"180065\", \"184440\", \"201358\", \"242848\", \"248610\", \"120351\", \"159107\", \"160711\", \"166107\", \"166429\", \"176241\", \"184826\", \"194335\", \"210354\", \"201650\", \"232524\", \"239264\", \"255460\", \"217153\", \"218449\", \"225991\", \"218414\", \"232464\", \"244451\", \"253034\"], \"outgoing_citations\": [\"200633\", \"228693\", \"239264\", \"241333\", \"242848\", \"253034\", \"265492\", \"282410\", \"296559\", \"302867\", \"303467\", \"311530\", \"319813\", \"321229\", \"323014\", \"328183\", \"329670\", \"360017\"]}","task_split":"paper_retrieval"}
{"document_id":"272202","document_content":"# Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to $35\\%$ less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.04393v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"272220","document_content":"# Activation Maximization Generative Adversarial Nets\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nClass labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training. Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality. We thus propose a new metric, called AM Score, to provide a more accurate estimation of the sample quality. Our proposed model also outperforms the baseline methods in the new metric.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.02000v9\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"11541\", \"34155\", \"3527\", \"12157\", \"21384\", \"74374\", \"78355\", \"99468\", \"139629\", \"203401\", \"84846\", \"207486\", \"160723\", \"171505\", \"176457\", \"183542\", \"192355\", \"209983\", \"204863\", \"228339\", \"229612\", \"242751\", \"257138\", \"206352\", \"233233\", \"258286\", \"259288\"], \"outgoing_citations\": [\"255264\", \"270105\", \"272345\", \"272918\", \"273438\", \"274632\", \"278560\", \"279091\", \"281002\", \"281128\", \"281144\", \"284132\", \"284986\", \"290683\", \"291302\", \"291537\", \"302935\", \"303913\"]}","task_split":"paper_retrieval"}
{"document_id":"272225","document_content":"# Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3: Lesion Classification with a system for automatic diagnosis of nevus, melanoma and seborrheic keratosis. Our approach aims to incorporate the expert knowledge of dermatologists into the well known framework of Convolutional Neural Networks (CNN), which have shown impressive performance in many visual recognition tasks. In particular, we have designed several networks providing lesion area identification, lesion segmentation into structural patterns and final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been designed to integrate this information with the diagnosis processing pipeline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.01976v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"72064\", \"5510\", \"31957\", \"115984\", \"103536\", \"160808\", \"195957\", \"251699\", \"188540\", \"199630\", \"223851\", \"229865\", \"246117\"], \"outgoing_citations\": [\"311347\"]}","task_split":"paper_retrieval"}
{"document_id":"272390","document_content":"# Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDetecting incidental scene text is a challenging task because of multi-orientation, perspective distortion, and variation of text size, color and scale. Retrospective research has only focused on using rectangular bounding box or horizontal sliding window to localize text, which may result in redundant background noise, unnecessary overlap or even information loss. To address these issues, we propose a new Convolutional Neural Networks (CNNs) based method, named Deep Matching Prior Network (DMPNet), to detect text with tighter quadrangle. First, we use quadrilateral sliding windows in several specific intermediate convolutional layers to roughly recall the text with higher overlapping area and then a shared Monte-Carlo method is proposed for fast and accurate computing of the polygonal areas. After that, we designed a sequential protocol for relative regression which can exactly predict text with compact quadrangle. Moreover, a auxiliary smooth Ln loss is also proposed for further regressing the position of text, which has better overall performance than L2 loss and smooth L1 loss in terms of robustness and stability. The effectiveness of our approach is evaluated on a public word-level, multi-oriented scene text database, ICDAR 2015 Robust Reading Competition Challenge 4 \"Incidental scene text localization\". The performance of our method is evaluated by using F-measure and found to be 70.64%, outperforming the existing state-of-the-art method with F-measure 63.76%.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.01425v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155242\", \"186590\", \"142099\", \"2918\", \"6741\", \"13927\", \"14003\", \"37135\", \"50917\", \"66255\", \"180823\", \"25132\", \"35003\", \"35666\", \"189429\", \"62320\", \"74776\", \"102102\", \"113025\", \"123348\", \"136651\", \"150258\", \"155066\", \"155495\", \"207887\", \"78601\", \"194005\", \"84406\", \"98220\", \"120420\", \"122763\", \"122949\", \"126431\", \"135263\", \"140006\", \"140478\", \"151319\", \"154995\", \"176257\", \"179503\", \"210634\", \"150452\", \"166479\", \"169067\", \"166370\", \"170061\", \"170332\", \"171686\", \"173837\", \"174928\", \"182255\", \"186238\", \"189470\", \"192471\", \"193513\", \"194314\", \"195963\", \"201515\", \"203525\", \"207348\", \"208801\", \"209083\", \"209860\", \"234390\", \"236404\", \"204872\", \"204166\", \"209603\", \"217122\", \"218516\", \"225195\", \"225336\", \"225795\", \"232509\", \"239002\", \"243851\", \"245591\", \"256364\", \"272481\", \"248812\", \"257197\", \"257954\", \"262570\", \"270668\", \"248133\"], \"outgoing_citations\": [\"284871\", \"294606\", \"302303\", \"314049\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"272395","document_content":"# Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset, which is fine-tuned for skin lesion classification using two different scales of input images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.01402v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"115984\", \"251699\", \"272909\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"272433","document_content":"# Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback\n## Categories\n- Artificial Intelligence\n- Human-Computer Interaction\n- Robotics\n## Abstract\nThis paper contributes a first study into how different human users deliver simultaneous control and feedback signals during human-robot interaction. As part of this work, we formalize and present a general interactive learning framework for online cooperation between humans and reinforcement learning agents. In many human-machine interaction settings, there is a growing gap between the degrees-of-freedom of complex semi-autonomous systems and the number of human control channels. Simple human control and feedback mechanisms are required to close this gap and allow for better collaboration between humans and machines on complex tasks. To better inform the design of concurrent control and feedback interfaces, we present experimental results from a human-robot collaborative domain wherein the human must simultaneously deliver both control and feedback signals to interactively train an actor-critic reinforcement learning robot. We compare three experimental conditions: 1) human delivered control signals, 2) reward-shaping feedback signals, and 3) simultaneous control and feedback. Our results suggest that subjects provide less feedback when simultaneously delivering feedback and control signals and that control signal quality is not significantly diminished. Our data suggest that subjects may also modify when and how they provide feedback. Through algorithmic development and tuning informed by this study, we expect semi-autonomous actions of robotic agents can be better shaped by human feedback, allowing for seamless collaboration and improved performance in difficult interactive domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.01274v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"199875\", \"209926\", \"221550\"], \"outgoing_citations\": [\"275728\", \"276547\", \"289831\"]}","task_split":"paper_retrieval"}
{"document_id":"272481","document_content":"# Arbitrary-Oriented Scene Text Detection via Rotation Proposals\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present the Rotation Region Proposal Networks (RRPN), which are designed to generate inclined proposals with text orientation angle information. The angle information is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation. The Rotation Region-of-Interest (RRoI) pooling layer is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. The whole framework is built upon a region-proposal-based architecture, which ensures the computational efficiency of the arbitrary-oriented text detection compared with previous text detection systems. We conduct experiments using the rotation-based framework on three real-world scene text detection datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TMM.2018.2818020\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"128508\", \"155242\", \"186590\", \"77004\", \"142099\", \"5320\", \"7829\", \"14003\", \"14016\", \"16703\", \"18949\", \"21323\", \"22020\", \"31017\", \"39902\", \"45536\", \"47752\", \"51497\", \"52248\", \"53850\", \"54017\", \"55987\", \"61613\", \"61729\", \"67237\", \"73190\", \"74974\", \"83916\", \"104773\", \"126408\", \"137208\", \"180823\", \"3406\", \"6018\", \"8137\", \"8230\", \"9921\", \"10580\", \"11650\", \"23852\", \"25132\", \"29950\", \"31607\", \"31720\", \"33142\", \"69366\", \"35003\", \"35713\", \"35782\", \"39096\", \"44361\", \"45056\", \"43037\", \"189429\", \"50136\", \"53987\", \"55220\", \"59725\", \"59727\", \"59917\", \"62678\", \"63261\", \"72891\", \"74776\", \"75097\", \"75104\", \"43191\", \"81960\", \"82049\", \"85992\", \"93400\", \"93577\", \"113025\", \"131052\", \"136651\", \"150258\", \"155066\", \"155495\", \"155848\", \"171645\", \"173937\", \"207887\", \"59016\", \"78601\", \"79741\", \"194005\", \"98220\", \"105122\", \"107649\", \"110101\", \"111059\", \"114759\", \"120420\", \"121146\", \"119526\", \"122345\", \"122981\", \"123921\", \"123286\", \"131755\", \"136466\", \"139566\", \"140006\", \"141887\", \"147908\", \"149858\", \"154995\", \"161127\", \"174062\", \"176257\", \"179503\", \"194384\", \"205450\", \"209427\", \"210634\", \"150452\", \"153142\", \"155716\", \"161220\", \"166479\", \"167606\", \"170332\", \"171543\", \"171686\", \"176583\", \"177984\", \"180472\", \"182255\", \"186238\", \"186748\", \"189470\", \"190708\", \"193382\", \"194314\", \"195963\", \"207348\", \"209083\", \"209227\", \"209722\", \"209860\", \"214869\", \"218907\", \"234390\", \"236404\", \"204166\", \"204872\", \"207786\", \"195200\", \"208400\", \"209603\", \"209767\", \"210746\", \"217122\", \"218516\", \"219278\", \"225446\", \"225690\", \"225795\", \"228248\", \"228664\", \"232509\", \"233366\", \"235742\", \"240965\", \"243863\", \"243851\", \"228406\", \"245899\", \"245970\", \"250738\", \"256364\", \"263462\", \"248812\", \"257954\", \"262570\"], \"outgoing_citations\": [\"268829\", \"269821\", \"271112\", \"272390\", \"273862\", \"274944\", \"279950\", \"284871\", \"289389\", \"290682\", \"291983\", \"293979\", \"294023\", \"294606\", \"295564\", \"298359\", \"320837\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"272506","document_content":"# A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma Detection, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.01025v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5510\", \"32627\", \"87658\", \"126306\", \"126799\", \"195957\", \"251699\", \"172320\", \"222227\"], \"outgoing_citations\": [\"282613\", \"299339\"]}","task_split":"paper_retrieval"}
{"document_id":"272549","document_content":"# Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper describes the participation of Araguaia Medical Vision Lab at the International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We describe the use of deep convolutional neural networks in attempt to classify images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic Keratosis and Melanoma respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.00856v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"224554\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"272621","document_content":"# Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin lesion is a severe disease in world-wide extent. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons, e.g. low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is a challenge focusing on the automatic analysis of skin lesion. In this paper, we proposed two deep learning methods to address all the three tasks announced in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully-convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. To our best knowledges, we are not aware of any previous work proposed for this task. The proposed deep learning frameworks were evaluated on the ISIC 2017 testing set. Experimental results show the promising accuracies of our frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were achieved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.00577v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6649\", \"22199\", \"37102\", \"61633\", \"1919\", \"9153\", \"70616\", \"87658\", \"96875\", \"96900\", \"104761\", \"110938\", \"113339\", \"125254\", \"132215\", \"137548\", \"166993\", \"176840\", \"168885\", \"189065\", \"198754\", \"199710\", \"271595\", \"216827\", \"220448\", \"220792\", \"226850\", \"234334\", \"241584\", \"246117\"], \"outgoing_citations\": [\"272835\", \"273071\", \"282613\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"272624","document_content":"# Structural Embedding of Syntactic Trees for Machine Comprehension\n## Categories\n- Computation and Language\n## Abstract\nDeep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.00572v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"81322\", \"94453\", \"101017\", \"148368\", \"145873\", \"193228\", \"218444\", \"234577\", \"247824\", \"250304\", \"251798\", \"267031\", \"213353\", \"251770\", \"253866\", \"259954\", \"260450\"], \"outgoing_citations\": [\"278104\", \"279213\", \"281127\", \"281162\", \"281210\", \"281526\", \"285811\", \"288385\", \"291045\", \"291099\"]}","task_split":"paper_retrieval"}
{"document_id":"272642","document_content":"# Skin cancer reorganization and classification with deep neural network\n## Categories\n- Computer Vision and Pattern Recognition\n- 68T20\n## Abstract\nAs one kind of skin cancer, melanoma is very dangerous. Dermoscopy based early detection and recarbonization strategy is critical for melanoma therapy. However, well-trained dermatologists dominant the diagnostic accuracy. In order to solve this problem, many effort focus on developing automatic image analysis systems. Here we report a novel strategy based on deep learning technique, and achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1) we build a segmentation neural network (skin_segnn), which achieved very high lesion boundary detection accuracy; 2) We build another very deep neural network based on Google inception v3 network (skin_recnn) and its well-trained weight. The novel designed transfer learning based deep neural network skin_inceptions_v3_nn helps to achieve a high prediction accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.00534v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68T20\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68T20\"], \"incoming_citations\": [\"87658\", \"204810\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"272675","document_content":"# Lossy Image Compression with Compressive Autoencoders\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1703.00395v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"11467\", \"32952\", \"47239\", \"125962\", \"10\", \"179\", \"2345\", \"3535\", \"3584\", \"4502\", \"8763\", \"16579\", \"28362\", \"39792\", \"40317\", \"48210\", \"58666\", \"85979\", \"97572\", \"97830\", \"123186\", \"143178\", \"181316\", \"898\", \"1558\", \"2018\", \"4181\", \"7664\", \"8452\", \"11354\", \"16587\", \"19978\", \"22411\", \"24786\", \"30766\", \"33728\", \"35121\", \"35592\", \"36887\", \"40729\", \"44575\", \"44576\", \"40378\", \"47341\", \"47505\", \"48406\", \"46569\", \"51703\", \"54372\", \"54885\", \"55403\", \"56443\", \"56971\", \"57667\", \"59267\", \"59559\", \"59824\", \"59979\", \"60017\", \"63979\", \"63928\", \"65334\", \"70549\", \"74237\", \"77691\", \"143298\", \"76720\", \"93339\", \"93754\", \"98494\", \"114038\", \"113821\", \"115360\", \"116426\", \"129657\", \"134573\", \"138585\", \"142696\", \"146619\", \"151708\", \"160172\", \"183113\", \"192903\", \"211111\", \"83381\", \"27863\", \"86844\", \"88783\", \"96164\", \"97042\", \"98307\", \"100949\", \"103060\", \"104348\", \"104873\", \"106518\", \"108590\", \"111470\", \"105895\", \"115743\", \"117730\", \"117925\", \"117930\", \"120449\", \"118608\", \"122829\", \"123706\", \"125050\", \"126210\", \"126666\", \"127222\", \"130143\", \"133310\", \"134242\", \"135025\", \"135412\", \"136318\", \"118710\", \"140558\", \"140596\", \"140899\", \"141379\", \"141734\", \"141894\", \"143347\", \"145566\", \"146623\", \"147245\", \"147608\", \"147933\", \"149895\", \"151604\", \"151563\", \"151876\", \"154339\", \"155387\", \"155615\", \"158600\", \"158691\", \"160257\", \"160928\", \"161706\", \"162788\", \"165298\", \"172387\", \"173284\", \"176021\", \"179246\", \"180563\", \"191822\", \"193985\", \"208605\", \"225233\", \"149183\", \"149165\", \"150526\", \"163308\", \"166254\", \"167493\", \"170337\", \"171806\", \"172350\", \"175457\", \"177300\", \"177794\", \"172600\", \"183117\", \"184331\", \"184339\", \"184711\", \"185438\", \"161723\", \"188251\", \"188610\", \"188660\", \"189982\", \"192107\", \"195076\", \"195538\", \"192950\", \"199280\", \"201958\", \"202159\", \"202886\", \"204814\", \"205024\", \"207798\", \"207812\", \"207875\", \"209536\", \"212193\", \"214540\", \"215245\", \"215653\", \"223673\", \"224867\", \"225593\", \"228729\", \"236298\", \"236595\", \"238842\", \"242751\", \"247298\", \"265829\", \"201747\", \"206880\", \"206908\", \"214139\", \"217824\", \"218722\", \"222890\", \"225475\", \"227137\", \"229841\", \"230221\", \"230218\", \"230266\", \"231681\", \"231842\", \"232128\", \"230216\", \"234240\", \"235085\", \"235796\", \"241131\", \"238760\", \"243653\", \"243971\", \"245227\", \"247493\", \"270142\", \"270241\", \"248428\", \"251064\", \"251580\", \"256057\", \"254860\", \"266304\", \"259513\", \"269868\"], \"outgoing_citations\": [\"286385\", \"288248\", \"293570\", \"298700\", \"303010\", \"303132\", \"303913\", \"311388\", \"331847\"]}","task_split":"paper_retrieval"}
{"document_id":"272756","document_content":"# Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nLesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). DRFI method incorporates multi-level segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and post-processing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/JBHI.2018.2839647\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"71829\", \"85135\", \"141137\", \"216827\"], \"outgoing_citations\": [\"282613\", \"299339\", \"319427\", \"320665\", \"322867\", \"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"272829","document_content":"# An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAn automated method to detect and analyze the melanoma is presented to improve diagnosis which will leads to the exact treatment. Image processing techniques such as segmentation, feature descriptors and classification models are involved in this method. In the First phase the lesion region is segmented using CIELAB Color space Based Segmentation. Then feature descriptors such as shape, color and texture are extracted. Finally, in the third phase lesion region is classified as melanoma, seborrheic keratosis or nevus using multi class O-A SVM model. Experiment with ISIC 2017 Archive skin image database has been done and analyzed the results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.08717v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"272852","document_content":"# Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nScene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition, especially in the context of convolutional neural networks (ConvNets), has not been previously studied. In this paper, we propose the extraction and use of scene flow for action recognition from RGB-D data. Previous works have considered the depth and RGB modalities as separate channels and extract features for later fusion. We take a different approach and consider the modalities as one entity, thus allowing feature extraction for action recognition at the beginning. Two key questions about the use of scene flow for action recognition are addressed: how to organize the scene flow vectors and how to represent the long term dynamics of videos based on scene flow. In order to calculate the scene flow correctly on the available datasets, we propose an effective self-calibration method to align the RGB and depth data spatially without knowledge of the camera parameters. Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition. We adopt a channel transform kernel to transform the scene flow vectors to an optimal color space analogous to RGB. This transformation takes better advantage of the trained ConvNets models over ImageNet. Experimental results indicate that this new representation can surpass the performance of state-of-the-art methods on two large public datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.08652v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79061\", \"2417\", \"76854\", \"118848\", \"174035\", \"31544\", \"108350\", \"89843\", \"89857\", \"90560\", \"84518\", \"118847\", \"186694\", \"223780\", \"231906\", \"155435\", \"186866\", \"188668\", \"16449\", \"219003\", \"229010\", \"234986\", \"235906\", \"238633\", \"248294\", \"251877\", \"261919\", \"267497\"], \"outgoing_citations\": [\"102898\", \"252747\", \"276667\", \"280943\", \"286118\", \"295919\", \"299830\", \"301959\", \"303465\", \"312644\", \"313936\", \"321340\", \"324540\", \"329514\"]}","task_split":"paper_retrieval"}
{"document_id":"272909","document_content":"# Skin Lesion Classification Using Hybrid Deep Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment. While there are many computerised methods for skin lesion classification, convolutional neural networks (CNNs) have been shown to be superior over classical methods. In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels. We use three pre-trained deep models, namely AlexNet, VGG16 and ResNet-18, as deep feature generators. The extracted features then are used to train support vector machine classifiers. In the final stage, the classifier outputs are fused to obtain a classification. Evaluated on the 150 validation images from the ISIC 2017 classification challenge, the proposed method is shown to achieve very good classification performance, yielding an area under receiver operating characteristic curve of 83.83% for melanoma classification and of 97.55% for seborrheic keratosis classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.08434v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"61633\", \"53022\", \"115984\", \"96900\", \"103536\", \"149055\", \"220448\"], \"outgoing_citations\": [\"271634\", \"272395\"]}","task_split":"paper_retrieval"}
{"document_id":"272912","document_content":"# Age Progression\/Regression by Conditional Adversarial Autoencoder\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\n\"If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5?\" The answer is probably a \"No.\" Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.08423v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"61864\", \"146184\", \"3475\", \"6642\", \"6844\", \"8254\", \"9626\", \"14324\", \"14789\", \"15218\", \"15867\", \"18439\", \"23296\", \"27969\", \"42173\", \"57703\", \"57874\", \"63205\", \"71528\", \"86899\", \"101224\", \"102723\", \"115438\", \"118784\", \"120045\", \"129666\", \"152939\", \"164483\", \"174773\", \"518\", \"8342\", \"10715\", \"12349\", \"12409\", \"13931\", \"15044\", \"15453\", \"17449\", \"18762\", \"25548\", \"27137\", \"31142\", \"34402\", \"34704\", \"40366\", \"42798\", \"44531\", \"44824\", \"45234\", \"46853\", \"47365\", \"49398\", \"50114\", \"51451\", \"52186\", \"52453\", \"53058\", \"54025\", \"134736\", \"56116\", \"58322\", \"63976\", \"63908\", \"64571\", \"65923\", \"66756\", \"67285\", \"70811\", \"72323\", \"75358\", \"78884\", \"82331\", \"82701\", \"83095\", \"84624\", \"87548\", \"89358\", \"90923\", \"92822\", \"94726\", \"96103\", \"97608\", \"99907\", \"102558\", \"113260\", \"114764\", \"115498\", \"116261\", \"117846\", \"118930\", \"126070\", \"133669\", \"137594\", \"152768\", \"156229\", \"56028\", \"167231\", \"170486\", \"171905\", \"203401\", \"212096\", \"234433\", \"71456\", \"78912\", \"83657\", \"84551\", \"84709\", \"84680\", \"86923\", \"94132\", \"95272\", \"95804\", \"96908\", \"42296\", \"98827\", \"101311\", \"101598\", \"113541\", \"114341\", \"115290\", \"120590\", \"127610\", \"127805\", \"128776\", \"129267\", \"131265\", \"135603\", \"136271\", \"136896\", \"138014\", \"138178\", \"138488\", \"140003\", \"139925\", \"143559\", \"143847\", \"144489\", \"148748\", \"152832\", \"155103\", \"155582\", \"159884\", \"160268\", \"172656\", \"174956\", \"180065\", \"184045\", \"186541\", \"188872\", \"196341\", \"204754\", \"210368\", \"225725\", \"250249\", \"164707\", \"149164\", \"155785\", \"156027\", \"157434\", \"161911\", \"163323\", \"162567\", \"165923\", \"167409\", \"170176\", \"170293\", \"174325\", \"177410\", \"177898\", \"178610\", \"149578\", \"183899\", \"185337\", \"186064\", \"187553\", \"116950\", \"183287\", \"189350\", \"191293\", \"191545\", \"193090\", \"196765\", \"198677\", \"198908\", \"201077\", \"206324\", \"208447\", \"208420\", \"208421\", \"210979\", \"217177\", \"217418\", \"219464\", \"223969\", \"235749\", \"241448\", \"249029\", \"208144\", \"208654\", \"212094\", \"211157\", \"214256\", \"188744\", \"210607\", \"213905\", \"224827\", \"226473\", \"230371\", \"233157\", \"233233\", \"236382\", \"239738\", \"241048\", \"242334\", \"243165\", \"244220\", \"244597\", \"245478\", \"248731\", \"249067\", \"249421\", \"249855\", \"250238\", \"256227\", \"258495\", \"264744\", \"249092\", \"249314\", \"243612\", \"257386\"], \"outgoing_citations\": [\"284986\", \"298195\", \"304665\"]}","task_split":"paper_retrieval"}
{"document_id":"273057","document_content":"# Detecting (Un)Important Content for Single-Document News Summarization\n## Categories\n- Computation and Language\n## Abstract\nWe present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the \"beginning of document\" heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.07998v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"84343\", \"92063\", \"181713\", \"182629\", \"263905\", \"269913\"], \"outgoing_citations\": [\"296029\"]}","task_split":"paper_retrieval"}
{"document_id":"273257","document_content":"# k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis abstract briefly describes a segmentation algorithm developed for the ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of the competition is to perform a segmentation (in the form of a binary mask image) of skin lesions in dermoscopic images as close as possible to a segmentation performed by trained clinicians, which is taken as ground truth. This project only takes part in the segmentation phase of the challenge. The other phases of the competition (feature extraction and lesion identification) are not considered.   The proposed algorithm consists of 4 steps: (1) lesion image preprocessing, (2) image segmentation using k-means clustering of pixel colors, (3) calculation of a set of features describing the properties of each segmented region, and (4) calculation of a final score for each region, representing the likelihood of corresponding to a suitable lesion segmentation. The scores in step (4) are obtained by averaging the results of 2 different regression models using the scores of each region as input. Before using the algorithm these regression models must be trained using the training set of images and ground truth masks provided by the Competition. Steps 2 to 4 are repeated with an increasing number of clusters (and therefore the image is segmented into more regions) until there is no further improvement of the calculated scores.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.07333v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"254674\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"273341","document_content":"# Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSkin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnoses worldwide. Early detection influences the outcome of the disease and motivates our work. We investigate the composition of CNN committees and data augmentation for the the ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards Melanoma Detection) facing the peculiarities of dealing with such a small, unbalanced, biological database. For that, we explore committees of Convolutional Neural Networks trained over the ISBI challenge training dataset artificially augmented by both classical image processing transforms and image warping guided by specialist knowledge about the lesion axis and improve the final classifier invariance to common melanoma variations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.07025v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"194892\", \"251699\", \"154082\", \"198754\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"273380","document_content":"# Knowledge Graph Completion via Complex Tensor Factorization\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Spectral Theory\n- Machine Learning\n## Abstract\nIn statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices---thus all possible relation\/adjacency matrices---are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.06879v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"math.SP\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Spectral Theory\", \"Machine Learning\"], \"incoming_citations\": [\"38327\", \"3809\", \"10987\", \"13009\", \"29824\", \"46197\", \"63513\", \"20248\", \"26849\", \"27229\", \"28039\", \"33347\", \"38797\", \"45046\", \"47183\", \"50479\", \"54342\", \"55682\", \"54230\", \"71159\", \"74062\", \"80138\", \"107283\", \"156524\", \"171033\", \"183316\", \"64686\", \"82195\", \"82358\", \"91201\", \"155991\", \"91585\", \"101184\", \"105725\", \"104196\", \"114729\", \"122929\", \"134233\", \"142243\", \"143277\", \"156074\", \"177273\", \"183162\", \"184180\", \"184542\", \"188873\", \"220492\", \"167883\", \"171139\", \"174761\", \"177903\", \"180988\", \"216611\", \"188690\", \"193499\", \"201341\", \"205803\", \"206025\", \"206766\", \"255709\", \"208043\", \"210969\", \"213734\", \"223711\", \"223631\", \"238635\", \"242267\", \"242422\", \"250440\", \"252012\", \"257933\", \"261990\", \"262644\", \"265002\"], \"outgoing_citations\": [\"261990\", \"273751\", \"281711\", \"291991\", \"292003\", \"294178\", \"298621\", \"304901\", \"311858\", \"330994\", \"345467\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"273444","document_content":"# Social Learning and Diffusion of Pervasive Goods: An Empirical Study of an African App Store\n## Categories\n- Machine Learning\n- Social and Information Networks\n- Applications\n## Abstract\nIn this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a large data set from an African app store and Bayesian estimation methods, the authors quantify the effect of social influence and investigate the impact of ignoring this process in estimating customer choices. The findings show that customer choices in the app store are explained better by offline than online density of adopters and that ignoring social influence in estimations results in biased estimates. Furthermore, the findings show that the mobile app adoption process is similar to adoption of music CDs, among all other classic economy goods. A counterfactual analysis shows that the app store can increase its revenue by 13.6% through a viral marketing policy (e.g., a sharing with friends and family button).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.06661v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.SI\", \"stat.ML\", \"stat.AP\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Social and Information Networks\", \"Machine Learning\", \"Applications\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"273727","document_content":"# Reproducing and learning new algebraic operations on word embeddings using genetic programming\n## Categories\n- Computation and Language\n## Abstract\nWord-vector representations associate a high dimensional real-vector to every word from a corpus. Recently, neural-network based methods have been proposed for learning this representation from large corpora. This type of word-to-vector embedding is able to keep, in the learned vector space, some of the syntactic and semantic relationships present in the original word corpus. This, in turn, serves to address different types of language classification tasks by doing algebraic operations defined on the vectors. The general practice is to assume that the semantic relationships between the words can be inferred by the application of a-priori specified algebraic operations. Our general goal in this paper is to show that it is possible to learn methods for word composition in semantic spaces. Instead of expressing the compositional method as an algebraic operation, we will encode it as a program, which can be linear, nonlinear, or involve more intricate expressions. More remarkably, this program will be evolved from a set of initial random programs by means of genetic programming (GP). We show that our method is able to reproduce the same behavior as human-designed algebraic operators. Using a word analogy task as benchmark, we also show that GP-generated programs are able to obtain accuracy values above those produced by the commonly used human-designed rule for algebraic manipulation of word vectors. Finally, we show the robustness of our approach by executing the evolved programs on the word2vec GoogleNews vectors, learned over 3 billion running words, and assessing their accuracy in the same word analogy task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.05624v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"323701\"]}","task_split":"paper_retrieval"}
{"document_id":"273946","document_content":"# Distributed deep learning on edge-devices: feasibility via adaptive compression\n## Categories\n- Machine Learning\n## Abstract\nA large portion of data mining and analytic services use modern machine learning techniques, such as deep learning. The state-of-the-art results by deep learning come at the price of an intensive use of computing resources. The leading frameworks (e.g., TensorFlow) are executed on GPUs or on high-end servers in datacenters. On the other end, there is a proliferation of personal devices with possibly free CPU cycles; this can enable services to run in users' homes, embedding machine learning operations. In this paper, we ask the following question: Is distributed deep learning computation on WAN connected devices feasible, in spite of the traffic caused by learning tasks? We show that such a setup rises some important challenges, most notably the ingress traffic that the servers hosting the up-to-date model have to sustain.   In order to reduce this stress, we propose adaComp, a novel algorithm for compressing worker updates to the model on the server. Applicable to stochastic gradient descent based approaches, it combines efficient gradient selection and learning rate modulation. We then experiment and measure the impact of compression, device heterogeneity and reliability on the accuracy of learned models, with an emulator platform that embeds TensorFlow into Linux containers. We report a reduction of the total amount of data sent by workers to the server by two order of magnitude (e.g., 191-fold reduction for a convolutional network on the MNIST dataset), when compared to a standard asynchronous stochastic gradient descent, while preserving model accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.04683v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"87768\", \"120909\", \"173279\", \"200635\", \"127222\", \"129191\", \"134725\", \"147242\", \"162883\", \"165272\", \"210777\", \"235592\", \"239264\", \"259380\"], \"outgoing_citations\": [\"285951\", \"300079\", \"303050\", \"303573\", \"306613\", \"310485\"]}","task_split":"paper_retrieval"}
{"document_id":"274102","document_content":"# Cognitive Mapping and Planning for Visual Navigation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nWe introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as 'going to a chair'. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.03920v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"2138\", \"6637\", \"5373\", \"6613\", \"16494\", \"22954\", \"24124\", \"27350\", \"30806\", \"31846\", \"35753\", \"36507\", \"36637\", \"38664\", \"48063\", \"56182\", \"60623\", \"64409\", \"75621\", \"84594\", \"128249\", \"141042\", \"186811\", \"1414\", \"2239\", \"5820\", \"6704\", \"11137\", \"13764\", \"15383\", \"15829\", \"16975\", \"17198\", \"22720\", \"22936\", \"25684\", \"27520\", \"28168\", \"30950\", \"31155\", \"3896\", \"36974\", \"37143\", \"37932\", \"39279\", \"40343\", \"41883\", \"42082\", \"43678\", \"21619\", \"49226\", \"49451\", \"50758\", \"51975\", \"51698\", \"52301\", \"54096\", \"54696\", \"55121\", \"56049\", \"57729\", \"59216\", \"59387\", \"59410\", \"59470\", \"61081\", \"61747\", \"62318\", \"62680\", \"64870\", \"65095\", \"66007\", \"66404\", \"68278\", \"69178\", \"71624\", \"77464\", \"79162\", \"79903\", \"81574\", \"88041\", \"88014\", \"88546\", \"91301\", \"92592\", \"93649\", \"97038\", \"97665\", \"100401\", \"101182\", \"104690\", \"110042\", \"112741\", \"120758\", \"132558\", \"135780\", \"142303\", \"148172\", \"152072\", \"153902\", \"161638\", \"163326\", \"183561\", \"228445\", \"83765\", \"81676\", \"82126\", \"84377\", \"85766\", \"88294\", \"90871\", \"96979\", \"101886\", \"102971\", \"104828\", \"104772\", \"106160\", \"109133\", \"110273\", \"110466\", \"110941\", \"112798\", \"112949\", \"114824\", \"117896\", \"117841\", \"118153\", \"114744\", \"120092\", \"121716\", \"123031\", \"126377\", \"124069\", \"130190\", \"131180\", \"131310\", \"131814\", \"131891\", \"132720\", \"135977\", \"136058\", \"136760\", \"138485\", \"139112\", \"140039\", \"139900\", \"140089\", \"144133\", \"145753\", \"147314\", \"148111\", \"149565\", \"149647\", \"150096\", \"151229\", \"151367\", \"152732\", \"155676\", \"155831\", \"157007\", \"158547\", \"160895\", \"161154\", \"161947\", \"162505\", \"164490\", \"164699\", \"161717\", \"165819\", \"180340\", \"181697\", \"181795\", \"191583\", \"197663\", \"200261\", \"136757\", \"155641\", \"157901\", \"160530\", \"166667\", \"167688\", \"170410\", \"170354\", \"172441\", \"173034\", \"173704\", \"174221\", \"177036\", \"177674\", \"179594\", \"179954\", \"181081\", \"183699\", \"184325\", \"185747\", \"186524\", \"190556\", \"190948\", \"191394\", \"191570\", \"191878\", \"192533\", \"192738\", \"193993\", \"194269\", \"195772\", \"196148\", \"196226\", \"196393\", \"196361\", \"196532\", \"196898\", \"198524\", \"200941\", \"202152\", \"202904\", \"203854\", \"204320\", \"205901\", \"207499\", \"208962\", \"209152\", \"214195\", \"220390\", \"225224\", \"227049\", \"231955\", \"231593\", \"230187\", \"204289\", \"207509\", \"187262\", \"209052\", \"211644\", \"209402\", \"212893\", \"210666\", \"215818\", \"216221\", \"216647\", \"199792\", \"220377\", \"223033\", \"224223\", \"224255\", \"225266\", \"225768\", \"227125\", \"227747\", \"227881\", \"228105\", \"219658\", \"229726\", \"230208\", \"230558\", \"231155\", \"231153\", \"233165\", \"234280\", \"237119\", \"237245\", \"237357\", \"239283\", \"239320\", \"240052\", \"241006\", \"241625\", \"240323\", \"234517\", \"243090\", \"243591\", \"244266\", \"245731\", \"247900\", \"248742\", \"249906\", \"249876\", \"250190\", \"252309\", \"254494\", \"255699\", \"257128\", \"272930\", \"246823\", \"247749\", \"254490\", \"255126\", \"261604\", \"262593\", \"268935\", \"270473\", \"271062\", \"280570\", \"277504\"], \"outgoing_citations\": [\"219658\", \"224223\", \"225036\", \"240323\", \"244266\", \"245731\", \"247749\", \"247900\", \"249906\", \"252658\", \"255699\", \"272930\", \"272954\", \"275003\", \"277797\", \"278999\", \"280570\", \"280681\", \"280870\", \"287362\", \"290433\", \"291568\", \"292013\", \"296637\", \"297268\", \"297285\", \"298651\", \"301502\", \"310079\", \"310233\", \"326874\"]}","task_split":"paper_retrieval"}
{"document_id":"274201","document_content":"# Vector Embedding of Wikipedia Concepts and Entities\n## Categories\n- Computation and Language\n## Abstract\nUsing deep learning for different machine learning tasks such as image classification and word embedding has recently gained many attentions. Its appealing performance reported across specific Natural Language Processing (NLP) tasks in comparison with other approaches is the reason for its popularity. Word embedding is the task of mapping words or phrases to a low dimensional numerical vector. In this paper, we use deep learning to embed Wikipedia Concepts and Entities. The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English Entities, Phrases, and Concepts. Each Wikipedia page is considered as a concept. Some concepts correspond to entities, such as a person's name, an organization or a place. Contrary to word embedding, Wikipedia Concepts Embedding is not ambiguous, so there are different vectors for concepts with similar surface form but different mentions. We proposed several approaches and evaluated their performance based on Concept Analogy and Concept Similarity tasks. The results show that proposed approaches have the performance comparable and in some cases even higher than the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.03470v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"165613\", \"194372\", \"232527\", \"273377\"], \"outgoing_citations\": [\"71722\", \"178230\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"274313","document_content":"# Reconstruction-Based Disentanglement for Pose-invariant Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop methods capable of handling large pose variations that are relatively underrepresented in training data. This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data. We first propose to generate non-frontal views from a single frontal face, in order to increase the diversity of training data while preserving accurate facial details that are critical for identity discrimination. Our next contribution is to seek a rich embedding that encodes identity features, as well as non-identity ones such as pose and landmark locations. Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject. Experiments on both controlled and in-the-wild face datasets, such as MultiPIE, 300WLP and the profile view database CFP, show that our method consistently outperforms the state-of-the-art, especially on images with large head pose variations. Detail results and resource are referred to https:\/\/sites.google.com\/site\/xipengcshomepage\/iccv2017","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.03041v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"68390\", \"27417\", \"31142\", \"32695\", \"33535\", \"39275\", \"59833\", \"62160\", \"63366\", \"79555\", \"83220\", \"235160\", \"37004\", \"89576\", \"89654\", \"95106\", \"109683\", \"111342\", \"113541\", \"118810\", \"125661\", \"133398\", \"133736\", \"139770\", \"139829\", \"143198\", \"149540\", \"154108\", \"165999\", \"187702\", \"206202\", \"247447\", \"220959\", \"226432\", \"234246\", \"235070\", \"236123\", \"237222\", \"237920\", \"243198\", \"244891\", \"247558\", \"249874\", \"251215\", \"273937\", \"263714\", \"268405\"], \"outgoing_citations\": [\"218034\", \"268405\", \"277948\", \"293303\", \"294284\", \"296061\", \"302701\", \"316183\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"274427","document_content":"# Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization\n## Categories\n- Computation and Language\n## Abstract\nA fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.02535v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"224850\", \"240432\", \"305094\"], \"outgoing_citations\": [\"292576\", \"297221\", \"305094\", \"305591\", \"314300\", \"323098\"]}","task_split":"paper_retrieval"}
{"document_id":"274450","document_content":"# Trainable Greedy Decoding for Neural Machine Translation\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.02429v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2603\", \"27127\", \"71522\", \"120964\", \"184591\", \"86824\", \"99469\", \"127430\", \"131385\", \"142985\", \"155086\", \"152729\", \"152767\", \"167598\", \"179505\", \"180695\", \"181585\", \"242690\", \"218543\", \"223201\", \"230212\", \"234742\", \"238496\", \"256347\", \"263027\", \"268886\", \"269199\"], \"outgoing_citations\": [\"275595\", \"279543\", \"280447\", \"280973\", \"281076\", \"281508\", \"282440\", \"282931\", \"283451\", \"287823\", \"290510\", \"290766\", \"290778\", \"292690\", \"295308\", \"299936\", \"300543\", \"300548\", \"301840\", \"303003\", \"304250\"]}","task_split":"paper_retrieval"}
{"document_id":"274469","document_content":"# Iterative Multi-document Neural Attention for Multiple Answer Prediction\n## Categories\n- Computation and Language\n## Abstract\nPeople have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. In a scenario in which the user profile can be considered as a question, intelligent agents able to answer questions can be used to find the most relevant answers for a given user. In this work we propose a novel model based on Artificial Neural Networks to answer questions with multiple answers by exploiting multiple facts retrieved from a knowledge base. The model is evaluated on the factoid Question Answering and top-n recommendation tasks of the bAbI Movie Dialog dataset. After assessing the performance of the model on both tasks, we try to define the long-term goal of a conversational recommender system able to interact using natural language and to support users in their information seeking processes in a personalized way.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.02367v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"290937\", \"297106\", \"302771\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"274499","document_content":"# Neural Machine Translation with Source-Side Latent Graph Parsing\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.02265v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"52679\", \"80977\", \"144222\", \"168201\", \"187326\", \"202454\", \"219049\", \"223095\", \"219758\", \"226756\", \"230281\", \"256740\", \"268812\", \"274183\", \"281169\"], \"outgoing_citations\": [\"260977\", \"261055\", \"267460\", \"268812\", \"274183\", \"279384\", \"281121\", \"281169\", \"281205\", \"286225\", \"288697\", \"290777\", \"291192\", \"292552\", \"296274\", \"302777\", \"304842\", \"316741\"]}","task_split":"paper_retrieval"}
{"document_id":"274560","document_content":"# Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approach\n## Categories\n- Social and Information Networks\n- Machine Learning\n## Abstract\nUser modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements, are among those factors that are neglected, while they have a strong impact on user participation in online services. In this paper, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community based question and answering websites, and propose an inference algorithm based on Variational-EM that can efficiently learn the model parameters. Extensive experiments on both synthetic and real data gathered from Stack Overflow show that our inference algorithm learns the parameters efficiently and the proposed method can better predict the user behavior compared to the alternatives.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.01948v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [\"166735\", \"49068\", \"185543\"], \"outgoing_citations\": [\"279731\", \"283491\", \"304589\", \"305551\", \"306540\", \"309877\", \"328896\", \"332537\", \"334129\", \"341471\", \"345012\", \"358150\"]}","task_split":"paper_retrieval"}
{"document_id":"274665","document_content":"# Contextually Customized Video Summaries via Natural Language\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nThe best summary of a long video differs among different people due to its highly subjective nature. Even for the same person, the best summary may change with time or mood. In this paper, we introduce the task of generating customized video summaries through simple text. First, we train a deep architecture to effectively learn semantic embeddings of video frames by leveraging the abundance of image-caption data via a progressive and residual manner. Given a user-specific text description, our algorithm is able to select semantically relevant video segments and produce a temporally aligned video summary. In order to evaluate our textually customized video summaries, we conduct experimental comparison with baseline methods that utilize ground-truth information. Despite the challenging baselines, our method still manages to show comparable or even exceeding performance. We also show that our method is able to generate semantically diverse video summaries by only utilizing the learned visual embeddings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.01528v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"33472\", \"101085\", \"109128\", \"168553\", \"195170\", \"216400\", \"216387\"], \"outgoing_citations\": [\"258932\", \"288206\", \"290378\", \"292486\", \"302943\", \"305469\", \"312423\", \"314050\", \"328015\"]}","task_split":"paper_retrieval"}
{"document_id":"274675","document_content":"# Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDeep Convolutional Neural Networks (DCNN) have been proven to be effective for various computer vision problems. In this work, we demonstrate its effectiveness on a continuous object orientation estimation task, which requires prediction of 0 to 360 degrees orientation of the objects. We do so by proposing and comparing three continuous orientation prediction approaches designed for the DCNNs. The first two approaches work by representing an orientation as a point on a unit circle and minimizing either L2 loss or angular difference loss. The third method works by first converting the continuous orientation estimation task into a set of discrete orientation estimation tasks and then converting the discrete orientation outputs back to the continuous orientation using a mean-shift algorithm. By evaluating on a vehicle orientation estimation task and a pedestrian orientation estimation task, we demonstrate that the discretization-based approach not only works better than the other two approaches but also achieves state-of-the-art performance. We also demonstrate that finding an appropriate feature representation is critical to achieve a good performance when adapting a DCNN trained for an image recognition task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.01499v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"4367\", \"86493\", \"88834\", \"79212\", \"99502\", \"84335\", \"153082\", \"176843\", \"209284\", \"120049\", \"183250\", \"158284\", \"166873\", \"179298\", \"187663\", \"228415\", \"231648\", \"235628\", \"249869\", \"250094\"], \"outgoing_citations\": [\"284812\", \"312499\", \"321449\", \"327107\", \"336151\"]}","task_split":"paper_retrieval"}
{"document_id":"274886","document_content":"# Procedural Content Generation via Machine Learning (PCGML)\n## Categories\n- Artificial Intelligence\n## Abstract\nThis survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1702.00539v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"24178\", \"31752\", \"61575\", \"87663\", \"133218\", \"140757\", \"18382\", \"18372\", \"22009\", \"33228\", \"33220\", \"33214\", \"33227\", \"35228\", \"37071\", \"37296\", \"38564\", \"39374\", \"39691\", \"40705\", \"41052\", \"41101\", \"41462\", \"51107\", \"51175\", \"52723\", \"56433\", \"67124\", \"83991\", \"95477\", \"107442\", \"112786\", \"114931\", \"142583\", \"47009\", \"94739\", \"96766\", \"96756\", \"96760\", \"94524\", \"61740\", \"104574\", \"104584\", \"107909\", \"109632\", \"109729\", \"111464\", \"112572\", \"117983\", \"122922\", \"124521\", \"124853\", \"125408\", \"133812\", \"139814\", \"140395\", \"143545\", \"145664\", \"148086\", \"153614\", \"163503\", \"257674\", \"155146\", \"168558\", \"170002\", \"176932\", \"181323\", \"189201\", \"194062\", \"202457\", \"214945\", \"218411\", \"193790\", \"216608\", \"216607\", \"218887\", \"218884\", \"216605\", \"59937\", \"226436\", \"233501\", \"234345\", \"261387\"], \"outgoing_citations\": [\"61579\", \"283868\", \"284708\", \"289709\", \"292503\", \"296883\", \"297233\", \"340939\", \"357151\"]}","task_split":"paper_retrieval"}
{"document_id":"275067","document_content":"# Language Independent Single Document Image Super-Resolution using CNN for improved recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognition of document images have important applications in restoring old and classical texts. The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text. The image enhancement and quality improvement constitute important steps as subsequent recognition depends upon the quality of the input image. There are scenarios when high resolution images are not available and our experiments show that the OCR accuracy reduces significantly with decrease in the spatial resolution of document images. Thus the only option is to improve the resolution of such document images. The goal is to construct a high resolution image, given a single low resolution binary image, which constitutes the problem of single image super-resolution. Most of the previous work in super-resolution deal with natural images which have more information-content than the document images. Here, we use Convolution Neural Network to learn the mapping between low and the corresponding high resolution images. We experiment with different number of layers, parameter settings and non-linear functions to build a fast end-to-end framework for document image super-resolution. Our proposed model shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images, resulting in a 3 % improvement of word level accuracy by the OCR. It takes less time than the recent sparse based natural image super-resolution technique, making it useful for real-time document recognition applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.08835v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"93552\", \"5077\", \"165873\", \"207005\", \"220337\"], \"outgoing_citations\": [\"301105\"]}","task_split":"paper_retrieval"}
{"document_id":"275194","document_content":"# Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy\n## Categories\n- Artificial Intelligence\n## Abstract\nWhen AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a \"model reconciliation problem\" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.08317v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"51923\", \"71823\", \"129980\", \"22157\", \"32730\", \"51298\", \"57877\", \"64321\", \"86001\", \"144265\", \"18775\", \"20843\", \"25679\", \"30840\", \"40109\", \"47489\", \"54303\", \"59886\", \"62367\", \"73743\", \"76888\", \"80016\", \"130931\", \"79100\", \"84240\", \"85487\", \"86101\", \"86274\", \"89311\", \"102965\", \"114745\", \"126632\", \"127229\", \"137316\", \"139877\", \"148027\", \"184564\", \"241614\", \"161527\", \"171849\", \"177549\", \"180659\", \"194509\", \"194783\", \"194802\", \"195041\", \"196464\", \"200132\", \"200543\", \"200101\", \"208919\", \"241886\", \"263098\", \"202683\", \"214227\", \"227270\", \"239909\", \"243401\", \"243879\", \"256056\", \"36384\", \"248571\", \"259593\", \"261163\", \"280276\"], \"outgoing_citations\": [\"302521\", \"303133\"]}","task_split":"paper_retrieval"}
{"document_id":"275272","document_content":"# A Comparative Study on Different Types of Approaches to Bengali document Categorization\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nDocument categorization is a technique where the category of a document is determined. In this paper three well-known supervised learning techniques which are Support Vector Machine(SVM), Na\\\"ive Bayes(NB) and Stochastic Gradient Descent(SGD) compared for Bengali document categorization. Besides classifier, classification also depends on how feature is selected from dataset. For analyzing those classifier performances on predicting a document against twelve categories several feature selection techniques are also applied in this article namely Chi square distribution, normalized TFIDF (term frequency-inverse document frequency) with word analyzer. So, we attempt to explore the efficiency of those three-classification algorithms by using two different feature selection techniques in this article.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.08694v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"73813\", \"131671\"], \"outgoing_citations\": [\"323508\", \"328734\"]}","task_split":"paper_retrieval"}
{"document_id":"275273","document_content":"# Document Decomposition of Bangla Printed Text\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nToday all kind of information is getting digitized and along with all this digitization, the huge archive of various kinds of documents is being digitized too. We know that, Optical Character Recognition is the method through which, newspapers and other paper documents convert into digital resources. But, it is a fact that this method works on texts only. As a result, if we try to process any document which contains non-textual zones, then we will get garbage texts as output. That is why; in order to digitize documents properly they should be prepossessed carefully. And while preprocessing, segmenting document in different regions according to the category properly is most important. But, the Optical Character Recognition processes available for Bangla language have no such algorithm that can categorize a newspaper\/book page fully. So we worked to decompose a document into its several parts like headlines, sub headlines, columns, images etc. And if the input is skewed and rotated, then the input was also deskewed and de-rotated. To decompose any Bangla document we found out the edges of the input image. Then we find out the horizontal and vertical area of every pixel where it lies in. Later on the input image was cut according to these areas. Then we pick each and every sub image and found out their height-width ratio, line height. Then according to these values the sub images were categorized. To deskew the image we found out the skew angle and de skewed the image according to this angle. To de-rotate the image we used the line height, matra line, pixel ratio of matra line.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.08706v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"275409","document_content":"# Learning Word-Like Units from Joint Audio-Visual Analysis\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nGiven a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word 'lighthouse' within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.07481v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"43252\", \"53303\", \"738\", \"10669\", \"16315\", \"37518\", \"38189\", \"41626\", \"58270\", \"77371\", \"77697\", \"85634\", \"118223\", \"127826\", \"80706\", \"90581\", \"112765\", \"126708\", \"132497\", \"154976\", \"173405\", \"176833\", \"176876\", \"184285\", \"189109\", \"190511\", \"198064\", \"199724\", \"205074\", \"205161\", \"209132\", \"221547\", \"225313\", \"236772\", \"254061\", \"221600\", \"230159\", \"236276\", \"236717\", \"237945\", \"237968\", \"249745\", \"287521\", \"251409\", \"256062\", \"260141\", \"263945\", \"268936\", \"270711\", \"274549\", \"280501\"], \"outgoing_citations\": [\"282874\", \"302621\", \"303546\", \"316584\", \"318558\", \"319938\", \"321621\", \"324544\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"275449","document_content":"# Deep Reinforcement Learning: An Overview\n## Categories\n- Machine Learning\n## Abstract\nWe give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions.   Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.07274v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"275486","document_content":"# Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code\n## Categories\n- Programming Languages\n- I.2.6; C.1.4\n- Artificial Intelligence\n## Abstract\nThe current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.237.4\", \"primary_category\": \"cs.PL\", \"categories\": [\"cs.PL\", \"I.2.6; C.1.4\", \"cs.AI\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"Programming Languages\", \"I.2.6; C.1.4\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"275566","document_content":"# Imitating Driver Behavior with Generative Adversarial Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nThe ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.06699v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"28573\", \"52134\", \"119790\", \"13755\", \"26978\", \"38318\", \"39639\", \"68506\", \"69750\", \"76756\", \"80340\", \"88471\", \"132642\", \"135117\", \"191103\", \"1686\", \"3012\", \"7156\", \"9553\", \"19552\", \"23300\", \"28675\", \"36422\", \"36400\", \"42382\", \"155602\", \"57738\", \"66140\", \"67037\", \"67309\", \"71419\", \"75571\", \"76456\", \"76456\", \"98587\", \"105761\", \"161638\", \"163665\", \"61535\", \"78509\", \"99595\", \"113006\", \"115020\", \"118382\", \"116566\", \"120882\", \"126757\", \"126686\", \"135586\", \"137647\", \"137942\", \"138073\", \"138403\", \"145993\", \"147984\", \"153464\", \"162735\", \"164146\", \"166815\", \"186194\", \"197149\", \"217927\", \"223299\", \"152241\", \"158269\", \"161182\", \"163838\", \"175629\", \"176463\", \"188143\", \"191356\", \"192305\", \"195236\", \"196618\", \"200294\", \"201059\", \"206814\", \"210901\", \"230605\", \"253478\", \"256317\", \"212080\", \"217856\", \"221580\", \"231623\", \"232095\", \"232096\", \"232224\", \"164502\", \"240202\", \"243241\", \"243457\", \"252398\", \"253348\", \"261653\", \"268573\", \"269043\", \"270529\"], \"outgoing_citations\": [\"291738\", \"301502\"]}","task_split":"paper_retrieval"}
{"document_id":"275976","document_content":"# Compression of Deep Neural Networks for Image Instance Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the dominant approach for generating {\\it global image descriptors} for the instance retrieval problem. One major drawback of CNN-based {\\it global descriptors} is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware. In this work, we study the problem of neural network model compression focusing on the image instance retrieval task. We study quantization, coding, pruning and weight sharing techniques for reducing model size for the instance retrieval problem. We provide extensive experimental results on the trade-off between retrieval performance and model size for different types of networks on several data sets providing the most comprehensive study on this topic. We compress models to the order of a few MBs: two orders of magnitude smaller than the uncompressed models while achieving negligible loss in retrieval performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.04923v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"52459\", \"138007\", \"150829\", \"187195\", \"217816\", \"260972\"], \"outgoing_citations\": [\"294668\", \"296534\", \"300357\", \"303067\", \"308236\", \"320127\", \"331445\", \"337753\"]}","task_split":"paper_retrieval"}
{"document_id":"276123","document_content":"# 3D tracking of water hazards with polarized stereo cameras\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nCurrent self-driving car systems operate well in sunny weather but struggle in adverse conditions. One of the most commonly encountered adverse conditions involves water on the road caused by rain, sleet, melting snow or flooding. While some advances have been made in using conventional RGB camera and LIDAR technology for detecting water hazards, other sources of information such as polarization offer a promising and potentially superior approach to this problem in terms of performance and cost. In this paper, we present a novel stereo-polarization system for detecting and tracking water hazards based on polarization and color variation of reflected light, with consideration of the effect of polarized light from sky as function of reflection and azimuth angles. To evaluate this system, we present a new large `water on road' datasets spanning approximately 2 km of driving in various on-road and off-road conditions and demonstrate for the first time reliable water detection and tracking over a wide range of realistic car driving water conditions using polarized vision as the primary sensing modality. Our system successfully detects water hazards up to more than 100m. Finally, we discuss several interesting challenges and propose future research directions for further improving robust autonomous car perception in hazardous wet conditions using polarization sensors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.04175v2\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"111979\", \"123508\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"276159","document_content":"# Neural Models for Sequence Chunking\n## Categories\n- Computation and Language\n## Abstract\nMany natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside-Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.04027v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"174296\", \"205022\", \"23429\", \"11169\", \"30876\", \"65563\", \"74641\", \"81520\", \"118786\", \"170375\", \"87226\", \"95080\", \"128934\", \"129221\", \"135568\", \"191706\", \"182220\", \"211603\", \"216615\", \"237235\", \"221599\", \"223096\", \"225791\", \"226966\", \"228348\", \"243475\", \"249624\", \"264648\", \"261113\", \"260847\", \"264389\"], \"outgoing_citations\": [\"287040\", \"289624\", \"295473\", \"296221\", \"300449\", \"309633\", \"309965\", \"312009\", \"312066\", \"313962\"]}","task_split":"paper_retrieval"}
{"document_id":"276436","document_content":"# Context-aware Captions from Context-agnostic Supervision\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n## Abstract\nWe introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of \"siamese cat\" and \"tiger cat\", we generate language that describes the \"siamese cat\" in a way that distinguishes it from \"tiger cat\". Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.02870v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\"], \"incoming_citations\": [\"3065\", \"17298\", \"20640\", \"22556\", \"22651\", \"24419\", \"28498\", \"61394\", \"74768\", \"80747\", \"86707\", \"110600\", \"88113\", \"90867\", \"99291\", \"101699\", \"125091\", \"131507\", \"132390\", \"128086\", \"134875\", \"135758\", \"146788\", \"154854\", \"166562\", \"161738\", \"174277\", \"167782\", \"169938\", \"180149\", \"180976\", \"182584\", \"187315\", \"192701\", \"197647\", \"202386\", \"203562\", \"204730\", \"206058\", \"272636\", \"204263\", \"215976\", \"219687\", \"223389\", \"223815\", \"228933\", \"228916\", \"230373\", \"233661\", \"235544\", \"238125\", \"239242\", \"242651\", \"250098\", \"250567\", \"253934\", \"257442\", \"268223\", \"271137\"], \"outgoing_citations\": [\"271085\", \"271497\", \"277491\", \"283076\", \"287377\", \"290891\", \"292386\", \"295368\", \"295742\", \"303603\", \"303833\", \"312704\", \"321621\", \"323775\", \"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"276547","document_content":"# Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n- Robotics\n## Abstract\nThis paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each other in systems which model human reward. We demonstrate that simultaneously incorporating human control and feedback signals can improve interactive robotic systems' performance on a self-mirrored movement control task where an RL-agent controlled right arm attempts to match the preprogrammed movement pattern of the left arm. We illustrate the impact of varying human feedback parameters on task performance by investigating the probability of giving feedback on each time step and the likelihood of given feedback being correct. We further illustrate that varying the temporal decay with which the agent incorporates human feedback has a significant impact on task performance. We found that smearing human feedback over time steps improves performance and we show varying the probability of feedback at each time step, and an increased likelihood of those feedbacks being 'correct' can impact agent performance. We conclude that understanding latent variables in human feedback is crucial for learning algorithms acting in human-machine interaction domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.02369v3\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"272433\"], \"outgoing_citations\": [\"289831\"]}","task_split":"paper_retrieval"}
{"document_id":"276603","document_content":"# Neural Personalized Response Generation as Domain Adaptation\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.02073v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42093\", \"23160\", \"24186\", \"45263\", \"168075\", \"68923\", \"78504\", \"192175\", \"90048\", \"130818\", \"128060\", \"156814\", \"186662\", \"170180\", \"201322\", \"210493\", \"253914\", \"282955\", \"293508\", \"251348\", \"267727\"], \"outgoing_citations\": [\"252860\", \"256448\", \"274567\", \"279897\", \"286291\", \"291102\", \"292261\", \"295552\", \"296250\", \"297170\", \"308378\", \"308381\", \"309886\", \"310740\", \"316328\", \"317966\"]}","task_split":"paper_retrieval"}
{"document_id":"276686","document_content":"# Spotting Information biases in Chinese and Western Media\n## Categories\n- Information Retrieval\n## Abstract\nNewswire and Social Media are the major sources of information in our time. While the topical demographic of Western Media was subjects of studies in the past, less is known about Chinese Media. In this paper, we apply event detection and tracking technology to examine the information overlap and differences between Chinese and Western - Traditional Media and Social Media. Our experiments reveal a biased interest of China towards the West, which becomes particularly apparent when comparing the interest in celebrities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.01737v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"276875","document_content":"# Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences\n## Categories\n- Computation and Language\n## Abstract\nWith the rapid growth of social media on the web, emotional polarity computation has become a flourishing frontier in the text mining community. However, it is challenging to understand the latest trends and summarize the state or general opinions about products due to the big diversity and size of social media data and this creates the need of automated and real time opinion extraction and mining. On the other hand, the bulk of current research has been devoted to study the subjective sentences which contain opinion keywords and limited work has been reported for objective statements that imply sentiment. In this paper, fuzzy based knowledge engineering model has been developed for sentiment classification of special group of such sentences including the change or deviation from desired range or value. Drug reviews are the rich source of such statements. Therefore, in this research, some experiments were carried out on patient's reviews on several different cholesterol lowering drugs to determine their sentiment polarity. The main conclusion through this study is, in order to increase the accuracy level of existing drug opinion mining systems, objective sentences which imply opinion should be taken into account. Our experimental results demonstrate that our proposed model obtains over 72 percent F1 value.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1701.00798v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"253253\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"276918","document_content":"# Knowledge Engineering for Hybrid Deductive Databases\n## Categories\n- Databases\n- Artificial Intelligence\n- D.1.6\n- Programming Languages\n## Abstract\nModern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these different formats of knowledge and reason about them. Even the file systems on different computers can be part of the knowledge base. Often, it is necessary to handle different versions of a knowledge base. E.g., we might want to find out common parts or differences of two versions of a relational database.   We will examine the use of abstractions of rule bases by predicate dependency and rule predicate graphs. Also the proof trees of derived atoms can help to compare different versions of a rule base. Moreover, it might be possible to have derivations joining rules with other formalisms of knowledge representation.   Ontologies have shown their benefits in many applications of intelligent systems, and there have been many proposals for rule languages compatible with the semantic web stack, e.g., SWRL, the semantic web rule language. Recently, ontologies are used in hybrid systems for specifying the provenance of the different components.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4204\/EPTCS.234.1\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.AI\", \"D.1.6\", \"cs.DB\", \"cs.PL\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Artificial Intelligence\", \"D.1.6\", \"Databases\", \"Programming Languages\"], \"incoming_citations\": [\"276915\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"277052","document_content":"# When the map is better than the territory\n## Categories\n- Information Theory\n- Artificial Intelligence\n- 94A15\n## Abstract\nThe causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus (be more informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a system (a map) can be more informative than a fully detailed model of the system (the territory). This has been called causal emergence. While causal emergence may at first glance seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different causal models of those systems take advantage of that capacity to various degrees. For some systems, only macroscale causal models use the full causal capacity. Such macroscale causal models can either be coarse-grains, or may leave variables and states out of the model (exogenous) in various ways, which can improve the model's efficacy and its informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel's capacity. As model choice increase, the causal capacity of a system approaches the channel capacity. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscopic model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/e19050188\", \"primary_category\": \"cs.IT\", \"categories\": [\"cs.IT\", \"cs.AI\", \"94A15\", \"math.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Information Theory\", \"Artificial Intelligence\", \"94A15\", \"Information Theory\"], \"incoming_citations\": [\"52466\", \"53248\", \"6590\", \"59817\", \"93233\", \"119035\", \"91140\", \"124354\", \"177011\", \"210631\", \"170912\", \"185143\"], \"outgoing_citations\": [\"241939\", \"323987\"]}","task_split":"paper_retrieval"}
{"document_id":"277061","document_content":"# PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThis paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities. In this paper we present a simple and efficient named entity extraction algorithm. The method, named PAMPO (PAttern Matching and POs tagging based algorithm for NER), relies on flexible pattern matching, part-of-speech tagging and lexical-based rules. It was developed to process texts written in Portuguese, however it is potentially applicable to other languages as well.   We compare our approach with current alternatives that support Named Entity Recognition (NER) for content written in Portuguese. These are Alchemy, Zemanta and Rembrandt. Evaluation of the efficacy of the entity extraction method on several texts written in Portuguese indicates a considerable improvement on $recall$ and $F_1$ measures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.09535v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"277229","document_content":"# Distributed Real-Time Sentiment Analysis for Big Data Social Streams\n## Categories\n- Machine Learning\n- Information Retrieval\n- Databases\n- Distributed, Parallel, and Cluster Computing\n- Computation and Language\n## Abstract\nBig data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about what-is-happening-now with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that incoming instances are not lost without being captured. Lastly, the learner needs to provide high analytical accuracy measures. Sentinel is a distributed system written in Java that aims to solve this challenge by enforcing both the processing and learning process to be done in distributed form. Sentinel is built on top of Apache Storm, a distributed computing platform. Sentinels learner, Vertical Hoeffding Tree, is a parallel decision tree-learning algorithm based on the VFDT, with ability of enabling parallel classification in distributed environments. Sentinel also uses SpaceSaving to keep a summary of the data stream and stores its summary in a synopsis data structure. Application of Sentinel on Twitter Public Stream API is shown and the results are discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CoDIT.2014.6996998\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.IR\", \"stat.ML\", \"cs.DB\", \"cs.DC\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Databases\", \"Distributed, Parallel, and Cluster Computing\", \"Computation and Language\"], \"incoming_citations\": [\"245363\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"277259","document_content":"# Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling\n## Categories\n- Computation and Language\n## Abstract\nHeadline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summarization, for which the summaries are generated word by word from scratch without using any part of the original content. Many deep learning approaches for headline generation from text document have been proposed recently, all requiring huge quantities of training data, which is difficult for spoken document summarization. In this paper, we propose an ASR error modeling approach to learn the underlying structure of ASR error patterns and incorporate this model in an Attentive Recurrent Neural Network (ARNN) architecture. In this way, the model for abstractive headline generation for spoken content can be learned from abundant text data and the ASR data for some recognizers. Experiments showed very encouraging results and verified that the proposed ASR error model works well even when the input spoken content is recognized by a recognizer very different from the one the model learned from.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.08375v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"237064\", \"255136\", \"255769\"], \"outgoing_citations\": [\"295815\", \"296029\", \"301996\", \"310904\", \"316328\", \"320814\"]}","task_split":"paper_retrieval"}
{"document_id":"277386","document_content":"# Learning from Simulated and Unsupervised Images through Adversarial Training\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWith recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.07828v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"40706\", \"49008\", \"50480\", \"74019\", \"145629\", \"146468\", \"154069\", \"193208\", \"54091\", \"2323\", \"5269\", \"6227\", \"8780\", \"11067\", \"14538\", \"20136\", \"25451\", \"28087\", \"31138\", \"32976\", \"37053\", \"38526\", \"39164\", \"39439\", \"40254\", \"41058\", \"42817\", \"43327\", \"43497\", \"49020\", \"55628\", \"56611\", \"59907\", \"71126\", \"74294\", \"74970\", \"78718\", \"80332\", \"84856\", \"103079\", \"124477\", \"146891\", \"147814\", \"226818\", \"738\", \"2356\", \"2942\", \"7178\", \"7167\", \"13407\", \"13412\", \"13878\", \"17147\", \"17540\", \"17671\", \"18423\", \"18600\", \"13587\", \"19680\", \"19959\", \"20208\", \"21102\", \"24549\", \"26072\", \"27366\", \"27358\", \"27759\", \"30013\", \"30059\", \"30261\", \"31189\", \"32679\", \"33798\", \"37187\", \"37811\", \"38502\", \"39482\", \"39517\", \"39627\", \"35842\", \"40343\", \"40366\", \"40725\", \"41613\", \"43187\", \"44694\", \"40901\", \"46195\", \"45904\", \"48483\", \"49810\", \"51347\", \"52725\", \"53512\", \"53635\", \"54548\", \"54934\", \"56160\", \"57754\", \"57747\", \"58032\", \"58097\", \"58346\", \"58768\", \"58840\", \"59374\", \"59332\", \"60151\", \"60473\", \"61611\", \"61901\", \"62020\", \"65688\", \"66429\", \"68726\", \"69347\", \"70577\", \"71397\", \"72567\", \"76267\", \"77117\", \"77077\", \"77592\", \"78879\", \"81719\", \"82433\", \"82606\", \"82828\", \"45549\", \"83775\", \"84117\", \"86391\", \"93453\", \"95428\", \"96057\", \"96040\", \"101841\", \"106870\", \"111580\", \"112869\", \"115089\", \"122178\", \"127037\", \"128735\", \"141252\", \"171456\", \"154282\", \"158514\", \"165015\", \"139836\", \"60686\", \"179558\", \"189636\", \"201828\", \"222542\", \"227244\", \"235160\", \"235567\", \"78197\", \"80084\", \"81784\", \"83133\", \"87563\", \"90074\", \"90871\", \"47170\", \"91923\", \"94107\", \"95957\", \"97058\", \"98764\", \"99856\", \"100072\", \"100616\", \"101036\", \"186719\", \"105538\", \"108347\", \"108653\", \"108736\", \"109826\", \"110088\", \"111568\", \"112162\", \"113846\", \"114689\", \"115738\", \"116281\", \"117660\", \"118174\", \"118288\", \"120770\", \"120835\", \"120848\", \"89847\", \"118161\", \"120569\", \"122164\", \"124044\", \"125446\", \"125960\", \"126722\", \"127805\", \"127870\", \"128519\", \"128731\", \"130238\", \"131060\", \"131160\", \"131265\", \"131180\", \"132347\", \"132662\", \"134310\", \"135921\", \"136212\", \"137477\", \"137775\", \"138014\", \"138930\", \"132376\", \"140003\", \"140195\", \"140653\", \"141166\", \"142414\", \"143825\", \"145885\", \"146111\", \"147114\", \"147179\", \"147274\", \"147696\", \"150505\", \"152649\", \"154569\", \"154726\", \"155142\", \"156090\", \"156056\", \"158820\", \"159126\", \"163314\", \"164850\", \"165999\", \"167541\", \"168570\", \"168626\", \"169073\", \"170255\", \"172819\", \"174575\", \"176756\", \"178744\", \"181038\", \"181795\", \"183254\", \"186824\", \"188872\", \"190940\", \"192202\", \"193153\", \"193197\", \"201572\", \"201824\", \"202976\", \"204853\", \"206901\", \"208148\", \"208385\", \"208915\", \"212979\", \"219137\", \"222119\", \"225497\", \"228255\", \"250524\", \"147691\", \"149371\", \"151057\", \"151156\", \"151790\", \"143588\", \"154577\", \"155707\", \"155974\", \"158679\", \"159364\", \"161397\", \"197762\", \"101253\", \"157385\", \"160163\", \"161496\", \"162616\", \"162888\", \"134558\", \"149030\", \"155845\", \"163558\", \"166096\", \"163763\", \"168628\", \"169631\", \"170417\", \"170573\", \"170865\", \"171564\", \"172274\", \"173235\", \"173485\", \"174325\", \"174928\", \"175637\", \"175881\", \"176296\", \"177083\", \"177291\", \"177490\", \"179183\", \"149578\", \"181193\", \"182541\", \"183044\", \"31650\", \"182649\", \"183317\", \"183583\", \"183793\", \"183923\"], \"outgoing_citations\": [\"283807\", \"285351\", \"287117\", \"287377\", \"288864\", \"289906\", \"292178\", \"296033\", \"296355\", \"298195\", \"302736\", \"302940\", \"309702\", \"314659\", \"316185\", \"319888\", \"320837\"]}","task_split":"paper_retrieval"}
{"document_id":"277666","document_content":"# Neural Machine Translation from Simplified Translations\n## Categories\n- Computation and Language\n## Abstract\nText simplification aims at reducing the lexical, grammatical and structural complexity of a text while keeping the same meaning. In the context of machine translation, we introduce the idea of simplified translations in order to boost the learning ability of deep neural translation models. We conduct preliminary experiments showing that translation complexity is actually reduced in a translation of a source bi-text compared to the target reference of the bi-text while using a neural machine translation (NMT) system learned on the exact same bi-text. Based on knowledge distillation idea, we then train an NMT system using the simplified bi-text, and show that it outperforms the initial system that was built over the reference data set. Performance is further boosted when both reference and automatic translations are used to learn the network. We perform an elementary analysis of the translated corpus and report accuracy results of the proposed approach on English-to-French and English-to-German translation tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.06139v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"193488\", \"256234\"], \"outgoing_citations\": [\"282440\", \"282494\", \"284853\", \"289589\"]}","task_split":"paper_retrieval"}
{"document_id":"277773","document_content":"# A User Simulator for Task-Completion Dialogues\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nDespite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Third, collecting and annotating human-machine or human-human conversations for task-oriented dialogues requires extensive domain knowledge. Because building an appropriate dataset can be both financially costly and time-consuming, one popular approach is to build a user simulator based upon a corpus of example dialogues. Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Dialogue agents trained on these simulators can serve as an effective starting point. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online. To ease empirical algorithmic comparisons in dialogues, this paper introduces a new, publicly available simulation framework, where our simulator, designed for the movie-booking domain, leverages both rules and collected data. The simulator supports two tasks: movie ticket booking and movie seeking. Finally, we demonstrate several agents and detail the procedure to add and test your own agent in the proposed framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.05688v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"20797\", \"50552\", \"56667\", \"14126\", \"16630\", \"21525\", \"24007\", \"33447\", \"41639\", \"45527\", \"46324\", \"48120\", \"66042\", \"71580\", \"78231\", \"85983\", \"91687\", \"103709\", \"122086\", \"123530\", \"126501\", \"129963\", \"132536\", \"136859\", \"156164\", \"157373\", \"167618\", \"168852\", \"171139\", \"171725\", \"182986\", \"195886\", \"220326\", \"220801\", \"220866\", \"219992\", \"222981\", \"216953\", \"228360\", \"229491\", \"233661\", \"233905\", \"234768\", \"237065\", \"238397\", \"241769\", \"243546\", \"251914\", \"247726\", \"248882\", \"158824\", \"255580\", \"268168\", \"269232\", \"270985\", \"272513\", \"285454\"], \"outgoing_citations\": [\"285454\", \"289260\", \"290697\", \"290819\", \"290847\", \"291195\", \"308381\"]}","task_split":"paper_retrieval"}
{"document_id":"277797","document_content":"# Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments\n## Categories\n- Robotics\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.05533v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"1158\", \"17143\", \"84594\", \"128249\", \"12745\", \"2452\", \"2455\", \"29854\", \"34787\", \"36974\", \"42563\", \"54266\", \"63163\", \"65435\", \"65953\", \"69040\", \"67832\", \"76209\", \"97665\", \"101182\", \"112741\", \"152072\", \"86980\", \"93235\", \"94162\", \"100245\", \"106478\", \"109438\", \"113866\", \"132020\", \"133552\", \"134432\", \"142941\", \"143049\", \"160935\", \"163351\", \"161717\", \"180340\", \"197663\", \"200791\", \"153864\", \"150072\", \"171332\", \"176652\", \"177381\", \"180851\", \"185747\", \"190948\", \"194738\", \"196770\", \"198524\", \"202443\", \"205488\", \"243597\", \"208372\", \"206231\", \"221998\", \"225036\", \"225748\", \"200927\", \"231623\", \"199779\", \"237008\", \"237929\", \"240717\", \"241006\", \"241625\", \"253933\", \"254599\", \"249137\", \"259708\", \"262593\", \"272668\", \"274102\", \"280570\", \"290243\", \"277504\"], \"outgoing_citations\": [\"277504\", \"283200\", \"290243\", \"290904\", \"302952\", \"302967\", \"306266\"]}","task_split":"paper_retrieval"}
{"document_id":"277856","document_content":"# A Survey of Inductive Biases for Factorial Representation-Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWith the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the discovery of useful encodings of data that make domain-relevant information explicit. Factorial representations identify underlying independent causal factors of variation in data. A factorial representation is compact and faithful, makes the causal factors explicit, and facilitates human interpretation of data. Factorial representations support a variety of applications, including the generation of novel examples, indexing and search, novelty detection, and transfer learning.   This article surveys various constraints that encourage a learning algorithm to discover factorial representations. I dichotomize the constraints in terms of unsupervised and supervised inductive bias. Unsupervised inductive biases exploit assumptions about the environment, such as the statistical distribution of factor coefficients, assumptions about the perturbations a factor should be invariant to (e.g. a representation of an object can be invariant to rotation, translation or scaling), and assumptions about how factors are combined to synthesize an observation. Supervised inductive biases are constraints on the representations based on additional information connected to observations. Supervisory labels come in variety of types, which vary in how strongly they constrain the representation, how many factors are labeled, how many observations are labeled, and whether or not we know the associations between the constraints and the factors they are related to.   This survey brings together a wide variety of models that all touch on the problem of learning factorial representations and lays out a framework for comparing these models based on the strengths of the underlying supervised and unsupervised inductive biases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.05299v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"80015\", \"103983\", \"146022\", \"20747\", \"33537\", \"63434\", \"70211\", \"70382\", \"72074\", \"179978\", \"110493\", \"120747\", \"93568\", \"112145\", \"132073\", \"141826\", \"143581\", \"165999\", \"168518\", \"170071\", \"180811\", \"149569\", \"179975\", \"186515\", \"205697\", \"227322\", \"236093\", \"241951\", \"242239\", \"251597\", \"260136\"], \"outgoing_citations\": [\"300606\", \"311052\", \"316183\", \"320031\", \"324717\", \"336507\", \"353174\"]}","task_split":"paper_retrieval"}
{"document_id":"277906","document_content":"# Adversarial Message Passing For Graphical Models\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nBayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn parameters of generators by fooling discriminators. Typically, GANs are considered to be models themselves and are not understood in the context of inference. Current techniques rely on inefficient global discrimination of joint distributions to perform learning, or only consider discriminating a single output variable. We overcome these limitations by treating GANs as a basis for likelihood-free inference in generative models and generalize them to Bayesian posterior inference over factor graphs. We propose local learning rules based on message passing minimizing a global divergence criterion involving cooperating local adversaries used to sidestep explicit likelihood evaluations. This allows us to compose models and yields a unified inference and learning framework for adversarial learning. Our framework treats model specification and inference separately and facilitates richly structured models within the family of Directed Acyclic Graphs, including components such as intractable likelihoods, non-differentiable models, simulators and generally cumbersome models. A key result of our treatment is the insight that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions. As a side-result, we discuss the link to likelihood maximization. These approaches hold promise to be useful in the toolbox of probabilistic modelers and enrich the gamut of current probabilistic programming applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.05048v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"113063\", \"127805\", \"183688\", \"250393\", \"205756\", \"236143\", \"243380\", \"266009\", \"263662\", \"272783\", \"272965\", \"276018\", \"282844\"], \"outgoing_citations\": [\"281749\", \"281750\", \"282643\", \"282844\", \"291537\", \"298700\", \"303764\", \"312814\", \"331033\", \"350478\"]}","task_split":"paper_retrieval"}
{"document_id":"277987","document_content":"# Encapsulating models and approximate inference programs in probabilistic modules\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nThis paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inference system. The interface can be seen as a stochastic generalization of a standard simulation and density interface for probabilistic primitives. We show that sound approximate inference algorithms can be constructed for networks of probabilistic modules, and we demonstrate that the interface can be implemented using learned stochastic inference networks and MCMC and SMC approximate inference programs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.04759v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"245393\"], \"outgoing_citations\": [\"278553\", \"286333\", \"291429\", \"301432\", \"310143\", \"331740\", \"350477\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"278208","document_content":"# Context-aware Sentiment Word Identification: sentiword2vec\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nTraditional sentiment analysis often uses sentiment dictionary to extract sentiment information in text and classify documents. However, emerging informal words and phrases in user generated content call for analysis aware to the context. Usually, they have special meanings in a particular context. Because of its great performance in representing inter-word relation, we use sentiment word vectors to identify the special words. Based on the distributed language model word2vec, in this paper we represent a novel method about sentiment representation of word under particular context, to be detailed, to identify the words with abnormal sentiment polarity in long answers. Result shows the improved model shows better performance in representing the words with special meaning, while keep doing well in representing special idiomatic pattern. Finally, we will discuss the meaning of vectors representing in the field of sentiment, which may be different from general object-based conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.03769v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"278317","document_content":"# StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nSynthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.03242v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"278443","document_content":"# Towards better decoding and language model integration in sequence to sequence models\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n- Machine Learning\n- Computation and Language\n## Abstract\nThe recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion. In this contribution, we analyse an attention-based seq2seq speech recognition system that directly transcribes recordings into characters. We observe two shortcomings: overconfidence in its predictions and a tendency to produce incomplete transcriptions when language models are used. We propose practical solutions to both problems achieving competitive speaker independent word error rates on the Wall Street Journal dataset: without separate language models we reach 10.6% WER, while together with a trigram language model, we reach 6.7% WER.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.02695v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"14962\", \"17516\", \"43854\", \"128866\", \"137262\", \"5243\", \"6021\", \"7521\", \"15739\", \"17365\", \"18986\", \"23831\", \"26270\", \"26596\", \"24533\", \"34324\", \"37730\", \"37881\", \"38130\", \"39390\", \"43395\", \"44842\", \"52010\", \"56582\", \"57166\", \"58142\", \"58866\", \"60570\", \"63669\", \"70412\", \"72010\", \"72108\", \"78621\", \"79918\", \"80995\", \"81688\", \"86255\", \"88750\", \"89215\", \"90949\", \"91752\", \"92302\", \"92414\", \"94772\", \"97056\", \"101935\", \"152859\", \"56672\", \"80931\", \"83167\", \"83724\", \"87840\", \"80535\", \"90510\", \"91148\", \"68729\", \"91765\", \"94192\", \"99584\", \"100077\", \"104345\", \"106960\", \"109393\", \"110653\", \"114312\", \"117211\", \"117729\", \"123650\", \"123861\", \"124040\", \"124230\", \"127445\", \"137065\", \"137935\", \"138324\", \"140430\", \"141055\", \"142629\", \"143720\", \"145112\", \"150338\", \"151603\", \"154476\", \"159352\", \"165878\", \"180293\", \"184247\", \"152767\", \"140063\", \"148309\", \"148312\", \"155439\", \"159446\", \"166036\", \"200072\", \"168634\", \"169714\", \"174786\", \"177078\", \"173669\", \"179677\", \"182142\", \"181585\", \"189849\", \"192212\", \"193516\", \"194255\", \"198379\", \"206550\", \"210294\", \"211835\", \"215676\", \"165575\", \"210400\", \"212246\", \"218973\", \"221204\", \"221208\", \"221967\", \"223071\", \"223083\", \"223730\", \"227564\", \"227765\", \"228859\", \"232580\", \"232274\", \"233779\", \"234164\", \"234172\", \"237515\", \"247045\", \"247430\", \"248184\", \"248220\", \"248246\", \"248229\", \"251358\", \"258039\", \"260505\", \"246706\", \"260414\", \"262349\", \"269836\", \"270174\", \"270599\", \"272753\"], \"outgoing_citations\": [\"275596\", \"280313\", \"282924\", \"282929\", \"284215\", \"287209\", \"290766\", \"293522\", \"296283\", \"299936\", \"307902\", \"308796\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"278553","document_content":"# Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nA key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Metropolis-Hastings, produce output samples drawn from a distribution that may be far from the target posterior distribution. This paper shows how to upper-bound the symmetric KL divergence between the output distribution of a broad class of sequential Monte Carlo (SMC) samplers and their target posterior distributions, subject to assumptions about the accuracy of a separate gold-standard sampler. The proposed method applies to samplers that combine multiple particles, multinomial resampling, and rejuvenation kernels. The experiments show the technique being used to estimate bounds on the divergence of SMC samplers for posterior inference in a Bayesian linear regression model and a Dirichlet process mixture model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.02161v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"277987\"], \"outgoing_citations\": [\"290933\", \"291429\", \"303764\"]}","task_split":"paper_retrieval"}
{"document_id":"278675","document_content":"# AI Researchers, Video Games Are Your Friends!\n## Categories\n- Artificial Intelligence\n## Abstract\nIf you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question \"what can video games do for AI\", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question \"what can AI do for video games\", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-48506-5_1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"45163\", \"65296\", \"178386\", \"222345\", \"239033\"], \"outgoing_citations\": [\"173070\"]}","task_split":"paper_retrieval"}
{"document_id":"278863","document_content":"# Unit Dependency Graph and its Application to Arithmetic Word Problem Solving\n## Categories\n- Computation and Language\n## Abstract\nMath word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.00969v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96833\", \"12817\", \"25273\", \"101113\", \"37796\", \"45526\", \"50647\", \"56186\", \"78252\", \"79536\", \"102856\", \"98821\", \"38612\", \"124449\", \"220655\", \"153178\", \"161669\", \"169320\", \"233912\", \"238788\", \"246516\"], \"outgoing_citations\": [\"287181\"]}","task_split":"paper_retrieval"}
{"document_id":"278945","document_content":"# Alleviating Overfitting for Polysemous Words for Word Representation Estimation Using Lexicons\n## Categories\n- Computation and Language\n## Abstract\nThough there are some works on improving distributed word representations using lexicons, the improper overfitting of the words that have multiple meanings is a remaining issue deteriorating the learning when lexicons are used, which needs to be solved. An alternative method is to allocate a vector per sense instead of a vector per word. However, the word representations estimated in the former way are not as easy to use as the latter one. Our previous work uses a probabilistic method to alleviate the overfitting, but it is not robust with a small corpus. In this paper, we propose a new neural network to estimate distributed word representations using a lexicon and a corpus. We add a lexicon layer in the continuous bag-of-words model and a threshold node after the output of the lexicon layer. The threshold rejects the unreliable outputs of the lexicon layer that are less likely to be the same with their inputs. In this way, it alleviates the overfitting of the polysemous words. The proposed neural network can be trained using negative sampling, which maximizing the log probabilities of target words given the context words, by distinguishing the target words from random noises. We compare the proposed neural network with the continuous bag-of-words model, the other works improving it, and the previous works estimating distributed word representations using both a lexicon and a corpus. The experimental results show that the proposed neural network is more efficient and balanced for both semantic tasks and syntactic tasks than the previous works, and robust to the size of the corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.00584v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"302903\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"278984","document_content":"# Generalizing Skills with Semi-Supervised Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n## Abstract\nDeep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the vast variability of the real world. Deep networks are known to achieve remarkable generalization when provided with massive amounts of labeled data, but can we provide this breadth of experience to an RL agent, such as a robot? The robot might continuously learn as it explores the world around it, even while deployed. However, this learning requires access to a reward function, which is often hard to measure in real-world domains, where the reward could depend on, for example, unknown positions of objects or the emotional state of the user. Conversely, it is often quite practical to provide the agent with reward functions in a limited set of situations, such as when a human supervisor is present or in a controlled setting. Can we make use of this limited supervision, and still benefit from the breadth of experience an agent might collect on its own? In this paper, we formalize this problem as semisupervised reinforcement learning, where the reward function can only be evaluated in a set of \"labeled\" MDPs, and the agent must generalize its behavior to the wide range of states it might encounter in a set of \"unlabeled\" MDPs, by using experience from both settings. Our proposed method infers the task objective in the unlabeled MDPs through an algorithm that resembles inverse RL, using the agent's own prior experience in the labeled MDPs as a kind of demonstration of optimal behavior. We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available. We also show that our method outperforms direct supervised learning of the reward.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.00429v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [\"114662\", \"35918\", \"42423\", \"72575\", \"90448\", \"94181\", \"149549\", \"81790\", \"86323\", \"160670\", \"151700\", \"178280\", \"202162\", \"212704\", \"234583\", \"233926\", \"245316\", \"245731\", \"253681\", \"255189\", \"258580\", \"260136\", \"262591\", \"263957\", \"265464\"], \"outgoing_citations\": [\"283318\", \"284134\", \"288338\", \"289906\", \"291568\", \"291738\", \"297317\", \"302720\", \"302952\"]}","task_split":"paper_retrieval"}
{"document_id":"279022","document_content":"# An Evaluation of Models for Runtime Approximation in Link Discovery\n## Categories\n- Artificial Intelligence\n## Abstract\nTime-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 400 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1612.00240v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"279119","document_content":"# Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making\n## Categories\n- Machine Learning\n- Computer Science and Game Theory\n- J.4; I.2.6; H.1.2\n## Abstract\nA large body of work in behavioral fields attempts to develop models that describe the way people, as opposed to rational agents, make decisions. A recent Choice Prediction Competition (2015) challenged researchers to suggest a model that captures 14 classic choice biases and can predict human decisions under risk and ambiguity. The competition focused on simple decision problems, in which human subjects were asked to repeatedly choose between two gamble options.   In this paper we present our approach for predicting human decision behavior: we suggest to use machine learning algorithms with features that are based on well-established behavioral theories. The basic idea is that these psychological features are essential for the representation of the data and are important for the success of the learning process. We implement a vanilla model in which we train SVM models using behavioral features that rely on the psychological properties underlying the competition baseline model. We show that this basic model captures the 14 choice biases and outperforms all the other learning-based models in the competition. The preliminary results suggest that such hybrid models can significantly improve the prediction of human decision making, and are a promising direction for future research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.10228v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.GT\", \"J.4; I.2.6; H.1.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Science and Game Theory\", \"J.4; I.2.6; H.1.2\"], \"incoming_citations\": [\"26479\", \"172\", \"185117\", \"161546\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"279337","document_content":"# Hierarchical Boundary-Aware Neural Encoder for Video Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell, which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2017.339\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5789\", \"59368\", \"60281\", \"25304\", \"26526\", \"72227\", \"93476\", \"100481\", \"183829\", \"223084\", \"83901\", \"5781\", \"102731\", \"104154\", \"104610\", \"109275\", \"133574\", \"134565\", \"136352\", \"139805\", \"140281\", \"144578\", \"70437\", \"157944\", \"225421\", \"125246\", \"175214\", \"181452\", \"187914\", \"191680\", \"197355\", \"200501\", \"208615\", \"220798\", \"238229\", \"266771\", \"223293\", \"240071\", \"248855\", \"279263\", \"216396\", \"262828\", \"280247\"], \"outgoing_citations\": [\"284230\", \"285259\", \"295122\", \"303593\", \"304307\", \"304481\", \"307386\", \"311742\", \"313189\", \"316564\", \"316803\", \"319169\", \"320332\", \"328469\", \"332079\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"279351","document_content":"# Improving Multi-Document Summarization via Text Classification\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nDeveloped so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization. TCSum projects documents onto distributed representations which act as a bridge between text classification and summarization. It also utilizes the classification results to produce summaries of different styles. Extensive experiments on DUC generic multi-document summarization datasets show that, TCSum can achieve the state-of-the-art performance without using any hand-crafted features and has the capability to catch the variations of summary styles with respect to different text categories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.09238v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"87999\", \"48962\", \"108725\", \"14013\", \"36093\", \"60807\", \"97507\", \"118456\", \"121501\", \"161045\", \"165773\", \"169466\", \"171414\", \"168708\", \"182486\", \"173805\", \"190165\", \"192342\", \"220188\", \"220955\", \"233391\", \"249229\", \"230466\", \"263243\"], \"outgoing_citations\": [\"295470\", \"296029\", \"302471\", \"309926\"]}","task_split":"paper_retrieval"}
{"document_id":"279439","document_content":"# Voronoi-based compact image descriptors: Efficient Region-of-Interest retrieval with VLAD and deep-learning-based descriptors\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regions-of-interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoi-based spatial partitioning of each dataset image. The proposed multi-level Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a content-based descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: (i) we utilize the tree structure of the spatial hierarchical K-means to perform a top-to-bottom pruning for local similarity maxima; (ii) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; (iii) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoi-based descriptors achieve comparable or higher mean Average Precision against conventional grid-based spatial search, while offering more than two-fold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-the-art on whole image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.08906v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"231046\", \"261255\", \"266014\"], \"outgoing_citations\": [\"294983\", \"297319\", \"308236\", \"328024\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"279522","document_content":"# Knowledge Graph Representation with Jointly Structural and Textual Encoding\n## Categories\n- Computation and Language\n## Abstract\nThe objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.08661v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"10131\", \"161623\", \"174296\", \"11894\", \"50715\", \"59753\", \"71008\", \"127923\", \"159408\", \"92027\", \"106164\", \"127758\", \"129613\", \"136153\", \"155316\", \"171420\", \"164092\", \"168182\", \"168277\", \"179907\", \"185605\", \"185665\", \"193708\", \"243422\", \"204585\", \"205710\", \"221660\", \"216863\", \"250937\"], \"outgoing_citations\": [\"280589\", \"284138\", \"313944\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"279528","document_content":"# Patient-Driven Privacy Control through Generalized Distillation\n## Categories\n- Cryptography and Security\n- Machine Learning\n- Machine Learning\n- Computers and Society\n## Abstract\nThe introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to privacy concerns, patients often desire to withhold sensitive information. This self-censorship can impede proper diagnosis and treatment, which may lead to serious health complications and even death over time. In this paper, we present privacy distillation, a mechanism which allows patients to control the type and amount of information they wish to disclose to the healthcare providers for use in statistical models. Meanwhile, it retains the accuracy of models that have access to all patient data under a sufficient but not full set of privacy-relevant information. We validate privacy distillation using a corpus of patients prescribed to warfarin for a personalized dosage. We use a deep neural network to implement privacy distillation for training and making dose predictions. We find that privacy distillation with sufficient privacy-relevant information i) retains accuracy almost as good as having all patient data (only 3\\% worse), and ii) is effective at preventing errors that introduce health-related risks (only 3.9\\% worse under- or over-prescriptions).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.08648v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"stat.ML\", \"cs.CR\", \"cs.LG\", \"cs.CY\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Machine Learning\", \"Computers and Society\"], \"incoming_citations\": [\"83696\", \"111831\", \"123758\", \"196418\", \"46940\", \"272935\", \"295510\", \"261890\"], \"outgoing_citations\": [\"272935\", \"295510\", \"303553\", \"329958\"]}","task_split":"paper_retrieval"}
{"document_id":"279543","document_content":"# A Simple, Fast Diverse Decoding Algorithm for Neural Generation\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an inter-sibling ranking penalty, favoring choosing hypotheses from diverse parents. We evaluate the proposed model on the tasks of dialogue response generation, abstractive summarization and machine translation. We find that diverse decoding helps across all tasks, especially those for which reranking is needed.   We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL). We observe a further performance boost from this RL technique. This paper includes material from the unpublished script \"Mutual Information and Diverse Decoding Improve Neural Machine Translation\" (Li and Jurafsky, 2016).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.08562v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25078\", \"25308\", \"50552\", \"25140\", \"26367\", \"40912\", \"43892\", \"44007\", \"45496\", \"45293\", \"46304\", \"47416\", \"54008\", \"65915\", \"168075\", \"73111\", \"80354\", \"86922\", \"114380\", \"132550\", \"132683\", \"153393\", \"207215\", \"91095\", \"91645\", \"93839\", \"94898\", \"95282\", \"96200\", \"99469\", \"101280\", \"108688\", \"126112\", \"130062\", \"132304\", \"132799\", \"142985\", \"143496\", \"151320\", \"151627\", \"154597\", \"155080\", \"157308\", \"157905\", \"171380\", \"172088\", \"186662\", \"189504\", \"44423\", \"154886\", \"164879\", \"168634\", \"166959\", \"180695\", \"182471\", \"183158\", \"192166\", \"195137\", \"197329\", \"197647\", \"198014\", \"198185\", \"201867\", \"210158\", \"211804\", \"211664\", \"213954\", \"216351\", \"216280\", \"217366\", \"218973\", \"219198\", \"220073\", \"221433\", \"68965\", \"230212\", \"234742\", \"235345\", \"238496\", \"243272\", \"256194\", \"269757\", \"144800\", \"249259\", \"251348\", \"261897\", \"269199\", \"274450\", \"275597\", \"275595\", \"276439\", \"278174\"], \"outgoing_citations\": [\"279384\", \"283076\", \"283809\", \"285454\", \"290515\", \"292690\", \"295552\", \"300543\", \"301483\", \"310740\", \"311849\", \"313427\", \"316102\"]}","task_split":"paper_retrieval"}
{"document_id":"279563","document_content":"# Neural Machine Translation with Latent Semantic of Image and Text\n## Categories\n- Computation and Language\n## Abstract\nAlthough attention-based Neural Machine Translation have achieved great success, attention-mechanism cannot capture the entire meaning of the source sentence because the attention mechanism generates a target word depending heavily on the relevant parts of the source sentence. The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation. We follow this approach and we believe that the capturing meaning of sentence benefits from image information because human beings understand the meaning of language not only from textual information but also from perceptual information such as that gained from vision. As described herein, we propose a neural machine translation model that introduces a continuous latent variable containing an underlying semantic extracted from texts and images. Our model, which can be trained end-to-end, requires image information only when training. Experiments conducted with an English--German translation task show that our model outperforms over the baseline.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.08459v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33741\", \"76974\", \"153733\", \"211982\", \"230056\", \"266669\"], \"outgoing_citations\": [\"289710\", \"291554\", \"291858\", \"293418\", \"299936\", \"300096\", \"304945\"]}","task_split":"paper_retrieval"}
{"document_id":"279852","document_content":"# Learning to Distill: The Essence Vector Modeling Framework\n## Categories\n- Computation and Language\n## Abstract\nIn the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is a pioneering study in this school of research. However, paragraph (or sentence and document) embedding learning is more suitable\/reasonable for some tasks, such as sentiment classification and document summarization. Nevertheless, as far as we are aware, there is relatively less work focusing on the development of unsupervised paragraph embedding methods. Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph. Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation. Motivated by these observations, our major contributions in this paper are twofold. First, we propose a novel unsupervised paragraph embedding method, named the essence vector (EV) model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph. Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector (D-EV) model, is proposed. The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.07206v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"316977\"]}","task_split":"paper_retrieval"}
{"document_id":"279883","document_content":"# A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nReinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment.   In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly. Our contribution is to extend a recently developed deep neural network for video frame prediction in Atari games to enable reward prediction as well. To this end, we phrase a joint optimization problem for minimizing both video frame and reward reconstruction loss, and adapt network parameters accordingly. Empirical evaluations on five Atari games demonstrate accurate cumulative reward prediction of up to 200 frames. We consider these results as opening up important directions for model-based reinforcement learning in complex, initially unknown environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.07078v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"68934\", \"16626\", \"33207\", \"50249\", \"88382\", \"135413\", \"140949\", \"180340\", \"184007\", \"196921\", \"161097\", \"162703\", \"174609\", \"188362\", \"192743\", \"211473\", \"211884\", \"218983\", \"228324\", \"229171\", \"260770\", \"264940\"], \"outgoing_citations\": [\"260770\", \"297268\", \"306266\", \"308711\", \"310624\", \"311482\", \"316183\", \"317863\", \"321553\"]}","task_split":"paper_retrieval"}
{"document_id":"280004","document_content":"# A Hierarchical Approach for Generating Descriptive Image Paragraphs\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nRecent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail. While one new captioning approach, dense captioning, can potentially describe images in finer levels of detail by captioning many regions within an image, it in turn is unable to produce a coherent story for an image. In this paper we overcome these limitations by generating entire paragraphs for describing images, which can tell detailed, unified stories. We develop a model that decomposes both images and paragraphs into their constituent parts, detecting semantic regions in images and using a hierarchical recurrent neural network to reason about language. Linguistic analysis confirms the complexity of the paragraph generation task, and thorough experiments on a new dataset of image and paragraph pairs demonstrate the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.06607v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"35427\", \"50659\", \"71234\", \"76299\", \"109545\", \"182259\", \"93187\", \"10837\", \"15263\", \"24435\", \"27283\", \"30208\", \"42357\", \"42494\", \"42496\", \"46309\", \"58373\", \"62999\", \"64334\", \"67525\", \"74954\", \"82846\", \"129502\", \"133496\", \"149360\", \"175031\", \"207215\", \"95771\", \"101699\", \"108688\", \"109415\", \"112590\", \"117173\", \"118565\", \"124665\", \"125299\", \"127984\", \"129776\", \"133574\", \"137358\", \"139876\", \"141319\", \"142162\", \"152418\", \"182386\", \"223463\", \"157229\", \"157529\", \"163104\", \"164667\", \"169105\", \"107350\", \"171101\", \"172812\", \"173531\", \"173493\", \"174744\", \"128955\", \"180695\", \"180742\", \"181110\", \"189378\", \"189964\", \"192208\", \"190441\", \"193573\", \"193908\", \"203169\", \"205896\", \"208184\", \"210016\", \"218332\", \"231203\", \"240605\", \"208776\", \"212441\", \"212792\", \"214396\", \"219342\", \"230900\", \"231949\", \"234549\", \"240071\", \"249639\", \"250098\", \"270455\", \"258987\", \"262828\", \"267523\", \"264856\", \"271004\", \"271234\"], \"outgoing_citations\": [\"302621\", \"304481\", \"311849\", \"316803\", \"320003\", \"328051\", \"332079\", \"333638\"]}","task_split":"paper_retrieval"}
{"document_id":"280202","document_content":"# Learning to reinforcement learn\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nIn recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05763v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"8658\", \"13743\", \"24950\", \"357157\", \"2655\", \"2687\", \"3143\", \"4353\", \"8426\", \"11995\", \"12230\", \"12599\", \"17457\", \"17410\", \"18599\", \"19580\", \"21768\", \"22458\", \"22537\", \"23564\", \"24689\", \"32352\", \"38393\", \"44890\", \"51800\", \"68455\", \"68934\", \"73192\", \"73051\", \"76756\", \"77439\", \"78473\", \"83792\", \"95517\", \"109082\", \"113552\", \"131702\", \"144508\", \"153312\", \"76\", \"2370\", \"4687\", \"4718\", \"5873\", \"6028\", \"11237\", \"11416\", \"14799\", \"15911\", \"16345\", \"18892\", \"19209\", \"19231\", \"22789\", \"25074\", \"25343\", \"25558\", \"25823\", \"26894\", \"30750\", \"32248\", \"36325\", \"37762\", \"38270\", \"41291\", \"44280\", \"45498\", \"20450\", \"46780\", \"48723\", \"37221\", \"52691\", \"53275\", \"55110\", \"55486\", \"57802\", \"60613\", \"61181\", \"62970\", \"64961\", \"64890\", \"65059\", \"67803\", \"70370\", \"71387\", \"73917\", \"75738\", \"76066\", \"76342\", \"77916\", \"81860\", \"84266\", \"84405\", \"84445\", \"96490\", \"85594\", \"85855\", \"89958\", \"95850\", \"95935\", \"97074\", \"97096\", \"97906\", \"103594\", \"104546\", \"106120\", \"107437\", \"113159\", \"114384\", \"115267\", \"117531\", \"118140\", \"119548\", \"120402\", \"120758\", \"121163\", \"122991\", \"139881\", \"141195\", \"142637\", \"142989\", \"166512\", \"192512\", \"80071\", \"80295\", \"80589\", \"86327\", \"86659\", \"87182\", \"89599\", \"90966\", \"91106\", \"92418\", \"92465\", \"92636\", \"92827\", \"93409\", \"97845\", \"98427\", \"98682\", \"100158\", \"101382\", \"102600\", \"103292\", \"105682\", \"106138\", \"107505\", \"108787\", \"111185\", \"111431\", \"111603\", \"113227\", \"113866\", \"113915\", \"115013\", \"113549\", \"115040\", \"116687\", \"117323\", \"117623\", \"118362\", \"119084\", \"119122\", \"120091\", \"121170\", \"118089\", \"122583\", \"122822\", \"123032\", \"127245\", \"127461\", \"128120\", \"128699\", \"130402\", \"130677\", \"134432\", \"135066\", \"136046\", \"136400\", \"137299\", \"138211\", \"138836\", \"138908\", \"140801\", \"141455\", \"142202\", \"142796\", \"143394\", \"143420\", \"144444\", \"145753\", \"147642\", \"148867\", \"151618\", \"152616\", \"161071\", \"162155\", \"162581\", \"164077\", \"165065\", \"145113\", \"150501\", \"167235\", \"169398\", \"175541\", \"179214\", \"180496\", \"181883\", \"182477\", \"182823\", \"184463\", \"82999\", \"189825\", \"192812\", \"201197\", \"205105\", \"225557\", \"117347\", \"152041\", \"152946\", \"153576\", \"155001\", \"157209\", \"158874\", \"159036\", \"159298\", \"160036\", \"160310\", \"161097\", \"161413\", \"159273\", \"167551\", \"166416\", \"174232\", \"174888\", \"176210\", \"176222\", \"176597\", \"177879\", \"179798\", \"181037\", \"181812\", \"181967\", \"182450\", \"182338\", \"184374\", \"184898\", \"186101\", \"187278\", \"187399\", \"187845\", \"192827\", \"193351\", \"194494\", \"195018\", \"195255\", \"196162\", \"196671\", \"197982\", \"198248\", \"198373\", \"200264\", \"199890\", \"201452\", \"201574\", \"201860\", \"203834\", \"205085\", \"205521\", \"205477\", \"205630\", \"213370\", \"214631\", \"140345\", \"209452\", \"215710\", \"215384\", \"216060\", \"216429\", \"223101\", \"228335\", \"229737\", \"232248\", \"236923\", \"237229\", \"239153\", \"266109\", \"205592\", \"207110\", \"207983\", \"211473\", \"213308\", \"215015\", \"215931\", \"215938\", \"212511\", \"215924\", \"217919\", \"218770\", \"221419\", \"223336\", \"223487\", \"225779\", \"226929\", \"227881\", \"228299\", \"228556\", \"229101\", \"229324\", \"230669\", \"230702\", \"231097\", \"231155\", \"205457\", \"234045\", \"235228\", \"235452\", \"236495\", \"237386\", \"237731\", \"238475\", \"239080\", \"239268\", \"241304\", \"241496\", \"241813\", \"240177\", \"233926\", \"242281\", \"242689\", \"244283\", \"244629\", \"250026\", \"249870\", \"251819\", \"252334\", \"253658\", \"260548\", \"261577\", \"261128\", \"275071\", \"253181\", \"232314\", \"258199\", \"258331\", \"224192\", \"259762\", \"263135\", \"263197\", \"266845\", \"270925\", \"271536\", \"271828\", \"272222\", \"275495\", \"280657\", \"277504\", \"285995\"], \"outgoing_citations\": [\"277193\", \"280657\", \"280681\", \"280870\", \"298651\", \"321960\", \"332226\", \"358661\"]}","task_split":"paper_retrieval"}
{"document_id":"280296","document_content":"# Reinforcement Learning with Unsupervised Auxiliary Tasks\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nDeep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many other pseudo-reward functions simultaneously by reinforcement learning. All of these tasks share a common representation that, like unsupervised learning, continues to develop in the absence of extrinsic rewards. We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task. Our agent significantly outperforms the previous state-of-the-art on Atari, averaging 880\\% expert human performance, and a challenging suite of first-person, three-dimensional \\emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\\times$ and averaging 87\\% expert human performance on Labyrinth.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05397v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"2138\", \"41571\", \"94177\", \"170695\", \"122\", \"3429\", \"4004\", \"4300\", \"4492\", \"4639\", \"11364\", \"13214\", \"17457\", \"20579\", \"22919\", \"26795\", \"27411\", \"29801\", \"34320\", \"35753\", \"36000\", \"36637\", \"44916\", \"49806\", \"50552\", \"63621\", \"68339\", \"69933\", \"77439\", \"78473\", \"79803\", \"84802\", \"97093\", \"115575\", \"128249\", \"131702\", \"148134\", \"148742\", \"225257\", \"1414\", \"2253\", \"2483\", \"3984\", \"4687\", \"5396\", \"8478\", \"8515\", \"9444\", \"12959\", \"13922\", \"13877\", \"16063\", \"17153\", \"17814\", \"18954\", \"19203\", \"19209\", \"19231\", \"25074\", \"26797\", \"28168\", \"30979\", \"31593\", \"31581\", \"33080\", \"34357\", \"35440\", \"35389\", \"35657\", \"36974\", \"37552\", \"37671\", \"38125\", \"40343\", \"40615\", \"41252\", \"41625\", \"41981\", \"41922\", \"42082\", \"42181\", \"42315\", \"43379\", \"43758\", \"43851\", \"45058\", \"44969\", \"45507\", \"301461\", \"46570\", \"48065\", \"49676\", \"50187\", \"50843\", \"51976\", \"52886\", \"53037\", \"54866\", \"55346\", \"55964\", \"56413\", \"56365\", \"57818\", \"60082\", \"61081\", \"60938\", \"61747\", \"62373\", \"63102\", \"63020\", \"64439\", \"64870\", \"64820\", \"65332\", \"66832\", \"66920\", \"66864\", \"67718\", \"67624\", \"69108\", \"69178\", \"69408\", \"69685\", \"70492\", \"70439\", \"71387\", \"76820\", \"76800\", \"79257\", \"83887\", \"88041\", \"88224\", \"88751\", \"89316\", \"90882\", \"92592\", \"93901\", \"96468\", \"97223\", \"97320\", \"97665\", \"98092\", \"100693\", \"101951\", \"108208\", \"110042\", \"112076\", \"113327\", \"114931\", \"116827\", \"117704\", \"121288\", \"128392\", \"128664\", \"150379\", \"152072\", \"163235\", \"163705\", \"180897\", \"192512\", \"201315\", \"224222\", \"79679\", \"80273\", \"82316\", \"84549\", \"86323\", \"91456\", \"93344\", \"93409\", \"93795\", \"96262\", \"96451\", \"96718\", \"96979\", \"167176\", \"101301\", \"102470\", \"104122\", \"106604\", \"109438\", \"109956\", \"111603\", \"111868\", \"112604\", \"112328\", \"112858\", \"112949\", \"113050\", \"113236\", \"113565\", \"113549\", \"115361\", \"115832\", \"115796\", \"116835\", \"117057\", \"119105\", \"120731\", \"121735\", \"121716\", \"123442\", \"124909\", \"125657\", \"125851\", \"126444\", \"127978\", \"128799\", \"129546\", \"130999\", \"131061\", \"132020\", \"132207\", \"132719\", \"133337\", \"133619\", \"134432\", \"135201\", \"135645\", \"136039\", \"103314\", \"136760\", \"136767\", \"136920\", \"137441\", \"137546\", \"138211\", \"138456\", \"138753\", \"138758\", \"138724\", \"139426\", \"139653\", \"139807\", \"139794\", \"139759\", \"139808\", \"139834\", \"140039\", \"140689\", \"141294\", \"142272\", \"143204\", \"145619\", \"145753\", \"146476\", \"146684\", \"152680\", \"152690\", \"152912\", \"153069\", \"153686\", \"154418\", \"155499\", \"155676\", \"161071\", \"161373\", \"164729\", \"164984\", \"166368\", \"166672\", \"167303\", \"167845\", \"168588\", \"168853\", \"175394\", \"176738\", \"178094\", \"178846\", \"180340\", \"186749\", \"191583\", \"194964\", \"196909\", \"200261\", \"215214\", \"148522\", \"222837\", \"230774\", \"239666\", \"257674\", \"149805\", \"151260\", \"152091\", \"156990\", \"159106\", \"160673\", \"164235\", \"164644\", \"164842\", \"154419\", \"154139\", \"167562\", \"167688\", \"214440\", \"169739\", \"171112\", \"172538\", \"174221\", \"174383\", \"174609\", \"174954\", \"176054\", \"176503\", \"177336\", \"177367\", \"177674\", \"178620\", \"179221\", \"174863\", \"179471\", \"179594\", \"179610\", \"180089\", \"179966\", \"181463\", \"181469\", \"181476\", \"182602\", \"183337\", \"183608\", \"184126\", \"184119\", \"184374\", \"182108\", \"185008\", \"185747\", \"185722\", \"185767\", \"185773\", \"186101\", \"186656\", \"187768\", \"183187\", \"188953\", \"190556\", \"190812\", \"191198\", \"191671\", \"191589\", \"192391\", \"192743\", \"193234\", \"193391\", \"193972\", \"194209\", \"194640\", \"194738\", \"195416\", \"195713\", \"196528\", \"197467\", \"197586\", \"198481\", \"198524\", \"198915\", \"200000\", \"200682\", \"200753\", \"200941\", \"200920\", \"201399\", \"201602\", \"202443\", \"202394\", \"203248\", \"205179\", \"205452\", \"205630\", \"209238\", \"210596\", \"212832\", \"211558\", \"216952\", \"218050\", \"218124\", \"218114\", \"218533\", \"218435\", \"218589\", \"224491\", \"228319\", \"229145\", \"228031\", \"239153\", \"252042\", \"266109\", \"191970\", \"207094\", \"207983\", \"207836\", \"208372\", \"209779\", \"209926\", \"211201\", \"203355\", \"213513\", \"215979\", \"209957\", \"207096\", \"216221\", \"216925\", \"217188\", \"218828\", \"218983\", \"219728\", \"219776\", \"220377\", \"222446\", \"222117\", \"223033\", \"223395\", \"224223\", \"225670\", \"225841\", \"227065\", \"218923\", \"229008\", \"229185\", \"230669\", \"231155\", \"232229\", \"231376\", \"235228\", \"236093\", \"237119\", \"237232\", \"237386\", \"237929\", \"239335\", \"239450\", \"240052\", \"240506\", \"240517\", \"240600\", \"240800\", \"241185\", \"241354\", \"241436\", \"241426\", \"241503\", \"228028\", \"240323\", \"240918\", \"240233\", \"241013\", \"242211\", \"232723\", \"233926\", \"242444\", \"242891\", \"243090\", \"241533\", \"243235\", \"242141\", \"244283\", \"245731\", \"246853\", \"247900\", \"248885\", \"249876\", \"250016\", \"251797\", \"251914\", \"251957\", \"252309\", \"253448\", \"253828\", \"253974\", \"255613\", \"258608\", \"261260\", \"261512\", \"263288\", \"267428\", \"268821\", \"273608\", \"288597\", \"225886\", \"247716\", \"247749\", \"248432\", \"248738\", \"248809\", \"249137\", \"249196\", \"248272\", \"251601\", \"251789\", \"250822\", \"254938\", \"255936\", \"258199\", \"258580\", \"259143\", \"258384\", \"259623\", \"260136\", \"260770\", \"260881\", \"260583\", \"261316\", \"261493\", \"261535\", \"261786\", \"261780\", \"263135\", \"263270\", \"263460\", \"263811\", \"263852\", \"264087\", \"262646\", \"265091\", \"265263\", \"265883\", \"265946\", \"266419\", \"267427\", \"267589\", \"268584\", \"269420\", \"269850\", \"270315\", \"270823\", \"270855\", \"271757\", \"272464\", \"271601\"], \"outgoing_citations\": [\"280681\", \"284468\", \"290243\", \"290904\", \"291568\", \"293071\", \"293876\", \"298696\", \"306157\", \"306812\", \"308711\", \"357232\"]}","task_split":"paper_retrieval"}
{"document_id":"280302","document_content":"# Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMulti-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by hand-designed network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the space of possible multi-task deep architectures is combinatorially large and often the final architecture is arrived at by manual exploration of this space subject to designer's bias, which can be both error-prone and tedious. In this work, we propose a principled approach for designing compact multi-task deep learning architectures. Our approach starts with a thin network and dynamically widens it in a greedy manner during training using a novel criterion that promotes grouping of similar tasks together. Our Extensive evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05377v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"6545\", \"9421\", \"17710\", \"29062\", \"35331\", \"43243\", \"52164\", \"82707\", \"143188\", \"202087\", \"8324\", \"19442\", \"22088\", \"22951\", \"24628\", \"30214\", \"32662\", \"8657\", \"36773\", \"37249\", \"38069\", \"42927\", \"46652\", \"47652\", \"48497\", \"52336\", \"53025\", \"57374\", \"64033\", \"80098\", \"80581\", \"95298\", \"177481\", \"117999\", \"122408\", \"128244\", \"151523\", \"162728\", \"173927\", \"183623\", \"85914\", \"90521\", \"101301\", \"78021\", \"104405\", \"104571\", \"106199\", \"109911\", \"111184\", \"112300\", \"114937\", \"118810\", \"119447\", \"121414\", \"119213\", \"123764\", \"132853\", \"133975\", \"134837\", \"135426\", \"143596\", \"146484\", \"150695\", \"153883\", \"166679\", \"173646\", \"185730\", \"192101\", \"204765\", \"204754\", \"162632\", \"163641\", \"165435\", \"167797\", \"170176\", \"170741\", \"172444\", \"174188\", \"174288\", \"174727\", \"179183\", \"183187\", \"192306\", \"193301\", \"201534\", \"201656\", \"206055\", \"209178\", \"209875\", \"218124\", \"244243\", \"205421\", \"206910\", \"208872\", \"212348\", \"213452\", \"219902\", \"220565\", \"224249\", \"224502\", \"224679\", \"230672\", \"235067\", \"237654\", \"239335\", \"239450\", \"243346\", \"251209\", \"251797\", \"256223\", \"251780\", \"252610\", \"254741\", \"260082\", \"263636\"], \"outgoing_citations\": [\"285178\", \"291055\", \"292812\", \"294072\", \"294130\", \"294694\", \"295202\", \"296073\", \"302585\", \"302812\", \"303018\", \"305427\", \"306222\", \"312090\", \"317653\", \"323693\", \"325660\", \"337553\", \"357176\", \"357245\"]}","task_split":"paper_retrieval"}
{"document_id":"280323","document_content":"# A Semi-supervised Framework for Image Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nState-of-the-art approaches for image captioning require supervised training data consisting of captions with paired image data. These methods are typically unable to use unsupervised data such as textual data with no corresponding images, which is a much more abundant commodity. We here propose a novel way of using such textual data by artificially generating missing visual information. We evaluate this learning approach on a newly designed model that detects visual concepts present in an image and feed them to a reviewer-decoder architecture with an attention mechanism. Unlike previous approaches that encode visual concepts using word embeddings, we instead suggest using regional image features which capture more intrinsic information. The main benefit of this architecture is that it synthesizes meaningful thought vectors that capture salient image properties and then applies a soft attentive decoder to decode the thought vectors and generate image captions. We evaluate our model on both Microsoft COCO and Flickr30K datasets and demonstrate that this model combined with our semi-supervised learning method can largely improve performance and help the model to generate more accurate and diverse captions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05321v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60640\", \"208555\", \"220477\"], \"outgoing_citations\": [\"291851\", \"303209\", \"303715\", \"310816\", \"313934\", \"321552\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"280378","document_content":"# Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nQuery expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05113v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5681\", \"34971\", \"73146\", \"144613\", \"5101\", \"13707\", \"15391\", \"31118\", \"36193\", \"52533\", \"61377\", \"76927\", \"110152\", \"176317\", \"104567\", \"111811\", \"111919\", \"130424\", \"135052\", \"136732\", \"142604\", \"147155\", \"148469\", \"163979\", \"193362\", \"221269\", \"237093\", \"150708\", \"171120\", \"171869\", \"174176\", \"186126\", \"189938\", \"191383\", \"207283\", \"208333\", \"208493\", \"231046\", \"250135\", \"261255\", \"208687\", \"212048\", \"221064\", \"223644\", \"237248\", \"237299\", \"255989\", \"239048\", \"248935\", \"250094\", \"256343\", \"264316\", \"271025\", \"287103\", \"258948\", \"261707\", \"268341\", \"272445\"], \"outgoing_citations\": [\"281953\", \"294983\", \"295204\", \"301572\", \"303067\", \"304501\", \"313957\", \"320036\", \"320578\", \"328917\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"280394","document_content":"# Learning Detailed Face Reconstruction from a Single Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. The reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. The proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). The proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. Therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. The accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.05053v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"42280\", \"5179\", \"7345\", \"14816\", \"17613\", \"49287\", \"57572\", \"94953\", \"95716\", \"5380\", \"9232\", \"12174\", \"16773\", \"17589\", \"19959\", \"32023\", \"32365\", \"33225\", \"41321\", \"42098\", \"221215\", \"63042\", \"72586\", \"53101\", \"81253\", \"82106\", \"84175\", \"87603\", \"96763\", \"102774\", \"104420\", \"180606\", \"194156\", \"219961\", \"229811\", \"234433\", \"235160\", \"109928\", \"112307\", \"105662\", \"133828\", \"134002\", \"134658\", \"135163\", \"135637\", \"137181\", \"140504\", \"145468\", \"150974\", \"151076\", \"159733\", \"168874\", \"170169\", \"180084\", \"188208\", \"192217\", \"208867\", \"259000\", \"151397\", \"158372\", \"163559\", \"168953\", \"169032\", \"171500\", \"181617\", \"182589\", \"174524\", \"185746\", \"187576\", \"190860\", \"191310\", \"191463\", \"191844\", \"192520\", \"193589\", \"193656\", \"194387\", \"194402\", \"197211\", \"198793\", \"199069\", \"200905\", \"202404\", \"202710\", \"205494\", \"205998\", \"234539\", \"245824\", \"257958\", \"259476\", \"207377\", \"218032\", \"216762\", \"220416\", \"224621\", \"227699\", \"227856\", \"230031\", \"234210\", \"234872\", \"235754\", \"236050\", \"237222\", \"237958\", \"238254\", \"238709\", \"243530\", \"247495\", \"247558\", \"248005\", \"248918\", \"250290\", \"270042\", \"250754\", \"257765\", \"257846\", \"263714\", \"270134\", \"270235\", \"270795\"], \"outgoing_citations\": [\"284716\", \"294914\", \"295199\", \"295972\", \"302377\", \"302701\", \"306261\", \"309106\", \"312499\", \"321117\"]}","task_split":"paper_retrieval"}
{"document_id":"280468","document_content":"# Knowledge Enhanced Hybrid Neural Network for Text Matching\n## Categories\n- Computation and Language\n## Abstract\nLong text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures. To tackle the challenge, we consider using prior knowledge to help identify useful information and filter out noise to matching in long text. To this end, we propose a knowledge enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into word representations by knowledge gates and establishes three matching channels with words, sequential structures of sentences given by Gated Recurrent Units (GRU), and knowledge enhanced representations. The three channels are processed by a convolutional neural network to generate high level features for matching, and the features are synthesized as a matching score by a multilayer perceptron. The model extends the existing methods by conducting matching on words, local structures of sentences, and global context of sentences. Evaluation results from extensive experiments on public data sets for question answering and conversation show that KEHNN can significantly outperform the-state-of-the-art matching models and particularly improve the performance on pairs with long text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.04684v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54317\", \"75583\", \"118948\", \"137340\", \"138091\", \"194919\", \"198875\", \"241428\", \"215208\", \"229493\"], \"outgoing_citations\": [\"154227\", \"292353\", \"292443\", \"294532\", \"297950\", \"297955\", \"300803\", \"301397\", \"302507\", \"303469\", \"306182\", \"310377\", \"316165\"]}","task_split":"paper_retrieval"}
{"document_id":"280478","document_content":"# Link Prediction using Embedded Knowledge Graphs\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nSince large knowledge bases are typically incomplete, missing facts need to be inferred from observed facts in a task called knowledge base completion. The most successful approaches to this task have typically explored explicit paths through sequences of triples. These approaches have usually resorted to human-designed sampling procedures, since large knowledge graphs produce prohibitively large numbers of possible paths, most of which are uninformative. As an alternative approach, we propose performing a single, short sequence of interactive lookup operations on an embedded knowledge graph which has been trained through end-to-end backpropagation to be an optimized and compressed version of the initial knowledge base. Our proposed model, called Embedded Knowledge Graph Network (EKGN), achieves new state-of-the-art results on popular knowledge base completion benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.04642v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"216953\", \"248147\", \"250221\", \"252012\", \"265002\", \"272851\", \"272929\"], \"outgoing_citations\": [\"250322\", \"260644\", \"280288\", \"284528\", \"288979\", \"289536\", \"290747\", \"304901\", \"310740\", \"311834\", \"311858\", \"311970\", \"313944\"]}","task_split":"paper_retrieval"}
{"document_id":"280498","document_content":"# Zero-resource Machine Translation by Multimodal Encoder-decoder Network with Multimedia Pivot\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nWe propose an approach to build a neural machine translation system with no supervised resources (i.e., no parallel corpora) using multimodal embedded representation over texts and images. Based on the assumption that text documents are often likely to be described with other multimedia information (e.g., images) somewhat related to the content, we try to indirectly estimate the relevance between two languages. Using multimedia as the \"pivot\", we project all modalities into one common hidden space where samples belonging to similar semantic concepts should come close to each other, whatever the observed space of each sample is. This modality-agnostic representation is the key to bridging the gap between different modalities. Putting a decoder on top of it, our network can flexibly draw the outputs from any input modality. Notably, in the testing phase, we need only source language texts as the input for translation. In experiments, we tested our method on two benchmarks to show that it can achieve reasonable translation performance. We compared and investigated several possible implementations and found that an end-to-end model that simultaneously optimized both rank loss in multimodal encoders and cross-entropy loss in decoders performed the best.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.04503v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [\"2460\", \"207958\", \"23204\", \"84886\", \"89663\", \"126586\", \"153733\", \"157257\", \"167598\", \"182830\", \"208350\", \"217125\", \"242857\", \"251816\", \"253508\", \"266669\", \"267524\", \"268957\"], \"outgoing_citations\": [\"290356\", \"290510\", \"293418\", \"300096\", \"301840\", \"302621\", \"303003\", \"304945\", \"305132\", \"322022\"]}","task_split":"paper_retrieval"}
{"document_id":"280589","document_content":"# Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion\n## Categories\n- Computation and Language\n## Abstract\nJoint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.04125v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"20997\", \"71008\", \"156620\", \"140904\", \"166443\", \"169296\", \"185889\", \"204585\", \"208559\", \"218055\", \"219306\", \"279522\"], \"outgoing_citations\": [\"308043\", \"308507\", \"313962\", \"341939\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"280681","document_content":"# Learning to Navigate in Complex Environments\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Robotics\n## Abstract\nLearning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks leveraging multimodal sensory inputs. In particular we consider jointly learning the goal-driven reinforcement learning problem with auxiliary depth prediction and loop closure classification tasks. This approach can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics, showing that the agent implicitly learns key navigation abilities.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.03673v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"2138\", \"28573\", \"2048\", \"7483\", \"17155\", \"17143\", \"17457\", \"19526\", \"20192\", \"20912\", \"26795\", \"30806\", \"31846\", \"35753\", \"36637\", \"50552\", \"60623\", \"64019\", \"68339\", \"75621\", \"78473\", \"136611\", \"115575\", \"128249\", \"148134\", \"165617\", \"186811\", \"225257\", \"93187\", \"1414\", \"4687\", \"6704\", \"10257\", \"12281\", \"15055\", \"15584\", \"15651\", \"16502\", \"17198\", \"19209\", \"19231\", \"9192\", \"25684\", \"26678\", \"35156\", \"36974\", \"37552\", \"38125\", \"39279\", \"51764\", \"48479\", \"54096\", \"54266\", \"55346\", \"57326\", \"61081\", \"63867\", \"64482\", \"64556\", \"65953\", \"66404\", \"66864\", \"67718\", \"71624\", \"73441\", \"79162\", \"80696\", \"81574\", \"82597\", \"82807\", \"86161\", \"88041\", \"88546\", \"88552\", \"89316\", \"93649\", \"97665\", \"98092\", \"99170\", \"101182\", \"104690\", \"110042\", \"111433\", \"132558\", \"152072\", \"157670\", \"163326\", \"172223\", \"192512\", \"218318\", \"79299\", \"84805\", \"85467\", \"85766\", \"86980\", \"90607\", \"90871\", \"93344\", \"93795\", \"102971\", \"104772\", \"107917\", \"59900\", \"108608\", \"109133\", \"110466\", \"110941\", \"111800\", \"112949\", \"113236\", \"113074\", \"114824\", \"115361\", \"116835\", \"117896\", \"118153\", \"110182\", \"122583\", \"123031\", \"124206\", \"124909\", \"86743\", \"127978\", \"129794\", \"131180\", \"131814\", \"131891\", \"132020\", \"132207\", \"134432\", \"136760\", \"139112\", \"139227\", \"140039\", \"140619\", \"145753\", \"148111\", \"148433\", \"149565\", \"149647\", \"152690\", \"152732\", \"155676\", \"155831\", \"160895\", \"161603\", \"162100\", \"162505\", \"164699\", \"161717\", \"164984\", \"165819\", \"168254\", \"180340\", \"191583\", \"196909\", \"197663\", \"200261\", \"257674\", \"262325\", \"153864\", \"155641\", \"156747\", \"158874\", \"159449\", \"159273\", \"158905\", \"164842\", \"159921\", \"166667\", \"167562\", \"167688\", \"168162\", \"170410\", \"171660\", \"172315\", \"172538\", \"174609\", \"174954\", \"161277\", \"176782\", \"177674\", \"178620\", \"179471\", \"179594\", \"180851\", \"181081\", \"182615\", \"183049\", \"183699\", \"184126\", \"184119\", \"182108\", \"185747\", \"185809\", \"186101\", \"189086\", \"189154\", \"190556\", \"190812\", \"190948\", \"191671\", \"192391\", \"193972\", \"193993\", \"194738\", \"195086\", \"195713\", \"195772\", \"196218\", \"196226\", \"196393\", \"196361\", \"196525\", \"196598\", \"196801\", \"196898\", \"198481\", \"198524\", \"200941\", \"202443\", \"202904\", \"203398\", \"203854\", \"26939\", \"204226\", \"205179\", \"207296\", \"207499\", \"208773\", \"214068\", \"211558\", \"214195\", \"216952\", \"218124\", \"228031\", \"231955\", \"236923\", \"241900\", \"230187\", \"249939\", \"263264\", \"266109\", \"213236\", \"207509\", \"207983\", \"209249\", \"211644\", \"209402\", \"203355\", \"214018\", \"216221\", \"207096\", \"215693\", \"216647\", \"199792\", \"219728\", \"221441\", \"222446\", \"221283\", \"223033\", \"224223\", \"224255\", \"225036\", \"225768\", \"227065\", \"228913\", \"229185\", \"231155\", \"231623\", \"231877\", \"232229\", \"231376\", \"234516\", \"237008\", \"237119\", \"237361\", \"237386\", \"238475\", \"239320\", \"240052\", \"240506\", \"241006\", \"241625\", \"240323\", \"241099\", \"242444\", \"243090\", \"241533\", \"243838\", \"243943\", \"244283\", \"244266\", \"244984\", \"245731\", \"246853\", \"247002\", \"247161\", \"249906\", \"249876\", \"252309\", \"253933\", \"257128\", \"264673\", \"246823\", \"247242\", \"247776\", \"247749\", \"249137\", \"253215\", \"254490\", \"254491\", \"254965\", \"255126\", \"256513\", \"258199\", \"260770\", \"261316\", \"262106\", \"262593\", \"262503\", \"265091\", \"265263\", \"266419\", \"267898\", \"269420\", \"271188\", \"271062\", \"272668\", \"274102\", \"275003\", \"277472\", \"280202\", \"280657\", \"280296\", \"277504\", \"278195\"], \"outgoing_citations\": [\"278195\", \"283200\", \"284468\", \"289906\", \"290904\", \"291568\", \"293876\", \"306812\", \"309498\", \"310370\", \"311613\", \"336246\"]}","task_split":"paper_retrieval"}
{"document_id":"280721","document_content":"# Importance Sampling with Unequal Support\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nImportance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based estimates by orders of magnitude when the supports of the training and testing distributions differ. After motivating and presenting our new importance sampling estimator, we provide a detailed theoretical analysis that characterizes both its bias and variance relative to the ordinary importance sampling estimator (in various settings, which include cases where ordinary importance sampling is biased, while our new estimator is not, and vice versa). We conclude with an example of how our new importance sampling estimator can be used to improve estimates of how well a new treatment policy for diabetes will work for an individual, using only data from when the individual used a previous treatment policy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.03451v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"53458\", \"181476\", \"205143\", \"258305\", \"271865\"], \"outgoing_citations\": [\"303539\"]}","task_split":"paper_retrieval"}
{"document_id":"280830","document_content":"# Increasing the throughput of machine translation systems using clouds\n## Categories\n- Computation and Language\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nThe manuscript presents an experiment at implementation of a Machine Translation system in a MapReduce model. The empirical evaluation was done using fully implemented translation systems embedded into the MapReduce programming model. Two machine translation paradigms were studied: shallow transfer Rule Based Machine Translation and Statistical Machine Translation.   The results show that the MapReduce model can be successfully used to increase the throughput of a machine translation system. Furthermore this method enhances the throughput of a machine translation system without decreasing the quality of the translation output.   Thus, the present manuscript also represents a contribution to the seminal work in natural language processing, specifically Machine Translation. It first points toward the importance of the definition of the metric of throughput of translation system and, second, the applicability of the machine translation task to the MapReduce paradigm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.02944v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"280976","document_content":"# Balotage in Argentina 2015, a sentiment analysis of tweets\n## Categories\n- Information Retrieval\n- Social and Information Networks\n- Computation and Language\n## Abstract\nTwitter social network contains a large amount of information generated by its users. That information is composed of opinions and comments that may reflect trends in social behavior. There is talk of trend when it is possible to identify opinions and comments geared towards the same shared by a lot of people direction. To determine if two or more written opinions share the same address, techniques Natural Language Processing (NLP) are used. This paper proposes a methodology for predicting reflected in Twitter from the use of sentiment analysis functions NLP based on social behaviors. The case study was selected the 2015 Presidential in Argentina, and a software architecture Big Data composed Vertica data base with the component called Pulse was used. Through the analysis it was possible to detect trends in voting intentions with regard to the presidential candidates, achieving greater accuracy in predicting that achieved with traditional systems surveys.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.02337v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.SI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Social and Information Networks\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"281002","document_content":"# Unrolled Generative Adversarial Networks\n## Categories\n- Machine Learning\n## Abstract\nWe introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.02163v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"25184\", \"55768\", \"117785\", \"146468\", \"940\", \"1091\", \"1751\", \"8848\", \"10263\", \"11379\", \"13255\", \"16115\", \"25661\", \"42811\", \"43181\", \"45243\", \"68876\", \"68785\", \"73051\", \"74717\", \"81994\", \"116895\", \"120045\", \"145520\", \"422\", \"7178\", \"10367\", \"10202\", \"10416\", \"11238\", \"10799\", \"13260\", \"17497\", \"18687\", \"19257\", \"19673\", \"24411\", \"27691\", \"27572\", \"28452\", \"29030\", \"28970\", \"31381\", \"31471\", \"32318\", \"33799\", \"35080\", \"35923\", \"38243\", \"40736\", \"41754\", \"42085\", \"43267\", \"43339\", \"44575\", \"45681\", \"47068\", \"82357\", \"49209\", \"53198\", \"54915\", \"56202\", \"56995\", \"57919\", \"58058\", \"58792\", \"59598\", \"61187\", \"61506\", \"61677\", \"65384\", \"67326\", \"68784\", \"71021\", \"73927\", \"78355\", \"79174\", \"80387\", \"82486\", \"83250\", \"93790\", \"99525\", \"101173\", \"104778\", \"107223\", \"113223\", \"113442\", \"114764\", \"116899\", \"117052\", \"118061\", \"119803\", \"119954\", \"121127\", \"128046\", \"139873\", \"142822\", \"149919\", \"150238\", \"180450\", \"180450\", \"235848\", \"265988\", \"118261\", \"83479\", \"84664\", \"86789\", \"87963\", \"88524\", \"88525\", \"67271\", \"72129\", \"90447\", \"92161\", \"93990\", \"115913\", \"96592\", \"97549\", \"98358\", \"98696\", \"99545\", \"100067\", \"101266\", \"103007\", \"103483\", \"105166\", \"105513\", \"106343\", \"107008\", \"107122\", \"108688\", \"109270\", \"112637\", \"105895\", \"90140\", \"113578\", \"116027\", \"116030\", \"116148\", \"117330\", \"117768\", \"118802\", \"119099\", \"120280\", \"117607\", \"122731\", \"123015\", \"127805\", \"129773\", \"130759\", \"132506\", \"133139\", \"134861\", \"135640\", \"137040\", \"137050\", \"138674\", \"140392\", \"140488\", \"141157\", \"142198\", \"145603\", \"146031\", \"147766\", \"148002\", \"150695\", \"153324\", \"164379\", \"164513\", \"174832\", \"184207\", \"193197\", \"195141\", \"206901\", \"228877\", \"242724\", \"247713\", \"156050\", \"156273\", \"158139\", \"159440\", \"159260\", \"159583\", \"159886\", \"161604\", \"162520\", \"164285\", \"153151\", \"184658\", \"165186\", \"169530\", \"172222\", \"175648\", \"177560\", \"178446\", \"181107\", \"181019\", \"182695\", \"182585\", \"182973\", \"183689\", \"183898\", \"184423\", \"185009\", \"185885\", \"186444\", \"186431\", \"187464\", \"186631\", \"189614\", \"190500\", \"190533\", \"191226\", \"191641\", \"191795\", \"234514\", \"184712\", \"195268\", \"196113\", \"196052\", \"198077\", \"198861\", \"199163\", \"199401\", \"199764\", \"199766\", \"199864\", \"200644\", \"200963\", \"201355\", \"201254\", \"201577\", \"201641\", \"201674\", \"201731\", \"202492\", \"202492\", \"202742\", \"203098\", \"203467\", \"204094\", \"204880\", \"206266\", \"207829\", \"210224\", \"210670\", \"212075\", \"212521\", \"212521\", \"217027\", \"219077\", \"220240\", \"224841\", \"225467\", \"224974\", \"226660\", \"226982\", \"231164\", \"240321\", \"240514\", \"241887\", \"242751\", \"247194\", \"260518\", \"280232\", \"207012\", \"209016\", \"210846\", \"213123\", \"215894\", \"218620\", \"218973\", \"222278\", \"222900\", \"223821\", \"225102\", \"226051\", \"226369\", \"226469\", \"226473\", \"229452\", \"229773\", \"230480\", \"230962\", \"231428\", \"232143\", \"232167\", \"232459\", \"233720\", \"219188\", \"237128\", \"237269\", \"237320\", \"237705\", \"237755\", \"237962\", \"238027\", \"239216\", \"133817\", \"240640\", \"240885\", \"241031\", \"241296\", \"241627\", \"241758\", \"245283\", \"265970\", \"242052\", \"240046\", \"239930\", \"238925\", \"242351\", \"242307\", \"243020\", \"243047\", \"243190\", \"243470\", \"244043\", \"244219\", \"244597\", \"244719\", \"245518\", \"246281\", \"248099\", \"250699\", \"251563\", \"252221\", \"252621\", \"252935\", \"256103\", \"257452\", \"258493\", \"262489\", \"264073\", \"264747\", \"265386\", \"265517\", \"265907\", \"269271\", \"270098\", \"272220\", \"248735\", \"249523\", \"250246\", \"250563\", \"250542\", \"251097\", \"251343\", \"251866\", \"254518\", \"255463\", \"256229\", \"257087\", \"258286\", \"258510\", \"259052\", \"259288\", \"259717\", \"259890\", \"260304\", \"261238\", \"262011\", \"262816\", \"263220\", \"263355\", \"263662\", \"263859\", \"263935\", \"264984\", \"265075\", \"265398\", \"265975\", \"266137\", \"267006\", \"269002\", \"270024\", \"271122\", \"271301\", \"272073\", \"272662\", \"265869\", \"274838\", \"276542\", \"277021\", \"280600\", \"278560\", \"302907\"], \"outgoing_citations\": [\"278423\", \"278560\", \"282643\", \"283070\", \"284986\", \"289471\", \"291537\", \"297062\", \"302947\", \"303913\", \"311303\", \"311388\", \"315761\", \"316183\", \"317642\"]}","task_split":"paper_retrieval"}
{"document_id":"281015","document_content":"# Building a comprehensive syntactic and semantic corpus of Chinese clinical texts\n## Categories\n- Computation and Language\n## Abstract\nObjective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.   Materials and methods: An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations. Inter-annotator agreement (IAA) was calculated to evaluate the annotation quality and a Chinese clinical text processing and information extraction system (CCTPIES) was developed based on our annotated corpus.   Results: The syntactic corpus consists of 138 Chinese clinical documents with 47,424 tokens and 2553 full parsing trees, while the semantic corpus includes 992 documents that annotated 39,511 entities with their assertions and 7695 relations. IAA evaluation shows that this comprehensive corpus is of good quality, and the system modules are effective.   Discussion: The annotated corpus makes a considerable contribution to natural language processing (NLP) research into Chinese texts in the clinical domain. However, this corpus has a number of limitations. Some additional types of clinical text should be introduced to improve corpus coverage and active learning methods should be utilized to promote annotation efficiency.   Conclusions: In this study, several annotation guidelines and an annotation method for Chinese clinical texts were proposed, and a comprehensive corpus with its NLP modules were constructed, providing a foundation for further study of applying NLP techniques to Chinese texts in the clinical domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.02091v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"131886\", \"222998\", \"255363\", \"270408\", \"279401\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"281106","document_content":"# Modular Multitask Reinforcement Learning with Policy Sketches\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWe describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate tasks with sequences of named subtasks, providing information about high-level structural relationships among tasks but not how to implement them---specifically not providing the detailed guidance used by much previous work on learning policy abstractions for RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivations). To learn from sketches, we present a model that associates every subtask with a modular subpolicy, and jointly maximizes reward over full task-specific policies by tying parameters across shared subpolicies. Optimization is accomplished via a decoupled actor--critic training objective that facilitates learning common behaviors from multiple dissimilar reward functions. We evaluate the effectiveness of our approach in three environments featuring both discrete and continuous control, and with sparse rewards that can be obtained only after completing a number of high-level subgoals. Experiments show that using our approach to learn policies guided by sketches gives better performance than existing techniques for learning task-specific or shared policies, while naturally inducing a library of interpretable primitive behaviors that can be recombined to rapidly adapt to new tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01796v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"5297\", \"244291\", \"14\", \"8773\", \"15387\", \"16608\", \"17854\", \"18466\", \"19003\", \"19140\", \"22954\", \"36650\", \"73951\", \"78473\", \"96439\", \"260314\", \"952\", \"8969\", \"9356\", \"14182\", \"14861\", \"17680\", \"20310\", \"22124\", \"26076\", \"26276\", \"26664\", \"31471\", \"39374\", \"39395\", \"35842\", \"91688\", \"46314\", \"46531\", \"154720\", \"14180\", \"55964\", \"56227\", \"63319\", \"64051\", \"67087\", \"67670\", \"67594\", \"69616\", \"69948\", \"74771\", \"74858\", \"89875\", \"93649\", \"101721\", \"111075\", \"115571\", \"155930\", \"92109\", \"100245\", \"101301\", \"107240\", \"109549\", \"89234\", \"112798\", \"113866\", \"114011\", \"115004\", \"115366\", \"116841\", \"119188\", \"119357\", \"121678\", \"121716\", \"125366\", \"125969\", \"126363\", \"134149\", \"138724\", \"142281\", \"148867\", \"164125\", \"164930\", \"163895\", \"168328\", \"167124\", \"177629\", \"178846\", \"179050\", \"180894\", \"192812\", \"104825\", \"151617\", \"157202\", \"158349\", \"158907\", \"159110\", \"161338\", \"162602\", \"165375\", \"164433\", \"171778\", \"173039\", \"165614\", \"173914\", \"180270\", \"181620\", \"182685\", \"182870\", \"184898\", \"183535\", \"188866\", \"190013\", \"191394\", \"195240\", \"196061\", \"196898\", \"197711\", \"198161\", \"205488\", \"207327\", \"216027\", \"216060\", \"217088\", \"217069\", \"217974\", \"225224\", \"191970\", \"209806\", \"212893\", \"215924\", \"217523\", \"223689\", \"223990\", \"225036\", \"224981\", \"229324\", \"231384\", \"240075\", \"240209\", \"240348\", \"240336\", \"241691\", \"241751\", \"241013\", \"242963\", \"243782\", \"244272\", \"245731\", \"247002\", \"248742\", \"251691\", \"254096\", \"256040\", \"259693\", \"248805\", \"249398\", \"251098\", \"252179\", \"258331\", \"263650\", \"270783\", \"275029\"], \"outgoing_citations\": [\"284134\", \"284567\", \"290371\", \"294142\", \"300421\", \"302970\", \"303306\"]}","task_split":"paper_retrieval"}
{"document_id":"281135","document_content":"# End-to-end Optimized Image Compression\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Theory\n- Information Theory\n## Abstract\nWe describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01704v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Theory\", \"Information Theory\"], \"incoming_citations\": [\"32952\", \"47239\", \"64601\", \"125962\", \"10\", \"179\", \"1684\", \"2235\", \"3535\", \"3584\", \"4502\", \"4434\", \"8763\", \"8961\", \"9247\", \"9912\", \"13936\", \"25262\", \"28362\", \"34498\", \"39792\", \"40317\", \"43729\", \"44763\", \"48257\", \"49272\", \"52169\", \"68918\", \"79886\", \"82132\", \"83996\", \"85979\", \"87941\", \"97572\", \"97830\", \"123186\", \"129842\", \"130790\", \"134192\", \"137860\", \"143178\", \"862\", \"898\", \"2018\", \"2592\", \"2875\", \"5645\", \"7664\", \"11255\", \"11335\", \"12436\", \"15540\", \"16587\", \"20061\", \"19978\", \"20073\", \"21213\", \"21744\", \"21826\", \"22411\", \"22585\", \"24786\", \"28322\", \"28395\", \"30766\", \"32956\", \"33077\", \"34766\", \"35592\", \"36887\", \"38208\", \"38255\", \"40729\", \"44585\", \"44575\", \"8882\", \"40378\", \"47341\", \"47505\", \"48406\", \"51508\", \"53584\", \"54372\", \"54617\", \"54885\", \"54989\", \"55403\", \"56443\", \"56971\", \"57667\", \"138021\", \"59267\", \"59824\", \"60017\", \"59979\", \"63503\", \"63928\", \"65334\", \"65198\", \"66567\", \"67687\", \"68318\", \"71399\", \"70549\", \"72511\", \"77691\", \"83013\", \"143298\", \"93339\", \"93754\", \"98216\", \"98494\", \"104397\", \"108257\", \"112154\", \"113228\", \"113821\", \"116426\", \"129657\", \"134573\", \"138585\", \"142696\", \"144653\", \"151708\", \"160882\", \"168702\", \"173475\", \"192903\", \"27863\", \"85863\", \"86183\", \"88783\", \"89245\", \"92508\", \"97042\", \"98307\", \"100949\", \"102609\", \"103060\", \"104348\", \"104873\", \"106286\", \"106518\", \"107514\", \"108590\", \"109062\", \"111470\", \"112004\", \"114008\", \"114362\", \"104670\", \"115500\", \"117047\", \"117730\", \"117930\", \"117925\", \"118280\", \"120449\", \"123706\", \"124326\", \"124751\", \"125050\", \"125595\", \"127209\", \"138579\", \"132713\", \"134242\", \"135025\", \"135757\", \"136240\", \"136318\", \"118710\", \"139561\", \"140596\", \"140899\", \"141734\", \"141894\", \"143086\", \"143213\", \"143347\", \"144118\", \"145230\", \"145566\", \"146623\", \"147245\", \"147712\", \"151353\", \"151604\", \"154339\", \"154939\", \"155387\", \"160928\", \"162788\", \"172387\", \"176021\", \"177451\", \"179246\", \"180563\", \"191822\", \"193191\", \"193985\", \"208605\", \"225233\", \"149183\", \"149165\", \"150526\", \"154066\", \"164798\", \"160749\", \"167493\", \"170337\", \"171806\", \"172350\", \"172377\", \"175457\", \"177909\", \"182691\", \"184331\", \"184711\", \"185438\", \"219287\", \"161723\", \"188251\", \"188610\", \"189501\", \"189982\", \"190500\", \"191688\", \"191828\", \"192107\", \"194397\", \"195076\", \"195179\", \"192950\", \"197777\", \"199006\", \"199280\", \"201968\", \"202886\", \"204814\", \"205024\", \"207812\", \"207875\", \"209536\", \"214540\", \"215245\", \"224867\", \"225593\", \"225766\", \"228729\", \"236298\", \"238842\", \"247298\", \"265182\", \"201747\", \"206908\", \"212910\", \"216677\", \"217824\", \"218722\", \"227137\", \"229261\", \"229841\", \"230133\", \"230221\", \"230266\", \"231842\", \"230216\", \"234240\", \"235085\", \"235796\", \"240876\", \"241131\", \"238760\", \"242306\", \"243971\", \"245087\", \"245227\", \"247493\", \"251693\", \"270142\", \"270241\"], \"outgoing_citations\": [\"286385\", \"288248\", \"293570\", \"302969\", \"303913\", \"311324\"]}","task_split":"paper_retrieval"}
{"document_id":"281152","document_content":"# Boosting Image Captioning with Attributes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing. In this paper, we present Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner. To incorporate attributes, we construct variants of architectures by feeding image representations and attributes into RNNs in different ways to explore the mutual but also fuzzy relationship between them. Extensive experiments are conducted on COCO image captioning dataset and our framework achieves superior results when compared to state-of-the-art deep models. Most remarkably, we obtain METEOR\/CIDEr-D of 25.2%\/98.6% on testing data of widely used and publicly available splits in (Karpathy & Fei-Fei, 2015) when extracting image representations by GoogleNet and achieve to date top-1 performance on COCO captioning Leaderboard.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01646v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23109\", \"35427\", \"113488\", \"22827\", \"39309\", \"64130\", \"89494\", \"97719\", \"101277\", \"113677\", \"114091\", \"209688\", \"182259\", \"1725\", \"2116\", \"6500\", \"23020\", \"26827\", \"27365\", \"27871\", \"28908\", \"31372\", \"39592\", \"40810\", \"43864\", \"53424\", \"54685\", \"62999\", \"69490\", \"73032\", \"73316\", \"74508\", \"78606\", \"80837\", \"110600\", \"137828\", \"142094\", \"145513\", \"180868\", \"183623\", \"80061\", \"85583\", \"89648\", \"99840\", \"101620\", \"105379\", \"106820\", \"154779\", \"111062\", \"112250\", \"114995\", \"117131\", \"133970\", \"135506\", \"138218\", \"97920\", \"141957\", \"147001\", \"149491\", \"154752\", \"168443\", \"183087\", \"184636\", \"185622\", \"204751\", \"150464\", \"151345\", \"152816\", \"154763\", \"157229\", \"158839\", \"161738\", \"161715\", \"165896\", \"166757\", \"167838\", \"168476\", \"168193\", \"169417\", \"169841\", \"170299\", \"170604\", \"171096\", \"78600\", \"172668\", \"172811\", \"112826\", \"139529\", \"173493\", \"177226\", \"179730\", \"180788\", \"184640\", \"186189\", \"186602\", \"187914\", \"188386\", \"189018\", \"189964\", \"191680\", \"192114\", \"192208\", \"192659\", \"193328\", \"193735\", \"194885\", \"200636\", \"201599\", \"205750\", \"207034\", \"208555\", \"212034\", \"215181\", \"215178\", \"221675\", \"229734\", \"231619\", \"238229\", \"240608\", \"205740\", \"206567\", \"206686\", \"210290\", \"211875\", \"212441\", \"212792\", \"213330\", \"214245\", \"214856\", \"217955\", \"217310\", \"218240\", \"220232\", \"218012\", \"221169\", \"223302\", \"223314\", \"226398\", \"226908\", \"228401\", \"230900\", \"231212\", \"231647\", \"231673\", \"232192\", \"234549\", \"234596\", \"234602\", \"234599\", \"236918\", \"236897\", \"237150\", \"237199\", \"238125\", \"238941\", \"239242\", \"243630\", \"244272\", \"249394\", \"254809\", \"256347\", \"257947\", \"270455\", \"250157\", \"234607\", \"252706\", \"253934\", \"258570\", \"260521\", \"258345\", \"270154\", \"271189\", \"278604\", \"279002\", \"279740\"], \"outgoing_citations\": [\"258345\", \"279740\", \"290392\", \"291851\", \"295639\", \"311824\", \"313189\", \"313196\", \"321621\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"281195","document_content":"# Estimating Causal Direction and Confounding of Two Discrete Variables\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presupposes that the probability distributions $P(C)$ of a cause $C$ is independent from the probability distribution $P(E\\mid C)$ of the cause-effect mechanism. While our classifier is trained with a Bayesian assumption of flat hyperpriors, we do not make this assumption about our test data. This work connects to recent developments on the identifiability of causal models over continuous variables under the assumption of \"independent mechanisms\". Carefully-commented Python notebooks that reproduce all our experiments are available online at http:\/\/vision.caltech.edu\/~kchalupk\/code.html.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01504v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"124247\", \"98189\", \"291800\"], \"outgoing_citations\": [\"320501\", \"346846\", \"348264\", \"348587\", \"348959\", \"358895\", \"358945\"]}","task_split":"paper_retrieval"}
{"document_id":"281206","document_content":"# Multi-task learning with deep model based reinforcement learning\n## Categories\n- Machine Learning\n## Abstract\nIn recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. Most successful approaches focus on solving a single task, while multi-task reinforcement learning remains an open problem. In this paper, we present a model based approach to deep reinforcement learning which we use to solve different tasks simultaneously. We show that our approach not only does not degrade but actually benefits from learning multiple tasks. For our model, we also present a new kind of recurrent neural network inspired by residual networks that decouples memory from computation allowing to model complex environments that do not require lots of memory.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01457v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"229559\", \"254494\", \"277504\"], \"outgoing_citations\": [\"287219\", \"302295\", \"302952\"]}","task_split":"paper_retrieval"}
{"document_id":"281225","document_content":"# Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies\n## Categories\n- Computation and Language\n## Abstract\nThe success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01368v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"30305\", \"2436\", \"2946\", \"3345\", \"20003\", \"23438\", \"24928\", \"26824\", \"91674\", \"106423\", \"6883\", \"6880\", \"9935\", \"11408\", \"12125\", \"16463\", \"19895\", \"21101\", \"21263\", \"21669\", \"22041\", \"21998\", \"23087\", \"23257\", \"23484\", \"24464\", \"25228\", \"26685\", \"27231\", \"29129\", \"35389\", \"35857\", \"38189\", \"41709\", \"42955\", \"44434\", \"45199\", \"45348\", \"45420\", \"45432\", \"45684\", \"44710\", \"45930\", \"45962\", \"46118\", \"46654\", \"49051\", \"49530\", \"49943\", \"50449\", \"52669\", \"53442\", \"53602\", \"54472\", \"54783\", \"56222\", \"57867\", \"103917\", \"64743\", \"64707\", \"65799\", \"66215\", \"68774\", \"72160\", \"72914\", \"73219\", \"73263\", \"73361\", \"73423\", \"73757\", \"76678\", \"77246\", \"77760\", \"77789\", \"78444\", \"81015\", \"89750\", \"91690\", \"92478\", \"95252\", \"98383\", \"107839\", \"117453\", \"121691\", \"128048\", \"129558\", \"133976\", \"153444\", \"175565\", \"85004\", \"85213\", \"88284\", \"89769\", \"87375\", \"87179\", \"89022\", \"89829\", \"91141\", \"92151\", \"92103\", \"94152\", \"94886\", \"95313\", \"95436\", \"95690\", \"95816\", \"96162\", \"96275\", \"96238\", \"97403\", \"98333\", \"99542\", \"100467\", \"112497\", \"113544\", \"115421\", \"116425\", \"115344\", \"121907\", \"122609\", \"124598\", \"124609\", \"125586\", \"126370\", \"127213\", \"127202\", \"127267\", \"127295\", \"127318\", \"127056\", \"127392\", \"127739\", \"127806\", \"127822\", \"128002\", \"127831\", \"127922\", \"128999\", \"130821\", \"131152\", \"131846\", \"131801\", \"132217\", \"132223\", \"132661\", \"132871\", \"127752\", \"127907\", \"134662\", \"140456\", \"141406\", \"141674\", \"147697\", \"149018\", \"153289\", \"153825\", \"155766\", \"158818\", \"160751\", \"167134\", \"173615\", \"180511\", \"157157\", \"30949\", \"159712\", \"161736\", \"163070\", \"159742\", \"165362\", \"166307\", \"166757\", \"168415\", \"164061\", \"165928\", \"166310\", \"167553\", \"170102\", \"171238\", \"169266\", \"171642\", \"140986\", \"175168\", \"177550\", \"180180\", \"180252\", \"180302\", \"180873\", \"181441\", \"181468\", \"181575\", \"182116\", \"182390\", \"182552\", \"182508\", \"180658\", \"181125\", \"184006\", \"184687\", \"185112\", \"185364\", \"186130\", \"187807\", \"183680\", \"186017\", \"190887\", \"191046\", \"191350\", \"181969\", \"192036\", \"192703\", \"188812\", \"189174\", \"193091\", \"193497\", \"194731\", \"194904\", \"195050\", \"195989\", \"194308\", \"196722\", \"197540\", \"197834\", \"197863\", \"198134\", \"195444\", \"200362\", \"201070\", \"201546\", \"203285\", \"205161\", \"205056\", \"205219\", \"212036\", \"213362\", \"217135\", \"218227\", \"223095\", \"225119\", \"229768\", \"202773\", \"208560\", \"211421\", \"211195\", \"212813\", \"214875\", \"217749\", \"218082\", \"218282\", \"218677\", \"219156\", \"219670\", \"219586\", \"219846\", \"219836\", \"220137\", \"220160\", \"220159\", \"220460\", \"219965\", \"219758\", \"218291\", \"224182\", \"224837\", \"225176\", \"228470\", \"228530\", \"228574\", \"230051\", \"232479\", \"233400\", \"233292\", \"232457\", \"232355\", \"234189\", \"234421\", \"234628\", \"234587\", \"237283\", \"238328\", \"239503\", \"239948\", \"240912\", \"230281\", \"232616\", \"244688\", \"244755\", \"249243\", \"256948\", \"265431\", \"287521\", \"244357\", \"252144\", \"252307\", \"256298\", \"258691\", \"258762\", \"258274\", \"263992\", \"263945\", \"265895\", \"268812\", \"267692\", \"271527\", \"276493\", \"278016\", \"297423\"], \"outgoing_citations\": [\"296583\", \"297423\", \"297648\", \"298750\", \"311098\", \"311585\", \"311683\", \"311845\", \"316576\", \"319833\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"281249","document_content":"# Generalized Topic Modeling\n## Categories\n- Machine Learning\n- Computation and Language\n- Data Structures and Algorithms\n- Information Retrieval\n## Abstract\nRecently there has been significant activity in developing algorithms with provable guarantees for topic modeling. In standard topic models, a topic (such as sports, business, or politics) is viewed as a probability distribution $\\vec a_i$ over words, and a document is generated by first selecting a mixture $\\vec w$ over topics, and then generating words i.i.d. from the associated mixture $A{\\vec w}$. Given a large collection of such documents, the goal is to recover the topic vectors and then to correctly classify new documents according to their topic mixture.   In this work we consider a broad generalization of this framework in which words are no longer assumed to be drawn i.i.d. and instead a topic is a complex distribution over sequences of paragraphs. Since one could not hope to even represent such a distribution in general (even if paragraphs are given using some natural feature representation), we aim instead to directly learn a document classifier. That is, we aim to learn a predictor that given a new document, accurately predicts its topic mixture, without learning the distributions explicitly. We present several natural conditions under which one can do this efficiently and discuss issues such as noise tolerance and sample complexity in this model. More generally, our model can be viewed as a generalization of the multi-view or co-training setting in machine learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.01259v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.DS\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Data Structures and Algorithms\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"281470","document_content":"# Recurrent Neural Network Language Model Adaptation Derived Document Vector\n## Categories\n- Computation and Language\n## Abstract\nIn many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document, and they can be important in some NLP tasks such as genre classification. This paper proposes a novel distributed vector representation of a document: a simple recurrent-neural-network language model (RNN-LM) or a long short-term memory RNN language model (LSTM-LM) is first created from all documents in a task; some of the LM parameters are then adapted by each document, and the adapted parameters are vectorized to represent the document. The new document vectors are labeled as DV-RNN and DV-LSTM respectively. We believe that our new document vectors can capture some high-level sequential information in the documents, which other current document representations fail to capture. The new document vectors were evaluated in the genre classification of documents in three corpora: the Brown Corpus, the BNC Baby Corpus and an artificially created Penn Treebank dataset. Their classification performances are compared with the performance of TF-IDF vector and the state-of-the-art distributed memory model of paragraph vector (PV-DM). The results show that DV-LSTM significantly outperforms TF-IDF and PV-DM in most cases, and combinations of the proposed document vectors with TF-IDF or PV-DM may further improve performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1611.00196v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"280967\", \"302425\", \"308857\"]}","task_split":"paper_retrieval"}
{"document_id":"281546","document_content":"# Named Entity Recognition for Novel Types by Transfer Learning\n## Categories\n- Computation and Language\n## Abstract\nIn named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.09914v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42791\", \"205022\", \"49866\", \"154623\", \"214921\", \"185216\", \"198964\", \"249726\", \"262107\"], \"outgoing_citations\": [\"289601\", \"314195\"]}","task_split":"paper_retrieval"}
{"document_id":"281551","document_content":"# Inference Compilation and Universal Probabilistic Programming\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n- 68T37, 68T05\n- G.3; I.2.6\n## Abstract\nWe introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do \"compilation of inference\" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.09900v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\", \"68T37, 68T05\", \"G.3; I.2.6\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\", \"68T37, 68T05\", \"G.3; I.2.6\"], \"incoming_citations\": [\"159670\", \"159677\", \"160783\", \"18807\", \"50901\", \"66284\", \"75835\", \"76921\", \"92019\", \"129616\", \"79224\", \"79646\", \"90426\", \"111291\", \"126274\", \"137224\", \"158109\", \"159130\", \"204508\", \"175263\", \"200075\", \"229861\", \"159160\", \"161185\", \"161379\", \"177174\", \"178478\", \"183751\", \"175519\", \"198641\", \"200760\", \"223974\", \"230086\", \"230419\", \"231591\", \"208906\", \"215715\", \"215841\", \"216257\", \"229117\", \"239288\", \"242366\", \"245393\", \"248638\", \"251000\", \"258121\", \"246851\", \"224192\", \"264782\", \"266025\", \"266415\", \"272546\", \"317200\"], \"outgoing_citations\": [\"296252\", \"306407\", \"306972\", \"310143\", \"311424\", \"316185\", \"320332\", \"328595\", \"331740\", \"336231\", \"343126\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"281552","document_content":"# Mining Social Media for Open Innovation in Transportation Systems\n## Categories\n- Computers and Society\n- Social and Information Networks\n- Artificial Intelligence\n## Abstract\nThis work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.09894v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.SI\", \"cs.AI\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Social and Information Networks\", \"Artificial Intelligence\"], \"incoming_citations\": [\"256730\", \"256344\", \"263641\"], \"outgoing_citations\": [\"290260\", \"302324\", \"329370\"]}","task_split":"paper_retrieval"}
{"document_id":"281681","document_content":"# Improving Sampling from Generative Autoencoders with Markov Chains\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior. Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.09296v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"171020\", \"199764\", \"247668\", \"264374\", \"279091\"], \"outgoing_citations\": [\"284708\", \"298592\", \"298700\", \"302928\", \"344049\", \"344331\"]}","task_split":"paper_retrieval"}
{"document_id":"281732","document_content":"# Representation Learning Models for Entity Search\n## Categories\n- Computation and Language\n## Abstract\nWe focus on the problem of learning distributed representations for entity search queries, named entities, and their short descriptions. With our representation learning models, the entity search query, named entity and description can be represented as low-dimensional vectors. Our goal is to develop a simple but effective model that can make the distributed representations of query related entities similar to the query in the vector space. Hence, we propose three kinds of learning strategies, and the difference between them mainly lies in how to deal with the relationship between an entity and its description. We analyze the strengths and weaknesses of each learning strategy and validate our methods on public datasets which contain four kinds of named entities, i.e., movies, TV shows, restaurants and celebrities. The experimental results indicate that our proposed methods can adapt to different types of entity search queries, and outperform the current state-of-the-art methods based on keyword matching and vanilla word2vec models. Besides, the proposed methods can be trained fast and be easily extended to other similar tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.09091v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"296702\", \"303469\", \"308414\", \"309271\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"281792","document_content":"# CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nExtracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object \"translation\" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.08763v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"47698\", \"19536\", \"28251\", \"39247\", \"41188\", \"48175\", \"41475\", \"54221\", \"59299\", \"69363\", \"98792\", \"85101\", \"93121\", \"98933\", \"101184\", \"77370\", \"65065\", \"127793\", \"129874\", \"133025\", \"142709\", \"154860\", \"167703\", \"168168\", \"194478\", \"63915\", \"170546\", \"185443\", \"189694\", \"189975\", \"190349\", \"195966\", \"198186\", \"210744\", \"211666\", \"213451\", \"225740\", \"224786\", \"233941\", \"236156\", \"239991\", \"250998\", \"251947\", \"250958\", \"262328\", \"264389\"], \"outgoing_citations\": [\"298157\", \"313074\", \"320886\", \"327951\"]}","task_split":"paper_retrieval"}
{"document_id":"281925","document_content":"# A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns\n## Categories\n- Artificial Intelligence\n- Programming Languages\n## Abstract\nManagement of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as chronic heart failure (CHF) is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. In the case of CHF management, the guidelines run nearly 80 pages. In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming. Our approach is based on developing reasoning templates (that we call knowledge patterns) and using these patterns to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system will work even in the presence of incomplete information. Our work makes two contributions: (i) it shows that highly complex guidelines can be successfully coded as ASP rules, and (ii) it develops a series of knowledge patterns that facilitate the coding of knowledge expressed in a natural language and that can be used for other application domains. This paper is under consideration for acceptance in TPLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1017\/S1471068416000429\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.PL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Programming Languages\"], \"incoming_citations\": [\"1580\", \"14296\", \"15686\", \"17310\", \"51642\", \"253174\", \"99312\", \"103583\", \"119016\", \"225819\", \"257131\", \"261098\", \"269429\"], \"outgoing_citations\": [\"329643\"]}","task_split":"paper_retrieval"}
{"document_id":"281930","document_content":"# Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nProduct review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single `correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system; and (2) What makes a `good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions---and over 3.1 million answers---and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.08095v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"756\", \"50552\", \"47809\", \"48665\", \"72334\", \"73497\", \"89873\", \"103764\", \"115623\", \"136978\", \"157249\", \"158044\", \"158978\", \"161107\", \"168879\", \"172079\", \"172076\", \"182577\", \"190742\", \"196978\", \"203554\"], \"outgoing_citations\": [\"301143\"]}","task_split":"paper_retrieval"}
{"document_id":"281931","document_content":"# Dis-S2V: Discourse Informed Sen2Vec\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nVector representation of sentences is important for many text processing tasks that involve clustering, classifying, or ranking sentences. Recently, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform the traditional bag-of-words representation. However, most of these learning methods consider only the content of a sentence and disregard the relations among sentences in a discourse by and large.   In this paper, we propose a series of novel models for learning latent representations of sentences (Sen2Vec) that consider the content of a sentence as well as inter-sentence relations. We first represent the inter-sentence relations with a language network and then use the network to induce contextual information into the content-based Sen2Vec models. Two different approaches are introduced to exploit the information in the network. Our first approach retrofits (already trained) Sen2Vec vectors with respect to the network in two different ways: (1) using the adjacency relations of a node, and (2) using a stochastic sampling method which is more flexible in sampling neighbors of a node. The second approach uses a regularizer to encode the information in the network into the existing Sen2Vec model. Experimental results show that our proposed models outperform existing methods in three fundamental information system tasks demonstrating the effectiveness of our approach. The models leverage the computational power of multi-core CPUs to achieve fine-grained computational efficiency. We make our code publicly available upon acceptance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.08078v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"94619\", \"100287\"], \"outgoing_citations\": [\"298543\", \"308627\"]}","task_split":"paper_retrieval"}
{"document_id":"281953","document_content":"# End-to-end Learning of Deep Visual Representations for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhile deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: i) noisy training data, ii) inappropriate deep architecture, and iii) suboptimal training procedure. We address all three issues.   First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy. For additional material, please see www.xrce.xerox.com\/Deep-Image-Retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.07940v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"94405\", \"458\", \"1850\", \"5681\", \"7819\", \"22567\", \"27638\", \"34971\", \"73146\", \"85376\", \"100302\", \"107975\", \"1407\", \"2721\", \"5101\", \"13707\", \"352180\", \"17603\", \"19708\", \"21970\", \"22193\", \"25890\", \"28959\", \"31118\", \"36782\", \"38289\", \"41288\", \"41988\", \"42853\", \"183974\", \"28791\", \"28833\", \"48708\", \"48791\", \"52533\", \"55349\", \"55955\", \"57807\", \"59695\", \"60247\", \"61377\", \"62045\", \"65937\", \"66717\", \"77995\", \"84765\", \"106263\", \"107454\", \"107996\", \"123303\", \"124630\", \"176317\", \"198822\", \"87306\", \"87595\", \"85055\", \"103466\", \"103697\", \"107967\", \"109645\", \"111071\", \"111143\", \"111811\", \"112422\", \"129986\", \"133586\", \"134085\", \"135052\", \"135489\", \"136732\", \"137767\", \"139112\", \"135639\", \"147155\", \"148469\", \"155931\", \"124460\", \"157545\", \"165769\", \"169444\", \"169975\", \"171120\", \"172435\", \"174176\", \"176318\", \"176580\", \"176978\", \"177755\", \"180184\", \"180670\", \"181615\", \"181568\", \"180562\", \"186126\", \"187122\", \"188194\", \"189938\", \"191383\", \"192344\", \"193735\", \"193535\", \"198922\", \"208787\", \"211595\", \"220216\", \"225586\", \"236908\", \"241439\", \"243056\", \"250135\", \"251473\", \"256887\", \"261255\", \"206586\", \"207010\", \"208288\", \"208687\", \"209520\", \"212048\", \"212243\", \"214392\", \"216394\", \"221285\", \"222277\", \"223644\", \"227120\", \"227893\", \"233661\", \"237248\", \"255989\", \"244938\", \"248935\", \"250094\", \"251572\", \"265276\", \"267404\", \"271025\", \"286767\", \"287103\", \"261707\", \"269054\", \"272445\", \"280378\"], \"outgoing_citations\": [\"285231\", \"294087\", \"294983\", \"295204\", \"301572\", \"303067\", \"304501\", \"314231\", \"314584\", \"328024\", \"328051\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"282088","document_content":"# Introduction: Cognitive Issues in Natural Language Processing\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nThis special issue is dedicated to get a better picture of the relationships between computational linguistics and cognitive science. It specifically raises two questions: \"what is the potential contribution of computational language modeling to cognitive science?\" and conversely: \"what is the influence of cognitive science in contemporary computational linguistics?\"","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1201\/b21583-2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"311336\", \"325742\"]}","task_split":"paper_retrieval"}
{"document_id":"282177","document_content":"# Learning Cost-Effective Treatment Regimes using Markov Decision Processes\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nDecision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning \\emph{cost-effective, interpretable and actionable treatment regimes}. We formulate this as a problem of learning a decision list -- a sequence of if-then-else rules -- which maps characteristics of subjects (eg., diagnostic test results of patients) to treatments. We propose a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs. We model the problem of learning such a list as a Markov Decision Process (MDP) and employ a variant of the Upper Confidence Bound for Trees (UCT) strategy which leverages customized checks for pruning the search space effectively. Experimental results on real world observational data capturing judicial bail decisions and treatment recommendations for asthma patients demonstrate the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.06972v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"230084\"], \"outgoing_citations\": [\"303951\", \"316549\"]}","task_split":"paper_retrieval"}
{"document_id":"282199","document_content":"# Deep Models for Engagement Assessment With Scarce Label Information\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nTask engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition). It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/THMS.2016.2608933\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85392\", \"92576\", \"202735\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"282240","document_content":"# Proposing Plausible Answers for Open-ended Visual Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nAnswering open-ended questions is an essential capability for any intelligent agent. One of the most interesting recent open-ended question answering challenges is Visual Question Answering (VQA) which attempts to evaluate a system's visual understanding through its answers to natural language questions about images. There exist many approaches to VQA, the majority of which do not exhibit deeper semantic understanding of the candidate answers they produce. We study the importance of generating plausible answers to a given question by introducing the novel task of `Answer Proposal': for a given open-ended question, a system should generate a ranked list of candidate answers informed by the semantics of the question. We experiment with various models including a neural generative model as well as a semantic graph matching one. We provide both intrinsic and extrinsic evaluations for the task of Answer Proposal, showing that our best model learns to propose plausible answers with a high recall and performs competitively with some other solutions to VQA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.06620v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"289503\", \"300421\", \"301884\", \"303603\", \"311976\", \"323775\"]}","task_split":"paper_retrieval"}
{"document_id":"282263","document_content":"# An Image Dataset of Text Patches in Everyday Scenes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper describes a dataset containing small images of text from everyday scenes. The purpose of the dataset is to support the development of new automated systems that can detect and analyze text. Although much research has been devoted to text detection and recognition in scanned documents, relatively little attention has been given to text detection in other types of images, such as photographs that are posted on social-media sites. This new dataset, known as COCO-Text-Patch, contains approximately 354,000 small images that are each labeled as \"text\" or \"non-text\". This dataset particularly addresses the problem of text verification, which is an essential stage in the end-to-end text detection and recognition pipeline. In order to evaluate the utility of this dataset, it has been used to train two deep convolution neural networks to distinguish text from non-text. One network is inspired by the GoogLeNet architecture, and the second one is based on CaffeNet. Accuracy levels of 90.2% and 90.9% were obtained using the two networks, respectively. All of the images, source code, and deep-learning trained models described in this paper will be publicly available","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.06494v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"265634\"], \"outgoing_citations\": [\"299461\"]}","task_split":"paper_retrieval"}
{"document_id":"282435","document_content":"# Identifiability and Transportability in Dynamic Causal Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Net- works, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the iden- tification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the re- sult of causal experiments in a source domain may be used for the identification of causal effects in a target domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.05556v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"28310\", \"104275\", \"142994\", \"233111\"], \"outgoing_citations\": [\"348244\", \"356542\", \"357586\", \"357904\", \"360769\", \"361834\"]}","task_split":"paper_retrieval"}
{"document_id":"282494","document_content":"# Pre-Translation for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nRecently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur.   When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach.   In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence.   We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.05243v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"6698\", \"42619\", \"56479\", \"69766\", \"81522\", \"277667\", \"171437\", \"152767\", \"157281\", \"160997\", \"180146\", \"180805\", \"188815\", \"192272\", \"186654\", \"192142\", \"181283\", \"213126\", \"218521\", \"219616\", \"221363\", \"233131\", \"258164\", \"259199\", \"259586\", \"262894\", \"262535\", \"268344\", \"268370\", \"277666\"], \"outgoing_citations\": [\"290995\", \"292493\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"282532","document_content":"# Spatio-Temporal Attention Models for Grounded Video Captioning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomatic video captioning is challenging due to the complex interactions in dynamic real scenes. A comprehensive system would ultimately localize and track the objects, actions and interactions present in a video and generate a description that relies on temporal localization in order to ground the visual concepts. However, most existing automatic video captioning systems map from raw video data to high level textual description, bypassing localization and recognition, thus discarding potentially valuable information for content localization and generalization. In this work we present an automatic video captioning model that combines spatio-temporal attention and image classification by means of deep neural network structures based on long short-term memory. The resulting system is demonstrated to produce state-of-the-art results in the standard YouTube captioning benchmark while also offering the advantage of localizing the visual concepts (subjects, verbs, objects), with no grounding supervision, over space and time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.04997v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9633\", \"5837\", \"46309\", \"5843\", \"104610\", \"110637\", \"146748\", \"183087\", \"158601\", \"205750\", \"206058\", \"250211\", \"269640\"], \"outgoing_citations\": [\"302621\", \"302909\", \"303593\", \"304481\", \"311742\", \"312453\", \"313189\", \"316803\", \"320332\", \"321453\"]}","task_split":"paper_retrieval"}
{"document_id":"282570","document_content":"# To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs). While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and correctly labelled training data. If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step? To address this question, we evaluate a number of popular facial landmarking and pose correction algorithms to understand their effect on facial recognition performance. Additionally, we introduce a new, automatic, single-image frontalization scheme that exceeds the performance of current algorithms. CNNs trained using sets of different pre-processing methods are used to extract features from the Point and Shoot Challenge (PaSC) and CMU Multi-PIE datasets. We assert that the subsequent verification and recognition performance serves to quantify the effectiveness of each pose correction scheme.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.04823v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"65496\", \"91208\", \"135421\", \"209731\", \"211561\", \"275470\"], \"outgoing_citations\": [\"278071\", \"290682\", \"296001\", \"296061\", \"302157\", \"303402\", \"303481\", \"307292\", \"317441\", \"318091\", \"321117\", \"356560\"]}","task_split":"paper_retrieval"}
{"document_id":"282712","document_content":"# Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nThis paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.04120v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"42736\", \"170044\", \"123278\", \"173239\", \"176426\", \"191455\", \"219359\", \"228036\"], \"outgoing_citations\": [\"310667\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"282900","document_content":"# Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation\n## Categories\n- Robotics\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nModern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a navigational guide model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then \"translate\" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% when compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants that demonstrate that our method generates instructions that people follow as accurately and easily as those produced by humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.03164v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.CL\", \"cs.RO\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Computation and Language\", \"Robotics\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"7377\", \"48063\", \"14861\", \"17795\", \"42402\", \"74771\", \"77756\", \"140187\", \"195860\", \"182870\", \"184657\", \"247393\", \"235942\", \"239749\", \"250567\"], \"outgoing_citations\": [\"307195\", \"311272\"]}","task_split":"paper_retrieval"}
{"document_id":"282995","document_content":"# Emergence of linguistic laws in human voice\n## Categories\n- Physics and Society\n- Computation and Language\n## Abstract\nLinguistic laws constitute one of the quantitative cornerstones of modern cognitive sciences and have been routinely investigated in written corpora, or in the equivalent transcription of oral corpora. This means that inferences of statistical patterns of language in acoustics are biased by the arbitrary, language-dependent segmentation of the signal, and virtually precludes the possibility of making comparative studies between human voice and other animal communication systems. Here we bridge this gap by proposing a method that allows to measure such patterns in acoustic signals of arbitrary origin, without needs to have access to the language corpus underneath. The method has been applied to six different human languages, recovering successfully some well-known laws of human communication at timescales even below the phoneme and finding yet another link between complexity and criticality in a biological system. These methods further pave the way for new comparative studies in animal communication or the analysis of signals of unknown code.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.02736v1\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"cs.CL\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Computation and Language\"], \"incoming_citations\": [\"57245\", \"187234\", \"193486\"], \"outgoing_citations\": [\"196119\", \"317675\", \"320574\", \"326364\", \"328234\", \"347066\", \"359755\", \"361881\"]}","task_split":"paper_retrieval"}
{"document_id":"283076","document_content":"# Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nNeural sequence models are widely used to model time-series data. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top-B candidates - resulting in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose Diverse Beam Search (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing for a diversity-augmented objective. We observe that our method finds better top-1 solutions by controlling for the exploration and exploitation of the search space - implying that DBS is a better search algorithm. Moreover, these gains are achieved with minimal computational or memory over- head as compared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation and visual question generation using both standard quantitative metrics and qualitative human studies. Further, we study the role of diversity for image-grounded language generation tasks as the complexity of the image changes. We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.02424v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3028\", \"15964\", \"16293\", \"17400\", \"42093\", \"46280\", \"50552\", \"56086\", \"67244\", \"94351\", \"113677\", \"13799\", \"16630\", \"17327\", \"25140\", \"29197\", \"30599\", \"31808\", \"44007\", \"44997\", \"45054\", \"45293\", \"50478\", \"52679\", \"55960\", \"61394\", \"65268\", \"66247\", \"168075\", \"78504\", \"83599\", \"86315\", \"86922\", \"105667\", \"129680\", \"131285\", \"153393\", \"167796\", \"175031\", \"207215\", \"79035\", \"132273\", \"82572\", \"84437\", \"89861\", \"90065\", \"93839\", \"94752\", \"95282\", \"95286\", \"95594\", \"100574\", \"101699\", \"105599\", \"108688\", \"109847\", \"110233\", \"112729\", \"113473\", \"114461\", \"123532\", \"123670\", \"125415\", \"127829\", \"129573\", \"139825\", \"142985\", \"145829\", \"145838\", \"157308\", \"158786\", \"171679\", \"152767\", \"154038\", \"44423\", \"159960\", \"174277\", \"164270\", \"160521\", \"165308\", \"180638\", \"180695\", \"182982\", \"190945\", \"190949\", \"192166\", \"192811\", \"193573\", \"193661\", \"197057\", \"198014\", \"198185\", \"200268\", \"197016\", \"206314\", \"209504\", \"212046\", \"211664\", \"248119\", \"211939\", \"214294\", \"213954\", \"216280\", \"218973\", \"219198\", \"220696\", \"225791\", \"68965\", \"228859\", \"229961\", \"231133\", \"231949\", \"232274\", \"235236\", \"236477\", \"236907\", \"237271\", \"238125\", \"240486\", \"243272\", \"243764\", \"243904\", \"246808\", \"256194\", \"249972\", \"251348\", \"250206\", \"265551\", \"269030\", \"269122\", \"270154\", \"272653\", \"275595\", \"276436\", \"278174\", \"278199\", \"279914\", \"279543\"], \"outgoing_citations\": [\"265680\", \"290337\", \"296279\", \"300652\", \"322167\"]}","task_split":"paper_retrieval"}
{"document_id":"283115","document_content":"# Weakly supervised learning of actions from transcripts\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present an approach for weakly supervised learning of human actions from video transcriptions. Our system is based on the idea that, given a sequence of input data and a transcript, i.e. a list of the order the actions occur in the video, it is possible to infer the actions within the video stream, and thus, learn the related action models without the need for any frame-based annotation. Starting from the transcript information at hand, we split the given data sequences uniformly based on the number of expected actions. We then learn action models for each class by maximizing the probability that the training video sequences are generated by the action models given the sequence order as defined by the transcripts. The learned model can be used to temporally segment an unseen video with or without transcript. We evaluate our approach on four distinct activity datasets, namely Hollywood Extended, MPII Cooking, Breakfast and CRIM13. We show that our system is able to align the scripted actions with the video data and that the learned models localize and classify actions competitively in comparison to models trained with full supervision, i.e. with frame level annotations, and that they outperform any current state-of-the-art approach for aligning transcripts with video data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.02237v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19821\", \"3517\", \"46854\", \"192035\", \"15831\", \"19925\", \"29561\", \"30657\", \"39008\", \"44055\", \"52435\", \"58191\", \"58193\", \"61811\", \"63687\", \"77136\", \"85803\", \"92195\", \"112252\", \"193069\", \"124017\", \"126373\", \"127506\", \"133920\", \"55847\", \"139778\", \"144125\", \"182786\", \"164400\", \"182790\", \"182794\", \"191393\", \"191573\", \"194508\", \"203523\", \"162966\", \"223200\", \"225372\", \"231686\", \"237402\", \"254641\", \"264711\", \"270325\", \"270713\", \"271891\"], \"outgoing_citations\": [\"287541\", \"302753\", \"307001\", \"309902\", \"315012\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"283173","document_content":"# A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n- 68Txx\n- I.2.4\n## Abstract\nOne essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug. The mining is even more challenging due to the lack of labeled dataset sources and external knowledge, as well as multiple token representations for a single drug name that is more common in the real application setting. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked Denoising Encoders). The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term Memory). In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.01891v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\", \"cs.NE\", \"68Txx\", \"I.2.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"68Txx\", \"I.2.4\"], \"incoming_citations\": [\"6508\"], \"outgoing_citations\": [\"361420\"]}","task_split":"paper_retrieval"}
{"document_id":"283299","document_content":"# Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy\n## Categories\n- Robotics\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.01238v3\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"23360\", \"31989\", \"59126\", \"80383\", \"120418\", \"12943\", \"17807\", \"34016\", \"37621\", \"85146\", \"92812\", \"152375\", \"86463\", \"114257\", \"125778\", \"132001\", \"141942\", \"144121\", \"144182\", \"146096\", \"153792\", \"166698\", \"181133\", \"154012\", \"112652\", \"137504\", \"190427\", \"200930\", \"214018\", \"225867\", \"227881\", \"246970\", \"254494\", \"262370\", \"307097\", \"271148\", \"272376\"], \"outgoing_citations\": [\"303632\", \"312185\", \"317774\"]}","task_split":"paper_retrieval"}
{"document_id":"283391","document_content":"# Deep Visual Foresight for Planning Robot Motion\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nA key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1610.00696v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"34882\", \"62549\", \"65465\", \"71698\", \"237562\", \"7646\", \"11364\", \"11995\", \"13611\", \"20668\", \"20946\", \"22458\", \"23602\", \"30074\", \"34544\", \"46115\", \"47774\", \"60584\", \"67280\", \"68295\", \"80534\", \"131327\", \"952\", \"6081\", \"13246\", \"15055\", \"15365\", \"19780\", \"21756\", \"21975\", \"22978\", \"26076\", \"32935\", \"33029\", \"36569\", \"36974\", \"37291\", \"38148\", \"38666\", \"39374\", \"39704\", \"41381\", \"45498\", \"41270\", \"48758\", \"50249\", \"50487\", \"50498\", \"51243\", \"52954\", \"55486\", \"55721\", \"57818\", \"59341\", \"59977\", \"60420\", \"60555\", \"61513\", \"62577\", \"64704\", \"64770\", \"64984\", \"66036\", \"68900\", \"69303\", \"71556\", \"71397\", \"75992\", \"76641\", \"77736\", \"79175\", \"80750\", \"85068\", \"85146\", \"86412\", \"86509\", \"88072\", \"92122\", \"107635\", \"116577\", \"116293\", \"123568\", \"135815\", \"135951\", \"137035\", \"140979\", \"163235\", \"177272\", \"204512\", \"244182\", \"176979\", \"52065\", \"81864\", \"82573\", \"86275\", \"86598\", \"87198\", \"87318\", \"88325\", \"90880\", \"72949\", \"92011\", \"93419\", \"196578\", \"96979\", \"100059\", \"101153\", \"107583\", \"107896\", \"108319\", \"109667\", \"111868\", \"112129\", \"98736\", \"113157\", \"114309\", \"114961\", \"116044\", \"116576\", \"118123\", \"119929\", \"120189\", \"121027\", \"121025\", \"122699\", \"125165\", \"125685\", \"127099\", \"121911\", \"38123\", \"86743\", \"128386\", \"128799\", \"129794\", \"132719\", \"135887\", \"135913\", \"136868\", \"136920\", \"137263\", \"137626\", \"137786\", \"139633\", \"140607\", \"140833\", \"140949\", \"140954\", \"142769\", \"143609\", \"145052\", \"146902\", \"149198\", \"157471\", \"159096\", \"159664\", \"159918\", \"160619\", \"163091\", \"164394\", \"127802\", \"165320\", \"165513\", \"166282\", \"167147\", \"174114\", \"178006\", \"178094\", \"180340\", \"186563\", \"190367\", \"190955\", \"196563\", \"196921\", \"224848\", \"38653\", \"152041\", \"154954\", \"155441\", \"156707\", \"159110\", \"159320\", \"159949\", \"160120\", \"162902\", \"163888\", \"152047\", \"167688\", \"167904\", \"164433\", \"169856\", \"173034\", \"174871\", \"179216\", \"179954\", \"180452\", \"181081\", \"181647\", \"183158\", \"185607\", \"186557\", \"186850\", \"189208\", \"191065\", \"192827\", \"193043\", \"194417\", \"194477\", \"195463\", \"195687\", \"196148\", \"196387\", \"198915\", \"203127\", \"203250\", \"204145\", \"206363\", \"210517\", \"213161\", \"214631\", \"217706\", \"220103\", \"220390\", \"220531\", \"223212\", \"226824\", \"231360\", \"237162\", \"237229\", \"207601\", \"212016\", \"213049\", \"203355\", \"213699\", \"215168\", \"215736\", \"210522\", \"216221\", \"216438\", \"220761\", \"222083\", \"221364\", \"223371\", \"223487\", \"224293\", \"224840\", \"227078\", \"228299\", \"227175\", \"229683\", \"230557\", \"231587\", \"233924\", \"234280\", \"235942\", \"236568\", \"236752\", \"236847\", \"237605\", \"238287\", \"238328\", \"238333\", \"239845\", \"239935\", \"240602\", \"241436\", \"240495\", \"233926\", \"242891\", \"243591\", \"245255\", \"249613\", \"251921\", \"252510\", \"254680\", \"258608\", \"259045\", \"115175\", \"204330\", \"249790\", \"253291\", \"254273\", \"254341\", \"256513\", \"256976\", \"258174\", \"260235\", \"260770\", \"260780\", \"261493\", \"261786\", \"265456\", \"267589\", \"270800\", \"271717\", \"272498\", \"277021\", \"278194\", \"277504\", \"281468\", \"284833\"], \"outgoing_citations\": [\"288746\", \"289605\", \"289719\", \"290911\", \"292009\", \"294601\", \"306156\", \"306266\", \"310624\"]}","task_split":"paper_retrieval"}
{"document_id":"283598","document_content":"# Social Computing for Mobile Big Data in Wireless Networks\n## Categories\n- Social and Information Networks\n- Machine Learning\n- Machine Learning\n## Abstract\nMobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. In this paper, we categorize and analyze the big data collected from real wireless cellular networks. Then, we study the social characteristics of mobile big data and highlight several research directions for mobile big data in the social computing areas.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/MC.2016.267\", \"primary_category\": \"cs.SI\", \"categories\": [\"stat.ML\", \"cs.LG\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"283901","document_content":"# Learning to Translate for Multilingual Question Answering\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn multilingual question answering, either the question needs to be translated into the document language, or vice versa. In addition to direction, there are multiple methods to perform the translation, four of which we explore in this paper: word-based, 10-best, context-based, and grammar-based. We build a feature for each combination of translation direction and method, and train a model that learns optimal feature weights. On a large forum dataset consisting of posts in English, Arabic, and Chinese, our novel learn-to-translate approach was more effective than a strong baseline (p<0.05): translating all text into English, then training a classifier based only on English (original or translated) text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.08210v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"33414\", \"121561\", \"151701\", \"171719\", \"218485\", \"254150\", \"263220\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"284114","document_content":"# Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems\n## Categories\n- Computation and Language\n## Abstract\nWe propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was constructed from the word problem. To accomplish this, we develop an algorithm for checking the equivalence between two derivations, and show how derivation an- notations can be semi-automatically added to existing datasets. To make our experiments more comprehensive, we include the derivation annotation for DRAW-1K, a new dataset containing 1000 general algebra word problems. In our experiments, we found that the annotated derivations enable a more accurate evaluation of automatic solvers than previously used metrics. We release derivation annotations for over 2300 algebra word problems for future evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.07197v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"26187\", \"96833\", \"80713\", \"94434\", \"129004\", \"220655\", \"183403\", \"192616\", \"211865\", \"231217\"], \"outgoing_citations\": [\"287181\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"284134","document_content":"# Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer\n## Categories\n- Machine Learning\n- Robotics\n## Abstract\nReinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations. Transfer learning can mitigate this problem by enabling us to transfer information from one skill to another and even from one robot to another. We show that neural network policies can be decomposed into \"task-specific\" and \"robot-specific\" modules, where the task-specific modules are shared across robots, and the robot-specific modules are shared across all tasks on that robot. This allows for sharing task information, such as perception, between robots and sharing robot information, such as dynamics and kinematics, between tasks. We exploit this decomposition to train mix-and-match modules that can solve new robot-task combinations that were not seen during training. Using a novel neural network architecture, we demonstrate the effectiveness of our transfer method for enabling zero-shot generalization with a variety of robots and tasks in simulation for both visual and non-visual tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.07088v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"14\", \"7646\", \"16608\", \"19003\", \"22458\", \"34544\", \"34545\", \"44437\", \"78473\", \"117672\", \"2261\", \"9075\", \"9814\", \"22439\", \"26085\", \"38914\", \"39672\", \"91688\", \"49451\", \"53844\", \"64984\", \"69249\", \"69948\", \"70865\", \"73515\", \"84405\", \"120350\", \"218296\", \"77336\", \"79679\", \"87588\", \"100245\", \"101301\", \"111344\", \"113866\", \"117327\", \"125378\", \"126363\", \"123759\", \"134149\", \"134338\", \"138724\", \"139874\", \"146533\", \"162505\", \"164419\", \"164719\", \"164930\", \"165176\", \"166762\", \"180204\", \"185463\", \"174708\", \"151617\", \"157202\", \"158907\", \"159949\", \"160156\", \"166326\", \"171778\", \"179602\", \"183187\", \"190450\", \"249412\", \"192847\", \"193057\", \"194729\", \"196713\", \"197436\", \"205488\", \"212717\", \"217069\", \"224873\", \"225224\", \"226824\", \"231050\", \"191970\", \"208860\", \"214230\", \"215537\", \"200927\", \"231633\", \"234287\", \"237051\", \"237119\", \"238333\", \"239335\", \"239450\", \"242292\", \"249870\", \"251797\", \"254096\", \"256051\", \"261617\", \"249398\", \"249790\", \"260087\", \"261190\", \"261786\", \"261983\", \"263306\", \"263463\", \"263650\", \"265464\", \"271975\", \"281106\", \"269251\", \"278984\", \"280870\", \"281468\", \"282205\"], \"outgoing_citations\": [\"291568\", \"302029\"]}","task_split":"paper_retrieval"}
{"document_id":"284187","document_content":"# Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nHigh-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary.   We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https:\/\/sites.google.com\/view\/deepmaca.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.06838v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"284201","document_content":"# Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning\n## Categories\n- Computation and Language\n## Abstract\nRecently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. One approach is the attention-based encoder-decoder framework that learns a mapping between variable-length input and output sequences in one step using a purely data-driven method. The attention model has often been shown to improve the performance over another end-to-end approach, the Connectionist Temporal Classification (CTC), mainly because it explicitly uses the history of the target character without any conditional independence assumptions. However, we observed that the performance of the attention has shown poor results in noisy condition and is hard to learn in the initial training stage with long input sequences. This is because the attention model is too flexible to predict proper alignments in such cases due to the lack of left-to-right constraints as used in CTC. This paper presents a novel method for end-to-end speech recognition to improve robustness and achieve fast convergence by using a joint CTC-attention model within the multi-task learning framework, thereby mitigating the alignment issue. An experiment on the WSJ and CHiME-4 tasks demonstrates its advantages over both the CTC and attention-based encoder-decoder baselines, showing 5.4-14.6% relative improvements in Character Error Rate (CER).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.06773v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"66419\", \"3399\", \"11959\", \"12258\", \"17966\", \"18285\", \"20283\", \"48910\", \"52814\", \"59368\", \"68214\", \"69085\", \"1147\", \"3717\", \"8202\", \"8562\", \"9376\", \"10315\", \"13416\", \"15739\", \"17751\", \"21791\", \"23136\", \"24648\", \"88348\", \"27783\", \"28255\", \"28261\", \"31803\", \"32477\", \"34042\", \"35176\", \"37881\", \"38470\", \"38524\", \"39097\", \"41759\", \"41794\", \"42626\", \"42707\", \"43136\", \"43051\", \"49805\", \"51911\", \"52178\", \"52588\", \"54855\", \"55835\", \"55881\", \"57076\", \"57537\", \"57835\", \"57846\", \"58416\", \"59107\", \"59397\", \"60580\", \"63477\", \"67039\", \"67526\", \"69240\", \"69401\", \"70578\", \"71938\", \"71926\", \"75038\", \"78777\", \"79047\", \"82075\", \"83100\", \"83815\", \"85193\", \"85553\", \"88346\", \"89202\", \"90104\", \"90106\", \"90834\", \"90949\", \"91107\", \"91138\", \"91392\", \"92036\", \"92681\", \"93837\", \"99252\", \"102095\", \"112973\", \"119059\", \"123732\", \"123766\", \"123965\", \"124154\", \"131947\", \"152859\", \"81486\", \"81702\", \"87404\", \"89403\", \"80535\", \"74626\", \"90711\", \"91120\", \"92466\", \"69230\", \"94956\", \"189229\", \"106331\", \"106675\", \"108617\", \"111029\", \"112747\", \"114312\", \"109780\", \"117729\", \"117722\", \"118586\", \"121034\", \"121083\", \"121408\", \"122457\", \"123744\", \"123724\", \"124180\", \"124457\", \"124385\", \"124484\", \"124745\", \"124641\", \"125873\", \"129570\", \"132488\", \"134542\", \"134532\", \"136281\", \"141055\", \"141969\", \"142877\", \"143122\", \"144173\", \"149329\", \"150060\", \"150626\", \"150770\", \"152478\", \"154476\", \"155009\", \"158938\", \"159352\", \"160137\", \"170312\", \"176965\", \"180293\", \"184247\", \"148838\", \"149354\", \"157202\", \"158040\", \"159831\", \"160402\", \"161022\", \"161477\", \"161692\", \"159047\", \"164757\", \"89150\", \"164451\", \"200072\", \"166863\", \"169714\", \"172493\", \"174558\", \"178701\", \"179517\", \"182947\", \"185363\", \"185877\", \"188717\", \"189807\", \"190056\", \"190242\", \"191320\", \"192389\", \"193547\", \"194746\", \"195383\", \"201913\", \"202047\", \"204302\", \"208995\", \"209901\", \"210397\", \"210625\", \"210813\", \"211159\", \"211349\", \"211380\", \"211835\", \"211849\", \"212239\", \"215676\", \"219801\", \"165575\", \"208295\", \"210400\", \"210510\", \"210900\", \"211383\", \"212246\", \"212602\", \"221967\", \"222092\", \"223730\", \"223784\", \"224137\", \"224386\", \"224760\", \"227564\", \"230043\", \"232007\", \"232580\", \"232706\", \"234172\", \"237441\", \"237515\", \"238928\", \"240191\", \"241674\", \"251229\", \"251232\", \"252035\", \"260505\", \"250653\", \"255148\", \"256711\", \"258542\", \"234309\", \"264220\", \"265720\", \"269624\", \"271544\", \"272753\", \"273509\", \"297349\"], \"outgoing_citations\": [\"307902\", \"308796\", \"320874\"]}","task_split":"paper_retrieval"}
{"document_id":"284208","document_content":"# How should we evaluate supervised hashing?\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nHashing produces compact representations for documents, to perform tasks like classification or retrieval based on these short codes. When hashing is supervised, the codes are trained using labels on the training data. This paper first shows that the evaluation protocols used in the literature for supervised hashing are not satisfactory: we show that a trivial solution that encodes the output of a classifier significantly outperforms existing supervised or semi-supervised methods, while using much shorter codes. We then propose two alternative protocols for supervised hashing: one based on retrieval on a disjoint set of classes, and another based on transfer learning to new classes. We provide two baseline methods for image-related tasks to assess the performance of (semi-)supervised hashing: without coding and with unsupervised codes. These baselines give a lower- and upper-bound on the performance of a supervised hashing scheme.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.06753v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155021\", \"138613\", \"148035\", \"144914\", \"5346\", \"28066\", \"38256\", \"38777\", \"109376\", \"125848\", \"131592\", \"225824\", \"140055\", \"155366\", \"186821\", \"239660\", \"243039\", \"150534\", \"172238\", \"182055\", \"183361\", \"184396\", \"189084\", \"196540\", \"199414\", \"202820\", \"228766\", \"232040\", \"241439\", \"249538\", \"252948\", \"287562\", \"265613\", \"278480\", \"258937\", \"271841\", \"278226\"], \"outgoing_citations\": [\"200534\", \"288169\", \"288218\", \"303372\", \"316473\", \"318535\", \"331445\", \"331485\"]}","task_split":"paper_retrieval"}
{"document_id":"284263","document_content":"# One Sentence One Model for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nNeural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.06490v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"46280\", \"56086\", \"46950\", \"59303\", \"93421\", \"95078\", \"277667\", \"131513\", \"148355\", \"160143\", \"164809\", \"170203\", \"210107\", \"211064\", \"217917\", \"219825\", \"229654\", \"233206\", \"236449\", \"240808\", \"240519\"], \"outgoing_citations\": [\"290402\", \"290766\", \"299936\", \"300096\", \"300604\", \"301840\", \"320814\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"284357","document_content":"# Enhanced LSTM for Natural Language Inference\n## Categories\n- Computation and Language\n## Abstract\nReasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result---it further improves the performance even when added to the already very strong model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/P17-1152\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"10543\", \"31126\", \"96167\", \"103740\", \"61905\", \"2671\", \"2947\", \"13226\", \"16108\", \"20888\", \"20995\", \"25514\", \"55862\", \"62492\", \"66512\", \"78437\", \"83039\", \"160740\", \"1631\", \"3480\", \"4341\", \"6131\", \"9135\", \"10935\", \"11742\", \"12008\", \"14057\", \"19139\", \"19793\", \"21092\", \"21573\", \"22406\", \"22549\", \"22596\", \"23095\", \"23780\", \"23915\", \"24663\", \"24923\", \"25894\", \"25987\", \"27160\", \"30207\", \"101113\", \"35820\", \"41371\", \"41541\", \"42919\", \"43329\", \"43502\", \"43943\", \"45199\", \"45354\", \"45490\", \"45407\", \"46486\", \"51577\", \"54802\", \"46806\", \"55247\", \"55370\", \"55811\", \"57077\", \"60198\", \"60207\", \"62785\", \"63195\", \"67034\", \"67730\", \"67722\", \"71484\", \"74057\", \"75186\", \"75752\", \"76238\", \"77302\", \"77342\", \"77760\", \"77894\", \"80172\", \"80638\", \"81522\", \"81532\", \"86359\", \"94572\", \"99146\", \"119059\", \"127466\", \"127502\", \"127746\", \"158023\", \"128114\", \"56976\", \"39323\", \"120764\", \"127819\", \"77619\", \"80135\", \"88174\", \"88336\", \"88412\", \"92275\", \"92511\", \"92597\", \"92805\", \"94030\", \"94026\", \"94063\", \"94065\", \"94054\", \"94133\", \"95013\", \"94970\", \"95192\", \"95817\", \"95809\", \"95639\", \"95841\", \"96086\", \"96129\", \"96499\", \"96864\", \"97669\", \"98518\", \"99110\", \"99497\", \"100437\", \"100533\", \"104637\", \"108688\", \"115465\", \"115623\", \"115939\", \"117167\", \"118413\", \"119381\", \"119518\", \"123448\", \"124171\", \"125926\", \"126346\", \"126870\", \"125776\", \"130818\", \"131151\", \"132061\", \"132002\", \"132176\", \"132366\", \"133083\", \"128406\", \"128024\", \"134513\", \"136310\", \"136978\", \"137730\", \"138342\", \"138862\", \"138479\", \"142346\", \"143050\", \"143679\", \"148999\", \"149018\", \"151647\", \"151803\", \"152317\", \"156438\", \"159864\", \"160459\", \"163052\", \"164142\", \"166494\", \"166973\", \"167906\", \"168571\", \"171420\", \"171585\", \"171616\", \"171936\", \"171990\", \"174139\", \"194126\", \"202296\", \"99870\", \"155237\", \"157261\", \"157629\", \"161848\", \"159863\", \"165663\", \"165935\", \"166141\", \"166223\", \"168281\", \"168247\", \"165627\", \"167028\", \"167930\", \"169008\", \"169330\", \"169362\", \"169784\", \"170064\", \"170083\", \"171039\", \"170946\", \"171777\", \"171894\", \"172343\", \"173239\", \"169415\", \"174318\", \"175093\", \"176478\", \"176848\", \"176966\", \"173474\", \"155859\", \"178092\", \"176822\", \"179414\", \"180652\", \"181441\", \"182618\", \"182640\", \"180536\", \"180097\", \"185541\", \"184854\", \"184848\", \"185429\", \"185629\", \"188708\", \"188738\", \"189235\", \"189672\", \"191923\", \"192195\", \"192271\", \"192402\", \"192756\", \"192782\", \"193560\", \"195157\", \"195288\", \"196210\", \"197036\", \"197347\", \"197572\", \"200362\", \"197674\", \"200579\", \"201405\", \"202353\", \"203186\", \"203275\", \"203519\", \"203557\", \"204274\", \"204401\", \"206112\", \"208526\", \"209764\", \"209765\", \"211259\", \"211869\", \"211890\", \"212916\", \"213284\", \"213566\", \"220223\", \"221086\", \"230131\", \"251892\", \"165844\", \"211788\", \"203633\", \"207203\", \"207412\", \"207566\", \"208889\", \"210207\", \"208471\", \"212057\", \"212931\", \"203631\", \"215208\", \"218274\", \"218693\", \"219367\", \"219352\", \"219933\", \"219902\", \"219977\", \"220159\", \"203638\", \"220285\", \"220797\", \"221212\", \"218698\", \"221609\", \"168542\", \"174908\", \"226758\", \"226854\", \"227426\", \"227488\", \"227981\", \"228407\", \"229493\", \"226956\", \"230234\", \"232482\", \"233045\", \"234538\", \"234679\", \"234716\", \"234456\", \"239738\", \"242069\", \"242126\", \"241469\", \"242461\", \"243004\", \"246273\", \"249504\", \"250441\", \"250722\", \"251893\", \"254332\", \"256098\", \"259009\", \"265431\", \"268604\", \"271683\", \"240157\", \"257223\", \"259120\", \"259393\", \"260008\", \"260223\", \"261587\", \"272883\", \"274120\", \"274817\", \"281119\", \"281847\"], \"outgoing_citations\": [\"271583\", \"280456\", \"288364\", \"288415\", \"291575\", \"296289\", \"299541\", \"300803\", \"300867\", \"302943\", \"306182\", \"316298\"]}","task_split":"paper_retrieval"}
{"document_id":"284481","document_content":"# SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAs a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.05473v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"8099\", \"28060\", \"36981\", \"50437\", \"63787\", \"75234\", \"82640\", \"103854\", \"146468\", \"3028\", \"3382\", \"6369\", \"6697\", \"16013\", \"16718\", \"18197\", \"28272\", \"30683\", \"36081\", \"43280\", \"46539\", \"114076\", \"48532\", \"50472\", \"50669\", \"56667\", \"63381\", \"64005\", \"120045\", \"131165\", \"152195\", \"159645\", \"182259\", \"1315\", \"1327\", \"3289\", \"6417\", \"8460\", \"10390\", \"10262\", \"11397\", \"14286\", \"15127\", \"15372\", \"15614\", \"17582\", \"20468\", \"21191\", \"21237\", \"21404\", \"22231\", \"22716\", \"23762\", \"24581\", \"26251\", \"26417\", \"27285\", \"27220\", \"27317\", \"29197\", \"29079\", \"30471\", \"30826\", \"31590\", \"32981\", \"33764\", \"35580\", \"38687\", \"38714\", \"38841\", \"41949\", \"42488\", \"42583\", \"42619\", \"42802\", \"44204\", \"45297\", \"45963\", \"50637\", \"50827\", \"51789\", \"53198\", \"56445\", \"56832\", \"57559\", \"57617\", \"57919\", \"58010\", \"58597\", \"58792\", \"60640\", \"61648\", \"62059\", \"63484\", \"64463\", \"168075\", \"67539\", \"67582\", \"70142\", \"70826\", \"72111\", \"73662\", \"73991\", \"75537\", \"76150\", \"76748\", \"77135\", \"77389\", \"77851\", \"79732\", \"80148\", \"80747\", \"80873\", \"81412\", \"81845\", \"86200\", \"89417\", \"91982\", \"98832\", \"99999\", \"100711\", \"104778\", \"111881\", \"116209\", \"120814\", \"120964\", \"121127\", \"126432\", \"127232\", \"130991\", \"131145\", \"132645\", \"132791\", \"132683\", \"132977\", \"138423\", \"139401\", \"149064\", \"150238\", \"160220\", \"163095\", \"11690\", \"158973\", \"165015\", \"170376\", \"171667\", \"184591\", \"187506\", \"191789\", \"197366\", \"246560\", \"34976\", \"77716\", \"78047\", \"78501\", \"80713\", \"80923\", \"81571\", \"81761\", \"82763\", \"83785\", \"84129\", \"84410\", \"84799\", \"87187\", \"87795\", \"88487\", \"90175\", \"90412\", \"90795\", \"91421\", \"91841\", \"93065\", \"93153\", \"93811\", \"94976\", \"94789\", \"94978\", \"95588\", \"95901\", \"96009\", \"96435\", \"96877\", \"97273\", \"97590\", \"97743\", \"98696\", \"99759\", \"99937\", \"100503\", \"102506\", \"103070\", \"103483\", \"103709\", \"104007\", \"104482\", \"104915\", \"105294\", \"106559\", \"103617\", \"107683\", \"108688\", \"108975\", \"109874\", \"110896\", \"110821\", \"113240\", \"114461\", \"115081\", \"117173\", \"119254\", \"120280\", \"120303\", \"120600\", \"120601\", \"112623\", \"121171\", \"121600\", \"122373\", \"122733\", \"123347\", \"123672\", \"124375\", \"124614\", \"125756\", \"126401\", \"126337\", \"126682\", \"126818\", \"127236\", \"127048\", \"127112\", \"128023\", \"130874\", \"132536\", \"132799\", \"133013\", \"132990\", \"128917\", \"134175\", \"135296\", \"136216\", \"137197\", \"137942\", \"138084\", \"138162\", \"138406\", \"138393\", \"138350\", \"138547\", \"138581\", \"138930\", \"140644\", \"140666\", \"142633\", \"143508\", \"143498\", \"143811\", \"143060\", \"139674\", \"142043\", \"144806\", \"145603\", \"145838\", \"146462\", \"146981\", \"147766\", \"148729\", \"148970\", \"149063\", \"149821\", \"152126\", \"154714\", \"155361\"], \"outgoing_citations\": [\"287823\", \"295156\", \"303250\", \"308381\", \"311378\", \"317966\", \"344331\"]}","task_split":"paper_retrieval"}
{"document_id":"284561","document_content":"# No-Regret Replanning under Uncertainty\n## Categories\n- Robotics\n- Machine Learning\n## Abstract\nThis paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modeled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information for better planning later and also achieves the task as quick as possible. We propose UCB style algorithms that are popular in the bandit settings and show how those analyses can be adapted to the online robotic path planning problems. The proposed algorithm trades-off exploration and exploitation in near-optimal manner and has appealing no-regret properties. We demonstrate the efficacy of the framework on the application of aircraft flight path planning when the winds are partially observed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.05162v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.LG\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Machine Learning\"], \"incoming_citations\": [\"79929\", \"164736\", \"11916\", \"208769\"], \"outgoing_citations\": [\"321392\", \"356537\"]}","task_split":"paper_retrieval"}
{"document_id":"284713","document_content":"# Bayesian Reinforcement Learning: A Survey\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nBayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration\/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1561\/2200000049\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"170695\", \"4074\", \"9804\", \"21373\", \"29746\", \"73192\", \"80915\", \"109259\", \"144230\", \"2253\", \"3583\", \"4271\", \"12751\", \"21340\", \"22789\", \"23098\", \"27991\", \"33207\", \"35156\", \"35657\", \"36901\", \"37762\", \"38662\", \"43441\", \"44791\", \"46695\", \"46780\", \"53818\", \"56104\", \"64078\", \"64836\", \"66458\", \"66813\", \"68016\", \"74229\", \"76066\", \"76115\", \"81860\", \"87412\", \"97074\", \"97096\", \"120514\", \"121163\", \"139582\", \"141780\", \"142989\", \"143524\", \"165242\", \"188032\", \"223398\", \"96718\", \"100132\", \"101003\", \"107505\", \"92991\", \"105910\", \"47007\", \"116584\", \"117367\", \"120163\", \"137299\", \"138045\", \"139400\", \"139821\", \"140891\", \"141682\", \"143490\", \"148651\", \"149907\", \"161071\", \"199850\", \"205105\", \"210747\", \"159327\", \"160789\", \"162749\", \"163903\", \"166991\", \"167373\", \"171112\", \"176782\", \"177276\", \"177282\", \"179655\", \"181429\", \"182212\", \"183193\", \"185531\", \"191839\", \"192074\", \"195903\", \"200573\", \"202761\", \"203534\", \"215786\", \"218661\", \"247075\", \"247892\", \"255154\", \"270851\", \"207983\", \"208763\", \"215165\", \"209957\", \"219430\", \"212458\", \"226220\", \"229683\", \"230656\", \"239105\", \"240362\", \"239380\", \"235276\", \"242413\", \"246290\", \"256399\", \"261844\", \"259927\", \"265880\", \"280457\", \"283289\", \"287702\", \"280870\", \"291460\"], \"outgoing_citations\": [\"306633\", \"311408\", \"327768\", \"332227\", \"335161\", \"335323\", \"344075\", \"347530\", \"349880\", \"350403\", \"354489\", \"354490\", \"357569\", \"357731\", \"357896\", \"358661\", \"358783\", \"358968\", \"360762\", \"361788\"]}","task_split":"paper_retrieval"}
{"document_id":"284716","document_content":"# 3D Face Reconstruction by Learning from Synthetic Data\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them properly requires a large dataset of annotated examples. In the case of three-dimensional faces, currently, there are no large volume data sets, while acquiring such big-data is a tedious task. As an alternative, we propose to generate random, yet nearly photo-realistic, facial images for which the geometric form is known. The suggested model successfully recovers facial shapes from real images, even for faces with extreme expressions and under various lighting conditions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.04387v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"42280\", \"76110\", \"17613\", \"49287\", \"57572\", \"431\", \"3767\", \"12190\", \"16773\", \"19959\", \"25578\", \"29040\", \"32365\", \"33225\", \"42098\", \"53885\", \"57586\", \"59791\", \"221215\", \"75463\", \"53101\", \"76752\", \"82106\", \"87603\", \"95988\", \"96763\", \"102099\", \"102774\", \"180606\", \"194156\", \"219961\", \"235160\", \"79243\", \"93727\", \"104989\", \"109928\", \"118390\", \"125661\", \"134076\", \"135163\", \"135637\", \"137181\", \"136024\", \"140504\", \"145468\", \"159733\", \"168874\", \"192217\", \"151397\", \"163559\", \"169420\", \"171500\", \"182589\", \"174524\", \"191310\", \"191463\", \"193656\", \"194402\", \"194387\", \"197211\", \"198793\", \"202404\", \"205494\", \"205998\", \"257958\", \"259476\", \"218032\", \"216762\", \"220416\", \"224621\", \"227144\", \"227856\", \"230031\", \"235754\", \"236050\", \"236073\", \"237090\", \"238254\", \"247495\", \"248005\", \"248918\", \"250290\", \"250292\", \"270042\", \"250754\", \"257713\", \"257846\", \"263714\", \"268703\", \"270134\", \"270235\", \"277948\", \"280394\"], \"outgoing_citations\": [\"302701\", \"306261\", \"309106\"]}","task_split":"paper_retrieval"}
{"document_id":"284747","document_content":"# Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning\n## Categories\n- Machine Learning\n## Abstract\nHigher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of four experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1037\/rev0000061\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"72387\", \"8319\", \"138924\", \"121248\", \"130231\", \"147262\", \"154335\", \"190184\", \"201860\"], \"outgoing_citations\": [\"351399\"]}","task_split":"paper_retrieval"}
{"document_id":"284853","document_content":"# An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nText simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.03663v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"23347\", \"67907\", \"105099\", \"116150\", \"213790\", \"214753\", \"250620\", \"277666\"], \"outgoing_citations\": [\"302970\"]}","task_split":"paper_retrieval"}
{"document_id":"284865","document_content":"# Joint Extraction of Events and Entities within a Document Context\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nEvents and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.03632v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"134\", \"16358\", \"19755\", \"21108\", \"27030\", \"34067\", \"37458\", \"40033\", \"7146\", \"24252\", \"28061\", \"41477\", \"46065\", \"52468\", \"62628\", \"91671\", \"94628\", \"96163\", \"104847\", \"52215\", \"91325\", \"100385\", \"125248\", \"128398\", \"133025\", \"136010\", \"152920\", \"168008\", \"170438\", \"180410\", \"191957\", \"192327\", \"190349\", \"207770\", \"93297\", \"216701\", \"235470\", \"197767\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"285107","document_content":"# DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification\n## Categories\n- Machine Learning\n## Abstract\nExtreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-art approaches for extreme multi-label classification attempt to capture correlation among labels by embedding the label matrix to a low-dimensional linear sub-space. However, in the presence of power-law distributed extremely large and diverse label spaces, structural assumptions such as low rank can be easily violated.   In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size. Unlike most state-of-the-art methods, DiSMEC does not make any low rank assumptions on the label matrix. Using double layer of parallelization, DiSMEC can learn classifiers for datasets consisting hundreds of thousands labels within few hours. The explicit capacity control mechanism filters out spurious parameters which keep the model compact in size, without losing prediction accuracy. We conduct extensive empirical evaluation on publicly available real-world datasets consisting upto 670,000 labels. We compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which is a leading approach for learning sparse local embeddings, and FastXML which is a tree-based approach optimizing ranking based loss function. On some of the datasets, DiSMEC can significantly boost prediction accuracies - 10% better compared to SLECC and 15% better compared to FastXML, in absolute terms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.02521v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3081\", \"81678\", \"85362\", \"94831\", \"19544\", \"19989\", \"20760\", \"21578\", \"23828\", \"25645\", \"27873\", \"14810\", \"39883\", \"39934\", \"45613\", \"32229\", \"9917\", \"32179\", \"69363\", \"76288\", \"77282\", \"81604\", \"98296\", \"113290\", \"179985\", \"50057\", \"81342\", \"98939\", \"114054\", \"114987\", \"116238\", \"122424\", \"129525\", \"131489\", \"119143\", \"133786\", \"140683\", \"147067\", \"181069\", \"187501\", \"184689\", \"190089\", \"150742\", \"159293\", \"161081\", \"174836\", \"175433\", \"184031\", \"190932\", \"193965\", \"211954\", \"214047\", \"226678\", \"228822\", \"208659\", \"211637\", \"212737\", \"214818\", \"239584\", \"240025\", \"242536\", \"269062\", \"255619\", \"255765\"], \"outgoing_citations\": [\"315250\", \"326956\", \"342373\"]}","task_split":"paper_retrieval"}
{"document_id":"285138","document_content":"# Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMelanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11548-017-1567-8\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"223595\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"285139","document_content":"# Ear-to-ear Capture of Facial Intrinsics\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present a practical approach to capturing ear-to-ear face models comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular albedo). Our approach is a hybrid of geometric and photometric methods and requires no geometric calibration. Photometric measurements made in a lightstage are used to estimate view dependent high resolution normal maps. We overcome the problem of having a single photometric viewpoint by capturing in multiple poses. We use uncalibrated multiview stereo to estimate a coarse base mesh to which the photometric views are registered. We propose a novel approach to robustly stitching surface normal and intrinsic texture data into a seamless, complete and highly detailed face model. The resulting relightable models provide photorealistic renderings in any view.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.02368v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"168874\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"285557","document_content":"# Reward Augmented Maximum Likelihood for Neural Structured Prediction\n## Categories\n- Machine Learning\n## Abstract\nA key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework. By establishing a link between the log-likelihood and expected reward objectives, we show that an optimal regularized expected reward is achieved when the conditional distribution of the outputs given the inputs is proportional to their exponentiated scaled rewards. Accordingly, we present a framework to smooth the predictive probability of the outputs using their corresponding rewards. We optimize the conditional log-probability of augmented outputs that are sampled proportionally to their exponentiated scaled rewards. Experiments on neural sequence to sequence models for speech recognition and machine translation show notable improvements over a maximum likelihood baseline by using reward augmented maximum likelihood (RAML), where the rewards are defined as the negative edit distance between the outputs and the ground truth labels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1609.00150v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"2603\", \"29108\", \"42171\", \"97465\", \"25034\", \"34863\", \"38562\", \"41949\", \"44376\", \"45054\", \"45967\", \"46443\", \"46724\", \"52583\", \"56761\", \"62239\", \"62234\", \"66974\", \"69926\", \"69954\", \"73991\", \"74463\", \"99660\", \"120964\", \"151443\", \"59162\", \"94192\", \"94789\", \"95845\", \"96495\", \"99332\", \"107946\", \"112623\", \"128609\", \"133702\", \"128053\", \"137935\", \"142267\", \"142825\", \"149063\", \"78439\", \"171886\", \"183412\", \"183942\", \"230774\", \"150606\", \"157804\", \"159333\", \"160310\", \"163994\", \"163364\", \"165584\", \"167962\", \"171014\", \"175028\", \"181153\", \"177296\", \"188236\", \"192328\", \"192827\", \"196775\", \"198373\", \"199671\", \"200429\", \"204972\", \"215676\", \"208908\", \"211763\", \"217666\", \"218088\", \"220477\", \"220565\", \"220611\", \"225551\", \"228307\", \"232192\", \"233811\", \"234742\", \"247045\", \"261881\", \"262672\", \"263764\", \"266671\", \"267031\", \"254759\", \"262458\", \"266002\", \"266490\", \"267246\", \"267980\", \"269108\", \"271635\", \"272328\", \"272785\", \"273062\", \"275596\", \"279002\", \"280866\", \"281159\", \"279333\", \"280655\", \"283908\"], \"outgoing_citations\": [\"287823\", \"290766\", \"296283\", \"301840\", \"302893\", \"303553\", \"312406\", \"320874\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"285977","document_content":"# Learning Points and Routes to Recommend Trajectories\n## Categories\n- Machine Learning\n- Information Retrieval\n## Abstract\nThe problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F$_1$ score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2983323.2983672\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"25078\", \"26129\", \"41095\", \"21404\", \"40113\", \"65503\", \"175383\", \"220472\", \"240140\", \"258375\", \"261949\", \"268790\"], \"outgoing_citations\": [\"331338\"]}","task_split":"paper_retrieval"}
{"document_id":"286028","document_content":"# Robust Named Entity Recognition in Idiosyncratic Domains\n## Categories\n- Computation and Language\n## Abstract\nNamed entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domain-specific language, such as news documents (e.g. Reuters) or biomedical text (e.g. Medline). Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks. Our model is trained with only few hundred labeled sentences and does not rely on further external knowledge. We report from our results F1 scores in the range of 84-94% on standard datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.06757v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"31981\", \"260390\", \"270018\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"286363","document_content":"# Refining Geometry from Depth Sensors using IR Shading Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight (ToF) technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.05204v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26199\", \"78435\", \"216437\", \"269940\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"286385","document_content":"# Full Resolution Image Compression with Recurrent Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study \"one-shot\" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.05148v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"32952\", \"47239\", \"125962\", \"3535\", \"3584\", \"4502\", \"4434\", \"7260\", \"8961\", \"9247\", \"16579\", \"25262\", \"28362\", \"39792\", \"40317\", \"49272\", \"52279\", \"79886\", \"85979\", \"97572\", \"97830\", \"105755\", \"116171\", \"123186\", \"143178\", \"898\", \"2018\", \"11722\", \"12436\", \"3681\", \"20061\", \"20073\", \"19978\", \"22411\", \"28322\", \"28395\", \"28556\", \"30766\", \"32956\", \"36887\", \"40998\", \"44576\", \"44585\", \"25712\", \"48406\", \"54488\", \"54885\", \"56443\", \"56971\", \"59267\", \"59412\", \"59824\", \"59979\", \"60017\", \"65270\", \"65334\", \"77691\", \"83013\", \"143298\", \"93339\", \"95031\", \"99964\", \"113228\", \"113821\", \"116426\", \"126787\", \"127959\", \"134573\", \"138585\", \"151708\", \"160882\", \"158506\", \"173475\", \"192903\", \"81184\", \"27863\", \"86844\", \"96707\", \"98307\", \"100681\", \"100949\", \"103060\", \"104348\", \"104873\", \"106518\", \"111470\", \"112004\", \"117730\", \"117925\", \"73831\", \"125050\", \"132713\", \"135025\", \"135412\", \"136318\", \"140558\", \"141379\", \"141734\", \"141894\", \"141895\", \"143347\", \"145230\", \"146623\", \"147245\", \"147712\", \"147901\", \"132172\", \"151353\", \"151563\", \"151604\", \"155387\", \"160928\", \"162788\", \"163027\", \"172387\", \"176021\", \"179246\", \"180641\", \"191822\", \"193985\", \"225233\", \"149165\", \"149183\", \"166254\", \"167493\", \"170337\", \"171806\", \"172007\", \"172350\", \"174214\", \"177794\", \"79389\", \"181309\", \"182691\", \"184339\", \"184711\", \"185272\", \"188660\", \"189501\", \"190339\", \"191688\", \"192107\", \"264586\", \"192950\", \"201958\", \"201968\", \"202159\", \"204814\", \"205024\", \"206509\", \"207812\", \"207875\", \"209536\", \"215245\", \"225593\", \"228729\", \"236298\", \"238842\", \"247042\", \"247298\", \"201747\", \"206908\", \"214139\", \"216677\", \"218722\", \"222579\", \"227137\", \"229261\", \"247714\", \"229841\", \"230133\", \"230221\", \"230266\", \"231681\", \"231842\", \"230216\", \"234240\", \"235085\", \"235796\", \"241634\", \"238760\", \"243512\", \"243653\", \"243971\", \"245087\", \"245227\", \"247493\", \"252185\", \"252472\", \"256272\", \"270142\", \"270241\", \"273390\", \"255687\", \"242995\", \"254860\", \"266118\", \"266304\", \"267121\", \"259513\", \"269868\", \"272675\", \"275726\", \"277207\", \"281135\", \"281618\", \"282914\"], \"outgoing_citations\": [\"288248\", \"293518\", \"293570\", \"298621\", \"303010\"]}","task_split":"paper_retrieval"}
{"document_id":"286392","document_content":"# SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification\n## Categories\n- Computation and Language\n## Abstract\nSentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.05129v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"31906\", \"205900\", \"236711\", \"255928\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"286594","document_content":"# Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks\n## Categories\n- Computation and Language\n## Abstract\nThere is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We propose a framework that facilitates better understanding of the encoded representations. We define prediction tasks around isolated aspects of sentence structure (namely sentence length, word content, and word order), and score representations by the ability to train a classifier to solve each prediction task when using the representation as input. We demonstrate the potential contribution of the approach by analyzing different sentence representation mechanisms. The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.04207v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"27690\", \"30305\", \"3345\", \"15940\", \"16353\", \"19990\", \"26824\", \"55849\", \"66794\", \"67128\", \"94453\", \"2981\", \"9935\", \"11203\", \"14944\", \"17826\", \"19536\", \"21496\", \"22428\", \"23812\", \"24452\", \"24562\", \"24848\", \"24964\", \"25228\", \"27588\", \"42396\", \"43485\", \"135681\", \"45962\", \"46212\", \"49051\", \"49530\", \"50076\", \"50044\", \"50439\", \"52669\", \"53442\", \"54271\", \"54572\", \"55494\", \"56568\", \"57520\", \"57573\", \"59769\", \"61915\", \"65799\", \"66215\", \"66349\", \"67764\", \"73062\", \"73232\", \"77820\", \"81532\", \"121728\", \"87953\", \"98383\", \"122635\", \"123965\", \"127502\", \"127562\", \"128048\", \"129558\", \"175565\", \"67678\", \"78131\", \"87454\", \"88284\", \"89632\", \"87179\", \"86291\", \"89829\", \"90067\", \"91128\", \"92103\", \"94200\", \"95106\", \"95436\", \"95690\", \"95816\", \"96142\", \"96273\", \"96238\", \"96316\", \"96658\", \"97403\", \"97490\", \"97563\", \"100467\", \"101455\", \"115797\", \"116876\", \"118253\", \"120468\", \"122609\", \"124868\", \"125586\", \"126795\", \"127278\", \"127056\", \"125835\", \"127922\", \"127844\", \"128059\", \"128960\", \"128999\", \"130625\", \"131070\", \"128087\", \"137785\", \"143271\", \"149018\", \"153289\", \"158554\", \"159720\", \"160751\", \"173615\", \"194126\", \"153933\", \"159925\", \"165301\", \"166307\", \"164327\", \"167584\", \"168097\", \"168298\", \"168415\", \"169266\", \"169337\", \"169340\", \"140986\", \"174318\", \"176876\", \"178043\", \"180304\", \"180302\", \"181468\", \"181749\", \"182390\", \"182546\", \"182935\", \"183017\", \"180658\", \"181125\", \"179924\", \"186130\", \"186418\", \"190887\", \"191046\", \"192036\", \"192641\", \"188933\", \"194617\", \"194731\", \"194904\", \"195157\", \"194308\", \"197253\", \"198618\", \"200362\", \"201070\", \"201401\", \"203285\", \"205056\", \"205161\", \"206664\", \"210158\", \"211702\", \"218589\", \"229768\", \"209174\", \"214875\", \"216455\", \"217465\", \"218131\", \"218282\", \"218881\", \"219419\", \"220276\", \"220797\", \"220950\", \"218291\", \"219965\", \"220460\", \"230051\", \"230664\", \"233400\", \"232355\", \"232457\", \"234743\", \"237283\", \"240090\", \"238963\", \"237157\", \"232616\", \"239686\", \"232691\", \"249109\", \"249243\", \"256935\", \"274696\", \"251412\", \"244357\", \"254432\", \"256062\", \"263945\", \"264555\", \"265895\", \"266180\", \"268619\", \"269127\", \"269801\", \"270040\", \"274549\", \"279224\", \"283971\", \"297423\", \"280427\"], \"outgoing_citations\": [\"293610\", \"297423\", \"298543\", \"302578\", \"303997\", \"311683\", \"311849\"]}","task_split":"paper_retrieval"}
{"document_id":"286869","document_content":"# Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nCurrent approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be possible, in principle, to learn compatible vector representations just by analyzing the monolingual distribution of words.   In order to evaluate this hypothesis, we propose a scheme to map word vectors trained on a source language to vectors semantically compatible with word vectors trained on a target language using an adversarial autoencoder.   We present preliminary qualitative results and discuss possible future developments of this technique, such as applications to cross-lingual sentence representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.02996v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"34494\", \"56253\", \"161470\", \"93714\", \"86841\", \"84547\", \"103258\", \"111128\", \"128326\", \"145018\", \"162387\", \"170843\", \"204279\", \"168976\", \"168735\", \"183811\", \"185967\", \"189639\", \"192245\", \"200307\", \"211635\", \"220218\", \"263680\", \"208559\", \"218392\", \"219577\", \"228868\", \"231863\", \"232631\", \"247071\", \"251974\", \"264750\"], \"outgoing_citations\": [\"298884\", \"303913\", \"317771\", \"323451\", \"334040\", \"336196\"]}","task_split":"paper_retrieval"}
{"document_id":"286978","document_content":"# Learning Joint Representations of Videos and Sentences with Web Image Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nOur objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.02367v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"60281\", \"101354\", \"24732\", \"31466\", \"46080\", \"62318\", \"83648\", \"107454\", \"130722\", \"105151\", \"110926\", \"131888\", \"155521\", \"187454\", \"227207\", \"172394\", \"173627\", \"182192\", \"180066\", \"180702\", \"221984\", \"228427\", \"256883\", \"259318\", \"267523\", \"269042\", \"293986\"], \"outgoing_citations\": [\"307389\", \"313397\", \"316803\", \"320332\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"287028","document_content":"# Adapting Deep Network Features to Capture Psychological Representations\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nDeep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often reaching or surpassing human-level accuracy. This success is due in part to the ability of DNNs to learn useful representations of high-dimensional inputs, a problem that humans must also solve. We examine the relationship between the representations learned by these networks and human psychological representations recovered from similarity judgments. We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images. However, these features do not capture some qualitative distinctions that are a key part of human representations. To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.02164v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"9175\", \"130091\", \"170319\", \"85435\", \"127461\", \"187462\", \"193646\", \"218251\", \"231441\", \"231438\", \"240053\", \"264295\", \"250604\", \"266305\", \"266909\", \"270022\", \"276471\", \"295435\"], \"outgoing_citations\": [\"326874\"]}","task_split":"paper_retrieval"}
{"document_id":"287134","document_content":"# A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a recursive framework to recognize facial expressions from images in real scenes. Unlike traditional approaches that typically focus on developing and refining algorithms for improving recognition performance on an existing dataset, we integrate three important components in a recursive manner: facial dataset generation, facial expression recognition model building, and interactive interfaces for testing and new data collection. To start with, we first create a candid-images-for-facial-expression (CIFE) dataset. We then apply a convolutional neural network (CNN) to CIFE and build a CNN model for web image expression classification. In order to increase the expression recognition accuracy, we also fine-tune the CNN model and thus obtain a better CNN facial expression recognition model. Based on the fine-tuned CNN model, we design a facial expression game engine and collect a new and more balanced dataset, GaMo. The images of this dataset are collected from the different expressions our game users make when playing the game. Finally, we evaluate the GaMo and CIFE datasets and show that our recursive framework can help build a better facial expression model for dealing with real scene facial expression tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.01647v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"288727\"]}","task_split":"paper_retrieval"}
{"document_id":"287181","document_content":"# Solving General Arithmetic Word Problems\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of {\\em quantity schemas} that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.01413v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"39958\", \"48678\", \"72953\", \"96833\", \"13400\", \"24666\", \"25239\", \"25273\", \"101113\", \"37796\", \"45526\", \"56186\", \"61074\", \"60990\", \"175554\", \"92714\", \"98424\", \"38612\", \"124449\", \"132063\", \"149351\", \"151631\", \"220655\", \"153178\", \"161625\", \"161669\", \"169358\", \"169320\", \"203186\", \"211865\", \"210184\", \"218313\", \"221849\", \"231217\", \"230273\", \"233912\", \"238788\", \"246516\", \"266702\", \"278863\", \"284114\", \"292141\", \"282328\", \"283758\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"287475","document_content":"# World Knowledge as Indirect Supervision for Document Clustering\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nOne of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1608.00104v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"157200\", \"153263\", \"187923\", \"189804\", \"267001\"], \"outgoing_citations\": [\"288699\", \"335299\", \"336263\", \"339118\", \"340201\", \"352955\", \"356583\"]}","task_split":"paper_retrieval"}
{"document_id":"287504","document_content":"# SPICE: Semantic Propositional Image Caption Evaluation\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThere is considerable interest in the task of automatically generating image captions. However, evaluation is challenging. Existing automatic evaluation metrics are primarily sensitive to n-gram overlap, which is neither necessary nor sufficient for the task of simulating human judgment. We hypothesize that semantic propositional content is an important component of human caption evaluation, and propose a new automated caption evaluation metric defined over scene graphs coined SPICE. Extensive evaluations across a range of models and datasets indicate that SPICE captures human judgments over model-generated captions better than other automatic metrics (e.g., system-level correlation of 0.88 with human judgments on the MS COCO dataset, versus 0.43 for CIDEr and 0.53 for METEOR). Furthermore, SPICE can answer questions such as `which caption-generator best understands colors?' and `can caption-generators count?'","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.08822v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"2123\", \"23109\", \"35427\", \"38226\", \"103740\", \"1156\", \"2579\", \"2790\", \"3461\", \"3595\", \"4080\", \"4498\", \"5083\", \"5842\", \"6697\", \"7377\", \"7692\", \"8053\", \"8982\", \"11273\", \"14401\", \"15880\", \"15861\", \"16718\", \"17660\", \"18648\", \"19075\", \"19710\", \"22827\", \"24165\", \"24945\", \"29778\", \"30354\", \"32570\", \"35623\", \"38766\", \"39309\", \"39368\", \"39414\", \"55418\", \"60281\", \"64841\", \"67993\", \"74810\", \"79138\", \"84343\", \"92767\", \"96985\", \"97719\", \"104145\", \"113677\", \"148583\", \"159132\", \"182259\", \"93187\", \"3925\", \"5837\", \"6500\", \"7939\", \"8573\", \"8679\", \"12569\", \"14111\", \"14525\", \"14851\", \"15266\", \"15902\", \"16751\", \"16991\", \"17327\", \"17378\", \"17795\", \"19258\", \"20379\", \"20540\", \"22802\", \"22804\", \"23457\", \"23625\", \"24023\", \"23922\", \"24290\", \"24386\", \"25081\", \"25362\", \"26454\", \"26526\", \"27110\", \"27160\", \"27522\", \"27871\", \"29379\", \"29595\", \"31202\", \"31502\", \"31878\", \"32961\", \"32981\", \"33289\", \"34188\", \"34961\", \"35401\", \"38068\", \"39327\", \"40685\", \"40652\", \"40810\", \"41801\", \"43997\", \"44372\", \"44781\", \"45268\", \"45277\", \"47214\", \"47726\", \"48826\", \"49108\", \"49388\", \"49933\", \"50852\", \"51417\", \"53028\", \"53793\", \"53992\", \"54502\", \"55207\", \"58201\", \"58980\", \"59720\", \"60572\", \"61394\", \"61851\", \"61915\", \"63484\", \"65077\", \"66217\", \"67563\", \"68626\", \"69434\", \"71449\", \"72906\", \"73451\", \"73707\", \"73799\", \"74508\", \"74954\", \"75147\", \"77378\", \"77697\", \"77995\", \"78606\", \"80837\", \"81451\", \"81622\", \"81855\", \"82221\", \"89845\", \"91656\", \"92505\", \"98408\", \"104974\", \"106415\", \"110600\", \"114991\", \"115124\", \"119349\", \"120951\", \"130188\", \"136492\", \"137828\", \"142435\", \"142464\", \"168082\", \"173243\", \"175031\", \"31178\", \"53205\", \"79450\", \"80061\", \"80728\", \"80904\", \"81724\", \"83601\", \"83901\", \"83854\", \"78599\", \"86053\", \"88355\", \"89629\", \"88096\", \"90597\", \"5843\", \"95617\", \"95885\", \"96329\", \"97743\", \"98561\", \"98852\", \"101699\", \"98475\", \"105379\", \"107106\", \"107473\", \"108688\", \"110219\", \"103084\", \"154779\", \"111062\", \"112250\", \"113188\", \"113955\", \"115608\", \"115717\", \"115939\", \"117131\", \"118272\", \"110207\", \"122297\", \"124686\", \"124665\", \"127707\", \"127968\", \"131507\", \"132879\", \"128175\", \"133695\", \"133970\", \"134604\", \"135154\", \"135255\", \"135934\", \"137915\", \"138132\", \"138218\", \"139263\", \"139684\", \"139825\", \"141196\", \"141978\", \"142162\", \"157244\", \"147001\", \"147698\", \"70437\", \"150669\", \"151123\", \"152418\", \"152646\", \"154318\", \"154933\", \"162227\", \"166562\", \"167323\", \"168443\", \"172586\", \"183087\", \"184636\", \"189542\", \"204751\", \"205147\", \"230774\", \"150464\", \"151345\", \"152816\", \"157229\", \"158332\", \"158839\", \"162378\", \"162661\", \"174277\", \"163104\", \"163994\", \"165588\", \"165591\", \"165896\", \"166757\", \"166989\", \"167642\", \"167650\", \"167838\", \"168543\", \"168553\", \"168616\", \"168596\", \"168673\", \"168193\", \"169417\", \"169599\", \"169774\", \"170299\", \"170606\", \"171096\", \"171225\", \"168405\", \"171902\", \"78600\", \"172581\", \"172668\", \"172811\", \"112826\", \"139529\", \"173655\", \"174060\", \"176591\", \"173572\", \"175035\", \"153420\", \"180838\", \"180695\", \"181226\", \"182657\", \"182877\", \"184473\"], \"outgoing_citations\": [\"300099\", \"303627\", \"311285\", \"311462\", \"312704\", \"313196\", \"313934\", \"320003\"]}","task_split":"paper_retrieval"}
{"document_id":"287584","document_content":"# Local Feature Detectors, Descriptors, and Image Representations: A Survey\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWith the advances in both stable interest region detectors and robust and distinctive descriptors, local feature-based image or object retrieval has become a popular research topic. %All of the local feature-based image retrieval system involves two important processes: local feature extraction and image representation. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we review local features and image representations for image retrieval. Because many and many methods are proposed in this area, these methods are grouped into several classes and summarized. In addition, recent deep learning-based approaches for image retrieval are briefly reviewed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.08368v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"29611\"], \"outgoing_citations\": [\"283881\", \"295615\", \"300357\", \"302867\", \"303423\", \"304501\", \"308236\", \"314231\", \"320036\", \"320127\", \"321680\", \"322263\", \"331445\", \"332726\", \"333779\"]}","task_split":"paper_retrieval"}
{"document_id":"287676","document_content":"# Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nDue to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to com- pute meaningful semantic relatedness between entities and categories. Our framework can han- dle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.07956v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"82028\", \"97752\", \"77782\", \"89675\", \"90183\", \"131610\", \"145988\", \"147146\", \"148012\", \"149656\", \"153033\", \"155045\", \"205857\", \"150061\", \"150803\", \"151247\", \"151771\", \"169329\", \"228842\", \"246202\", \"246780\", \"262315\", \"267001\", \"267123\", \"279891\", \"297491\"], \"outgoing_citations\": [\"289339\", \"300498\", \"313946\", \"321628\", \"330797\"]}","task_split":"paper_retrieval"}
{"document_id":"287980","document_content":"# Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEvent cameras or neuromorphic cameras mimic the human perception system as they measure the per-pixel intensity change rather than the actual intensity level. In contrast to traditional cameras, such cameras capture new information about the scene at MHz frequency in the form of sparse events. The high temporal resolution comes at the cost of losing the familiar per-pixel intensity information. In this work we propose a variational model that accurately models the behaviour of event cameras, enabling reconstruction of intensity images with arbitrary frame rate in real-time. Our method is formulated on a per-event-basis, where we explicitly incorporate information about the asynchronous nature of events via an event manifold induced by the relative timestamps of events. In our experiments we verify that solving the variational model on the manifold produces high-quality images without explicitly estimating optical flow.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.06283v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"3629\", \"6051\", \"190036\", \"195521\", \"81097\", \"81085\", \"81435\", \"92948\", \"100061\", \"180645\", \"79965\", \"98108\", \"111652\", \"131521\", \"135873\", \"136384\", \"139039\", \"153050\", \"207147\", \"189162\", \"190078\", \"197597\", \"207669\", \"208731\", \"209333\", \"223380\", \"231930\", \"211978\", \"288554\", \"243051\", \"255512\", \"271427\", \"281870\"], \"outgoing_citations\": [\"281870\", \"305392\"]}","task_split":"paper_retrieval"}
{"document_id":"288027","document_content":"# Sequence to sequence learning for unconstrained scene text recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like \"g\" and \"9\", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.06125v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": [\"317966\", \"320148\", \"322955\", \"328595\", \"358782\", \"359628\"]}","task_split":"paper_retrieval"}
{"document_id":"288166","document_content":"# Discriminating between similar languages in Twitter using label propagation\n## Categories\n- Computation and Language\n- I.2.7\n## Abstract\nIdentifying the language of social media messages is an important first step in linguistic processing. Existing models for Twitter focus on content analysis, which is successful for dissimilar language pairs. We propose a label propagation approach that takes the social graph of tweet authors into account as well as content to better tease apart similar languages. This results in state-of-the-art shared task performance of $76.63\\%$, $1.4\\%$ higher than the top system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.05408v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.7\"], \"incoming_citations\": [\"234635\", \"237788\", \"286860\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"288243","document_content":"# Intelligent Biohybrid Neurotechnologies: Are They Really What They Claim?\n## Categories\n- Artificial Intelligence\n- A.1\n## Abstract\nIn the era of intelligent biohybrid neurotechnologies for brain repair, new fanciful terms are appearing in the scientific dictionary to define what has so far been unimaginable. As the emerging neurotechnologies are becoming increasingly polyhedral and sophisticated, should we talk about evolution and rank the intelligence of these devices?","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.05023v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"A.1\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"A.1\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"288254","document_content":"# Dependency Language Models for Transition-based Dependency Parsing\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English and Chinese data where the base parser could achieve competitive accuracy scores. Our enhanced parser achieved state-of-the-art accuracy on Chinese data and competitive results on English data. We gained a large absolute improvement of one point (UAS) on Chinese and 0.5 points for English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.04982v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"215471\"], \"outgoing_citations\": [\"261088\", \"281121\", \"296283\", \"310837\", \"312066\", \"336720\"]}","task_split":"paper_retrieval"}
{"document_id":"288569","document_content":"# Multi-modal image retrieval with random walk on multi-layer graphs\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nThe analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data. The similarity between images could be computed using different and possibly multimodal features such as color or edge information or even text labels. This motivates the design of image analysis solutions that are able to effectively integrate the multi-view information provided by different feature sets. We therefore propose a new image retrieval solution that is able to sort images through a random walk on a multi-layer graph, where each layer corresponds to a different type of information about the image data. We study in depth the design of the image graph and propose in particular an effective method to select the edge weights for the multi-layer graph, such that the image ranking scores are optimised. We then provide extensive experiments in different real-world photo collections, which confirm the high performance of our new image retrieval algorithm that generally surpasses state-of-the-art solutions due to a more meaningful image similarity computation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISM.2016.0011\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.MM\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Multimedia\"], \"incoming_citations\": [\"160335\"], \"outgoing_citations\": [\"354628\"]}","task_split":"paper_retrieval"}
{"document_id":"288968","document_content":"# An optimal learning method for developing personalized treatment regimes\n## Categories\n- Machine Learning\n## Abstract\nA treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information and the selected treatment. The goal is to find the treatments that lead to the best patient responses on average. Each experiment is expensive, forcing us to learn the most from each experiment. We adopt a Bayesian approach both to incorporate possible prior information and to update our treatment regime continuously as information accrues, with the potential to allow smaller yet more informative trials and for patients to receive better treatment. By formulating the problem as contextual bandits, we introduce a knowledge gradient policy to guide the treatment assignment by maximizing the expected value of information, for which an approximation method is used to overcome computational challenges. We provide a detailed study on how to make sequential medical decisions under uncertainty to reduce health care costs on a real world knee replacement dataset. We use clustering and LASSO to deal with the intrinsic sparsity in health datasets. We show experimentally that even though the problem is sparse, through careful selection of physicians (versus picking them at random), we can significantly improve the success rates.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.01462v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"77053\", \"210057\", \"240952\"], \"outgoing_citations\": [\"316249\", \"350403\", \"354490\"]}","task_split":"paper_retrieval"}
{"document_id":"289106","document_content":"# Coarse2Fine: Two-Layer Fusion For Image Retrieval\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nThis paper addresses the problem of large-scale image retrieval. We propose a two-layer fusion method which takes advantage of global and local cues and ranks database images from coarse to fine (C2F). Departing from the previous methods fusing multiple image descriptors simultaneously, C2F is featured by a layered procedure composed by filtering and refining. In particular, C2F consists of three components. 1) Distractor filtering. With holistic representations, noise images are filtered out from the database, so the number of candidate images to be used for comparison with the query can be greatly reduced. 2) Adaptive weighting. For a certain query, the similarity of candidate images can be estimated by holistic similarity scores in complementary to the local ones. 3) Candidate refining. Accurate retrieval is conducted via local features, combining the pre-computed adaptive weights. Experiments are presented on two benchmarks, \\emph{i.e.,} Holidays and Ukbench datasets. We show that our method outperforms recent fusion methods in terms of storage consumption and computation complexity, and that the accuracy is competitive to the state-of-the-arts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.00719v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.CV\", \"cs.MM\", \"cs.IR\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\", \"Information Retrieval\"], \"incoming_citations\": [\"258475\"], \"outgoing_citations\": [\"328873\", \"332966\", \"333779\"]}","task_split":"paper_retrieval"}
{"document_id":"289144","document_content":"# Context-Dependent Word Representation for Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nWe first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En-Fr and En-De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.00578v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"7836\", \"204766\", \"55504\", \"78041\", \"77215\", \"128829\", \"141629\", \"154167\", \"165937\", \"173254\", \"174373\", \"176605\", \"182668\", \"198836\", \"210594\", \"227079\", \"215296\", \"234721\", \"237210\", \"248881\", \"249315\", \"258004\", \"259721\", \"256804\", \"264202\", \"268909\"], \"outgoing_citations\": [\"295815\", \"296251\", \"300548\", \"301840\", \"302570\", \"315078\", \"320814\", \"322537\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"289268","document_content":"# Review Based Rating Prediction\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nRecommendation systems are an important units in today's e-commerce applications, such as targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated generation of personalized recommendations according to the available contextual information of users.   Compared to the traditional systems which mainly utilize users' rating history, review-based recommendation hopefully provide more relevant results to users. We introduce a review-based recommendation approach that obtains contextual information by mining user reviews. The proposed approach relate to features obtained by analyzing textual reviews using methods developed in Natural Language Processing (NLP) and information retrieval discipline to compute a utility function over a given item. An item utility is a measure that shows how much it is preferred according to user's current context.   In our system, the context inference is modeled as similarity between the users reviews history and the item reviews history. As an example application, we used our method to mine contextual data from customers' reviews of movies and use it to produce review-based rating prediction. The predicted ratings can generate recommendations that are item-based and should appear at the recommended items list in the product page. Our evaluations suggest that our system can help produce better prediction rating scores in comparison to the standard prediction methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.00024v4\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"289389","document_content":"# Scene Text Detection via Holistic, Multi-Channel Prediction\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vast majority of the existing methods detect text within local regions, typically through extracting character, word or line level candidates followed by candidate aggregation and false positive elimination, which potentially exclude the effect of wide-scope and long-range contextual cues in the scene. To take full advantage of the rich information available in the whole natural image, we propose to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem. The proposed algorithm directly runs on full images and produces global, pixel-wise prediction maps, in which detections are subsequently formed. To better make use of the properties of text, three types of information regarding text region, individual characters and their relationship are estimated, with a single Fully Convolutional Network (FCN) model. With such predictions of text properties, the proposed algorithm can simultaneously handle horizontal, multi-oriented and curved text in real-world natural images. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015 and MSRA-TD500, demonstrate that the proposed algorithm substantially outperforms previous state-of-the-art approaches. Moreover, we report the first baseline result on the recently-released, large-scale dataset COCO-Text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.09002v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"186590\", \"180823\", \"35003\", \"50136\", \"54412\", \"62678\", \"84406\", \"98220\", \"105122\", \"106579\", \"120420\", \"122949\", \"122981\", \"128870\", \"129484\", \"139566\", \"140006\", \"155530\", \"156029\", \"174062\", \"209980\", \"210634\", \"171543\", \"176583\", \"177579\", \"182255\", \"192471\", \"194314\", \"201515\", \"207348\", \"208801\", \"209083\", \"209227\", \"209860\", \"218589\", \"234390\", \"216006\", \"218516\", \"223981\", \"225195\", \"225336\", \"225690\", \"225795\", \"231139\", \"232509\", \"233366\", \"239535\", \"240965\", \"245591\", \"245970\", \"272481\", \"248812\", \"252159\", \"257197\", \"257954\", \"269205\", \"269821\", \"271112\"], \"outgoing_citations\": [\"293979\", \"299461\", \"314541\", \"320148\", \"320837\", \"328595\", \"337927\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"289536","document_content":"# STransE: a novel embedding model of entities and relationships in knowledge bases\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nKnowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/N16-1054\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"10131\", \"98478\", \"11022\", \"14777\", \"55459\", \"59753\", \"73615\", \"144356\", \"92027\", \"95933\", \"100267\", \"104196\", \"114253\", \"126798\", \"128558\", \"131904\", \"155250\", \"114970\", \"171916\", \"177273\", \"184180\", \"184542\", \"270722\", \"184958\", \"152851\", \"157049\", \"164877\", \"169175\", \"167167\", \"169826\", \"193708\", \"195502\", \"201341\", \"188111\", \"210557\", \"197408\", \"220727\", \"221542\", \"204585\", \"218830\", \"225536\", \"230422\", \"230744\", \"242267\", \"244172\", \"248147\", \"261989\", \"258451\", \"265002\", \"267141\", \"268485\", \"278237\", \"280634\", \"280478\", \"289931\", \"298065\"], \"outgoing_citations\": [\"280288\", \"281711\", \"288979\", \"289931\", \"294402\", \"304901\", \"308219\", \"311834\", \"311858\", \"311970\", \"313944\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"289601","document_content":"# Corpus-level Fine-grained Entity Typing Using Contextual Information\n## Categories\n- Computation and Language\n## Abstract\nThis paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as \"food\" or \"artist\". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.07901v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2021\", \"2021\", \"4523\", \"16195\", \"21536\", \"117616\", \"132948\", \"147649\", \"205857\", \"164680\", \"169340\", \"177343\", \"181770\", \"187755\", \"190200\", \"190739\", \"93297\", \"213085\", \"224111\", \"224786\", \"228334\", \"228842\", \"230379\", \"232329\", \"234714\", \"259124\", \"276843\", \"268014\", \"270019\", \"276616\", \"277440\", \"283434\", \"281546\", \"289600\", \"300498\"], \"outgoing_citations\": [\"313946\", \"341939\", \"345467\"]}","task_split":"paper_retrieval"}
{"document_id":"289632","document_content":"# Issues in evaluating semantic spaces using word analogies\n## Categories\n- Computation and Language\n## Abstract\nThe offset method for solving word analogies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflates offset consistency with largely irrelevant neighborhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.07736v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"48324\", \"59401\", \"22000\", \"25230\", \"50322\", \"67430\", \"95965\", \"96217\", \"113988\", \"127785\", \"133462\", \"137785\", \"137974\", \"153748\", \"184943\", \"194126\", \"153545\", \"156490\", \"157684\", \"182626\", \"191332\", \"193478\", \"196180\", \"201292\", \"203658\", \"225003\", \"229885\", \"219136\", \"225253\", \"231554\", \"235923\", \"247071\", \"247025\", \"256931\", \"264335\", \"265631\", \"269546\", \"281186\"], \"outgoing_citations\": [\"340248\"]}","task_split":"paper_retrieval"}
{"document_id":"289637","document_content":"# A Game-Theoretic Approach to Word Sense Disambiguation\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Computer Science and Game Theory\n## Abstract\nThis paper presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The paper provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1162\/COLI_a_00274\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.GT\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Computer Science and Game Theory\"], \"incoming_citations\": [\"79448\", \"111897\", \"157067\", \"220054\", \"219222\", \"232527\", \"247876\", \"284668\", \"288788\"], \"outgoing_citations\": [\"308446\"]}","task_split":"paper_retrieval"}
{"document_id":"289709","document_content":"# The VGLC: The Video Game Level Corpus\n## Categories\n- Human-Computer Interaction\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nLevels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.07487v2\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"47005\", \"33213\", \"33220\", \"33214\", \"38564\", \"39691\", \"41462\", \"67124\", \"72454\", \"74578\", \"94739\", \"61740\", \"104584\", \"104574\", \"107909\", \"109632\", \"109729\", \"111464\", \"112572\", \"117983\", \"125408\", \"133342\", \"133812\", \"139814\", \"147140\", \"189201\", \"189837\", \"218411\", \"216608\", \"224233\", \"233501\", \"230010\", \"234345\", \"274886\", \"261387\", \"261528\", \"261529\"], \"outgoing_citations\": [\"297233\", \"297797\"]}","task_split":"paper_retrieval"}
{"document_id":"289816","document_content":"# Emulating Human Conversations using Convolutional Neural Network-based IR\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Information Retrieval\n- H.3.3; I.2.7\n## Abstract\nConversational agents (\"bots\") are beginning to be widely used in conversational interfaces. To design a system that is capable of emulating human-like interactions, a conversational layer that can serve as a fabric for chat-like interaction with the agent is needed. In this paper, we introduce a model that employs Information Retrieval by utilizing convolutional deep structured semantic neural network-based features in the ranker to present human-like responses in ongoing conversation with a user. In conversations, accounting for context is critical to the retrieval model; we show that our context-sensitive approach using a Convolutional Deep Structured Semantic Model (cDSSM) with character trigrams significantly outperforms several conventional baselines in terms of the relevance of responses retrieved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.07056v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.IR\", \"H.3.3; I.2.7\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\", \"H.3.3; I.2.7\"], \"incoming_citations\": [\"170156\", \"213413\", \"260591\", \"267727\"], \"outgoing_citations\": [\"302107\", \"310740\", \"320868\"]}","task_split":"paper_retrieval"}
{"document_id":"289831","document_content":"# Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n- Robotics\n## Abstract\nThis paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement learning robotic agent. While robotic human-machine interfaces have become increasingly complex in both form and function, control remains challenging for users. This has resulted in an increasing gap between user control approaches and the number of robotic motors which can be controlled. One way to address this gap is to shift some autonomy to the robot. Semi-autonomous actions of the robotic agent can then be shaped by human feedback, simplifying user control. Most prior work on agent shaping by humans has incorporated training with feedback, or has included indirect control signals. By contrast, in this paper we explore how a human can provide concurrent feedback signals and real-time myoelectric control signals to train a robot's actor-critic reinforcement learning control system. Using both a physical and a simulated robotic system, we compare training performance on a simple movement task when reward is derived from the environment, when reward is provided by the human, and combinations of these two approaches. Our results indicate that some benefit can be gained with the inclusion of human generated feedback.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.06979v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.AI\", \"cs.HC\", \"cs.RO\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Human-Computer Interaction\", \"Robotics\"], \"incoming_citations\": [\"30309\", \"123527\", \"103709\", \"200163\", \"186139\", \"200476\", \"250883\", \"272433\", \"276547\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290032","document_content":"# Mining Local Process Models\n## Categories\n- Databases\n- Machine Learning\n## Abstract\nIn this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as \\emph{local process models}. Local process model mining can be positioned in-between process discovery and episode \/ sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode \/ sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jides.2016.11.001\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"cs.LG\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"Machine Learning\"], \"incoming_citations\": [\"5446\", \"39591\", \"16713\", \"72880\", \"148652\", \"212108\", \"153591\", \"168838\", \"195728\", \"253526\", \"265401\", \"251489\", \"270967\", \"247682\", \"269118\", \"265404\", \"282960\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290180","document_content":"# Adding Context to Concept Trees\n## Categories\n- Artificial Intelligence\n## Abstract\nA Concept Tree is a structure for storing knowledge where the trees are stored in a database called a Concept Base. It sits between the highly distributed neural architectures and the distributed information systems, with the intention of bringing brain-like and computer systems closer together. Concept Trees can grow from the semi-structured sources when consistent sequences of concepts are presented. Each tree ideally represents a single cohesive concept and the trees can link with each other for navigation and semantic purposes. The trees are therefore also a type of semantic network and would benefit from having a consistent level of context for each node. A consistent build process is managed through a 'counting rule' and some other rules that can normalise the database structure. This restricted structure can then be complimented and enriched by the more dynamic context. It is also suggested to use the linking structure of the licas system [15] as a basis for the context links, where the mathematical model is extended further to define this. A number of tests have demonstrated the soundness of the architecture. Building the trees from text documents shows that the tree structure could be inherent in natural language. Then, two types of query language are described. Both of these can perform consistent query processes to return knowledge to the user and even enhance the query with new knowledge. This is supported even further with direct comparisons to a cognitive model, also being developed by the author.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.05597v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"61552\", \"279853\"], \"outgoing_citations\": [\"191735\", \"279853\", \"332473\"]}","task_split":"paper_retrieval"}
{"document_id":"290182","document_content":"# Introspective Agents: Confidence Measures for General Value Functions\n## Categories\n- Artificial Intelligence\n## Abstract\nAgents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashion. This position paper builds on the idea of encoding knowledge as temporally extended predictions through the use of general value functions. Prior work has focused on learning predictions about externally derived signals about a task or environment (e.g. battery level, joint position). Here we advocate that the agent should also predict internally generated signals regarding its own learning process - for example, an agent's confidence in its learned predictions. Finally, we suggest how such information would be beneficial in creating an introspective agent that is able to learn to make good decisions in a complex, changing world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.05593v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"16639\", \"171614\", \"237929\", \"250883\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290230","document_content":"# Deep Image Set Hashing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets. These methods are slow to compute and not compact to use in a large scale scenario. Learning-based hashing is often used in large scale image retrieval as they provide a compact representation of each sample and the Hamming distance can be used to efficiently compare two samples. However, most hashing methods encode each image separately and discard knowledge that multiple images in the same set represent the same object or person. We investigate the set hashing problem by combining both set representation and hashing in a single deep neural network. An image set is first passed to a CNN module to extract image features, then these features are aggregated using two types of set feature to capture both set specific and database-wide distribution information. The computed set feature is then fed into a multilayer perceptron to learn a compact binary embedding. Triplet loss is used to train the network by forming set similarity relations using class labels. We extensively evaluate our approach on datasets used for image matching and show highly competitive performance compared to state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.05381v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290243","document_content":"# Successor Features for Transfer in Reinforcement Learning\n## Categories\n- Artificial Intelligence\n## Abstract\nTransfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: \"successor features\", a value function representation that decouples the dynamics of the environment from the rewards, and \"generalized policy improvement\", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.05312v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"23640\", \"53008\", \"218\", \"7388\", \"15486\", \"16608\", \"20487\", \"27411\", \"36644\", \"44495\", \"44916\", \"62518\", \"64409\", \"67332\", \"79803\", \"97832\", \"99608\", \"6628\", \"6744\", \"7252\", \"8634\", \"11406\", \"11825\", \"12745\", \"17153\", \"26551\", \"34787\", \"35918\", \"35958\", \"37807\", \"42563\", \"43996\", \"45058\", \"45285\", \"45631\", \"46516\", \"50187\", \"51080\", \"53903\", \"55721\", \"61195\", \"63102\", \"63163\", \"64566\", \"65615\", \"65435\", \"66864\", \"67095\", \"67153\", \"69249\", \"70370\", \"70741\", \"75808\", \"76687\", \"79812\", \"83169\", \"85115\", \"86305\", \"96180\", \"114310\", \"119760\", \"121288\", \"122847\", \"149549\", \"184991\", \"77902\", \"90884\", \"100245\", \"101382\", \"113227\", \"113866\", \"114800\", \"113549\", \"115832\", \"117210\", \"119692\", \"124909\", \"125378\", \"126363\", \"132686\", \"132719\", \"134247\", \"137062\", \"137645\", \"138724\", \"139834\", \"140833\", \"140983\", \"143204\", \"143420\", \"143504\", \"139874\", \"148433\", \"155692\", \"158625\", \"163351\", \"167687\", \"181163\", \"184260\", \"197465\", \"200791\", \"148522\", \"217040\", \"222837\", \"39730\", \"149096\", \"151963\", \"161870\", \"162568\", \"154419\", \"160289\", \"171332\", \"171778\", \"177381\", \"178186\", \"179483\", \"179594\", \"180158\", \"181789\", \"183234\", \"182108\", \"186669\", \"186339\", \"194738\", \"196539\", \"193925\", \"200000\", \"200753\", \"200941\", \"204013\", \"205488\", \"209651\", \"214196\", \"207100\", \"209469\", \"216060\", \"217092\", \"227313\", \"228561\", \"229059\", \"231418\", \"191970\", \"208372\", \"214018\", \"215093\", \"219397\", \"224929\", \"225320\", \"225748\", \"200927\", \"230923\", \"231364\", \"199779\", \"235227\", \"237929\", \"239160\", \"240506\", \"241185\", \"233926\", \"236060\", \"251957\", \"255032\", \"259693\", \"258963\", \"259708\", \"260136\", \"265722\", \"270792\", \"277797\", \"280296\", \"277504\", \"278427\", \"284470\"], \"outgoing_citations\": [\"277797\", \"290904\"]}","task_split":"paper_retrieval"}
{"document_id":"290304","document_content":"# No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering \"Simple\" Questions)\n## Categories\n- Computation and Language\n## Abstract\nFirst-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%-76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results --- even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast's X1 entertainment platform with millions of users every day.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.05029v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"188713\", \"95011\", \"123115\", \"144703\", \"192346\", \"206987\", \"214874\", \"234460\", \"248193\", \"267877\"], \"outgoing_citations\": [\"245908\", \"290690\", \"295325\", \"296068\", \"304701\", \"310639\", \"311684\", \"317213\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"290444","document_content":"# Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task. Being able to pick out representative words within a document can lead to substantial improvements in document clustering. In the case of web documents, the HTML markup that defines the layout of the content provides additional structural information that can be further exploited to identify representative words. In this paper we introduce a fuzzy term weighing approach that makes the most of the HTML structure for document clustering. We set forth and build on the hypothesis that a good representation can take advantage of how humans skim through documents to extract the most representative words. The authors of web pages make use of HTML tags to convey the most important message of a web page through page elements that attract the readers' attention, such as page titles or emphasized elements. We define a set of criteria to exploit the information provided by these page elements, and introduce a fuzzy combination of these criteria that we evaluate within the context of a web page clustering task. Our proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can adapt to datasets whose features are distributed differently, achieving good results compared to other similar fuzzy logic based approaches and TF-IDF across different datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.04429v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290504","document_content":"# Micro-interventions in urban transport from pattern discovery on the flow of passengers and on the bus network\n## Categories\n- Artificial Intelligence\n- Social and Information Networks\n## Abstract\nIn this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ISC2.2016.7580776\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.SI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Social and Information Networks\"], \"incoming_citations\": [\"9436\", \"150579\", \"267438\", \"267618\", \"268076\", \"269785\", \"270203\", \"281767\", \"290598\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290510","document_content":"# Zero-Resource Translation with Multi-Lingual Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nIn this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.04164v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"16217\", \"38855\", \"81858\", \"22374\", \"24303\", \"35515\", \"43377\", \"44225\", \"55869\", \"58974\", \"59110\", \"63988\", \"65641\", \"68902\", \"91649\", \"98946\", \"131248\", \"132595\", \"157509\", \"77651\", \"89339\", \"89348\", \"92861\", \"93229\", \"94612\", \"96371\", \"96469\", \"103430\", \"106711\", \"106707\", \"126586\", \"129165\", \"132812\", \"148542\", \"153039\", \"153733\", \"228971\", \"149588\", \"119925\", \"152767\", \"159855\", \"162397\", \"162838\", \"164879\", \"165748\", \"167598\", \"169587\", \"171399\", \"173024\", \"175775\", \"176571\", \"178555\", \"181277\", \"182716\", \"186464\", \"187452\", \"192335\", \"194844\", \"197007\", \"211950\", \"227804\", \"211710\", \"212018\", \"220351\", \"220330\", \"220017\", \"234632\", \"234807\", \"238941\", \"242857\", \"251816\", \"251974\", \"253508\", \"254850\", \"211601\", \"251555\", \"259471\", \"260373\", \"262139\", \"265947\", \"267524\", \"268957\", \"269960\", \"268370\", \"273586\", \"274450\", \"280486\", \"280498\", \"280884\", \"282931\", \"280421\"], \"outgoing_citations\": [\"291554\", \"295308\", \"296251\", \"300548\", \"300604\", \"303003\"]}","task_split":"paper_retrieval"}
{"document_id":"290515","document_content":"# Rationalizing Neural Predictions\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nPrediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications -- rationales -- that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.04155v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"30305\", \"80233\", \"1862\", \"2601\", \"2537\", \"5579\", \"6220\", \"9230\", \"16325\", \"16320\", \"24197\", \"24381\", \"24996\", \"25947\", \"27250\", \"27576\", \"34473\", \"39414\", \"40004\", \"42101\", \"53210\", \"55483\", \"128043\", \"169936\", \"13015\", \"13529\", \"14944\", \"16272\", \"16435\", \"17700\", \"17833\", \"20615\", \"22596\", \"22925\", \"23095\", \"23774\", \"23881\", \"24169\", \"24663\", \"25311\", \"26821\", \"27011\", \"27297\", \"8948\", \"40037\", \"41860\", \"42580\", \"43967\", \"45086\", \"45080\", \"45293\", \"45716\", \"47146\", \"47697\", \"45790\", \"50372\", \"51299\", \"53003\", \"55157\", \"55765\", \"56426\", \"57157\", \"57165\", \"58346\", \"58831\", \"60179\", \"60207\", \"61008\", \"61354\", \"61496\", \"61915\", \"62920\", \"65385\", \"65995\", \"67313\", \"70633\", \"73354\", \"75999\", \"78260\", \"79632\", \"80148\", \"80515\", \"84836\", \"85205\", \"86707\", \"91690\", \"106171\", \"108663\", \"115953\", \"121749\", \"121691\", \"127012\", \"132897\", \"148058\", \"153970\", \"165061\", \"171784\", \"178428\", \"183668\", \"183675\", \"198460\", \"120322\", \"77839\", \"78576\", \"82385\", \"83050\", \"87814\", \"89450\", \"92903\", \"93421\", \"94387\", \"94934\", \"95087\", \"95246\", \"95395\", \"95987\", \"96728\", \"97297\", \"99542\", \"99779\", \"104775\", \"110028\", \"107813\", \"115797\", \"117830\", \"118685\", \"121171\", \"122133\", \"125214\", \"126862\", \"127086\", \"127408\", \"127544\", \"122766\", \"122343\", \"127494\", \"127779\", \"127851\", \"127844\", \"128172\", \"128337\", \"128829\", \"130056\", \"133083\", \"108356\", \"128058\", \"135600\", \"136528\", \"142607\", \"143408\", \"143508\", \"147166\", \"149523\", \"154257\", \"154880\", \"157352\", \"157528\", \"164142\", \"165547\", \"174554\", \"198512\", \"199332\", \"265119\", \"150318\", \"159144\", \"159313\", \"160684\", \"164111\", \"163311\", \"165848\", \"167137\", \"167584\", \"167726\", \"168243\", \"167615\", \"169573\", \"171988\", \"171987\", \"171991\", \"175946\", \"175365\", \"179925\", \"181040\", \"182261\", \"182289\", \"182304\", \"182490\", \"182935\", \"179945\", \"181717\", \"183237\", \"183178\", \"183204\", \"184091\", \"184002\", \"184188\", \"184740\", \"185541\", \"188047\", \"190137\", \"191253\", \"192342\", \"192608\", \"189174\", \"194333\", \"197556\", \"197416\", \"197927\", \"200101\", \"205161\", \"206170\", \"209451\", \"209654\", \"210401\", \"211202\", \"212053\", \"214937\", \"220419\", \"226576\", \"234308\", \"243145\", \"192554\", \"207813\", \"216029\", \"212464\", \"217361\", \"217926\", \"152837\", \"218162\", \"219560\", \"217010\", \"220022\", \"218291\", \"224858\", \"226265\", \"227415\", \"227448\", \"227824\", \"230052\", \"230373\", \"230464\", \"231957\", \"232584\", \"232457\", \"234708\", \"208359\", \"234801\", \"235452\", \"234844\", \"239137\", \"239653\", \"241110\", \"241376\", \"241464\", \"264749\", \"242037\", \"243765\", \"244688\", \"245009\", \"245199\", \"249243\", \"250234\", \"253401\", \"251515\", \"257223\", \"260144\", \"261871\", \"264168\", \"265614\", \"265674\", \"265723\", \"266702\", \"265330\", \"265814\", \"268252\", \"268833\", \"267877\", \"271811\", \"272328\", \"272864\", \"277007\", \"278430\", \"277288\", \"279543\", \"281092\"], \"outgoing_citations\": [\"292576\", \"298842\", \"299541\", \"301316\", \"303047\", \"303224\", \"303951\", \"306182\", \"307951\", \"311683\", \"311696\", \"311845\", \"319781\", \"350457\", \"353488\"]}","task_split":"paper_retrieval"}
{"document_id":"290569","document_content":"# Neural Associative Memory for Dual-Sequence Modeling\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nMany important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target-sequence can be bridged effectively using Dual AM-RNNs. However, an initial experiment on auto-encoding reveals that these benefits are not exploited by the system when learning to solve sequence-to-sequence tasks which indicates that additional supervision or regularization is needed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.03864v2\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"288590\"], \"outgoing_citations\": [\"298621\", \"299541\", \"300803\", \"306182\", \"311585\", \"311849\", \"316745\", \"319833\"]}","task_split":"paper_retrieval"}
{"document_id":"290679","document_content":"# Generative Adversarial Imitation Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nConsider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.03476v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [\"43417\", \"135015\", \"30438\", \"75234\", \"93745\", \"119790\", \"121351\", \"146468\", \"211084\", \"208474\", \"15\", \"800\", \"1042\", \"1269\", \"1769\", \"1752\", \"2695\", \"3212\", \"3694\", \"3724\", \"4773\", \"5033\", \"5280\", \"7093\", \"7249\", \"8426\", \"10698\", \"11227\", \"11670\", \"13755\", \"13710\", \"14284\", \"15090\", \"15486\", \"16951\", \"17854\", \"18163\", \"20668\", \"21579\", \"23199\", \"23944\", \"24764\", \"29891\", \"30327\", \"30806\", \"30777\", \"33306\", \"34545\", \"34855\", \"35649\", \"36644\", \"39034\", \"38950\", \"40128\", \"39969\", \"46115\", \"46555\", \"52057\", \"55689\", \"199815\", \"62061\", \"64019\", \"68506\", \"69573\", \"73051\", \"181001\", \"75603\", \"76756\", \"79803\", \"84981\", \"97832\", \"104477\", \"107773\", \"115575\", \"116594\", \"118238\", \"131263\", \"144508\", \"160740\", \"191103\", \"86\", \"235\", \"597\", \"883\", \"2483\", \"3384\", \"4818\", \"5892\", \"6763\", \"7833\", \"9025\", \"9356\", \"9485\", \"9593\", \"10822\", \"10657\", \"11802\", \"12989\", \"13020\", \"13156\", \"13426\", \"13246\", \"13366\", \"14557\", \"14671\", \"14799\", \"15614\", \"16542\", \"16665\", \"17289\", \"17658\", \"17752\", \"18138\", \"19347\", \"19478\", \"19709\", \"19039\", \"19552\", \"20155\", \"20356\", \"20692\", \"21340\", \"21810\", \"22916\", \"23632\", \"25074\", \"26007\", \"26365\", \"26474\", \"28886\", \"29825\", \"31582\", \"31874\", \"32052\", \"32449\", \"32573\", \"33837\", \"34081\", \"34133\", \"34863\", \"35136\", \"34925\", \"35112\", \"35306\", \"36315\", \"36422\", \"36423\", \"36692\", \"36887\", \"36964\", \"37526\", \"37807\", \"38356\", \"39481\", \"39733\", \"40149\", \"35842\", \"40489\", \"41463\", \"41291\", \"41518\", \"41883\", \"42082\", \"42423\", \"42747\", \"42771\", \"43198\", \"43202\", \"43421\", \"43475\", \"43996\", \"43971\", \"44355\", \"44329\", \"44473\", \"45285\", \"45638\", \"45629\", \"155602\", \"41497\", \"46314\", \"46858\", \"46862\", \"47373\", \"47465\", \"48229\", \"48247\", \"48390\", \"139151\", \"49426\", \"49757\", \"50637\", \"50855\", \"51162\", \"51963\", \"52082\", \"53818\", \"54266\", \"54834\", \"55951\", \"56203\", \"56269\", \"56385\", \"56445\", \"56409\", \"57053\", \"57391\", \"57363\", \"57738\", \"58158\", \"58531\", \"58859\", \"137475\", \"59140\", \"59216\", \"59236\", \"60330\", \"60527\", \"60681\", \"61080\", \"61181\", \"61333\", \"61195\", \"61401\", \"61513\", \"62362\", \"62450\", \"62732\", \"63319\", \"64007\", \"64355\", \"64482\", \"65194\", \"66140\", \"66915\", \"67037\", \"66896\", \"67095\", \"67374\", \"67358\", \"67424\", \"68519\", \"69303\", \"69452\", \"69620\", \"69799\", \"70281\", \"70604\", \"70713\", \"71419\", \"72877\", \"70806\", \"74451\", \"74900\", \"75097\", \"75338\", \"75816\", \"77389\", \"77464\", \"77735\", \"79012\", \"79130\", \"80435\", \"82750\", \"83472\", \"85256\", \"85867\", \"85855\", \"86496\", \"89090\", \"89521\", \"92888\", \"93063\", \"95856\", \"97665\", \"99953\", \"101579\", \"105601\", \"105761\", \"106323\", \"107853\", \"109264\", \"110042\", \"113681\", \"114596\", \"114931\", \"116450\", \"116668\", \"117217\", \"117664\", \"120100\", \"120259\", \"120714\", \"120873\", \"121387\", \"124867\", \"127333\", \"130096\", \"132645\", \"132434\", \"134976\", \"137424\", \"139572\", \"144521\", \"144726\", \"152072\", \"155930\", \"161444\", \"161638\", \"163665\", \"167888\", \"167814\", \"173946\", \"177272\", \"183561\", \"184991\", \"190509\", \"206988\", \"211674\", \"227635\"], \"outgoing_citations\": [\"291738\", \"297317\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"290705","document_content":"# Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages\n## Categories\n- Artificial Intelligence\n## Abstract\nReasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast reasoning is highly desirable. We introduce a new framework called structured factored inference (SFI) that provides the foundation for such a system. Using models encoded in a probabilistic programming language, SFI provides a sound means to decompose a model into sub-models, apply an inference algorithm to each sub-model, and combine the resulting information to answer a query. Our results show that SFI is nearly as accurate as exact inference yet retains the benefits of approximate inference methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.03298v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"180115\"], \"outgoing_citations\": [\"335884\", \"349215\", \"350210\"]}","task_split":"paper_retrieval"}
{"document_id":"290737","document_content":"# Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nUnsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with similar syntactic or semantic meaning to vectors close to each other. However, extracting context-aware word-sequence embedding remains a challenging task. Training over large corpus is difficult as labels are difficult to get. More importantly, it is challenging for pre-trained models to obtain word-sequence embeddings that are universally good for all downstream tasks or for any new datasets. We propose a two-phased ConvDic+DeconvDec framework to solve the problem by combining a word-sequence dictionary learning model with a word-sequence embedding decode model. We propose a convolutional tensor decomposition mechanism to learn good word-sequence phrase dictionary in the learning phase. It is proved to be more accurate and much more efficient than the popular alternating minimization method. In the decode phase, we introduce a deconvolution framework that is immune to the problem of varying sentence lengths. The word-sequence embeddings we extracted using ConvDic+DeconvDec are universally good for a few downstream tasks we test on. The framework requires neither pre-training nor prior\/outside information.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.03153v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"154737\", \"272080\"], \"outgoing_citations\": [\"290387\", \"300867\", \"302516\", \"311376\", \"314240\", \"317517\", \"321035\", \"328571\", \"333356\", \"352900\"]}","task_split":"paper_retrieval"}
{"document_id":"290747","document_content":"# Key-Value Memory Networks for Directly Reading Documents\n## Categories\n- Computation and Language\n## Abstract\nDirectly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WikiQA benchmark.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.03126v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"291957\", \"224\", \"103740\", \"128468\", \"814\", \"3809\", \"6554\", \"8356\", \"12316\", \"13997\", \"17151\", \"18380\", \"29168\", \"29573\", \"30389\", \"45539\", \"50552\", \"59368\", \"73800\", \"113490\", \"169593\", \"199\", \"2103\", \"3695\", \"10232\", \"10837\", \"12774\", \"13340\", \"16054\", \"21167\", \"21528\", \"22846\", \"23997\", \"23992\", \"24562\", \"28492\", \"28553\", \"28745\", \"29056\", \"29212\", \"32958\", \"28429\", \"41437\", \"45225\", \"45697\", \"45832\", \"47547\", \"48502\", \"48619\", \"52887\", \"53317\", \"54101\", \"55916\", \"55695\", \"56855\", \"58427\", \"58742\", \"58997\", \"63245\", \"69510\", \"71004\", \"71580\", \"73497\", \"73658\", \"73917\", \"76113\", \"78578\", \"81225\", \"86983\", \"95850\", \"111774\", \"137940\", \"142497\", \"104940\", \"51468\", \"78352\", \"81775\", \"82594\", \"88937\", \"73765\", \"90494\", \"90377\", \"94054\", \"96894\", \"155429\", \"103442\", \"109697\", \"109913\", \"111678\", \"112181\", \"112404\", \"114439\", \"114729\", \"117098\", \"121547\", \"120882\", \"123551\", \"126615\", \"127748\", \"128361\", \"61111\", \"132413\", \"132631\", \"128545\", \"132227\", \"134304\", \"137730\", \"137736\", \"140317\", \"142243\", \"143089\", \"138662\", \"140782\", \"145535\", \"148271\", \"149550\", \"149565\", \"150226\", \"154645\", \"157803\", \"162894\", \"168344\", \"171990\", \"178593\", \"191285\", \"212028\", \"150219\", \"157022\", \"158040\", \"158513\", \"158893\", \"161119\", \"161422\", \"162246\", \"162857\", \"163127\", \"163173\", \"163778\", \"166960\", \"167475\", \"167835\", \"168228\", \"170492\", \"170864\", \"171566\", \"171791\", \"173016\", \"175493\", \"176990\", \"177592\", \"178032\", \"177883\", \"173474\", \"176129\", \"178950\", \"179166\", \"180575\", \"180895\", \"181844\", \"181988\", \"180362\", \"181781\", \"184694\", \"185680\", \"185894\", \"186337\", \"188009\", \"188039\", \"185903\", \"189326\", \"191258\", \"191350\", \"192697\", \"189600\", \"188736\", \"192932\", \"193257\", \"196314\", \"202015\", \"202917\", \"203208\", \"205569\", \"209764\", \"210208\", \"210420\", \"211838\", \"212641\", \"218998\", \"219107\", \"233659\", \"207767\", \"208713\", \"209388\", \"213616\", \"215928\", \"217625\", \"218189\", \"219306\", \"219669\", \"219774\", \"220351\", \"220858\", \"221363\", \"221433\", \"221316\", \"197765\", \"212887\", \"225721\", \"228401\", \"226415\", \"228231\", \"231239\", \"231338\", \"231467\", \"231981\", \"233970\", \"234280\", \"234624\", \"234735\", \"235307\", \"261037\", \"234599\", \"235408\", \"239537\", \"240596\", \"239633\", \"217294\", \"243759\", \"244172\", \"244479\", \"245072\", \"245101\", \"245474\", \"249268\", \"249315\", \"250322\", \"250483\", \"256171\", \"255954\", \"264636\", \"265431\", \"269757\", \"272930\", \"247016\", \"250084\", \"250703\", \"251348\", \"252012\", \"254760\", \"255095\", \"257223\", \"257933\", \"258130\", \"260998\", \"261389\", \"197767\", \"262644\", \"263120\", \"265947\", \"266411\", \"266955\", \"267878\", \"268356\", \"270020\", \"270519\", \"272059\", \"272092\", \"272222\", \"272929\", \"273411\", \"273977\", \"274520\", \"277662\", \"278611\", \"279648\", \"280030\", \"274425\", \"277941\", \"278163\", \"280854\", \"279215\", \"282697\", \"278896\", \"280478\", \"281092\", \"288590\", \"289265\", \"297473\"], \"outgoing_citations\": [\"297795\", \"298513\", \"302771\", \"303029\", \"303827\", \"311684\", \"317213\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"290762","document_content":"# Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nWord embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low-dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.02979v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290804","document_content":"# Face valuing: Training user interfaces with facial expressions and reinforcement learning\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nAn important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive feedback; explicit feedback imposes a cognitive load on the user and is expensive in terms of human effort. The primary objective of the current work is to demonstrate that a learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions---we call this face valuing. We empirically evaluate face valuing on a grip selection task. Our preliminary results suggest that an agent can quickly adapt to a user's changing preferences with minimal explicit feedback by learning a value function that maps facial features extracted from a camera image to expected future reward. We believe that an agent learning to perceive a value from the body language of its human user is complementary to existing interactive machine learning approaches and will help in creating successful human-machine interactive applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.02807v1\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [\"98034\", \"103709\", \"106567\", \"145848\", \"163867\", \"200476\", \"250883\", \"265464\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290875","document_content":"# Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task\n## Categories\n- Robotics\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nAs robots enter human environments, they will be expected to accomplish a tremendous range of tasks. It is not feasible for robot designers to pre-program these behaviors or know them in advance, so one way to address this is through end-user programming, such as via learning from demonstration (LfD). While significant work has been done on the mechanics of enabling robot learning from human teachers, one unexplored aspect is enabling mutual feedback between both the human teacher and robot during the learning process, i.e., implicit learning. In this paper, we explore one aspect of this mutual understanding, grounding sequences, where both a human and robot provide non-verbal feedback to signify their mutual understanding during interaction. We conducted a study where people taught an autonomous humanoid robot a dance, and performed gesture analysis to measure people's responses to the robot during correct and incorrect demonstrations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ROMAN.2016.7745138\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.AI\", \"cs.HC\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Artificial Intelligence\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"213766\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"290904","document_content":"# Deep Successor Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nLearning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (SR), which decomposes the value function into two components -- a reward predictor and a successor map. The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards. The value function of a state can be computed as the inner product between the successor map and the reward weights. In this paper, we present DSR, which generalizes SR within an end-to-end deep reinforcement learning framework. DSR has several appealing properties including: increased sensitivity to distal reward changes due to factorization of reward and world dynamics, and the ability to extract bottleneck states (subgoals) given successor maps trained under a random policy. We show the efficacy of our approach on two diverse environments given raw pixel observations -- simple grid-world domains (MazeBase) and the Doom game engine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.02396v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"cs.NE\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"12555\", \"7928\", \"17165\", \"20487\", \"36644\", \"46165\", \"128249\", \"6744\", \"6744\", \"8634\", \"12745\", \"13629\", \"17153\", \"26551\", \"36974\", \"42563\", \"53903\", \"57788\", \"61195\", \"65615\", \"65435\", \"67095\", \"70182\", \"96180\", \"141419\", \"90884\", \"100245\", \"107851\", \"113549\", \"117297\", \"124909\", \"125657\", \"126377\", \"200791\", \"148522\", \"222837\", \"149096\", \"157061\", \"160827\", \"171112\", \"171332\", \"179483\", \"179594\", \"184311\", \"186669\", \"186339\", \"190812\", \"194738\", \"193925\", \"205488\", \"212832\", \"214196\", \"216060\", \"191970\", \"208372\", \"218445\", \"225320\", \"200927\", \"199779\", \"237929\", \"238475\", \"240506\", \"233926\", \"236060\", \"242444\", \"243090\", \"245731\", \"249906\", \"251957\", \"253974\", \"263104\", \"255936\", \"258199\", \"261493\", \"265722\", \"266845\", \"270823\", \"271757\", \"277797\", \"280681\", \"281110\", \"280296\", \"290243\", \"277504\"], \"outgoing_citations\": [\"291908\", \"293071\", \"294142\", \"295736\", \"296448\", \"297838\", \"298281\", \"302660\", \"302920\", \"302942\", \"305802\", \"309498\", \"310171\", \"316183\"]}","task_split":"paper_retrieval"}
{"document_id":"290949","document_content":"# A Minimax Approach to Supervised Learning\n## Categories\n- Machine Learning\n- Information Theory\n- Information Theory\n## Abstract\nGiven a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $\\Gamma$ on $(X,Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $\\Gamma$? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying this principle to sets of distributions with marginal on $X$ constrained to be the empirical marginal from the data, we develop a general minimax approach for supervised learning problems. While for some loss functions such as squared-error and log loss, the minimax approach rederives well-knwon regression models, for the 0-1 loss it results in a new linear classifier which we call the maximum entropy machine. The maximum entropy machine minimizes the worst-case 0-1 loss over the structured set of distribution, and by our numerical experiments can outperform other well-known linear classifiers such as SVM. We also prove a bound on the generalization worst-case error in the minimax approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.02206v5\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"math.IT\", \"cs.IT\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [\"12116\", \"42817\", \"77928\", \"112884\", \"40729\", \"66595\", \"83045\", \"95428\", \"110231\", \"133207\", \"77559\", \"93944\", \"98358\", \"205076\", \"120579\", \"122521\", \"201720\", \"172059\", \"200489\", \"200704\", \"210300\", \"252257\", \"204997\", \"212705\", \"247902\", \"229718\", \"247906\", \"265793\", \"246378\"], \"outgoing_citations\": [\"293473\", \"303931\", \"345643\", \"356592\"]}","task_split":"paper_retrieval"}
{"document_id":"291115","document_content":"# Deep Q-Networks for Accelerating the Training of Deep Neural Networks\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN. The state features of the agent are learned from the weight statistics of the optimizee during training. The reward function of this agent is designed to learn policies that minimize the optimizee's training time given a certain performance goal. The actions of the agent correspond to changing the learning rate for the optimizee during training. As far as we know, this is the first attempt to use deep RL to learn how to optimize a large-sized DNN. We perform extensive experiments on a standard benchmark dataset and demonstrate the effectiveness of the policies learned by our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.01467v10\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"146987\", \"234682\", \"255420\", \"268720\", \"272663\"], \"outgoing_citations\": [\"298418\", \"302951\", \"302952\", \"306812\", \"307136\", \"309933\", \"317642\", \"337405\", \"358178\"]}","task_split":"paper_retrieval"}
{"document_id":"291350","document_content":"# A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment\n## Categories\n- Computer Vision and Pattern Recognition\n- Human-Computer Interaction\n- Robotics\n- 68T45, 68T40, 68T10\n- I.2.9; I.2.10; I.5.4\n## Abstract\nFace analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.00474v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.HC\", \"cs.RO\", \"68T45, 68T40, 68T10\", \"I.2.9; I.2.10; I.5.4\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Human-Computer Interaction\", \"Robotics\", \"68T45, 68T40, 68T10\", \"I.2.9; I.2.10; I.5.4\"], \"incoming_citations\": [\"257958\"], \"outgoing_citations\": [\"299047\", \"316340\"]}","task_split":"paper_retrieval"}
{"document_id":"291356","document_content":"# How to advance general game playing artificial intelligence by player modelling\n## Categories\n- Human-Computer Interaction\n- Artificial Intelligence\n## Abstract\nGeneral game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling:   Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1606.00401v3\", \"primary_category\": \"cs.HC\", \"categories\": [\"cs.HC\", \"cs.AI\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"Human-Computer Interaction\", \"Artificial Intelligence\"], \"incoming_citations\": [\"288241\"], \"outgoing_citations\": [\"295656\", \"296084\"]}","task_split":"paper_retrieval"}
{"document_id":"291983","document_content":"# DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the inception region proposal network (Inception-RPN) and design a set of text characteristic prior bounding boxes to achieve high word recall with only hundred level candidate proposals. Next, we present a powerful textdetection network that embeds ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP) for text and non-text classification and accurate localization. Finally, we apply an iterative bounding box voting scheme to pursue high recall in a complementary manner and introduce a filtering algorithm to retain the most suitable bounding box, while removing redundant inner and outer boxes for each text instance. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.07314v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"150258\", \"98220\", \"111116\", \"122981\", \"140478\", \"166479\", \"169067\", \"177579\", \"236404\", \"225690\", \"243851\", \"245591\", \"250729\", \"268829\", \"272481\", \"248812\", \"250766\", \"253398\", \"258388\", \"261365\", \"262570\", \"266391\", \"269205\", \"270668\", \"279950\"], \"outgoing_citations\": [\"298359\", \"299461\", \"313205\", \"314541\", \"320837\", \"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"291993","document_content":"# Classifying discourse in a CSCL platform to evaluate correlations with Teacher Participation and Progress\n## Categories\n- Computers and Society\n- Computation and Language\n## Abstract\nIn Computer-Supported learning, monitoring and engaging a group of learners is a complex task for teachers, especially when learners are working collaboratively: Are my students motivated? What kind of progress are they making? Should I intervene? Is my communication and the didactic design adapted to my students? Our hypothesis is that the analysis of natural language interactions between students, and between students and teachers, provide very valuable information and could be used to produce qualitative indicators to help teachers' decisions. We develop an automatic approach in three steps (1) to explore the discursive functions of messages in a CSCL platform, (2) to classify the messages automatically and (3) to evaluate correlations between discursive attitudes and other variables linked to the learning activity. Results tend to show that some types of discourse are correlated with a notion of Progress on the learning activities and the importance of emotive participation from the Teacher.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.07268v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"cs.CL\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"336894\"]}","task_split":"paper_retrieval"}
{"document_id":"292069","document_content":"# Embedding based on function approximation for large scale image search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the stateof-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.06914v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73146\", \"241679\", \"247969\", \"189084\", \"231046\", \"241439\", \"243056\", \"249448\", \"262169\", \"269591\", \"300530\"], \"outgoing_citations\": [\"198138\", \"288218\", \"297319\", \"303067\", \"319454\", \"328917\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"292141","document_content":"# Programming with a Differentiable Forth Interpreter\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGiven that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.06640v3\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"117\", \"39958\", \"82120\", \"180115\", \"4298\", \"26664\", \"27558\", \"52278\", \"58831\", \"60420\", \"66817\", \"75085\", \"92118\", \"111903\", \"142497\", \"151824\", \"175554\", \"112404\", \"114128\", \"136139\", \"150693\", \"154153\", \"164108\", \"168571\", \"149106\", \"175055\", \"178997\", \"183535\", \"190023\", \"198641\", \"203445\", \"224873\", \"212792\", \"218066\", \"219352\", \"223816\", \"229420\", \"230273\", \"237102\", \"217975\", \"236828\", \"232449\", \"241060\", \"243353\", \"244854\", \"245201\", \"245418\", \"246129\", \"248846\", \"250651\", \"255562\", \"271294\", \"246446\", \"224192\", \"263306\", \"264931\", \"267980\", \"270893\", \"273953\", \"281011\", \"281041\", \"281214\", \"281085\", \"279777\", \"281042\", \"281367\", \"278913\", \"286548\"], \"outgoing_citations\": [\"286548\", \"287181\", \"291841\", \"302515\", \"302803\", \"302933\", \"302970\", \"303306\", \"303714\", \"311585\", \"316576\", \"317642\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"292186","document_content":"# Residual Networks Behave Like Ensembles of Relatively Shallow Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nIn this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training. To support this observation, we rewrite residual networks as an explicit collection of paths. Unlike traditional models, paths through residual networks vary in length. Further, a lesion study reveals that these paths show ensemble-like behavior in the sense that they do not strongly depend on each other. Finally, and most surprising, most paths are shorter than one might expect, and only the short paths are needed during training, as longer paths do not contribute any gradient. For example, most of the gradient in a residual network with 110 layers comes from paths that are only 10-34 layers deep. Our results reveal one of the key characteristics that seem to enable the training of very deep networks: Residual networks avoid the vanishing gradient problem by introducing short paths which can carry gradient throughout the extent of very deep networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.06431v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"1683\", \"3103\", \"24184\", \"36542\", \"194836\", \"4254\", \"16532\", \"18135\", \"37987\", \"47139\", \"48115\", \"58046\", \"84761\", \"122264\", \"149977\", \"189448\", \"209105\", \"8153\", \"10396\", \"12335\", \"14238\", \"15363\", \"17268\", \"19466\", \"29017\", \"30292\", \"30498\", \"32086\", \"36240\", \"38528\", \"39230\", \"40383\", \"44052\", \"44168\", \"41879\", \"46630\", \"47707\", \"53245\", \"54173\", \"57038\", \"57735\", \"59891\", \"60361\", \"63911\", \"64322\", \"64690\", \"65129\", \"65911\", \"68445\", \"68510\", \"70895\", \"73312\", \"76139\", \"77117\", \"88809\", \"97296\", \"99392\", \"105077\", \"117150\", \"122260\", \"129418\", \"133583\", \"148058\", \"153368\", \"154992\", \"156837\", \"171837\", \"178323\", \"186364\", \"205408\", \"76374\", \"231517\", \"231793\", \"78612\", \"89807\", \"98149\", \"98831\", \"98932\", \"96388\", \"105235\", \"107598\", \"108995\", \"114822\", \"115226\", \"116149\", \"117451\", \"117618\", \"118573\", \"118493\", \"119664\", \"116707\", \"127630\", \"131087\", \"132228\", \"132551\", \"132569\", \"132789\", \"132755\", \"133191\", \"137241\", \"137585\", \"138774\", \"138919\", \"139738\", \"140391\", \"140604\", \"141446\", \"141692\", \"142286\", \"142181\", \"145957\", \"151944\", \"152384\", \"153883\", \"154693\", \"167993\", \"174353\", \"174675\", \"176746\", \"179441\", \"202926\", \"206691\", \"209071\", \"199241\", \"149438\", \"150547\", \"159122\", \"160305\", \"161914\", \"162253\", \"164917\", \"165186\", \"166517\", \"167546\", \"169901\", \"171177\", \"172482\", \"173411\", \"177237\", \"179445\", \"179780\", \"181105\", \"181661\", \"183454\", \"184402\", \"185645\", \"188316\", \"17110\", \"191908\", \"192500\", \"192613\", \"274191\", \"193236\", \"194322\", \"196329\", \"199356\", \"200056\", \"208547\", \"208392\", \"208583\", \"209178\", \"209416\", \"210494\", \"214491\", \"216744\", \"220309\", \"221777\", \"228572\", \"238384\", \"242135\", \"248753\", \"250062\", \"279147\", \"256265\", \"204501\", \"207779\", \"208640\", \"209813\", \"210580\", \"213260\", \"213867\", \"214704\", \"216533\", \"215006\", \"217824\", \"223582\", \"223745\", \"224528\", \"225838\", \"225944\", \"228939\", \"229062\", \"229381\", \"229693\", \"232849\", \"232934\", \"233266\", \"233518\", \"233694\", \"233084\", \"235335\", \"235415\", \"236718\", \"237042\", \"238575\", \"239990\", \"240158\", \"240936\", \"242370\", \"234081\", \"244812\", \"246832\", \"249590\", \"250759\", \"251797\", \"252168\", \"252185\", \"259759\", \"262065\", \"263667\", \"269618\", \"273848\", \"279728\", \"247388\", \"234074\", \"248666\", \"251167\", \"251523\", \"251011\", \"251913\", \"251946\", \"252170\", \"253150\", \"254541\", \"255188\", \"256466\", \"257818\", \"257826\", \"291785\", \"258393\", \"258609\", \"260702\", \"261733\", \"265022\", \"266107\", \"269332\", \"270175\", \"270780\", \"271680\", \"272177\", \"272278\", \"272870\", \"273129\", \"277019\", \"278798\", \"280217\", \"280213\", \"280285\", \"277393\", \"282949\", \"283223\", \"291925\", \"280925\", \"284432\", \"289496\"], \"outgoing_citations\": [\"302920\"]}","task_split":"paper_retrieval"}
{"document_id":"292337","document_content":"# The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response\n## Categories\n- Optimization and Control\n- Systems and Control\n- Artificial Intelligence\n## Abstract\nUtilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of the faults causing the outage. Instead, utilities have to depend primarily on phone calls (trouble calls) from customers who have lost power to guide the dispatching of utility trucks. In this paper, we develop a policy that routes a utility truck to restore outages in the power grid as quickly as possible, using phone calls to create beliefs about outages, but also using utility trucks as a mechanism for collecting additional information. This means that routing decisions change not only the physical state of the truck (as it moves from one location to another) and the grid (as the truck performs repairs), but also our belief about the network, creating the first stochastic vehicle routing problem that explicitly models information collection and belief modeling. We address the problem of managing a single utility truck, which we start by formulating as a sequential stochastic optimization model which captures our belief about the state of the grid. We propose a stochastic lookahead policy, and use Monte Carlo tree search (MCTS) to produce a practical policy that is asymptotically optimal. Simulation results show that the developed policy restores the power grid much faster compared to standard industry heuristics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.05711v1\", \"primary_category\": \"math.OC\", \"categories\": [\"cs.SY\", \"math.OC\", \"cs.AI\"], \"primary_category_human_readable\": \"Optimization and Control\", \"categories_human_readable\": [\"Systems and Control\", \"Optimization and Control\", \"Artificial Intelligence\"], \"incoming_citations\": [\"77053\", \"94466\", \"213778\", \"268468\", \"231983\"], \"outgoing_citations\": [\"325992\"]}","task_split":"paper_retrieval"}
{"document_id":"292371","document_content":"# Beyond Caption To Narrative: Video Captioning With Multiple Sentences\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.05440v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"159645\", \"173243\", \"83901\", \"104610\", \"115717\", \"155074\", \"197355\", \"206058\", \"190250\", \"216400\", \"262465\", \"269640\", \"269646\", \"280986\"], \"outgoing_citations\": [\"301758\", \"311710\", \"313189\", \"316564\", \"319169\", \"321798\"]}","task_split":"paper_retrieval"}
{"document_id":"292443","document_content":"# Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM\n## Categories\n- Computation and Language\n## Abstract\nModeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability. Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge. In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling. Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations. In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate. Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.05110v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"41328\", \"45320\", \"101441\", \"77654\", \"94942\", \"130345\", \"132887\", \"157654\", \"157654\", \"194919\", \"157789\", \"161259\", \"145237\", \"185665\", \"192896\", \"199077\", \"251892\", \"165331\", \"219419\", \"218515\", \"229493\", \"242123\", \"249820\", \"280468\", \"293508\", \"275597\", \"278671\", \"291102\"], \"outgoing_citations\": [\"294537\", \"301313\", \"305098\", \"308381\", \"310377\", \"310740\", \"311886\", \"316328\", \"325200\"]}","task_split":"paper_retrieval"}
{"document_id":"292523","document_content":"# A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations\n## Categories\n- Machine Learning\n- Databases\n- Artificial Intelligence\n## Abstract\nLink prediction in large knowledge graphs has received a lot of attention recently because of its importance for inferring missing relations and for completing and improving noisily extracted knowledge graphs. Over the years a number of machine learning researchers have presented various models for predicting the presence of missing relations in a knowledge base. Although all the previous methods are presented with empirical results that show high performance on select datasets, there is almost no previous work on understanding the connection between properties of a knowledge base and the performance of a model. In this paper we analyze the RESCAL method and prove that it can not encode asymmetric transitive relations in knowledge bases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.04672v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.DB\", \"cs.AI\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Databases\", \"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"322879\"]}","task_split":"paper_retrieval"}
{"document_id":"292570","document_content":"# Anchoring and Agreement in Syntactic Annotations\n## Categories\n- Computation and Language\n## Abstract\nWe present a study on two key characteristics of human syntactic annotations: anchoring and agreement. Anchoring is a well known cognitive bias in human decision making, where judgments are drawn towards pre-existing values. We study the influence of anchoring on a standard approach to creation of syntactic resources where syntactic annotations are obtained via human editing of tagger and parser output. Our experiments demonstrate a clear anchoring effect and reveal unwanted consequences, including overestimation of parsing performance and lower quality of annotations in comparison with human-based annotations. Using sentences from the Penn Treebank WSJ, we also report systematically obtained inter-annotator agreement estimates for English dependency parsing. Our agreement results control for parser bias, and are consequential in that they are on par with state of the art parsing performance for English newswire. We discuss the impact of our findings on strategies for future annotation efforts and parser evaluations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.04481v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21712\", \"29188\", \"117633\", \"141471\", \"213525\", \"258064\", \"264358\"], \"outgoing_citations\": [\"292611\", \"296283\", \"310837\"]}","task_split":"paper_retrieval"}
{"document_id":"292834","document_content":"# PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFor applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomfort are desirable. Facial recognition is particularly appealing because it is minimally invasive yet offers relatively good recognition performance. Unfortunately, the combination of weak supervision and minimal invasiveness makes even highly accurate facial recognition systems susceptible to spoofing via presentation attacks. Thus, there is great demand for an effective and low cost system capable of rejecting such attacks.To this end we introduce PARAPH -- a novel hardware extension that exploits different measurements of light polarization to yield an image space in which presentation media are readily discernible from Bona Fide facial characteristics. The PARAPH system is inexpensive with an added cost of less than 10 US dollars. The system makes two polarization measurements in rapid succession, allowing them to be approximately pixel-aligned, with a frame rate limited by the camera, not the system. There are no moving parts above the molecular level, due to the efficient use of twisted nematic liquid crystals. We present evaluation images using three presentation attack media next to an actual face -- high quality photos on glossy and matte paper and a video of the face on an LCD. In each case, the actual face in the image generated by PARAPH is structurally discernible from the presentations, which appear either as noise (print attacks) or saturated images (replay attacks).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.03124v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"136247\", \"1562\", \"245834\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"292869","document_content":"# Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States\n## Categories\n- Physics and Society\n- Social and Information Networks\n## Abstract\nRecently, numerous approaches have emerged in the social sciences to exploit the opportunities made possible by the vast amounts of data generated by online social networks (OSNs). Having access to information about users on such a scale opens up a range of possibilities, all without the limitations associated with often slow and expensive paper-based polls. A question that remains to be satisfactorily addressed, however, is how demography is represented in the OSN content? Here, we study language use in the US using a corpus of text compiled from over half a billion geo-tagged messages from the online microblogging platform Twitter. Our intention is to reveal the most important spatial patterns in language use in an unsupervised manner and relate them to demographics. Our approach is based on Latent Semantic Analysis (LSA) augmented with the Robust Principal Component Analysis (RPCA) methodology. We find spatially correlated patterns that can be interpreted based on the words associated with them. The main language features can be related to slang use, urbanization, travel, religion and ethnicity, the patterns of which are shown to correlate plausibly with traditional census data. Our findings thus validate the concept of demography being represented in OSN language use and show that the traits observed are inherently present in the word frequencies without any previous assumptions about the dataset. Thus, they could form the basis of further research focusing on the evaluation of demographic data estimation from other big data sources, or on the dynamical processes that result in the patterns found here.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1057\/palcomms.2016.10.\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"cs.SI\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Social and Information Networks\"], \"incoming_citations\": [\"39225\", \"172685\", \"195623\", \"214462\", \"281289\"], \"outgoing_citations\": [\"317660\", \"321937\", \"323698\", \"328238\", \"338275\", \"338305\", \"339235\", \"339391\", \"348888\", \"351055\", \"351470\", \"351594\", \"353272\"]}","task_split":"paper_retrieval"}
{"document_id":"293072","document_content":"# The Actias system: supervised multi-strategy learning paradigm using categorical logic\n## Categories\n- Databases\n- 18C30, 03G30\n- Software Engineering\n- Artificial Intelligence\n## Abstract\nOne of the most difficult problems in the development of intelligent systems is the construction of the underlying knowledge base. As a consequence, the rate of progress in the development of this type of system is directly related to the speed with which knowledge bases can be assembled, and on its quality. We attempt to solve the knowledge acquisition problem, for a Business Information System, developing a supervised multistrategy learning paradigm. This paradigm is centred on a collaborative data mining strategy, where groups of experts collaborate using data-mining process on the supervised acquisition of new knowledge extracted from heterogeneous machine learning data models.   The Actias system is our approach to this paradigm. It is the result of applying the graphic logic based language of sketches to knowledge integration. The system is a data mining collaborative workplace, where the Information System knowledge base is an algebraic structure. It results from the integration of background knowledge with new insights extracted from data models, generated for specific data modelling tasks, and represented as rules using the sketches language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1607.08098v1\", \"primary_category\": \"cs.DB\", \"categories\": [\"18C30, 03G30\", \"cs.SE\", \"cs.AI\", \"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"18C30, 03G30\", \"Software Engineering\", \"Artificial Intelligence\", \"Databases\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"293105","document_content":"# User Reviews and Language: How Language Influences Ratings\n## Categories\n- Human-Computer Interaction\n- H.5.m; H.3.5\n- Computation and Language\n- Computers and Society\n## Abstract\nThe number of user reviews of tourist attractions, restaurants, mobile apps, etc. is increasing for all languages; yet, research is lacking on how reviews in multiple languages should be aggregated and displayed. Speakers of different languages may have consistently different experiences, e.g., different information available in different languages at tourist attractions or different user experiences with software due to internationalization\/localization choices. This paper assesses the similarity in the ratings given by speakers of different languages to London tourist attractions on TripAdvisor. The correlations between different languages are generally high, but some language pairs are more correlated than others. The results question the common practice of computing average ratings from reviews in many languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2851581.2892466\", \"primary_category\": \"cs.HC\", \"categories\": [\"H.5.m; H.3.5\", \"cs.HC\", \"cs.CL\", \"cs.CY\"], \"primary_category_human_readable\": \"Human-Computer Interaction\", \"categories_human_readable\": [\"H.5.m; H.3.5\", \"Human-Computer Interaction\", \"Computation and Language\", \"Computers and Society\"], \"incoming_citations\": [\"57015\", \"60949\", \"129326\", \"274948\"], \"outgoing_citations\": [\"192572\", \"311933\", \"337085\"]}","task_split":"paper_retrieval"}
{"document_id":"293137","document_content":"# Shape from Mixed Polarization\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nShape from Polarization (SfP) estimates surface normals using photos captured at different polarizer rotations. Fundamentally, the SfP model assumes that light is reflected either diffusely or specularly. However, this model is not valid for many real-world surfaces exhibiting a mixture of diffuse and specular properties. To address this challenge, previous methods have used a sequential solution: first, use an existing algorithm to separate the scene into diffuse and specular components, then apply the appropriate SfP model. In this paper, we propose a new method that jointly uses viewpoint and polarization data to holistically separate diffuse and specular components, recover refractive index, and ultimately recover 3D shape. By involving the physics of polarization in the separation process, we demonstrate competitive results with a benchmark method, while recovering additional information (e.g. refractive index).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.02066v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"257717\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"293168","document_content":"# Parallels of human language in the behavior of bottlenose dolphins\n## Categories\n- Neurons and Cognition\n- Computation and Language\n## Abstract\nA short review of similarities between dolphins and humans with the help of quantitative linguistics and information theory.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.2478\/lf-2022-0002\", \"primary_category\": \"q-bio.NC\", \"categories\": [\"q-bio.NC\", \"cs.CL\"], \"primary_category_human_readable\": \"Neurons and Cognition\", \"categories_human_readable\": [\"Neurons and Cognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"47608\", \"182576\", \"225709\", \"320716\", \"338769\", \"347066\"]}","task_split":"paper_retrieval"}
{"document_id":"293215","document_content":"# Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.01397v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"293275","document_content":"# A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Neurons and Cognition\n## Abstract\nHumans demonstrate remarkable abilities to predict physical events in complex scenes. Two classes of models for physical scene understanding have recently been proposed: \"Intuitive Physics Engines\", or IPEs, which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video-game physics engines, and memory-based models, which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes. Versions of the latter have recently been instantiated in convolutional neural network (CNN) architectures. Here we report four experiments that, to our knowledge, are the first rigorous comparisons of simulation-based and CNN-based models, where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall. Both approaches can achieve super-human accuracy levels and can quantitatively predict human judgments to a similar degree, but only the simulation-based models generalize to novel situations in ways that people do, and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1605.01138v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Neurons and Cognition\"], \"incoming_citations\": [\"34887\", \"9040\", \"17103\", \"41902\", \"130091\", \"127494\", \"142217\", \"146902\", \"127802\", \"171629\", \"204145\", \"219835\", \"238328\", \"270800\", \"278194\", \"297162\"], \"outgoing_citations\": [\"295487\", \"297162\", \"302659\", \"303476\"]}","task_split":"paper_retrieval"}
{"document_id":"293545","document_content":"# Faster R-CNN Features for Instance Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.08893v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"73146\", \"144613\", \"36193\", \"47623\", \"48708\", \"228640\", \"103466\", \"223220\", \"171342\", \"212041\", \"225828\", \"241439\", \"256887\", \"208288\", \"230600\", \"234521\", \"255989\", \"287103\", \"261707\", \"262166\", \"275310\"], \"outgoing_citations\": [\"301572\", \"303067\", \"331445\"]}","task_split":"paper_retrieval"}
{"document_id":"293801","document_content":"# Spot On: Action Localization from Pointly-Supervised Proposals\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe strive for spatio-temporal localization of actions in videos. The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations. Annotating action boxes in video is cumbersome, tedious, and error prone. Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only. We introduce an overlap measure between action proposals and points and incorporate them all into the objective of a non-convex Multiple Instance Learning optimization. Experimental evaluation on the UCF Sports and UCF 101 datasets shows that (i) spatio-temporal proposals can be used to train classifiers while retaining the localization performance, (ii) point annotations yield results comparable to box annotations while being significantly faster to annotate, (iii) with a minimum amount of supervision our approach is competitive to the state-of-the-art. Finally, we introduce spatio-temporal action annotations on the train and test videos of Hollywood2, resulting in Hollywood2Tubes, available at http:\/\/tinyurl.com\/hollywood2tubes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.07602v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"7545\", \"116568\", \"56325\", \"30227\", \"63687\", \"81115\", \"84482\", \"101255\", \"80403\", \"110579\", \"131199\", \"136718\", \"163427\", \"236665\", \"169552\", \"170707\", \"183340\", \"184611\", \"191393\", \"191556\", \"201099\", \"26939\", \"208075\", \"208972\", \"209268\", \"220193\", \"223768\", \"223871\", \"225414\", \"226384\", \"226467\", \"227420\", \"230140\", \"237493\", \"246830\", \"255915\", \"263830\", \"264711\", \"265650\", \"288919\", \"253115\", \"259728\", \"259939\", \"259940\", \"292421\", \"265791\", \"262578\", \"268408\", \"268417\", \"271019\", \"271198\", \"272096\", \"272360\", \"277625\"], \"outgoing_citations\": [\"304596\", \"311710\", \"312475\", \"312826\", \"318558\", \"321453\"]}","task_split":"paper_retrieval"}
{"document_id":"294023","document_content":"# Synthetic Data for Text Localisation in Natural Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene geometry. Second, we use the synthetic images to train a Fully-Convolutional Regression Network (FCRN) which efficiently performs text detection and bounding-box regression at all locations and multiple scales in an image. We discuss the relation of FCRN to the recently-introduced YOLO detector, as well as other end-to-end object detection systems based on deep learning. The resulting detection network significantly out performs current methods for text detection in natural images, achieving an F-measure of 84.2% on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per second on a GPU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.06646v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38757\", \"41858\", \"155242\", \"1039\", \"2918\", \"3394\", \"5857\", \"6379\", \"6241\", \"6741\", \"7829\", \"13927\", \"14003\", \"31775\", \"35464\", \"39902\", \"50775\", \"50917\", \"52248\", \"54017\", \"56985\", \"66255\", \"78763\", \"80383\", \"83916\", \"84505\", \"89127\", \"94155\", \"94535\", \"108704\", \"137208\", \"180823\", \"4188\", \"6043\", \"7154\", \"7620\", \"7580\", \"8109\", \"8735\", \"9220\", \"11202\", \"3699\", \"17176\", \"21983\", \"24824\", \"25132\", \"25198\", \"28270\", \"29428\", \"29777\", \"31607\", \"32623\", \"33360\", \"33363\", \"33358\", \"33798\", \"34424\", \"35713\", \"35782\", \"36052\", \"23043\", \"37811\", \"38854\", \"39862\", \"40057\", \"42497\", \"43153\", \"43037\", \"47089\", \"48702\", \"48822\", \"48901\", \"49128\", \"49805\", \"50125\", \"50136\", \"50591\", \"53750\", \"53987\", \"55556\", \"55562\", \"55703\", \"56713\", \"57555\", \"58438\", \"58673\", \"61171\", \"63761\", \"64528\", \"64650\", \"67749\", \"74776\", \"78355\", \"79422\", \"81960\", \"84238\", \"85941\", \"86413\", \"93345\", \"99456\", \"103980\", \"113794\", \"120486\", \"122764\", \"123348\", \"133054\", \"136651\", \"145945\", \"147463\", \"155066\", \"163092\", \"164827\", \"165015\", \"173324\", \"207887\", \"210925\", \"218990\", \"81667\", \"83582\", \"84406\", \"92285\", \"98220\", \"99077\", \"99423\", \"100417\", \"102479\", \"105122\", \"105295\", \"78416\", \"110306\", \"111035\", \"111116\", \"111991\", \"114759\", \"115934\", \"118390\", \"120420\", \"122981\", \"123078\", \"123550\", \"125177\", \"126244\", \"126431\", \"126484\", \"128870\", \"130807\", \"134664\", \"135021\", \"135263\", \"124322\", \"136218\", \"136190\", \"136831\", \"136843\", \"140006\", \"140021\", \"140478\", \"141887\", \"143280\", \"144173\", \"145587\", \"147253\", \"147536\", \"149329\", \"149580\", \"150060\", \"151049\", \"151319\", \"152219\", \"154995\", \"155530\", \"156029\", \"158242\", \"162477\", \"170229\", \"174062\", \"176257\", \"189663\", \"192671\", \"202305\", \"210634\", \"151110\", \"154825\", \"155455\", \"78380\", \"161220\", \"149030\", \"166479\", \"166353\", \"166621\", \"148689\", \"150251\", \"165784\", \"166370\", \"169411\", \"169711\", \"170008\", \"171543\", \"171686\", \"171807\", \"172172\", \"172530\", \"172540\", \"172884\", \"173076\", \"174894\", \"174928\", \"177490\", \"178095\", \"178153\", \"180472\", \"180931\", \"182255\", \"182603\", \"183044\", \"184992\", \"189470\", \"190056\", \"190708\", \"191096\", \"192483\", \"192471\", \"192870\", \"193382\", \"193824\", \"197469\", \"201515\", \"203406\", \"203525\", \"203735\", \"205988\", \"205980\", \"207348\", \"207554\", \"208801\", \"209083\", \"209227\", \"211851\", \"212064\", \"213640\", \"216651\", \"217463\", \"218589\", \"218907\", \"222513\", \"229426\", \"230086\", \"234390\", \"236404\", \"270363\", \"205602\", \"206178\", \"204872\", \"202645\", \"204166\", \"207071\", \"209438\", \"209603\", \"212719\", \"216006\", \"216815\", \"218516\", \"219146\", \"219278\", \"220129\", \"220364\", \"78367\", \"220896\", \"223189\", \"223824\", \"224056\", \"162242\", \"225195\", \"225336\", \"225266\", \"225666\", \"225690\", \"225795\", \"231129\", \"231139\", \"232509\", \"232706\", \"239002\", \"239535\", \"240965\", \"241968\", \"243863\", \"243851\", \"245591\", \"245899\", \"245970\", \"247460\", \"247753\", \"250119\", \"250738\", \"250729\", \"250852\", \"252062\", \"256364\", \"257694\", \"259334\", \"268829\", \"272481\"], \"outgoing_citations\": [\"149627\", \"310698\", \"311530\", \"320837\", \"321377\", \"328595\", \"336231\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"294084","document_content":"# Row-less Universal Schema\n## Categories\n- Computation and Language\n## Abstract\nUniversal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'column-less' versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a 'row-less' model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model per- forms with nearly the same accuracy on entity pairs never seen during training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.06361v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"294144","document_content":"# Dialog-based Language Learning\n## Categories\n- Computation and Language\n## Abstract\nA long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.06045v7\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"73800\", \"14126\", \"66630\", \"128396\", \"129558\", \"142497\", \"89813\", \"116873\", \"126335\", \"127002\", \"137928\", \"139785\", \"175055\", \"156062\", \"202748\", \"231741\", \"232584\", \"234141\", \"241921\", \"248407\", \"301313\", \"144800\", \"259264\", \"262814\", \"265139\", \"265222\", \"264856\", \"272851\", \"275029\", \"277456\", \"278174\", \"279215\", \"277941\", \"278163\", \"278244\", \"280103\", \"282827\", \"287413\", \"292019\"], \"outgoing_citations\": [\"302660\", \"302771\", \"303827\", \"308104\", \"308711\", \"310171\", \"310370\", \"310740\", \"311684\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"294344","document_content":"# Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization\n## Categories\n- Computation and Language\n## Abstract\nWe address the problem of automatically finding the parameters of a statistical machine translation system that maximize BLEU scores while ensuring that decoding speed exceeds a minimum value. We propose the use of Bayesian Optimization to efficiently tune the speed-related decoding parameters by easily incorporating speed as a noisy constraint function. The obtained parameter values are guaranteed to satisfy the speed constraint with an associated confidence margin. Across three language pairs and two speed constraint values, we report overall optimization time reduction compared to grid and random search. We also show that Bayesian Optimization can decouple speed and BLEU measurements, resulting in a further reduction of overall optimization time as speed is measured over a small subset of sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.05073v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"108349\", \"269174\"], \"outgoing_citations\": [\"158252\", \"316639\", \"319881\", \"328309\", \"332181\", \"333060\"]}","task_split":"paper_retrieval"}
{"document_id":"294460","document_content":"# Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nContent-based image retrieval (CBIR) in large medical image archives is a challenging and necessary task. Generally, different feature extraction methods are used to assign expressive and invariant features to each image such that the search for similar images comes down to feature classification and\/or matching. The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes. We combine neural codes for global classification with Radon barcodes for the final retrieval. We also examine image search based on regions of interest (ROI) matching after image retrieval. The IRMA dataset with more than 14,000 x-rays images is used to evaluate the performance of our method. Experimental results show that our approach is superior to many published works.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.04676v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"61327\", \"87413\", \"119495\", \"184473\", \"249570\", \"254673\", \"273689\", \"276945\"], \"outgoing_citations\": [\"298712\", \"306547\", \"310085\", \"312582\"]}","task_split":"paper_retrieval"}
{"document_id":"294495","document_content":"# Accessing accurate documents by mining auxiliary document information\n## Categories\n- Information Retrieval\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nEarlier techniques of text mining included algorithms like k-means, Naive Bayes, SVM which classify and cluster the text document for mining relevant information about the documents. The need for improving the mining techniques has us searching for techniques using the available algorithms. This paper proposes one technique which uses the auxiliary information that is present inside the text documents to improve the mining. This auxiliary information can be a description to the content. This information can be either useful or completely useless for mining. The user should assess the worth of the auxiliary information before considering this technique for text mining. In this paper, a combination of classical clustering algorithms is used to mine the datasets. The algorithm runs in two stages which carry out mining at different levels of abstraction. The clustered documents would then be classified based on the necessary groups. The proposed technique is aimed at improved results of document clustering.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.04558v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.LG\", \"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Machine Learning\", \"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"335857\", \"335885\"]}","task_split":"paper_retrieval"}
{"document_id":"294545","document_content":"# Facial expression recognition based on local region specific features and support vector machines\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFacial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s11042-016-3418-y\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"13026\", \"45256\", \"199281\", \"206532\", \"244237\", \"290805\"], \"outgoing_citations\": [\"294778\"]}","task_split":"paper_retrieval"}
{"document_id":"294694","document_content":"# Cross-stitch Networks for Multi-task Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nMulti-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning. Specifically, we propose a new sharing unit: \"cross-stitch\" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.03539v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"5297\", \"37300\", \"42089\", \"124304\", \"163138\", \"14\", \"6545\", \"9421\", \"13040\", \"16486\", \"18688\", \"26974\", \"32344\", \"33660\", \"39614\", \"40848\", \"43243\", \"44437\", \"44833\", \"48797\", \"54923\", \"65442\", \"72728\", \"82707\", \"83553\", \"90200\", \"123946\", \"128476\", \"260314\", \"6837\", \"7309\", \"8324\", \"8322\", \"9075\", \"7883\", \"10457\", \"12088\", \"13378\", \"13461\", \"13740\", \"13843\", \"14741\", \"16826\", \"18244\", \"19442\", \"17487\", \"22088\", \"22482\", \"22951\", \"23326\", \"24628\", \"27582\", \"28470\", \"28999\", \"30404\", \"32662\", \"32621\", \"33043\", \"33424\", \"33634\", \"33838\", \"8657\", \"36773\", \"38069\", \"38428\", \"39028\", \"39363\", \"40461\", \"42790\", \"42927\", \"44039\", \"86256\", \"46777\", \"48497\", \"137129\", \"49676\", \"49741\", \"50803\", \"51657\", \"52808\", \"52846\", \"53025\", \"55486\", \"57374\", \"59409\", \"59678\", \"60699\", \"61705\", \"62867\", \"64767\", \"64896\", \"65955\", \"65968\", \"58688\", \"66236\", \"67030\", \"73085\", \"74758\", \"75031\", \"76635\", \"76685\", \"79908\", \"80098\", \"87603\", \"91541\", \"96468\", \"97085\", \"87000\", \"98172\", \"99185\", \"102495\", \"102974\", \"105733\", \"106274\", \"111756\", \"112862\", \"116302\", \"116729\", \"117999\", \"126963\", \"128244\", \"141709\", \"142773\", \"142782\", \"143357\", \"160371\", \"165813\", \"236349\", \"80084\", \"87456\", \"90521\", \"91517\", \"92546\", \"93830\", \"94441\", \"94626\", \"96196\", \"97643\", \"100171\", \"101301\", \"104405\", \"104571\", \"106199\", \"107106\", \"107695\", \"109911\", \"110845\", \"111367\", \"112109\", \"112242\", \"114607\", \"114822\", \"114937\", \"117394\", \"119017\", \"119262\", \"119213\", \"124290\", \"127383\", \"130456\", \"131531\", \"131753\", \"132583\", \"133352\", \"133975\", \"180220\", \"135426\", \"135865\", \"136759\", \"140256\", \"140480\", \"143844\", \"130630\", \"145595\", \"146533\", \"146484\", \"148164\", \"148987\", \"152680\", \"153883\", \"154392\", \"155591\", \"156718\", \"160609\", \"170312\", \"171585\", \"173646\", \"192101\", \"204212\", \"245416\", \"266686\", \"91517\", \"157044\", \"161137\", \"162328\", \"159829\", \"159047\", \"164278\", \"151201\", \"165675\", \"166964\", \"167462\", \"168546\", \"170176\", \"170741\", \"171834\", \"172237\", \"172964\", \"175672\", \"176158\", \"176337\", \"176918\", \"178397\", \"178372\", \"181809\", \"183821\", \"183760\", \"184140\", \"187872\", \"183187\", \"188380\", \"188515\", \"191434\", \"191495\", \"191589\", \"192072\", \"193301\", \"193901\", \"195445\", \"189949\", \"197741\", \"201602\", \"201962\", \"202158\", \"203580\", \"199250\", \"206055\", \"208310\", \"209172\", \"216181\", \"212647\", \"218124\", \"228999\", \"232067\", \"234604\", \"237399\", \"240608\", \"242504\", \"244243\", \"246082\", \"257928\", \"265708\", \"296192\", \"208067\", \"211725\", \"212348\", \"212772\", \"213232\", \"213452\", \"214692\", \"219902\", \"220565\", \"220126\", \"224249\", \"225149\", \"224679\", \"230234\", \"230672\", \"233502\", \"231489\", \"235067\", \"235122\", \"235681\", \"239335\", \"239450\", \"251209\", \"251499\", \"251797\", \"266003\", \"273937\", \"289331\", \"251780\", \"252170\", \"255272\", \"258218\", \"259546\", \"175604\", \"260702\", \"263636\", \"264009\", \"268518\", \"274612\", \"279725\", \"280302\", \"281169\", \"282387\", \"283724\", \"285178\", \"311668\", \"287879\"], \"outgoing_citations\": [\"300646\", \"313205\", \"319827\", \"320908\", \"321620\", \"327379\", \"328124\"]}","task_split":"paper_retrieval"}
{"document_id":"294778","document_content":"# Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines\n## Categories\n- Computer Vision and Pattern Recognition\n- 68T01\n- I.4; I.5\n## Abstract\nFacial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3390\/s130607714\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"68T01\", \"I.4; I.5\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"68T01\", \"I.4; I.5\"], \"incoming_citations\": [\"13026\", \"25172\", \"45256\", \"51376\", \"64085\", \"190298\", \"199281\", \"206532\", \"233170\", \"249993\", \"246180\", \"286523\", \"294545\", \"294546\", \"300285\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"294799","document_content":"# Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text\n## Categories\n- Computation and Language\n## Abstract\nIn this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at http:\/\/bit.ly\/csmt-parser-api .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.03136v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"72513\", \"73442\", \"121671\", \"74103\", \"106795\", \"110853\", \"180252\", \"193839\", \"95512\", \"231014\", \"235425\", \"270085\", \"278030\", \"283117\", \"164467\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"294983","document_content":"# CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nConvolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task. In this work, we propose to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner. We employ state-of-the-art retrieval and Structure-from-Motion (SfM) methods to obtain 3D models, which are used to guide the selection of the training data for CNN fine-tuning. We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.02426v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"5294\", \"5681\", \"34971\", \"41272\", \"52513\", \"70613\", \"73146\", \"85376\", \"112364\", \"118203\", \"17603\", \"19708\", \"21970\", \"26135\", \"29310\", \"31118\", \"42853\", \"183974\", \"46262\", \"28833\", \"52533\", \"141725\", \"77995\", \"83678\", \"90913\", \"100159\", \"107996\", \"115346\", \"129504\", \"138773\", \"89986\", \"78868\", \"218749\", \"98899\", \"101649\", \"105747\", \"106988\", \"109113\", \"110044\", \"112934\", \"120624\", \"106927\", \"129986\", \"134085\", \"135052\", \"135459\", \"136732\", \"137528\", \"139475\", \"140411\", \"142055\", \"142604\", \"145646\", \"145738\", \"146346\", \"148469\", \"153842\", \"173124\", \"178329\", \"221269\", \"223220\", \"162905\", \"144958\", \"169268\", \"169444\", \"172394\", \"174419\", \"176318\", \"178605\", \"180184\", \"180750\", \"180562\", \"186173\", \"186126\", \"188194\", \"189487\", \"189920\", \"190669\", \"190678\", \"191270\", \"191610\", \"193945\", \"200203\", \"204128\", \"205616\", \"207283\", \"208242\", \"208493\", \"220216\", \"225967\", \"231046\", \"236908\", \"239587\", \"241439\", \"243056\", \"250135\", \"251473\", \"254770\", \"256887\", \"261255\", \"274746\", \"206947\", \"207767\", \"208427\", \"208687\", \"209520\", \"212048\", \"134853\", \"221064\", \"156995\", \"222623\", \"223359\", \"224369\", \"227262\", \"228831\", \"232564\", \"234121\", \"237248\", \"237299\", \"238392\", \"255989\", \"244938\", \"245750\", \"248935\", \"249448\", \"249591\", \"250094\", \"256343\", \"267404\", \"271025\", \"280225\", \"287103\", \"221412\", \"249642\", \"250152\", \"254346\", \"258948\", \"259307\", \"259795\", \"260702\", \"260972\", \"261297\", \"261707\", \"262169\", \"260306\", \"265083\", \"264978\", \"266559\", \"268341\", \"269054\", \"269848\", \"270054\", \"271841\", \"271856\", \"272445\", \"272805\", \"274926\", \"275310\", \"277635\", \"279439\", \"280378\", \"281953\", \"280325\", \"282939\", \"295204\", \"301572\"], \"outgoing_citations\": [\"295204\", \"295468\", \"301572\", \"303067\", \"304501\", \"314584\", \"320036\", \"320052\", \"327036\", \"327312\", \"331429\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"295204","document_content":"# Deep Image Retrieval: Learning global representations for image search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors. In contrast to previous works employing pre-trained deep networks as a black box to produce features, our method leverages a deep architecture trained for the specific task of image retrieval. Our contribution is twofold: (i) we leverage a ranking framework to learn convolution and projection weights that are used to build the region features; and (ii) we employ a region proposal network to learn which regions should be pooled to form the final global descriptor. We show that using clean training data is key to the success of our approach. To that aim, we use a large scale but noisy landmark dataset and develop an automatic cleaning approach. The proposed architecture produces a global image representation in a single forward pass. Our approach significantly outperforms previous approaches based on global descriptors on standard datasets. It even surpasses most prior works based on costly local descriptor indexing and spatial verification. Additional material is available at www.xrce.xerox.com\/Deep-Image-Retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.01325v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9497\", \"36808\", \"267\", \"4014\", \"5681\", \"7251\", \"26324\", \"34971\", \"41058\", \"41272\", \"43453\", \"52513\", \"63954\", \"70613\", \"70868\", \"73146\", \"85376\", \"97991\", \"102580\", \"107975\", \"109960\", \"136098\", \"518\", \"1407\", \"2169\", \"6827\", \"13707\", \"15391\", \"21970\", \"22094\", \"25550\", \"27673\", \"28439\", \"31095\", \"31214\", \"31545\", \"31743\", \"32028\", \"37306\", \"42005\", \"183974\", \"28833\", \"48791\", \"51736\", \"52533\", \"54765\", \"56145\", \"59292\", \"59357\", \"59934\", \"64950\", \"70156\", \"70460\", \"76106\", \"77995\", \"78142\", \"222397\", \"85889\", \"87691\", \"90913\", \"106263\", \"109376\", \"115346\", \"120031\", \"124630\", \"272822\", \"78966\", \"81646\", \"98899\", \"100788\", \"103466\", \"186010\", \"105896\", \"106210\", \"106588\", \"110044\", \"111248\", \"112422\", \"112888\", \"112934\", \"122539\", \"123150\", \"126349\", \"129986\", \"133188\", \"134808\", \"87866\", \"135489\", \"137528\", \"138397\", \"139321\", \"140411\", \"142055\", \"142604\", \"145738\", \"147155\", \"148469\", \"190577\", \"193218\", \"221269\", \"246254\", \"260452\", \"112122\", \"159424\", \"162905\", \"163030\", \"164610\", \"167361\", \"169444\", \"170436\", \"171120\", \"172155\", \"172394\", \"174176\", \"175014\", \"177081\", \"177755\", \"179469\", \"180184\", \"180750\", \"181202\", \"181615\", \"186173\", \"187195\", \"188194\", \"190678\", \"191788\", \"193735\", \"193945\", \"194673\", \"198922\", \"200458\", \"205616\", \"207283\", \"207598\", \"208242\", \"208493\", \"211598\", \"213700\", \"220216\", \"231046\", \"236908\", \"239587\", \"243056\", \"245667\", \"248322\", \"249538\", \"250135\", \"251473\", \"254643\", \"256887\", \"261255\", \"206776\", \"207767\", \"208829\", \"209520\", \"212048\", \"212243\", \"215570\", \"217551\", \"219262\", \"221285\", \"222623\", \"223359\", \"223466\", \"224853\", \"227120\", \"227893\", \"228130\", \"228831\", \"229628\", \"233440\", \"233661\", \"233733\", \"234121\", \"237299\", \"238392\", \"255989\", \"244938\", \"245223\", \"247613\", \"249591\", \"256343\", \"260279\", \"262552\", \"264032\", \"267404\", \"271025\", \"286767\", \"287103\", \"215570\", \"249199\", \"216387\", \"254812\", \"258441\", \"258948\", \"258937\", \"259795\", \"260577\", \"260972\", \"262166\", \"262169\", \"263387\", \"266559\", \"268341\", \"269848\", \"270753\", \"272445\", \"274926\", \"277635\", \"280378\", \"281953\", \"289755\", \"292884\", \"280325\", \"294983\", \"301572\"], \"outgoing_citations\": [\"294983\", \"301572\", \"303067\", \"304501\", \"314231\", \"314584\", \"328024\", \"328051\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"295222","document_content":"# Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language\n## Categories\n- Physics and Society\n- Social and Information Networks\n- Computation and Language\n## Abstract\nIn this work we extend previous analyses of linguistic networks by adopting a multi-layer network framework for modelling the human mental lexicon, i.e. an abstract mental repository where words and concepts are stored together with their linguistic patterns. Across a three-layer linguistic multiplex, we model English words as nodes and connect them according to (i) phonological similarities, (ii) synonym relationships and (iii) free word associations. Our main aim is to exploit this multi-layered structure to explore the influence of phonological and semantic relationships on lexicon assembly over time. We propose a model of lexicon growth which is driven by the phonological layer: words are suggested according to different orderings of insertion (e.g. shorter word length, highest frequency, semantic multiplex features) and accepted or rejected subject to constraints. We then measure times of network assembly and compare these to empirical data about the age of acquisition of words. In agreement with empirical studies in psycholinguistics, our results provide quantitative evidence for the hypothesis that word acquisition is driven by features at multiple levels of organisation within language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-3-319-30569-1_20\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"cs.SI\", \"cs.CL\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Social and Information Networks\", \"Computation and Language\"], \"incoming_citations\": [\"240555\"], \"outgoing_citations\": [\"323109\", \"341400\", \"345544\"]}","task_split":"paper_retrieval"}
{"document_id":"295291","document_content":"# Automatic Knowledge Base Evolution by Learning Instances\n## Categories\n- Artificial Intelligence\n- I.2.6\n## Abstract\nKnowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose full-automated ontology learning algorithm to generate refined knowledge base from incomplete knowledge base and rdf-triples. Our algorithm is data-driven approach which is based on the property of each instance. Ontology class is being elaborated by generalizing frequent property of its instances. By using that developed class information, each instance can find its most relatively matching class. By repeating these two steps, we achieve fully-automated ontology evolution from incomplete basic knowledge base.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.00869v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"I.2.6\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.6\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"295435","document_content":"# Building Machines That Learn and Think Like People\n## Categories\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nRecent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.00289v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CV\", \"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"264898\", \"26642\", \"81335\", \"109132\", \"112333\", \"129980\", \"170695\", \"187349\", \"193208\", \"209605\", \"504\", \"1087\", \"3045\", \"3797\", \"4639\", \"4722\", \"6214\", \"6445\", \"7249\", \"7529\", \"7794\", \"11995\", \"14192\", \"15670\", \"15887\", \"16208\", \"17868\", \"18980\", \"19940\", \"22043\", \"23386\", \"25315\", \"30636\", \"34887\", \"35435\", \"36000\", \"38128\", \"38319\", \"36251\", \"47401\", \"49462\", \"51371\", \"60854\", \"63054\", \"64734\", \"70887\", \"83889\", \"95451\", \"95534\", \"105385\", \"107932\", \"114079\", \"146022\", \"148742\", \"162248\", \"182259\", \"227779\", \"250219\", \"318\", \"950\", \"2320\", \"3784\", \"5578\", \"8285\", \"9040\", \"10004\", \"10395\", \"10416\", \"11406\", \"11424\", \"12943\", \"14022\", \"14068\", \"16582\", \"17103\", \"17809\", \"18244\", \"18432\", \"19486\", \"19478\", \"19895\", \"20214\", \"20920\", \"21483\", \"21606\", \"21837\", \"22395\", \"24451\", \"24452\", \"25164\", \"31658\", \"32234\", \"32844\", \"33207\", \"33320\", \"33601\", \"35445\", \"35833\", \"35918\", \"36325\", \"37142\", \"39599\", \"41041\", \"41104\", \"42116\", \"42269\", \"42376\", \"42276\", \"42387\", \"43379\", \"43557\", \"43653\", \"43651\", \"43851\", \"45254\", \"34531\", \"45803\", \"46808\", \"47882\", \"48192\", \"48311\", \"48660\", \"48672\", \"52082\", \"52988\", \"55273\", \"55652\", \"56560\", \"56885\", \"58236\", \"58382\", \"58965\", \"58915\", \"59150\", \"59175\", \"59467\", \"60348\", \"60526\", \"60961\", \"61509\", \"62099\", \"62117\", \"62373\", \"63038\", \"63434\", \"63559\", \"63850\", \"64437\", \"64499\", \"64910\", \"65114\", \"65332\", \"65982\", \"66198\", \"66233\", \"66630\", \"67061\", \"67358\", \"67522\", \"67829\", \"68053\", \"68587\", \"70171\", \"70211\", \"70382\", \"70922\", \"71164\", \"71387\", \"72785\", \"73174\", \"75085\", \"75643\", \"77246\", \"81987\", \"82744\", \"82806\", \"84445\", \"96490\", \"84667\", \"84768\", \"86412\", \"86722\", \"88340\", \"90350\", \"90959\", \"91715\", \"94315\", \"95428\", \"95935\", \"97289\", \"98711\", \"100538\", \"107208\", \"107682\", \"107839\", \"108069\", \"114384\", \"114972\", \"116081\", \"116200\", \"119177\", \"119579\", \"120758\", \"126896\", \"132041\", \"133135\", \"134858\", \"143580\", \"146400\", \"154847\", \"163235\", \"184569\", \"188761\", \"195033\", \"212942\", \"225974\", \"78162\", \"79679\", \"79914\", \"80799\", \"81601\", \"81778\", \"83802\", \"85661\", \"86326\", \"49082\", \"90324\", \"90658\", \"91004\", \"90831\", \"91429\", \"91567\", \"91844\", \"93568\", \"196578\", \"94710\", \"95058\", \"96451\", \"97845\", \"97877\", \"99316\", \"99988\", \"100660\", \"101310\", \"101382\", \"102464\", \"102538\", \"103955\", \"104523\", \"104568\", \"105688\", \"105893\", \"107126\", \"107129\", \"109953\", \"110210\", \"110241\", \"112980\", \"113052\", \"113227\", \"113356\", \"113551\", \"113715\", \"113549\", \"115164\", \"115348\", \"118558\", \"119217\", \"119002\", \"119715\", \"119937\", \"120540\", \"120466\", \"121542\", \"116023\", \"120643\", \"118089\", \"122438\", \"122857\", \"123128\", \"123819\", \"126128\", \"126130\", \"127218\", \"127376\", \"127461\", \"127678\", \"127845\", \"128000\", \"130780\", \"130767\", \"131431\", \"131660\", \"132207\", \"132719\", \"133949\", \"135134\", \"135912\", \"137220\", \"137359\", \"137873\", \"138836\", \"136991\"], \"outgoing_citations\": [\"278541\", \"287028\", \"290433\", \"293570\", \"294142\", \"295736\", \"296448\", \"297162\", \"298182\", \"302924\", \"302952\", \"302970\", \"303003\", \"303553\", \"304880\", \"305802\", \"310171\", \"311436\", \"311585\", \"316183\", \"317213\", \"322384\", \"331740\", \"333502\", \"334299\", \"350010\", \"353417\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"295473","document_content":"# Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding\n## Categories\n- Computation and Language\n## Abstract\nThe goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.00117v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"35001\", \"30876\", \"65563\", \"211624\", \"84921\", \"89849\", \"95921\", \"124328\", \"127777\", \"129221\", \"135568\", \"173100\", \"156185\", \"180447\", \"219814\", \"211411\", \"226294\", \"234683\", \"234684\", \"243475\", \"246324\", \"244978\", \"248799\", \"261770\", \"276159\", \"286860\"], \"outgoing_citations\": [\"298750\", \"300449\", \"300505\", \"308310\"]}","task_split":"paper_retrieval"}
{"document_id":"295483","document_content":"# Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection\n## Categories\n- Computation and Language\n## Abstract\nRecurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recognition, image caption generation, visual question answering and machine translation. In this paper, neural attention model is applied on two sequence classification tasks, dialogue act detection and key term extraction. In the sequence labeling tasks, the model input is a sequence, and the output is the label of the input sequence. The major difficulty of sequence labeling is that when the input sequence is long, it can include many noisy or irrelevant part. If the information in the whole sequence is treated equally, the noisy or irrelevant part may degrade the classification performance. The attention mechanism is helpful for sequence classification task because it is capable of highlighting important part among the entire sequence for the classification task. The experimental results show that with the attention mechanism, discernible improvements were achieved in the sequence labeling task considered here. The roles of the attention mechanism in the tasks are further analyzed and visualized in this paper.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1604.00077v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"265\", \"89900\", \"129355\", \"132711\", \"152736\", \"164111\", \"182190\", \"187315\", \"188064\", \"191150\", \"192223\", \"193410\", \"210090\", \"213458\", \"213935\", \"233064\", \"240264\", \"259051\", \"270825\", \"163409\"], \"outgoing_citations\": [\"296068\", \"303224\", \"311886\"]}","task_split":"paper_retrieval"}
{"document_id":"295499","document_content":"# Neural Language Correction with Character-Based Attention\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nNatural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism. By operating at the character level, the network avoids the problem of out-of-vocabulary words. We illustrate the flexibility of our approach on dataset of noisy, user-generated text collected from an English learner forum. When combined with a language model, our method achieves a state-of-the-art $F_{0.5}$-score on the CoNLL 2014 Shared Task. We further demonstrate that training the network on additional data with synthesized errors can improve performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.09727v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"132173\", \"9180\", \"20314\", \"23566\", \"33363\", \"72331\", \"145195\", \"164336\", \"96435\", \"125866\", \"127163\", \"127512\", \"145190\", \"162976\", \"170712\", \"172036\", \"173424\", \"175234\", \"182495\", \"183261\", \"191276\", \"193753\", \"198379\", \"220934\", \"212029\", \"216791\", \"219068\", \"225890\", \"231519\", \"233496\", \"230192\", \"233722\", \"235398\", \"235400\", \"244130\", \"255492\", \"259275\", \"261850\", \"259966\", \"269294\", \"281976\", \"282874\", \"292208\"], \"outgoing_citations\": [\"322537\", \"338253\"]}","task_split":"paper_retrieval"}
{"document_id":"295547","document_content":"# Learning Compatibility Across Categories for Heterogeneous Item Recommendation\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIdentifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible. In domains like clothing recommendation, this task is particularly challenging since a successful system should be capable of handling a large corpus of items, a huge amount of relationships among them, as well as the high-dimensional and semantically complicated features involved. Furthermore, the human notion of \"compatibility\" to capture goes beyond mere similarity: For two items to be compatible---whether jeans and a t-shirt, or a laptop and a charger---they should be similar in some ways, but systematically different in others.   In this paper we propose a novel method, Monomer, to learn complicated and heterogeneous relationships between items in product recommendation settings. Recently, scalable methods have been developed that address this task by learning similarity metrics on top of the content of the products involved. Here our method relaxes the metricity assumption inherent in previous work and models multiple localized notions of 'relatedness,' so as to uncover ways in which related items should be systematically similar, and systematically different. Quantitatively, we show that our system achieves state-of-the-art performance on large-scale compatibility prediction tasks, especially in cases where there is substantial heterogeneity between related items. Qualitatively, we demonstrate that richer notions of compatibility can be learned that go beyond similarity, and that our model can make effective recommendations of heterogeneous content.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.09473v3\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Information Retrieval\"], \"incoming_citations\": [\"7582\", \"27805\", \"37684\", \"51144\", \"51736\", \"58028\", \"160391\", \"105275\", \"117597\", \"120377\", \"122928\", \"130988\", \"143622\", \"151265\", \"169834\", \"180307\", \"151405\", \"170088\", \"174295\", \"173684\", \"188249\", \"189714\", \"198113\", \"204827\", \"216802\", \"226422\", \"233849\", \"237849\", \"248034\", \"248570\", \"258652\", \"261756\"], \"outgoing_citations\": [\"306030\", \"310387\", \"335603\"]}","task_split":"paper_retrieval"}
{"document_id":"295639","document_content":"# Rich Image Captioning in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include high quality caption quality with respect to human judgments, out-of-domain data handling, and low latency required in many applications. Built on top of a state-of-the-art framework, we developed a deep vision model that detects a broad range of visual concepts, an entity recognition model that identifies celebrities and landmarks, and a confidence model for the caption output. Experimental results show that our caption engine outperforms previous state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO) and out of-domain datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.09016v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"95561\", \"96985\", \"25846\", \"47101\", \"98018\", \"142094\", \"145513\", \"194216\", \"141957\", \"149491\", \"154752\", \"157078\", \"174008\", \"192648\", \"208555\", \"215181\", \"205227\", \"189921\", \"225101\", \"227880\", \"229793\", \"234753\", \"235190\", \"248164\", \"278948\", \"279002\", \"279676\", \"279740\", \"281152\", \"296279\"], \"outgoing_citations\": [\"302621\", \"313196\"]}","task_split":"paper_retrieval"}
{"document_id":"295951","document_content":"# Semantic Properties of Customer Sentiment in Tweets\n## Categories\n- Computation and Language\n- Information Retrieval\n- Social and Information Networks\n- Machine Learning\n## Abstract\nAn increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/WAINA.2014.151\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"296049","document_content":"# BreakingNews: Article Annotation by Image and Text Processing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nBuilding upon recent Deep Neural Network architectures, current approaches lying in the intersection of computer vision and natural language processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of News articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce new deep learning methods that address source detection, popularity prediction, article illustration and geolocation of articles. An adaptive CNN architecture is proposed, that shares most of the structure for all the tasks, and is suitable for multitask and transfer learning. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and popularity metrics). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.07141v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"35427\", \"55418\", \"65753\", \"3784\", \"25348\", \"33417\", \"95829\", \"31505\", \"105987\", \"129737\", \"130657\", \"125259\", \"135336\", \"162660\", \"157257\", \"166303\", \"187732\", \"192648\", \"260612\", \"260611\", \"298347\"], \"outgoing_citations\": [\"298153\", \"302621\", \"303011\", \"303756\", \"306975\", \"320003\", \"321621\", \"322022\", \"323775\", \"336196\", \"345941\", \"362304\"]}","task_split":"paper_retrieval"}
{"document_id":"296150","document_content":"# Learning Executable Semantic Parsers for Natural Language Understanding\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nFor building question answering systems and natural language interfaces, semantic parsing has emerged as an important and powerful paradigm. Semantic parsers map natural language into logical forms, the classic representation for many important linguistic phenomena. The modern twist is that we are interested in learning semantic parsers from data, which introduces a new layer of statistical and computational issues. This article lays out the components of a statistical semantic parser, highlighting the key challenges. We will see that semantic parsing is a rich fusion of the logical and the statistical world, and that this fusion will play an integral role in the future of natural language understanding systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.06677v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"1631\", \"21262\", \"37142\", \"40436\", \"41041\", \"42116\", \"43967\", \"63559\", \"65216\", \"77129\", \"91749\", \"81185\", \"85729\", \"89712\", \"97826\", \"117275\", \"127678\", \"128387\", \"132812\", \"137584\", \"144895\", \"158894\", \"160242\", \"175055\", \"178997\", \"188236\", \"200747\", \"203087\", \"207494\", \"218187\", \"219107\", \"250549\", \"269608\", \"213822\", \"217956\", \"220575\", \"225309\", \"221540\", \"232138\", \"232584\", \"234382\", \"234579\", \"239867\", \"250256\", \"245533\", \"256438\", \"266567\", \"268224\", \"272408\", \"279428\", \"277361\", \"281251\"], \"outgoing_citations\": [\"308610\", \"321546\", \"340201\", \"356833\", \"357182\"]}","task_split":"paper_retrieval"}
{"document_id":"296274","document_content":"# Tree-to-Sequence Attentional Neural Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nMost of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. Experimental results on the WAT'15 English-to-Japanese dataset demonstrate that our proposed model considerably outperforms sequence-to-sequence attentional NMT models and compares favorably with the state-of-the-art tree-to-string SMT system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.06075v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"54897\", \"7836\", \"55676\", \"59368\", \"72647\", \"5730\", \"35733\", \"54802\", \"56945\", \"69733\", \"77990\", \"77667\", \"77726\", \"95403\", \"96726\", \"98120\", \"115783\", \"126672\", \"127543\", \"128185\", \"133899\", \"141406\", \"150155\", \"158554\", \"168201\", \"116488\", \"161898\", \"168539\", \"168516\", \"169436\", \"170618\", \"171238\", \"173424\", \"179996\", \"181468\", \"182271\", \"180658\", \"187326\", \"193497\", \"197572\", \"202454\", \"218858\", \"219049\", \"220350\", \"221557\", \"234729\", \"217899\", \"218997\", \"209678\", \"219933\", \"219758\", \"220020\", \"168542\", \"225736\", \"226756\", \"234587\", \"236917\", \"239777\", \"242644\", \"249990\", \"250737\", \"252642\", \"256948\", \"269792\", \"240157\", \"256740\", \"259199\", \"261055\", \"260977\", \"267365\", \"267460\", \"268684\", \"268788\", \"268812\", \"269888\", \"272328\", \"274183\", \"274499\", \"274776\", \"281169\", \"290777\", \"290992\"], \"outgoing_citations\": [\"300543\", \"300604\", \"301483\", \"302777\", \"304842\", \"320814\", \"322537\", \"324965\"]}","task_split":"paper_retrieval"}
{"document_id":"296685","document_content":"# Robust Scene Text Recognition with Automatic Rectification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. RARE is a specially-designed deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing, an image is firstly rectified via a predicted Thin-Plate-Spline (TPS) transformation, into a more \"readable\" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State-of-the-art or highly-competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.03915v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"77004\", \"3662\", \"6241\", \"13927\", \"25766\", \"37135\", \"50775\", \"50917\", \"57829\", \"66255\", \"78763\", \"94155\", \"121818\", \"180823\", \"4188\", \"7620\", \"8109\", \"21983\", \"24824\", \"27768\", \"29428\", \"29640\", \"33363\", \"33358\", \"33798\", \"35666\", \"36974\", \"42497\", \"43153\", \"43037\", \"48822\", \"48901\", \"49128\", \"50591\", \"53750\", \"55703\", \"59727\", \"62619\", \"63261\", \"63761\", \"64528\", \"64650\", \"67749\", \"78142\", \"79422\", \"81960\", \"82221\", \"93345\", \"99340\", \"101055\", \"113794\", \"116951\", \"122764\", \"133054\", \"147463\", \"163092\", \"184587\", \"247723\", \"84406\", \"90549\", \"99077\", \"100417\", \"100709\", \"111035\", \"111991\", \"114759\", \"115073\", \"120420\", \"122765\", \"123550\", \"126244\", \"126431\", \"134664\", \"135021\", \"136218\", \"136843\", \"136831\", \"140021\", \"141887\", \"143280\", \"144173\", \"145587\", \"149329\", \"150060\", \"152219\", \"162477\", \"170229\", \"189663\", \"192671\", \"202305\", \"210634\", \"225279\", \"155455\", \"156244\", \"162650\", \"166353\", \"166621\", \"167879\", \"169411\", \"169711\", \"170332\", \"172884\", \"173076\", \"173837\", \"174894\", \"174928\", \"180931\", \"183268\", \"192483\", \"193513\", \"193824\", \"195963\", \"201515\", \"203406\", \"205988\", \"211597\", \"211851\", \"217463\", \"222513\", \"229426\", \"205602\", \"202645\", \"204872\", \"207071\", \"207786\", \"208400\", \"211645\", \"216815\", \"176625\", \"223824\", \"225336\", \"229565\", \"232706\", \"234523\", \"239535\", \"240965\", \"245899\", \"247460\", \"250738\", \"251341\", \"253692\", \"256108\", \"256711\", \"256785\", \"258388\", \"260012\", \"261365\", \"269108\", \"294753\", \"248133\"], \"outgoing_citations\": [\"294606\", \"320148\", \"320837\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"296798","document_content":"# Instance-Aware Hashing for Multi-Label Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSimilarity-preserving hashing is a commonly used method for nearest neighbour search in large-scale image retrieval. For image retrieval, deep-networks-based hashing methods are appealing since they can simultaneously learn effective image representations and compact hash codes. This paper focuses on deep-networks-based hashing for multi-label images, each of which may contain objects of multiple categories. In most existing hashing methods, each image is represented by one piece of hash code, which is referred to as semantic hashing. This setting may be suboptimal for multi-label image retrieval. To solve this problem, we propose a deep architecture that learns \\textbf{instance-aware} image representations for multi-label image data, which are organized in multiple groups, with each group containing the features for one category. The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category. Extensive evaluations conducted on several benchmark datasets demonstrate that, for both semantic hashing and category-aware hashing, the proposed method shows substantial improvement over the state-of-the-art supervised and unsupervised hashing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2016.2545300\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85376\", \"30132\", \"72302\", \"71954\", \"114350\", \"82163\", \"124432\", \"239660\", \"155612\", \"211595\", \"232040\", \"240768\", \"237774\", \"264646\", \"267210\", \"281123\", \"294367\"], \"outgoing_citations\": [\"314568\", \"318535\", \"321153\", \"328038\", \"336408\", \"340567\", \"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"296839","document_content":"# Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code\n## Categories\n- Programming Languages\n- I.2.6; C.1.4\n- 68N20\n## Abstract\nThe current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.03022v2\", \"primary_category\": \"cs.PL\", \"categories\": [\"I.2.6; C.1.4\", \"68N20\", \"cs.PL\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"I.2.6; C.1.4\", \"68N20\", \"Programming Languages\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"296862","document_content":"# Fast Training of Triplet-based Deep Binary Embedding Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task.   We make use of a triplet loss because this has been shown to be most effective for ranking problems.   However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples.   To address this issue, we propose to formulate high-order binary codes learning as a multi-label classification problem by explicitly separating learning into two interleaved stages.   To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary code which serve as the label of each training datum.   In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training binary CNN classifiers.   An incremental\/interleaved optimization strategy is proffered to ensure that these two steps are interactive with each other during training for better accuracy.   We conduct experiments on several benchmark datasets, which demonstrate both improved training time (by as much as two orders of magnitude) as well as producing state-of-the-art hashing for various retrieval tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.02844v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"85376\", \"35695\", \"48708\", \"72768\", \"71954\", \"113783\", \"208542\", \"146612\", \"146770\", \"158244\", \"235344\", \"239316\", \"136142\", \"170190\", \"192549\", \"200515\", \"208322\", \"208382\", \"232040\", \"240768\", \"241439\", \"252948\", \"222134\", \"192280\", \"237849\", \"236814\", \"249448\", \"251069\", \"259748\", \"265613\", \"256474\", \"259061\", \"263034\", \"269692\", \"269545\", \"284456\", \"298792\", \"283789\", \"287659\"], \"outgoing_citations\": [\"307882\", \"308960\", \"314568\", \"316473\", \"318091\", \"318535\", \"319454\", \"321751\", \"331485\", \"340567\", \"346675\", \"348109\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"297233","document_content":"# Super Mario as a String: Platformer Level Generation Via LSTMs\n## Categories\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nThe procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long Short-Term Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario Brothers levels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.00930v2\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.NE\", \"cs.LG\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"140757\", \"33214\", \"33220\", \"33227\", \"33228\", \"35228\", \"40705\", \"67124\", \"95477\", \"112786\", \"94739\", \"96766\", \"104584\", \"104574\", \"107909\", \"109632\", \"109729\", \"111464\", \"112572\", \"117983\", \"122922\", \"124521\", \"125408\", \"133812\", \"139814\", \"143545\", \"145664\", \"153614\", \"163503\", \"202457\", \"218411\", \"216607\", \"216608\", \"59937\", \"226436\", \"233501\", \"274886\", \"289709\"], \"outgoing_citations\": [\"297797\", \"320332\"]}","task_split":"paper_retrieval"}
{"document_id":"297268","document_content":"# Continuous Deep Q-Learning with Model-based Acceleration\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Robotics\n- Systems and Control\n## Abstract\nModel-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However, the sample complexity of model-free algorithms, particularly when using high-dimensional function approximators, tends to limit their applicability to physical systems. In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks. We propose two complementary techniques for improving the efficiency of such algorithms. First, we derive a continuous variant of the Q-learning algorithm, which we call normalized adantage functions (NAF), as an alternative to the more commonly used policy gradient and actor-critic methods. NAF representation allows us to apply Q-learning with experience replay to continuous tasks, and substantially improves performance on a set of simulated robotic control tasks. To further improve the efficiency of our approach, we explore the use of learned models for accelerating model-free reinforcement learning. We show that iteratively refitted local linear models are especially effective for this, and demonstrate substantially faster learning on domains where such models are applicable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1603.00748v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.RO\", \"cs.SY\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Robotics\", \"Systems and Control\"], \"incoming_citations\": [\"1585\", \"18939\", \"34882\", \"8426\", \"15009\", \"15090\", \"20642\", \"29391\", \"29359\", \"40876\", \"44419\", \"63862\", \"73680\", \"76756\", \"87712\", \"109082\", \"110593\", \"111065\", \"114662\", \"138124\", \"138348\", \"148134\", \"189255\", \"4334\", \"4627\", \"7653\", \"9326\", \"17658\", \"18761\", \"25823\", \"27991\", \"20632\", \"34811\", \"34863\", \"37807\", \"38349\", \"37777\", \"39300\", \"40116\", \"43894\", \"143967\", \"52145\", \"54437\", \"54549\", \"56639\", \"57438\", \"58000\", \"58236\", \"58685\", \"60511\", \"61107\", \"61166\", \"62612\", \"62805\", \"67807\", \"69040\", \"71397\", \"74841\", \"80939\", \"85594\", \"87213\", \"89758\", \"91052\", \"96415\", \"96784\", \"99925\", \"99953\", \"103481\", \"110439\", \"120766\", \"122888\", \"126744\", \"136776\", \"141419\", \"149899\", \"149829\", \"155590\", \"177272\", \"204512\", \"233639\", \"59162\", \"115252\", \"78162\", \"79836\", \"87269\", \"87950\", \"88020\", \"93340\", \"196578\", \"94703\", \"98452\", \"100937\", \"101003\", \"193470\", \"112547\", \"112714\", \"113443\", \"114689\", \"118995\", \"119020\", \"120385\", \"124294\", \"125165\", \"127236\", \"86650\", \"128488\", \"128637\", \"129599\", \"130329\", \"130430\", \"132951\", \"133480\", \"134432\", \"135645\", \"135919\", \"135897\", \"103314\", \"139927\", \"142373\", \"146034\", \"146535\", \"147046\", \"149121\", \"152531\", \"152732\", \"154131\", \"158625\", \"159175\", \"159918\", \"161295\", \"162810\", \"163248\", \"164670\", \"167862\", \"167829\", \"168418\", \"178094\", \"179805\", \"180884\", \"181883\", \"184341\", \"190367\", \"215780\", \"224848\", \"245461\", \"146751\", \"148969\", \"151909\", \"152120\", \"153521\", \"154211\", \"154513\", \"157947\", \"157162\", \"160120\", \"160980\", \"161097\", \"162568\", \"164054\", \"164141\", \"150331\", \"149726\", \"165829\", \"168803\", \"169797\", \"170765\", \"171722\", \"172569\", \"173229\", \"162379\", \"174225\", \"178186\", \"179221\", \"179948\", \"180123\", \"180319\", \"181678\", \"181593\", \"178932\", \"184645\", \"186841\", \"187389\", \"187685\", \"188362\", \"189111\", \"190556\", \"190693\", \"192739\", \"192830\", \"193972\", \"195601\", \"196361\", \"197904\", \"199000\", \"198355\", \"200574\", \"200708\", \"203127\", \"205317\", \"205323\", \"209631\", \"211172\", \"215975\", \"216112\", \"208351\", \"215627\", \"217589\", \"220103\", \"224491\", \"225108\", \"227836\", \"231360\", \"232394\", \"236923\", \"237044\", \"237229\", \"239153\", \"239264\", \"213236\", \"207479\", \"207983\", \"210573\", \"211473\", \"212130\", \"214765\", \"60407\", \"217715\", \"217919\", \"206231\", \"218936\", \"219167\", \"219842\", \"220513\", \"219992\", \"222363\", \"224416\", \"225147\", \"225464\", \"225762\", \"226635\", \"228303\", \"229171\", \"229333\", \"229638\", \"229890\", \"225539\", \"231459\", \"232414\", \"231408\", \"234762\", \"234984\", \"235189\", \"237232\", \"237848\", \"238287\", \"238475\", \"238577\", \"239159\", \"239841\", \"240405\", \"240471\", \"240602\", \"240617\", \"240815\", \"240800\", \"240918\", \"228028\", \"240664\", \"240208\", \"243943\", \"244081\", \"244838\", \"245316\", \"246692\", \"249438\", \"249685\", \"250811\", \"251867\", \"259045\", \"263658\", \"115175\", \"204330\", \"250337\", \"250707\", \"251941\", \"256193\", \"256402\", \"258199\", \"258265\", \"260018\", \"260770\", \"261190\", \"261493\", \"261983\", \"258834\", \"264786\", \"265091\", \"266490\", \"269235\", \"270783\", \"271952\", \"272030\", \"270826\", \"272668\", \"272987\", \"274102\", \"275056\", \"280866\", \"280992\", \"282684\", \"283410\", \"284122\", \"291677\", \"292020\", \"218934\", \"277163\", \"277504\", \"279883\", \"281468\", \"281549\", \"282503\", \"283200\", \"284470\", \"289357\", \"292763\"], \"outgoing_citations\": [\"303459\", \"304250\", \"306153\", \"310624\", \"317863\"]}","task_split":"paper_retrieval"}
{"document_id":"297423","document_content":"# Representation of linguistic form and function in recurrent neural networks\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe present novel methods for analyzing the activation patterns of RNNs from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings trained on predicting the representations of the visual scene corresponding to an input sentence, and predicting the next word in the same sentence. Based on our proposed method to estimate the amount of contribution of individual tokens in the input to the final prediction of the networks we show that the image prediction pathway: a) is sensitive to the information structure of the sentence b) pays selective attention to lexical categories and grammatical functions that carry semantic information c) learns to treat the same input token differently depending on its grammatical functions in the sentence. In contrast the language model is comparatively more sensitive to words with a syntactic function. Furthermore, we propose methods to ex- plore the function of individual hidden units in RNNs and show that the two pathways of the architecture in our case study contain specialized units tuned to patterns informative for the task, some of which can carry activations to later time steps to encode long-term dependencies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.08952v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"2537\", \"26824\", \"14944\", \"23774\", \"26390\", \"43485\", \"49051\", \"55157\", \"65235\", \"68753\", \"71845\", \"73130\", \"148058\", \"94387\", \"94934\", \"96238\", \"96273\", \"96658\", \"121303\", \"123279\", \"130699\", \"157528\", \"158554\", \"167134\", \"222721\", \"157229\", \"165362\", \"164070\", \"166110\", \"171991\", \"180302\", \"175039\", \"186017\", \"192036\", \"188812\", \"193560\", \"193634\", \"199724\", \"201960\", \"205056\", \"205161\", \"205219\", \"212036\", \"211702\", \"218314\", \"221448\", \"209174\", \"213840\", \"214464\", \"219156\", \"217233\", \"219575\", \"220159\", \"219965\", \"220460\", \"218291\", \"225176\", \"230056\", \"231957\", \"233400\", \"235234\", \"241113\", \"244688\", \"249243\", \"289715\", \"251412\", \"244357\", \"257080\", \"263945\", \"266669\", \"269127\", \"274549\", \"283764\", \"286594\", \"277288\", \"281225\", \"282874\", \"283007\"], \"outgoing_citations\": [\"277288\", \"281225\", \"286594\", \"298512\", \"301913\", \"302634\", \"302734\", \"304481\", \"306182\", \"311336\", \"311683\", \"311845\", \"319833\", \"319938\", \"336950\"]}","task_split":"paper_retrieval"}
{"document_id":"297452","document_content":"# Investigating practical linear temporal difference learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nOff-policy reinforcement learning has many applications including: learning from demonstration, learning multiple goal seeking policies in parallel, and representing predictive knowledge. Recently there has been an proliferation of new policy-evaluation algorithms that fill a longstanding algorithmic void in reinforcement learning: combining robustness to off-policy sampling, function approximation, linear complexity, and temporal difference (TD) updates. This paper contains two main contributions. First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms. Second, we perform an empirical comparison to elicit which of these new linear TD methods should be preferred in different situations, and make concrete suggestions about practical use.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.08771v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"14421\", \"24389\", \"45507\", \"53037\", \"177912\", \"114837\", \"179107\", \"120594\", \"120596\", \"209897\", \"220095\", \"225320\", \"273312\", \"274323\", \"279330\"], \"outgoing_citations\": [\"120607\", \"306461\", \"315982\", \"329162\", \"345989\", \"353380\"]}","task_split":"paper_retrieval"}
{"document_id":"297795","document_content":"# Sentence Similarity Learning by Lexical Decomposition and Composition\n## Categories\n- Computation and Language\n## Abstract\nMost conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.07019v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"31126\", \"49207\", \"130260\", \"25894\", \"52138\", \"72284\", \"135183\", \"142002\", \"160220\", \"76633\", \"80135\", \"94065\", \"97000\", \"149419\", \"173451\", \"129134\", \"158062\", \"167612\", \"163156\", \"186162\", \"220932\", \"213606\", \"215208\", \"225721\", \"229493\", \"244596\", \"245787\", \"248015\", \"248226\", \"255009\", \"248047\", \"245428\", \"257686\", \"268848\", \"274120\", \"278104\", \"289485\", \"290747\", \"294251\"], \"outgoing_citations\": [\"298513\", \"301397\", \"303029\", \"309810\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"297797","document_content":"# Toward Game Level Generation from Gameplay Videos\n## Categories\n- Artificial Intelligence\n## Abstract\nAlgorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.07721v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"140757\", \"33220\", \"33214\", \"41101\", \"61740\", \"175602\", \"230010\", \"261529\", \"289709\", \"296883\", \"297233\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"298067","document_content":"# Overview of Annotation Creation: Processes & Tools\n## Categories\n- Computation and Language\n- Human-Computer Interaction\n## Abstract\nCreating linguistic annotations requires more than just a reliable annotation scheme. Annotation can be a complex endeavour potentially involving many people, stages, and tools. This chapter outlines the process of creating end-to-end linguistic annotations, identifying specific tasks that researchers often perform. Because tool support is so central to achieving high quality, reusable annotations with low cost, the focus is on identifying capabilities that are necessary or useful for annotation tools, as well as common problems these tools present that reduce their utility. Although examples of specific tools are provided in many cases, this chapter concentrates more on abstract capabilities and problems because new tools appear continuously, while old tools disappear into disuse or disrepair. The two core capabilities tools must have are support for the chosen annotation scheme and the ability to work on the language under study. Additional capabilities are organized into three categories: those that are widely provided; those that often useful but found in only a few tools; and those that have as yet little or no available tool support.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.05753v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.HC\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"298095","document_content":"# Multi-layer Representation Learning for Medical Concepts\n## Categories\n- Machine Learning\n## Abstract\nLearning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skip-gram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.05568v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"8107\", \"19289\", \"34202\", \"37082\", \"41810\", \"52848\", \"88816\", \"103465\", \"152571\", \"158052\", \"193031\", \"3177\", \"23606\", \"25266\", \"33035\", \"34436\", \"34530\", \"39933\", \"45603\", \"48486\", \"57213\", \"58318\", \"61482\", \"71515\", \"73577\", \"79087\", \"83141\", \"102327\", \"109554\", \"152180\", \"177687\", \"189593\", \"140301\", \"74998\", \"87739\", \"90568\", \"91841\", \"96241\", \"97751\", \"98857\", \"107793\", \"109837\", \"116351\", \"117298\", \"118587\", \"122601\", \"156599\", \"144074\", \"145538\", \"148310\", \"149817\", \"152344\", \"172203\", \"181285\", \"196679\", \"282465\", \"152203\", \"149923\", \"161728\", \"163419\", \"164046\", \"165848\", \"166725\", \"167008\", \"169228\", \"153847\", \"175331\", \"176014\", \"180986\", \"182128\", \"189987\", \"190960\", \"194434\", \"209654\", \"215929\", \"230325\", \"231472\", \"207643\", \"213345\", \"218056\", \"223355\", \"223616\", \"225253\", \"229111\", \"234690\", \"237750\", \"242280\", \"243416\", \"255363\", \"264024\", \"249817\", \"256812\", \"259805\", \"263454\", \"264335\", \"274340\", \"275412\", \"279891\", \"286253\", \"287396\", \"289575\", \"297924\", \"303053\"], \"outgoing_citations\": [\"298490\", \"301647\", \"303053\", \"303550\", \"328529\"]}","task_split":"paper_retrieval"}
{"document_id":"298157","document_content":"# Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nCurrent systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e., incorrect for the entity mention's local context). We define a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic identification of correct type labels (type-paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.05307v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"27854\", \"144530\", \"24038\", \"43879\", \"71639\", \"73179\", \"77391\", \"94883\", \"96581\", \"128110\", \"129874\", \"132948\", \"132446\", \"181780\", \"164781\", \"180937\", \"182188\", \"187755\", \"188381\", \"190739\", \"196210\", \"204585\", \"221972\", \"224598\", \"224786\", \"224723\", \"234714\", \"239564\", \"252098\", \"255202\", \"259124\", \"262896\", \"271675\", \"276616\", \"277440\", \"281792\", \"291175\", \"304559\"], \"outgoing_citations\": [\"308627\", \"320886\"]}","task_split":"paper_retrieval"}
{"document_id":"298353","document_content":"# Science Question Answering using Instructional Materials\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n## Abstract\nWe provide a solution for elementary science test using instructional materials. We posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs and instructional materials), and uses what it learns to answer novel elementary science questions. Our evaluation shows that our framework outperforms several strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.04375v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"50013\", \"165679\", \"200370\", \"245787\", \"245858\", \"260768\", \"274244\", \"294554\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"298758","document_content":"# Disentangled Representations in Neural Models\n## Categories\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nRepresentation learning is the foundation for the recent success of neural network models. However, the distributed representations generated by neural networks are far from ideal. Due to their highly entangled nature, they are di cult to reuse and interpret, and they do a poor job of capturing the sparsity which is present in real- world transformations. In this paper, I describe methods for learning disentangled representations in the two domains of graphics and computation. These methods allow neural methods to learn representations which are easy to interpret and reuse, yet they incur little or no penalty to performance. In the Graphics section, I demonstrate the ability of these methods to infer the generating parameters of images and rerender those images under novel conditions. In the Computation section, I describe a model which is able to factorize a multitask learning problem into subtasks and which experiences no catastrophic forgetting. Together these techniques provide the tools to design a wide range of models that learn disentangled representations and better model the factors of variation in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.02383v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"81601\", \"208685\", \"225984\", \"229311\", \"234979\", \"290911\", \"297838\"], \"outgoing_citations\": [\"300606\", \"302688\", \"302970\", \"303306\", \"303715\", \"311388\", \"313427\", \"316183\", \"327472\", \"333252\", \"333469\", \"336181\", \"343126\", \"353174\", \"357204\"]}","task_split":"paper_retrieval"}
{"document_id":"298776","document_content":"# Scalable Text Mining with Sparse Generative Models\n## Categories\n- Information Retrieval\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nThe information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods.   This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines.   The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.02332v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"330213\", \"332883\", \"333448\", \"336720\", \"345587\", \"349550\", \"349847\", \"351402\", \"353222\", \"355824\", \"358303\", \"358967\", \"361803\"]}","task_split":"paper_retrieval"}
{"document_id":"298944","document_content":"# Many Languages, One Parser\n## Categories\n- Computation and Language\n## Abstract\nWe train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser's performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.01595v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15848\", \"57059\", \"99327\", \"4693\", \"17684\", \"29188\", \"44974\", \"44997\", \"49929\", \"66050\", \"72915\", \"122192\", \"133976\", \"144897\", \"139456\", \"118167\", \"88056\", \"91648\", \"91883\", \"94893\", \"96675\", \"117465\", \"119083\", \"126439\", \"127555\", \"127787\", \"128134\", \"130833\", \"131195\", \"132153\", \"128513\", \"137472\", \"157502\", \"162124\", \"160039\", \"161226\", \"162114\", \"165830\", \"168298\", \"168532\", \"168812\", \"166025\", \"159372\", \"182131\", \"183673\", \"189639\", \"190200\", \"190470\", \"191113\", \"192418\", \"195260\", \"197584\", \"197656\", \"197729\", \"200678\", \"203509\", \"210090\", \"211936\", \"212163\", \"263680\", \"215455\", \"218881\", \"217117\", \"217125\", \"219866\", \"225305\", \"226011\", \"228745\", \"232339\", \"232185\", \"230064\", \"233612\", \"234421\", \"234621\", \"234760\", \"235359\", \"235951\", \"237243\", \"246542\", \"268046\", \"268625\", \"250429\", \"251523\", \"258346\", \"259840\", \"253612\", \"264791\", \"265144\", \"266157\", \"268263\", \"270019\", \"270085\", \"277444\", \"282324\", \"286324\", \"282869\", \"284904\", \"289031\", \"291209\", \"292448\", \"292691\", \"308757\"], \"outgoing_citations\": [\"298884\", \"308559\", \"308757\", \"312066\"]}","task_split":"paper_retrieval"}
{"document_id":"299198","document_content":"# DeepCare: A Deep Dynamic Memory Model for Predictive Medicine\n## Categories\n- Machine Learning\n## Abstract\nPersonalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1602.00357v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"26737\", \"36797\", \"152571\", \"10339\", \"34436\", \"40596\", \"51328\", \"62832\", \"152180\", \"140301\", \"87739\", \"111399\", \"135885\", \"136869\", \"145697\", \"148310\", \"153944\", \"174985\", \"152203\", \"165848\", \"168233\", \"169228\", \"153847\", \"173246\", \"174406\", \"182974\", \"194434\", \"203003\", \"207097\", \"209654\", \"204959\", \"208650\", \"217086\", \"219028\", \"223616\", \"226687\", \"229111\", \"233107\", \"242280\", \"244197\", \"264024\", \"291026\", \"251450\", \"244024\", \"286738\", \"287396\", \"287480\", \"287747\"], \"outgoing_citations\": [\"303997\", \"338303\", \"360754\"]}","task_split":"paper_retrieval"}
{"document_id":"299339","document_content":"# An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- I.4; I.5.4; J.3\n## Abstract\nThe incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.07843v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"stat.ML\", \"I.4; I.5.4; J.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"I.4; I.5.4; J.3\"], \"incoming_citations\": [\"22199\", \"84559\", \"25182\", \"99927\", \"129337\", \"131171\", \"176840\", \"182674\", \"189887\", \"271920\", \"272756\", \"224178\", \"236071\", \"272506\", \"282613\"], \"outgoing_citations\": [\"336018\"]}","task_split":"paper_retrieval"}
{"document_id":"299461","document_content":"# COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. The images were not collected with text in mind and thus contain a broad variety of text instances. To reflect the diversity of text in natural scenes, we annotate text with (a) location in terms of a bounding box, (b) fine-grained classification into machine printed text and handwritten text, (c) classification into legible and illegible text, (d) script of the text and (e) transcriptions of legible text. The dataset contains over 173k text annotations in over 63k images. We provide a statistical analysis of the accuracy of our annotations. In addition, we present an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on our dataset. While scene text detection and recognition enjoys strong advances in recent years, we identify significant shortcomings motivating future work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.07140v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"91575\", \"186590\", \"142099\", \"1156\", \"17761\", \"28244\", \"50917\", \"52248\", \"121818\", \"180823\", \"236\", \"4188\", \"7154\", \"10369\", \"3699\", \"24577\", \"28596\", \"30168\", \"33798\", \"50125\", \"53987\", \"58438\", \"59727\", \"64650\", \"43191\", \"81622\", \"81968\", \"81855\", \"81960\", \"84238\", \"99456\", \"113025\", \"113794\", \"120486\", \"150258\", \"247723\", \"85935\", \"98220\", \"100417\", \"105122\", \"110306\", \"116312\", \"120420\", \"122949\", \"126431\", \"129737\", \"140478\", \"140469\", \"144173\", \"147253\", \"149580\", \"152219\", \"154248\", \"154995\", \"155530\", \"156029\", \"156340\", \"194598\", \"209980\", \"150452\", \"163839\", \"166303\", \"166353\", \"168875\", \"166370\", \"166606\", \"176199\", \"178095\", \"178256\", \"182603\", \"183268\", \"189847\", \"192483\", \"192870\", \"195963\", \"209860\", \"211851\", \"224507\", \"234390\", \"236404\", \"255907\", \"206178\", \"212589\", \"213283\", \"218516\", \"220129\", \"162242\", \"225336\", \"239002\", \"240477\", \"240965\", \"242276\", \"245591\", \"245970\", \"252159\", \"253692\", \"253398\", \"254795\", \"257197\", \"257954\", \"260702\", \"264526\", \"265634\", \"266391\", \"269205\", \"269108\", \"273862\", \"248133\", \"278231\", \"282263\", \"274093\", \"289389\", \"291983\", \"296828\"], \"outgoing_citations\": [\"306023\", \"320837\", \"328595\", \"331178\"]}","task_split":"paper_retrieval"}
{"document_id":"299531","document_content":"# P-values: misunderstood and misused\n## Categories\n- Applications\n- Computers and Society\n- Data Analysis, Statistics and Probability\n## Abstract\nP-values are widely used in both the social and natural sciences to quantify the statistical significance of observed results. The recent surge of big data research has made the p-value an even more popular tool to test the significance of a study. However, substantial literature has been produced critiquing how p-values are used and understood. In this paper we review this recent critical literature, much of which is routed in the life sciences, and consider its implications for social scientific research. We provide a coherent picture of what the main criticisms are, and draw together and disambiguate common themes. In particular, we explain how the False Discovery Rate is calculated, and how this differs from a p-value. We also make explicit the Bayesian nature of many recent criticisms, a dimension that is often underplayed or ignored. We conclude by identifying practical steps to help remediate some of the concerns identified. We recommend that (i) far lower significance levels are used, such as $0.01$ or $0.001$, and (ii) p-values are interpreted contextually, and situated within both the findings of the individual study and the broader field of inquiry (through, for example, meta-analyses).","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.3389\/fphy.2016.00006\", \"primary_category\": \"stat.AP\", \"categories\": [\"stat.AP\", \"cs.CY\", \"physics.data-an\"], \"primary_category_human_readable\": \"Applications\", \"categories_human_readable\": [\"Applications\", \"Computers and Society\", \"Data Analysis, Statistics and Probability\"], \"incoming_citations\": [\"99003\", \"177865\", \"238085\", \"194813\", \"292497\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"299878","document_content":"# Improved Spoken Document Summarization with Coverage Modeling Techniques\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nExtractive summarization aims at selecting a set of indicative sentences from a source document as a summary that can express the major theme of the document. A general consensus on extractive summarization is that both relevance and coverage are critical issues to address. The existing methods designed to model coverage can be characterized by either reducing redundancy or increasing diversity in the summary. Maximal margin relevance (MMR) is a widely-cited method since it takes both relevance and redundancy into account when generating a summary for a given document. In addition to MMR, there is only a dearth of research concentrating on reducing redundancy or increasing diversity for the spoken document summarization task, as far as we are aware. Motivated by these observations, two major contributions are presented in this paper. First, in contrast to MMR, which considers coverage by reducing redundancy, we propose two novel coverage-based methods, which directly increase diversity. With the proposed methods, a set of representative sentences, which not only are relevant to the given document but also cover most of the important sub-themes of the document, can be selected automatically. Second, we make a step forward to plug in several document\/sentence representation methods into the proposed framework to further enhance the summarization performance. A series of empirical evaluations demonstrate the effectiveness of our proposed methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.05194v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"41482\"], \"outgoing_citations\": [\"311204\"]}","task_split":"paper_retrieval"}
{"document_id":"300066","document_content":"# Learning the Semantics of Structured Data Sources\n## Categories\n- Artificial Intelligence\n## Abstract\nInformation sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.   We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. ...","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.websem.2015.12.003\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"55902\", \"75060\", \"194478\", \"243908\", \"301322\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"300096","document_content":"# Multimodal Pivots for Image Caption Translation\n## Categories\n- Computation and Language\n## Abstract\nWe present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over a database of images that are captioned in the target language, and use the captions of the most similar images for crosslingual reranking of translation outputs. Our approach does not depend on the availability of large amounts of in-domain parallel data, but only relies on available large datasets of monolingually captioned images, and on state-of-the-art convolutional neural networks to compute image similarities. Our experimental evaluation shows improvements of 1 BLEU point over strong baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.18653\/v1\/p16-1227\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"2460\", \"18911\", \"47275\", \"65751\", \"67594\", \"98055\", \"175031\", \"129142\", \"132574\", \"153733\", \"168031\", \"157257\", \"162846\", \"167598\", \"171726\", \"180146\", \"191237\", \"193738\", \"211982\", \"231022\", \"222846\", \"230056\", \"238941\", \"242857\", \"253508\", \"284263\", \"252903\", \"260373\", \"261917\", \"263333\", \"266669\", \"268861\", \"274729\", \"275603\", \"280498\", \"279563\", \"293418\", \"304945\"], \"outgoing_citations\": [\"304945\", \"310723\", \"330651\"]}","task_split":"paper_retrieval"}
{"document_id":"300153","document_content":"# Question Answering on Linked Data: Challenges and Future Directions\n## Categories\n- Information Retrieval\n## Abstract\nQuestion Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched metadata, but still question answering systems involve serious challenges which cause to be far beyond desired expectations. In this paper, we raise the challenges for building a Question Answering (QA) system especially with the focus of employing structured data (i.e. knowledge graph). This paper provide an exhaustive insight of the known challenges, so far. Thus, it helps researchers to easily spot open rooms for the future research agenda.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.03541v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\"], \"incoming_citations\": [\"57292\", \"129168\", \"196613\"], \"outgoing_citations\": [\"300505\", \"320505\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"300182","document_content":"# Document image classification, with a specific view on applications of patent images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe main focus of this paper is document image classification and retrieval, where we analyze and compare different parameters for the RunLeght Histogram (RL) and Fisher Vector (FV) based image representations. We do an exhaustive experimental study using different document image datasets, including the MARG benchmarks, two datasets built on customer data and the images from the Patent Image Classification task of the Clef-IP 2011. The aim of the study is to give guidelines on how to best choose the parameters such that the same features perform well on different tasks. As an example of such need, we describe the Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same image representation to predict the image type and retrieve relevant patents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.03295v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"297196\"], \"outgoing_citations\": [\"316961\"]}","task_split":"paper_retrieval"}
{"document_id":"300208","document_content":"# Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.cviu.2016.01.002\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"94155\", \"64650\", \"93345\", \"163092\", \"111991\", \"126431\", \"174894\", \"208801\", \"273862\"], \"outgoing_citations\": [\"320148\", \"320837\", \"360756\"]}","task_split":"paper_retrieval"}
{"document_id":"300357","document_content":"# Group Invariant Deep Representations for Image Instance Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nMost image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts.   In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness.   Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.02093v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [\"85376\", \"182431\", \"287103\", \"310303\", \"260972\", \"275976\", \"287584\"], \"outgoing_citations\": [\"303664\", \"308236\", \"318817\", \"331445\", \"331690\", \"337753\"]}","task_split":"paper_retrieval"}
{"document_id":"300393","document_content":"# Visual Script and Language Identification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper we introduce a script identification method based on hand-crafted texture features and an artificial neural network. The proposed pipeline achieves near state-of-the-art performance for script identification of video-text and state-of-the-art performance on visual language identification of handwritten text. More than using the deep network as a classifier, the use of its intermediary activations as a learned metric demonstrates remarkable results and allows the use of discriminative models on unknown classes. Comparative experiments in video-text and text in the wild datasets provide insights on the internals of the proposed deep network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.01885v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"6043\", \"87876\", \"152162\", \"246175\"], \"outgoing_citations\": [\"312979\", \"314036\"]}","task_split":"paper_retrieval"}
{"document_id":"300498","document_content":"# Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation\n## Categories\n- Computation and Language\n## Abstract\nNamed Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.01343v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21023\", \"2549\", \"3328\", \"20997\", \"26715\", \"48823\", \"61237\", \"64775\", \"80770\", \"121898\", \"159550\", \"169211\", \"11203\", \"23626\", \"24055\", \"24567\", \"24722\", \"24980\", \"28819\", \"30029\", \"37401\", \"45196\", \"55194\", \"66795\", \"67681\", \"72429\", \"77042\", \"78994\", \"80326\", \"81410\", \"97185\", \"98792\", \"104921\", \"127923\", \"156620\", \"86291\", \"94753\", \"97100\", \"97423\", \"21536\", \"106440\", \"41806\", \"121381\", \"124889\", \"127481\", \"128180\", \"129562\", \"127760\", \"126671\", \"140904\", \"116263\", \"122351\", \"142709\", \"143723\", \"147649\", \"148404\", \"157181\", \"157168\", \"157255\", \"167165\", \"205857\", \"236572\", \"137257\", \"167916\", \"168589\", \"168892\", \"165378\", \"171151\", \"171884\", \"169340\", \"175560\", \"175482\", \"179831\", \"179907\", \"182677\", \"185889\", \"181078\", \"200032\", \"200632\", \"200632\", \"243378\", \"208559\", \"208638\", \"220553\", \"209228\", \"222811\", \"224451\", \"225253\", \"228842\", \"227437\", \"232120\", \"232591\", \"233941\", \"238024\", \"240614\", \"243397\", \"246202\", \"246780\", \"248226\", \"262673\", \"267123\", \"268728\", \"276616\", \"287676\"], \"outgoing_citations\": [\"178230\", \"289601\", \"313713\"]}","task_split":"paper_retrieval"}
{"document_id":"301009","document_content":"# The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nWe consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several stochastic arms, each a source of i.i.d. rewards of unknown distribution. At each time step the agent chooses an arm, and observes the reward of the obtained sample. Each sample is considered here as a separate item with the reward designating its value, and the goal is to find an item with the highest possible value. Our basic assumption is a known lower bound on the {\\em tail function} of the reward distributions. Under the PAC framework, we provide a lower bound on the sample complexity of any $(\\epsilon,\\delta)$-correct algorithm, and propose an algorithm that attains this bound up to logarithmic factors. We analyze the robustness of the proposed algorithm and in addition, we compare the performance of this algorithm to the variant in which the arms are not distinguishable by the agent and are chosen randomly at each stage. Interestingly, when the maximal rewards of the arms happen to be similar, the latter approach may provide better performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.07650v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"312672\"]}","task_split":"paper_retrieval"}
{"document_id":"301138","document_content":"# Predicting the Co-Evolution of Event and Knowledge Graphs\n## Categories\n- Machine Learning\n## Abstract\nEmbedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Knowledge graphs are typically treated as static: A knowledge graph grows more links when more facts become available but the ground truth values associated with links is considered time invariant. In this paper we address the issue of knowledge graphs where triple states depend on time. We assume that changes in the knowledge graph always arrive in form of events, in the sense that the events are the gateway to the knowledge graph. We train an event prediction model which uses both knowledge graph background information and information on recent events. By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well. Our experiments demonstrate that our approach performs well in a clinical application, a recommendation engine and a sensor network application.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.06900v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"27229\", \"54464\", \"62199\", \"97620\", \"159408\", \"79036\", \"127904\", \"134233\", \"184180\", \"204585\", \"218512\", \"258941\", \"266319\", \"302555\"], \"outgoing_citations\": [\"302555\", \"337747\"]}","task_split":"paper_retrieval"}
{"document_id":"301305","document_content":"# Learning the Preferences of Ignorant, Inconsistent Agents\n## Categories\n- Artificial Intelligence\n## Abstract\nAn important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.05832v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"26479\", \"78387\", \"5872\", \"9814\", \"13327\", \"26056\", \"39686\", \"61430\", \"214965\", \"87472\", \"74639\", \"87237\", \"87679\", \"107425\", \"108216\", \"118955\", \"122106\", \"147399\", \"164299\", \"179418\", \"200697\", \"202556\", \"204227\", \"206875\", \"209432\", \"211165\", \"209453\", \"219230\", \"224746\", \"228980\", \"231267\", \"239105\", \"239917\", \"241354\", \"247344\", \"247676\", \"247911\", \"246855\", \"246897\", \"247196\", \"251082\", \"265196\", \"265652\", \"266287\", \"287602\", \"289913\", \"292832\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"301423","document_content":"# Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning\n## Categories\n- Computation and Language\n## Abstract\nMorpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.05030v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"259703\", \"276843\", \"260562\", \"273344\", \"290475\"], \"outgoing_citations\": [\"292691\", \"301264\", \"311013\", \"311696\", \"315614\", \"316338\", \"328648\", \"339198\", \"343869\"]}","task_split":"paper_retrieval"}
{"document_id":"301437","document_content":"# Conditions for Normative Decision Making at the Fire Ground\n## Categories\n- Artificial Intelligence\n## Abstract\nWe discuss the changes in an attitude to decision making at the fire ground. The changes are driven by the recent technological shift. The emerging new approaches in sensing and data processing (under common umbrella of Cyber-Physical Systems) allow for leveling off the gap, between humans and machines, in perception of the fire ground. Furthermore, results from descriptive decision theory question the rationality of human choices. This creates the need for searching and testing new approaches for decision making during emergency. We propose the framework that addresses this need. The primary feature of the framework are possibilities for incorporation of normative and prescriptive approaches to decision making. The framework also allows for comparison of the performance of decisions, between human and machine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.04976v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"301504","document_content":"# Sentence Entailment in Compositional Distributional Semantics\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Category Theory\n- 03B65\n- I.2.7\n## Abstract\nDistributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10472-017-9570-x\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"math.CT\", \"03B65\", \"I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Category Theory\", \"03B65\", \"I.2.7\"], \"incoming_citations\": [\"66952\", \"150356\", \"35451\", \"56270\", \"60438\", \"75959\", \"113507\", \"43318\", \"82195\", \"94905\", \"101732\", \"122385\", \"125845\", \"126175\", \"126620\", \"170964\", \"183142\", \"185823\", \"270350\", \"210973\", \"257176\", \"275641\", \"282656\", \"287186\", \"287190\", \"299918\"], \"outgoing_citations\": [\"291108\", \"298930\", \"299918\", \"310767\", \"312382\", \"313529\", \"318101\", \"325323\", \"328199\", \"330808\"]}","task_split":"paper_retrieval"}
{"document_id":"301592","document_content":"# Action Recognition with Image Based CNN Features\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nMost of human actions consist of complex temporal compositions of more simple actions. Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model. Convolutional Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for designing handcrafted features. Usually, the output of the last layer in CNN (a layer before the classification layer -known as fc7) is used as a generic feature for images. In this paper, we show that fc7 features, per se, can not get a good performance for the task of action recognition, when the network is trained only on images. We present a feature structure on top of fc7 features, which can capture the temporal variation in a video. To represent the temporal components, which is needed to capture motion information, we introduced a hierarchical structure. The hierarchical model enables to capture sub-actions from a complex action. At the higher levels of the hierarchy, it represents a coarse capture of action sequence and lower levels represent fine action elements. Furthermore, we introduce a method for extracting key-frames using binary coding of each frame in a video, which helps to improve the performance of our hierarchical model. We experimented our method on several action datasets and show that our method achieves superior results compared to other state-of-the-arts methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.03980v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"241652\", \"111720\", \"148550\", \"160356\", \"191374\", \"283479\", \"269728\", \"287341\"], \"outgoing_citations\": [\"307386\", \"311357\", \"311710\", \"329514\", \"337553\"]}","task_split":"paper_retrieval"}
{"document_id":"301770","document_content":"# Where You Are Is Who You Are: User Identification by Matching Statistics\n## Categories\n- Machine Learning\n- Cryptography and Security\n- Social and Information Networks\n- Applications\n## Abstract\nMost users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting in which we are given histograms of users' data collected during two different experiments. We assume that, in the first dataset, the users' identities are anonymized or hidden and that, in the second dataset, their identities are known. We study the task of identifying the users by matching the histograms of their data in the first dataset with the histograms from the second dataset. In recent works, the optimal algorithm for this user identification task is introduced. In this paper, we evaluate the effectiveness of this method on three different types of datasets and in multiple scenarios. Using datasets such as call data records, web browsing histories, and GPS trajectories, we show that a large fraction of users can be easily identified given only histograms of their data; hence these histograms can act as users' fingerprints. We also verify that simultaneous identification of users achieves better performance compared to one-by-one user identification. We show that using the optimal method for identification gives higher identification accuracy than heuristics-based approaches in practical scenarios. The accuracy obtained under this optimal method can thus be used to quantify the maximum level of user identification that is possible in such settings. We show that the key factors affecting the accuracy of the optimal identification algorithm are the duration of the data collection, the number of users in the anonymized dataset, and the resolution of the dataset. We analyze the effectiveness of k-anonymization in resisting user identification attacks on these datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIFS.2015.2498131\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CR\", \"cs.SI\", \"stat.AP\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Cryptography and Security\", \"Social and Information Networks\", \"Applications\", \"Machine Learning\"], \"incoming_citations\": [\"27194\", \"244582\", \"48400\", \"112566\", \"193677\", \"226487\", \"198623\", \"202031\", \"254435\", \"228926\", \"233472\", \"302245\"], \"outgoing_citations\": [\"320705\", \"353995\"]}","task_split":"paper_retrieval"}
{"document_id":"301918","document_content":"# Jointly Modeling Topics and Intents with Global Order Structure\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nModeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.02009v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"335270\", \"353790\"]}","task_split":"paper_retrieval"}
{"document_id":"302047","document_content":"# Topic segmentation via community detection in complex networks\n## Categories\n- Computation and Language\n- Social and Information Networks\n## Abstract\nMany real systems have been modelled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting findings, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such representation fails in capturing other textual features, such as the organization in topics or subjects. In this context, we propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favours the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We have found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study semantical features of texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1063\/1.4954215\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.SI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Social and Information Networks\"], \"incoming_citations\": [\"38586\", \"60833\", \"227153\", \"240844\", \"289273\"], \"outgoing_citations\": [\"308446\", \"308777\", \"310342\", \"310929\", \"318041\", \"334235\", \"348105\", \"348705\", \"348770\", \"355138\"]}","task_split":"paper_retrieval"}
{"document_id":"302056","document_content":"# Neural Generative Question Answering\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.01337v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"29573\", \"50552\", \"95561\", \"2943\", \"9670\", \"29970\", \"47641\", \"71008\", \"76036\", \"127734\", \"110206\", \"128194\", \"149064\", \"77716\", \"78011\", \"88975\", \"96296\", \"108688\", \"121517\", \"127675\", \"129959\", \"154484\", \"156419\", \"157274\", \"189612\", \"160015\", \"163891\", \"168355\", \"176426\", \"180957\", \"190863\", \"192378\", \"192361\", \"194535\", \"196187\", \"200431\", \"195502\", \"188746\", \"203266\", \"210121\", \"206516\", \"225275\", \"227251\", \"264290\", \"204585\", \"205684\", \"210551\", \"217625\", \"218973\", \"219502\", \"220145\", \"68965\", \"228360\", \"229477\", \"226956\", \"232341\", \"230172\", \"234735\", \"239682\", \"240158\", \"243189\", \"244088\", \"251736\", \"207186\", \"251348\", \"256119\", \"256956\", \"264189\", \"278430\", \"284154\", \"285454\", \"287413\", \"289061\", \"291043\", \"293171\", \"301178\"], \"outgoing_citations\": [\"302107\", \"308378\", \"308381\", \"311684\", \"328352\", \"330994\", \"352060\"]}","task_split":"paper_retrieval"}
{"document_id":"302126","document_content":"# Actions ~ Transformations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWhat defines an action like \"kicking ball\"? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the environment before the action happens (precondition) to the state after the action (effect). Motivated by recent advancements of video representation using deep learning, we design a Siamese network which models the action as a transformation on a high-level feature space. We show that our model gives improvements on standard action recognition datasets including UCF101 and HMDB51. More importantly, our approach is able to generalize beyond learned action categories and shows significant performance improvement on cross-category generalization on our new ACT dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1512.00795v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"79061\", \"2783\", \"12199\", \"45869\", \"84568\", \"112550\", \"151983\", \"267294\", \"83133\", \"95573\", \"111954\", \"113259\", \"183677\", \"230177\", \"159506\", \"162083\", \"165768\", \"169806\", \"184573\", \"191546\", \"191861\", \"192147\", \"220531\", \"235738\", \"250981\", \"270115\", \"206483\", \"180473\", \"209786\", \"210761\", \"210373\", \"220662\", \"221428\", \"229179\", \"231723\", \"234473\", \"235406\", \"237654\", \"180480\", \"242228\", \"242943\", \"243029\", \"244483\", \"247535\", \"248108\", \"248813\", \"249401\", \"249567\", \"249868\", \"251877\", \"263762\", \"269928\", \"278370\", \"284199\", \"294503\", \"253971\", \"258412\", \"259142\", \"259415\", \"196680\", \"265228\", \"265722\", \"265811\", \"266053\", \"269276\", \"270479\", \"271003\", \"274379\", \"275433\", \"277625\", \"278889\", \"279623\", \"292471\", \"280271\", \"281003\", \"282188\", \"284487\", \"285965\", \"286564\", \"293555\"], \"outgoing_citations\": [\"305615\", \"307386\", \"309902\", \"313122\", \"313397\", \"314890\", \"315012\", \"320052\", \"321340\", \"321453\", \"321798\", \"329514\", \"362075\"]}","task_split":"paper_retrieval"}
{"document_id":"302202","document_content":"# Technical Privacy Metrics: a Systematic Survey\n## Categories\n- Cryptography and Security\n- Information Theory\n- Performance\n- Information Theory\n## Abstract\nThe goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/3168389\", \"primary_category\": \"cs.CR\", \"categories\": [\"math.IT\", \"cs.PF\", \"cs.CR\", \"cs.IT\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Information Theory\", \"Performance\", \"Cryptography and Security\", \"Information Theory\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302383","document_content":"# Semantic Folding Theory And its Application in Semantic Fingerprinting\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Neurons and Cognition\n## Abstract\nHuman language is recognized as a very complex domain since decades. No computer system has been able to reach human levels of performance so far. The only known computational system capable of proper language processing is the human brain. While we gather more and more data about the brain, its fundamental computational processes still remain obscure. The lack of a sound computational brain theory also prevents the fundamental understanding of Natural Language Processing. As always when science lacks a theoretical foundation, statistical modeling is applied to accommodate as many sampled real-world data as possible. An unsolved fundamental issue is the actual representation of language (data) within the brain, denoted as the Representational Problem. Starting with Jeff Hawkins' Hierarchical Temporal Memory (HTM) theory, a consistent computational theory of the human cortex, we have developed a corresponding theory of language data representation: The Semantic Folding Theory. The process of encoding words, by using a topographic semantic space as distributional reference frame into a sparse binary representational vector is called Semantic Folding and is the central topic of this document. Semantic Folding describes a method of converting language from its symbolic representation (text) into an explicit, semantically grounded representation that can be generically processed by Hawkins' HTM networks. As it turned out, this change in representation, by itself, can solve many complex NLP problems by applying Boolean operators and a generic similarity function like the Euclidian Distance. Many practical problems of statistical NLP systems, like the high cost of computation, the fundamental incongruity of precision and recall , the complex tuning procedures etc., can be elegantly overcome by applying Semantic Folding.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.08855v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Neurons and Cognition\"], \"incoming_citations\": [\"78431\", \"294176\", \"221780\", \"298035\", \"301386\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302471","document_content":"# TGSum: Build Tweet Guided Multi-Document Summarization Dataset\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions. We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.08417v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [\"129959\", \"141876\", \"168453\", \"210408\", \"240697\", \"241070\", \"268861\", \"280557\", \"279351\", \"280552\"], \"outgoing_citations\": [\"311754\", \"351546\"]}","task_split":"paper_retrieval"}
{"document_id":"302527","document_content":"# A Roadmap towards Machine Intelligence\n## Categories\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nThe development of intelligent machines is one of the biggest unsolved challenges in computer science. In this paper, we propose some fundamental properties these machines should have, focusing in particular on communication and learning. We discuss a simple environment that could be used to incrementally teach a machine the basics of natural-language-based communication, as a prerequisite to more complex interaction with human users. We also present some conjectures on the sort of algorithms the machine should support in order to profitably learn from the environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.08130v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302617","document_content":"# Fine-Grain Annotation of Cricket Videos\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThe recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of ctions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into \"scenes\", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying video-shots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots. The novel aspect of this work is the fine temporal scale at which semantic information is assigned to the video. As a result of our approach, we enable retrieval of specific actions that last only a few seconds, from several hours of video. This solution yields a large number of labeled exemplars, with no manual effort, that could be used by machine learning algorithms to learn complex actions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.07607v2\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.MM\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Multimedia\"], \"incoming_citations\": [\"122949\", \"191519\", \"203376\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302771","document_content":"# Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nA long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks (Weston et al., 2015b) which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering 75k movie entities and with 3.5M training examples. We present results of various models on these tasks, and evaluate their performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06931v6\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"73800\", \"133609\", \"7939\", \"14656\", \"20917\", \"45484\", \"68923\", \"73917\", \"119906\", \"142497\", \"161344\", \"175031\", \"77867\", \"73765\", \"95678\", \"98259\", \"116873\", \"124030\", \"126246\", \"154820\", \"230774\", \"130674\", \"150091\", \"146606\", \"161259\", \"161419\", \"167475\", \"170492\", \"171725\", \"171733\", \"181179\", \"194535\", \"210493\", \"211804\", \"212046\", \"211664\", \"233659\", \"247393\", \"210690\", \"205490\", \"216965\", \"218963\", \"219198\", \"219154\", \"216953\", \"232285\", \"234630\", \"236043\", \"239849\", \"243004\", \"243759\", \"244479\", \"249677\", \"255716\", \"255778\", \"256171\", \"259009\", \"301313\", \"144800\", \"253593\", \"259264\", \"263059\", \"263622\", \"272752\", \"277662\", \"279518\", \"279678\", \"277941\", \"278065\", \"279215\", \"291916\", \"274469\", \"278244\", \"280247\", \"289265\", \"290747\", \"290697\", \"292379\", \"294144\", \"296257\"], \"outgoing_citations\": [\"303827\", \"310370\", \"310377\", \"310740\", \"311684\", \"316576\", \"317213\", \"328352\"]}","task_split":"paper_retrieval"}
{"document_id":"302775","document_content":"# Screen Content Image Segmentation Using Sparse-Smooth Decomposition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACSSC.2015.7421331\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"236510\", \"232277\", \"274582\", \"271585\", \"276302\", \"283326\", \"287485\", \"318236\", \"279686\", \"284816\", \"295635\", \"296270\", \"298740\", \"306739\"], \"outgoing_citations\": [\"306739\", \"309899\", \"318236\", \"318996\", \"320275\", \"321085\", \"325260\"]}","task_split":"paper_retrieval"}
{"document_id":"302795","document_content":"# Semi-supervised Bootstrapping approach for Named Entity Recognition\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nThe aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06833v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302822","document_content":"# Top-N recommendations from expressive recommender systems\n## Categories\n- Machine Learning\n## Abstract\nNormalized nonnegative models assign probability distributions to users and random variables to items; see [Stark, 2015]. Rating an item is regarded as sampling the random variable assigned to the item with respect to the distribution assigned to the user who rates the item. Models of that kind are highly expressive. For instance, using normalized nonnegative models we can understand users' preferences as mixtures of interpretable user stereotypes, and we can arrange properties of users and items in a hierarchical manner. These features would not be useful if the predictive power of normalized nonnegative models was poor. Thus, we analyze here the performance of normalized nonnegative models for top-N recommendation and observe that their performance matches the performance of methods like PureSVD which was introduced in [Cremonesi et al., 2010]. We conclude that normalized nonnegative models not only provide accurate recommendations but they also deliver (for free) representations that are interpretable. We deepen the discussion of normalized nonnegative models by providing further theoretical insights. In particular, we introduce total variational distance as an operational similarity measure, we discover scenarios where normalized nonnegative models yield unique representations of users and items, we prove that the inference of optimal normalized nonnegative models is NP-hard and finally, we discuss the relationship between normalized nonnegative models and nonnegative matrix factorization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06718v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"299723\"], \"outgoing_citations\": [\"303321\", \"326978\"]}","task_split":"paper_retrieval"}
{"document_id":"302833","document_content":"# Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nIntegrating higher level visual and linguistic interpretations is at the heart of human intelligence. As automatic visual category recognition in images is approaching human performance, the high level understanding in the dynamic spatiotemporal domain of videos and its translation into natural language is still far from being solved. While most works on vision-to-text translations use pre-learned or pre-established computational linguistic models, in this paper we present an approach that uses vision alone to efficiently learn how to translate into language the video content. We discover, in simple form, the story played by main actors, while using only visual cues for representing objects and their interactions. Our method learns in a hierarchical manner higher level representations for recognizing subjects, actions and objects involved, their relevant contextual background and their interaction to one another over time. We have a three stage approach: first we take in consideration features of the individual entities at the local level of appearance, then we consider the relationship between these objects and actions and their video background, and third, we consider their spatiotemporal relations as inputs to classifiers at the highest level of interpretation. Thus, our approach finds a coherent linguistic description of videos in the form of a subject, verb and object based on their role played in the overall visual story learned directly from training data, without using a known language model. We test the efficiency of our approach on a large scale dataset containing YouTube clips taken in the wild and demonstrate state-of-the-art performance, often superior to current approaches that use more complex, pre-learned linguistic knowledge.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06674v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"316803\", \"320332\", \"332079\"]}","task_split":"paper_retrieval"}
{"document_id":"302903","document_content":"# Joint Word Representation Learning using a Corpus and a Semantic Lexicon\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nMethods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by defining the various relationships that exist among the words in a language. We consider the question, can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon. We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus. Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into word representations on several benchmark datasets for semantic similarity and word analogy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06438v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"73891\", \"96324\", \"146677\", \"224091\", \"152436\", \"234585\", \"231282\", \"232527\", \"255250\", \"260365\", \"278945\", \"281373\", \"225222\"], \"outgoing_citations\": [\"312209\", \"320743\"]}","task_split":"paper_retrieval"}
{"document_id":"302936","document_content":"# sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNeural word representations have proven useful in Natural Language Processing (NLP) tasks due to their ability to efficiently model complex semantic and syntactic word relationships. However, most techniques model only one representation per word, despite the fact that a single word can have multiple meanings or \"senses\". Some techniques model words by using multiple vectors that are clustered based on context. However, recent neural approaches rarely focus on the application to a consuming NLP algorithm. Furthermore, the training process of recent word-sense models is expensive relative to single-sense embedding processes. This paper presents a novel approach which addresses these concerns by modeling multiple embeddings for each word based on supervised disambiguation, which provides a fast and accurate way for a consuming NLP model to select a sense-disambiguated embedding. We demonstrate that these embeddings can disambiguate both contrastive senses such as nominal and verbal senses as well as nuanced senses such as sarcasm. We further evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing, yielding a greater than 8% average error reduction in unlabeled attachment scores across 6 languages.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06388v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"16015\", \"21653\", \"6319\", \"14508\", \"23606\", \"46200\", \"26523\", \"138895\", \"97442\", \"102352\", \"122196\", \"127318\", \"146235\", \"147539\", \"161657\", \"74228\", \"192655\", \"218191\", \"226435\", \"264269\", \"271009\", \"273395\", \"291855\"], \"outgoing_citations\": [\"311618\", \"313947\", \"323243\", \"342907\"]}","task_split":"paper_retrieval"}
{"document_id":"302943","document_content":"# Order-Embeddings of Images and Language\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nHypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06361v6\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"113797\", \"3797\", \"39143\", \"72957\", \"75853\", \"7414\", \"13075\", \"13331\", \"13589\", \"24161\", \"24428\", \"39115\", \"39537\", \"41718\", \"50398\", \"57132\", \"62806\", \"76703\", \"76974\", \"77391\", \"106415\", \"130188\", \"134933\", \"138177\", \"161696\", \"92329\", \"95273\", \"95344\", \"96188\", \"96329\", \"105151\", \"105922\", \"106210\", \"110926\", \"94488\", \"114253\", \"116342\", \"118087\", \"124391\", \"126620\", \"129340\", \"131626\", \"132981\", \"133497\", \"133502\", \"139259\", \"141225\", \"142371\", \"142294\", \"147671\", \"153286\", \"154774\", \"114970\", \"165364\", \"168031\", \"184485\", \"220492\", \"144119\", \"152424\", \"157257\", \"157878\", \"158121\", \"161383\", \"162846\", \"164107\", \"164254\", \"155415\", \"165588\", \"166050\", \"166307\", \"166989\", \"167256\", \"167642\", \"168374\", \"168851\", \"169105\", \"170590\", \"171357\", \"181437\", \"182699\", \"183392\", \"187201\", \"188867\", \"189520\", \"189628\", \"191966\", \"181078\", \"193442\", \"193503\", \"194673\", \"195097\", \"196153\", \"199273\", \"200395\", \"207637\", \"209113\", \"213042\", \"214465\", \"214193\", \"218730\", \"220223\", \"230673\", \"233934\", \"235648\", \"265384\", \"269128\", \"204263\", \"212437\", \"217125\", \"220529\", \"221007\", \"221984\", \"224064\", \"225802\", \"174908\", \"224723\", \"226758\", \"227989\", \"228704\", \"228417\", \"229558\", \"231768\", \"232070\", \"232329\", \"232719\", \"95270\", \"234168\", \"234814\", \"236843\", \"236910\", \"237852\", \"238205\", \"239242\", \"241716\", \"242069\", \"239686\", \"242228\", \"244721\", \"248171\", \"248817\", \"250183\", \"250722\", \"252172\", \"253087\", \"253508\", \"254266\", \"255999\", \"256098\", \"256883\", \"260746\", \"260926\", \"264628\", \"264994\", \"266046\", \"273190\", \"274665\", \"246446\", \"248167\", \"250346\", \"253082\", \"257559\", \"259393\", \"259591\", \"259867\", \"260373\", \"256940\", \"260467\", \"263270\", \"264567\", \"264645\", \"264931\", \"265524\", \"265661\", \"265739\", \"266669\", \"267161\", \"269031\", \"270548\", \"271934\", \"274120\", \"274549\", \"276299\", \"278948\", \"280249\", \"280501\", \"279384\", \"284357\", \"287036\", \"300722\", \"303293\", \"280264\", \"280456\", \"282023\", \"282874\", \"283914\", \"287949\", \"288279\", \"289508\", \"289706\", \"291019\", \"291145\", \"291311\", \"291575\", \"296289\", \"300803\", \"300867\", \"302972\"], \"outgoing_citations\": [\"306182\", \"314050\", \"320008\", \"336312\"]}","task_split":"paper_retrieval"}
{"document_id":"302948","document_content":"# Generating Sentences from a Continuous Space\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nThe standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06349v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\"], \"incoming_citations\": [\"5447\", \"28060\", \"36981\", \"37430\", \"42229\", \"49223\", \"63787\", \"112333\", \"90924\", \"1031\", \"2863\", \"3382\", \"7448\", \"7769\", \"9824\", \"15842\", \"16013\", \"16334\", \"16590\", \"18326\", \"23584\", \"24427\", \"24755\", \"25308\", \"25505\", \"25636\", \"26001\", \"26304\", \"26860\", \"34447\", \"36081\", \"38362\", \"40851\", \"46735\", \"50472\", \"53587\", \"58538\", \"64407\", \"68140\", \"68743\", \"72764\", \"78228\", \"84326\", \"90014\", \"91666\", \"94351\", \"98368\", \"103338\", \"124470\", \"179998\", \"182975\", \"91676\", \"4556\", \"4898\", \"6121\", \"6122\", \"7087\", \"9185\", \"9412\", \"10367\", \"10457\", \"7840\", \"13062\", \"13160\", \"14278\", \"16207\", \"16185\", \"16405\", \"16506\", \"16775\", \"17665\", \"18314\", \"18410\", \"19800\", \"19998\", \"17900\", \"19929\", \"20549\", \"21060\", \"21826\", \"23120\", \"23400\", \"23275\", \"23468\", \"24623\", \"24883\", \"25034\", \"25136\", \"25776\", \"25918\", \"26417\", \"26367\", \"26515\", \"27517\", \"27415\", \"27905\", \"29356\", \"29970\", \"31331\", \"14140\", \"32174\", \"32963\", \"32953\", \"33189\", \"33240\", \"33363\", \"34438\", \"34586\", \"35257\", \"37444\", \"38116\", \"38366\", \"38475\", \"38520\", \"38574\", \"39812\", \"40239\", \"42007\", \"42224\", \"43034\", \"43140\", \"43236\", \"43329\", \"43773\", \"43607\", \"44100\", \"44204\", \"44098\", \"44212\", \"44295\", \"45169\", \"45423\", \"46102\", \"46157\", \"46005\", \"46104\", \"46976\", \"46983\", \"49136\", \"49658\", \"50351\", \"50455\", \"50610\", \"50687\", \"52074\", \"52037\", \"53035\", \"53457\", \"53533\", \"55134\", \"55481\", \"55461\", \"56433\", \"56945\", \"58035\", \"57919\", \"58075\", \"58539\", \"59669\", \"162677\", \"59897\", \"61157\", \"61631\", \"61648\", \"61764\", \"61917\", \"62332\", \"62794\", \"63928\", \"64508\", \"64902\", \"64915\", \"66243\", \"66531\", \"168075\", \"67296\", \"67539\", \"69618\", \"70206\", \"70369\", \"72431\", \"72861\", \"73415\", \"73920\", \"73962\", \"74530\", \"75407\", \"75658\", \"76708\", \"76748\", \"76964\", \"77164\", \"78548\", \"79732\", \"79685\", \"80578\", \"80697\", \"81823\", \"81799\", \"150602\", \"85712\", \"86200\", \"89239\", \"90921\", \"91598\", \"91982\", \"92175\", \"92381\", \"95582\", \"95412\", \"95850\", \"98752\", \"100542\", \"100630\", \"101292\", \"103539\", \"104774\", \"105401\", \"108602\", \"112480\", \"116577\", \"119517\", \"170321\", \"127810\", \"127928\", \"129567\", \"41767\", \"130189\", \"132550\", \"132791\", \"133302\", \"146128\", \"155850\", \"158023\", \"162550\", \"179282\", \"184128\", \"195123\", \"197666\", \"132906\", \"78655\", \"79187\", \"80713\", \"81397\", \"82594\", \"82887\", \"83487\", \"82662\", \"84129\", \"86112\", \"157079\", \"88685\", \"89565\", \"85759\", \"90194\", \"90732\", \"90795\", \"91705\", \"91856\", \"92405\", \"92638\", \"92542\", \"92747\", \"93407\", \"89331\", \"94543\", \"94673\", \"95012\", \"94789\", \"94898\", \"95573\", \"95549\", \"96069\", \"96107\", \"96175\", \"92996\", \"96487\", \"96780\", \"96881\", \"96894\", \"97263\", \"97297\", \"98221\", \"98620\", \"98642\", \"99116\", \"99469\", \"99373\", \"100337\", \"100365\", \"101774\", \"102470\", \"103430\", \"104915\", \"105668\", \"105670\", \"107358\", \"107733\", \"108688\", \"110734\", \"111025\", \"111464\", \"112095\", \"112232\", \"113001\", \"113240\", \"113479\", \"113578\", \"114145\", \"114483\", \"114171\", \"115081\", \"115193\", \"115345\", \"117959\", \"118224\", \"118364\", \"118623\", \"118981\", \"119271\", \"119533\", \"119768\", \"119895\", \"120543\", \"120641\", \"121188\", \"121542\", \"115344\", \"121733\", \"121899\", \"122438\", \"122432\", \"122889\", \"123289\", \"124250\", \"124496\", \"124651\", \"124684\", \"125048\", \"125666\", \"126112\", \"126210\", \"126318\", \"126782\", \"126614\", \"126818\", \"127048\", \"122474\", \"127848\", \"127927\", \"128792\", \"128766\", \"128874\", \"128975\", \"129534\", \"129945\", \"129810\", \"130810\", \"130874\", \"131445\", \"131678\", \"131969\", \"132506\", \"133034\", \"133147\", \"133457\", \"130164\", \"128060\", \"134175\", \"134520\", \"135398\", \"133767\", \"135597\", \"137033\", \"137608\", \"137718\", \"137742\", \"137976\", \"138646\"], \"outgoing_citations\": [\"303029\", \"303997\", \"311388\", \"311849\", \"314240\", \"317771\", \"320032\", \"321177\", \"328469\", \"349217\"]}","task_split":"paper_retrieval"}
{"document_id":"302952","document_content":"# Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning\n## Categories\n- Machine Learning\n## Abstract\nThe ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed \"Actor-Mimic\", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06342v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"1158\", \"4353\", \"12230\", \"12849\", \"18466\", \"22458\", \"34545\", \"73192\", \"74365\", \"162248\", \"172302\", \"204254\", \"260314\", \"4901\", \"5981\", \"13461\", \"18635\", \"22951\", \"22978\", \"23294\", \"26894\", \"27284\", \"29648\", \"31901\", \"37206\", \"37807\", \"42119\", \"41922\", \"41980\", \"53844\", \"54100\", \"14180\", \"55486\", \"56552\", \"58392\", \"68374\", \"68459\", \"69040\", \"69480\", \"69948\", \"70816\", \"71387\", \"71397\", \"72943\", \"74507\", \"75992\", \"77413\", \"79812\", \"84405\", \"85115\", \"94549\", \"96663\", \"100560\", \"114431\", \"115375\", \"119726\", \"139597\", \"201315\", \"207011\", \"78625\", \"81777\", \"87840\", \"94162\", \"96179\", \"96752\", \"100245\", \"101301\", \"112242\", \"112186\", \"113394\", \"113866\", \"119171\", \"123032\", \"184639\", \"124206\", \"132686\", \"134149\", \"134432\", \"137299\", \"138724\", \"139807\", \"140129\", \"141197\", \"141701\", \"143420\", \"143410\", \"139874\", \"145493\", \"146533\", \"152680\", \"157007\", \"162155\", \"162581\", \"160320\", \"164419\", \"165065\", \"167763\", \"175541\", \"192812\", \"257674\", \"149260\", \"149771\", \"160036\", \"161097\", \"161870\", \"161921\", \"164856\", \"165569\", \"154618\", \"171239\", \"175602\", \"175563\", \"176597\", \"176642\", \"177381\", \"183608\", \"187871\", \"189620\", \"190947\", \"191589\", \"192827\", \"194100\", \"194729\", \"195428\", \"195663\", \"197982\", \"199156\", \"200000\", \"205179\", \"205521\", \"209651\", \"208234\", \"217092\", \"218124\", \"219433\", \"228561\", \"191970\", \"207983\", \"211473\", \"212811\", \"213477\", \"216076\", \"205943\", \"219314\", \"220865\", \"223990\", \"224025\", \"229185\", \"229453\", \"229559\", \"231707\", \"231847\", \"236067\", \"237060\", \"239419\", \"241037\", \"241774\", \"241899\", \"242292\", \"243393\", \"247002\", \"251765\", \"253681\", \"255032\", \"255613\", \"259693\", \"262228\", \"264787\", \"273608\", \"204330\", \"252179\", \"252176\", \"252251\", \"253876\", \"254503\", \"255100\", \"255936\", \"258199\", \"258580\", \"258963\", \"259498\", \"259094\", \"260136\", \"261316\", \"262591\", \"263650\", \"264960\", \"270792\", \"277797\", \"278903\", \"281011\", \"278984\", \"284565\", \"293876\", \"296348\", \"305239\", \"279205\", \"280804\", \"281206\", \"280870\", \"284470\", \"290378\", \"290915\", \"291115\", \"293702\", \"295435\", \"298696\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302962","document_content":"# Good, Better, Best: Choosing Word Embedding Context\n## Categories\n- Computation and Language\n## Abstract\nWe propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees. Using several variations of neural network classifier, we show that these combined methods lead to improved performance when used as input features for supervised term-matching.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06312v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"302999","document_content":"# Detection of Slang Words in e-Data using semi-Supervised Learning\n## Categories\n- Computation and Language\n## Abstract\nThe proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day,in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/ijaia.2013.4504\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"303010","document_content":"# Variable Rate Image Compression with Recurrent Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nA large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements. Due to these factors, it has become the norm for modern graphics-heavy websites to transmit low-resolution, low-bytecount image previews (thumbnails) as part of the initial page load process to improve apparent page responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore a current research focus, as any byte savings will significantly enhance the experience of mobile device users. Toward this end, we propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional LSTM recurrent networks. Our models address the main issues that have prevented autoencoder neural networks from competing with existing image compression algorithms: (1) our networks only need to be trained once (not per-image), regardless of input image dimensions and the desired compression rate; (2) our networks are progressive, meaning that the more bits are sent, the more accurate the image reconstruction; and (3) the proposed architecture is at least as efficient as a standard purpose-trained autoencoder for a given number of bits. On a large-scale benchmark of 32$\\times$32 thumbnails, our LSTM-based approaches provide better visual quality than (headerless) JPEG, JPEG2000 and WebP, with a storage size that is reduced by 10% or more.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.06085v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"47239\", \"125962\", \"2345\", \"3535\", \"3584\", \"4502\", \"4434\", \"8961\", \"9247\", \"25262\", \"39792\", \"49272\", \"79886\", \"83996\", \"87941\", \"97572\", \"97830\", \"123186\", \"129842\", \"143178\", \"898\", \"2018\", \"11335\", \"11354\", \"19978\", \"22411\", \"30766\", \"35592\", \"36887\", \"38255\", \"38620\", \"44576\", \"44585\", \"25712\", \"48406\", \"51703\", \"52736\", \"54885\", \"56443\", \"59267\", \"59412\", \"59824\", \"59979\", \"65334\", \"71399\", \"70549\", \"77691\", \"83013\", \"143298\", \"93754\", \"98494\", \"113228\", \"114038\", \"113821\", \"116426\", \"129657\", \"134573\", \"138585\", \"151708\", \"160882\", \"192903\", \"81184\", \"83091\", \"98307\", \"100949\", \"103060\", \"104348\", \"104873\", \"106286\", \"106518\", \"117730\", \"117930\", \"117925\", \"120449\", \"124326\", \"126210\", \"111943\", \"132713\", \"135025\", \"136318\", \"118710\", \"140558\", \"141734\", \"141894\", \"143347\", \"145566\", \"146623\", \"146681\", \"147245\", \"147608\", \"147712\", \"132172\", \"151563\", \"151604\", \"154339\", \"155387\", \"158691\", \"160928\", \"162788\", \"172196\", \"172387\", \"176021\", \"179246\", \"193985\", \"225233\", \"149165\", \"150526\", \"151704\", \"166254\", \"172350\", \"177300\", \"79389\", \"182691\", \"183117\", \"184339\", \"185272\", \"188610\", \"189982\", \"190339\", \"192107\", \"195076\", \"199280\", \"201968\", \"201958\", \"202159\", \"204814\", \"205024\", \"207812\", \"209536\", \"215245\", \"215653\", \"228729\", \"236298\", \"238842\", \"247298\", \"201747\", \"216677\", \"218722\", \"218873\", \"227137\", \"229326\", \"229261\", \"247714\", \"229841\", \"230221\", \"231842\", \"230216\", \"234240\", \"235796\", \"241131\", \"241634\", \"238760\", \"243971\", \"245087\", \"245227\", \"247493\", \"249132\", \"252290\", \"270142\", \"270241\", \"273390\", \"254860\", \"242995\", \"266118\", \"266304\", \"259513\", \"269868\", \"272675\", \"289981\", \"286385\", \"281822\", \"302515\"], \"outgoing_citations\": [\"328469\"]}","task_split":"paper_retrieval"}
{"document_id":"303067","document_content":"# Particular object retrieval with integral max-pooling of CNN activations\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.05879v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9497\", \"145777\", \"5681\", \"7251\", \"23117\", \"34971\", \"36437\", \"39682\", \"52513\", \"73146\", \"85376\", \"97279\", \"99264\", \"144051\", \"144613\", \"211153\", \"7275\", \"13253\", \"13707\", \"15391\", \"352180\", \"21691\", \"22193\", \"23675\", \"24332\", \"25890\", \"26135\", \"30179\", \"31118\", \"36193\", \"41288\", \"183974\", \"45771\", \"28833\", \"46590\", \"48708\", \"49887\", \"50585\", \"51376\", \"52533\", \"52816\", \"55955\", \"56767\", \"61377\", \"62537\", \"65943\", \"67484\", \"68658\", \"68885\", \"70156\", \"72768\", \"76106\", \"64004\", \"86145\", \"87691\", \"93329\", \"93567\", \"97325\", \"100159\", \"107454\", \"107996\", \"115346\", \"124630\", \"157313\", \"176317\", \"228640\", \"8448\", \"35418\", \"79185\", \"84799\", \"87306\", \"85055\", \"78868\", \"218749\", \"99702\", \"100788\", \"103466\", \"103697\", \"106588\", \"104567\", \"109113\", \"109645\", \"112934\", \"122090\", \"122880\", \"127476\", \"128712\", \"129986\", \"103266\", \"134012\", \"134085\", \"137528\", \"139623\", \"140573\", \"145738\", \"147155\", \"211067\", \"223220\", \"103597\", \"150708\", \"161866\", \"165966\", \"166252\", \"167361\", \"166416\", \"165769\", \"166405\", \"169444\", \"170303\", \"170359\", \"170948\", \"143691\", \"172435\", \"174176\", \"176318\", \"176978\", \"177755\", \"178783\", \"180184\", \"180670\", \"181615\", \"180562\", \"186126\", \"187122\", \"187855\", \"188194\", \"189084\", \"189938\", \"190213\", \"191610\", \"192344\", \"192353\", \"193735\", \"194234\", \"195314\", \"196519\", \"194115\", \"198922\", \"200458\", \"201111\", \"204128\", \"207283\", \"207598\", \"207610\", \"208493\", \"211595\", \"218263\", \"225586\", \"231046\", \"236908\", \"239587\", \"241439\", \"243056\", \"245667\", \"248322\", \"250135\", \"251473\", \"256887\", \"261255\", \"267922\", \"274746\", \"206586\", \"206698\", \"193076\", \"207767\", \"208288\", \"208687\", \"209520\", \"209652\", \"210585\", \"212048\", \"212243\", \"215570\", \"213333\", \"221285\", \"222623\", \"223359\", \"223644\", \"70590\", \"224292\", \"224597\", \"227120\", \"227893\", \"228831\", \"230895\", \"233440\", \"234121\", \"235501\", \"237299\", \"237248\", \"237722\", \"238392\", \"255989\", \"245750\", \"248935\", \"249266\", \"249448\", \"256343\", \"262552\", \"264032\", \"264316\", \"265276\", \"267404\", \"271025\", \"280225\", \"287103\", \"292069\", \"215570\", \"247853\", \"249199\", \"254812\", \"258948\", \"259317\", \"260577\", \"260972\", \"261707\", \"262166\", \"262169\", \"263387\", \"266559\", \"268341\", \"269054\", \"269814\", \"272445\", \"272633\", \"269927\", \"274926\", \"275310\", \"275976\", \"277635\", \"280378\", \"280519\", \"281953\", \"288173\", \"294367\", \"296534\", \"281155\", \"289866\", \"293545\", \"294470\", \"294983\", \"295204\", \"297319\", \"298153\", \"301572\"], \"outgoing_citations\": [\"301572\", \"304501\", \"314584\", \"321070\", \"327401\", \"331429\", \"331445\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"303122","document_content":"# A Distribution Adaptive Framework for Prediction Interval Estimation Using Nominal Variables\n## Categories\n- Machine Learning\n## Abstract\nProposed methods for prediction interval estimation so far focus on cases where input variables are numerical. In datasets with solely nominal input variables, we observe records with the exact same input $x^u$, but different real valued outputs due to the inherent noise in the system. Existing prediction interval estimation methods do not use representations that can accurately model such inherent noise in the case of nominal inputs. We propose a new prediction interval estimation method tailored for this type of data, which is prevalent in biology and medicine. We call this method Distribution Adaptive Prediction Interval Estimation given Nominal inputs (DAPIEN) and has four main phases. First, we select a distribution function that can best represent the inherent noise of the system for all unique inputs. Then we infer the parameters $\\theta_i$ (e.g. $\\theta_i=[mean_i, variance_i]$) of the selected distribution function for all unique input vectors $x^u_i$ and generate a new corresponding training set using pairs of $x^u_i, \\theta_i$. III). Then, we train a model to predict $\\theta$ given a new $x_u$. Finally, we calculate the prediction interval for a new sample using the inverse of the cumulative distribution function once the parameters $\\theta$ is predicted by the trained model. We compared DAPIEN to the commonly used Bootstrap method on three synthetic datasets. Our results show that DAPIEN provides tighter prediction intervals while preserving the requested coverage when compared to Bootstrap. This work can facilitate broader usage of regression methods in medicine and biology where it is necessary to provide tight prediction intervals while preserving coverage when input variables are nominal.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.05688v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"303146","document_content":"# Topic Model Based Behaviour Modeling and Clustering Analysis for Wireless Network Users\n## Categories\n- Social and Information Networks\n## Abstract\nUser behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.05618v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"303306","document_content":"# Neural Programmer: Inducing Latent Programs with Gradient Descent\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nDeep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning. However, this success has not been translated to applications like question answering that may involve complex arithmetic and logic reasoning. A major limitation of these models is in their inability to learn even simple arithmetic and logic operations. For example, it has been shown that neural networks fail to learn to add two binary numbers reliably. In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations. Neural Programmer can call these augmented operations over several steps, thereby inducing compositional programs that are more complex than the built-in operations. The model learns from a weak supervision signal which is the result of execution of the correct program, hence it does not require expensive annotation of the correct program itself. The decisions of what operations to call, and what data segments to apply to are inferred by Neural Programmer. Such decisions, during training, are done in a differentiable fashion so that the entire network can be trained jointly by gradient descent. We find that training the model is difficult, but it can be greatly improved by adding random noise to the gradient. On a fairly complex synthetic table-comprehension dataset, traditional recurrent networks and attentional models perform poorly while Neural Programmer typically obtains nearly perfect accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.04834v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"35134\", \"59368\", \"82120\", \"149987\", \"26664\", \"31147\", \"37320\", \"49764\", \"59677\", \"61465\", \"65740\", \"70595\", \"93230\", \"142497\", \"74193\", \"49082\", \"90379\", \"90377\", \"96129\", \"96227\", \"113586\", \"117275\", \"118679\", \"124544\", \"132918\", \"138113\", \"144603\", \"168571\", \"188441\", \"149371\", \"158917\", \"165951\", \"171683\", \"175028\", \"176190\", \"176946\", \"177099\", \"181093\", \"180362\", \"188866\", \"196980\", \"198161\", \"210090\", \"211735\", \"224563\", \"226444\", \"234648\", \"250549\", \"264290\", \"269608\", \"207983\", \"210276\", \"213652\", \"213780\", \"215409\", \"218723\", \"218674\", \"218944\", \"219950\", \"223827\", \"223816\", \"227881\", \"227326\", \"231384\", \"232097\", \"240094\", \"217975\", \"232449\", \"241060\", \"242415\", \"242963\", \"243063\", \"244165\", \"245072\", \"245201\", \"245418\", \"246779\", \"249693\", \"250651\", \"250990\", \"250483\", \"252324\", \"254907\", \"255562\", \"265646\", \"271683\", \"296823\", \"246446\", \"250084\", \"253541\", \"256438\", \"259956\", \"260320\", \"263120\", \"263306\", \"264931\", \"266702\", \"266825\", \"266878\", \"267878\", \"267980\", \"268336\", \"270315\", \"270893\", \"272929\", \"273742\", \"278430\", \"281011\", \"281106\", \"279428\", \"281175\", \"279333\", \"281041\", \"281214\", \"281085\", \"288979\", \"292141\", \"279777\", \"281042\", \"281251\", \"281367\", \"282185\", \"278913\", \"286548\", \"289848\", \"290231\", \"292763\", \"298439\", \"298627\", \"298758\", \"302107\", \"302970\", \"303296\", \"303714\", \"302803\", \"333520\"], \"outgoing_citations\": [\"300421\", \"302107\", \"302803\", \"302970\", \"307732\", \"307902\", \"308610\", \"311970\", \"313944\", \"316576\", \"322537\", \"328352\", \"356833\"]}","task_split":"paper_retrieval"}
{"document_id":"303348","document_content":"# Word Embedding based Correlation Model for Question\/Answer Matching\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nWith the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.04646v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"61450\", \"183151\", \"164432\", \"197910\", \"234687\", \"234538\", \"245858\", \"152460\", \"301316\"], \"outgoing_citations\": [\"152460\", \"153040\", \"154227\", \"324358\"]}","task_split":"paper_retrieval"}
{"document_id":"303349","document_content":"# Deep Reinforcement Learning with a Natural Language Action Space\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nThis paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.04636v5\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"15887\", \"18344\", \"22323\", \"34793\", \"36587\", \"43279\", \"45156\", \"56667\", \"13824\", \"22124\", \"22368\", \"41323\", \"41525\", \"42570\", \"44832\", \"45523\", \"54834\", \"55605\", \"59320\", \"60832\", \"89002\", \"95805\", \"96855\", \"97289\", \"91154\", \"92228\", \"92085\", \"96195\", \"96449\", \"98703\", \"100477\", \"103709\", \"110974\", \"89234\", \"119028\", \"120211\", \"128388\", \"132686\", \"135201\", \"140959\", \"145784\", \"145805\", \"149907\", \"153843\", \"163329\", \"167303\", \"182477\", \"257674\", \"147596\", \"156421\", \"158642\", \"162609\", \"166985\", \"171239\", \"171942\", \"180270\", \"184995\", \"187527\", \"188486\", \"189110\", \"179567\", \"199295\", \"200321\", \"207273\", \"207533\", \"205490\", \"226327\", \"226321\", \"216953\", \"228241\", \"231741\", \"232759\", \"234141\", \"214848\", \"239749\", \"242422\", \"249569\", \"259693\", \"265222\", \"267779\", \"268410\", \"274244\", \"290615\", \"291102\", \"295884\", \"298789\", \"301005\"], \"outgoing_citations\": [\"298789\", \"310370\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"303801","document_content":"# A Survey of the Trends in Facial and Expression Recognition Databases and Methods\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAutomated facial identification and facial expression recognition have been topics of active research over the past few decades. Facial and expression recognition find applications in human-computer interfaces, subject tracking, real-time security surveillance systems and social networking. Several holistic and geometric methods have been developed to identify faces and expressions using public and local facial image databases. In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger, sadness, happiness, disgust, fear and surprise. We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images. However, the recent methodologies have shifted focus towards robust implementation of facial\/expression recognition from large image databases that vary with space (gathered from the internet) and time (video recordings). The evolution trends in databases and methodologies for facial and expression recognition can be useful for assessing the next-generation topics that may have applications in security systems or personal identification systems that involve \"Quantitative face\" assessments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/ijcses.2015.6501\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"99238\", \"271259\"], \"outgoing_citations\": [\"311792\", \"360291\"]}","task_split":"paper_retrieval"}
{"document_id":"303827","document_content":"# The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations\n## Categories\n- Computation and Language\n## Abstract\nWe introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency words, which carry greater semantic content. We compare a range of state-of-the-art models, each with a different way of encoding what has been previously read. We show that models which store explicit representations of long-term contexts outperform state-of-the-art neural language models at predicting semantic content words, although this advantage is not observed for syntactic function words. Interestingly, we find that the amount of text encoded in a single memory representation is highly influential to the performance: there is a sweet-spot, not too big and not too small, between single words and full sentences that allows the most meaningful information in a text to be effectively retained and recalled. Further, the attention over such window-based memories can be trained effectively through self-supervision. We then assess the generality of this principle by applying it to the CNN QA benchmark, which involves identifying named entities in paraphrased summaries of news articles, and achieve state-of-the-art performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.02301v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"33114\", \"2641\", \"14340\", \"25135\", \"49550\", \"50667\", \"66794\", \"73800\", \"117433\", \"148368\", \"719\", \"3488\", \"4719\", \"11114\", \"11540\", \"14433\", \"16405\", \"18340\", \"21167\", \"21101\", \"23573\", \"26502\", \"36345\", \"38043\", \"45832\", \"54560\", \"58432\", \"72141\", \"73497\", \"76611\", \"77192\", \"77947\", \"79143\", \"94230\", \"126474\", \"142497\", \"145454\", \"200289\", \"46092\", \"133131\", \"87222\", \"89042\", \"146890\", \"97341\", \"97587\", \"100873\", \"103778\", \"109913\", \"117098\", \"124679\", \"125312\", \"125364\", \"127916\", \"130763\", \"132603\", \"116972\", \"128656\", \"128932\", \"136564\", \"145873\", \"147374\", \"150446\", \"156086\", \"156548\", \"158701\", \"161377\", \"171817\", \"189538\", \"130674\", \"149087\", \"156401\", \"157292\", \"150389\", \"161422\", \"161783\", \"169278\", \"169799\", \"171719\", \"171777\", \"171683\", \"176190\", \"178032\", \"176129\", \"178327\", \"179348\", \"181662\", \"181662\", \"182670\", \"181585\", \"186432\", \"192197\", \"192378\", \"192756\", \"188736\", \"194419\", \"197855\", \"197927\", \"200431\", \"200693\", \"200553\", \"203165\", \"208346\", \"209224\", \"210888\", \"211259\", \"212117\", \"220723\", \"221086\", \"234648\", \"213967\", \"169239\", \"208172\", \"210690\", \"212304\", \"213271\", \"214463\", \"215234\", \"218189\", \"218693\", \"218944\", \"219048\", \"219292\", \"220016\", \"220570\", \"218698\", \"222022\", \"221316\", \"220927\", \"197765\", \"216953\", \"226958\", \"228435\", \"228482\", \"228637\", \"229429\", \"223867\", \"229631\", \"231338\", \"231929\", \"232590\", \"233062\", \"230393\", \"231981\", \"232355\", \"233970\", \"234754\", \"234814\", \"236156\", \"233791\", \"236958\", \"237682\", \"237939\", \"238610\", \"238895\", \"239013\", \"225669\", \"192203\", \"234577\", \"241099\", \"246393\", \"247060\", \"247824\", \"249315\", \"250547\", \"250999\", \"251589\", \"250483\", \"251821\", \"252059\", \"253068\", \"255322\", \"267031\", \"268100\", \"269098\", \"255006\", \"144800\", \"250863\", \"250996\", \"251770\", \"253866\", \"259954\", \"260280\", \"260768\", \"261257\", \"261389\", \"262511\", \"263691\", \"265139\", \"266155\", \"267246\", \"267351\", \"266850\", \"267878\", \"268252\", \"268671\", \"268809\", \"270020\", \"271533\", \"271583\", \"271935\", \"272516\", \"273977\", \"279213\", \"279680\", \"281162\", \"281163\", \"278163\", \"281127\", \"281119\", \"281526\", \"281853\", \"282278\", \"282697\", \"285811\", \"288385\", \"290408\", \"291099\", \"280584\", \"281092\", \"279343\", \"281210\", \"281586\", \"281754\", \"283345\", \"284528\", \"286317\", \"287413\", \"287982\", \"288590\", \"288821\", \"289265\", \"290041\", \"290525\", \"290789\", \"290747\", \"290937\", \"290934\", \"290697\", \"294144\", \"295667\", \"295931\", \"297106\", \"302771\", \"317213\", \"303538\"], \"outgoing_citations\": [\"311585\", \"311684\", \"312066\", \"316576\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"304091","document_content":"# Computing Feasible Vehicle Platooning Opportunities for Transport Assignments\n## Categories\n- Systems and Control\n## Abstract\nVehicle platooning facilitates the partial automation of vehicles and can significantly reduce fuel consumption. Mobile communication infrastructure makes it possible to dynamically coordinate the formation of platoons en route. We consider a centralized system that provides trucks with routes and speed profiles allowing them to dynamically form platoons during their journeys. For this to work, all possible pairs of vehicles that can platoon based on their location, destination, and other constraints have to be identified. The presented approach scales well to large vehicle fleets and realistic road networks by extracting features from the transport assignments of the vehicles and rules out a majority of possible pairs based on these features only. Merely a small number of remaining pairs are considered in depth by a complete and computationally expensive algorithm. This algorithm conclusively decides if platooning is possible for a pair based on the complete data associated with the two vehicles. We derive appropriate features for the problem and demonstrate the effectiveness of the approach in a simulation example.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1511.00849v2\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [\"236061\"], \"outgoing_citations\": [\"304647\", \"304663\"]}","task_split":"paper_retrieval"}
{"document_id":"304409","document_content":"# A Fast Randomized Method to Find Homotopy Classes for Socially-Aware Navigation\n## Categories\n- Robotics\n- Computer Vision and Pattern Recognition\n## Abstract\nWe introduce and show preliminary results of a fast randomized method that finds a set of K paths lying in distinct homotopy classes. We frame the path planning task as a graph search problem, where the navigation graph is based on a Voronoi diagram. The search is biased by a cost function derived from the social force model that is used to generate and select the paths. We compare our method to Yen's algorithm, and empirically show that our approach is faster to find a subset of homotopy classes. Furthermore our approach computes a set of more diverse paths with respect to the baseline while obtaining a negligible loss in path quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.08233v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.RO\", \"cs.CV\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Robotics\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"304501","document_content":"# Aggregating Deep Convolutional Features for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSeveral recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation approaches developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptors.   In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides arguably the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.07493v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1485\", \"62443\", \"5681\", \"21691\", \"49887\", \"52533\", \"61377\", \"70156\", \"64004\", \"100159\", \"110152\", \"120701\", \"228640\", \"88683\", \"85055\", \"109645\", \"111079\", \"111143\", \"113961\", \"127476\", \"145738\", \"149517\", \"154718\", \"223220\", \"260452\", \"160966\", \"165769\", \"170359\", \"176580\", \"181615\", \"186126\", \"194378\", \"196737\", \"198922\", \"251473\", \"208427\", \"212048\", \"218237\", \"237248\", \"255989\", \"245421\", \"286767\", \"310303\", \"249199\", \"260306\", \"268341\", \"269927\", \"274705\", \"274926\", \"275310\", \"277404\", \"279638\", \"280378\", \"280275\", \"281953\", \"294367\", \"305554\", \"287584\", \"289866\", \"294983\", \"295204\", \"295253\", \"297319\", \"301572\", \"303067\", \"308693\"], \"outgoing_citations\": [\"320036\", \"321201\", \"322263\", \"332726\"]}","task_split":"paper_retrieval"}
{"document_id":"304647","document_content":"# Fuel-Optimal Centralized Coordination of Truck Platooning Based on Shortest Paths\n## Categories\n- Systems and Control\n## Abstract\nPlatooning is a way to significantly reduce fuel consumption of trucks. Vehicles that drive at close inter-vehicle distance assisted by automatic controllers experience substantially lower air-drag. In this paper, we deal with the problem of coordinating the formation and the breakup of platoons in a fuel-optimal way. We formulate an optimization problem which accounts for routing, speed-dependent fuel consumption, and platooning decisions. An algorithm to obtain an approximate solution to the problem is presented. It first determines the shortest path for each truck. Then, possible platoon configurations are identified. For a certain platoon configuration the optimal speed profile is the solution to a convex program. The algorithm is illustrated by a realistic example.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACC.2015.7171911\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [\"224940\", \"236061\", \"267763\", \"313444\", \"309656\", \"304091\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"304657","document_content":"# Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling\n## Categories\n- Computation and Language\n- Distributed, Parallel, and Cluster Computing\n- Machine Learning\n## Abstract\nThere is an explosion of data, documents, and other content, and people require tools to analyze and interpret these, tools to turn the content into information and knowledge. Topic modeling have been developed to solve these problems. Topic models such as LDA [Blei et. al. 2003] allow salient patterns in data to be extracted automatically. When analyzing texts, these patterns are called topics. Among numerous extensions of LDA, few of them can reliably analyze multiple groups of documents and extract topic similarities. Recently, the introduction of differential topic modeling (SPDP) [Chen et. al. 2012] performs uniformly better than many topic models in a discriminative setting.   There is also a need to improve the sampling speed for topic models. While some effort has been made for distributed algorithms, there is no work currently done using graphical processing units (GPU). Note the GPU framework has already become the most cost-efficient platform for many problems.   In this thesis, I propose and implement a scalable multi-GPU distributed parallel framework which approximates SPDP. Through experiments, I have shown my algorithms have a gain in speed of about 50 times while being almost as accurate, with only one single cheap laptop GPU. Furthermore, I have shown the speed improvement is sublinearly scalable when multiple GPUs are used, while fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the speed improvement could potentially reach a factor of a thousand.   Note SPDP is just a representative of other extensions of LDA. Although my algorithm is implemented to work with SPDP, it is designed to be a general enough to work with other topic models. The speed-up on smaller collections (i.e., 1000s of documents), means that these more complex LDA extensions could now be done in real-time, thus opening up a new way of using these LDA models in industry.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.06549v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DC\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Distributed, Parallel, and Cluster Computing\", \"Machine Learning\"], \"incoming_citations\": [\"304610\", \"304705\"], \"outgoing_citations\": [\"356534\"]}","task_split":"paper_retrieval"}
{"document_id":"304663","document_content":"# Coordinating Truck Platooning by Clustering Pairwise Fuel-Optimal Plans\n## Categories\n- Systems and Control\n## Abstract\nWe consider the fuel-optimal coordination of trucks into platoons. Truck platooning is a promising technology that enables trucks to save significant amounts of fuel by driving close together and thus reducing air drag. We study how fuel-optimal speed profiles for platooning can be computed. A first-order fuel model is considered and pairwise optimal plans are derived. We formulate an optimization problem that combines these pairwise plans into an overall plan for a large number of trucks. The problem resembles a medoids clustering problem. We propose an approximation algorithm similar to the partitioning around medoids algorithm and discuss its convergence. The method is evaluated with Monte Carlo simulations. We demonstrate that the proposed algorithm can compute a plan for thousands of trucks and that significant fuel savings can be achieved.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ITSC.2015.75\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [\"83881\", \"236061\", \"267763\", \"266688\", \"304091\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"304823","document_content":"# Latent Space Model for Multi-Modal Social Data\n## Categories\n- Social and Information Networks\n- Physics and Society\n- Data Analysis, Statistics and Probability\n- Machine Learning\n## Abstract\nWith the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2872427.2883031\", \"primary_category\": \"cs.SI\", \"categories\": [\"physics.soc-ph\", \"cs.SI\", \"physics.data-an\", \"cs.LG\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Physics and Society\", \"Social and Information Networks\", \"Data Analysis, Statistics and Probability\", \"Machine Learning\"], \"incoming_citations\": [\"63296\", \"79547\", \"192888\", \"194519\", \"232752\", \"266518\"], \"outgoing_citations\": [\"309553\", \"310856\", \"327691\", \"332327\", \"332602\", \"333989\", \"334460\", \"339003\", \"339235\", \"346647\", \"347335\", \"361187\", \"362636\"]}","task_split":"paper_retrieval"}
{"document_id":"304825","document_content":"# Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis\n## Categories\n- Social and Information Networks\n- Information Retrieval\n## Abstract\nThe growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/UKCI.2014.6930158\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"cs.IR\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"341593\"]}","task_split":"paper_retrieval"}
{"document_id":"305162","document_content":"# Text-Attentional Convolutional Neural Networks for Scene Text Detection\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text\/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text\/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2016.2547588\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"150258\", \"111116\", \"208801\", \"204166\", \"225336\", \"239535\", \"260510\", \"253398\", \"257197\", \"257954\", \"273862\", \"299382\", \"284871\", \"295564\", \"311196\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"305317","document_content":"# Distilling Model Knowledge\n## Categories\n- Machine Learning\n## Abstract\nTop-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate into larger systems. Ideally, we would like to replace such cumbersome models with simpler models that perform equally well.   In this thesis, we study knowledge distillation, the idea of extracting the knowledge contained in a complex model and injecting it into a more convenient model. We present a general framework for knowledge distillation, whereby a convenient model of our choosing learns how to mimic a complex model, by observing the latter's behaviour and being penalized whenever it fails to reproduce it.   We develop our framework within the context of three distinct machine learning applications: (a) model compression, where we compress large discriminative models, such as ensembles of neural networks, into models of much smaller size; (b) compact predictive distributions for Bayesian inference, where we distil large bags of MCMC samples into compact predictive distributions in closed form; (c) intractable generative models, where we distil unnormalizable models such as RBMs into tractable models such as NADEs.   We contribute to the state of the art with novel techniques and ideas. In model compression, we describe and implement derivative matching, which allows for better distillation when data is scarce. In compact predictive distributions, we introduce online distillation, which allows for significant savings in memory. Finally, in intractable generative models, we show how to use distilled models to robustly estimate intractable quantities of the original model, such as its intractable partition function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.02437v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"79173\", \"188296\", \"171705\", \"216619\", \"256480\", \"270965\", \"284428\"], \"outgoing_citations\": [\"339406\", \"344185\", \"350478\"]}","task_split":"paper_retrieval"}
{"document_id":"305448","document_content":"# Parameterized Neural Network Language Models for Information Retrieval\n## Categories\n- Information Retrieval\n- H.3.3; I.2.6\n- Computation and Language\n## Abstract\nInformation Retrieval (IR) models need to deal with two difficult issues, vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to the difficulty of retrieving relevant documents that do not contain exact query terms but semantically related terms. Term dependencies refers to the need of considering the relationship between the words of the query when estimating the relevance of a document. A multitude of solutions has been proposed to solve each of these two problems, but no principled model solve both. In parallel, in the last few years, language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection. Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required. This is both computationally unfeasible and prone to over-fitting. Based on a recent work that proposed to learn a generic language model that can be modified through a set of document-specific parameters, we explore use of new neural network models that are adapted to ad-hoc IR tasks. Within the language model IR framework, we propose and study the use of a generic language model as well as a document-specific language model. Both can be used as a smoothing component, but the latter is more adapted to the document at hand and has the potential of being used as a full document language model. We experiment with such models and analyze their results on TREC-1 to 8 datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.01562v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"H.3.3; I.2.6\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"H.3.3; I.2.6\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"320006\"]}","task_split":"paper_retrieval"}
{"document_id":"305591","document_content":"# A Primer on Neural Network Models for Natural Language Processing\n## Categories\n- Computation and Language\n## Abstract\nOver the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.00726v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"103740\", \"6532\", \"11588\", \"16209\", \"87060\", \"99594\", \"250219\", \"135\", \"6910\", \"6911\", \"14706\", \"17665\", \"19913\", \"32123\", \"36100\", \"48496\", \"49789\", \"51461\", \"53670\", \"48118\", \"60198\", \"69643\", \"71391\", \"71695\", \"105829\", \"180141\", \"89642\", \"95288\", \"97327\", \"100288\", \"103530\", \"103548\", \"104194\", \"102699\", \"113263\", \"116213\", \"63156\", \"119187\", \"125848\", \"126398\", \"127108\", \"127213\", \"131656\", \"129844\", \"135274\", \"143517\", \"147849\", \"109592\", \"134053\", \"152344\", \"159984\", \"160005\", \"174222\", \"185063\", \"148476\", \"152767\", \"150320\", \"153545\", \"157014\", \"157684\", \"148823\", \"161845\", \"163634\", \"169804\", \"172976\", \"175028\", \"177944\", \"179907\", \"182985\", \"183315\", \"186173\", \"186873\", \"189371\", \"192901\", \"167649\", \"197314\", \"197826\", \"198527\", \"201825\", \"201960\", \"209356\", \"210770\", \"211869\", \"213346\", \"218156\", \"221723\", \"223677\", \"228413\", \"237749\", \"204960\", \"192552\", \"207670\", \"216265\", \"218192\", \"217010\", \"219068\", \"219885\", \"220026\", \"220144\", \"225069\", \"226854\", \"231590\", \"231957\", \"232120\", \"232527\", \"95270\", \"234721\", \"238889\", \"239405\", \"242451\", \"241072\", \"244918\", \"220101\", \"249243\", \"250441\", \"255162\", \"259009\", \"261912\", \"267398\", \"270292\", \"247830\", \"251523\", \"253594\", \"255324\", \"256084\", \"258512\", \"244690\", \"263475\", \"264791\", \"264902\", \"265194\", \"266275\", \"267631\", \"267838\", \"268510\", \"271105\", \"272328\", \"272331\", \"274521\", \"274427\", \"278531\", \"291192\", \"305094\", \"317200\", \"280161\", \"280923\", \"282878\", \"283729\", \"179675\", \"284144\", \"286993\", \"289403\", \"290337\", \"292484\", \"292576\", \"293123\", \"294248\", \"296583\", \"297221\", \"297329\", \"302570\"], \"outgoing_citations\": [\"296163\", \"296712\", \"298319\", \"302570\", \"303003\", \"308310\", \"308559\", \"309429\", \"309633\", \"309965\", \"310667\", \"310740\", \"310837\", \"311683\", \"312066\", \"312490\", \"313962\", \"315023\", \"315890\", \"319782\", \"320072\", \"321035\", \"321994\", \"323451\", \"329561\", \"330941\", \"341939\", \"342934\"]}","task_split":"paper_retrieval"}
{"document_id":"305655","document_content":"# A Generative Model of Words and Relationships from Multiple Sources\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nNeural language models are a powerful tool to embed words into semantic vector spaces. However, learning such models generally relies on the availability of abundant and diverse training examples. In highly specialised domains this requirement may not be met due to difficulties in obtaining a large corpus, or the limited range of expression in average use. Such domains may encode prior knowledge about entities in a knowledge base or ontology. We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information. We achieve this by generalising the concept of co-occurrence from distributional semantics to include other relationships between entities or words, which we model as affine transformations on the embedding space. We demonstrate the effectiveness of this approach by outperforming recent models on a link prediction task and demonstrating its ability to profit from partially or fully unobserved data training labels. We further demonstrate the usefulness of learning from different data sources with overlapping vocabularies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1510.00259v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"298534\"], \"outgoing_citations\": [\"235915\", \"341939\"]}","task_split":"paper_retrieval"}
{"document_id":"305817","document_content":"# Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts\n## Categories\n- Computation and Language\n- Computers and Society\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nThe quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.procs.2015.08.456\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CY\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computers and Society\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"15289\", \"149489\", \"199966\", \"256004\", \"292561\"], \"outgoing_citations\": [\"305744\", \"305749\", \"305785\"]}","task_split":"paper_retrieval"}
{"document_id":"306266","document_content":"# Deep Spatial Autoencoders for Visuomotor Learning\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n- Robotics\n## Abstract\nReinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot's arm.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1509.06113v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\", \"cs.RO\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Robotics\"], \"incoming_citations\": [\"1051\", \"3519\", \"4184\", \"14157\", \"18771\", \"20668\", \"23602\", \"34545\", \"61139\", \"62791\", \"114662\", \"115404\", \"4583\", \"8621\", \"9356\", \"9593\", \"12124\", \"13246\", \"13406\", \"15055\", \"23792\", \"27533\", \"29648\", \"30961\", \"33029\", \"35918\", \"37671\", \"39704\", \"42015\", \"42315\", \"43745\", \"301461\", \"47686\", \"48758\", \"57260\", \"60496\", \"63020\", \"64961\", \"65884\", \"66446\", \"66514\", \"70370\", \"72057\", \"71397\", \"77643\", \"79175\", \"86509\", \"87072\", \"87613\", \"97223\", \"100693\", \"101182\", \"103818\", \"105871\", \"112124\", \"123568\", \"81864\", \"86028\", \"86628\", \"87242\", \"88020\", \"91106\", \"96979\", \"97599\", \"97993\", \"101310\", \"107896\", \"109667\", \"111344\", \"114889\", \"119929\", \"123008\", \"125685\", \"129794\", \"132719\", \"136185\", \"136663\", \"137034\", \"138960\", \"139794\", \"148993\", \"153714\", \"160619\", \"166368\", \"166705\", \"166762\", \"168799\", \"178006\", \"178094\", \"178420\", \"185730\", \"196921\", \"222669\", \"150902\", \"155441\", \"160156\", \"152047\", \"166332\", \"166296\", \"174441\", \"174927\", \"179899\", \"181928\", \"183158\", \"184488\", \"185153\", \"181405\", \"171783\", \"191711\", \"193043\", \"195713\", \"196853\", \"196955\", \"198915\", \"202595\", \"204013\", \"205521\", \"212832\", \"214068\", \"215643\", \"220103\", \"223212\", \"226824\", \"231360\", \"237162\", \"206698\", \"207601\", \"240996\", \"215168\", \"215931\", \"216438\", \"218983\", \"219180\", \"220377\", \"222446\", \"223487\", \"224840\", \"225289\", \"229546\", \"219658\", \"229890\", \"231587\", \"232186\", \"230042\", \"231376\", \"233969\", \"234583\", \"235501\", \"236969\", \"237051\", \"237245\", \"237361\", \"238333\", \"239636\", \"240602\", \"242478\", \"243236\", \"258608\", \"275068\", \"251098\", \"255401\", \"255861\", \"255939\", \"258199\", \"259119\", \"260136\", \"260489\", \"265464\", \"265500\", \"264092\", \"266207\", \"271717\", \"272585\", \"276392\", \"277472\", \"277564\", \"277797\", \"283391\", \"284805\", \"290911\", \"277504\", \"279883\", \"281468\", \"282967\", \"284833\", \"286689\", \"289719\", \"295194\", \"297317\", \"302720\", \"306153\", \"315055\"], \"outgoing_citations\": [\"310624\", \"311482\", \"311613\", \"318666\", \"361126\"]}","task_split":"paper_retrieval"}
{"document_id":"306611","document_content":"# Deep Learning Applied to Image and Text Matching\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nThe ability to describe images with natural language sentences is the hallmark for image and language understanding. Such a system has wide ranging applications such as annotating images and using natural sentences to search for images.In this project we focus on the task of bidirectional image retrieval: such asystem is capable of retrieving an image based on a sentence (image search) andretrieve sentence based on an image query (image annotation). We present asystem based on a global ranking objective function which uses a combinationof convolutional neural networks (CNN) and multi layer perceptrons (MLP).It takes a pair of image and sentence and processes them in different channels,finally embedding it into a common multimodal vector space. These embeddingsencode abstract semantic information about the two inputs and can be comparedusing traditional information retrieval approaches. For each such pair, the modelreturns a score which is interpretted as a similarity metric. If this score is high,the image and sentence are likely to convey similar meaning, and if the score is low then they are likely not to.   The visual input is modeled via deep convolutional neural network. On theother hand we explore three models for the textual module. The first one isbag of words with an MLP. The second one uses n-grams (bigram, trigrams,and a combination of trigram & skip-grams) with an MLP. The third is morespecialized deep network specific for modeling variable length sequences (SSE).We report comparable performance to recent work in the field, even though ouroverall model is simpler. We also show that the training time choice of how wecan generate our negative samples has a significant impact on performance, and can be used to specialize the bi-directional system in one particular task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1601.03478v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"306728","document_content":"# Lazy Factored Inference for Functional Probabilistic Programming\n## Categories\n- Artificial Intelligence\n## Abstract\nProbabilistic programming provides the means to represent and reason about complex probabilistic models using programming language constructs. Even simple probabilistic programs can produce models with infinitely many variables. Factored inference algorithms are widely used for probabilistic graphical models, but cannot be applied to these programs because all the variables and factors have to be enumerated. In this paper, we present a new inference framework, lazy factored inference (LFI), that enables factored algorithms to be used for models with infinitely many variables. LFI expands the model to a bounded depth and uses the structure of the program to precisely quantify the effect of the unexpanded part of the model, producing lower and upper bounds to the probability of the query.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1509.03564v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"346962\", \"346982\", \"348268\", \"350478\", \"356565\", \"357555\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"306751","document_content":"# OCR accuracy improvement on document images through a novel pre-processing approach\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDigital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/sipij.2015.6401\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"49071\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"306921","document_content":"# A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nAn approach for game bot detection in MMORPGs is proposed based on the analysis of game playing behavior. Since MMORPGs are large scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. In order to cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. For a practical purpose of reducing the workloads of the game servers in service, the game data is collected at a low resolution in time. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4218\/etrij.13.2013.0049\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"306962","document_content":"# Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\\% in mean average precision on the TIMIT corpus.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1509.02213v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"222073\", \"249139\", \"261065\", \"263012\", \"297211\", \"299178\", \"311620\", \"306961\"], \"outgoing_citations\": [\"306963\"]}","task_split":"paper_retrieval"}
{"document_id":"306979","document_content":"# Future Localization from an Egocentric Depth Image\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a method for future localization: to predict a set of plausible trajectories of ego-motion given a depth image. We predict paths avoiding obstacles, between objects, even paths turning around a corner into space behind objects. As a byproduct of the predicted trajectories of ego-motion, we discover in the image the empty space occluded by foreground objects. We use no image based features such as semantic labeling\/segmentation or object detection\/recognition for this algorithm. Inspired by proxemics, we represent the space around a person using an EgoSpace map, akin to an illustrated tourist map, that measures a likelihood of occlusion at the egocentric coordinate system. A future trajectory of ego-motion is modeled by a linear combination of compact trajectory bases allowing us to constrain the predicted trajectory. We learn the relationship between the EgoSpace map and trajectory from the EgoMotion dataset providing in-situ measurements of the future trajectory. A cost function that takes into account partial occlusion due to foreground objects is minimized to predict a trajectory. This cost function generates a trajectory that passes through the occluded space, which allows us to discover the empty space behind the foreground objects. We quantitatively evaluate our method to show predictive validity and apply to various real world scenes including walking, shopping, and social interactions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1509.02094v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"320057\", \"357727\"]}","task_split":"paper_retrieval"}
{"document_id":"307007","document_content":"# Integrate Document Ranking Information into Confidence Measure Calculation for Spoken Term Detection\n## Categories\n- Computation and Language\n## Abstract\nThis paper proposes an algorithm to improve the calculation of confidence measure for spoken term detection (STD). Given an input query term, the algorithm first calculates a measurement named document ranking weight for each document in the speech database to reflect its relevance with the query term by summing all the confidence measures of the hypothesized term occurrences in this document. The confidence measure of each term occurrence is then re-estimated through linear interpolation with the calculated document ranking weight to improve its reliability by integrating document-level information. Experiments are conducted on three standard STD tasks for Tamil, Vietnamese and English respectively. The experimental results all demonstrate that the proposed algorithm achieves consistent improvements over the state-of-the-art method for confidence measure calculation. Furthermore, this algorithm is still effective even if a high accuracy speech recognizer is not available, which makes it applicable for the languages with limited speech resources.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1509.01899v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"307406","document_content":"# Computational Sociolinguistics: A Survey\n## Categories\n- Computation and Language\n## Abstract\nLanguage is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of \"Computational Sociolinguistics\" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1508.07544v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"15030\", \"21667\", \"24518\", \"25095\", \"49962\", \"56479\", \"69640\", \"91985\", \"132077\", \"23414\", \"121866\", \"173670\", \"177884\", \"180341\", \"201322\", \"262186\", \"234391\", \"236835\", \"303276\"], \"outgoing_citations\": [\"311628\", \"319428\", \"324761\", \"343228\", \"351055\", \"353216\", \"353272\", \"353350\"]}","task_split":"paper_retrieval"}
{"document_id":"307472","document_content":"# A Dynamical Model of Twitter Activity Profiles\n## Categories\n- Social and Information Networks\n- Computers and Society\n- Physics and Society\n- J.2; J.4; I.6\n- Human-Computer Interaction\n## Abstract\nThe advent of the era of Big Data has allowed many researchers to dig into various socio-technical systems, including social media platforms. In particular, these systems have provided them with certain verifiable means to look into certain aspects of human behavior. In this work, we are specifically interested in the behavior of individuals on social media platforms---how they handle the information they get, and how they share it. We look into Twitter to understand the dynamics behind the users' posting activities---tweets and retweets---zooming in on topics that peaked in popularity. Three mechanisms are considered: endogenous stimuli, exogenous stimuli, and a mechanism that dictates the decay of interest of the population in a topic. We propose a model involving two parameters $\\eta^\\star$ and $\\lambda$ describing the tweeting behaviour of users, which allow us to reconstruct the findings of Lehmann et al. (2012) on the temporal profiles of popular Twitter hashtags. With this model, we are able to accurately reproduce the temporal profile of user engagements on Twitter. Furthermore, we introduce an alternative in classifying the collective activities on the socio-technical system based on the model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2700171.2791029\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.CY\", \"cs.SI\", \"physics.soc-ph\", \"J.2; J.4; I.6\", \"cs.HC\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Computers and Society\", \"Social and Information Networks\", \"Physics and Society\", \"J.2; J.4; I.6\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": [\"341140\", \"346166\", \"348386\", \"358150\"]}","task_split":"paper_retrieval"}
{"document_id":"307800","document_content":"# Message Passing and Combinatorial Optimization\n## Categories\n- Artificial Intelligence\n- Computational Complexity\n- Data Structures and Algorithms\n- Commutative Algebra\n- Probability\n## Abstract\nGraphical models use the intuitive and well-studied methods of graph theory to implicitly represent dependencies between variables in large systems. They can model the global behaviour of a complex system by specifying only local factors. This thesis studies inference in discrete graphical models from an algebraic perspective and the ways inference can be used to express and approximate NP-hard combinatorial problems.   We investigate the complexity and reducibility of various inference problems, in part by organizing them in an inference hierarchy. We then investigate tractable approximations for a subset of these problems using distributive law in the form of message passing. The quality of the resulting message passing procedure, called Belief Propagation (BP), depends on the influence of loops in the graphical model. We contribute to three classes of approximations that improve BP for loopy graphs A) loop correction techniques; B) survey propagation, another message passing technique that surpasses BP in some settings; and C) hybrid methods that interpolate between deterministic message passing and Markov Chain Monte Carlo inference.   We then review the existing message passing solutions and provide novel graphical models and inference techniques for combinatorial problems under three broad classes: A) constraint satisfaction problems such as satisfiability, coloring, packing, set \/ clique-cover and dominating \/ independent set and their optimization counterparts; B) clustering problems such as hierarchical clustering, K-median, K-clustering, K-center and modularity optimization; C) problems over permutations including assignment, graph morphisms and alignment, finding symmetries and traveling salesman problem. In many cases we show that message passing is able to find solutions that are either near optimal or favourably compare with today's state-of-the-art approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1508.05013v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CC\", \"cs.DS\", \"math.AC\", \"math.PR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computational Complexity\", \"Data Structures and Algorithms\", \"Commutative Algebra\", \"Probability\"], \"incoming_citations\": [], \"outgoing_citations\": [\"323985\", \"324939\", \"328683\", \"328841\", \"332146\", \"334542\", \"339804\", \"339845\", \"340813\", \"343291\", \"349845\", \"349861\", \"350491\", \"350507\", \"351242\", \"351380\", \"353234\", \"353358\", \"353374\", \"356544\", \"356827\", \"357169\", \"357183\", \"357293\", \"357546\", \"357560\", \"357688\", \"357888\", \"357914\", \"357936\", \"358925\"]}","task_split":"paper_retrieval"}
{"document_id":"307882","document_content":"# Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nExtracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval . Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. Specifically, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between matched pairs and mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2015.2467315\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"138613\", \"19768\", \"85376\", \"155958\", \"173266\", \"750\", \"72768\", \"71954\", \"109376\", \"161125\", \"167557\", \"216643\", \"82163\", \"99948\", \"105810\", \"107920\", \"108421\", \"127880\", \"131090\", \"132522\", \"225824\", \"143795\", \"158244\", \"171742\", \"203861\", \"239660\", \"243039\", \"247969\", \"180528\", \"184377\", \"189084\", \"192213\", \"202820\", \"211595\", \"224544\", \"225826\", \"228642\", \"232040\", \"232982\", \"237343\", \"241439\", \"249538\", \"252948\", \"287562\", \"215048\", \"219553\", \"222134\", \"222151\", \"224682\", \"236021\", \"192280\", \"191832\", \"191827\", \"246331\", \"247613\", \"249448\", \"251069\", \"253907\", \"258284\", \"259748\", \"260176\", \"269511\", \"274046\", \"278480\", \"189932\", \"250269\", \"253038\", \"242809\", \"258937\", \"254156\", \"260175\", \"264939\", \"267210\", \"269814\", \"271027\", \"272827\", \"254157\", \"286024\", \"286731\", \"292657\", \"278179\", \"281599\", \"282939\", \"286673\", \"287800\", \"288138\", \"288218\", \"293935\", \"296862\", \"294533\", \"303372\", \"303437\", \"303512\", \"308354\", \"321975\"], \"outgoing_citations\": [\"301632\", \"312932\", \"318121\", \"318193\", \"318535\", \"331445\", \"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"308024","document_content":"# Causal Decision Trees\n## Categories\n- Artificial Intelligence\n## Abstract\nUncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree where nodes have causal interpretations. Our method follows a well established causal inference framework and makes use of a classic statistical test. The method is practical for finding causal signals in large data sets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TKDE.2016.2619350\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"36327\", \"69889\", \"93585\", \"327024\", \"339460\"]}","task_split":"paper_retrieval"}
{"document_id":"308311","document_content":"# Image Representations and New Domains in Neural Image Captioning\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nWe examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-the-art neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result in a new, fine-grained, transfer learned captioning domain, consisting of 66K recipe image\/title pairs. We also provide some experiments regarding the appropriateness of datasets for automatic captioning, and find that having multiple captions per image is beneficial, but not an absolute requirement.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1508.02091v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"157229\", \"204220\", \"270455\", \"259184\", \"303613\"], \"outgoing_citations\": [\"316472\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"308551","document_content":"# Factor Graphs for Quantum Probabilities\n## Categories\n- Information Theory\n- Artificial Intelligence\n- Statistics Theory\n- Quantum Physics\n- Statistics Theory\n## Abstract\nA factor-graph representation of quantum-mechanical probabilities (involving any number of measurements) is proposed. Unlike standard statistical models, the proposed representation uses auxiliary variables (state variables) that are not random variables. All joint probability distributions are marginals of some complex-valued function $q$, and it is demonstrated how the basic concepts of quantum mechanics relate to factorizations and marginals of $q$.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1508.00689v3\", \"primary_category\": \"cs.IT\", \"categories\": [\"cs.IT\", \"cs.AI\", \"math.ST\", \"quant-ph\", \"stat.TH\", \"math.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Information Theory\", \"Artificial Intelligence\", \"Statistics Theory\", \"Quantum Physics\", \"Statistics Theory\", \"Information Theory\"], \"incoming_citations\": [\"22968\", \"37605\", \"196965\", \"199517\", \"264699\", \"267457\", \"288283\"], \"outgoing_citations\": [\"264699\", \"267457\", \"298690\", \"318929\", \"357455\", \"362498\"]}","task_split":"paper_retrieval"}
{"document_id":"308622","document_content":"# Indexing of CNN Features for Large Scale Image Search\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe convolutional neural network (CNN) features can give a good description of image content, which usually represent images with unique global vectors. Although they are compact compared to local descriptors, they still cannot efficiently deal with large-scale image retrieval due to the cost of the linear incremental computation and storage. To address this issue, we build a simple but effective indexing framework based on inverted table, which significantly decreases both the search time and memory usage. In addition, several strategies are fully investigated under an indexing framework to adapt it to CNN features and compensate for quantization errors. First, we use multiple assignment for the query and database images to increase the probability of relevant images' co-existing in the same Voronoi cells obtained via the clustering algorithm. Then, we introduce embedding codes to further improve precision by removing false matches during a search. We demonstrate that by using hashing schemes to calculate the embedding codes and by changing the ranking rule, indexing framework speeds can be greatly improved. Extensive experiments conducted on several unsupervised and supervised benchmarks support these results and the superiority of the proposed indexing framework. We also provide a fair comparison between the popular CNN features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1508.00217v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"308743","document_content":"# Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems\n## Categories\n- Computation and Language\n## Abstract\nRecently, the focus of complex networks research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we propose the introduction of multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax, co-occurrence and its shuffled counterpart) and a subword level (syllables and graphemes) network layers, from five variations of original text (in the modeled language). The obtained results suggest that there are substantial differences between the networks structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems simultaneously and hence to provide a more unified view on language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.08539v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"221167\", \"262031\", \"282259\", \"284961\"], \"outgoing_citations\": [\"326922\", \"329526\", \"329628\", \"329827\", \"334266\", \"336687\", \"348991\", \"351528\"]}","task_split":"paper_retrieval"}
{"document_id":"308756","document_content":"# Unsupervised Sentence Simplification Using Deep Semantics\n## Categories\n- Computation and Language\n## Abstract\nWe present a novel approach to sentence simplification which departs from previous work in two main ways. First, it requires neither hand written rules nor a training corpus of aligned standard and simplified sentences. Second, sentence splitting operates on deep semantic structure. We show (i) that the unsupervised framework we propose is competitive with four state-of-the-art supervised systems and (ii) that our semantic based approach allows for a principled and effective handling of sentence splitting.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.08452v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"26460\", \"113240\", \"118053\", \"127956\", \"154650\", \"182778\", \"183012\", \"214312\", \"214400\", \"214570\", \"214590\", \"233722\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"308882","document_content":"# Classifying informative and imaginative prose using complex networks\n## Categories\n- Computation and Language\n## Abstract\nStatistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, which encompasses machine translation, automatic summarization and document classification. In the latter, many approaches have emphasized the semantical content of texts, as it is the case of bag-of-word language models. This approach has certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only on a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological\/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterizing texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1209\/0295-5075\/113\/28007\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"114526\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"309099","document_content":"# LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation\n## Categories\n- Information Retrieval\n- Human-Computer Interaction\n## Abstract\nWe present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.06593v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.HC\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Human-Computer Interaction\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"309239","document_content":"# An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nImage-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.05717v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"38757\", \"109659\", \"77004\", \"1039\", \"1799\", \"3394\", \"5857\", \"6241\", \"6379\", \"13927\", \"18285\", \"28244\", \"37135\", \"45982\", \"50775\", \"50917\", \"51057\", \"52248\", \"44535\", \"78763\", \"80383\", \"94155\", \"180823\", \"191748\", \"191\", \"4188\", \"7620\", \"7580\", \"8109\", \"9220\", \"10204\", \"11202\", \"10580\", \"14025\", \"14359\", \"16047\", \"21983\", \"24824\", \"25235\", \"27768\", \"28270\", \"29428\", \"29640\", \"29777\", \"29901\", \"19530\", \"31120\", \"32623\", \"33363\", \"33358\", \"33360\", \"33798\", \"35666\", \"39759\", \"40057\", \"40793\", \"42497\", \"43153\", \"43037\", \"46757\", \"48822\", \"48901\", \"49071\", \"49128\", \"49698\", \"49805\", \"50125\", \"50591\", \"51384\", \"52288\", \"53117\", \"53371\", \"53750\", \"55487\", \"55460\", \"55703\", \"56713\", \"57380\", \"62619\", \"63761\", \"64171\", \"64528\", \"64650\", \"67749\", \"68302\", \"78142\", \"79422\", \"81960\", \"82221\", \"82740\", \"90368\", \"90920\", \"91313\", \"93345\", \"99340\", \"103980\", \"103920\", \"104170\", \"116951\", \"120486\", \"122764\", \"124388\", \"130621\", \"133054\", \"147463\", \"150258\", \"152376\", \"158534\", \"163092\", \"172702\", \"77867\", \"77826\", \"80437\", \"84861\", \"85583\", \"86470\", \"90549\", \"94101\", \"98220\", \"99077\", \"99051\", \"99164\", \"99423\", \"99721\", \"100417\", \"100709\", \"101629\", \"189229\", \"105412\", \"106374\", \"108209\", \"198569\", \"110306\", \"110322\", \"111035\", \"111116\", \"111991\", \"112100\", \"112241\", \"112444\", \"113191\", \"114016\", \"115026\", \"115073\", \"117181\", \"118954\", \"155847\", \"120420\", \"119213\", \"122763\", \"123078\", \"123550\", \"126244\", \"126431\", \"126484\", \"124083\"], \"outgoing_citations\": [\"320148\", \"320837\", \"322955\", \"328595\", \"339394\"]}","task_split":"paper_retrieval"}
{"document_id":"309409","document_content":"# RBIR Based on Signature Graph\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper approaches the image retrieval system on the base of visual features local region RBIR (region-based image retrieval). First of all, the paper presents a method for extracting the interest points based on Harris-Laplace to create the feature region of the image. Next, in order to reduce the storage space and speed up query image, the paper builds the binary signature structure to describe the visual content of image. Based on the image's binary signature, the paper builds the SG (signature graph) to classify and store image's binary signatures. Since then, the paper builds the image retrieval algorithm on SG through the similar measure EMD (earth mover's distance) between the image's binary signatures. Last but not least, the paper gives an image retrieval model RBIR, experiments and assesses the image retrieval method on Corel image database over 10,000 images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.04816v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"309622","document_content":"# Using Automated Theorem Provers to Teach Knowledge Representation in First-Order Logic\n## Categories\n- Computers and Society\n- K.3\n- Logic in Computer Science\n## Abstract\nUndergraduate students of artificial intelligence often struggle with representing knowledge as logical sentences. This is a skill that seems to require extensive practice to obtain, suggesting a teaching strategy that involves the assignment of numerous exercises involving the formulation of some bit of knowledge, communicated using a natural language such as English, as a sentence in some logic. The number of such exercises needed to master this skill is far too large to allow typical artificial intelligence course teaching teams to provide prompt feedback on student efforts. Thus, an automated assessment system for such exercises is needed to ensure that students receive an adequate amount of practice, with the rapid delivery of feedback allowing students to identify errors in their understanding and correct them. This paper describes an automated grading system for knowledge representation exercises using first-order logic. A resolution theorem prover, \\textit{Prover9}, is used to check if a student-submitted formula is logically equivalent to a solution provided by the instructor. This system has been used by students enrolled in undergraduate artificial intelligence classes for several years. Use of this teaching tool resulted in a statistically significant improvement on first-order logic knowledge representation questions appearing on the course final examination. This article explains how this system works, provides an analysis of changes in student learning outcomes, and explores potential enhancements of this system, including the possibility of providing rich formative feedback by replacing the resolution theorem prover with a tableaux-based method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.03670v1\", \"primary_category\": \"cs.CY\", \"categories\": [\"cs.CY\", \"K.3\", \"cs.LO\"], \"primary_category_human_readable\": \"Computers and Society\", \"categories_human_readable\": [\"Computers and Society\", \"K.3\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"309656","document_content":"# Cyber-physical Control of Road Freight Transport\n## Categories\n- Systems and Control\n## Abstract\nFreight transportation is of outmost importance for our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of all energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.03466v1\", \"primary_category\": \"cs.SY\", \"categories\": [\"cs.SY\"], \"primary_category_human_readable\": \"Systems and Control\", \"categories_human_readable\": [\"Systems and Control\"], \"incoming_citations\": [\"165536\", \"59037\", \"60547\", \"83881\", \"85303\", \"165868\", \"172740\", \"194965\", \"202387\", \"234395\", \"236061\", \"260433\", \"267763\", \"266688\", \"272873\", \"313444\"], \"outgoing_citations\": [\"274796\", \"304647\", \"313444\"]}","task_split":"paper_retrieval"}
{"document_id":"309709","document_content":"# Using Bayesian Network Representations for Effective Sampling from Generative Network Models\n## Categories\n- Artificial Intelligence\n## Abstract\nBayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of graphical models where additional independence relations arise in the context of particular values of random variables (RVs). Identifying and exploiting CSI properties can simplify inference. Some generative network models (models that generate social\/information network samples from a network distribution P(G)), with complex interactions among a set of RVs, can be represented with probabilistic graphical models, in particular with BNs. In the present work we show one such a case. We discuss how a mixed Kronecker Product Graph Model can be represented as a BN, and study its BN properties that can be used for efficient sampling. Specifically, we show that instead of exhibiting CSI properties, the model has deterministic context-specific dependence (DCSD). Exploiting this property focuses the sampling method on a subset of the sampling space that improves efficiency.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.03168v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"339486\", \"348963\"]}","task_split":"paper_retrieval"}
{"document_id":"309726","document_content":"# Ontology Matching with Knowledge Rules\n## Categories\n- Artificial Intelligence\n## Abstract\nOntology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves substantially better F-score than the previous state-of-the-art on three ontology matching domains.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.03097v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"345749\", \"345932\", \"357895\"]}","task_split":"paper_retrieval"}
{"document_id":"309743","document_content":"# Toward Practical Differential Privacy in Smart Grid with Capacity-Limited Rechargeable Batteries\n## Categories\n- Cryptography and Security\n## Abstract\nThe technology of differential privacy, adding a noise drawn from the Laplace distribution, successfully overcomes a difficulty of keeping both the privacy of individual data and the utility of the statistical result simultaneously. Therefore, it is prevalent to use a rechargeable battery as the noise for achieving differential privacy in the application of smart grid. Unfortunately, to the best of our knowledge, we observe that the existing privacy protection mechanisms cannot satisfy differential privacy, when considering physical restrictions of the battery in practice. In this paper, we first classify two types of challenges caused by two physical restrictions, the maximum charging\/discharging rate and the capacity of battery. We then propose a stateless privacy protection scheme by exploring a boundary-changeable distribution for noise, and prove this scheme satisfies differential privacy, with regard to the first type of challenge. We further explain the difficulty to achieve differential privacy under the second type of challenge, and formalize the definition of a relaxed differential privacy. Finally, we present a stateful privacy protection scheme that satisfies the relaxed differential privacy. Experimental analysis shows that the maximum privacy leakage of our privacy protection schemes at each time point stably outperforms that of the existing work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.03000v2\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [\"182643\", \"207072\", \"236362\"], \"outgoing_citations\": [\"351552\"]}","task_split":"paper_retrieval"}
{"document_id":"309761","document_content":"# Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The proposed methods are based on the hierarchical combination of single-document summaries, and achieves state of the art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.02907v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"143404\", \"347097\", \"308437\"], \"outgoing_citations\": [\"284247\", \"326817\", \"335109\", \"343406\"]}","task_split":"paper_retrieval"}
{"document_id":"309926","document_content":"# Multi-Document Summarization via Discriminative Summary Reranking\n## Categories\n- Computation and Language\n## Abstract\nExisting multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries for different document sets with different topics. However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets. On the contrary, a baseline summarization model may produce high-quality summaries for some document sets. Based on the above observations, we treat the summaries produced by different summarization models as candidate summaries, and then explore discriminative reranking techniques to identify high-quality summaries from the candidates for difference document sets. We propose to extract a set of candidate summaries for each document set based on an ILP framework, and then leverage Ranking SVM for summary reranking. Various useful features have been developed for the reranking process, including word-level features, sentence-level features and summary-level features. Evaluation results on the benchmark DUC datasets validate the efficacy and robustness of our proposed approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.02062v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"14013\", \"53833\", \"130347\", \"279351\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"310047","document_content":"# The method of artificial systems\n## Categories\n- Artificial Intelligence\n## Abstract\nThis document is written with the intention to describe in detail a method and means by which a computer program can reason about the world and in so doing, increase its analogue to a living system. As the literature is rife and it is apparent we, as scientists and engineers, have not found the solution, this document will attempt the solution by grounding its intellectual arguments within tenets of human cognition in Western philosophy. The result will be a characteristic description of a method to describe an artificial system analogous to that performed for a human. The approach was the substance of my Master's thesis, explored more deeply during the course of my postdoc research. It focuses primarily on context awareness and choice set within a boundary of available epistemology, which serves to describe it. Expanded upon, such a description strives to discover agreement with Kant's critique of reason to understand how it could be applied to define the architecture of its design. The intention has never been to mimic human or biological systems, rather, to understand the profoundly fundamental rules, when leveraged correctly, results in an artificial consciousness as noumenon while in keeping with the perception of it as phenomenon.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.01384v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"272259\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"310085","document_content":"# Autoencoding the Retrieval Relevance of Medical Images\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nContent-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n\/p\/n$ autoencoder ($p\\!<\\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.01251v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"84595\", \"257015\", \"254675\", \"242851\", \"270624\", \"283476\", \"283477\", \"284576\", \"293935\", \"294460\", \"294461\", \"294462\", \"298712\"], \"outgoing_citations\": [\"312582\"]}","task_split":"paper_retrieval"}
{"document_id":"310279","document_content":"# Dimensionality on Summarization\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nSummarization is one of the key features of human intelligence. It plays an important role in understanding and representation. With rapid and continual expansion of texts, pictures and videos in cyberspace, automatic summarization becomes more and more desirable. Text summarization has been studied for over half century, but it is still hard to automatically generate a satisfied summary. Traditional methods process texts empirically and neglect the fundamental characteristics and principles of language use and understanding. This paper summarizes previous text summarization approaches in a multi-dimensional classification space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies the dimensions and forms of representations, and proposes a multi-dimensional evaluation mechanisms. Investigation extends to the incorporation of pictures into summary and to the summarization of videos, graphs and pictures, and then reaches a general summarization framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1507.00209v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"213187\", \"347097\", \"309341\"], \"outgoing_citations\": [\"359657\"]}","task_split":"paper_retrieval"}
{"document_id":"310303","document_content":"# Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TPAMI.2017.2666812\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"192339\", \"138613\", \"350506\", \"85376\", \"20231\", \"77972\", \"109376\", \"119839\", \"192719\", \"200851\", \"82163\", \"218749\", \"106896\", \"108421\", \"143795\", \"155331\", \"225359\", \"235344\", \"239316\", \"155612\", \"185437\", \"192549\", \"212041\", \"232040\", \"232982\", \"241439\", \"215048\", \"232223\", \"237842\", \"242340\", \"242865\", \"259070\", \"250269\", \"261828\", \"264939\"], \"outgoing_citations\": [\"300357\", \"304501\", \"314231\", \"314568\", \"316473\", \"318535\", \"331445\", \"331485\", \"332726\", \"340567\", \"350261\"]}","task_split":"paper_retrieval"}
{"document_id":"310342","document_content":"# A complex network approach to stylometry\n## Categories\n- Computation and Language\n## Abstract\nStatistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1371\/journal.pone.0136076\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"94490\", \"111151\", \"136175\", \"136959\", \"195226\", \"201513\", \"238076\", \"227153\", \"240844\", \"251138\", \"267578\", \"282259\", \"289273\", \"289588\", \"267932\", \"287489\", \"295299\", \"287856\", \"302047\", \"310932\", \"319646\"], \"outgoing_citations\": [\"318041\", \"319646\", \"324427\", \"338986\", \"345544\", \"348105\", \"348705\", \"348709\", \"348710\", \"348769\", \"361881\"]}","task_split":"paper_retrieval"}
{"document_id":"310370","document_content":"# Language Understanding for Text-based Games Using Deep Reinforcement Learning\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nIn this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the virtual world are through text and the underlying state is not observed. The resulting language barrier makes such environments challenging for automatic game players. We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback. This framework enables us to map text descriptions into vector representations that capture the semantics of the game states. We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations. Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.08941v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"15887\", \"22323\", \"36587\", \"42171\", \"43279\", \"50309\", \"56667\", \"144508\", \"20488\", \"22368\", \"41323\", \"45832\", \"54834\", \"60332\", \"60832\", \"63559\", \"66630\", \"88854\", \"95530\", \"95805\", \"97289\", \"170214\", \"183481\", \"82624\", \"82665\", \"84549\", \"86099\", \"86580\", \"91154\", \"92228\", \"96449\", \"96524\", \"98703\", \"108770\", \"110974\", \"117735\", \"119028\", \"121716\", \"127451\", \"132686\", \"137546\", \"140959\", \"145329\", \"145784\", \"153843\", \"158253\", \"160124\", \"143589\", \"167303\", \"168729\", \"168588\", \"169889\", \"220655\", \"224848\", \"255893\", \"257674\", \"166985\", \"169742\", \"171447\", \"171942\", \"180270\", \"181620\", \"184995\", \"187527\", \"196049\", \"199295\", \"207273\", \"222345\", \"232394\", \"198011\", \"207533\", \"211104\", \"212893\", \"226327\", \"226321\", \"216953\", \"218923\", \"229008\", \"231741\", \"214848\", \"237845\", \"238333\", \"239749\", \"243105\", \"240208\", \"243712\", \"243973\", \"245813\", \"247002\", \"249717\", \"259693\", \"264985\", \"265786\", \"158824\", \"256353\", \"258265\", \"259264\", \"263270\", \"266342\", \"267980\", \"268410\", \"274244\", \"280681\", \"281257\", \"281092\", \"282436\", \"290615\", \"291102\", \"291363\", \"291568\", \"292392\", \"292763\", \"292127\", \"294144\", \"294142\", \"295884\", \"298691\", \"298789\", \"302771\", \"303349\", \"302534\", \"304241\", \"306812\", \"307569\", \"309111\"], \"outgoing_citations\": [\"307569\", \"334986\"]}","task_split":"paper_retrieval"}
{"document_id":"310428","document_content":"# Exact and approximate inference in graphical models: variable elimination and beyond\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nProbabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.08544v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"333252\", \"339705\", \"345609\", \"349845\", \"351242\", \"351380\", \"351477\", \"358303\"]}","task_split":"paper_retrieval"}
{"document_id":"310609","document_content":"# Fast 3D Synthetic Aperture Radar Imaging from Polarization-Diverse Measurements\n## Categories\n- Information Theory\n## Abstract\nAn innovative 3-D radar imaging technique is developed for fast and efficient identification and characterization of radar backscattering components of complex objects, when the collected scattered field is made of polarization-diverse measurements. In this context, all the polarimetric information seems irretrievably mixed. A direct model, derived from a simple but original extension of the widespread \"multiple scattering model\" leads to a high dimensional linear inverse problem. It is solved by a fast dedicated imaging algorithm that performs to determine at a time three huge 3-D scatterer maps which correspond to HH, VV and HV polarizations at emission and reception. It is applied successfully to various mock-ups and data sets collected from an accurate and dedicated 3D spherical experimental layout that provides concentric polarization-diverse RCS measurements.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.07459v1\", \"primary_category\": \"cs.IT\", \"categories\": [\"cs.IT\", \"math.IT\"], \"primary_category_human_readable\": \"Information Theory\", \"categories_human_readable\": [\"Information Theory\", \"Information Theory\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"310772","document_content":"# Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.06490v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"73035\", \"76257\", \"87226\", \"109254\", \"203983\", \"216726\", \"153217\", \"153040\", \"155328\", \"310904\"], \"outgoing_citations\": [\"154227\", \"320868\"]}","task_split":"paper_retrieval"}
{"document_id":"310928","document_content":"# \"The Sum of Its Parts\": Joint Learning of Word and Phrase Representations with Autoencoders\n## Categories\n- Computation and Language\n## Abstract\nRecently, there has been a lot of effort to represent words in continuous vector spaces. Those representations have been shown to capture both semantic and syntactic information about words. However, distributed representations of phrases remain a challenge. We introduce a novel model that jointly learns word vector representations and their summation. Word representations are learnt using the word co-occurrence statistical information. To embed sequences of words (i.e. phrases) with different sizes into a common semantic space, we propose to average word vector representations. In contrast with previous methods which reported a posteriori some compositionality aspects by simple summation, we simultaneously train words to sum, while keeping the maximum information from the original vectors. We evaluate the quality of the word representations on several classical word evaluation tasks, and we introduce a novel task to evaluate the quality of the phrase representations. While our distributed representations compete with other methods of learning word representations on word evaluations, we show that they give better performance on the phrase evaluation. Such representations of phrases could be interesting for many tasks in natural language processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.05703v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"156272\", \"274521\", \"288001\"], \"outgoing_citations\": [\"319903\", \"336326\", \"349607\"]}","task_split":"paper_retrieval"}
{"document_id":"311126","document_content":"# Latent Regression Bayesian Network for Data Representation\n## Categories\n- Machine Learning\n## Abstract\nDeep directed generative models have attracted much attention recently due to their expressive representation power and the ability of ancestral sampling. One major difficulty of learning directed models with many latent variables is the intractable inference. To address this problem, most existing algorithms make assumptions to render the latent variables independent of each other, either by designing specific priors, or by approximating the true posterior using a factorized distribution. We believe the correlations among latent variables are crucial for faithful data representation. Driven by this idea, we propose an inference method based on the conditional pseudo-likelihood that preserves the dependencies among the latent variables. For learning, we propose to employ the hard Expectation Maximization (EM) algorithm, which avoids the intractability of the traditional EM by max-out instead of sum-out to compute the data likelihood. Qualitative and quantitative evaluations of our model against state of the art deep models on benchmark datasets demonstrate the effectiveness of the proposed algorithm in data representation and reconstruction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.04720v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"315063\", \"334299\", \"337458\", \"349865\", \"357708\"]}","task_split":"paper_retrieval"}
{"document_id":"311199","document_content":"# Linguistics and some aspects of its underlying dynamics\n## Categories\n- Computation and Language\n- Quantum Physics\n## Abstract\nIn recent years, central components of a new approach to linguistics, the Minimalist Program (MP) have come closer to physics. Features of the Minimalist Program, such as the unconstrained nature of recursive Merge, the operation of the Labeling Algorithm that only operates at the interface of Narrow Syntax with the Conceptual-Intentional and the Sensory-Motor interfaces, the difference between pronounced and un-pronounced copies of elements in a sentence and the build-up of the Fibonacci sequence in the syntactic derivation of sentence structures, are directly accessible to representation in terms of algebraic formalism. Although in our scheme linguistic structures are classical ones, we find that an interesting and productive isomorphism can be established between the MP structure, algebraic structures and many-body field theory opening new avenues of inquiry on the dynamics underlying some central aspects of linguistics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.08663v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"quant-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Quantum Physics\"], \"incoming_citations\": [\"254377\", \"277099\", \"284977\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"311204","document_content":"# Leveraging Word Embeddings for Spoken Document Summarization\n## Categories\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nOwing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation. On the other hand, word embedding has emerged as a newly favorite research subject because of its excellent performance in many natural language processing (NLP)-related tasks. However, as far as we are aware, there are relatively few studies investigating its use in extractive text or speech summarization. A common thread of leveraging word embeddings in the summarization process is to represent the document (or sentence) by averaging the word embeddings of the words occurring in the document (or sentence). Then, intuitively, the cosine similarity measure can be employed to determine the relevance degree between a pair of representations. Beyond the continued efforts made to improve the representation of words, this paper focuses on building novel and efficient ranking models based on the general word embedding methods for extractive speech summarization. Experimental results demonstrate the effectiveness of our proposed methods, compared to existing state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.04365v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\"], \"incoming_citations\": [\"97731\", \"287938\", \"299878\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"311253","document_content":"# On the accuracy of self-normalized log-linear models\n## Categories\n- Machine Learning\n- Computation and Language\n- Methodology\n## Abstract\nCalculation of the log-normalizer is a major computational obstacle in applications of log-linear models with large output spaces. The problem of fast normalizer computation has therefore attracted significant attention in the theoretical and applied machine learning literature. In this paper, we analyze a recently proposed technique known as \"self-normalization\", which introduces a regularization term in training to penalize log normalizers for deviating from zero. This makes it possible to use unnormalized model scores as approximate probabilities. Empirical evidence suggests that self-normalization is extremely effective, but a theoretical understanding of why it should work, and how generally it can be applied, is largely lacking. We prove generalization bounds on the estimated variance of normalizers and upper bounds on the loss in accuracy due to self-normalization, describe classes of input distributions that self-normalize easily, and construct explicit examples of high-variance input distributions. Our theoretical results make predictions about the difficulty of fitting self-normalized models to several classes of distributions, and we conclude with empirical validation of these predictions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.04147v2\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.CL\", \"stat.ME\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Computation and Language\", \"Methodology\"], \"incoming_citations\": [\"14614\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"311814","document_content":"# A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)\n## Categories\n- Computation and Language\n## Abstract\nThe interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a sentence from the source Language (English) to the target language (Arabic) automatically through efficiently incorporating different statistical and Natural Language Processing (NLP) models such as language model, alignment model, phrase based model, reordering model, and translation model. These models are combined to enhance the performance of statistical machine translation (SMT). Many implementation tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and BLEU. Based on the implementation, evaluation of this model, and comparing the generated translation with other implemented machine translation systems like Google Translate, it was proved that this proposed model has enhanced the results of the statistical machine translation, and forms a reliable and efficient model in this field of research.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.01171v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"311834","document_content":"# Traversing Knowledge Graphs in Vector Space\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Databases\n- Machine Learning\n## Abstract\nPath queries on a knowledge graph can be used to answer compositional questions such as \"What languages are spoken by people living in Lisbon?\". However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new \"compositional\" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43%) and achieving new state-of-the-art results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.01094v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.DB\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Databases\", \"Machine Learning\"], \"incoming_citations\": [\"38327\", \"54422\", \"816\", \"13090\", \"31362\", \"2081\", \"3657\", \"2653\", \"10043\", \"10232\", \"11894\", \"12220\", \"13183\", \"13340\", \"13589\", \"18122\", \"24482\", \"50715\", \"52632\", \"66421\", \"71008\", \"88604\", \"127614\", \"132807\", \"159408\", \"171033\", \"77679\", \"81789\", \"155991\", \"91201\", \"92329\", \"96322\", \"96639\", \"100267\", \"116351\", \"124613\", \"127408\", \"127552\", \"127904\", \"132413\", \"134530\", \"142243\", \"142294\", \"145854\", \"155250\", \"162419\", \"162971\", \"163124\", \"165082\", \"176220\", \"243824\", \"270722\", \"152722\", \"157837\", \"158498\", \"158668\", \"164877\", \"168766\", \"180988\", \"182892\", \"183498\", \"186633\", \"187436\", \"197395\", \"198175\", \"206025\", \"213912\", \"197408\", \"234139\", \"256639\", \"211718\", \"204585\", \"210690\", \"215850\", \"218055\", \"218512\", \"218834\", \"219691\", \"221660\", \"220268\", \"225536\", \"216953\", \"227492\", \"228334\", \"229275\", \"230531\", \"230744\", \"233005\", \"242422\", \"245072\", \"248147\", \"248817\", \"250322\", \"256171\", \"255134\", \"256006\", \"271216\", \"273190\", \"256804\", \"257933\", \"258845\", \"260644\", \"197767\", \"262644\", \"263488\", \"264724\", \"267141\", \"267878\", \"268485\", \"271918\", \"274569\", \"273751\", \"277968\", \"283190\", \"284063\", \"288979\", \"278530\", \"279845\", \"280478\", \"280985\", \"281576\", \"284138\", \"286209\", \"287413\", \"289536\", \"289848\", \"289931\", \"290231\", \"290885\", \"290997\", \"290632\", \"295884\", \"302930\", \"304737\", \"304901\", \"302941\", \"304844\"], \"outgoing_citations\": [\"313944\", \"328352\", \"328698\", \"336189\", \"345692\"]}","task_split":"paper_retrieval"}
{"document_id":"311849","document_content":"# A Hierarchical Neural Autoencoder for Paragraphs and Documents\n## Categories\n- Computation and Language\n## Abstract\nNatural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Long-short term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization\\footnote{Code for the three models described in this paper can be found at www.stanford.edu\/~jiweil\/ .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.01057v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"62580\", \"14600\", \"38362\", \"48976\", \"53486\", \"91658\", \"92767\", \"94351\", \"212503\", \"8100\", \"27209\", \"29376\", \"29356\", \"38455\", \"48659\", \"64334\", \"71008\", \"79923\", \"80697\", \"82930\", \"91625\", \"223084\", \"88013\", \"89825\", \"104775\", \"107493\", \"125128\", \"127807\", \"127856\", \"133034\", \"133082\", \"64810\", \"126544\", \"155383\", \"157124\", \"161757\", \"167323\", \"171392\", \"171459\", \"182386\", \"229516\", \"247445\", \"151142\", \"157878\", \"163655\", \"158046\", \"164105\", \"164175\", \"164236\", \"166509\", \"168505\", \"169432\", \"170712\", \"171491\", \"171518\", \"171217\", \"173531\", \"176111\", \"178337\", \"178736\", \"180636\", \"181306\", \"181749\", \"182394\", \"182438\", \"184644\", \"184851\", \"175599\", \"187341\", \"171029\", \"189813\", \"191253\", \"188736\", \"200224\", \"200698\", \"201057\", \"201539\", \"204839\", \"202815\", \"210047\", \"210090\", \"218708\", \"220385\", \"238332\", \"239974\", \"242815\", \"243081\", \"190250\", \"205728\", \"210586\", \"212285\", \"213975\", \"217166\", \"214015\", \"220740\", \"220145\", \"231001\", \"231610\", \"232261\", \"232887\", \"232274\", \"232285\", \"234302\", \"234385\", \"236881\", \"237801\", \"238205\", \"238964\", \"239076\", \"239653\", \"239534\", \"241070\", \"241329\", \"241634\", \"215243\", \"243291\", \"243821\", \"244869\", \"254332\", \"269098\", \"271458\", \"271683\", \"279024\", \"280892\", \"144800\", \"230466\", \"251885\", \"252965\", \"254432\", \"258818\", \"258494\", \"259932\", \"261215\", \"264429\", \"264202\", \"266230\", \"267123\", \"267838\", \"268861\", \"268911\", \"269385\", \"270825\", \"271004\", \"275597\", \"276586\", \"277511\", \"278993\", \"279688\", \"280004\", \"281407\", \"286594\", \"288039\", \"291100\", \"293413\", \"296193\", \"294966\", \"278018\", \"278244\", \"279543\", \"281847\", \"282136\", \"285066\", \"288348\", \"289107\", \"287750\", \"289239\", \"290102\", \"166518\", \"280967\", \"290569\", \"291617\", \"293976\", \"295225\", \"295308\", \"297133\", \"297211\", \"298016\", \"300652\", \"301397\", \"302088\", \"302516\", \"302948\", \"304481\", \"305200\", \"301288\", \"303493\", \"310904\"], \"outgoing_citations\": [\"316745\", \"319833\", \"322537\", \"324965\"]}","task_split":"paper_retrieval"}
{"document_id":"311858","document_content":"# Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Machine Learning\n## Abstract\nThis paper tackles the problem of endogenous link prediction for Knowledge Base completion. Knowledge Bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.00999v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"11022\", \"14473\", \"243824\", \"270722\", \"159804\", \"255709\", \"212076\", \"218834\", \"227492\", \"250718\", \"256006\", \"252012\", \"231820\", \"265002\", \"268485\", \"273380\", \"280478\", \"289536\", \"289931\", \"302930\"], \"outgoing_citations\": [\"308219\", \"321782\", \"350279\"]}","task_split":"paper_retrieval"}
{"document_id":"311971","document_content":"# RBIR using Interest Regions and Binary Signatures\n## Categories\n- Computer Vision and Pattern Recognition\n- H.2.8; H.3.3\n## Abstract\nIn this paper, we introduce an approach to overcome the low accuracy of the Content-Based Image Retrieval (CBIR) (when using the global features). To increase the accuracy, we use Harris-Laplace detector to identify the interest regions of image. Then, we build the Region-Based Image Retrieval (RBIR). For the efficient image storage and retrieval, we encode images into binary signatures. The binary signature of a image is created from its interest regions. Furthermore, this paper also provides an algorithm for image retrieval on S-tree by comparing the images' signatures on a metric similarly to EMD (earth mover's distance). Finally, we evaluate the created models on COREL's images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1506.00368v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"H.2.8; H.3.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"H.2.8; H.3.3\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"312497","document_content":"# Object Modelling with a Handheld RGB-D Camera\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions that are automatically merged together to reconstruct a full model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities for different applications, such as human-object interaction analysis or robot grasping. The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or a turn-table setup). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1505.05643v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"313435","document_content":"# Sequence to Sequence -- Video to Text\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nReal-world videos often have complex dynamics; and methods for generating open-domain video descriptions should be sensitive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem, we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1505.00487v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"158167\", \"1081\", \"2284\", \"5789\", \"6419\", \"7537\", \"8991\", \"9633\", \"14600\", \"15826\", \"33173\", \"44352\", \"47742\", \"50703\", \"60281\", \"64841\", \"114091\", \"159645\", \"207810\", \"163074\", \"93187\", \"116\", \"2724\", \"4719\", \"8514\", \"12778\", \"16751\", \"18278\", \"17057\", \"20800\", \"21531\", \"24639\", \"25304\", \"28951\", \"29109\", \"30775\", \"30940\", \"31373\", \"31466\", \"33300\", \"33542\", \"37422\", \"46309\", \"48946\", \"49800\", \"55460\", \"59690\", \"60390\", \"62318\", \"70400\", \"74195\", \"76934\", \"77383\", \"138681\", \"80826\", \"81752\", \"81968\", \"82713\", \"82930\", \"92195\", \"96230\", \"100481\", \"109073\", \"112298\", \"118726\", \"126319\", \"173243\", \"175031\", \"198711\", \"83901\", \"86053\", \"86252\", \"86777\", \"86824\", \"89825\", \"91237\", \"5843\", \"5781\", \"95107\", \"102190\", \"102731\", \"104154\", \"104120\", \"104610\", \"105783\", \"105788\", \"109275\", \"109375\", \"109955\", \"111415\", \"111318\", \"115296\", \"116152\", \"116207\", \"116406\", \"120547\", \"120890\", \"114237\", \"124561\", \"127571\", \"127728\", \"134024\", \"134565\", \"134719\", \"134923\", \"135934\", \"136352\", \"137358\", \"137473\", \"137936\", \"138944\", \"139135\", \"139876\", \"139932\", \"143440\", \"143679\", \"146748\", \"147305\", \"148485\", \"70437\", \"149248\", \"149491\", \"151907\", \"154810\", \"157944\", \"163549\", \"169352\", \"173548\", \"173854\", \"181520\", \"182940\", \"183087\", \"204751\", \"223463\", \"225421\", \"230774\", \"257295\", \"157635\", \"81691\", \"151549\", \"154686\", \"156557\", \"158081\", \"161371\", \"283406\", \"164397\", \"167684\", \"168374\", \"170020\", \"171357\", \"173101\", \"173484\", \"173627\", \"175214\", \"176385\", \"177326\", \"180149\", \"182088\", \"182461\", \"182657\", \"181452\", \"185116\", \"187005\", \"187303\", \"187722\", \"187914\", \"188775\", \"189518\", \"189878\", \"191680\", \"191861\", \"191966\", \"191676\", \"191926\", \"194040\", \"194045\", \"197355\", \"200501\", \"197001\", \"206058\", \"206997\", \"210413\", \"211154\", \"211144\", \"214171\", \"217992\", \"221547\", \"231064\", \"231203\", \"235648\", \"236120\", \"238229\", \"240605\", \"251214\", \"204263\", \"205291\", \"206567\", \"206888\", \"207702\", \"208634\", \"212708\", \"213578\", \"214230\", \"215488\", \"198597\", \"217244\", \"214015\", \"219495\", \"218099\", \"219683\", \"219958\", \"220204\", \"220232\", \"220662\", \"221551\", \"220145\", \"221655\", \"221984\", \"222846\", \"222858\", \"223048\", \"223302\", \"223293\", \"168542\", \"223862\", \"226908\", \"228174\", \"228718\", \"229142\", \"231298\", \"234374\", \"234602\", \"234941\", \"235633\", \"236042\", \"236918\", \"235531\", \"237150\", \"237201\", \"237375\", \"237945\", \"240071\", \"241716\", \"243080\", \"243821\", \"243999\", \"244447\", \"248855\", \"249677\", \"249862\", \"250211\", \"251821\", \"252642\", \"254396\", \"253991\", \"254361\", \"267403\", \"269485\", \"270292\", \"277875\", \"279024\", \"279697\", \"283025\", \"284199\", \"298885\", \"251409\", \"216400\", \"188669\", \"216387\", \"252830\"], \"outgoing_citations\": [\"311742\", \"316564\", \"316803\", \"319169\", \"320332\", \"321453\", \"321503\"]}","task_split":"paper_retrieval"}
{"document_id":"313521","document_content":"# Embedding Semantic Relations into Word Representations\n## Categories\n- Computation and Language\n## Abstract\nLearning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representations for individual words, learning word representations that explicitly capture the semantic relations between words remains under developed. We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words. First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words. Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern. Third, we train a binary classifier to detect relationally similar vs. non-similar lexical pattern pairs. The proposed method is unsupervised in the sense that the lexical pattern pairs we use as train data are automatically sampled from a corpus, without requiring any manual intervention. Our proposed method statistically significantly outperforms the current state-of-the-art word representations on three benchmark datasets for proportional analogy detection, demonstrating its ability to accurately capture the semantic relations among words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1505.00161v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"73891\", \"218458\", \"255431\", \"268485\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"313859","document_content":"# Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Information Retrieval\n## Abstract\nSocial media is becoming an increasingly important source of information to complement traditional pharmacovigilance methods. In order to identify signals of potential adverse drug reactions, it is necessary to first identify medical concepts in the social media text. Most of the existing studies use dictionary-based methods which are not evaluated independently from the overall signal detection task.   We compare different approaches to automatically identify and normalise medical concepts in consumer reviews in medical forums. Specifically, we implement several dictionary-based methods popular in the relevant literature, as well as a method we suggest based on a state-of-the-art machine learning method for entity recognition. MetaMap, a popular biomedical concept extraction tool, is used as a baseline. Our evaluations were performed in a controlled setting on a common corpus which is a collection of medical forum posts annotated with concepts and linked to controlled vocabularies such as MedDRA and SNOMED CT.   To our knowledge, our study is the first to systematically examine the effect of popular concept extraction methods in the area of signal detection for adverse reactions. We show that the choice of algorithm or controlled vocabulary has a significant impact on concept extraction, which will impact the overall signal detection process. We also show that our proposed machine learning approach significantly outperforms all the other methods in identification of both adverse reactions and drugs, even when trained with a relatively small set of annotated text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.06936v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"314047","document_content":"# Open Data Platform for Knowledge Access in Plant Health Domain : VESPA Mining\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nImportant data are locked in ancient literature. It would be uneconomic to produce these data again and today or to extract them without the help of text mining technologies. Vespa is a text mining project whose aim is to extract data on pest and crops interactions, to model and predict attacks on crops, and to reduce the use of pesticides. A few attempts proposed an agricultural information access. Another originality of our work is to parse documents with a dependency of the document architecture.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.06077v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"72714\"], \"outgoing_citations\": [\"314046\"]}","task_split":"paper_retrieval"}
{"document_id":"314050","document_content":"# Multimodal Convolutional Neural Networks for Matching Image and Sentence\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Neural and Evolutionary Computing\n## Abstract\nIn this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content, and one matching CNN learning the joint representation of image and sentence. The matching CNN composes words to different semantic fragments and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results on benchmark databases of bidirectional image and sentence retrieval demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. Specifically, our proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and Microsoft COCO databases achieve the state-of-the-art performances.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.06063v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"19710\", \"33924\", \"42740\", \"82418\", \"46136\", \"55096\", \"61064\", \"70460\", \"79143\", \"81968\", \"161696\", \"171871\", \"111384\", \"114646\", \"129340\", \"133742\", \"137906\", \"140566\", \"171465\", \"182940\", \"189381\", \"210499\", \"239905\", \"250405\", \"127498\", \"164236\", \"165046\", \"168851\", \"169858\", \"170020\", \"172576\", \"174872\", \"180438\", \"182042\", \"182169\", \"182947\", \"181437\", \"189628\", \"191076\", \"191552\", \"193442\", \"193503\", \"200501\", \"201599\", \"208727\", \"213042\", \"235648\", \"245484\", \"250228\", \"269128\", \"212441\", \"218710\", \"224064\", \"230900\", \"236696\", \"237150\", \"237201\", \"189653\", \"239242\", \"248171\", \"250183\", \"253508\", \"256883\", \"264628\", \"271077\", \"274665\", \"260397\", \"260373\", \"259061\", \"261030\", \"262222\", \"266120\", \"267161\", \"267351\", \"269848\", \"271187\", \"271841\", \"273876\", \"276299\", \"279990\", \"280249\", \"281407\", \"285796\", \"289755\", \"277261\", \"280161\", \"286996\", \"293220\", \"293986\", \"301594\", \"302943\", \"303011\", \"311976\", \"314287\", \"312643\", \"313934\"], \"outgoing_citations\": [\"321503\", \"323634\", \"324619\", \"328051\", \"343624\"]}","task_split":"paper_retrieval"}
{"document_id":"314541","document_content":"# Efficient Scene Text Localization and Recognition with Local Character Refinement\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nAn unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components.   Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture.   The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.03522v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"150258\", \"126431\", \"208801\", \"225690\", \"232509\", \"240965\", \"260510\", \"256316\", \"257197\", \"268281\", \"271112\", \"284871\", \"289389\", \"291983\", \"295564\"], \"outgoing_citations\": [\"350400\"]}","task_split":"paper_retrieval"}
{"document_id":"314568","document_content":"# Simultaneous Feature Learning and Hash Coding with Deep Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSimilarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2015.7298947\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"155021\", \"138613\", \"203505\", \"350506\", \"267\", \"2529\", \"50903\", \"51667\", \"85376\", \"100185\", \"144914\", \"155958\", \"750\", \"4177\", \"20231\", \"22627\", \"24332\", \"25550\", \"30132\", \"30649\", \"34977\", \"38777\", \"45534\", \"49247\", \"72302\", \"72768\", \"75521\", \"62576\", \"77972\", \"78925\", \"71954\", \"92127\", \"109376\", \"111427\", \"119839\", \"126493\", \"144622\", \"158041\", \"208542\", \"82328\", \"82163\", \"82141\", \"105810\", \"106896\", \"107920\", \"108421\", \"110187\", \"110589\", \"116615\", \"119811\", \"119811\", \"131122\", \"132522\", \"225824\", \"138627\", \"139775\", \"140055\", \"156383\", \"156960\", \"158244\", \"173455\", \"203861\", \"235344\", \"239316\", \"239660\", \"241679\", \"243039\", \"243040\", \"247969\", \"155599\", \"155612\", \"160718\", \"169305\", \"170190\", \"180529\", \"180528\", \"185437\", \"185753\", \"186825\", \"189084\", \"192549\", \"192543\", \"193175\", \"196737\", \"200515\", \"200516\", \"202820\", \"203388\", \"211595\", \"218854\", \"227937\", \"228642\", \"232040\", \"232982\", \"238528\", \"241439\", \"244229\", \"249538\", \"252948\", \"287562\", \"160311\", \"208899\", \"200699\", \"217551\", \"219189\", \"222134\", \"222151\", \"227036\", \"234316\", \"192280\", \"237774\", \"239654\", \"191832\", \"242538\", \"191827\", \"247613\", \"247578\", \"249448\", \"249596\", \"251069\", \"259070\", \"259748\", \"260176\", \"265830\", \"274046\", \"278480\", \"279057\", \"291400\", \"310303\", \"246768\", \"248895\", \"249322\", \"249314\", \"250269\", \"179310\", \"251855\", \"242809\", \"255632\", \"258937\", \"234601\", \"258809\", \"260702\", \"259061\", \"261828\", \"263387\", \"264123\", \"264939\", \"267210\", \"269814\", \"271002\", \"271290\", \"272119\", \"272827\", \"272805\", \"274846\", \"287298\", \"286731\", \"286574\", \"297863\", \"277829\", \"278179\", \"281599\", \"189958\", \"283789\", \"288138\", \"292486\", \"295221\", \"296798\", \"296862\", \"299230\", \"299634\", \"301389\", \"301771\", \"303512\", \"303664\", \"306406\", \"261672\", \"303629\"], \"outgoing_citations\": [\"337248\", \"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"314899","document_content":"# Voice based self help System: User Experience Vs Accuracy\n## Categories\n- Computation and Language\n## Abstract\nIn general, self help systems are being increasingly deployed by service based industries because they are capable of delivering better customer service and increasingly the switch is to voice based self help systems because they provide a natural interface for a human to interact with a machine. A speech based self help system ideally needs a speech recognition engine to convert spoken speech to text and in addition a language processing engine to take care of any misrecognitions by the speech recognition engine. Any off-the-shelf speech recognition engine is generally a combination of acoustic processing and speech grammar. While this is the norm, we believe that ideally a speech recognition application should have in addition to a speech recognition engine a separate language processing engine to give the system better performance. In this paper, we discuss ways in which the speech recognition engine and the language processing engine can be combined to give a better user experience.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/978-90-481-3658-2_18\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"314905","document_content":"# Mobile Phone Based Vehicle License Plate Recognition for Road Policing\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIdentity of a vehicle is done through the vehicle license plate by traffic police in general. Au- tomatic vehicle license plate recognition has several applications in intelligent traffic management systems. The security situation across the globe and particularly in India demands a need to equip the traffic police with a system that enables them to get instant details of a vehicle. The system should be easy to use, should be mobile, and work 24 x 7. In this paper, we describe a mobile phone based, client-server architected, license plate recognition system. While we use the state of the art image processing and pattern recognition algorithms tuned for Indian conditions to automatically recognize non-uniform license plates, the main contribution is in creating an end to end usable solution. The client application runs on a mobile device and a server application, with access to vehicle information database, is hosted centrally. The solution enables capture of license plate image captured by the phone camera and passes to the server; on the server the license plate number is recognized; the data associated with the number plate is then sent back to the mobile device, instantaneously. We describe the end to end system architecture in detail. A working prototype of the proposed system has been implemented in the lab environment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.01476v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"315078","document_content":"# Learning to Understand Phrases by Embedding the Dictionary\n## Categories\n- Computation and Language\n## Abstract\nDistributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: \"reverse dictionaries\" that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1504.00548v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21391\", \"47162\", \"50572\", \"52303\", \"56692\", \"82156\", \"94483\", \"96107\", \"97559\", \"132845\", \"137652\", \"137472\", \"146808\", \"148999\", \"150634\", \"151487\", \"167497\", \"151284\", \"175039\", \"192079\", \"192294\", \"192655\", \"197534\", \"200032\", \"221673\", \"265434\", \"210276\", \"218475\", \"219502\", \"219352\", \"220548\", \"228842\", \"226692\", \"228330\", \"230136\", \"231144\", \"235596\", \"255562\", \"272080\", \"246778\", \"258000\", \"259867\", \"264814\", \"267123\", \"268316\", \"273429\", \"278993\", \"283697\", \"285484\", \"289144\", \"283868\", \"285612\", \"286227\", \"286035\", \"291436\", \"298543\", \"306060\"], \"outgoing_citations\": [\"311684\", \"317213\", \"323451\", \"325323\", \"328352\", \"334040\", \"334997\", \"336196\"]}","task_split":"paper_retrieval"}
{"document_id":"315258","document_content":"# Nonparametric Relational Topic Models through Dependent Gamma Processes\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nTraditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model in this paper. Instead of using fixed-dimensional probability distributions in its generative model, we use stochastic processes. Specifically, a gamma process is assigned to each document, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different gamma process from the global gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.08542v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"323642\", \"339278\", \"346647\", \"352170\"]}","task_split":"paper_retrieval"}
{"document_id":"315591","document_content":"# Optimum Reject Options for Prototype-based Classification\n## Categories\n- Machine Learning\n## Abstract\nWe analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts. We compare reject schemes with global thresholds, and local thresholds for the Voronoi cells of the classifier. For the latter, we develop a polynomial-time algorithm to compute optimum thresholds based on a dynamic programming scheme, and we propose an intuitive linear time, memory efficient approximation thereof with competitive accuracy. Evaluating the performance in various benchmarks, we conclude that local reject options are beneficial in particular for simple prototype-based classifiers, while the improvement is less pronounced for advanced models. For the latter, an accuracy-reject curve which is comparable to support vector machine classifiers with state of the art reject options can be reached.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.06549v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"33711\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"315675","document_content":"# The RatioLog Project: Rational Extensions of Logical Reasoning\n## Categories\n- Artificial Intelligence\n## Abstract\nHigher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia \"Wikipedia\". In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase - because the knowledge may be incomplete and inconsistent -, we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s13218-015-0377-9\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"316228\", \"321641\", \"341148\"]}","task_split":"paper_retrieval"}
{"document_id":"315967","document_content":"# Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset\n## Categories\n- Databases\n## Abstract\nRecently, economic depression, which scoured all over the world, affects business organizations and banking sectors. Such economic pose causes a severe attrition for banks and customer retention becomes impossible. Accordingly, marketing managers are in need to increase marketing campaigns, whereas organizations evade both expenses and business expansion. In order to solve such riddle, data mining techniques is used as an uttermost factor in data analysis, data summarizations, hidden pattern discovery, and data interpretation. In this paper, rough set theory and decision tree mining techniques have been implemented, using a real marketing data obtained from Portuguese marketing campaign related to bank deposit subscription [Moro et al., 2011]. The paper aims to improve the efficiency of the marketing campaigns and helping the decision makers by reducing the number of features, that describes the dataset and spotting on the most significant ones, and predict the deposit customer retention criteria based on potential predictive rules.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5120\/19293-0725\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"316042","document_content":"# Approximating Sparse PCA from Incomplete Data\n## Categories\n- Machine Learning\n- Information Theory\n- Numerical Analysis\n- Information Theory\n## Abstract\nWe study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization problems, if the sketch is close (in the spectral norm) to the original data matrix, then one can recover a near optimal solution to the optimization problem by using the sketch. In particular, we use this approach to obtain sparse principal components and show that for \\math{m} data points in \\math{n} dimensions, \\math{O(\\epsilon^{-2}\\tilde k\\max\\{m,n\\})} elements gives an \\math{\\epsilon}-additive approximation to the sparse PCA problem (\\math{\\tilde k} is the stable rank of the data matrix). We demonstrate our algorithms extensively on image, text, biological and financial data. The results show that not only are we able to recover the sparse PCAs from the incomplete data, but by using our sparse sketch, the running time drops by a factor of five or more.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.03903v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.IT\", \"cs.NA\", \"math.IT\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Information Theory\", \"Numerical Analysis\", \"Information Theory\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"317219\", \"325478\"]}","task_split":"paper_retrieval"}
{"document_id":"316228","document_content":"# A Case Based Reasoning Approach for Answer Reranking in Question Answering\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this document I present an approach to answer validation and reranking for question answering (QA) systems. A cased-based reasoning (CBR) system judges answer candidates for questions from annotated answer candidates for earlier questions. The promise of this approach is that user feedback will result in improved answers of the QA system, due to the growing case base. In the paper, I present the adequate structuring of the case base and the appropriate selection of relevant similarity measures, in order to solve the answer validation problem. The structural case base is built from annotated MultiNet graphs, which provide representations for natural language expressions, and corresponding graph similarity measures. I cover a priori relations to experienced answer candidates for former questions. I compare the CBR System results to current approaches in an experiment integrating CBR into an existing framework for answer validation and reranking. This integration is achieved by adding CBR-related features to the input of a learned ranking model that determines the final answer ranking. In the experiments based on QA@CLEF questions, the best learned models make heavy use of CBR features. Observing the results with a continually growing case base, I present a positive effect of the size of the case base on the accuracy of the CBR subsystem.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.02917v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"315675\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"316502","document_content":"# Bethe Projections for Non-Local Inference\n## Categories\n- Machine Learning\n- Computation and Language\n## Abstract\nMany inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference objectives, capturing powerful non-local statistics of the latent variables, while maintaining tractable and provably fast inference using non-Euclidean projected gradient descent with a distance-generating function given by the Bethe entropy. We demonstrate the performance and flexibility of our method by (1) extracting structured citations from research papers by learning soft global constraints, (2) achieving state-of-the-art results on a widely-used handwriting recognition task using a novel learned non-convex inference procedure, and (3) providing a fast and highly scalable algorithm for the challenging problem of inference in a collective graphical model applied to bird migration.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.01397v3\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CL\", \"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"85340\", \"33562\", \"68530\", \"88997\", \"134026\", \"119851\", \"158680\", \"201535\", \"263734\", \"271301\"], \"outgoing_citations\": [\"332797\", \"339843\", \"344844\", \"355658\"]}","task_split":"paper_retrieval"}
{"document_id":"316539","document_content":"# Statistical modality tagging from rule-based annotations and crowdsourcing\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n- I.2.7; I.2.6; I.5.1; I.5.4\n## Abstract\nWe explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.01190v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"incoming_citations\": [\"17166\", \"17177\", \"22357\", \"286353\"], \"outgoing_citations\": [\"317974\", \"323040\"]}","task_split":"paper_retrieval"}
{"document_id":"316558","document_content":"# Complexity and universality in the long-range order of words\n## Categories\n- Computation and Language\n- Data Analysis, Statistics and Probability\n- Physics and Society\n## Abstract\nAs is the case of many signals produced by complex systems, language presents a statistical structure that is balanced between order and disorder. Here we review and extend recent results from quantitative characterisations of the degree of order in linguistic sequences that give insights into two relevant aspects of language: the presence of statistical universals in word ordering, and the link between semantic information and the statistical linguistic structure. We first analyse a measure of relative entropy that assesses how much the ordering of words contributes to the overall statistical structure of language. This measure presents an almost constant value close to 3.5 bits\/word across several linguistic families. Then, we show that a direct application of information theory leads to an entropy measure that can quantify and extract semantic structures from linguistic samples, even without prior knowledge of the underlying language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1503.01129v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"physics.data-an\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Data Analysis, Statistics and Probability\", \"Physics and Society\"], \"incoming_citations\": [\"98997\", \"104174\", \"289827\"], \"outgoing_citations\": [\"356974\"]}","task_split":"paper_retrieval"}
{"document_id":"316803","document_content":"# Describing Videos by Exploiting Temporal Structure\n## Categories\n- Machine Learning\n- Computation and Language\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nRecent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.08029v5\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.CL\", \"cs.AI\", \"stat.ML\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"5789\", \"7537\", \"9633\", \"29678\", \"44352\", \"53912\", \"59368\", \"60281\", \"114091\", \"159645\", \"163074\", \"4719\", \"8514\", \"3079\", \"16751\", \"24639\", \"25304\", \"28908\", \"29109\", \"30775\", \"31378\", \"31466\", \"33542\", \"34158\", \"37041\", \"34847\", \"46309\", \"48946\", \"62318\", \"72227\", \"74195\", \"76934\", \"77383\", \"79397\", \"80826\", \"82930\", \"85426\", \"109073\", \"154007\", \"171211\", \"173243\", \"192118\", \"79943\", \"81067\", \"83901\", \"86053\", \"86777\", \"52845\", \"5781\", \"5843\", \"97962\", \"102731\", \"103606\", \"104154\", \"104610\", \"105783\", \"109275\", \"109955\", \"110141\", \"111461\", \"111318\", \"116152\", \"116207\", \"116406\", \"87013\", \"124561\", \"124665\", \"127571\", \"133329\", \"136352\", \"137473\", \"137936\", \"137915\", \"139444\", \"139805\", \"139876\", \"139932\", \"141526\", \"146462\", \"146748\", \"146928\", \"147305\", \"149491\", \"151281\", \"154286\", \"154810\", \"157944\", \"181520\", \"182940\", \"188448\", \"194926\", \"204751\", \"205130\", \"208120\", \"223463\", \"225421\", \"249514\", \"152767\", \"155074\", \"161619\", \"166851\", \"170020\", \"169997\", \"160405\", \"172405\", \"173101\", \"174419\", \"175214\", \"176538\", \"177326\", \"182088\", \"182209\", \"182420\", \"181452\", \"181738\", \"183514\", \"185533\", \"186562\", \"187005\", \"187303\", \"187914\", \"188775\", \"190450\", \"191680\", \"192210\", \"194040\", \"194045\", \"197086\", \"197355\", \"197627\", \"200322\", \"202686\", \"197001\", \"205750\", \"206058\", \"206997\", \"208280\", \"210413\", \"211144\", \"212327\", \"220390\", \"231203\", \"235648\", \"238229\", \"240605\", \"242690\", \"258754\", \"265384\", \"204263\", \"204574\", \"204624\", \"205291\", \"207160\", \"210290\", \"212708\", \"217244\", \"219149\", \"220662\", \"221655\", \"222988\", \"223293\", \"168542\", \"224214\", \"225096\", \"226908\", \"227052\", \"227114\", \"229142\", \"230894\", \"231320\", \"231298\", \"232045\", \"229891\", \"234602\", \"236918\", \"235531\", \"237150\", \"237201\", \"237375\", \"237362\", \"238466\", \"238566\", \"240071\", \"241309\", \"241716\", \"243821\", \"244721\", \"244891\", \"246361\", \"248855\", \"249677\", \"250068\", \"250211\", \"250762\", \"257744\", \"259635\", \"260892\", \"263762\", \"265235\", \"267403\", \"269485\", \"273754\", \"279024\", \"279697\", \"284199\", \"296867\", \"153770\", \"250203\", \"216400\", \"252131\", \"252706\", \"252830\", \"256200\", \"256861\", \"257266\", \"257303\", \"258598\", \"258629\", \"259121\", \"258928\", \"260951\", \"261347\", \"260810\", \"264136\", \"257267\", \"264568\", \"264135\", \"265371\", \"267280\", \"267476\", \"267523\", \"268083\", \"268871\", \"269640\", \"269646\", \"270264\", \"269728\", \"270658\", \"271004\", \"271705\", \"276376\", \"277467\", \"277530\", \"277786\", \"278574\", \"278846\", \"278997\", \"279394\", \"279337\", \"279623\", \"279740\", \"279703\", \"280004\", \"280030\", \"280246\", \"280338\", \"280615\", \"282943\", \"288062\", \"290628\", \"291183\", \"295636\", \"295802\", \"295994\", \"296193\", \"300099\", \"309165\", \"309231\", \"277561\", \"278848\", \"280247\", \"280426\", \"280986\", \"282293\", \"282532\", \"282723\", \"283389\", \"153766\", \"283780\", \"283914\", \"284530\", \"285775\", \"285973\", \"286311\", \"286978\", \"288757\", \"289265\", \"290390\", \"290909\", \"291311\", \"279851\", \"291817\", \"292639\", \"292922\", \"293986\", \"294726\", \"294698\", \"294765\", \"294898\", \"295122\", \"295744\", \"297328\", \"299352\", \"302756\", \"302909\", \"303224\", \"303359\", \"303534\", \"303593\", \"303621\", \"303698\", \"304481\", \"311824\", \"313189\", \"302833\", \"302914\", \"303158\", \"303341\", \"303465\", \"307840\", \"310136\", \"311742\", \"312453\", \"313435\", \"313461\", \"317717\", \"321708\"], \"outgoing_citations\": [\"316564\", \"319169\", \"320022\", \"320332\", \"327491\"]}","task_split":"paper_retrieval"}
{"document_id":"316824","document_content":"# Local Translation Prediction with Global Sentence Representation\n## Categories\n- Computation and Language\n## Abstract\nStatistical machine translation models have made great progress in improving the translation quality. However, the existing models predict the target translation with only the source- and target-side local context information. In practice, distinguishing good translations from bad ones does not only depend on the local features, but also rely on the global sentence-level information. In this paper, we explore the source-side global sentence-level features for target-side local translation prediction. We propose a novel bilingually-constrained chunk-based convolutional neural network to learn sentence semantic representations. With the sentence-level feature representation, we further design a feed-forward neural network to better predict translations using both local and global information. The large-scale experiments show that our method can obtain substantial improvements in translation quality over the strong baseline: the hierarchical phrase-based translation model augmented with the neural network joint model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.07920v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"182668\", \"231748\", \"316329\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"316936","document_content":"# Breaking Sticks and Ambiguities with Adaptive Skip-gram\n## Categories\n- Computation and Language\n## Abstract\nRecently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.07257v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"56152\", \"6319\", \"21888\", \"73486\", \"98008\", \"94962\", \"98901\", \"119031\", \"135303\", \"136809\", \"151487\", \"155664\", \"156490\", \"156495\", \"162931\", \"163411\", \"166015\", \"165410\", \"220821\", \"116579\", \"203774\", \"213533\", \"215296\", \"217768\", \"223096\", \"228662\", \"226692\", \"230845\", \"232527\", \"233049\", \"234679\", \"236218\", \"238862\", \"246643\", \"251059\", \"260586\", \"261133\", \"261908\", \"262903\", \"263218\", \"268833\", \"279053\", \"258826\", \"295613\", \"303183\"], \"outgoing_citations\": [\"313947\", \"336720\", \"357464\"]}","task_split":"paper_retrieval"}
{"document_id":"316961","document_content":"# Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nThis paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.07058v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"3102\", \"6241\", \"10970\", \"15859\", \"17886\", \"19755\", \"30431\", \"9109\", \"12833\", \"26331\", \"28390\", \"31120\", \"31235\", \"36476\", \"38915\", \"40383\", \"44251\", \"47716\", \"48498\", \"48483\", \"50580\", \"52829\", \"55130\", \"59992\", \"64008\", \"68388\", \"73913\", \"76956\", \"77979\", \"95084\", \"97694\", \"111843\", \"111989\", \"114770\", \"118235\", \"130460\", \"130415\", \"146516\", \"149004\", \"149580\", \"220646\", \"152003\", \"154495\", \"176090\", \"184301\", \"224507\", \"241864\", \"220369\", \"244011\", \"250276\", \"253798\", \"248560\", \"251500\", \"258765\", \"258857\", \"269105\", \"271441\", \"273240\", \"281706\", \"297196\", \"300182\"], \"outgoing_citations\": [\"327567\", \"328477\", \"331445\", \"337553\"]}","task_split":"paper_retrieval"}
{"document_id":"316977","document_content":"# Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nThis paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks with Long Short-Term Memory (LSTM) cells. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detects the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms it for web document retrieval task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TASLP.2016.2520371\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"8635\", \"15754\", \"98936\", \"109523\", \"115150\", \"135649\", \"159550\", \"2039\", \"2422\", \"6830\", \"16007\", \"16383\", \"16983\", \"25115\", \"29275\", \"29376\", \"34861\", \"41442\", \"44225\", \"51180\", \"55431\", \"59425\", \"66247\", \"67375\", \"69479\", \"71008\", \"70974\", \"75166\", \"76510\", \"79143\", \"81870\", \"105251\", \"128063\", \"142002\", \"149064\", \"75876\", \"78287\", \"89759\", \"91824\", \"92081\", \"94054\", \"97469\", \"98578\", \"107565\", \"119822\", \"121274\", \"119245\", \"126105\", \"127709\", \"129023\", \"138847\", \"138930\", \"144352\", \"148691\", \"150457\", \"156419\", \"157078\", \"166589\", \"186662\", \"194919\", \"183406\", \"202296\", \"204279\", \"221784\", \"157029\", \"156911\", \"158283\", \"283406\", \"168551\", \"170669\", \"170681\", \"169337\", \"173600\", \"175491\", \"177947\", \"143698\", \"191350\", \"194358\", \"194297\", \"197168\", \"204274\", \"202815\", \"210025\", \"211946\", \"221879\", \"224210\", \"241642\", \"207430\", \"209434\", \"170895\", \"214317\", \"215841\", \"215499\", \"216774\", \"217735\", \"217799\", \"219051\", \"220972\", \"226675\", \"229477\", \"231144\", \"237436\", \"239420\", \"244923\", \"245813\", \"265646\", \"286346\", \"108619\", \"250820\", \"257393\", \"260591\", \"260724\", \"261049\", \"262685\", \"264621\", \"267351\", \"267395\", \"270631\", \"271934\", \"274408\", \"277010\", \"281915\", \"283697\", \"163409\", \"279852\", \"280923\", \"283004\", \"286459\", \"287578\", \"289646\", \"289666\", \"289779\", \"290013\", \"290488\", \"294532\", \"295325\", \"301594\", \"302507\", \"307816\", \"308507\"], \"outgoing_citations\": [\"311683\", \"330941\", \"337473\", \"349865\", \"351685\"]}","task_split":"paper_retrieval"}
{"document_id":"317089","document_content":"# The fundamental nature of the log loss function\n## Categories\n- Machine Learning\n- Methodology\n- 68T05, 68T37, 60G25, 62M20\n## Abstract\nThe standard loss functions used in the literature on probabilistic prediction are the log loss function, the Brier loss function, and the spherical loss function; however, any computable proper loss function can be used for comparison of prediction algorithms. This note shows that the log loss function is most selective in that any prediction algorithm that is optimal for a given data sequence (in the sense of the algorithmic theory of randomness) under the log loss function will be optimal under any computable proper mixable loss function; on the other hand, there is a data sequence and a prediction algorithm that is optimal for that sequence under either of the two other standard loss functions but not under the log loss function.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.06254v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ME\", \"68T05, 68T37, 60G25, 62M20\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Methodology\", \"68T05, 68T37, 60G25, 62M20\"], \"incoming_citations\": [\"45096\", \"15398\", \"22813\", \"37645\", \"89516\", \"89633\", \"241577\", \"304196\"], \"outgoing_citations\": [\"327984\"]}","task_split":"paper_retrieval"}
{"document_id":"317124","document_content":"# Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nArtificial agents today can answer factual questions. But they fall short on questions that require common sense reasoning. Perhaps this is because most existing common sense databases rely on text to learn and represent knowledge. But much of common sense knowledge is unwritten - partly because it tends not to be interesting enough to talk about, and partly because some common sense is unnatural to articulate in text. While unwritten, it is not unseen. In this paper we leverage semantic common sense knowledge learned from images - i.e. visual common sense - in two textual tasks: fill-in-the-blank and visual paraphrasing. We propose to \"imagine\" the scene behind the text, and leverage visual cues from the \"imagined\" scenes in addition to textual cues while answering these questions. We imagine the scenes as a visual abstraction. Our approach outperforms a strong text-only baseline on these tasks. Our proposed tasks can serve as benchmarks to quantitatively evaluate progress in solving tasks that go \"beyond recognition\". Our code and datasets are publicly available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.06108v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"39620\", \"71008\", \"103762\", \"139684\", \"149495\", \"143643\", \"213917\", \"230558\", \"237062\", \"250049\", \"264994\", \"296867\", \"258433\", \"260702\", \"261600\", \"266669\", \"268871\", \"288062\", \"169080\", \"293220\", \"301507\", \"302731\", \"302756\", \"303251\", \"303341\", \"309247\", \"310738\", \"311989\", \"313440\"], \"outgoing_citations\": [\"323775\", \"337553\"]}","task_split":"paper_retrieval"}
{"document_id":"317305","document_content":"# Supervised cross-modal factor analysis for multiple modal data classification\n## Categories\n- Machine Learning\n## Abstract\nIn this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.05134v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"294823\", \"280083\", \"285446\", \"286504\", \"290931\", \"291754\", \"292128\", \"294029\", \"300356\", \"303110\", \"308694\", \"307610\", \"307934\"], \"outgoing_citations\": [\"295987\", \"295990\", \"323931\", \"326002\", \"338083\", \"355266\"]}","task_split":"paper_retrieval"}
{"document_id":"317334","document_content":"# A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena\n## Categories\n- Computation and Language\n## Abstract\nWord reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1162\/COLI_a_00245\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"38588\", \"65235\", \"100303\", \"169393\", \"180871\", \"195260\", \"249276\", \"284752\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"317384","document_content":"# TimeMachine: Timeline Generation for Knowledge-Base Entities\n## Categories\n- Databases\n- H.2.8\n- Information Retrieval\n## Abstract\nWe present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2783258.2783325\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\", \"H.2.8\", \"cs.IR\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\", \"H.2.8\", \"Information Retrieval\"], \"incoming_citations\": [\"34067\", \"161115\", \"82734\", \"136010\", \"165613\", \"185370\", \"186398\", \"206236\", \"232948\", \"233315\", \"235814\", \"238645\", \"302555\"], \"outgoing_citations\": [\"339752\", \"353402\"]}","task_split":"paper_retrieval"}
{"document_id":"317410","document_content":"# Image Specificity\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFor some images, descriptions written by multiple people are consistent with each other. But for other images, descriptions across people vary considerably. In other words, some images are specific $-$ they elicit consistent descriptions from different people $-$ while other images are ambiguous. Applications involving images and text can benefit from an understanding of which images are specific and which ones are ambiguous. For instance, consider text-based image retrieval. If a query description is moderately similar to the caption (or reference description) of an ambiguous image, that query may be considered a decent match to the image. But if the image is very specific, a moderate similarity between the query and the reference description may not be sufficient to retrieve the image.   In this paper, we introduce the notion of image specificity. We present two mechanisms to measure specificity given multiple descriptions of an image: an automated measure and a measure that relies on human judgement. We analyze image specificity with respect to image content and properties to better understand what makes an image specific. We then train models to automatically predict the specificity of an image from image features alone without requiring textual descriptions of the image. Finally, we show that modeling image specificity leads to improvements in a text-based image retrieval application.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.04569v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"26236\", \"90065\", \"141196\", \"238451\", \"242423\", \"172079\", \"228859\", \"235117\", \"236907\", \"267621\", \"251639\", \"253934\", \"278348\", \"300099\", \"285760\", \"302731\"], \"outgoing_citations\": [\"321503\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"317592","document_content":"# Phrase-based Image Captioning\n## Categories\n- Computation and Language\n## Abstract\nGenerating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.03671v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"25304\", \"110589\", \"129340\", \"154774\", \"157944\", \"182699\", \"265384\", \"218240\", \"235248\", \"243816\", \"250774\", \"251143\", \"257266\", \"257267\", \"264856\", \"268218\", \"277126\", \"277511\", \"300099\", \"286237\", \"291448\", \"295566\", \"296029\", \"297735\", \"303341\", \"311989\", \"312423\", \"312643\", \"312704\", \"313143\", \"315108\", \"321621\"], \"outgoing_citations\": [\"320303\", \"320332\", \"321503\", \"323634\"]}","task_split":"paper_retrieval"}
{"document_id":"317974","document_content":"# Use of Modality and Negation in Semantically-Informed Syntactic MT\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n- I.2.7; I.2.6; I.5.1; I.5.4\n## Abstract\nThis paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality\/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation) and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set.   We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. While the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described in this paper. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.01682v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"incoming_citations\": [\"33328\", \"95020\", \"133899\", \"176190\", \"198349\", \"234587\", \"277595\", \"314319\", \"316539\"], \"outgoing_citations\": [\"323040\"]}","task_split":"paper_retrieval"}
{"document_id":"318041","document_content":"# Authorship recognition via fluctuation analysis of network topology and word intermittency\n## Categories\n- Computation and Language\n## Abstract\nStatistical methods have been widely employed in many practical natural language processing applications. More specifically, complex networks concepts and methods from dynamical systems theory have been successfully applied to recognize stylistic patterns in written texts. Despite the large amount of studies devoted to represent texts with physical models, only a few studies have assessed the relevance of attributes derived from the analysis of stylistic fluctuations. Because fluctuations represent a pivotal factor for characterizing a myriad of real systems, this study focused on the analysis of the properties of stylistic fluctuations in texts via topological analysis of complex networks and intermittency measurements. The results showed that different authors display distinct fluctuation patterns. In particular, it was found that it is possible to identify the authorship of books using the intermittency of specific words. Taken together, the results described here suggest that the patterns found in stylistic fluctuations could be used to analyze other related complex systems. Furthermore, the discovery of novel patterns related to textual stylistic fluctuations indicates that these patterns could be useful to improve the state of the art of many stylistic-based natural language processing tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1088\/1742-5468\/2015\/03\/P03005\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"94490\", \"277258\", \"251138\", \"267578\", \"282259\", \"289273\", \"289588\", \"259269\", \"266695\", \"267932\", \"271880\", \"284461\", \"287489\", \"302047\", \"310342\", \"319646\"], \"outgoing_citations\": [\"319646\", \"338986\", \"340444\", \"348104\", \"348105\", \"348705\", \"348709\", \"348710\", \"348725\", \"348768\", \"348769\", \"359810\", \"362490\"]}","task_split":"paper_retrieval"}
{"document_id":"318097","document_content":"# Face frontalization for Alignment and Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, it was shown that excellent results can be achieved in both face landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D faces data. In this paper, we propose a novel method for joint face landmark localization and frontal face reconstruction (pose correction) using a small set of frontal images only. By observing that the frontal facial image is the one with the minimum rank from all different poses we formulate an appropriate model which is able to jointly recover the facial landmarks as well as the frontalized version of the face. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix $\\ell_1$ norm, is solved. The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases. The experimental results demonstrate the effectiveness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1502.00852v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"141765\", \"217801\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"318346","document_content":"# Regression and Learning to Rank Aggregation for User Engagement Evaluation\n## Categories\n- Information Retrieval\n- H.2.8; J.4\n- Machine Learning\n## Abstract\nUser engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain.   For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1145\/2668067.2668077\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"H.2.8; J.4\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"H.2.8; J.4\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"318418","document_content":"# Survey:Natural Language Parsing For Indian Languages\n## Categories\n- Computation and Language\n## Abstract\nSyntactic parsing is a necessary task which is required for NLP applications including machine translation. It is a challenging task to develop a qualitative parser for morphological rich and agglutinative languages. Syntactic analysis is used to understand the grammatical structure of a natural language sentence. It outputs all the grammatical information of each word and its constituent. Also issues related to it help us to understand the language in a more detailed way. This literature survey is groundwork to understand the different parser development for Indian languages and various approaches that are used to develop such tools and techniques. This paper provides a survey of research papers from well known journals and conferences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1501.07005v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"318535","document_content":"# Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWith the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multilevel semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1501.06272v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"138613\", \"203505\", \"9942\", \"17770\", \"85376\", \"155958\", \"750\", \"20231\", \"25550\", \"30132\", \"72302\", \"72768\", \"78925\", \"71954\", \"109376\", \"158041\", \"208542\", \"82163\", \"86068\", \"103466\", \"105810\", \"106896\", \"108421\", \"110187\", \"113620\", \"131122\", \"132522\", \"225824\", \"143497\", \"143660\", \"143795\", \"156220\", \"158244\", \"174506\", \"203861\", \"235344\", \"239660\", \"243039\", \"247969\", \"169305\", \"172184\", \"182899\", \"180528\", \"186825\", \"189084\", \"191400\", \"192213\", \"192346\", \"193175\", \"196540\", \"197124\", \"202820\", \"211595\", \"213838\", \"228642\", \"232982\", \"237343\", \"238528\", \"240768\", \"241439\", \"249538\", \"252948\", \"268960\", \"287562\", \"206852\", \"208345\", \"217551\", \"222134\", \"222151\", \"222817\", \"227549\", \"232009\", \"233835\", \"192280\", \"237774\", \"236780\", \"190365\", \"247613\", \"249448\", \"251069\", \"259748\", \"260176\", \"265613\", \"269511\", \"274046\", \"278223\", \"278480\", \"287103\", \"291400\", \"310303\", \"250269\", \"255632\", \"256480\", \"256474\", \"258937\", \"259093\", \"260702\", \"264939\", \"267210\", \"269814\", \"269848\", \"274683\", \"274846\", \"275310\", \"277417\", \"284208\", \"286731\", \"298792\", \"299317\", \"278179\", \"281599\", \"282939\", \"288138\", \"288218\", \"289180\", \"200534\", \"292486\", \"296798\", \"296862\", \"299079\", \"303372\", \"303512\", \"306406\", \"314287\", \"307882\"], \"outgoing_citations\": [\"327501\", \"336408\", \"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"318551","document_content":"# Dynamics of Belief: Abduction, Horn Knowledge Base And Database Updates\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n- Databases\n## Abstract\nThe dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality. The present paper provides a comparative study of view update algorithms in rational approach. For, understand the basic concepts with abduction, we provide an abductive framework for knowledge base dynamics. Finally, we demonstrate how belief base dynamics can provide an axiomatic characterization for insertion a view atom to the database. We give a quick overview of the main operators for belief change, in particular, belief update versus database update.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1501.06206v2\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.AI\", \"cs.DB\", \"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Databases\", \"Logic in Computer Science\"], \"incoming_citations\": [], \"outgoing_citations\": [\"322029\", \"329836\", \"349949\", \"358598\"]}","task_split":"paper_retrieval"}
{"document_id":"319021","document_content":"# Privacy by Design: On the Conformance Between Protocols and Architectures\n## Categories\n- Cryptography and Security\n- Logic in Computer Science\n## Abstract\nIn systems design, we generally distinguish the architecture and the protocol levels. In the context of privacy by design, in the first case, we talk about privacy architectures, which define the privacy goals and the main features of the system at high level. In the latter case, we consider the underlying concrete protocols and privacy enhancing technologies that implement the architectures. In this paper, we address the question that whether a given protocol conforms to a privacy architecture and provide the answer based on formal methods. We propose a process algebra variant to define protocols and reason about privacy properties, as well as a mapping procedure from protocols to architectures that are defined in a high-level architecture language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1501.03593v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.LO\", \"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Cryptography and Security\"], \"incoming_citations\": [\"104964\", \"193602\", \"272945\", \"304031\"], \"outgoing_citations\": [\"49785\", \"323794\", \"326018\"]}","task_split":"paper_retrieval"}
{"document_id":"319159","document_content":"# Combining Language and Vision with a Multimodal Skip-gram Model\n## Categories\n- Computation and Language\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1501.02598v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"9431\", \"1270\", \"16884\", \"21998\", \"24604\", \"25261\", \"34874\", \"92094\", \"55830\", \"60658\", \"107839\", \"122192\", \"127562\", \"77833\", \"82482\", \"87156\", \"88824\", \"90607\", \"93223\", \"94460\", \"116433\", \"122648\", \"126620\", \"128186\", \"130491\", \"141066\", \"143229\", \"157078\", \"179665\", \"157257\", \"55798\", \"143643\", \"162846\", \"283406\", \"154895\", \"168270\", \"169660\", \"171357\", \"176543\", \"183080\", \"191237\", \"205435\", \"230083\", \"196994\", \"263680\", \"218363\", \"218789\", \"225087\", \"227757\", \"231455\", \"233746\", \"235117\", \"234721\", \"243105\", \"246155\", \"250927\", \"250411\", \"251821\", \"255628\", \"248549\", \"248860\", \"253098\", \"259403\", \"260980\", \"262549\", \"264567\", \"264902\", \"265661\", \"269268\", \"272294\", \"274595\", \"287036\", \"289435\", \"296180\", \"279600\", \"282874\", \"283762\", \"227700\", \"290932\", \"294021\", \"294422\", \"296605\", \"302731\", \"303483\", \"305927\", \"295841\", \"311336\", \"312704\", \"315108\", \"320039\"], \"outgoing_citations\": [\"323634\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"319420","document_content":"# Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention\n## Categories\n- Computer Vision and Pattern Recognition\n- Computers and Society\n## Abstract\nMelanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the infection; early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due to doctors subjectivity. This paper proposes an innovative and fully functional smart-phone based application to assist in melanoma early detection and prevention. The application has two major components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image database from Pedro Hispano Hospital for development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the normal, atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/sipij.2014.5601\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CY\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computers and Society\"], \"incoming_citations\": [\"249252\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"319696","document_content":"# Improving Persian Document Classification Using Semantic Relations between Words\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nWith the increase of information, document classification as one of the methods of text mining, plays vital role in many management and organizing information. Document classification is the process of assigning a document to one or more predefined category labels. Document classification includes different parts such as text processing, term selection, term weighting and final classification. The accuracy of document classification is very important. Thus improvement in each part of classification should lead to better results and higher precision. Term weighting has a great impact on the accuracy of the classification. Most of the existing weighting methods exploit the statistical information of terms in documents and do not consider semantic relations between words. In this paper, an automated document classification system is presented that uses a novel term weighting method based on semantic relations between terms. To evaluate the proposed method, three standard Persian corpuses are used. Experiment results show 2 to 4 percent improvement in classification accuracy compared with the best previous designed system for Persian documents.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.8147v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"319833","document_content":"# Grammar as a Foreign Language\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nSyntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.7449v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"3606\", \"14960\", \"67056\", \"2988\", \"17300\", \"20995\", \"21743\", \"26860\", \"59368\", \"129123\", \"204766\", \"12821\", \"16435\", \"22981\", \"29970\", \"32114\", \"38617\", \"40537\", \"41477\", \"42653\", \"45199\", \"49746\", \"58856\", \"63074\", \"70842\", \"73442\", \"75427\", \"77573\", \"86922\", \"101065\", \"101935\", \"119795\", \"125476\", \"161918\", \"187044\", \"200289\", \"223084\", \"247723\", \"83070\", \"88790\", \"89826\", \"89832\", \"90858\", \"91687\", \"94133\", \"95287\", \"96298\", \"105893\", \"108688\", \"116427\", \"117120\", \"117465\", \"122660\", \"123059\", \"123236\", \"125291\", \"125560\", \"127951\", \"147190\", \"147804\", \"153185\", \"158180\", \"189507\", \"250140\", \"152767\", \"44423\", \"156950\", \"166623\", \"95622\", \"167179\", \"168711\", \"169587\", \"169780\", \"170882\", \"171057\", \"172405\", \"173076\", \"173391\", \"173424\", \"176753\", \"176837\", \"177928\", \"182999\", \"181717\", \"182540\", \"183339\", \"183514\", \"180289\", \"185364\", \"185475\", \"188107\", \"189378\", \"191377\", \"191777\", \"197278\", \"199658\", \"197125\", \"203509\", \"205085\", \"207494\", \"211433\", \"215676\", \"230166\", \"234729\", \"234820\", \"240757\", \"241536\", \"242690\", \"242815\", \"260108\", \"272440\", \"274843\", \"205593\", \"208852\", \"210586\", \"210936\", \"211195\", \"212819\", \"213527\", \"213822\", \"214294\", \"215409\", \"215499\", \"216458\", \"216791\", \"218958\", \"218973\", \"217486\", \"218997\", \"219586\", \"219829\", \"220606\", \"220797\", \"212422\", \"221339\", \"219758\", \"221344\", \"223048\", \"224679\", \"225340\", \"228464\", \"228470\", \"228628\", \"228740\", \"230051\", \"230109\", \"231660\", \"231880\", \"232243\", \"232288\", \"232472\", \"233400\", \"233407\", \"233779\", \"225558\", \"234421\", \"234523\", \"236925\", \"236927\", \"239563\", \"240434\", \"239581\", \"237157\", \"217294\", \"264749\", \"237494\", \"242430\", \"242644\", \"244339\", \"244654\", \"250108\", \"250620\", \"250829\", \"251778\", \"253284\", \"254641\", \"259009\", \"259826\", \"260505\", \"262672\", \"264560\", \"268121\", \"268465\", \"296823\", \"144800\", \"246446\", \"247016\", \"247721\", \"248710\", \"248712\", \"249146\", \"250689\", \"251523\", \"253743\", \"254230\", \"254784\", \"254923\", \"255190\", \"255604\", \"257132\", \"259083\", \"258278\", \"259618\", \"260396\", \"260853\", \"261088\", \"262124\", \"262127\", \"259980\", \"261587\", \"261601\", \"262973\", \"263808\", \"263951\", \"263691\", \"262646\", \"265144\", \"265525\", \"265859\", \"266411\", \"266758\", \"266878\", \"267460\", \"267551\", \"267881\", \"268251\", \"268356\", \"268354\", \"268464\", \"268410\", \"268788\", \"269624\", \"268177\", \"268067\", \"267877\", \"270019\", \"270234\", \"270599\", \"270893\", \"271610\", \"272753\", \"272680\", \"273197\", \"273270\", \"273562\", \"273718\", \"270726\", \"273795\", \"273761\", \"273872\", \"276160\", \"277160\", \"277694\", \"278951\", \"280201\", \"280884\", \"281121\", \"281085\", \"281380\", \"282924\", \"284379\", \"284605\", \"287040\", \"288229\", \"288364\", \"288415\", \"291440\", \"297423\", \"277150\", \"278244\", \"278633\", \"279606\", \"279947\", \"280106\", \"280339\", \"280708\", \"281225\", \"281081\", \"277612\", \"281822\", \"282249\", \"282914\", \"283618\", \"283665\", \"179675\", \"284043\", \"284904\", \"286459\", \"286462\", \"287046\", \"285615\", \"287880\", \"288348\", \"288779\", \"288978\", \"289239\", \"289192\", \"289589\", \"290356\", \"166518\", \"290999\", \"291145\", \"290201\", \"289951\", \"290569\", \"290632\", \"290766\", \"291319\", \"291691\", \"292998\", \"292181\", \"294054\", \"294480\", \"295325\", \"294830\", \"294999\", \"296198\", \"296283\", \"296517\", \"296583\", \"296563\", \"297955\", \"297648\", \"300505\", \"300652\", \"302515\", \"302516\", \"302934\", \"303003\", \"302930\", \"302933\", \"302927\", \"302947\", \"303020\", \"303296\", \"305200\", \"306182\", \"307195\", \"302908\", \"303997\", \"310136\", \"309633\", \"310781\", \"310903\", \"310837\", \"311098\", \"311464\", \"311470\", \"311849\", \"312066\", \"312008\", \"313944\", \"315023\", \"316745\", \"316741\", \"317517\"], \"outgoing_citations\": [\"320814\", \"320874\", \"322537\"]}","task_split":"paper_retrieval"}
{"document_id":"319889","document_content":"# Pragmatic Neural Language Modelling in Machine Translation\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents an in-depth investigation on integrating neural language models in translation systems. Scaling neural language models is a difficult task, but crucial for real-world applications. This paper evaluates the impact on end-to-end MT quality of both new and existing scaling techniques. We show when explicitly normalising neural models is necessary and what optimisation tricks one should use in such scenarios. We also focus on scalable training algorithms and investigate noise contrastive estimation and diagonal contexts as sources for further speed improvements. We explore the trade-offs between neural models and back-off n-gram models and find that neural models make strong candidates for natural language applications in memory constrained environments, yet still lag behind traditional models in raw translation quality. We conclude with a set of recommendations one should follow to build a scalable neural language model for MT.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.7119v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"42093\", \"100365\", \"153286\", \"160643\", \"216217\", \"255165\", \"259525\", \"263943\", \"283147\", \"291343\", \"295028\", \"301447\", \"302570\", \"303538\"], \"outgoing_citations\": [\"329561\", \"336720\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"319947","document_content":"# Extraction of Salient Sentences from Labelled Documents\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nWe present a hierarchical convolutional document model with an architecture designed to support introspection of the document structure. Using this model, we show how to use visualisation techniques from the computer vision literature to identify and extract topic-relevant sentences.   We also introduce a new scalable evaluation technique for automatic sentence extraction systems that avoids the need for time consuming human annotation of validation data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.6815v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"2537\", \"9635\", \"14944\", \"23774\", \"55157\", \"56568\", \"68753\", \"83757\", \"84229\", \"83050\", \"93210\", \"94934\", \"100392\", \"125075\", \"127086\", \"127806\", \"164111\", \"165047\", \"188812\", \"192891\", \"193560\", \"220682\", \"244688\", \"249229\", \"263108\", \"270565\", \"277380\", \"274425\", \"285066\", \"285181\", \"289752\", \"310136\"], \"outgoing_citations\": [\"320868\", \"328352\", \"330941\", \"343624\", \"350125\"]}","task_split":"paper_retrieval"}
{"document_id":"320003","document_content":"# Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Machine Learning\n- I.2.6; I.2.7; I.2.10\n## Abstract\nIn this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu\/~junhua.mao\/m-RNN.html .","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.6632v5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.LG\", \"I.2.6; I.2.7; I.2.10\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Machine Learning\", \"I.2.6; I.2.7; I.2.10\"], \"incoming_citations\": [\"35427\", \"6697\", \"22827\", \"29778\", \"30117\", \"60281\", \"160859\", \"182259\", \"247224\", \"15665\", \"25304\", \"28498\", \"30940\", \"31347\", \"33924\", \"37422\", \"37574\", \"38068\", \"38289\", \"82418\", \"46681\", \"47510\", \"49326\", \"52654\", \"53385\", \"55096\", \"56947\", \"64312\", \"64411\", \"66734\", \"67637\", \"77697\", \"78606\", \"81968\", \"82846\", \"82930\", \"102192\", \"110600\", \"118569\", \"138177\", \"142094\", \"145513\", \"169181\", \"175031\", \"183829\", \"268025\", \"67532\", \"80061\", \"82482\", \"89629\", \"90048\", \"94076\", \"97825\", \"98487\", \"101085\", \"108290\", \"110219\", \"111384\", \"111512\", \"112250\", \"112491\", \"113777\", \"115081\", \"116569\", \"118957\", \"123245\", \"132709\", \"135561\", \"137915\", \"139876\", \"140566\", \"141978\", \"141957\", \"145573\", \"146920\", \"149997\", \"153733\", \"154807\", \"154752\", \"156930\", \"157078\", \"168443\", \"172586\", \"172900\", \"185622\", \"213237\", \"223463\", \"230774\", \"250405\", \"143743\", \"154381\", \"157229\", \"162846\", \"283406\", \"165046\", \"165663\", \"166989\", \"167962\", \"168851\", \"167615\", \"168193\", \"169599\", \"169858\", \"170604\", \"172576\", \"172668\", \"174060\", \"174419\", \"153669\", \"171422\", \"173572\", \"128955\", \"180228\", \"180838\", \"182062\", \"189628\", \"189699\", \"190585\", \"190945\", \"190949\", \"191058\", \"193442\", \"193906\", \"194673\", \"196234\", \"200073\", \"189596\", \"204038\", \"204220\", \"207647\", \"209504\", \"212054\", \"212034\", \"215181\", \"229734\", \"237749\", \"241536\", \"255907\", \"265384\", \"269128\", \"204813\", \"206741\", \"206888\", \"207057\", \"210290\", \"212441\", \"212792\", \"216687\", \"218240\", \"219342\", \"217125\", \"219553\", \"219698\", \"220232\", \"220529\", \"220729\", \"218012\", \"221169\", \"221662\", \"223302\", \"224064\", \"164380\", \"225101\", \"216953\", \"227880\", \"230900\", \"231212\", \"231647\", \"236042\", \"236907\", \"237199\", \"237178\", \"237270\", \"237271\", \"237655\", \"237852\", \"238125\", \"189653\", \"238941\", \"239242\", \"239425\", \"241584\", \"241716\", \"239686\", \"243764\", \"243816\", \"244082\", \"244721\", \"245695\", \"246422\", \"246397\", \"246399\", \"248164\", \"249394\", \"250183\", \"254809\", \"254903\", \"256883\", \"256347\", \"260746\", \"260954\", \"264628\", \"269098\", \"269468\", \"270292\", \"270455\", \"279024\", \"279697\", \"296049\", \"296867\", \"144800\", \"237265\", \"249972\", \"250500\", \"250610\", \"153429\", \"250774\", \"252099\", \"253029\", \"258204\", \"258511\", \"258570\", \"258987\", \"259197\", \"259184\", \"260163\", \"260373\", \"260521\", \"259061\", \"261733\", \"259835\", \"262564\", \"264289\", \"265445\", \"265506\", \"266666\", \"267127\", \"267223\", \"267500\", \"267973\", \"268218\", \"268935\", \"269016\", \"269030\", \"269122\", \"269640\", \"269646\", \"269848\", \"270154\", \"270658\", \"270767\", \"271004\", \"273696\", \"276110\", \"276376\", \"277126\", \"277511\", \"278243\", \"278604\", \"278846\", \"278948\", \"279676\", \"279688\", \"279914\", \"280004\", \"280246\", \"280277\", \"281407\", \"283247\", \"284230\", \"285969\", \"288062\", \"289627\", \"289755\", \"290392\", \"300099\", \"300722\", \"303293\", \"277261\", \"277385\", \"277936\", \"279600\", \"284530\", \"284627\", \"284800\", \"285742\", \"286237\", \"286996\", \"287427\", \"287504\", \"288000\", \"290500\", \"291311\", \"291724\"], \"outgoing_citations\": [\"312704\", \"313196\", \"313934\", \"321503\", \"323634\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"320105","document_content":"# N-gram-Based Low-Dimensional Representation for Document Classification\n## Categories\n- Computation and Language\n## Abstract\nThe bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been designed to summarize documents in a lower dimension with the least semantic information loss. Some semantic information is nevertheless always lost, since only words are considered. Instead, we aim at using information coming from n-grams to overcome this limitation, while remaining in a low-dimension space. Many approaches, such as the Skip-gram model, provide good word vector representations very quickly. We propose to average these representations to obtain representations of n-grams. All n-grams are thus embedded in a same semantic space. A K-means clustering can then group them into semantic concepts. The number of features is therefore dramatically reduced and documents can be represented as bag of semantic concepts. We show that this model outperforms LSA and LDA on a sentiment classification task, and yields similar results than a traditional BOW-model with far less features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.6277v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"198545\", \"232887\", \"289148\"], \"outgoing_citations\": [\"336326\"]}","task_split":"paper_retrieval"}
{"document_id":"320414","document_content":"# Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar\n## Categories\n- Artificial Intelligence\n- Logic\n- Probability\n- Machine Learning\n## Abstract\nArriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. That is, although precise answers to some queries, in principle, cannot be achieved, a range of plausible answers is attainable for each query given the available partial knowledge. In this paper, we propose the Multi-Context Model (MCM), a new graphical model to represent the state of partial knowledge as to a domain. MCM is a middle ground between Probabilistic Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to form partial beliefs regarding a domain in a gradual and probabilistically consistent way, and (ii) how to perform inference, i.e., to evaluate a probability of interest involving some variables of the domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.4271v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"math.LO\", \"math.PR\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic\", \"Probability\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"347278\", \"347539\", \"348627\", \"348963\", \"357936\"]}","task_split":"paper_retrieval"}
{"document_id":"320434","document_content":"# A survey of modern optical character recognition techniques\n## Categories\n- Computer Vision and Pattern Recognition\n- 62-04\n- I.7.5; I.4.1; I.5.4; I.4.1; I.4.3; I.4.6; I.4.7\n## Abstract\nThis report explores the latest advances in the field of digital document recognition. With the focus on printed document imagery, we discuss the major developments in optical character recognition (OCR) and document image enhancement\/restoration in application to Latin and non-Latin scripts. In addition, we review and discuss the available technologies for hand-written document recognition. In this report, we also provide some company-accumulated benchmark results on available OCR engines.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.4183v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"62-04\", \"I.7.5; I.4.1; I.5.4; I.4.1; I.4.3; I.4.6; I.4.7\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"62-04\", \"I.7.5; I.4.1; I.5.4; I.4.1; I.4.3; I.4.6; I.4.7\"], \"incoming_citations\": [\"91603\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"320716","document_content":"# Optimization models of natural communication\n## Categories\n- Physics and Society\n- Data Analysis, Statistics and Probability\n- Computation and Language\n## Abstract\nA family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf's law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy. The family also sheds light on the origins of three other patterns: the principle of contrast, a related vocabulary learning bias and the meaning-frequency law. Here two important components of the family, namely the information theoretic principles and the energy function that combines them linearly, are reviewed from the perspective of psycholinguistics, language learning, information theory and synergetic linguistics. The minimization of this linear function is linked to the problem of compression of standard information theory and might be tuned by self-organization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1080\/09296174.2017.1366095\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.data-an\", \"cs.CL\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Data Analysis, Statistics and Probability\", \"Computation and Language\", \"Physics and Society\"], \"incoming_citations\": [\"108829\", \"182576\", \"293168\", \"22615\", \"47608\", \"193486\", \"279475\", \"265212\"], \"outgoing_citations\": [\"279475\", \"282423\", \"324003\", \"326364\", \"331394\", \"338769\", \"347066\", \"351470\", \"351626\"]}","task_split":"paper_retrieval"}
{"document_id":"320743","document_content":"# Learning Word Representations from Relational Graphs\n## Categories\n- Computation and Language\n## Abstract\nAttributes of words and relations between two words are central to numerous tasks in Artificial Intelligence such as knowledge representation, similarity measurement, and analogy detection. Often when two words share one or more attributes in common, they are connected by some semantic relations. On the other hand, if there are numerous semantic relations between two words, we can expect some of the attributes of one of the words to be inherited by the other. Motivated by this close connection between attributes and relations, given a relational graph in which words are inter- connected via numerous semantic relations, we propose a method to learn a latent representation for the individual words. The proposed method considers not only the co-occurrences of words as done by existing approaches for word representation learning, but also the semantic relations in which two words co-occur. To evaluate the accuracy of the word representations learnt using the proposed method, we use the learnt word representations to solve semantic word analogy problems. Our experimental results show that it is possible to learn better word representations by using semantic semantics between words.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.2378v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"73891\", \"88174\", \"255431\", \"302903\"], \"outgoing_citations\": [\"345692\"]}","task_split":"paper_retrieval"}
{"document_id":"320868","document_content":"# Deep Learning for Answer Sentence Selection\n## Categories\n- Computation and Language\n## Abstract\nAnswer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that---despite its simplicity---our model matches state of the art performance on the answer sentence selection task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.1632v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"21752\", \"12048\", \"25894\", \"53849\", \"104539\", \"142002\", \"94065\", \"97393\", \"138479\", \"154484\", \"194919\", \"155968\", \"158062\", \"158283\", \"168956\", \"173474\", \"86766\", \"180604\", \"182073\", \"191156\", \"186273\", \"197770\", \"199945\", \"203265\", \"204955\", \"192445\", \"152436\", \"211788\", \"208737\", \"210964\", \"215208\", \"220043\", \"220529\", \"221934\", \"222235\", \"212857\", \"232590\", \"233065\", \"238024\", \"240441\", \"242123\", \"242488\", \"244439\", \"244596\", \"245787\", \"249796\", \"253691\", \"255322\", \"260297\", \"301087\", \"212994\", \"247748\", \"248047\", \"256632\", \"258130\", \"259127\", \"260724\", \"260989\", \"262155\", \"264394\", \"266737\", \"271360\", \"274681\", \"276586\", \"279224\", \"288364\", \"288415\", \"264368\", \"280161\", \"281092\", \"245908\", \"283169\", \"285483\", \"289485\", \"289575\", \"289816\", \"289808\", \"290387\", \"290690\", \"293976\", \"294251\", \"295235\", \"296257\", \"297226\", \"298513\", \"298362\", \"301397\", \"303029\", \"312651\", \"303469\", \"308414\", \"309810\", \"296555\", \"310377\", \"310772\", \"319947\"], \"outgoing_citations\": [\"328352\", \"330941\", \"330994\", \"336196\"]}","task_split":"paper_retrieval"}
{"document_id":"320908","document_content":"# Curriculum Learning of Multiple Tasks\n## Categories\n- Machine Learning\n## Abstract\nSharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover the favourable order of tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1412.1353v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"stat.ML\", \"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"41972\", \"91442\", \"62948\", \"66517\", \"73516\", \"163067\", \"196319\", \"16201\", \"38622\", \"42960\", \"43897\", \"60917\", \"66415\", \"82509\", \"93901\", \"149556\", \"178768\", \"108824\", \"114189\", \"114455\", \"114868\", \"127383\", \"139615\", \"133337\", \"142785\", \"155347\", \"178509\", \"248027\", \"158636\", \"163302\", \"174288\", \"176571\", \"191219\", \"194014\", \"202304\", \"203961\", \"206314\", \"207031\", \"212547\", \"217577\", \"224604\", \"291923\", \"208118\", \"212814\", \"222080\", \"239436\", \"240045\", \"215498\", \"244294\", \"255438\", \"249373\", \"253395\", \"257405\", \"257472\", \"260702\", \"263712\", \"262842\", \"269959\", \"270792\", \"272525\", \"278743\", \"279725\", \"282387\", \"285178\", \"295171\", \"297910\", \"280106\", \"286572\", \"294694\", \"296168\", \"300593\"], \"outgoing_citations\": [\"323750\", \"337980\", \"357176\"]}","task_split":"paper_retrieval"}
{"document_id":"321124","document_content":"# On Rendering Synthetic Images for Training an Object Detector\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nWe propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances.   A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.cviu.2014.12.006\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"1796\", \"19959\", \"164827\", \"165015\", \"79256\", \"107507\", \"128945\", \"188785\", \"150453\", \"172537\", \"188650\", \"190670\", \"241864\", \"296192\", \"234358\", \"243835\", \"259334\", \"296184\", \"245977\", \"247146\", \"253112\", \"272884\", \"273802\", \"277132\", \"278889\", \"286589\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"321133","document_content":"# Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge\n## Categories\n- Computation and Language\n## Abstract\nWe present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e.g. history, economy) are aligned based on induced multilingual topics. The method combines three ideas: a two-level LDA model that filters out words that do not convey themes, an HMM that models the ordering of themes in the collection of documents, and language-independent concept annotations to serve as a cross-language bridge and to strengthen the connection between paragraphs in the same segment through concept relations. The method is evaluated on English and French data previously used for monolingual alignment. The results show state-of-the-art performance in both monolingual and cross-lingual settings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.7820v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"335270\", \"358962\"]}","task_split":"paper_retrieval"}
{"document_id":"321305","document_content":"# Some Reflections on the Set-based and the Conditional-based Interpretations of Statements in Syllogistic Reasoning\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nTwo interpretations about syllogistic statements are described in this paper. One is the so-called set-based interpretation, which assumes that quantified statements and syllogisms talk about quantity-relationships between sets. The other one, the so-called conditional interpretation, assumes that quantified propositions talk about conditional propositions and how strong are the links between the antecedent and the consequent. Both interpretations are compared attending to three different questions (existential import, singular statements and non-proportional quantifiers) from the point of view of their impact on the further development of this type of reasoning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.6794v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [\"251021\"], \"outgoing_citations\": [\"321188\"]}","task_split":"paper_retrieval"}
{"document_id":"321490","document_content":"# A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- Machine Learning\n## Abstract\nEstimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categorization to identify two types of sentences in mathematical word problems, namely relevant and irrelevant sentences. A novel joint probabilistic classification model is proposed to estimate the joint probability of classification decisions for all sentences of a math word problem by utilizing the correlation among all sentences along with the correlation between the question sentence and other sentences, and sentence text. The proposed model is compared with i) a SVM classifier which makes independent classification decisions for individual sentences by only using the sentence text and ii) a novel SVM classifier that considers the correlation between the question sentence and other sentences along with the sentence text. An extensive set of experiments demonstrates the effectiveness of the joint probabilistic classification model for identifying relevant and irrelevant sentences as well as the novel SVM classifier that utilizes the correlation between the question sentence and other sentences. Furthermore, empirical results and analysis show that i) it is highly beneficial not to remove stopwords and ii) utilizing part of speech tagging does not make a significant improvement although it has been shown to be effective for the related task of math word problem type classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.5732v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"94695\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"321503","document_content":"# Learning a Recurrent Visual Representation for Image Caption Generation\n## Categories\n- Computer Vision and Pattern Recognition\n- Artificial Intelligence\n- Computation and Language\n## Abstract\nIn this paper we explore the bi-directional mapping between images and their sentence-based descriptions. We propose learning this mapping using a recurrent neural network. Unlike previous approaches that map both sentences and images to a common embedding, we enable the generation of novel sentences given an image. Using the same model, we can also reconstruct the visual features associated with an image given its visual description. We use a novel recurrent visual memory that automatically learns to remember long-term visual concepts to aid in both sentence generation and visual feature reconstruction. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are preferred by humans over $19.8\\%$ of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.5654v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.AI\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Artificial Intelligence\", \"Computation and Language\"], \"incoming_citations\": [\"8679\", \"107491\", \"204751\", \"157229\", \"283406\", \"212792\", \"216394\", \"219698\", \"189653\", \"243971\", \"250108\", \"270455\", \"271683\", \"279024\", \"259807\", \"264568\", \"267568\", \"269031\", \"273290\", \"273696\", \"277599\", \"278883\", \"320761\", \"277936\", \"279600\", \"285882\", \"286237\", \"294558\", \"297133\", \"301594\", \"301767\", \"302621\", \"302756\", \"302731\", \"303837\", \"304481\", \"310639\", \"311824\", \"311976\", \"312275\", \"301669\", \"302570\", \"303627\", \"306073\", \"311336\", \"311989\", \"312506\", \"312643\", \"312703\", \"313143\", \"313196\", \"313435\", \"313934\", \"314050\", \"315108\", \"316779\", \"317410\", \"317592\", \"317717\", \"319169\", \"320003\", \"321493\"], \"outgoing_citations\": [\"323634\", \"328051\"]}","task_split":"paper_retrieval"}
{"document_id":"321621","document_content":"# From Captions to Visual Concepts and Back\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n## Abstract\nThis paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.4952v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\"], \"incoming_citations\": [\"35427\", \"73684\", \"1295\", \"2504\", \"5789\", \"46635\", \"49748\", \"59368\", \"60281\", \"61316\", \"78623\", \"94351\", \"97719\", \"101277\", \"209688\", \"182259\", \"189753\", \"6500\", \"8679\", \"11360\", \"14882\", \"20379\", \"25735\", \"25732\", \"26367\", \"38693\", \"39327\", \"40652\", \"40810\", \"44869\", \"45373\", \"77306\", \"34531\", \"47101\", \"98018\", \"48826\", \"50398\", \"53515\", \"53571\", \"54685\", \"58270\", \"78606\", \"81451\", \"82846\", \"89845\", \"95829\", \"103610\", \"118569\", \"136492\", \"142094\", \"145107\", \"145513\", \"149892\", \"167495\", \"175031\", \"83854\", \"86053\", \"96188\", \"97825\", \"105922\", \"107304\", \"108290\", \"154779\", \"111384\", \"111776\", \"112281\", \"108389\", \"117131\", \"117945\", \"120980\", \"127571\", \"130657\", \"132002\", \"132709\", \"134565\", \"97920\", \"140820\", \"141957\", \"142649\", \"144120\", \"145827\", \"145829\", \"145838\", \"149248\", \"154807\", \"154752\", \"155397\", \"157078\", \"163614\", \"163648\", \"167323\", \"168031\", \"168443\", \"172900\", \"179422\", \"189642\", \"204177\", \"204751\", \"205147\", \"205306\", \"216658\", \"230774\", \"243013\", \"150299\", \"151345\", \"118605\", \"152816\", \"153482\", \"161371\", \"161767\", \"161983\", \"161715\", \"283406\", \"165588\", \"165896\", \"167256\", \"167642\", \"168616\", \"168851\", \"168193\", \"169268\", \"169599\", \"170299\", \"170604\", \"170984\", \"171096\", \"171369\", \"171357\", \"171726\", \"171902\", \"172416\", \"172576\", \"172768\", \"172811\", \"112826\", \"139529\", \"176543\", \"177226\", \"178885\", \"179730\", \"180838\", \"181643\", \"182062\", \"182578\", \"183823\", \"186189\", \"187303\", \"187744\", \"188775\", \"189018\", \"190585\", \"191237\", \"192210\", \"192648\", \"193246\", \"193328\", \"193442\", \"193738\", \"194846\", \"195852\", \"197689\", \"199453\", \"189596\", \"200553\", \"201774\", \"202386\", \"207034\", \"208242\", \"208773\", \"209504\", \"212034\", \"215181\", \"217992\", \"231203\", \"235648\", \"236772\", \"238664\", \"240605\", \"241536\", \"250263\", \"250228\", \"253921\", \"254061\", \"255907\", \"205227\", \"205647\", \"210290\", \"212441\", \"213413\", \"216649\", \"217526\", \"200466\", \"218240\", \"189921\", \"220232\", \"218012\", \"221169\", \"221579\", \"221830\", \"223302\", \"223456\", \"223733\", \"224064\", \"225101\", \"225211\", \"225585\", \"216953\", \"217194\", \"227880\", \"228231\", \"230900\", \"231173\", \"233869\", \"233924\", \"233986\", \"234549\", \"234602\", \"234753\", \"235117\", \"236276\", \"236897\", \"237655\", \"238205\", \"238941\", \"240071\", \"241716\", \"234545\", \"243764\", \"243816\", \"244082\", \"244341\", \"245831\", \"245930\", \"248164\", \"248171\", \"249031\", \"249591\", \"249639\", \"250108\", \"250474\", \"252694\", \"252885\", \"254903\", \"255035\", \"256347\", \"259007\", \"260746\", \"270292\", \"278280\", \"296049\", \"296867\", \"153770\", \"250157\", \"250774\", \"251143\", \"252099\", \"252641\", \"253934\", \"256019\", \"258570\", \"258987\", \"259307\", \"259559\", \"259807\", \"259865\", \"260163\", \"260373\", \"260521\", \"260702\", \"256940\", \"258345\", \"262511\", \"262564\", \"264289\", \"264514\", \"264568\", \"264645\", \"264889\", \"264937\", \"265524\", \"265551\", \"267385\", \"267568\", \"268096\", \"268218\", \"268356\", \"269030\", \"269031\", \"269216\", \"269122\", \"269646\", \"269959\", \"270711\", \"271085\", \"271120\", \"271234\", \"273290\", \"273696\", \"273718\", \"275220\", \"275409\", \"276436\", \"277126\", \"277511\", \"278243\", \"278604\", \"278846\", \"278883\", \"278948\", \"278894\", \"278997\", \"279518\", \"279604\", \"279676\", \"279740\", \"279990\", \"279914\", \"280030\", \"280249\", \"280277\", \"281152\", \"282943\", \"283082\", \"283247\", \"284230\", \"285235\", \"287380\", \"289627\", \"289755\", \"290392\", \"300099\", \"301835\", \"320761\", \"320332\", \"277385\", \"277605\", \"277936\"], \"outgoing_citations\": [\"317592\", \"323634\", \"328051\", \"328327\", \"332853\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"321913","document_content":"# Statistically Significant Detection of Linguistic Change\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n- H.3.3; I.2.6\n## Abstract\nWe propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts.   We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time.   We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.3315v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\", \"H.3.3; I.2.6\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\", \"H.3.3; I.2.6\"], \"incoming_citations\": [\"6044\", \"16892\", \"17972\", \"56152\", \"11212\", \"15030\", \"24558\", \"26279\", \"27150\", \"31139\", \"34034\", \"34205\", \"37962\", \"37904\", \"39459\", \"41890\", \"42833\", \"44973\", \"39225\", \"47674\", \"50267\", \"52054\", \"55328\", \"56302\", \"57457\", \"61937\", \"69148\", \"72476\", \"74072\", \"74650\", \"63352\", \"74821\", \"91742\", \"167741\", \"87126\", \"88467\", \"88670\", \"95952\", \"97205\", \"97252\", \"110076\", \"113263\", \"81398\", \"456\", \"124716\", \"127813\", \"128202\", \"128369\", \"131433\", \"138257\", \"135960\", \"146182\", \"146604\", \"147843\", \"153118\", \"159284\", \"177273\", \"189539\", \"229522\", \"151195\", \"160951\", \"163411\", \"164185\", \"168654\", \"165613\", \"171800\", \"175097\", \"175220\", \"179302\", \"180341\", \"180913\", \"182409\", \"182513\", \"182605\", \"182003\", \"186766\", \"195271\", \"205958\", \"218312\", \"218524\", \"220776\", \"237823\", \"209985\", \"207393\", \"223098\", \"224928\", \"228662\", \"230151\", \"230155\", \"236835\", \"243873\", \"249587\", \"272615\", \"227918\", \"250392\", \"254624\", \"243614\", \"269662\", \"270730\", \"271438\", \"271009\", \"279053\", \"280033\", \"280755\", \"283235\", \"285191\", \"284236\", \"269389\", \"288246\", \"290798\", \"291574\", \"293621\", \"296816\", \"304623\", \"309412\", \"319558\"], \"outgoing_citations\": [\"329536\", \"329672\", \"332807\", \"342907\", \"350314\"]}","task_split":"paper_retrieval"}
{"document_id":"321936","document_content":"# Distributed Representations for Compositional Semantics\n## Categories\n- Computation and Language\n## Abstract\nThe mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistics of large corpora --- have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP.   This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.3146v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"280164\", \"309271\"], \"outgoing_citations\": [\"330941\", \"335600\", \"336196\", \"338185\", \"340248\", \"342907\", \"342934\", \"343624\", \"343852\", \"345692\", \"349607\", \"350362\"]}","task_split":"paper_retrieval"}
{"document_id":"322022","document_content":"# Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models\n## Categories\n- Machine Learning\n- Computation and Language\n- Computer Vision and Pattern Recognition\n## Abstract\nInspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - \"blue\" + \"red\" is near images of red cars. Sample captions generated for 800 images are made available for comparison.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.2539v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"48408\", \"289574\", \"1081\", \"5197\", \"11273\", \"24165\", \"29778\", \"30117\", \"55118\", \"60281\", \"72957\", \"86254\", \"88607\", \"96143\", \"113677\", \"93187\", \"738\", \"5726\", \"6500\", \"8556\", \"10271\", \"15665\", \"20397\", \"20409\", \"21190\", \"24014\", \"25608\", \"32697\", \"37209\", \"37574\", \"40597\", \"42740\", \"45087\", \"82418\", \"46634\", \"46967\", \"47178\", \"47939\", \"49033\", \"49326\", \"50398\", \"53451\", \"54595\", \"55460\", \"56145\", \"57659\", \"58057\", \"58839\", \"58876\", \"59654\", \"59840\", \"60135\", \"60390\", \"61064\", \"61785\", \"62836\", \"62806\", \"64186\", \"66630\", \"67637\", \"70049\", \"70460\", \"72354\", \"76974\", \"81968\", \"82713\", \"85205\", \"86310\", \"88202\", \"92023\", \"92282\", \"96230\", \"99513\", \"107454\", \"115346\", \"130722\", \"128625\", \"138177\", \"142094\", \"145513\", \"161696\", \"175031\", \"183623\", \"191408\", \"203930\", \"211872\", \"77732\", \"78975\", \"80061\", \"86006\", \"86252\", \"89825\", \"90048\", \"96188\", \"101085\", \"102190\", \"105922\", \"106532\", \"108290\", \"111384\", \"113665\", \"114022\", \"118354\", \"87013\", \"124223\", \"126586\", \"126620\", \"127083\", \"127093\", \"127707\", \"128073\", \"129340\", \"131401\", \"131626\", \"133695\", \"133740\", \"136245\", \"137906\", \"137967\", \"139259\", \"141225\", \"141957\", \"142128\", \"140049\", \"146920\", \"147001\", \"147671\", \"148465\", \"150513\", \"154774\", \"154807\", \"154752\", \"155791\", \"157078\", \"162227\", \"168031\", \"172586\", \"209090\", \"209573\", \"223463\", \"230774\", \"144119\", \"153094\", \"153520\", \"156399\", \"157175\", \"157229\", \"158679\", \"161383\", \"154606\", \"162846\", \"283406\", \"164107\", \"164236\", \"165046\", \"165588\", \"166854\", \"167256\", \"168374\", \"168616\", \"169105\", \"169524\", \"170299\", \"170604\", \"171182\", \"172394\", \"173627\", \"174295\", \"174872\", \"173572\", \"178924\", \"179730\", \"180566\", \"182042\", \"181897\", \"181437\", \"182699\", \"185910\", \"188867\", \"189628\", \"191076\", \"191439\", \"191501\", \"191552\", \"191966\", \"192697\", \"193442\", \"194673\", \"194714\", \"195097\", \"196153\", \"196234\", \"187617\", \"189596\", \"200744\", \"202386\", \"204141\", \"199249\", \"205435\", \"207159\", \"209047\", \"212034\", \"213042\", \"215181\", \"215251\", \"218332\", \"219049\", \"219931\", \"221675\", \"230083\", \"233934\", \"238664\", \"245667\", \"255907\", \"265384\", \"269128\", \"190250\", \"205227\", \"204708\", \"212643\", \"216687\", \"217770\", \"218363\", \"214015\", \"214466\", \"217125\", \"220529\", \"221984\", \"220421\", \"223815\", \"224679\", \"225585\", \"225830\", \"225087\", \"227200\", \"228141\", \"227757\", \"226619\", \"231212\", \"231647\", \"232494\", \"232759\", \"233475\", \"233746\", \"233938\", \"234549\", \"235117\", \"236090\", \"236696\", \"236907\", \"237062\", \"237270\", \"238069\", \"238205\", \"189653\", \"239108\", \"240885\", \"241067\", \"241716\", \"241628\", \"244721\", \"245433\", \"245735\", \"245910\", \"245930\", \"247206\", \"248171\", \"248300\", \"249248\", \"250098\", \"250183\", \"253108\", \"253508\", \"254809\", \"254903\", \"256883\", \"260926\", \"264628\", \"266046\", \"267912\", \"268298\", \"270455\", \"279024\", \"281760\", \"296049\", \"296867\", \"233456\", \"237265\", \"248549\", \"250774\", \"252099\", \"257442\", \"257559\", \"258204\", \"258570\", \"251453\", \"259403\", \"259184\", \"248315\", \"259754\", \"260163\", \"260373\", \"260397\", \"260588\", \"260908\", \"261030\", \"261957\", \"262155\", \"256940\", \"264645\", \"265506\", \"265661\", \"267379\", \"267476\", \"267568\", \"264856\", \"268218\", \"269030\", \"269122\", \"269848\", \"271066\", \"271841\", \"272075\", \"274548\", \"274784\", \"275603\", \"277126\", \"278243\", \"278846\", \"278894\", \"279676\", \"279914\", \"280030\", \"280249\", \"280498\", \"281407\", \"284154\", \"284230\", \"285796\", \"286574\", \"287823\", \"289627\", \"289755\", \"300099\", \"303293\", \"308699\", \"320761\", \"320332\", \"277385\", \"277936\", \"279600\", \"283070\", \"283914\", \"285882\", \"286237\", \"286996\", \"287427\"], \"outgoing_citations\": [\"323634\", \"328051\", \"328529\", \"330410\", \"330941\", \"336196\"]}","task_split":"paper_retrieval"}
{"document_id":"322195","document_content":"# Efficient Representations for Life-Long Learning and Autoencoding\n## Categories\n- Machine Learning\n## Abstract\nIt has been a long-standing goal in machine learning, as well as in AI more generally, to develop life-long learning systems that learn many different tasks over time, and reuse insights from tasks learned, \"learning to learn\" as they do so. In this work we pose and provide efficient algorithms for several natural theoretical formulations of this goal. Specifically, we consider the problem of learning many different target functions over time, that share certain commonalities that are initially unknown to the learning algorithm. Our aim is to learn new internal representations as the algorithm learns new target functions, that capture this commonality and allow subsequent learning tasks to be solved more efficiently and from less data. We develop efficient algorithms for two very different kinds of commonalities that target functions might share: one based on learning common low-dimensional and unions of low-dimensional subspaces and one based on learning nonlinear Boolean combinations of features. Our algorithms for learning Boolean feature combinations additionally have a dual interpretation, and can be viewed as giving an efficient procedure for constructing near-optimal sparse Boolean autoencoders under a natural \"anchor-set\" assumption.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1411.1490v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"13433\", \"66853\", \"166134\", \"28672\", \"51479\", \"69425\", \"69489\", \"118021\", \"140107\", \"141047\", \"169908\", \"167881\", \"170651\", \"182115\", \"193831\", \"197245\", \"238193\", \"255962\", \"258331\", \"262390\", \"262842\", \"281817\", \"277152\", \"279076\", \"309817\"], \"outgoing_citations\": [\"322720\", \"340961\", \"341867\", \"345235\", \"351617\", \"352650\", \"357176\", \"359942\"]}","task_split":"paper_retrieval"}
{"document_id":"322750","document_content":"# Clustering Words by Projection Entropy\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nWe apply entropy agglomeration (EA), a recently introduced algorithm, to cluster the words of a literary text. EA is a greedy agglomerative procedure that minimizes projection entropy (PE), a function that can quantify the segmentedness of an element set. To apply it, the text is reduced to a feature allocation, a combinatorial object to represent the word occurences in the text's paragraphs. The experiment results demonstrate that EA, despite its reduction and simplicity, is useful in capturing significant relationships among the words in the text. This procedure was implemented in Python and published as a free software: REBUS.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1410.6830v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"259014\", \"299839\"], \"outgoing_citations\": [\"339569\"]}","task_split":"paper_retrieval"}
{"document_id":"322788","document_content":"# Enable Portrait Privacy Protection in Photo Capturing and Sharing\n## Categories\n- Cryptography and Security\n## Abstract\nThe wide adoption of wearable smart devices with onboard cameras greatly increases people's concern on privacy infringement. Here we explore the possibility of easing persons from photos captured by smart devices according to their privacy protection requirements. To make this work, we need to address two challenges: 1) how to let users explicitly express their privacy protection intention, and 2) how to associate the privacy requirements with persons in captured photos accurately and efficiently. Furthermore, the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a protocol, that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy.Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, we achieves high accuracy and low overhead.   We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1410.6582v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": [\"236069\"]}","task_split":"paper_retrieval"}
{"document_id":"322825","document_content":"# Capturing spatial interdependence in image features: the counting grid, an epitomic representation for bags of features\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nIn recent scene recognition research images or large image regions are often represented as disorganized \"bags\" of features which can then be analyzed using models originally developed to capture co-variation of word counts in text. However, image feature counts are likely to be constrained in different ways than word counts in text. For example, as a camera pans upwards from a building entrance over its first few floors and then further up into the sky Fig. 1, some feature counts in the image drop while others rise -- only to drop again giving way to features found more often at higher elevations. The space of all possible feature count combinations is constrained both by the properties of the larger scene and the size and the location of the window into it. To capture such variation, in this paper we propose the use of the counting grid model. This generative model is based on a grid of feature counts, considerably larger than any of the modeled images, and considerably smaller than the real estate needed to tile the images next to each other tightly. Each modeled image is assumed to have a representative window in the grid in which the feature counts mimic the feature distribution in the image. We provide a learning procedure that jointly maps all images in the training set to the counting grid and estimates the appropriate local counts in it. Experimentally, we demonstrate that the resulting representation captures the space of feature count combinations more accurately than the traditional models, not only when the input images come from a panning camera, but even when modeling images of different scenes from the same category.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1410.6264v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"44277\", \"295253\", \"305565\"], \"outgoing_citations\": [\"345453\", \"361823\"]}","task_split":"paper_retrieval"}
{"document_id":"322955","document_content":"# Supervised mid-level features for word image representation\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThis paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-length representation. Machine learning techniques can then be supplied with these representations to produce models useful for word retrieval or recognition tasks. Although many works have focused on the machine learning aspect once a global representation has been produced, little work has been devoted to the construction of those base image representations: most works use standard coding and aggregation techniques directly on top of standard computer vision features such as SIFT or HOG.   We propose to learn local mid-level features suitable for building word image representations. These features are learnt by leveraging character bounding box annotations on a small set of training images. However, contrary to other approaches that use character bounding box information, our approach does not rely on detecting the individual characters explicitly at testing time. Our local mid-level features can then be aggregated to produce a global word image signature. When pairing these features with the recent word attributes framework of Almaz\\'an et al., we obtain results comparable with or better than the state-of-the-art on matching and recognition tasks using global descriptors of only 96 dimensions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1410.5224v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"44535\", \"35666\", \"147463\", \"120420\", \"126431\", \"136218\", \"170229\", \"210634\", \"166621\", \"172884\", \"174894\", \"174928\", \"180931\", \"201515\", \"203406\", \"205988\", \"217463\", \"205602\", \"207071\", \"216815\", \"225336\", \"229565\", \"232706\", \"250738\", \"253692\", \"256108\", \"256711\", \"256785\", \"260702\", \"264526\", \"251168\", \"309239\", \"288027\", \"296685\", \"296828\", \"306246\", \"320148\", \"320837\", \"328595\"], \"outgoing_citations\": [\"328595\", \"351151\"]}","task_split":"paper_retrieval"}
{"document_id":"323719","document_content":"# Automatic Generation of Loop-Invariants for Matrix Operations\n## Categories\n- Mathematical Software\n- Symbolic Computation\n## Abstract\nIn recent years it has been shown that for many linear algebra operations it is possible to create families of algorithms following a very systematic procedure. We do not refer to the fine tuning of a known algorithm, but to a methodology for the actual generation of both algorithms and routines to solve a given target matrix equation. Although systematic, the methodology relies on complex algebraic manipulations and non-obvious pattern matching, making the procedure challenging to be performed by hand, our goal is the development of a fully automated system that from the sole description of a target equation creates multiple algorithms and routines. We present CL1ck, a symbolic system written in Mathematica, that starts with an equation, decomposes it into multiple equations, and returns a set of loop-invariants for the algorithms -- yet to be generated -- that will solve the equation. In a successive step each loop-invariant is then mapped to its corresponding algorithm and routine. For a large class of equations, the methodology generates known algorithms as well as many previously unknown ones. Most interestingly, the methodology unifies algorithms traditionally developed in isolation. As an example, the five well known algorithms for the LU factorization are for the first time unified under a common root.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1410.0564v1\", \"primary_category\": \"cs.MS\", \"categories\": [\"cs.MS\", \"cs.SC\"], \"primary_category_human_readable\": \"Mathematical Software\", \"categories_human_readable\": [\"Mathematical Software\", \"Symbolic Computation\"], \"incoming_citations\": [\"155044\", \"149136\", \"177415\", \"179813\", \"220814\", \"232298\", \"236084\", \"253519\", \"331150\", \"356127\", \"358448\"], \"outgoing_citations\": [\"323718\"]}","task_split":"paper_retrieval"}
{"document_id":"323826","document_content":"# Interference Effects in Quantum Belief Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nProbabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.   Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision.   In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.asoc.2014.09.008\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"6947\", \"6947\", \"122064\", \"208020\", \"256508\", \"224420\", \"254340\", \"307556\"], \"outgoing_citations\": [\"338531\"]}","task_split":"paper_retrieval"}
{"document_id":"324002","document_content":"# Causal Graph Justifications of Logic Programs\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nIn this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in terms of causal graphs formed by rule labels and edges that represent their application ordering. For positive programs, we show that the causal justifications obtained for a given atom have a direct correspon- dence to (relevant) syntactic proofs of that atom using the program rules involved in the graphs. The most interesting contribution is that this causal information is obtained in a purely semantic way, by algebraic op- erations (product, sum and application) on a lattice of causal values whose ordering relation expresses when a justification is stronger than another. Finally, for programs with negation, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct. As a result, default negation behaves as \"absence of proof\" and no justification is derived from negative liter","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.7281v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [\"7194\", \"99308\", \"99312\", \"102965\", \"107098\", \"167407\", \"217011\", \"287280\", \"287282\", \"297821\"], \"outgoing_citations\": [\"336212\", \"350497\"]}","task_split":"paper_retrieval"}
{"document_id":"324040","document_content":"# Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n- I.2.7; I.2.6; I.5.1; I.5.4\n## Abstract\nWe describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality---and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.7085v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\", \"I.2.7; I.2.6; I.5.1; I.5.4\"], \"incoming_citations\": [], \"outgoing_citations\": [\"323040\"]}","task_split":"paper_retrieval"}
{"document_id":"324543","document_content":"# A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nTopic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TPAMI.2015.2476802\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"67191\", \"113758\", \"158736\", \"164334\", \"214884\", \"217762\", \"183082\", \"221650\", \"225402\", \"190684\", \"271665\", \"280365\", \"296300\", \"289644\", \"291504\", \"293035\", \"305132\"], \"outgoing_citations\": [\"339406\"]}","task_split":"paper_retrieval"}
{"document_id":"324960","document_content":"# Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers\n## Categories\n- Databases\n## Abstract\nWe aim to provide table answers to keyword queries against knowledge bases. For queries referring to multiple entities, like \"Washington cities population\" and \"Mel Gibson movies\", it is better to represent each relevant answer as a table which aggregates a set of entities or entity-joins within the same table scheme or pattern. In this paper, we study how to find highly relevant patterns in a knowledge base for user-given keyword queries to compose table answers. A knowledge base can be modeled as a directed graph called knowledge graph, where nodes represent entities in the knowledge base and edges represent the relationships among them. Each node\/edge is labeled with type and text. A pattern is an aggregation of subtrees which contain all keywords in the texts and have the same structure and types on node\/edges. We propose efficient algorithms to find patterns that are relevant to the query for a class of scoring functions. We show the hardness of the problem in theory, and propose path-based indexes that are affordable in memory. Two query-processing algorithms are proposed: one is fast in practice for small queries (with small patterns as answers) by utilizing the indexes; and the other one is better in theory, with running time linear in the sizes of indexes and answers, which can handle large queries better. We also conduct extensive experimental study to compare our approaches with a naive adaption of known techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.1292v1\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Databases\"], \"incoming_citations\": [\"146546\", \"165730\", \"226284\", \"232257\"], \"outgoing_citations\": [\"357073\"]}","task_split":"paper_retrieval"}
{"document_id":"324964","document_content":"# On the Properties of Neural Machine Translation: Encoder-Decoder Approaches\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nNeural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.1259v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"11546\", \"12079\", \"23614\", \"37955\", \"55449\", \"62549\", \"62580\", \"68390\", \"76551\", \"88097\", \"90783\", \"129784\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"324965","document_content":"# Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nThe authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.1257v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [\"70033\", \"41427\", \"46157\", \"58974\", \"75537\", \"95286\", \"120122\", \"149448\", \"152767\", \"116187\", \"201546\", \"228350\", \"233420\", \"263927\", \"276661\", \"278923\", \"286498\", \"282755\", \"290687\", \"296274\", \"311849\", \"324659\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"324996","document_content":"# Visualizing and Quantifying Impact and Effect in Twitter Narrative using Geometric Data Analysis\n## Categories\n- Social and Information Networks\n- Physics and Society\n- Computers and Society\n- 66H25, 62H30, 91F99\n- I.7; I.5.3; H.3.1; H.2.8; G.3\n## Abstract\nWe use geometric multivariate data analysis which has been termed a methodology for both the visualization and verbalization of data. The general objectives are data mining and knowledge discovery. In the first case study, we use the narrative surrounding very highly profiled tweets, and thus a Twitter event of significance and importance. In the second case study, we use eight carefully planned Twitter campaigns relating to environmental issues. The aim of these campaigns was to increase environmental awareness and behaviour. Unlike current marketing, political and other communication campaigns using Twitter, we develop an innovative approach to measuring bevavioural change. We show also how we can assess statistical significance of social media behaviour.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.1039v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"physics.soc-ph\", \"cs.SI\", \"cs.CY\", \"66H25, 62H30, 91F99\", \"I.7; I.5.3; H.3.1; H.2.8; G.3\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Physics and Society\", \"Social and Information Networks\", \"Computers and Society\", \"66H25, 62H30, 91F99\", \"I.7; I.5.3; H.3.1; H.2.8; G.3\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"325098","document_content":"# Neural Machine Translation by Jointly Learning to Align and Translate\n## Categories\n- Computation and Language\n- Machine Learning\n- Neural and Evolutionary Computing\n- Machine Learning\n## Abstract\nNeural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1409.0473v7\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.NE\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Neural and Evolutionary Computing\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"325997","document_content":"# Automatic Removal of Marginal Annotations in Printed Text Document\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecovering the original printed texts from a document with added handwritten annotations in the marginal area is one of the challenging problems, especially when the original document is not available. Therefore, this paper aims at salvaging automatically the original document from the annotated document by detecting and removing any handwritten annotations that appear in the marginal area of the document without any loss of information. Here a two stage algorithm is proposed, where in the first stage due to approximate marginal boundary detection with horizontal and vertical projection profiles, all of the marginal annotations along with some part of the original printed text that may appear very close to the marginal boundary are removed. Therefore as a second stage, using the connected components, a strategy is applied to bring back the printed text components cropped during the first stage. The proposed method is validated using a dataset of 50 documents having complex handwritten annotations, which gives an overall accuracy of 89.01% in removing the marginal annotations and 97.74% in case of retrieving the original printed text document.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1408.2015v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"326657","document_content":"# Feature Engineering for Knowledge Base Construction\n## Categories\n- Databases\n- Computation and Language\n- Machine Learning\n## Abstract\nKnowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student.   Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user \"think about features---not algorithms.\" We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1407.6439v3\", \"primary_category\": \"cs.DB\", \"categories\": [\"cs.CL\", \"cs.LG\", \"cs.DB\"], \"primary_category_human_readable\": \"Databases\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Databases\"], \"incoming_citations\": [\"57197\", \"271458\", \"271674\", \"318126\", \"310262\", \"310786\"], \"outgoing_citations\": [\"328473\", \"331883\"]}","task_split":"paper_retrieval"}
{"document_id":"326789","document_content":"# PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using few random variables while each random variable has a massive set of values. With hierarchical data - data that is arranged in a treelike structure with several levels - one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node. In this paper we present a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data. We believe the proposed model will lead to an easily-scalable, more readable, and expressive implementation for problems that require probabilistic-based solutions for massive amounts of hierarchical data. We successfully applied this model to solve two different challenging probabilistic-based problems on massive hierarchical data sets for different domains, namely, bioinformatics and latent semantic discovery over search logs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1407.5656v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"284369\", \"285500\", \"295278\", \"300863\", \"324753\"], \"outgoing_citations\": [\"339474\", \"349874\", \"353236\"]}","task_split":"paper_retrieval"}
{"document_id":"326914","document_content":"# Efficient On-the-fly Category Retrieval using ConvNets and GPUs\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Neural and Evolutionary Computing\n## Abstract\nWe investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets.   We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1407.4764v3\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"70460\", \"110289\", \"134853\", \"258937\", \"301681\", \"286912\", \"313299\", \"320036\", \"322093\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"327088","document_content":"# A New Rational Algorithm for View Updating in Relational Databases\n## Categories\n- Artificial Intelligence\n- Databases\n## Abstract\nThe dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10489-014-0579-0\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.DB\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Databases\"], \"incoming_citations\": [\"322029\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"327491","document_content":"# Weakly Supervised Action Labeling in Videos Under Ordering Constraints\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nWe are given a set of video clips, each one annotated with an {\\em ordered} list of actions, such as \"walk\" then \"sit\" then \"answer phone\" extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1407.1208v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"19821\", \"149118\", \"3517\", \"116568\", \"46493\", \"192035\", \"226421\", \"5927\", \"19925\", \"29561\", \"29649\", \"30657\", \"39008\", \"53510\", \"58191\", \"58193\", \"61811\", \"62569\", \"63687\", \"79928\", \"85803\", \"120720\", \"145028\", \"193069\", \"56216\", \"106194\", \"108687\", \"110491\", \"126373\", \"126819\", \"133920\", \"139778\", \"144125\", \"151327\", \"182786\", \"190266\", \"163612\", \"164400\", \"170707\", \"169293\", \"182790\", \"185831\", \"190241\", \"191393\", \"194508\", \"198925\", \"203523\", \"206997\", \"210413\", \"233133\", \"265384\", \"204838\", \"208756\", \"223200\", \"225372\", \"225414\", \"226384\", \"231686\", \"237402\", \"237765\", \"247497\", \"264711\", \"268231\", \"270325\", \"296750\", \"259865\", \"259963\", \"292421\", \"268631\", \"268934\", \"270713\", \"271891\", \"274379\", \"283115\", \"294114\", \"287541\", \"288617\", \"292797\", \"301539\", \"310322\", \"307386\", \"310452\", \"310738\", \"312423\", \"316803\", \"317032\", \"317909\", \"319169\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"328015","document_content":"# VideoSET: Video Summary Evaluation through Text\n## Categories\n- Computer Vision and Pattern Recognition\n- Computation and Language\n- Information Retrieval\n## Abstract\nIn this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.5824v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"38120\", \"49750\", \"73446\", \"126319\", \"109123\", \"223463\", \"165655\", \"193523\", \"196093\", \"208053\", \"216743\", \"223048\", \"249441\", \"274665\", \"259318\", \"261010\", \"261096\", \"267523\", \"288206\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"328305","document_content":"# Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms\n## Categories\n- Machine Learning\n## Abstract\nIn both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. In particular, I aim to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. I developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the \"rewards\" are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. The censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.3922v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"170695\", \"234563\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"328327","document_content":"# Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nCapturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.3830v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"29376\", \"79143\", \"81398\", \"152222\", \"174293\", \"235298\", \"221609\", \"233064\", \"259009\", \"264668\", \"266699\", \"267351\", \"271951\", \"282533\", \"284110\", \"284764\", \"290471\", \"294251\", \"295235\", \"298627\", \"302425\", \"303493\", \"296555\", \"311754\", \"313779\", \"314616\", \"321621\", \"320512\", \"321939\"], \"outgoing_citations\": [\"343624\", \"350125\", \"361420\"]}","task_split":"paper_retrieval"}
{"document_id":"328352","document_content":"# Question Answering with Subgraph Embeddings\n## Categories\n- Computation and Language\n## Abstract\nThis paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few hand-crafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers. Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a competitive benchmark of the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.3676v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"172989\", \"8356\", \"12316\", \"16309\", \"17151\", \"22571\", \"29168\", \"29573\", \"32110\", \"32761\", \"39212\", \"130260\", \"230611\", \"10139\", \"10232\", \"23032\", \"29016\", \"31671\", \"32017\", \"38797\", \"38755\", \"42728\", \"43762\", \"53317\", \"64948\", \"76845\", \"79363\", \"87811\", \"91775\", \"93230\", \"98055\", \"131285\", \"159408\", \"95011\", \"91585\", \"96631\", \"106140\", \"106394\", \"108787\", \"109697\", \"112493\", \"93489\", \"115353\", \"117690\", \"670\", \"61111\", \"132572\", \"137900\", \"138930\", \"145854\", \"157274\", \"157280\", \"157487\", \"158598\", \"206415\", \"149574\", \"152851\", \"157513\", \"157837\", \"159418\", \"163778\", \"162259\", \"167875\", \"168277\", \"151551\", \"174060\", \"175028\", \"175493\", \"175775\", \"176900\", \"177261\", \"173688\", \"182892\", \"183095\", \"183201\", \"192128\", \"192346\", \"189598\", \"192896\", \"193226\", \"196314\", \"198632\", \"199752\", \"205803\", \"208184\", \"218741\", \"220326\", \"234730\", \"247502\", \"250549\", \"204585\", \"214874\", \"217522\", \"218189\", \"219207\", \"220575\", \"229275\", \"228231\", \"232527\", \"240297\", \"243759\", \"245072\", \"245858\", \"253068\", \"255202\", \"256171\", \"261912\", \"264636\", \"264668\", \"273458\", \"290213\", \"144800\", \"196257\", \"246677\", \"251341\", \"253019\", \"253098\", \"252012\", \"258130\", \"260231\", \"267877\", \"272929\", \"278439\", \"279428\", \"290304\", \"303756\", \"278530\", \"280161\", \"282901\", \"283919\", \"285849\", \"286110\", \"203410\", \"289779\", \"290231\", \"290747\", \"290997\", \"291251\", \"290690\", \"293171\", \"293606\", \"295325\", \"295511\", \"297226\", \"299709\", \"300153\", \"300421\", \"302056\", \"302516\", \"302771\", \"302913\", \"302930\", \"303029\", \"303224\", \"303306\", \"303837\", \"304481\", \"315078\", \"310201\", \"311684\", \"311834\", \"311989\", \"313713\", \"313946\", \"319947\", \"320868\"], \"outgoing_citations\": [\"330994\"]}","task_split":"paper_retrieval"}
{"document_id":"328801","document_content":"# A Geometric Method to Obtain the Generation Probability of a Sentence\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Statistics Theory\n- Computation\n- Methodology\n- Statistics Theory\n## Abstract\n\"How to generate a sentence\" is the most critical and difficult problem in all the natural language processing technologies. In this paper, we present a new approach to explain the generation process of a sentence from the perspective of mathematics. Our method is based on the premise that in our brain a sentence is a part of a word network which is formed by many word nodes. Experiments show that the probability of the entire sentence can be obtained by the probabilities of single words and the probabilities of the co-occurrence of word pairs, which indicate that human use the synthesis method to generate a sentence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.1234v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"math.ST\", \"stat.CO\", \"stat.ME\", \"stat.TH\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Statistics Theory\", \"Computation\", \"Methodology\", \"Statistics Theory\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"328961","document_content":"# Seeing the Big Picture: Deep Embedding with Contextual Evidences\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance. Conventionally, local cues of a keypoint are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem, this paper defines \"true match\" as a pair of keypoints which are similar on three levels, i.e., local, regional, and global. Then, a principled probabilistic framework is established, which is capable of implicitly integrating discriminative cues from all these feature levels.   Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches, leading to the so-called \"Deep Embedding\" framework. CNN has been shown to produce excellent performance on a dozen computer vision tasks such as image classification and detection, but few works have been done on BoW based image retrieval. In this paper, firstly we show that proper pre-processing techniques are necessary for effective usage of CNN feature. Then, in the attempt to fit it into our model, a novel indexing structure called \"Deep Indexing\" is introduced, which dramatically reduces memory usage.   Extensive experiments on three benchmark datasets demonstrate that, the proposed Deep Embedding method greatly promotes the retrieval accuracy when CNN feature is integrated. We show that our method is efficient in terms of both memory and time cost, and compares favorably with the state-of-the-art methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1406.0132v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"186957\", \"186959\", \"270282\", \"301086\", \"269295\", \"328885\"], \"outgoing_citations\": [\"332966\", \"333779\"]}","task_split":"paper_retrieval"}
{"document_id":"329570","document_content":"# Model revision inference for extensions of first order logic\n## Categories\n- Artificial Intelligence\n## Abstract\nI am Joachim Jansen and this is my research summary, part of my application to the Doctoral Consortium at ICLP'14. I am a PhD student in the Knowledge Representation and Reasoning (KRR) research group, a subgroup of the Declarative Languages and Artificial Intelligence (DTAI) group at the department of Computer Science at KU Leuven. I started my PhD in September 2012. My promotor is prof. dr. ir. Gerda Janssens and my co-promotor is prof. dr. Marc Denecker. I can be contacted at joachim.jansen@cs.kuleuven.be or at: Room 01.167 Celestijnenlaan 200A 3001 Heverlee Belgium An extended abstract \/ full version of a paper accepted to be presented at the Doctoral Consortium of the 30th International Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1405.4206v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"339800\"]}","task_split":"paper_retrieval"}
{"document_id":"329586","document_content":"# Distributed Representations of Sentences and Documents\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nMany machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, \"powerful,\" \"strong\" and \"Paris\" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1405.4053v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2956\", \"11639\", \"12647\", \"16257\", \"18076\", \"20028\", \"21023\", \"23929\", \"28479\", \"29029\", \"34819\", \"41599\", \"45065\", \"50353\", \"58301\", \"64244\", \"82136\", \"85576\", \"112333\", \"127659\", \"114994\", \"106\", \"894\", \"3011\", \"3124\", \"3323\", \"5197\", \"10265\", \"10616\", \"14728\", \"14728\", \"15816\", \"15903\", \"15954\", \"18335\", \"23900\", \"23979\", \"24351\", \"24730\", \"25942\", \"25921\", \"26223\", \"28244\", \"32386\", \"32617\", \"32877\", \"37082\", \"42581\", \"49207\", \"50728\", \"52464\", \"52848\", \"55563\", \"56667\", \"58316\", \"60281\", \"61503\", \"64074\", \"64447\", \"65521\", \"66289\", \"69129\", \"72933\", \"74365\", \"77332\", \"77984\", \"78835\", \"79026\", \"79821\", \"82680\", \"83086\", \"85806\", \"87445\", \"92767\", \"99044\", \"104525\", \"121565\", \"121898\", \"127275\", \"130260\", \"151832\", \"159564\", \"164881\", \"167536\", \"189633\", \"194630\", \"200295\", \"202795\", \"253233\", \"67959\"], \"outgoing_citations\": [\"339810\", \"349607\", \"361420\"]}","task_split":"paper_retrieval"}
{"document_id":"329686","document_content":"# Developing Corpus-based Translation Methods between Informal and Formal Mathematics: Project Description\n## Categories\n- Artificial Intelligence\n- Digital Libraries\n## Abstract\nThe goal of this project is to (i) accumulate annotated informal\/formal mathematical corpora suitable for training semi-automated translation between informal and formal mathematics by statistical machine-translation methods, (ii) to develop such methods oriented at the formalization task, and in particular (iii) to combine such methods with learning-assisted automated reasoning that will serve as a strong semantic component. We describe these ideas, the initial set of corpora, and some initial experiments done over them.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1405.3451v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.DL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Digital Libraries\"], \"incoming_citations\": [\"73654\", \"157585\", \"220884\", \"232558\"], \"outgoing_citations\": [\"339219\", \"340134\", \"344477\"]}","task_split":"paper_retrieval"}
{"document_id":"329735","document_content":"# Lifted Variable Elimination for Probabilistic Logic Programming\n## Categories\n- Artificial Intelligence\n## Abstract\nLifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1017\/S1471068414000283\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"226056\", \"234052\", \"286249\"], \"outgoing_citations\": [\"334208\", \"336353\", \"345528\", \"349828\", \"358940\"]}","task_split":"paper_retrieval"}
{"document_id":"329848","document_content":"# Sentiment Analysis: A Survey\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nSentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Mining opinions expressed in the user generated content is a challenging yet practically very useful problem. This survey would cover various approaches and methodology used in Sentiment Analysis and Opinion Mining in general. The focus would be on Internet text like, Product review, tweets and other social media.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1405.2584v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"330684","document_content":"# Gradual Classical Logic for Attributed Objects\n## Categories\n- Artificial Intelligence\n- Logic in Computer Science\n## Abstract\nThere is knowledge. There is belief. And there is tacit agreement.' 'We may talk about objects. We may talk about attributes of the objects. Or we may talk both about objects and their attributes.' This work inspects tacit agreements on assumptions about the relation between objects and their attributes, and studies a way of expressing them, presenting as the result what we term gradual logic in which the sense of truth gradually shifts. It extends classical logic instances with a new logical connective capturing the object-attribute relation. A formal semantics is presented. Decidability is proved. Para- consistent\/epistemic\/conditional\/intensional\/description\/combined logics are compared.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.6036v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [\"296373\", \"314293\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"330871","document_content":"# Opinion Mining In Hindi Language: A Survey\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nOpinions are very important in the life of human beings. These Opinions helped the humans to carry out the decisions. As the impact of the Web is increasing day by day, Web documents can be seen as a new source of opinion for human beings. Web contains a huge amount of information generated by the users through blogs, forum entries, and social networking websites and so on To analyze this large amount of information it is required to develop a method that automatically classifies the information available on the Web. This domain is called Sentiment Analysis and Opinion Mining. Opinion Mining or Sentiment Analysis is a natural language processing task that mine information from various text forms such as reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral. But, from the last few years, enormous increase has been seen in Hindi language on the Web. Research in opinion mining mostly carried out in English language but it is very important to perform the opinion mining in Hindi language also as large amount of information in Hindi is also available on the Web. This paper gives an overview of the work that has been done Hindi language.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.4935v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"301644\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"330955","document_content":"# A Novel Approach for Video Temporal Annotation\n## Categories\n- Multimedia\n- H.2.4\n- 68Uxx\n## Abstract\nRecent advances in computing, communication, and data storage have led to an increasing number of large digital libraries publicly available on the Internet. Main problem of content-based video retrieval is inferring semantics from raw video data. Video data play an important role in these libraries. Instead of words, a video retrieval system deals with collections of video records. Therefore, the system is confronted with the problem of video understanding. Because machine understanding of the video data is still an unsolved research problem, text annotations are usually used to describe the content of video data according to the annotator's understanding and the purpose of that video data. Most of proposed systems for video annotation are domain dependent. In addition, in many of these systems, an important feature of video data, temporality, is disregarded. In this paper, we proposed a framework for video temporal annotation. The proposed system uses domain knowledge and a time ontology to perform temporal annotation of input video.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.4543v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"H.2.4\", \"cs.MM\", \"68Uxx\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"H.2.4\", \"Multimedia\", \"68Uxx\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"330994","document_content":"# Open Question Answering with Weakly Supervised Embedding Models\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nBuilding computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.4326v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"29573\", \"32110\", \"39212\", \"10232\", \"13811\", \"26878\", \"30055\", \"37393\", \"37494\", \"42728\", \"64948\", \"76845\", \"88448\", \"131285\", \"183777\", \"188713\", \"90377\", \"90787\", \"96087\", \"96345\", \"105460\", \"93489\", \"115353\", \"116684\", \"120987\", \"136881\", \"158598\", \"157513\", \"168277\", \"171689\", \"63084\", \"177889\", \"193226\", \"196314\", \"205803\", \"208610\", \"211643\", \"224210\", \"168796\", \"204585\", \"211939\", \"214874\", \"218958\", \"219207\", \"227725\", \"226956\", \"240297\", \"243759\", \"243868\", \"245072\", \"245858\", \"253691\", \"259009\", \"273458\", \"290213\", \"212994\", \"252768\", \"257659\", \"258130\", \"259293\", \"260724\", \"261807\", \"273380\", \"274021\", \"277599\", \"280634\", \"199625\", \"280985\", \"290997\", \"291251\", \"295235\", \"295325\", \"296122\", \"302056\", \"302516\", \"303029\", \"310377\", \"311684\", \"311989\", \"320868\", \"328352\"], \"outgoing_citations\": [\"341939\"]}","task_split":"paper_retrieval"}
{"document_id":"331125","document_content":"# Recover Canonical-View Faces in the Wild with Deep Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications. This paper addresses this challenge by proposing a new deep learning framework that can recover the canonical view of face images. It dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness. Unlike the existing face reconstruction methods that were either evaluated in controlled 2D environment or employed 3D information, our approach directly learns the transformation from the face images with a complex set of variations to their canonical views. At the training stage, to avoid the costly process of labeling canonical-view images from the training set by hand, we have devised a new measurement to automatically select or synthesize a canonical-view image for each identity. As an application, this face recovery approach is used for face verification. Facial features are learned from the recovered canonical-view face images by using a facial component-based convolutional neural network. Our approach achieves the state-of-the-art performance on the LFW dataset.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.3543v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"19638\", \"98159\", \"143372\", \"235160\", \"198677\", \"240676\", \"236458\", \"292980\", \"317441\", \"325660\", \"199553\", \"318091\", \"318826\", \"321117\", \"331074\"], \"outgoing_citations\": [\"331074\", \"332590\"]}","task_split":"paper_retrieval"}
{"document_id":"331238","document_content":"# Distribution-Aware Sampling and Weighted Model Counting for SAT\n## Categories\n- Artificial Intelligence\n- Data Structures and Algorithms\n## Abstract\nGiven a CNF formula and a weight for each assignment of values to variables, two natural problems are weighted model counting and distribution-aware sampling of satisfying assignments. Both problems have a wide variety of important applications. Due to the inherent complexity of the exact versions of the problems, interest has focused on solving them approximately. Prior work in this area scaled only to small problems in practice, or failed to provide strong theoretical guarantees, or employed a computationally-expensive maximum a posteriori probability (MAP) oracle that assumes prior knowledge of a factored representation of the weight distribution. We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems. Our approach works under mild assumptions on the distribution of weights of satisfying assignments, provides strong theoretical guarantees, and scales to problems involving several thousand variables. We also show that the assumptions can be significantly relaxed while improving computational efficiency if a factored representation of the weights is known.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.2984v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.DS\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"43541\", \"161164\", \"77278\", \"83247\", \"100747\", \"113740\", \"92683\", \"118520\", \"120237\", \"127957\", \"141944\", \"147050\", \"147462\", \"161951\", \"91574\", \"226317\", \"153781\", \"160482\", \"179138\", \"195361\", \"204296\", \"229055\", \"229738\", \"263919\", \"253085\", \"259868\", \"274552\", \"276287\", \"281686\", \"322323\", \"272921\", \"301173\", \"302605\"], \"outgoing_citations\": [\"332066\", \"339845\", \"343270\", \"346373\", \"348310\", \"351398\"]}","task_split":"paper_retrieval"}
{"document_id":"331391","document_content":"# Optimizing The Selection of Strangers To Answer Questions in Social Media\n## Categories\n- Social and Information Networks\n- Physics and Society\n## Abstract\nMillions of people express themselves on public social media, such as Twitter. Through their posts, these people may reveal themselves as potentially valuable sources of information. For example, real-time information about an event might be collected through asking questions of people who tweet about being at the event location. In this paper, we explore how to model and select users to target with questions so as to improve answering performance while managing the load on people who must be asked. We first present a feature-based model that leverages users exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to respond to questions on Twitter. We then use the model to predict the likelihood for people to answer questions. To support real-world information collection applications, we present an optimization-based approach that selects a proper set of strangers to answer questions while achieving a set of application-dependent objectives, such as achieving a desired number of answers and minimizing the number of questions to be sent. Our cross-validation experiments using multiple real-world data sets demonstrate the effectiveness of our work.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.2013v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"physics.soc-ph\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Physics and Society\"], \"incoming_citations\": [], \"outgoing_citations\": [\"329359\"]}","task_split":"paper_retrieval"}
{"document_id":"331428","document_content":"# Int\u00e9gration des donn\u00e9es d'un lexique syntaxique dans un analyseur syntaxique probabiliste\n## Categories\n- Computation and Language\n## Abstract\nThis article reports the evaluation of the integration of data from a syntactic-semantic lexicon, the Lexicon-Grammar of French, into a syntactic parser. We show that by changing the set of labels for verbs and predicational nouns, we can improve the performance on French of a non-lexicalized probabilistic parser.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.1872v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"331445","document_content":"# Neural Codes for Image Retrieval\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIt has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application. In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e.g.\\ Image-Net). We also evaluate the improvement in the retrieval performance of neural codes, when the network is retrained on a dataset of images that are similar to images encountered at test time.   We further evaluate the performance of the compressed neural codes and show that a simple PCA compression provides very good short codes that give state-of-the-art accuracy on a number of datasets. In general, neural codes turn out to be much more resilient to such compression in comparison other state-of-the-art descriptors. Finally, we show that discriminative dimensionality reduction trained on a dataset of pairs of matched photographs improves the performance of PCA-compressed neural codes even further. Overall, our quantitative experiments demonstrate the promise of neural codes as visual descriptors for image retrieval.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.1777v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"9497\", \"145777\", \"458\", \"1850\", \"7251\", \"20465\", \"27638\", \"34971\", \"70613\", \"73146\", \"85376\", \"101518\", \"107975\", \"112364\", \"115076\", \"144914\", \"1407\", \"1439\", \"6827\", \"11592\", \"13707\", \"15391\", \"25394\", \"25890\", \"27673\", \"31118\", \"41294\", \"41682\", \"183974\", \"48708\", \"48791\", \"49789\", \"51376\", \"51731\", \"59695\", \"61377\", \"66717\", \"70156\", \"70765\", \"75928\", \"76106\", \"77995\", \"84653\", \"100159\", \"106263\", \"115346\", \"228640\", \"88683\", \"85055\", \"91958\", \"99702\", \"100605\", \"108701\", \"125153\", \"119949\", \"133188\", \"135489\", \"139321\", \"140652\", \"142055\", \"142604\", \"146346\", \"148469\", \"156383\", \"159420\", \"221269\", \"225359\", \"246350\", \"149810\", \"153745\", \"155322\", \"156386\", \"165767\", \"172435\", \"143691\", \"175014\", \"177755\", \"179469\", \"180184\", \"180562\", \"188194\", \"182718\", \"192173\", \"193735\", \"196737\", \"198922\", \"199414\", \"170479\", \"207283\", \"207378\", \"207610\", \"208493\", \"213700\", \"216478\", \"217894\", \"220216\", \"224604\", \"231046\", \"241439\", \"249538\", \"250135\", \"251473\", \"254643\", \"256887\", \"261255\", \"201747\", \"203110\", \"207767\", \"210585\", \"212048\", \"214691\", \"215998\", \"217551\", \"219134\", \"221285\", \"223359\", \"225272\", \"227893\", \"228831\", \"228895\", \"234152\", \"237299\", \"238392\", \"255989\", \"243175\", \"245421\", \"246331\", \"248935\", \"251572\", \"264032\", \"264177\", \"264316\", \"264624\", \"267404\", \"280225\", \"287103\", \"292069\", \"310303\", \"320578\", \"230902\", \"254537\", \"254812\", \"254889\", \"255771\", \"255901\", \"258674\", \"258937\", \"258948\", \"259795\", \"261707\", \"262169\", \"262166\", \"263387\", \"268341\", \"269591\", \"270753\", \"272445\", \"269927\", \"273802\", \"273840\", \"275310\", \"275932\", \"275976\", \"277532\", \"277635\", \"279439\", \"279638\", \"281953\", \"282928\", \"283481\", \"284208\", \"287298\", \"289391\", \"294367\", \"296534\", \"298965\", \"300357\", \"277669\", \"280209\", \"281155\", \"281599\", \"283967\", \"284025\", \"285156\", \"285338\", \"287050\", \"287584\", \"288138\", \"289593\", \"289866\", \"292025\", \"293322\", \"293545\", \"294067\", \"294068\", \"294470\", \"294983\", \"295204\", \"295253\", \"295468\", \"297209\", \"297196\", \"297319\", \"298153\", \"299564\", \"301572\", \"303067\", \"303664\", \"308236\", \"301469\", \"301916\", \"305615\", \"305777\", \"306246\", \"306916\", \"307109\", \"307882\", \"308541\", \"309121\", \"263960\", \"312090\", \"313318\", \"313957\", \"313964\", \"314231\", \"314213\", \"316961\", \"318817\", \"322546\", \"323963\", \"320869\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"331485","document_content":"# Fast Supervised Hashing with Decision Trees for High-Dimensional Data\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n## Abstract\nSupervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated the advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval performance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash functions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for high-dimensional data, our method is orders of magnitude faster than many methods in terms of training time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2014.253\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\"], \"incoming_citations\": [\"100185\", \"144914\", \"78925\", \"95269\", \"99781\", \"119839\", \"208542\", \"106896\", \"108421\", \"225824\", \"138139\", \"196949\", \"239316\", \"241679\", \"155241\", \"166634\", \"169305\", \"180528\", \"189084\", \"192543\", \"197931\", \"200516\", \"202820\", \"207729\", \"218854\", \"232040\", \"238528\", \"241439\", \"244229\", \"260178\", \"287562\", \"222134\", \"226674\", \"226676\", \"237774\", \"191832\", \"191827\", \"249596\", \"259070\", \"259748\", \"260176\", \"265613\", \"291400\", \"309853\", \"310303\", \"189932\", \"248561\", \"253404\", \"249175\", \"264939\", \"269814\", \"271480\", \"284208\", \"298792\", \"279165\", \"278179\", \"281599\", \"283789\", \"288169\", \"288218\", \"200534\", \"293858\", \"293860\", \"296640\", \"296862\", \"297878\", \"298956\", \"303372\", \"303512\", \"305006\", \"318717\", \"261672\", \"288034\", \"302900\", \"303629\", \"307457\", \"316473\", \"318281\", \"318801\", \"319454\", \"320280\", \"320988\", \"325441\", \"325820\", \"327501\"], \"outgoing_citations\": [\"340567\", \"346675\", \"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"331496","document_content":"# Text Based Approach For Indexing And Retrieval Of Image And Video: A Review\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n- Digital Libraries\n- Multimedia\n## Abstract\nText data present in multimedia contain useful information for automatic annotation, indexing. Extracted information used for recognition of the overlay or scene text from a given video or image. The Extracted text can be used for retrieving the videos and images. In this paper, firstly, we are discussed the different techniques for text extraction from images and videos. Secondly, we are reviewed the techniques for indexing and retrieval of image and videos by using extracted text.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.1514v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CV\", \"cs.DL\", \"cs.MM\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computer Vision and Pattern Recognition\", \"Digital Libraries\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"335612\", \"336089\", \"338951\"]}","task_split":"paper_retrieval"}
{"document_id":"331648","document_content":"# Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nDocument Image Analysis, like any Digital Image Analysis requires identification and extraction of proper features, which are generally extracted from uncompressed images, though in reality images are made available in compressed form for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation induces the motivation to research in extracting features directly from the compressed image. In this research, we propose to extract essential features such as projection profile, run-histogram and entropy for text document analysis directly from run-length compressed text-documents. The experimentation illustrates that features are extracted directly from the compressed image without going through the stage of decompression, because of which the computing time is reduced. The feature values so extracted are exactly identical to those extracted from uncompressed images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ACPR.2013.147\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"114603\", \"323375\", \"333477\", \"331390\", \"333944\", \"331837\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"331740","document_content":"# Venture: a higher-order probabilistic programming platform with programmable inference\n## Categories\n- Artificial Intelligence\n- Programming Languages\n- Computation\n- Machine Learning\n## Abstract\nWe describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies built out of scalable exact and approximate techniques. We also describe four key aspects of Venture's implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and ``likelihood-free'' stochastic simulators. It also supports external models that do inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. PETs capture conditional dependencies, existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds. Stochastic regeneration linear runtime scaling in cases where many previous approaches scaled quadratically. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on particle Markov chain Monte Carlo and mean-field variational inference techniques.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1404.0099v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.PL\", \"stat.CO\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Programming Languages\", \"Computation\", \"Machine Learning\"], \"incoming_citations\": [\"7292\", \"43477\", \"159670\", \"159677\", \"160783\", \"180115\", \"18807\", \"27185\", \"38415\", \"41016\", \"56948\", \"66284\", \"66317\", \"67425\", \"76921\", \"92019\", \"96168\", \"110164\", \"93486\", \"100293\", \"139092\", \"143658\", \"147983\", \"347563\", \"159130\", \"175263\", \"161185\", \"173259\", \"176443\", \"179792\", \"193782\", \"200760\", \"202739\", \"176146\", \"210659\", \"211807\", \"227116\", \"231798\", \"206362\", \"207288\", \"210088\", \"211364\", \"215639\", \"215715\", \"216257\", \"225411\", \"229117\", \"229681\", \"233193\", \"236460\", \"238712\", \"240789\", \"243739\", \"245393\", \"251000\", \"265502\", \"265965\", \"268714\", \"276499\", \"281551\", \"290570\", \"291287\", \"297260\", \"261281\", \"272627\", \"277987\", \"281570\", \"282411\", \"286348\", \"290492\", \"291429\", \"295435\", \"299451\", \"299915\", \"300785\", \"302068\", \"306972\", \"309432\", \"301326\", \"301432\", \"316930\", \"318444\", \"318655\", \"318828\", \"318969\", \"320780\", \"327242\", \"327472\", \"332931\"], \"outgoing_citations\": [\"144568\", \"310143\", \"343126\", \"348633\", \"350634\", \"353417\", \"357281\", \"357921\", \"360708\"]}","task_split":"paper_retrieval"}
{"document_id":"331837","document_content":"# Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nSegmentation of a text-document into lines, words and characters, which is considered to be the crucial pre-processing stage in Optical Character Recognition (OCR) is traditionally carried out on uncompressed documents, although most of the documents in real life are available in compressed form, for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation has motivated us to take up research in document image analysis using compressed documents. In this paper, we think in a new way to carry out segmentation at line, word and character level in run-length compressed printed-text-documents. We extract the horizontal projection profile curve from the compressed file and using the local minima points perform line segmentation. However, tracing vertical information which leads to tracking words-characters in a run-length compressed file is not very straight forward. Therefore, we propose a novel technique for carrying out simultaneous word and character segmentation by popping out column runs from each row in an intelligent sequence. The proposed algorithms have been validated with 1101 text-lines, 1409 words and 7582 characters from a data-set of 35 noise and skew free compressed documents of Bengali, Kannada and English Scripts.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/NCVPRIPG.2013.6776195\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"114603\", \"253786\", \"258271\", \"323375\", \"333477\", \"331390\", \"333944\"], \"outgoing_citations\": [\"331648\"]}","task_split":"paper_retrieval"}
{"document_id":"332099","document_content":"# Adaptive MCMC-Based Inference in Probabilistic Logic Programs\n## Categories\n- Artificial Intelligence\n## Abstract\nProbabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we present a technique for approximate inference of conditional probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo (MCMC) technique, where the distribution from which samples are drawn is modified as the Markov Chain is explored. In particular, the distribution is progressively modified to increase the likelihood that a generated sample is consistent with evidence. In our context, each sample is uniquely characterized by the outcomes of a set of random variables. Inspired by reinforcement learning, our technique propagates rewards to random variable\/outcome pairs used in a sample based on whether the sample was consistent or not. The cumulative rewards of each outcome is used to derive a new \"adapted distribution\" for each random variable. For a sequence of samples, the distributions are progressively adapted after each sample. For a query with \"Markovian evaluation structure\", we show that the adapted distribution of samples converges to the query's conditional probability distribution. For Markovian queries, we present a modified adaptation process that can be used in adaptive MCMC as well as adaptive independent sampling. We empirically evaluate the effectiveness of the adaptive sampling methods for queries with and without Markovian evaluation structure.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1403.6036v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"47839\"], \"outgoing_citations\": [\"350210\", \"350217\", \"351406\"]}","task_split":"paper_retrieval"}
{"document_id":"332701","document_content":"# Algebraic properties of word equations\n## Categories\n- Formal Languages and Automata Theory\n- 68R15, 13P05\n- Commutative Algebra\n## Abstract\nThe question about maximal size of independent system of word equations is one of the most striking problems in combinatorics on words. Recently, Aleksi Saarela has introduced a new approach to the problem that is based on linear-algebraic properties of polynomials encoding the equations and their solutions. In this paper we develop further this approach and take into account other algebraic properties of polynomials, namely their factorization. This, in particular, allows to improve the bound for the number of independent equations with maximal rank from quadratic to linear.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.jalgebra.2015.03.021\", \"primary_category\": \"cs.FL\", \"categories\": [\"cs.FL\", \"68R15, 13P05\", \"math.AC\"], \"primary_category_human_readable\": \"Formal Languages and Automata Theory\", \"categories_human_readable\": [\"Formal Languages and Automata Theory\", \"68R15, 13P05\", \"Commutative Algebra\"], \"incoming_citations\": [], \"outgoing_citations\": [\"334414\"]}","task_split":"paper_retrieval"}
{"document_id":"332921","document_content":"# Representing, reasoning and answering questions about biological pathways - various applications\n## Categories\n- Artificial Intelligence\n- Computational Engineering, Finance, and Science\n- Computation and Language\n## Abstract\nBiological organisms are composed of numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for diagnosing diseases, and drug development. Scientists studying these processes have identified various pathways responsible for drug metabolism, and signal transduction, etc.   Newer techniques and speed improvements have resulted in deeper knowledge about these pathways, resulting in refined models that tend to be large and complex, making it difficult for a person to remember all aspects of it. Thus, computer models are needed to analyze them. We want to build such a system that allows modeling of biological systems and pathways in such a way that we can answer questions about them.   Many existing models focus on structural and\/or factoid questions, using surface-level knowledge that does not require understanding the underlying model. We believe these are not the kind of questions that a biologist may ask someone to test their understanding of the biological processes. We want our system to answer the kind of questions a biologist may ask. Such questions appear in early college level text books.   Thus the main goal of our thesis is to develop a system that allows us to encode knowledge about biological pathways and answer such questions about them demonstrating understanding of the pathway. To that end, we develop a language that will allow posing such questions and illustrate the utility of our framework with various applications in the biological domain. We use some existing tools with modifications to accomplish our goal.   Finally, we apply our system to real world applications by extracting pathway knowledge from text and answering questions related to drug development.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1403.0541v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CE\", \"cs.CL\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computational Engineering, Finance, and Science\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"336207\", \"343630\", \"343633\"]}","task_split":"paper_retrieval"}
{"document_id":"332936","document_content":"# Face Recognition Methods & Applications\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nFace recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1403.0485v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"23735\", \"55958\", \"156209\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"333142","document_content":"# Large-margin Learning of Compact Binary Image Encodings\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe use of high-dimensional features has become a normal practice in many computer vision applications. The large dimension of these features is a limiting factor upon the number of data points which may be effectively stored and processed, however. We address this problem by developing a novel approach to learning a compact binary encoding, which exploits both pair-wise proximity and class-label information on training data set. Exploiting this extra information allows the development of encodings which, although compact, outperform the original high-dimensional features in terms of final classification or retrieval performance. The method is general, in that it is applicable to both non-parametric and parametric learning methods. This generality means that the embedded features are suitable for a wide variety of computer vision tasks, such as image classification and content-based image retrieval. Experimental results demonstrate that the new compact descriptor achieves an accuracy comparable to, and in some cases better than, the visual descriptor in the original space despite being significantly more compact. Moreover, any convex loss function and convex regularization penalty (e.g., $ \\ell_p $ norm with $ p \\ge 1 $) can be incorporated into the framework, which provides future flexibility.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2014.2337759\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"348109\", \"348893\", \"353654\"]}","task_split":"paper_retrieval"}
{"document_id":"333624","document_content":"# Finding Coordinated Paths for Multiple Holonomic Agents in 2-d Polygonal Environment\n## Categories\n- Artificial Intelligence\n- Robotics\n- 68T42 (Primary), 68T40 (Secondary)\n- I.2.11\n## Abstract\nAvoiding collisions is one of the vital tasks for systems of autonomous mobile agents. We focus on the problem of finding continuous coordinated paths for multiple mobile disc agents in a 2-d environment with polygonal obstacles. The problem is PSPACE-hard, with the state space growing exponentially in the number of agents. Therefore, the state of the art methods include mainly reactive techniques and sampling-based iterative algorithms.   We compare the performance of a widely-used reactive method ORCA with three variants of a popular planning algorithm RRT* applied to multi-agent path planning and find that an algorithm combining reactive collision avoidance and RRT* planning, which we call ORCA-RRT* can be used to solve instances that are out of the reach of either of the techniques. We experimentally show that: 1) the reactive part of the algorithm can efficiently solve many multi-agent path finding problems involving large number of agents, for which RRT* algorithm is often unable to find a solution in limited time and 2) the planning component of the algorithm is able to solve many instances containing local minima, where reactive techniques typically fail.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1402.3613v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.RO\", \"68T42 (Primary), 68T40 (Secondary)\", \"I.2.11\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"68T42 (Primary), 68T40 (Secondary)\", \"I.2.11\"], \"incoming_citations\": [\"129035\"], \"outgoing_citations\": [\"348999\"]}","task_split":"paper_retrieval"}
{"document_id":"334629","document_content":"# A Statistical Parsing Framework for Sentiment Classification\n## Categories\n- Computation and Language\n## Abstract\nWe present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark datasets show significant improvements over baseline sentiment classification approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1401.6330v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"106394\", \"115386\", \"178402\", \"207307\", \"244679\", \"261912\", \"253019\", \"280635\", \"288749\", \"232384\", \"296264\", \"325387\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"334675","document_content":"# Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection\n## Categories\n- Computation and Language\n## Abstract\nSemantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presented in this article integrates everything into one model, in hopes of achieving desirable integrity and practicality for real applications while maintaining a competitive performance. This integrative approach tackles semantic parsing as a word pair classification problem using a maximum entropy classifier. We leverage adaptive pruning of argument candidates and large-scale feature selection engineering to allow the largest feature space ever in use so far in this field, it achieves a state-of-the-art performance on the evaluation data set for CoNLL-2008 shared task, on top of all but one top pipeline system, confirming its feasibility and effectiveness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.3717\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"78281\", \"101065\", \"95433\", \"168549\", \"169520\", \"200951\", \"173016\", \"169264\", \"202775\", \"210903\", \"218692\", \"221642\"], \"outgoing_citations\": [\"357895\"]}","task_split":"paper_retrieval"}
{"document_id":"334724","document_content":"# COLIN: Planning with Continuous Linear Numeric Change\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the handling of continuous linear change, and the handling of duration-dependent effects in combination with duration inequalities, both of which require tightly coupled temporal and numeric reasoning during planning. COLIN combines FF-style forward chaining search, with the use of a Linear Program (LP) to check the consistency of the interacting temporal and numeric constraints at each state. The LP is used to compute bounds on the values of variables in each state, reducing the range of actions that need to be considered for application. In addition, we develop an extension of the Temporal Relaxed Planning Graph heuristic of CRIKEY3, to support reasoning directly with continuous change. We extend the range of task variables considered to be suitable candidates for specifying the gradient of the continuous numeric change effected by an action. Finally, we explore the potential for employing mixed integer programming as a tool for optimising the timestamps of the actions in the plan, once a solution has been found. To support this, we further contribute a selection of extended benchmark domains that include continuous numeric effects. We present results for COLIN that demonstrate its scalability on a range of benchmarks, and compare to existing state-of-the-art planners.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.3608\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"15151\", \"48165\", \"42053\", \"68944\", \"97087\", \"91463\", \"164108\", \"165494\", \"186347\", \"190421\", \"269101\", \"284818\", \"334209\", \"334194\"], \"outgoing_citations\": [\"335180\", \"335291\", \"335330\"]}","task_split":"paper_retrieval"}
{"document_id":"334888","document_content":"# An Identification System Using Eye Detection Based On Wavelets And Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThe randomness and uniqueness of human eye patterns is a major breakthrough in the search for quicker, easier and highly reliable forms of automatic human identification. It is being used extensively in security solutions. This includes access control to physical facilities, security systems and information databases, Suspect tracking, surveillance and intrusion detection and by various Intelligence agencies through out the world. We use the advantage of human eye uniqueness to identify people and approve its validity as a biometric. . Eye detection involves first extracting the eye from a digital face image, and then encoding the unique patterns of the eye in such a way that they can be compared with pre-registered eye patterns. The eye detection system consists of an automatic segmentation system that is based on the wavelet transform, and then the Wavelet analysis is used as a pre-processor for a back propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of the neural network is the classification of the input into an eye or non-eye region. An accuracy of 90% is observed for identifying test images under different conditions included in training stage.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1401.5108v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"334986","document_content":"# Learning to Win by Reading Manuals in a Monte-Carlo Framework\n## Categories\n- Computation and Language\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nDomain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance. Both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application. To effectively leverage textual information, our method automatically extracts the text segment most relevant to the current game state, and labels it with a task-centric predicate structure. This labeled text is then used to bias an action selection policy for the game, guiding it towards promising regions of the action space. We encode our model for text analysis and game playing in a multi-layer neural network, representing linguistic decisions via latent variables in the hidden layers, and game action quality via the output layer. Operating within the Monte-Carlo Search framework, we estimate model parameters using feedback from simulated games. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 34% absolute improvement and winning over 65% of games when playing against the built-in AI of Civilization.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.3484\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"15887\", \"56667\", \"14861\", \"16824\", \"22743\", \"44473\", \"63172\", \"74771\", \"89875\", \"95168\", \"95805\", \"108770\", \"111265\", \"116357\", \"125751\", \"127451\", \"131013\", \"126793\", \"145784\", \"147465\", \"161143\", \"171695\", \"202951\", \"153551\", \"168951\", \"174916\", \"181620\", \"196377\", \"206489\", \"234729\", \"198011\", \"211104\", \"228586\", \"243105\", \"245813\", \"251691\", \"259693\", \"261378\", \"248865\", \"264358\", \"266342\", \"268410\", \"276207\", \"290615\", \"291102\", \"303349\", \"304241\", \"305098\", \"307569\", \"310370\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"335079","document_content":"# A bi-level view of inpainting - based image compression\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nInpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression. The major challenge in these compression models is to find a small set of descriptive supporting points, which allow for an accurate reconstruction of the original image. It turns out in practice that this is a challenging problem even for the simplest Laplacian interpolation model. In this paper, we revisit the Laplacian interpolation compression model and introduce two fast algorithms, namely successive preconditioning primal dual algorithm and the recently proposed iPiano algorithm, to solve this problem efficiently. Furthermore, we extend the Laplacian interpolation based compression model to a more general form, which is based on principles from bi-level optimization. We investigate two different variants of the Laplacian model, namely biharmonic interpolation and smoothed Total Variation regularization. Our numerical results show that significant improvements can be obtained from the biharmonic interpolation model, and it can recover an image with very high quality from only 5% pixels.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1401.4112v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"18638\", \"40129\", \"116785\", \"171419\", \"210835\", \"263317\", \"301913\", \"311152\", \"321229\"], \"outgoing_citations\": [\"330900\", \"335080\", \"335420\"]}","task_split":"paper_retrieval"}
{"document_id":"335120","document_content":"# Interpolable Formulas in Equilibrium Logic and Answer Set Programming\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n## Abstract\nInterpolation is an important property of classical and many non-classical logics that has been shown to have interesting applications in computer science and AI. Here we study the Interpolation Property for the the non-monotonic system of equilibrium logic, establishing weaker or stronger forms of interpolation depending on the precise interpretation of the inference relation. These results also yield a form of interpolation for ground logic programs under the answer sets semantics. For disjunctive logic programs we also study the property of uniform interpolation that is closely related to the concept of variable forgetting. The first-order version of equilibrium logic has analogous Interpolation properties whenever the collection of equilibrium models is (first-order) definable. Since this is the case for so-called safe programs and theories, it applies to the usual situations that arise in practical answer set programming.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.3329\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\", \"cs.AI\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Artificial Intelligence\"], \"incoming_citations\": [\"35383\", \"330373\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"335150","document_content":"# Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution\n## Categories\n- Computation and Language\n## Abstract\nTraditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the pre-statistical NLP era in terms of sophistication. Two independent lines of recent research have attempted to improve the mention-pair model, one by acquiring the mention-ranking model to rank preceding mentions for a given anaphor, and the other by training the entity-mention model to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution, which combines the strengths of the mention-ranking model and the entity-mention model, and is therefore theoretically more appealing than both of these models. In addition, we seek to improve cluster rankers via two extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity by jointly modeling anaphoricity determination and coreference resolution. Experimental results on the ACE data sets demonstrate the superior performance of cluster rankers to competing approaches as well as the effectiveness of our two extensions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.3120\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"96243\", \"155003\", \"291177\", \"294814\", \"357234\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"335184","document_content":"# An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data\n## Categories\n- Machine Learning\n- Human-Computer Interaction\n## Abstract\nCrowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers. Unfortunately, poor worker performance frequently threatens to compromise annotation reliability, and requesting multiple labels for every instance can lead to large cost increases without guaranteeing good results. Minimizing the required training samples using an active learning selection procedure reduces the labeling requirement but can jeopardize classifier training by focusing on erroneous annotations. This paper presents an active learning approach in which worker performance, task difficulty, and annotation reliability are jointly estimated and used to compute the risk function guiding the sample selection procedure. We demonstrate that the proposed approach, which employs active learning with Bayesian networks, significantly improves training accuracy and correctly ranks the expertise of unknown labelers in the presence of annotation noise.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1401.3836v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.HC\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"21570\", \"51025\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"335270","document_content":"# Content Modeling Using Latent Permutations\n## Categories\n- Information Retrieval\n- Computation and Language\n- Machine Learning\n## Abstract\nWe present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.2830\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"182792\", \"182677\", \"237765\", \"289706\", \"301918\", \"321133\", \"330690\"], \"outgoing_citations\": [\"357568\"]}","task_split":"paper_retrieval"}
{"document_id":"335286","document_content":"# Variable Forgetting in Reasoning about Knowledge\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n## Abstract\nIn this paper, we investigate knowledge reasoning within a simple framework called knowledge structure. We use variable forgetting as a basic operation for one agent to reason about its own or other agents\\ knowledge. In our framework, two notions namely agents\\ observable variables and the weakest sufficient condition play important roles in knowledge reasoning. Given a background knowledge base and a set of observable variables for each agent, we show that the notion of an agent knowing a formula can be defined as a weakest sufficient condition of the formula under background knowledge base. Moreover, we show how to capture the notion of common knowledge by using a generalized notion of weakest sufficient condition. Also, we show that public announcement operator can be conveniently dealt with via our notion of knowledge structure. Further, we explore the computational complexity of the problem whether an epistemic formula is realized in a knowledge structure. In the general case, this problem is PSPACE-hard; however, for some interesting subcases, it can be reduced to co-NP. Finally, we discuss possible applications of our framework in some interesting domains such as the automated analysis of the well-known muddy children puzzle and the verification of the revised Needham-Schroeder protocol. We believe that there are many scenarios where the natural presentation of the available information about knowledge is under the form of a knowledge structure. What makes it valuable compared with the corresponding multi-agent S5 Kripke structure is that it can be much more succinct.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1613\/jair.2750\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.AI\", \"cs.LO\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic in Computer Science\"], \"incoming_citations\": [\"136884\", \"219399\", \"234791\", \"310120\", \"317224\", \"317761\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"336000","document_content":"# Quality Estimation of English-Hindi Outputs using Naive Bayes Classifier\n## Categories\n- Computation and Language\n## Abstract\nIn this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Na\\\"ive Bayes classifier to build model using features which are extracted from the input sentences. These features are used for finding the likelihood of each of the sentences of the training data which are then further used for determining the scores of the test data. On the basis of these scores we determine the class labels of the test data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.7223v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"309697\", \"327233\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"336189","document_content":"# Can recursive neural tensor networks learn logical reasoning?\n## Categories\n- Computation and Language\n- Machine Learning\n## Abstract\nRecursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of \"some animal walks\" from \"some dog walks\" or \"some cat walks,\" given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.6192v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"57826\", \"158350\", \"249946\", \"246446\", \"265116\", \"278530\", \"290231\", \"295931\", \"311834\", \"312437\", \"314319\", \"316741\", \"320035\", \"328698\", \"328732\"], \"outgoing_citations\": [\"345692\", \"350244\"]}","task_split":"paper_retrieval"}
{"document_id":"336196","document_content":"# Multilingual Distributed Representations without Word Alignment\n## Categories\n- Computation and Language\n## Abstract\nDistributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document classification task where we outperform the previous state of the art. Further, by employing parallel corpora of multiple language pairs we find that our model learns representations that capture semantic relationships across languages for which no parallel data was used.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.6173v4\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"17703\", \"81400\", \"158023\", \"229314\", \"84860\", \"88993\", \"89348\", \"106023\", \"120122\", \"102185\", \"141814\", \"134494\", \"149309\", \"177293\", \"181569\", \"189109\", \"193794\", \"193535\", \"195852\", \"210764\", \"263680\", \"211930\", \"215091\", \"226011\", \"228868\", \"229877\", \"238561\", \"239686\", \"244590\", \"266046\", \"296049\", \"251885\", \"253927\", \"257949\", \"261950\", \"262903\", \"264791\", \"274111\", \"280501\", \"286324\", \"279471\", \"282853\", \"286869\", \"288444\", \"290387\", \"299064\", \"305132\", \"306060\", \"313796\", \"315078\", \"303355\", \"300283\", \"308645\", \"312209\", \"320072\", \"323451\", \"320868\", \"321936\", \"322022\", \"324659\", \"328529\", \"330123\", \"330410\", \"330941\", \"334040\"], \"outgoing_citations\": [\"335600\", \"343624\"]}","task_split":"paper_retrieval"}
{"document_id":"336231","document_content":"# Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nRecognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.6082v4\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"70805\", \"74025\", \"110662\", \"203954\", \"6536\", \"9706\", \"18458\", \"29092\", \"12895\", \"42863\", \"50733\", \"65814\", \"65865\", \"69051\", \"70322\", \"92024\", \"100300\", \"102260\", \"103920\", \"117826\", \"118677\", \"131351\", \"155750\", \"81716\", \"84406\", \"90516\", \"93380\", \"99778\", \"58197\", \"108149\", \"115254\", \"117181\", \"118577\", \"120755\", \"125177\", \"138839\", \"143549\", \"145011\", \"156930\", \"166054\", \"171472\", \"181062\", \"184440\", \"204191\", \"204309\", \"152302\", \"163839\", \"181062\", \"175389\", \"189498\", \"190053\", \"196070\", \"196401\", \"199248\", \"201602\", \"203511\", \"213640\", \"208045\", \"208587\", \"209752\", \"206976\", \"216765\", \"222271\", \"223824\", \"224235\", \"224679\", \"226566\", \"227395\", \"228543\", \"235200\", \"237564\", \"240532\", \"241758\", \"245899\", \"245937\", \"247460\", \"288950\", \"297369\", \"301087\", \"303371\", \"257204\", \"248063\", \"252315\", \"257600\", \"258812\", \"260012\", \"267144\", \"265808\", \"263985\", \"270743\", \"272546\", \"273947\", \"274119\", \"275625\", \"281551\", \"278399\", \"278760\", \"279441\", \"283075\", \"283167\", \"289880\", \"290256\", \"294023\", \"295089\", \"295812\", \"296563\", \"296828\", \"297726\", \"302585\", \"300755\", \"301360\", \"302295\", \"303142\", \"269295\", \"311501\", \"311691\", \"311809\", \"313461\", \"314880\", \"317401\", \"319781\", \"319909\", \"320148\", \"320837\", \"324923\", \"330307\", \"320563\", \"322413\", \"328595\", \"329613\", \"336491\"], \"outgoing_citations\": [\"339394\", \"359628\"]}","task_split":"paper_retrieval"}
{"document_id":"336892","document_content":"# Budgeted Influence Maximization for Multiple Products\n## Categories\n- Machine Learning\n- Social and Information Networks\n## Abstract\nThe typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more constraints than a single product, endless user attentions, unlimited budget and unbounded time; in reality, multiple products need to be advertised, each user can tolerate only a small number of recommendations, influencing user has a cost and advertisers have only limited budgets, and the adoptions need to be maximized within a short time window.   Given theses myriads of user, monetary, and timing constraints, it is extremely challenging for the host to design principled and efficient viral market algorithms with provable guarantees. In this paper, we provide a novel solution by formulating the problem as a submodular maximization in a continuous-time diffusion model under an intersection of a matroid and multiple knapsack constraints. We also propose an adaptive threshold greedy algorithm which can be faster than the traditional greedy algorithm with lazy evaluation, and scalable to networks with million of nodes. Furthermore, our mathematical formulation allows us to prove that the algorithm can achieve an approximation factor of $k_a\/(2+2 k)$ when $k_a$ out of the $k$ knapsack constraints are active, which also improves over previous guarantees from combinatorial optimization literature. In the case when influencing each user has uniform cost, the approximation becomes even better to a factor of $1\/3$. Extensive synthetic and real world experiments demonstrate that our budgeted influence maximization algorithm achieves the-state-of-the-art in terms of both effectiveness and scalability, often beating the next best by significant margins.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.2164v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.SI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Social and Information Networks\", \"Machine Learning\"], \"incoming_citations\": [\"30900\", \"213461\", \"320893\"], \"outgoing_citations\": [\"337839\", \"351622\", \"351689\", \"359019\"]}","task_split":"paper_retrieval"}
{"document_id":"337109","document_content":"# Image Representation Learning Using Graph Regularized Auto-Encoders\n## Categories\n- Machine Learning\n- K.3.2\n## Abstract\nWe consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning. In those learning tasks, the raw image vectors may not provide enough representation for their intrinsic structures due to their highly dense feature space. To overcome this problem, the raw image vectors should be mapped to a proper representation space which can capture the latent structure of the original data and represent the data explicitly for further learning tasks such as clustering.   Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally invariant ideal to our image representation with auto-encoders and propose a novel method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure.   Extensive experiments on image clustering show encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-word cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1312.0786v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"K.3.2\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"K.3.2\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"337306","document_content":"# An Alternate Approach for Designing a Domain Specific Image Search Prototype Using Histogram\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n## Abstract\nEveryone knows that thousand of words are represented by a single image. As a result image search has become a very popular mechanism for the Web searchers. Image search means, the search results are produced by the search engine should be a set of images along with their Web page Unified Resource Locator. Now Web searcher can perform two types of image search, they are Text to Image and Image to Image search. In Text to Image search, search query should be a text. Based on the input text data system will generate a set of images along with their Web page URL as an output. On the other hand, in Image to Image search, search query should be an image and based on this image system will generate a set of images along with their Web page URL as an output. According to the current scenarios, Text to Image search mechanism always not returns perfect result. It matches the text data and then displays the corresponding images as an output, which is not always perfect. To resolve this problem, Web researchers have introduced the Image to Image search mechanism. In this paper, we have also proposed an alternate approach of Image to Image search mechanism using Histogram.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1401.2902v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"337458","document_content":"# Bounding the Test Log-Likelihood of Generative Models\n## Categories\n- Machine Learning\n## Abstract\nSeveral interesting generative learning algorithms involve a complex probability distribution over many random variables, involving intractable normalization constants or latent variable normalization. Some of them may even not have an analytic expression for the unnormalized probability function and no tractable approximation. This makes it difficult to estimate the quality of these models, once they have been trained, or to monitor their quality (e.g. for early stopping) while training. A previously proposed method is based on constructing a non-parametric density estimator of the model's probability function from samples generated by the model. We revisit this idea, propose a more efficient estimator, and prove that it provides a lower bound on the true test log-likelihood, and an unbiased estimator as the number of generated samples goes to infinity, although one that incorporates the effect of poor mixing. We further propose a biased variant of the estimator that can be used reliably with a finite number of samples for the purpose of model comparison.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1311.6184v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"16766\", \"280545\", \"311126\", \"305347\", \"315761\", \"319620\", \"320013\", \"336318\", \"344331\"], \"outgoing_citations\": [\"344049\", \"344331\", \"344867\", \"356259\", \"357193\", \"358901\"]}","task_split":"paper_retrieval"}
{"document_id":"337494","document_content":"# Dynamic Model of Facial Expression Recognition based on Eigen-face Approach\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nEmotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security, surveillance, marketing, advertisement, and human-computer interaction. To communicate with a computer in a natural way, it will be desirable to use more natural modes of human communication based on voice, gestures and facial expressions. In this paper, a holistic approach for facial expression recognition is proposed which captures the variation in facial features in temporal domain and classifies the sequence of images in different emotions. The proposed method uses Haar-like features to detect face in an image. The dimensionality of the eigenspace is reduced using Principal Component Analysis (PCA). By projecting the subsequent face images into principal eigen directions, the variation pattern of the obtained weight vector is modeled to classify it into different emotions. Owing to the variations of expressions for different people and its intensity, a person specific method for emotion recognition is followed. Using the gray scale images of the frontal face, the system is able to classify four basic emotions such as happiness, sadness, surprise, and anger.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1311.6007v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"312781\"]}","task_split":"paper_retrieval"}
{"document_id":"338744","document_content":"# Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nThere is a large collection of Handwritten English paper documents of Historical and Scientific importance. But paper documents are not recognized directly by computer. Hence the closest way of indexing these documents is by storing their document digital image. Hence a large database of document images can replace the paper documents. But the document and data corresponding to each image cannot be directly recognized by the computer.   This paper applies the technique of word spotting using Modified Character Shape Code to Handwritten English document images for quick and efficient query search of words on a database of document images. It is different from other Word Spotting techniques as it implements two level of selection for word segments to match search query. First based on word size and then based on character shape code of query. It makes the process faster and more efficient and reduces the need of multiple pre-processing.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1310.6063v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"338988","document_content":"# Distributed Representations of Words and Phrases and their Compositionality\n## Categories\n- Computation and Language\n- Machine Learning\n- Machine Learning\n## Abstract\nThe recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1310.4546v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"1409\", \"6316\", \"9851\", \"10617\", \"15132\", \"15330\", \"16257\", \"16733\", \"16981\", \"16953\", \"18076\", \"21023\", \"23929\", \"24337\", \"24677\", \"24801\", \"25516\", \"29450\", \"29883\", \"31126\", \"32774\", \"33267\", \"34819\", \"38891\", \"39785\", \"41599\", \"46822\", \"49891\", \"54087\", \"63787\", \"66904\", \"70327\", \"75234\", \"77041\", \"82409\", \"83866\", \"86401\", \"86665\", \"87999\", \"90365\", \"102138\", \"102816\", \"103854\", \"103928\", \"105371\", \"118617\", \"120407\", \"122728\", \"132547\", \"133240\", \"137020\", \"138382\", \"148035\", \"153306\", \"205640\", \"275063\"], \"outgoing_citations\": [\"338905\", \"357185\"]}","task_split":"paper_retrieval"}
{"document_id":"339118","document_content":"# Predicting Social Links for New Users across Aligned Heterogeneous Social Networks\n## Categories\n- Social and Information Networks\n- Machine Learning\n- H.2.8 Database Management, Database Applications-Data Mining\n- Physics and Society\n## Abstract\nOnline social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1310.3492v1\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.LG\", \"H.2.8 Database Management, Database Applications-Data Mining\", \"physics.soc-ph\", \"cs.SI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Machine Learning\", \"H.2.8 Database Management, Database Applications-Data Mining\", \"Physics and Society\", \"Social and Information Networks\"], \"incoming_citations\": [\"58769\", \"59835\", \"117653\", \"132726\", \"161980\", \"180282\", \"199320\", \"201149\", \"234153\", \"234649\", \"257907\", \"303300\", \"287475\", \"287506\", \"310964\", \"309059\", \"311029\", \"323823\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"339474","document_content":"# Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis\n## Categories\n- Computation and Language\n## Abstract\nSemantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1310.1285v3\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"300854\", \"297074\", \"268635\", \"313605\", \"317949\", \"326789\"], \"outgoing_citations\": [\"213447\", \"335299\", \"338487\", \"352174\", \"360209\", \"360990\", \"360998\"]}","task_split":"paper_retrieval"}
{"document_id":"339803","document_content":"# Integrating Document Clustering and Topic Modeling\n## Categories\n- Machine Learning\n- Computation and Language\n- Information Retrieval\n## Abstract\nDocument clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.6874v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CL\", \"cs.IR\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"8389\", \"67191\", \"148995\", \"41837\", \"100953\", \"127878\", \"190719\", \"201281\", \"213323\", \"214479\", \"204573\", \"215275\", \"223098\", \"265431\", \"276101\", \"283832\", \"247962\", \"301479\", \"304704\", \"214235\", \"300320\", \"290151\", \"335569\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"339819","document_content":"# Evaluating computational models of explanation using human judgments\n## Categories\n- Artificial Intelligence\n## Abstract\nWe evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments. In two experiments, we present human participants with causal structures for which the models make divergent predictions and either solicit the best explanation for an observed event (Experiment 1) or have participants rate provided explanations for an observed event (Experiment 2). Across two versions of two causal structures and across both experiments, we find that the Causal Explanation Tree and Most Relevant Explanation models provide better fits to human data than either Most Probable Explanation or Explanation Tree models. We identify strengths and shortcomings of these models and what they can reveal about human explanation. We conclude by suggesting the value of pursuing computational and psychological investigations of explanation in parallel.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.6855v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"87063\", \"121248\", \"200101\", \"223389\", \"275120\"], \"outgoing_citations\": [\"335124\", \"357901\"]}","task_split":"paper_retrieval"}
{"document_id":"339880","document_content":"# Characterness: An Indicator of Text in the Wild\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nText in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. Despite its value, however, identifying general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem which reflects the `characterness' of an image region. In this sense our approach mirrors the move from saliency detection methods to measures of `objectness'. In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field (MRF) model so as to exploit the inherent dependencies between characters.   We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multi-cue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection datasets. We also show that our measurement of `characterness' is superior than state-of-the-art saliency detection models when applied to the same task.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.6691v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"207348\", \"245591\", \"248420\", \"257954\", \"273862\", \"286020\", \"295564\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"339933","document_content":"# Sentiment Analysis in the News\n## Categories\n- Computation and Language\n- H.3.1; H.3.3; I.2.7; J.4\n## Abstract\nRecent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles - author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which (a) we test the relative suitability of various sentiment dictionaries and (b) we attempt to separate positive or negative opinion from good or bad news. In the experiments described here, we tested whether or not subject domain-defining vocabulary should be ignored. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.6202v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"H.3.1; H.3.3; I.2.7; J.4\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"H.3.1; H.3.3; I.2.7; J.4\"], \"incoming_citations\": [\"16203\", \"21526\", \"32818\", \"45802\", \"48380\", \"87468\", \"88858\", \"48374\", \"188154\", \"239550\", \"225821\", \"229533\", \"288798\", \"296912\", \"123171\", \"73780\", \"325925\", \"335403\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"340166","document_content":"# Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Robotics\n## Abstract\nIn this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.4714v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.RO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Robotics\"], \"incoming_citations\": [\"230826\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"340705","document_content":"# Learning to answer questions\n## Categories\n- Computation and Language\n## Abstract\nWe present an open-domain Question-Answering system that learns to answer questions based on successful past interactions. We follow a pattern-based approach to Answer-Extraction, where (lexico-syntactic) patterns that relate a question to its answer are automatically learned and used to answer future questions. Results show that our approach contributes to the system's best performance when it is conjugated with typical Answer-Extraction strategies. Moreover, it allows the system to learn with the answered questions and to rectify wrong or unsolved past questions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1309.1125v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"1405\", \"17412\", \"42630\", \"48946\", \"55757\", \"56463\", \"57930\", \"58215\", \"59046\", \"76349\", \"95003\", \"105379\", \"110653\", \"110910\", \"111461\", \"112444\", \"114237\", \"124479\", \"136352\", \"140281\", \"151953\", \"154963\", \"159997\", \"169487\", \"179017\", \"182209\", \"190949\", \"191515\", \"206164\", \"219048\", \"225313\", \"227507\", \"236252\", \"236717\", \"237974\", \"239415\", \"267591\", \"268871\", \"277538\", \"278894\", \"291430\", \"296867\", \"301767\", \"303251\", \"303714\", \"313440\", \"317213\"]}","task_split":"paper_retrieval"}
{"document_id":"341193","document_content":"# Distributed Online Learning via Cooperative Contextual Bandits\n## Categories\n- Machine Learning\n## Abstract\nIn this paper we propose a novel framework for decentralized, online learning by many learners. At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, the learner can select one of its own actions (which gives a reward and provides information) or request assistance from another learner. In the latter case, the requester pays a cost and receives the reward but the provider learns the information. In our framework, learners are modeled as cooperative contextual bandits. Each learner seeks to maximize the expected reward from its arrivals, which involves trading off the reward received from its own actions, the information learned from its own actions, the reward received from the actions requested of others and the cost paid for these actions - taking into account what it has learned about the value of assistance from each other learner. We develop distributed online learning algorithms and provide analytic bounds to compare the efficiency of these with algorithms with the complete knowledge (oracle) benchmark (in which the expected reward of every action in every context is known by every learner). Our estimates show that regret - the loss incurred by the algorithm - is sublinear in time. Our theoretical framework can be used in many practical applications including Big Data mining, event detection in surveillance sensor networks and distributed online recommendation systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1308.4568v4\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"38928\", \"141435\", \"154347\", \"194240\", \"201982\", \"215154\", \"258245\", \"266778\", \"290485\", \"301039\", \"293784\", \"298263\", \"308565\", \"339877\"], \"outgoing_citations\": [\"339877\", \"341194\"]}","task_split":"paper_retrieval"}
{"document_id":"341194","document_content":"# Decentralized Online Big Data Classification - a Bandit Framework\n## Categories\n- Machine Learning\n- Multiagent Systems\n## Abstract\nDistributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We assume that the classification functions available to each processing element are fixed, but their prediction accuracy for various types of incoming data are unknown and can change dynamically over time, and thus they need to be learned online. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop distributed online learning algorithms for which we can prove that they have sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1308.4565v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.MA\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Multiagent Systems\"], \"incoming_citations\": [\"341193\", \"339877\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"341410","document_content":"# System and Methods for Converting Speech to SQL\n## Categories\n- Computation and Language\n- Databases\n## Abstract\nThis paper concerns with the conversion of a Spoken English Language Query into SQL for retrieving data from RDBMS. A User submits a query as speech signal through the user interface and gets the result of the query in the text format. We have developed the acoustic and language models using which a speech utterance can be converted into English text query and thus natural language processing techniques can be applied on this English text query to generate an equivalent SQL query. For conversion of speech into English text HTK and Julius tools have been used and for conversion of English text query into SQL query we have implemented a System which uses rule based translation to translate English Language Query into SQL Query. The translation uses lexical analyzer, parser and syntax directed translation techniques like in compilers. JFLex and BYACC tools have been used to build lexical analyzer and parser respectively. System is domain independent i.e. system can run on different database as it generates lex files from the underlying database.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1308.3106v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.DB\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Databases\"], \"incoming_citations\": [\"210677\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"341761","document_content":"# Measure Transformer Semantics for Bayesian Machine Learning\n## Categories\n- Logic in Computer Science\n- Programming Languages\n- Artificial Intelligence\n## Abstract\nThe Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define measure-transformer combinators inspired by theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that is processed by an existing inference engine for factor graphs, which are data structures that enable many efficient inference algorithms. This allows efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.2168\/LMCS-9(3:11)2013\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\", \"cs.PL\", \"cs.AI\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Programming Languages\", \"Artificial Intelligence\"], \"incoming_citations\": [\"146685\", \"19600\", \"43477\", \"35096\", \"66317\", \"96168\", \"124267\", \"124299\", \"227298\", \"166454\", \"192427\", \"211807\", \"257470\", \"216257\", \"241196\", \"288083\", \"288696\", \"269793\", \"297937\", \"298080\", \"299915\", \"302299\", \"311709\", \"315128\", \"320464\", \"322686\"], \"outgoing_citations\": [\"357921\", \"358941\"]}","task_split":"paper_retrieval"}
{"document_id":"341923","document_content":"# Extracting Information-rich Part of Texts using Text Denoising\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe aim of this paper is to report on a novel text reduction technique, called Text Denoising, that highlights information-rich content when processing a large volume of text data, especially from the biomedical domain. The core feature of the technique, the text readability index, embodies the hypothesis that complex text is more information-rich than the rest. When applied on tasks like biomedical relation bearing text extraction, keyphrase indexing and extracting sentences describing protein interactions, it is evident that the reduced set of text produced by text denoising is more information-rich than the rest.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1307.8060v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CL\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": [\"341924\", \"359895\"]}","task_split":"paper_retrieval"}
{"document_id":"342102","document_content":"# An Architecture for Autonomously Controlling Robot with Embodiment in Real World\n## Categories\n- Robotics\n- Artificial Intelligence\n## Abstract\nIn the real world, robots with embodiment face various issues such as dynamic continuous changes of the environment and input\/output disturbances. The key to solving these issues can be found in daily life; people `do actions associated with sensing' and `dynamically change their plans when necessary'. We propose the use of a new concept, enabling robots to do these two things, for autonomously controlling mobile robots. We implemented our concept to make two experiments under static\/dynamic environments. The results of these experiments show that our idea provides a way to adapt to dynamic changes of the environment in the real world.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1307.7129v1\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"342370","document_content":"# Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation\n## Categories\n- Machine Learning\n## Abstract\nThe goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, collecting a large number of samples is often expensive in practice. On the other hand, the model-based RL approach first estimates the transition model of the environment and then learns the policy based on the estimated transition model. Thus, if the transition model is accurately learned from a small amount of data, the model-based approach can perform better than the model-free approach. In this paper, we propose a novel model-based RL method by combining a recently proposed model-free policy search method called policy gradients with parameter-based exploration and the state-of-the-art transition model estimator called least-squares conditional density estimation. Through experiments, we demonstrate the practical usefulness of the proposed method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1307.5118v1\", \"primary_category\": \"stat.ML\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"119242\", \"128143\", \"167774\", \"167829\", \"225557\", \"225108\", \"171187\", \"270939\"], \"outgoing_citations\": [\"350140\"]}","task_split":"paper_retrieval"}
{"document_id":"342530","document_content":"# Part of Speech Tagging of Marathi Text Using Trigram Method\n## Categories\n- Computation and Language\n## Abstract\nIn this paper we present a Marathi part of speech tagger. It is a morphologically rich language. It is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using trigram Method. The main concept of trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine which is the best sequence of a tag. In this paper we show the development of the tagger. Moreover we have also shown the evaluation done.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/ijait.2013.3203\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"342691","document_content":"# Genetic approach for arabic part of speech tagging\n## Categories\n- Computation and Language\n- Neural and Evolutionary Computing\n- 68T50\n## Abstract\nWith the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the part of speech in each sentence. Arabic is a morphologically rich language, wich presents a challenge for part of speech tagging. In this paper, our goal is to propose, improve and implement a part of speech tagger based on a genetic alorithm. The accuracy obtained with this method is comparable to that of other probabilistic approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1307.3489v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.NE\", \"68T50\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Neural and Evolutionary Computing\", \"68T50\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"342920","document_content":"# Using MathML to Represent Units of Measurement for Improved Ontology Alignment\n## Categories\n- Artificial Intelligence\n## Abstract\nOntologies provide a formal description of concepts and their relationships in a knowledge domain. The goal of ontology alignment is to identify semantically matching concepts and relationships across independently developed ontologies that purport to describe the same knowledge. In order to handle the widest possible class of ontologies, many alignment algorithms rely on terminological and structural meth- ods, but the often fuzzy nature of concepts complicates the matching process. However, one area that should provide clear matching solutions due to its mathematical nature, is units of measurement. Several on- tologies for units of measurement are available, but there has been no attempt to align them, notwithstanding the obvious importance for tech- nical interoperability. We propose a general strategy to map these (and similar) ontologies by introducing MathML to accurately capture the semantic description of concepts specified therein. We provide mapping results for three ontologies, and show that our approach improves on lexical comparisons.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1307.1568v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"343226","document_content":"# Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media\n## Categories\n- Social and Information Networks\n- Applications\n- Machine Learning\n- Physics and Society\n- Machine Learning\n## Abstract\nThere is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we consider a network of fifteen thousand users on Twitter over a seven week period. We apply two contrasting modeling paradigms: computational mechanics and echo state networks. Both methods attempt to model the behavior of users on the basis of their past behavior. We demonstrate that the behavior of users on Twitter can be well-modeled as processes with self-feedback. We find that the two modeling approaches perform very similarly for most users, but that they differ in performance on a small subset of the users. By exploring the properties of these performance-differentiated users, we highlight the challenges faced in applying predictive models to dynamic social data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1306.6111v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"stat.AP\", \"cs.LG\", \"cs.SI\", \"physics.soc-ph\", \"stat.ML\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Applications\", \"Machine Learning\", \"Social and Information Networks\", \"Physics and Society\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"325995\", \"349027\", \"351825\"]}","task_split":"paper_retrieval"}
{"document_id":"343624","document_content":"# Recurrent Convolutional Neural Networks for Discourse Compositionality\n## Categories\n- Computation and Language\n## Abstract\nThe compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1306.3584v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"138028\", \"76036\", \"78544\", \"96296\", \"126748\", \"127748\", \"132711\", \"105764\", \"122496\", \"137057\", \"149847\", \"167166\", \"169173\", \"173239\", \"169337\", \"173988\", \"122495\", \"177316\", \"187539\", \"192223\", \"192945\", \"197091\", \"213458\", \"223677\", \"235298\", \"274843\", \"92316\", \"206871\", \"210370\", \"216936\", \"217759\", \"218613\", \"220040\", \"222051\", \"226356\", \"231590\", \"231880\", \"231865\", \"240264\", \"250398\", \"256124\", \"253046\", \"259051\", \"261679\", \"263027\", \"266489\", \"277590\", \"293413\", \"280164\", \"286757\", \"288348\", \"292555\", \"296029\", \"297029\", \"314050\", \"321333\", \"319947\", \"321936\", \"325323\", \"328327\", \"325322\", \"330410\", \"330941\", \"331356\", \"334826\", \"336196\"], \"outgoing_citations\": [\"350362\"]}","task_split":"paper_retrieval"}
{"document_id":"343752","document_content":"# The Quantum Challenge in Concept Theory and Natural Language Processing\n## Categories\n- Computation and Language\n- Information Retrieval\n- Quantum Physics\n## Abstract\nThe mathematical formalism of quantum theory has been successfully used in human cognition to model decision processes and to deliver representations of human knowledge. As such, quantum cognition inspired tools have improved technologies for Natural Language Processing and Information Retrieval. In this paper, we overview the quantum cognition approach developed in our Brussels team during the last two decades, specifically our identification of quantum structures in human concepts and language, and the modeling of data from psychological and corpus-text-based experiments. We discuss our quantum-theoretic framework for concepts and their conjunctions\/disjunctions in a Fock-Hilbert space structure, adequately modeling a large amount of data collected on concept combinations. Inspired by this modeling, we put forward elements for a quantum contextual and meaning-based approach to information technologies in which 'entities of meaning' are inversely reconstructed from texts, which are considered as traces of these entities' states.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1306.2838v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"quant-ph\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Quantum Physics\"], \"incoming_citations\": [\"271074\"], \"outgoing_citations\": [\"338531\", \"346580\", \"355496\", \"358186\"]}","task_split":"paper_retrieval"}
{"document_id":"343799","document_content":"# DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm\n## Categories\n- Machine Learning\n## Abstract\nIn a regression setting we propose algorithms that reduce the dimensionality of the features while simultaneously maximizing a statistical measure of dependence known as distance correlation between the low-dimensional features and a response variable. This helps in solving the prediction problem with a low-dimensional set of features. Our setting is different from subset-selection algorithms where the problem is to choose the best subset of features for regression. Instead, we attempt to generate a new set of low-dimensional features as in a feature-learning setting. We attempt to keep our proposed approach as model-free and our algorithm does not assume the application of any specific regression model in conjunction with the low-dimensional features that it learns. The algorithm is iterative and is fomulated as a combination of the majorization-minimization and concave-convex optimization procedures. We also present spectral radius based convergence results for the proposed iterations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1306.2533v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"344188","document_content":"# Using a bag of Words for Automatic Medical Image Annotation with a Latent Semantic\n## Categories\n- Information Retrieval\n- Computer Vision and Pattern Recognition\n## Abstract\nWe present in this paper a new approach for the automatic annotation of medical images, using the approach of \"bag-of-words\" to represent the visual content of the medical image combined with text descriptors based approach tf.idf and reduced by latent semantic to extract the co-occurrence between terms and visual terms. A medical report is composed of a text describing a medical image. First, we are interested to index the text and extract all relevant terms using a thesaurus containing MeSH medical concepts. In a second phase, the medical image is indexed while recovering areas of interest which are invariant to change in scale, light and tilt. To annotate a new medical image, we use the approach of \"bagof-words\" to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical image from the database training. The calculation of the relevance value of an image to the query image is based on the cosine function. We conclude with an experiment carried out on five types of radiological imaging to evaluate the performance of our system of medical annotation. The results showed that our approach works better with more images from the radiology of the skull.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1306.0178v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.CV\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"254672\", \"305402\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"344459","document_content":"# Development of a Hindi Lemmatizer\n## Categories\n- Computation and Language\n## Abstract\nWe live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages. Learning all these languages is not at all possible; therefore we need a mechanism which can do this task for us. Machine translators have emerged as a tool which can perform this task. In order to develop a machine translator we need to develop several different rules. The very first module that comes in machine translation pipeline is morphological analysis. Stemming and lemmatization comes under morphological analysis. In this paper we have created a lemmatizer which generates rules for removing the affixes along with the addition of rules for creating a proper root word.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1305.6211v2\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"327233\", \"339556\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"344525","document_content":"# A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation\n## Categories\n- Computer Vision and Pattern Recognition\n- Machine Learning\n- Machine Learning\n## Abstract\nTopic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for document modeling. In this work, we show how to successfully apply and extend this model to the context of visual scene modeling. Specifically, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the hidden topic features by incorporating label information into the training objective of the model. We also describe how to leverage information about the spatial position of the visual words and how to embed additional image annotations, so as to simultaneously perform image classification and annotation. We test our model on the Scene15, LabelMe and UIUC-Sports datasets and show that it compares favorably to other topic models such as the supervised variant of LDA.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1305.5306v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"345301","document_content":"# Image Compression By Embedding Five Modulus Method Into JPEG\n## Categories\n- Computer Vision and Pattern Recognition\n- Multimedia\n## Abstract\nThe standard JPEG format is almost the optimum format in image compression. The compression ratio in JPEG sometimes reaches 30:1. The compression ratio of JPEG could be increased by embedding the Five Modulus Method (FMM) into the JPEG algorithm. The novel algorithm gives twice the time as the standard JPEG algorithm or more. The novel algorithm was called FJPEG (Five-JPEG). The quality of the reconstructed image after compression is approximately approaches the JPEG. Standard test images have been used to support and implement the suggested idea in this paper and the error metrics have been computed and compared with JPEG.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.5121\/sipij.2013.4203\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.MM\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Multimedia\"], \"incoming_citations\": [], \"outgoing_citations\": [\"352195\"]}","task_split":"paper_retrieval"}
{"document_id":"345355","document_content":"# A Hybrid Rule Based Fuzzy-Neural Expert System For Passive Network Monitoring\n## Categories\n- Artificial Intelligence\n- Networking and Internet Architecture\n## Abstract\nAn enhanced approach for network monitoring is to create a network monitoring tool that has artificial intelligence characteristics. There are a number of approaches available. One such approach is by the use of a combination of rule based, fuzzy logic and neural networks to create a hybrid ANFIS system. Such system will have a dual knowledge database approach. One containing membership function values to compare to and do deductive reasoning and another database with rules deductively formulated by an expert (a network administrator). The knowledge database will be updated continuously with newly acquired patterns. In short, the system will be composed of 2 parts, learning from data sets and fine-tuning the knowledge-base using neural network and the use of fuzzy logic in making decision based on the rules and membership functions inside the knowledge base. This paper will discuss the idea, steps and issues involved in creating such a system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.7843v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Networking and Internet Architecture\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"345528","document_content":"# Inference and learning in probabilistic logic programs using weighted Boolean formulas\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Logic in Computer Science\n## Abstract\nProbabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before.   The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms.   The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1017\/S1471068414000076\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"cs.LO\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Logic in Computer Science\"], \"incoming_citations\": [\"117\", \"146685\", \"67758\", \"105111\", \"27558\", \"31023\", \"31053\", \"47267\", \"47839\", \"60678\", \"73754\", \"126942\", \"166093\", \"175554\", \"124267\", \"90927\", \"107965\", \"109632\", \"121419\", \"136139\", \"140415\", \"141944\", \"146761\", \"147050\", \"155712\", \"197475\", \"208098\", \"165348\", \"165795\", \"66530\", \"183071\", \"192427\", \"195361\", \"197222\", \"217065\", \"220467\", \"308004\", \"352211\", \"210215\", \"216727\", \"217067\", \"221432\", \"223636\", \"225948\", \"226086\", \"227191\", \"230273\", \"229738\", \"238903\", \"278808\", \"250262\", \"260226\", \"260744\", \"260998\", \"264279\", \"265201\", \"269429\", \"275017\", \"292167\", \"303904\", \"309759\", \"309105\", \"321542\", \"324591\", \"329735\", \"336353\", \"336504\"], \"outgoing_citations\": [\"361790\"]}","task_split":"paper_retrieval"}
{"document_id":"345903","document_content":"# On the Benefits of Sampling in Privacy Preserving Statistical Analysis on Distributed Databases\n## Categories\n- Cryptography and Security\n- Databases\n- Data Structures and Algorithms\n## Abstract\nWe consider a problem where mutually untrusting curators possess portions of a vertically partitioned database containing information about a set of individuals. The goal is to enable an authorized party to obtain aggregate (statistical) information from the database while protecting the privacy of the individuals, which we formalize using Differential Privacy. This process can be facilitated by an untrusted server that provides storage and processing services but should not learn anything about the database. This work describes a data release mechanism that employs Post Randomization (PRAM), encryption and random sampling to maintain privacy, while allowing the authorized party to conduct an accurate statistical analysis of the data. Encryption ensures that the storage server obtains no information about the database, while PRAM and sampling ensures individual privacy is maintained against the authorized party. We characterize how much the composition of random sampling with PRAM increases the differential privacy of system compared to using PRAM alone. We also analyze the statistical utility of our system, by bounding the estimation error - the expected l2-norm error between the true empirical distribution and the estimated distribution - as a function of the number of samples, PRAM noise, and other system parameters. Our analysis shows a tradeoff between increasing PRAM noise versus decreasing the number of samples to maintain a desired level of privacy, and we determine the optimal number of samples that balances this tradeoff and maximizes the utility. In experimental simulations with the UCI \"Adult Data Set\" and with synthetically generated data, we confirm that the theoretically predicted optimal number of samples indeed achieves close to the minimal empirical error, and that our analytical error bounds match well with the empirical results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.4613v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\", \"cs.DB\", \"cs.DS\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\", \"Databases\", \"Data Structures and Algorithms\"], \"incoming_citations\": [\"254666\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346228","document_content":"# Corpus-based Web Document Summarization using Statistical and Linguistic Approach\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nSingle document summarization generates summary by extracting the representative sentences from the document. In this paper, we presented a novel technique for summarization of domain-specific text from a single web document that uses statistical and linguistic analysis on the text in a reference corpus and the web document. The proposed summarizer uses the combinational function of Sentence Weight (SW) and Subject Weight (SuW) to determine the rank of a sentence, where SW is the function of number of terms (t_n) and number of words (w_n) in a sentence, and term frequency (t_f) in the corpus and SuW is the function of t_n and w_n in a subject, and t_f in the corpus. 30 percent of the ranked sentences are considered to be the summary of the web document. We generated three web document summaries using our technique and compared each of them with the summaries developed manually from 16 different human subjects. Results showed that 68 percent of the summaries produced by our approach satisfy the manual summaries.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICCCE.2010.5556854\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"359309\", \"359310\", \"359891\"]}","task_split":"paper_retrieval"}
{"document_id":"346265","document_content":"# Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference\n## Categories\n- Computer Vision and Pattern Recognition\n- Information Retrieval\n- I.5.4; I.4\n## Abstract\nWhile existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the underlying resolution (eg. poor optics). To alleviate this degradation, we propose a compensation framework which dynamically chooses the most appropriate face recognition system for a given pair of image resolutions. This framework applies a novel resolution detection method which does not rely on the size of the input images, but instead exploits the sensitivity of local features to resolution using a probabilistic multi-region histogram approach. Experiments on a resolution-modified version of the \"Labeled Faces in the Wild\" dataset show that the proposed resolution detector frontend obtains a 99% average accuracy in selecting the most appropriate face recognition system, resulting in higher overall face discrimination accuracy (across several resolutions) compared to the individual baseline face recognition systems.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/ICPR.2010.299\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.IR\", \"I.5.4; I.4\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Information Retrieval\", \"I.5.4; I.4\"], \"incoming_citations\": [\"234204\", \"209118\", \"332431\", \"346465\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346292","document_content":"# Image Retrieval using Histogram Factorization and Contextual Similarity Learning\n## Categories\n- Computer Vision and Pattern Recognition\n- Databases\n- Machine Learning\n## Abstract\nImage retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.1995v2\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.DB\", \"cs.LG\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Databases\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"355266\"]}","task_split":"paper_retrieval"}
{"document_id":"346465","document_content":"# Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition\n## Categories\n- Computer Vision and Pattern Recognition\n- Applications\n- I.4.1; I.4.6; I.4.7; I.4.9; I.4.10; I.5.1; I.5.4; G.3\n## Abstract\nIn video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the \"best\" subset of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an \"ideal\" face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPRW.2011.5981881\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"stat.AP\", \"I.4.1; I.4.6; I.4.7; I.4.9; I.4.10; I.5.1; I.5.4; G.3\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Applications\", \"I.4.1; I.4.6; I.4.7; I.4.9; I.4.10; I.5.1; I.5.4; G.3\"], \"incoming_citations\": [\"7097\", \"14719\", \"71292\", \"102723\", \"32886\", \"63976\", \"69833\", \"72234\", \"74714\", \"84486\", \"237771\", \"93610\", \"102904\", \"133473\", \"135770\", \"158820\", \"163258\", \"172272\", \"199328\", \"238384\", \"245824\", \"264911\", \"257648\", \"262487\", \"259898\", \"283271\", \"283398\"], \"outgoing_citations\": [\"346265\", \"347052\"]}","task_split":"paper_retrieval"}
{"document_id":"346631","document_content":"# Exploring the Role of Logically Related Non-Question Phrases for Answering Why-Questions\n## Categories\n- Computation and Language\n- Information Retrieval\n- H.3.m\n## Abstract\nIn this paper, we show that certain phrases although not present in a given question\/query, play a very important role in answering the question. Exploring the role of such phrases in answering questions not only reduces the dependency on matching question phrases for extracting answers, but also improves the quality of the extracted answers. Here matching question phrases means phrases which co-occur in given question and candidate answers. To achieve the above discussed goal, we introduce a bigram-based word graph model populated with semantic and topical relatedness of terms in the given document. Next, we apply an improved version of ranking with a prior-based approach, which ranks all words in the candidate document with respect to a set of root words (i.e. non-stopwords present in the question and in the candidate document). As a result, terms logically related to the root words are scored higher than terms that are not related to the root words. Experimental results show that our devised system performs better than state-of-the-art for the task of answering Why-questions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.7310v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"H.3.m\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"H.3.m\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346675","document_content":"# Inductive Hashing on Manifolds\n## Categories\n- Machine Learning\n## Abstract\nLearning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE .","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/CVPR.2013.205\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"14101\", \"155958\", \"107920\", \"143998\", \"187947\", \"243144\", \"155241\", \"244229\", \"236561\", \"240311\", \"191832\", \"191827\", \"251069\", \"259748\", \"274566\", \"291400\", \"189932\", \"280246\", \"280270\", \"298792\", \"189958\", \"200534\", \"296862\", \"297878\", \"181932\", \"306406\", \"316473\", \"320988\", \"325441\", \"325820\", \"327501\", \"330116\", \"331485\", \"340567\"], \"outgoing_citations\": [\"348109\"]}","task_split":"paper_retrieval"}
{"document_id":"346695","document_content":"# Intelligent Probabilistic Inference\n## Categories\n- Artificial Intelligence\n## Abstract\nThe analysis of practical probabilistic models on the computer demands a convenient representation for the available knowledge and an efficient algorithm to perform inference. An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies. Recent advances have developed solution procedures based on the influence diagram. In this paper, we examine the fundamental properties that underlie those techniques, and the information about the probabilistic structure that is available in the influence diagram representation. The influence diagram is a convenient representation for computer processing while also being clear and non-mathematical. It displays probabilistic dependence precisely, in a way that is intuitive for decision makers and experts to understand and communicate. As a result, the same influence diagram can be used to build, assess and analyze a model, facilitating changes in the formulation and feedback from sensitivity analysis. The goal in this paper is to determine arbitrary conditional probability distributions from a given probabilistic model. Given qualitative information about the dependence of the random variables in the model we can, for a specific conditional expression, specify precisely what quantitative information we need to be able to determine the desired conditional probability distribution. It is also shown how we can find that probability distribution by performing operations locally, that is, over subspaces of the joint distribution. In this way, we can exploit the conditional independence present in the model to avoid having to construct or manipulate the full joint distribution. These results are extended to include maximal processing when the information available is incomplete, and optimal decision making in an uncertain environment. Influence diagrams as a computer-aided modeling tool were developed by Miller, Merkofer, and Howard [5] and extended by Howard and Matheson [2]. Good descriptions of how to use them in modeling are in Owen [7] and Howard and Matheson [2]. The notion of solving a decision problem through influence diagrams was examined by Olmsted [6] and such an algorithm was developed by Shachter [8]. The latter paper also shows how influence diagrams can be used to perform a variety of sensitivity analyses. This paper extends those results by developing a theory of the properties of the diagram that are used by the algorithm, and the information needed to solve arbitrary probability inference problems. Section 2 develops the notation and the framework for the paper and the relationship between influence diagrams and joint probability distributions. The general probabilistic inference problem is posed in Section 3. In Section 4 the transformations on the diagram are developed and then put together into a solution procedure in Section 5. In Section 6, this procedure is used to calculate the information requirement to solve an inference problem and the maximal processing that can be performed with incomplete information. Section 7 contains a summary of results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.3446v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"16229\", \"21521\", \"87370\", \"193208\", \"146685\", \"1721\", \"4736\", \"11669\", \"11643\", \"11843\", \"13193\", \"18253\", \"21420\", \"23835\", \"25303\", \"26307\", \"34001\", \"34908\", \"40159\", \"40224\", \"44520\", \"49823\", \"50113\", \"53122\", \"57823\", \"63619\", \"63736\", \"64734\", \"67026\", \"68801\", \"74882\", \"82852\", \"83736\", \"83792\", \"83972\", \"85340\", \"89070\", \"106767\", \"120578\", \"120912\", \"133427\", \"142247\", \"145884\", \"168845\", \"184206\", \"191606\", \"212164\", \"221737\", \"229562\", \"233570\", \"199\", \"1475\", \"1886\", \"2446\", \"2380\", \"3709\", \"6122\", \"92124\", \"7524\", \"14301\", \"15937\", \"18807\", \"20913\", \"21324\", \"22181\", \"22262\", \"23484\", \"24036\", \"24239\", \"26673\", \"26769\", \"27558\", \"29737\", \"33182\", \"33538\", \"33562\", \"35355\", \"35897\", \"36686\", \"37389\", \"37711\", \"37871\", \"38349\", \"38686\", \"39119\", \"39669\", \"41883\", \"42363\", \"43629\", \"43943\", \"44864\", \"45531\", \"45757\", \"46018\", \"46104\", \"46319\", \"47068\", \"47267\", \"47449\", \"48132\", \"48372\", \"49087\", \"49802\", \"50649\", \"53088\", \"53634\", \"54333\", \"54952\", \"58674\", \"58702\", \"59009\", \"59180\", \"60523\", \"60849\", \"61279\", \"63342\", \"65593\", \"65724\", \"264304\", \"70125\", \"70449\", \"74072\", \"74566\", \"75791\", \"76564\", \"76899\", \"78629\", \"78499\", \"78844\", \"82076\", \"82101\", \"90729\", \"95243\", \"96663\", \"97085\", \"97440\", \"100939\", \"102173\", \"104269\", \"108646\", \"110164\", \"110597\", \"112017\", \"115935\", \"115860\", \"116222\", \"122451\", \"124626\", \"60095\", \"138587\", \"138924\", \"138980\", \"141163\", \"143939\", \"152549\", \"159491\", \"171658\", \"172410\", \"174370\", \"175053\", \"175554\", \"177653\", \"187728\", \"191314\", \"201326\", \"202524\", \"206334\", \"232531\", \"241989\", \"266576\", \"270047\", \"339944\", \"278340\", \"95011\"], \"outgoing_citations\": [\"346718\", \"346719\", \"346756\", \"346759\", \"346787\", \"346792\", \"346798\", \"346804\", \"346806\", \"346814\", \"346816\", \"346822\", \"346846\"]}","task_split":"paper_retrieval"}
{"document_id":"346798","document_content":"# Structuring Causal Tree Models with Continuous Variables\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper considers the problem of invoking auxiliary, unobservable variables to facilitate the structuring of causal tree models for a given set of continuous variables. Paralleling the treatment of bi-valued variables in [Pearl 1986], we show that if a collection of coupled variables are governed by a joint normal distribution and a tree-structured representation exists, then both the topology and all internal relationships of the tree can be uncovered by observing pairwise dependencies among the observed variables (i.e., the leaves of the tree). Furthermore, the conditions for normally distributed variables are less restrictive than those governing bi-valued variables. The result extends the applications of causal tree models which were found useful in evidential reasoning tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.2730v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"346695\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346826","document_content":"# A General Non-Probabilistic Theory of Inductive Reasoning\n## Categories\n- Artificial Intelligence\n## Abstract\nProbability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through information or experience; induction and belief change are one and same topic, after all. The most basic of these rules is simply to conditionalize with respect to the information received; and there are similar and more general rules. 1 Hence, a fundamental reason for the epistemological success of probability theory is that there at all exists a well-behaved concept of conditional probability. Still, people have, and have reasons for, various concerns over probability theory. One of these is my starting point: Intuitively, we have the notion of plain belief; we believe propositions2 to be true (or to be false or neither). Probability theory, however, offers no formal counterpart to this notion. Believing A is not the same as having probability 1 for A, because probability 1 is incorrigible3; but plain belief is clearly corrigible. And believing A is not the same as giving A a probability larger than some 1 - c, because believing A and believing B is usually taken to be equivalent to believing A & B.4 Thus, it seems that the formal representation of plain belief has to take a non-probabilistic route. Indeed, representing plain belief seems easy enough: simply represent an epistemic state by the set of all propositions believed true in it or, since I make the common assumption that plain belief is deductively closed, by the conjunction of all propositions believed true in it. But this does not yet provide a theory of induction, i.e. an answer to the question how epistemic states so represented are changed tbrough information or experience. There is a convincing partial answer: if the new information is compatible with the old epistemic state, then the new epistemic state is simply represented by the conjunction of the new information and the old beliefs. This answer is partial because it does not cover the quite common case where the new information is incompatible with the old beliefs. It is, however, important to complete the answer and to cover this case, too; otherwise, we would not represent plain belief as conigible. The crucial problem is that there is no good completion. When epistemic states are represented simply by the conjunction of all propositions believed true in it, the answer cannot be completed; and though there is a lot of fruitful work, no other representation of epistemic states has been proposed, as far as I know, which provides a complete solution to this problem. In this paper, I want to suggest such a solution. In [4], I have more fully argued that this is the only solution, if certain plausible desiderata are to be satisfied. Here, in section 2, I will be content with formally defining and intuitively explaining my proposal. I will compare my proposal with probability theory in section 3. It will turn out that the theory I am proposing is structurally homomorphic to probability theory in important respects and that it is thus equally easily implementable, but moreover computationally simpler. Section 4 contains a very brief comparison with various kinds of logics, in particular conditional logic, with Shackle's functions of potential surprise and related theories, and with the Dempster - Shafer theory of belief functions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.2375v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"164789\", \"361789\", \"357266\", \"351433\", \"350501\", \"350205\", \"349871\", \"348965\", \"348571\", \"348572\", \"348282\", \"348228\", \"346956\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346829","document_content":"# Maintenance in Probabilistic Knowledge-Based Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nRecent developments using directed acyclical graphs (i.e., influence diagrams and Bayesian networks) for knowledge representation have lessened the problems of using probability in knowledge-based systems (KBS). Most current research involves the efficient propagation of new evidence, but little has been done concerning the maintenance of domain-specific knowledge, which includes the probabilistic information about the problem domain. By making use of conditional independencies represented in she graphs, however, probability assessments are required only for certain variables when the knowledge base is updated. The purpose of this study was to investigate, for those variables which require probability assessments, ways to reduce the amount of new knowledge required from the expert when updating probabilistic information in a probabilistic knowledge-based system. Three special cases (ignored outcome, split outcome, and assumed constraint outcome) were identified under which many of the original probabilities (those already in the knowledge-base) do not need to be reassessed when maintenance is required.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.2372v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346836","document_content":"# Probabilistic and Non-Monotonic Inference\n## Categories\n- Artificial Intelligence\n## Abstract\n(l) I have enough evidence to render the sentence S probable. (la) So, relative to what I know, it is rational of me to believe S. (2) Now that I have more evidence, S may no longer be probable. (2a) So now, relative to what I know, it is not rational of me to believe S. These seem a perfectly ordinary, common sense, pair of situations. Generally and vaguely, I take them to embody what I shall call probabilistic inference. This form of inference is clearly non-monotonic. Relatively few people have taken this form of inference, based on high probability, to serve as a foundation for non-monotonic logic or for a logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks that sometimes probability has something to do with non-monotonic reasoning. Judea Pearl [ 17] has recently been exploring the possibility. There are any number of people whom one might call probability enthusiasts who feel that probability provides all the answers by itself, with no need of help from logic. Cheeseman [1], Henrion [5] and others think it useful to look at a distribution of probabilities over a whole algebra of statements, to update that distribution in the light of new evidence, and to use the latest updated distribution of probability over the algebra as a basis for planning and decision making. A slightly weaker form of this approach is captured by Nilsson [15], where one assumes certain probabilities for certain statements, and infers the probabilities, or constraints on the probabilities of other statement. None of this corresponds to what I call probabilistic inference. All of the inference that is taking place, either in Bayesian updating, or in probabilistic logic, is strictly deductive. Deductive inference, particularly that concerned with the distribution of classical probabilities or chances, is of great importance. But this is not to say that there is no important role for what earlier logicians have called \"ampliative\" or \"inductive\" or \"scientific\" inference, in which the conclusion goes beyond the premises, asserts more than do the premises. This depends on what David Israel [6] has called \"real rules of inference\". It is characteristic of any such logic or inference procedure that it can go wrong: that statements accepted at one point may be rejected at a later point. Research underlying the results reported here has been partially supported by the Signals Warfare Center of the United States Army.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.2365v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"346744\"]}","task_split":"paper_retrieval"}
{"document_id":"346841","document_content":"# A Representation of Uncertainty to Aid Insight into Decision Models\n## Categories\n- Artificial Intelligence\n## Abstract\nMany real world models can be characterized as weak, meaning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users of computer decision aids to understand and have confidence in the models. This paper presents a representation for uncertainty and utilities that serves as a framework for graphical summary and computer-generated explanation of decision models. The application described that tests the methodology is a computer decision aid designed to enhance the clinician-patient consultation process for patients with angina (chest pain due to lack of blood flow to the heart muscle). The angina model is represented as a Bayesian decision network. Additionally, the probabilities and utilities are treated as random variables with probability distributions on their range of possible values. The initial distributions represent information on all patients with anginal symptoms, and the approach allows for rapid tailoring to more patientspecific distributions. This framework provides a metric for judging the importance of each variable in the model dynamically.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.2360v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346883","document_content":"# Inference Policies\n## Categories\n- Artificial Intelligence\n## Abstract\nIt is suggested that an AI inference system should reflect an inference policy that is tailored to the domain of problems to which it is applied -- and furthermore that an inference policy need not conform to any general theory of rational inference or induction. We note, for instance, that Bayesian reasoning about the probabilistic characteristics of an inference domain may result in the specification of an nonBayesian procedure for reasoning within the inference domain. In this paper, the idea of an inference policy is explored in some detail. To support this exploration, the characteristics of some standard and nonstandard inference policies are examined.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.1516v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"346965","document_content":"# Application of Confidence Intervals to the Autonomous Acquisition of High-level Spatial Knowledge\n## Categories\n- Artificial Intelligence\n## Abstract\nObjects in the world usually appear in context, participating in spatial relationships and interactions that are predictable and expected. Knowledge of these contexts can be used in the task of using a mobile camera to search for a specified object in a room. We call this the object search task. This paper is concerned with representing this knowledge in a manner facilitating its application to object search while at the same time lending itself to autonomous learning by a robot. The ability for the robot to learn such knowledge without supervision is crucial due to the vast number of possible relationships that can exist for any given set of objects. Moreover, since a robot will not have an infinite amount of time to learn, it must be able to determine an order in which to look for possible relationships so as to maximize the rate at which new knowledge is gained. In effect, there must be a \"focus of interest\" operator that allows the robot to choose which examples are likely to convey the most new information and should be examined first. This paper demonstrates how a representation based on statistical confidence intervals allows the construction of a system that achieves the above goals. An algorithm, based on the Highest Impact First heuristic, is presented as a means for providing a \"focus of interest\" with which to control the learning process, and examples are given.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1304.1109v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"346867\"]}","task_split":"paper_retrieval"}
{"document_id":"347242","document_content":"# Detecting Causal Relations in the Presence of Unmeasured Variables\n## Categories\n- Artificial Intelligence\n## Abstract\nThe presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it is possible to reliably infer the causal relation between two measured variables, regardless of whether latent variables are acting or not.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.5754v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"347288","document_content":"# Some Properties of Plausible Reasoning\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature of plausible reasoning. These issues have a common underlying theme: there should be an underlying belief calculus of which the many different reasoning forms are special cases, sometimes approximate. The system presented allows reasoning about defaults, likelihood, necessity and possibility in a manner similar to the earlier work of Adams. The system is based on the belief calculus of subjective Bayesian probability which itself is based on a few simple assumptions about how belief should be manipulated. Approximations, semantics, consistency and consequence results are presented for the system. While this puts these often discussed plausible reasoning forms on a probabilistic footing, useful application to practical problems remains an issue.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.5708v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"347567\"], \"outgoing_citations\": [\"346892\"]}","task_split":"paper_retrieval"}
{"document_id":"347548","document_content":"# Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems\n## Categories\n- Artificial Intelligence\n## Abstract\nAn expert classification system having statistical information about the prior probabilities of the different classes should be able to use this knowledge to reduce the amount of additional information that it must collect, e.g., through questions, in order to make a correct classification. This paper examines how best to use such prior information and additional information-collection opportunities to reduce uncertainty about the class to which a case belongs, thus minimizing the average cost or effort required to correctly classify new cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.5425v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"347568","document_content":"# Integrating Model Construction and Evaluation\n## Categories\n- Artificial Intelligence\n## Abstract\nTo date, most probabilistic reasoning systems have relied on a fixed belief network constructed at design time. The network is used by an application program as a representation of (in)dependencies in the domain. Probabilistic inference algorithms operate over the network to answer queries. Recognizing the inflexibility of fixed models has led researchers to develop automated network construction procedures that use an expressive knowledge base to generate a network that can answer a query. Although more flexible than fixed model approaches, these construction procedures separate construction and evaluation into distinct phases. In this paper we develop an approach to combining incremental construction and evaluation of a partial probability model. The combined method holds promise for improved methods for control of model construction based on a trade-off between fidelity of results and cost of construction.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.5405v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"349526\", \"347911\", \"347928\"], \"outgoing_citations\": [\"346896\", \"347257\"]}","task_split":"paper_retrieval"}
{"document_id":"347906","document_content":"# Using First-Order Probability Logic for the Construction of Bayesian Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nWe present a mechanism for constructing graphical models, specifically Bayesian networks, from a knowledge base of general probabilistic information. The unique feature of our approach is that it uses a powerful first-order probabilistic logic for expressing the general knowledge base. This logic allows for the representation of a wide range of logical and probabilistic information. The model construction procedure we propose uses notions from direct inference to identify pieces of local statistical information from the knowledge base that are most appropriate to the particular event we want to reason about. These pieces are composed to generate a joint probability distribution specified as a Bayesian network. Although there are fundamental difficulties in dealing with fully general knowledge, our procedure is practical for quite rich knowledge bases and it supports the construction of a far wider range of networks than allowed for by current template technology.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.1480v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"278808\", \"348268\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"348078","document_content":"# Genetic Programming for Document Segmentation and Region Classification Using Discipulus\n## Categories\n- Computer Vision and Pattern Recognition\n- Neural and Evolutionary Computing\n## Abstract\nDocument segmentation is a method of rending the document into distinct regions. A document is an assortment of information and a standard mode of conveying information to others. Pursuance of data from documents involves ton of human effort, time intense and might severely prohibit the usage of data systems. So, automatic information pursuance from the document has become a big issue. It is been shown that document segmentation will facilitate to beat such problems. This paper proposes a new approach to segment and classify the document regions as text, image, drawings and table. Document image is divided into blocks using Run length smearing rule and features are extracted from every blocks. Discipulus tool has been used to construct the Genetic programming based classifier model and located 97.5% classification accuracy.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1303.0460v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.NE\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"348105","document_content":"# Probing the statistical properties of unknown texts: application to the Voynich Manuscript\n## Categories\n- Physics and Society\n- Data Analysis, Statistics and Probability\n- Computation and Language\n## Abstract\nWhile the use of statistical physics methods to analyze large corpora has been useful to unveil many patterns in texts, no comprehensive investigation has been performed investigating the properties of statistical measurements across different languages and texts. In this study we propose a framework that aims at determining if a text is compatible with a natural language and which languages are closest to it, without any knowledge of the meaning of the words. The approach is based on three types of statistical measurements, i.e. obtained from first-order statistics of word properties in a text, from the topology of complex networks representing text, and from intermittency concepts where text is treated as a time series. Comparative experiments were performed with the New Testament in 15 different languages and with distinct books in English and Portuguese in order to quantify the dependency of the different measurements on the language and on the story being told in the book. The metrics found to be informative in distinguishing real texts from their shuffled versions include assortativity, degree and selectivity of words. As an illustration, we analyze an undeciphered medieval manuscript known as the Voynich Manuscript. We show that it is mostly compatible with natural languages and incompatible with random texts. We also obtain candidates for key-words of the Voynich Manuscript which could be helpful in the effort of deciphering it. Because we were able to identify statistical measurements that are more dependent on the syntax than on the semantics, the framework may also serve for text analysis in language-dependent applications.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1371\/journal.pone.0067310\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"physics.data-an\", \"cs.CL\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Data Analysis, Statistics and Probability\", \"Computation and Language\"], \"incoming_citations\": [\"36834\", \"37121\", \"90802\", \"94490\", \"155650\", \"185716\", \"195226\", \"227153\", \"267578\", \"282259\", \"289273\", \"289588\", \"256816\", \"259269\", \"267932\", \"287489\", \"287856\", \"302047\", \"310342\", \"317675\", \"318041\", \"319646\", \"320909\"], \"outgoing_citations\": [\"348768\", \"348769\", \"351470\", \"356974\"]}","task_split":"paper_retrieval"}
{"document_id":"348271","document_content":"# Learning Gaussian Networks\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n- I.2; G.3\n## Abstract\nWe describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Previous work has concentrated on metrics for domains containing only discrete variables, under the assumption that data represents a multinomial sample. In this paper, we extend this work, developing scoring metrics for domains containing all continuous variables or a mixture of discrete and continuous variables, under the assumption that continuous data is sampled from a multivariate normal distribution. Our work extends traditional statistical approaches for identifying vanishing regression coefficients in that we identify two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.6808v3\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\", \"I.2; G.3\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\", \"I.2; G.3\"], \"incoming_citations\": [\"21521\", \"65457\", \"83736\", \"36686\", \"39705\", \"47449\", \"49802\", \"50166\", \"264304\", \"201455\", \"81582\", \"96124\", \"97668\", \"105468\", \"181394\", \"189124\", \"200942\", \"289759\", \"168863\", \"212666\", \"213602\", \"225335\", \"241969\", \"310716\", \"207742\", \"332664\", \"335762\", \"327713\", \"340974\", \"360769\", \"358946\", \"357916\", \"356569\", \"348264\", \"350219\", \"350223\", \"349523\", \"349549\", \"349551\", \"348974\", \"348596\", \"348603\", \"348611\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"348277","document_content":"# Some Properties of Joint Probability Distributions\n## Categories\n- Artificial Intelligence\n## Abstract\nSeveral Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain domain models. Even though the correct working of these schemes is practically contingent upon the existence of a small number of probable states, no formal justification has been proposed of why this should be the case. This paper attempts to fill this apparent gap by studying asymmetries among probabilities of various states of uncertain models. By rewriting the joint probability distribution over a model's variables into a product of individual variables' prior and conditional probability distributions, and applying central limit theorem to this product, we can demonstrate that the probabilities of individual states of the model can be expected to be drawn from highly skewed, log-normal distributions. With sufficient asymmetry in individual prior and conditional probability distributions, a small fraction of states can be expected to cover a large portion of the total probability space with the remaining states having practically negligible probability. Theoretical discussion is supplemented by simulation results and an illustrative real-world example.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.6802v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"36149\", \"68533\", \"273748\", \"349541\", \"348916\", \"348923\", \"348941\", \"348942\", \"348626\"], \"outgoing_citations\": [\"347275\", \"347889\", \"347932\"]}","task_split":"paper_retrieval"}
{"document_id":"348295","document_content":"# Generating New Beliefs From Old\n## Categories\n- Artificial Intelligence\n## Abstract\nIn previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective (first-order, statistical, and default) information. But allowing a knowledge base to contain only objective information is sometimes limiting. We occasionally wish to include information about degrees of belief in the knowledge base as well, because there are contexts in which old beliefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective information to one that can make use of degrees of belief as well. All of our techniques are bloused on well-known approaches, such as cross-entropy. We discuss general connections between the techniques and in particular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.6783v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"349226\"], \"outgoing_citations\": [\"346718\", \"348257\"]}","task_split":"paper_retrieval"}
{"document_id":"348483","document_content":"# Stochastic dynamics of lexicon learning in an uncertain and nonuniform world\n## Categories\n- Physics and Society\n- Computation and Language\n- Statistical Mechanics\n- Neurons and Cognition\n## Abstract\nWe study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1103\/PhysRevLett.110.258701\", \"primary_category\": \"physics.soc-ph\", \"categories\": [\"physics.soc-ph\", \"cs.CL\", \"cond-mat.stat-mech\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Physics and Society\", \"categories_human_readable\": [\"Physics and Society\", \"Computation and Language\", \"Statistical Mechanics\", \"Neurons and Cognition\"], \"incoming_citations\": [\"205907\", \"320715\"], \"outgoing_citations\": [\"357946\", \"360032\"]}","task_split":"paper_retrieval"}
{"document_id":"348584","document_content":"# On the Testability of Causal Models with Latent and Instrumental Variables\n## Categories\n- Artificial Intelligence\n## Abstract\nCertain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality contraints on the observed distribution. This paper derives a general formula for such instrumental variables, that is, exogenous variables that directly affect some variables but not all. With the help of this formula, it is possible to test whether a model involving instrumental variables may account for the data, or, conversely, whether a given variables can be deemed instrumental.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.4976v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"11430\", \"68339\", \"20628\", \"40530\", \"64467\", \"101708\", \"119467\", \"131754\", \"338687\", \"277050\", \"357301\", \"351382\", \"350511\", \"350514\", \"348930\"], \"outgoing_citations\": [\"348294\", \"348583\", \"348612\"]}","task_split":"paper_retrieval"}
{"document_id":"348591","document_content":"# Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nWe show an alternative way of representing a Bayesian belief network by sensitivities and probability distributions. This representation is equivalent to the traditional representation by conditional probabilities, but makes dependencies between nodes apparent and intuitively easy to understand. We also propose a QR matrix representation for the sensitivities and\/or conditional probabilities which is more efficient, in both memory requirements and computational speed, than the traditional representation for computer-based implementations of probabilistic inference. We use sensitivities to show that for a certain class of binary networks, the computation time for approximate probabilistic inference with any positive upper bound on the error of the result is independent of the size of the network. Finally, as an alternative to traditional algorithms that use conditional probabilities, we describe an exact algorithm for probabilistic inference that uses the QR-representation for sensitivities and updates probability distributions of nodes in a network according to messages from the neighbors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.4969v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"348937\"], \"outgoing_citations\": [\"347247\", \"347916\"]}","task_split":"paper_retrieval"}
{"document_id":"348606","document_content":"# Probabilistic Temporal Reasoning with Endogenous Change\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper presents a probabilistic model for reasoning about the state of a system as it changes over time, both due to exogenous and endogenous influences. Our target domain is a class of medical prediction problems that are neither so urgent as to preclude careful diagnosis nor progress so slowly as to allow arbitrary testing and treatment options. In these domains there is typically enough time to gather information about the patient's state and consider alternative diagnoses and treatments, but the temporal interaction between the timing of tests, treatments, and the course of the disease must also be considered. Our approach is to elicit a qualitative structural model of the patient from a human expert---the model identifies important attributes, the way in which exogenous changes affect attribute values, and the way in which the patient's condition changes endogenously. We then elicit probabilistic information to capture the expert's uncertainty about the effects of tests and treatments and the nature and timing of endogenous state changes. This paper describes the model in the context of a problem in treating vehicle accident trauma, and suggests a method for solving the model based on the technique of sequential imputation. A complementary goal of this work is to understand and synthesize a disparate collection of research efforts all using the name ?probabilistic temporal reasoning.? This paper analyzes related work and points out essential differences between our proposed model and other approaches in the literature.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.4954v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"351399\", \"349896\", \"348973\"], \"outgoing_citations\": [\"346906\", \"347566\", \"347925\", \"347928\", \"348278\"]}","task_split":"paper_retrieval"}
{"document_id":"348612","document_content":"# Testing Identifiability of Causal Effects\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.4948v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"14293\", \"36961\", \"93169\", \"98349\", \"113457\", \"142994\", \"99465\", \"162159\", \"204153\", \"205883\", \"224298\", \"286478\", \"339831\", \"335960\", \"353430\", \"356542\", \"350165\", \"348583\", \"348584\"], \"outgoing_citations\": [\"348244\", \"348583\"]}","task_split":"paper_retrieval"}
{"document_id":"348618","document_content":"# Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians\n## Categories\n- Artificial Intelligence\n## Abstract\nBayesian networks provide a method of representing conditional independence between random variables and computing the probability distributions associated with these random variables. In this paper, we extend Bayesian network structures to compute probability density functions for continuous random variables. We make this extension by approximating prior and conditional densities using sums of weighted Gaussian distributions and then finding the propagation rules for updating the densities in terms of these weights. We present a simple example that illustrates the Bayesian network for continuous variables; this example shows the effect of the network structure and approximation errors on the computation of densities for variables in the network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.4942v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"107508\", \"351427\", \"350211\", \"349846\", \"349214\", \"348974\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"348963","document_content":"# Context-Specific Independence in Bayesian Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nBayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme - tree-structured CPTs - for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms. In particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on cutset conditioning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.3562v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"78333\", \"13193\", \"14293\", \"15247\", \"22169\", \"91024\", \"92134\", \"107863\", \"133427\", \"145884\", \"205555\", \"4822\", \"12164\", \"15047\", \"31525\", \"43732\", \"214965\", \"73754\", \"76564\", \"278340\", \"86266\", \"124267\", \"89462\", \"91959\", \"109628\", \"113870\", \"115497\", \"115501\", \"119256\", \"130453\", \"148055\", \"150111\", \"208098\", \"215387\", \"99465\", \"175233\", \"178709\", \"192427\", \"195361\", \"195534\", \"197252\", \"220917\", \"258035\", \"230167\", \"229738\", \"256947\", \"268874\", \"270880\", \"273931\", \"277439\", \"278815\", \"281133\", \"282437\", \"299688\", \"304272\", \"354206\", \"164789\", \"289281\", \"290418\", \"292688\", \"293877\", \"298607\", \"306972\", \"334355\", \"301871\", \"309709\", \"315460\", \"318727\", \"319391\", \"320414\", \"326399\", \"335683\", \"336353\", \"339486\", \"339825\", \"339840\", \"342534\", \"343831\", \"348726\", \"350208\", \"335129\", \"276819\", \"360726\", \"335304\", \"358943\", \"357880\", \"357883\", \"357286\", \"357298\", \"356866\", \"356881\", \"356567\", \"353189\", \"353248\", \"350459\", \"350470\", \"350471\", \"350087\", \"350200\", \"350210\", \"350223\", \"349822\", \"349837\", \"349843\", \"349853\", \"349866\", \"349884\", \"349510\", \"349518\", \"349552\", \"349196\", \"349211\", \"349212\", \"349241\", \"349242\", \"349247\", \"348948\"], \"outgoing_citations\": [\"157691\", \"346961\", \"346973\", \"347278\", \"347907\", \"347918\", \"348244\", \"348265\", \"348292\", \"348948\"]}","task_split":"paper_retrieval"}
{"document_id":"349206","document_content":"# The Cognitive Processing of Causal Knowledge\n## Categories\n- Artificial Intelligence\n## Abstract\nThere is a brief description of the probabilistic causal graph model for representing, reasoning with, and learning causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concerning how the presence of one cause of an effect makes another cause less probable) support the hypothesis that humans reach the same judgments as algorithms for doing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and dependencies as those used by certain algorithms for learning the structure of a Bayesian network. Based on this indication, a subjective definition of causality is forwarded. Finally, methods for further testing the accuracy of these claims are discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1563v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"349208","document_content":"# Structure and Parameter Learning for Causal Independence and Causal Interaction Models\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThis paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models that have independent mechanisms where a mechanism can have several causes. In addition to introducing several particular types of causal interaction models, we show how we can apply the Bayesian approach to learning causal interaction models obtaining approximate posterior distributions for the models and obtain MAP and ML estimates for the parameters. We illustrate the approach with a simulation study of learning model posteriors.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1561v2\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"160506\", \"349843\", \"349551\", \"349224\"], \"outgoing_citations\": [\"347907\", \"348912\", \"348939\", \"348945\", \"348948\", \"348958\", \"349199\", \"349241\"]}","task_split":"paper_retrieval"}
{"document_id":"349212","document_content":"# Network Fragments: Representing Knowledge for Constructing Probabilistic Models\n## Categories\n- Artificial Intelligence\n## Abstract\nIn most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex domains, problem-specific models must be constructed from a knowledge base encoding probabilistic relationships in the domain. Most work in knowledge-based model construction takes the rule as the basic unit of knowledge. We present a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units which we call network fragments. Our framework provides for representation of asymmetric independence and canonical intercausal interaction. We discuss the combination of network fragments to form problem-specific models to reason about particular problem instances. The framework is illustrated using examples from the domain of military situation awareness.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1557v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"138924\", \"114928\", \"350191\", \"350485\", \"350470\", \"349853\", \"349837\", \"349893\", \"349526\", \"349215\"], \"outgoing_citations\": [\"346834\", \"346882\", \"347278\", \"347918\", \"348240\", \"348586\", \"348934\", \"348963\", \"349215\"]}","task_split":"paper_retrieval"}
{"document_id":"349242","document_content":"# Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nWe present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alleviates some of the overhead associated with arc reversal, plays an important role in evidence integration and can be used to restrict sampling of variables in DPNs. We also provide an algorithm that detects the dynamic irrelevance of state variables in forward simulation. This algorithm exploits the structured CPTs in a reversed network to determine, in a time-independent fashion, the conditions under which a variable does or does not need to be sampled.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1527v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"134889\", \"226347\", \"350467\"], \"outgoing_citations\": [\"346873\", \"346895\", \"347278\", \"347554\", \"347566\", \"348595\", \"348929\", \"348948\", \"348963\"]}","task_split":"paper_retrieval"}
{"document_id":"349257","document_content":"# Towards the Rapid Development of a Natural Language Understanding Module\n## Categories\n- Computation and Language\n## Abstract\nWhen developing a conversational agent, there is often an urgent need to have a prototype available in order to test the application with real users. A Wizard of Oz is a possibility, but sometimes the agent should be simply deployed in the environment where it will be used. Here, the agent should be able to capture as many interactions as possible and to understand how people react to failure. In this paper, we focus on the rapid development of a natural language understanding module by non experts. Our approach follows the learning paradigm and sees the process of understanding natural language as a classification problem. We test our module with a conversational agent that answers questions in the art domain. Moreover, we show how our approach can be used by a natural language interface to a cinema database.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1380v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"318743\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"349267","document_content":"# Cloud Computing framework for Computer Vision Research:An Introduction\n## Categories\n- Computer Vision and Pattern Recognition\n- Distributed, Parallel, and Cluster Computing\n## Abstract\nCloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits can be obtained from cloud in order to help medical image analysis users (including scientists, clinicians, and research institutes). As security and privacy of algorithms are important for most of algorithms inventors, these algorithms can be hidden in a cloud to allow the users to use the algorithms as a package without any access to see\/change their inside. In another word, in the user part, users send their images to the cloud and configure the algorithm via an interface. In the cloud part, the algorithms are applied to this image and the results are returned back to the user. My proposal has two parts: (1) investigate the potential of cloud computing for computer vision problems and (2) study the components of a proposed cloud-based framework for medical image analysis application and develop them (depending on the length of the internship). The investigation part will involve a study on several aspects of the problem including security, usability (for medical end users of the service), appropriate programming abstractions for vision problems, scalability and resource requirements. In the second part of this proposal I am going to thoroughly study of the proposed framework components and their relations and develop them. The proposed cloud-based framework includes an integrated environment to enable scientists and clinicians to access to the previous and current medical image analysis algorithms using a handful user interface without any access to the algorithm codes and procedures.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1302.1326v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.DC\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Distributed, Parallel, and Cluster Computing\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"349526","document_content":"# Constructing Situation Specific Belief Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments. A situation-specific network is a minimal query complete network constructed from a knowledge base in response to a query for the probability distribution on a set of target variables given evidence and context variables. We present definitions of query completeness and situation-specific networks. We describe conditions on the knowledge base that guarantee query completeness. The relationship of our work to earlier work on KBMC is also discussed.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.7399v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"275337\", \"357285\", \"356907\", \"350485\", \"349853\"], \"outgoing_citations\": [\"346894\", \"346962\", \"347568\", \"347928\", \"348268\", \"348586\", \"349211\", \"349212\", \"349215\"]}","task_split":"paper_retrieval"}
{"document_id":"349543","document_content":"# Inferring Informational Goals from Free-Text Queries: A Bayesian Approach\n## Categories\n- Information Retrieval\n- Computation and Language\n- Artificial Intelligence\n## Abstract\nPeople using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals with common words and phrases that describe software functionality in terms of structure and objects they understand. We describe a Bayesian approach to modeling the relationship between words in a user's query for assistance and the informational goals of the user. After reviewing the general method, we describe several extensions that center on integrating additional distinctions and structure about language usage and user goals into the Bayesian models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.7382v2\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.AI\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Artificial Intelligence\"], \"incoming_citations\": [\"349540\"], \"outgoing_citations\": [\"349540\"]}","task_split":"paper_retrieval"}
{"document_id":"349885","document_content":"# Causal Discovery from a Mixture of Experimental and Observational Data\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the data, given that (1) the data contains a mixture of observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and observational data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and observational data were varied systematically. For each of these training datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.6686v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"21521\", \"70086\", \"124372\", \"95709\", \"104822\", \"119292\", \"167646\", \"279093\", \"162621\", \"163690\", \"171015\", \"271736\", \"244244\", \"256304\", \"272880\", \"280626\", \"281823\", \"332664\", \"342807\", \"353294\", \"354116\", \"353394\", \"361806\", \"360764\", \"357926\", \"357584\", \"357301\", \"351388\", \"350460\", \"350219\"], \"outgoing_citations\": [\"348602\", \"348611\"]}","task_split":"paper_retrieval"}
{"document_id":"349903","document_content":"# Multi-class Generalized Binary Search for Active Inverse Reinforcement Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nThis paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a reward function. Our contribution is a novel active learning algorithm that enables the learning agent to query the expert for more informative demonstrations, thus leading to more sample-efficient learning. For this novel algorithm (Generalized Binary Search for Inverse Reinforcement Learning, or GBS-IRL), we provide a theoretical bound on sample complexity and illustrate its applicability on several different tasks. To our knowledge, GBS-IRL is the first active IRL algorithm with provable sample complexity bounds. We also discuss our method in light of other existing methods in the literature and its general applicability in multi-class classification problems. Finally, motivated by recent work on learning from demonstration in robots, we also discuss how different forms of human feedback can be integrated in a transparent manner in our learning framework.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.5488v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"332776\"], \"outgoing_citations\": [\"335320\", \"357569\"]}","task_split":"paper_retrieval"}
{"document_id":"349949","document_content":"# A Rational and Efficient Algorithm for View Revision in Databases\n## Categories\n- Logic in Computer Science\n- Artificial Intelligence\n- Databases\n## Abstract\nThe dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In this paper, we argue that to apply rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first of all, it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented, along with the concept of a generalized revision algorithm for Horn knowledge bases. We show that Horn knowledge base dynamics has interesting connection with kernel change and abduction. Finally, we also show that both variants are rational in the sense that they satisfy certain rationality postulates stemming from philosophical works on belief dynamics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.12785\/amis\/070302\", \"primary_category\": \"cs.LO\", \"categories\": [\"cs.LO\", \"cs.AI\", \"cs.DB\"], \"primary_category_human_readable\": \"Logic in Computer Science\", \"categories_human_readable\": [\"Logic in Computer Science\", \"Artificial Intelligence\", \"Databases\"], \"incoming_citations\": [\"318551\", \"322029\", \"329836\"], \"outgoing_citations\": [\"358598\"]}","task_split":"paper_retrieval"}
{"document_id":"350181","document_content":"# Adaptive Importance Sampling for Estimation in Structured Domains\n## Categories\n- Artificial Intelligence\n- Machine Learning\n- Machine Learning\n## Abstract\nSampling is an important tool for estimating large, complex sums and integrals over high dimensional spaces. For instance, important sampling has been used as an alternative to exact methods for inference in belief networks. Ideally, we want to have a sampling distribution that provides optimal-variance estimators. In this paper, we present methods that improve the sampling distribution by systematically adapting it as we obtain information from the samples. We present a stochastic-gradient-descent method for sequentially updating the sampling distribution based on the direct minization of the variance. We also present other stochastic-gradient-descent methods based on the minimizationof typical notions of distance between the current sampling distribution and approximations of the target, optimal distribution. We finally validate and compare the different methods empirically by applying them to the problem of action evaluation in influence diagrams.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.3882v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"294901\", \"356831\"], \"outgoing_citations\": [\"346873\", \"346895\", \"346933\"]}","task_split":"paper_retrieval"}
{"document_id":"350208","document_content":"# Likelihood Computations Using Value Abstractions\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference. This is done by grouping variable values and treating the combined values as a single entity. As we show, such abstractions can exploit regularities in conditional probability distributions and also the specific values of observed variables. To formally justify value abstraction, we define the notion of safe value abstraction and devise inference algorithms that use it to reduce the cost of inference. Our procedure is particularly useful for learning complex networks with many hidden variables. In such cases, repeated likelihood computations are required for EM or other parameter optimization techniques. Since these computations are repeated with respect to the same evidence set, our methods can provide significant speedup to the learning procedure. We demonstrate the algorithm on genetic linkage problems where the use of value abstraction sometimes differentiates between a feasible and non-feasible solution.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.3855v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"348229\", \"348963\"]}","task_split":"paper_retrieval"}
{"document_id":"350277","document_content":"# Why Size Matters: Feature Coding as Nystrom Sampling\n## Categories\n- Machine Learning\n- Computer Vision and Pattern Recognition\n## Abstract\nRecently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency. In this paper we propose a novel view of this pipeline based on kernel methods and Nystrom sampling. In particular, we focus on the coding of a data point with a local representation based on a dictionary with fewer elements than the number of data points, and view it as an approximation to the actual function that would compute pair-wise similarity to all data points (often too many to compute in practice), followed by a Nystrom sampling step to select a subset of all data points.   Furthermore, since bounds are known on the approximation power of Nystrom sampling as a function of how many samples (i.e. dictionary size) we consider, we can derive bounds on the approximation of the exact (but expensive to compute) kernel matrix, and use it as a proxy to predict accuracy as a function of the dictionary size, which has been observed to increase but also to saturate as we increase its size. This model may help explaining the positive effect of the codebook size and justifying the need to stack more layers (often referred to as deep learning), as flat models empirically saturate as we add more complexity.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.5348v2\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"350289\"]}","task_split":"paper_retrieval"}
{"document_id":"350483","document_content":"# Exact Inference in Networks with Discrete Children of Continuous Parents\n## Categories\n- Artificial Intelligence\n## Abstract\nMany real life domains contain a mixture of discrete and continuous variables and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid BNs are conditional linear Gaussian (CLG) networks, where the conditional distribution of the continuous variables given an assignment to the discrete variables is a multivariate Gaussian. Lauritzen's extension to the clique tree algorithm can be used for exact inference in CLG networks. However, many domains also include discrete variables that depend on continuous ones, and CLG networks do not allow such dependencies to berepresented. No exact inference algorithm has been proposed for these enhanced CLG networks. In this paper, we generalize Lauritzen's algorithm, providing the first \"exact\" inference algorithm for augmented CLG networks - networks where continuous nodes are conditional linear Gaussians but that also allow discrete children ofcontinuous parents. Our algorithm is exact in the sense that it computes the exact distributions over the discrete nodes, and the exact first and second moments of the continuous ones, up to the accuracy obtained by numerical integration used within thealgorithm. When the discrete children are modeled with softmax CPDs (as is the case in many real world domains) the approximation of the continuous distributions using the first two moments is particularly accurate. Our algorithm is simple to implement and often comparable in its complexity to Lauritzen's algorithm. We show empirically that it achieves substantially higher accuracy than previous approximate algorithms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2289v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"25281\", \"296258\", \"357256\", \"357279\"], \"outgoing_citations\": [\"346873\", \"349214\", \"349846\", \"349861\", \"350162\", \"350484\"]}","task_split":"paper_retrieval"}
{"document_id":"350504","document_content":"# Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nGlobal variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model determines a tradeoff between the complexity of the approximation procedure and the quality of the approximation. In this paper, we consider variational approximations based on two classes of models that are richer than standard Bayesian networks, Markov networks or mixture models. As such, these classes allow to find better tradeoffs in the spectrum of approximations. The first class of models are chain graphs, which capture distributions that are partially directed. The second class of models are directed graphs (Bayesian networks) with additional latent variables. Both classes allow representation of multi-variable dependencies that cannot be easily represented within a Bayesian network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2268v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"353184\"], \"outgoing_citations\": [\"350162\"]}","task_split":"paper_retrieval"}
{"document_id":"350509","document_content":"# Linearity Properties of Bayes Nets with Binary Variables\n## Categories\n- Artificial Intelligence\n## Abstract\nIt is \"well known\" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of \"instrumental variables\" sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or trek can be computed directly from the parameter values associated with each edge in the path or trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables.These properties are exploited in search procedures. It is also known that properties (2)-(5) do not hold for all Bayes nets with binary variables. We show that (1) holds for all Bayes nets with binary variables and (5) holds for all singly trek-connected Bayes nets of that kind. We further show that all five properties hold for Bayes nets with any DAG and binary variables parameterized with noisy-or and noisy-and gates.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2263v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"99942\", \"361806\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"350512","document_content":"# Confidence Inference in Bayesian Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nWe present two sampling algorithms for probabilistic confidence inference in Bayesian networks. These two algorithms (we call them AIS-BN-mu and AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities are with a given probability within a desired precision bound. Our algorithms are based on recent advances in sampling algorithms for (1) estimating the mean of bounded random variables and (2) adaptive importance sampling in Bayesian networks. In addition to a simple stopping rule for sampling that they provide, the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning process in the AIS-BN algorithm. An empirical evaluation of the proposed algorithms shows excellent performance, even for very unlikely evidence.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2260v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"307565\"], \"outgoing_citations\": [\"346873\", \"346895\", \"348240\"]}","task_split":"paper_retrieval"}
{"document_id":"350515","document_content":"# Content-Based Video Browsing by Text Region Localization and Classification\n## Categories\n- Multimedia\n- Information Retrieval\n## Abstract\nThe amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can index, retrieve and browse this data by content. This can be achieved by identifying semantic description captured automatically from video structure. Among these descriptions, text within video is considered as rich features that enable a good way for video indexing and browsing. Unlike most video text detection and extraction methods that treat video sequences as collections of still images, we propose in this paper spatiotemporal. video-text localization and identification approach which proceeds in two main steps: text region localization and text region classification. In the first step we detect the significant appearance of the new objects in a frame by a split and merge processes applied on binarized edge frame pair differences. Detected objects are, a priori, considered as text. They are then filtered according to both local contrast variation and texture criteria in order to get the effective ones. The resulted text regions are classified based on a visual grammar descriptor containing a set of semantic text class regions characterized by visual features. A visual table of content is then generated based on extracted text regions occurring within video sequence enriched by a semantic identification. The experimentation performed on a variety of video sequences shows the efficiency of our approach.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2172v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.MM\", \"cs.IR\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Multimedia\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"350516","document_content":"# A Calculus for Causal Relevance\n## Categories\n- Artificial Intelligence\n## Abstract\nThis paper presents a sound and completecalculus for causal relevance, based onPearl's functional models semantics.The calculus consists of axioms and rulesof inference for reasoning about causalrelevance relationships.We extend the set of known axioms for causalrelevance with three new axioms, andintroduce two new rules of inference forreasoning about specific subclasses ofmodels.These subclasses give a more refinedcharacterization of causal models than the one given in Halpern's axiomatizationof counterfactual reasoning.Finally, we show how the calculus for causalrelevance can be used in the task ofidentifying causal structure from non-observational data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.2257v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"326077\", \"350460\"]}","task_split":"paper_retrieval"}
{"document_id":"350523","document_content":"# Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nIn the modern healthcare system, rapidly expanding costs\/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational\/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate\/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.artmed.2012.12.003\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"2340\", \"34568\", \"55591\", \"253174\", \"104316\", \"142752\", \"175709\", \"215167\", \"207983\", \"247676\", \"255363\", \"264565\", \"306647\"], \"outgoing_citations\": [\"335323\", \"358316\"]}","task_split":"paper_retrieval"}
{"document_id":"350898","document_content":"# Human-Recognizable Robotic Gestures\n## Categories\n- Robotics\n- Artificial Intelligence\n- Human-Computer Interaction\n## Abstract\nFor robots to be accommodated in human spaces and in humans daily activities, robots should be able to understand messages from the human conversation partner. In the same light, humans must also understand the messages that are being communicated by robots, including the non-verbal ones. We conducted a web-based video study wherein participants gave interpretations on the iconic gestures and emblems that were produced by an anthropomorphic robot. Out of the 15 gestures presented, we found 6 robotic gestures that can be accurately recognized by the human observer. These were nodding, clapping, hugging, expressing anger, walking, and flying. We reviewed these gestures for their meaning from literatures in human and animal behavior. We conclude by discussing the possible implications of these gestures for the design of social robots that are aimed to have engaging interactions with humans.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TAMD.2012.2208962\", \"primary_category\": \"cs.RO\", \"categories\": [\"cs.AI\", \"cs.RO\", \"cs.HC\"], \"primary_category_human_readable\": \"Robotics\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Robotics\", \"Human-Computer Interaction\"], \"incoming_citations\": [\"288185\", \"338362\"], \"outgoing_citations\": [\"356715\"]}","task_split":"paper_retrieval"}
{"document_id":"351109","document_content":"# Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence\n## Categories\n- Artificial Intelligence\n- Logic\n- Machine Learning\n- I.2; F.1.2; G.3; F.4.1\n## Abstract\nThe problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged as a fundamental tenet of Artificial Intelligence (AI) and cognitive science. But what kind of computation is cognition?   We describe a computational formalism centered around a probabilistic Turing machine called QUERY, which captures the operation of probabilistic conditioning via conditional simulation. Through several examples and analyses, we demonstrate how the QUERY abstraction can be used to cast common-sense reasoning as probabilistic inference in a statistical model of our observations and the uncertain structure of the world that generated that experience. This formulation is a recent synthesis of several research programs in AI and cognitive science, but it also represents a surprising convergence of several of Turing's pioneering insights in AI, the foundations of computation, and statistics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1017\/CBO9781107338579.007\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"math.LO\", \"stat.ML\", \"I.2; F.1.2; G.3; F.4.1\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Logic\", \"Machine Learning\", \"I.2; F.1.2; G.3; F.4.1\"], \"incoming_citations\": [\"37108\", \"59677\", \"177610\", \"204320\", \"222974\"], \"outgoing_citations\": [\"357281\", \"357794\", \"357812\", \"357921\", \"357936\"]}","task_split":"paper_retrieval"}
{"document_id":"351146","document_content":"# When you talk about \"Information processing\" what actually do you have in mind?\n## Categories\n- Artificial Intelligence\n- Neurons and Cognition\n## Abstract\n\"Information Processing\" is a recently launched buzzword whose meaning is vague and obscure even for the majority of its users. The reason for this is the lack of a suitable definition for the term \"information\". In my attempt to amend this bizarre situation, I have realized that, following the insights of Kolmogorov's Complexity theory, information can be defined as a description of structures observable in a given data set. Two types of structures could be easily distinguished in every data set - in this regard, two types of information (information descriptions) should be designated: physical information and semantic information. Kolmogorov's theory also posits that the information descriptions should be provided as a linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as a kind of text processing. The idea is not new - inspired by the observation that human information processing is deeply rooted in natural language handling customs, Lotfi Zadeh and his followers have introduced the so-called \"Computing With Words\" paradigm. Despite of promotional efforts, the idea is not taking off yet. The reason - a lack of a coherent understanding of what should be called \"information\", and, as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues would be helpful in avoiding common traps and pitfalls.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.4137v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"q-bio.NC\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neurons and Cognition\"], \"incoming_citations\": [], \"outgoing_citations\": [\"363131\"]}","task_split":"paper_retrieval"}
{"document_id":"351389","document_content":"# Reinforcement Learning with Partially Known World Dynamics\n## Categories\n- Machine Learning\n## Abstract\nReinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not provide a natural framework in which to specify knowledge about the domain dynamics. The designer must either admit to knowing nothing about the dynamics or completely specify the dynamics (thereby turning it into a planning problem). We propose a new framework called a partially known Markov decision process (PKMDP) which allows the designer to specify known dynamics while still leaving portions of the environment s dynamics unknown.The model represents NOT ONLY the environment dynamics but also the agents knowledge of the dynamics. We present a reinforcement learning algorithm for this model based on importance sampling. The algorithm incorporates planning based on the known dynamics and learning about the unknown dynamics. Our results clearly demonstrate the ability to add domain knowledge and the resulting benefits for learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1301.0601v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": [\"350462\"]}","task_split":"paper_retrieval"}
{"document_id":"351675","document_content":"# Compiling Relational Database Schemata into Probabilistic Graphical Models\n## Categories\n- Artificial Intelligence\n- Databases\n- Machine Learning\n- Machine Learning\n## Abstract\nInstead of requiring a domain expert to specify the probabilistic dependencies of the data, in this work we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for columns, latent variables that cluster the data, and factors that reflect and represent the foreign key links. Experiments demonstrate the accuracy of the model and the scalability of inference on synthetic and real-world data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1212.0967v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.DB\", \"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Databases\", \"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"294474\"], \"outgoing_citations\": [\"351386\", \"357269\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"351883","document_content":"# Letter counting: a stem cell for Cryptology, Quantitative Linguistics, and Statistics\n## Categories\n- History and Overview\n- Cryptography and Security\n- Computation and Language\n## Abstract\nCounting letters in written texts is a very ancient practice. It has accompanied the development of Cryptology, Quantitative Linguistics, and Statistics. In Cryptology, counting frequencies of the different characters in an encrypted message is the basis of the so called frequency analysis method. In Quantitative Linguistics, the proportion of vowels to consonants in different languages was studied long before authorship attribution. In Statistics, the alternation vowel-consonants was the only example that Markov ever gave of his theory of chained events. A short history of letter counting is presented. The three domains, Cryptology, Quantitative Linguistics, and Statistics, are then examined, focusing on the interactions with the other two fields through letter counting. As a conclusion, the eclectism of past centuries scholars, their background in humanities, and their familiarity with cryptograms, are identified as contributing factors to the mutual enrichment process which is described here.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1211.6847v1\", \"primary_category\": \"math.HO\", \"categories\": [\"cs.CR\", \"cs.CL\", \"math.HO\"], \"primary_category_human_readable\": \"History and Overview\", \"categories_human_readable\": [\"Cryptography and Security\", \"Computation and Language\", \"History and Overview\"], \"incoming_citations\": [], \"outgoing_citations\": [\"352945\"]}","task_split":"paper_retrieval"}
{"document_id":"351927","document_content":"# TwitterPaul: Extracting and Aggregating Twitter Predictions\n## Categories\n- Social and Information Networks\n- Physics and Society\n- Artificial Intelligence\n## Abstract\nThis paper introduces TwitterPaul, a system designed to make use of Social Media data to help to predict game outcomes for the 2010 FIFA World Cup tournament. To this end, we extracted over 538K mentions to football games from a large sample of tweets that occurred during the World Cup, and we classified into different types with a precision of up to 88%. The different mentions were aggregated in order to make predictions about the outcomes of the actual games. We attempt to learn which Twitter users are accurate predictors and explore several techniques in order to exploit this information to make more accurate predictions. We compare our results to strong baselines and against the betting line (prediction market) and found that the quality of extractions is more important than the quantity, suggesting that high precision methods working on a medium-sized dataset are preferable over low precision methods that use a larger amount of data. Finally, by aggregating some classes of predictions, the system performance is close to the one of the betting line. Furthermore, we believe that this domain independent framework can help to predict other sports, elections, product release dates and other future events that people talk about in social media.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1211.6496v2\", \"primary_category\": \"cs.SI\", \"categories\": [\"cs.SI\", \"physics.soc-ph\", \"cs.AI\"], \"primary_category_human_readable\": \"Social and Information Networks\", \"categories_human_readable\": [\"Social and Information Networks\", \"Physics and Society\", \"Artificial Intelligence\"], \"incoming_citations\": [\"322234\"], \"outgoing_citations\": [\"352667\", \"357319\", \"359297\", \"361037\"]}","task_split":"paper_retrieval"}
{"document_id":"352522","document_content":"# 3D Surface Reconstruction of Underwater Objects\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn this paper, we propose a novel technique to reconstruct 3D surface of an underwater object using stereo images. Reconstructing the 3D surface of an underwater object is really a challenging task due to degraded quality of underwater images. There are various reason of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Floating particles present in underwater produces Gaussian noise on the captured underwater images which degrades the quality of images. The degraded underwater images are preprocessed by applying homomorphic, wavelet denoising and anisotropic filtering sequentially. The uncalibrated rectification technique is applied to preprocessed images to rectify the left and right images. The rectified left and right image lies on a common plane. To find the correspondence points in a left and right images, we have applied dense stereo matching technique i.e., graph cut method. Finally, we estimate the depth of images using triangulation technique. The experimental result shows that the proposed method reconstruct 3D surface of underwater objects accurately using captured underwater stereo images.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1211.2082v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"353202","document_content":"# Dealing with uncertainty in fuzzy inductive reasoning methodology\n## Categories\n- Artificial Intelligence\n## Abstract\nThe aim of this research is to develop a reasoning under uncertainty strategy in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged from the General Systems Problem Solving developed by G. Klir. It is a data driven methodology based on systems behavior rather than on structural knowledge. It is a very useful tool for both the modeling and the prediction of those systems for which no previous structural knowledge is available. FIR reasoning is based on pattern rules synthesized from the available data. The size of the pattern rule base can be very large making the prediction process quite difficult. In order to reduce the size of the pattern rule base, it is possible to automatically extract classical Sugeno fuzzy rules starting from the set of pattern rules. The Sugeno rule base preserves pattern rules knowledge as much as possible. In this process some information is lost but robustness is considerably increased. In the forecasting process either the pattern rule base or the Sugeno fuzzy rule base can be used. The first option is desirable when the computational resources make it possible to deal with the overall pattern rule base or when the extracted fuzzy rules are not accurate enough due to uncertainty associated to the original data. In the second option, the prediction process is done by means of the classical Sugeno inference system. If the amount of uncertainty associated to the data is small, the predictions obtained using the Sugeno fuzzy rule base will be very accurate. In this paper a mixed pattern\/fuzzy rules strategy is proposed to deal with uncertainty in such a way that the best of both perspectives is used. Areas in the data space with a higher level of uncertainty are identified by means of the so-called error models. The prediction process in these areas makes use of a mixed pattern\/fuzzy rules scheme, whereas areas identified with a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed strategy is applied to a real biomedical system, i.e., the central nervous system control of the cardiovascular system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1212.2501v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"353204","document_content":"# Practically Perfect\n## Categories\n- Artificial Intelligence\n- Machine Learning\n## Abstract\nThe property of perfectness plays an important role in the theory of Bayesian networks. First, the existence of perfect distributions for arbitrary sets of variables and directed acyclic graphs implies that various methods for reading independence from the structure of the graph (e.g., Pearl, 1988; Lauritzen, Dawid, Larsen & Leimer, 1990) are complete. Second, the asymptotic reliability of various search methods is guaranteed under the assumption that the generating distribution is perfect (e.g., Spirtes, Glymour & Scheines, 2000; Chickering & Meek, 2002). We provide a lower-bound on the probability of sampling a non-perfect distribution when using a fixed number of bits to represent the parameters of the Bayesian network. This bound approaches zero exponentially fast as one increases the number of bits used to represent the parameters. This result implies that perfect distributions with fixed-length representations exist. We also provide a lower-bound on the number of bits needed to guarantee that a distribution sampled from a uniform Dirichlet distribution is perfect with probability greater than 1\/2. This result is useful for constructing randomized reductions for hardness proofs.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1212.2503v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"353212","document_content":"# Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries\n## Categories\n- Artificial Intelligence\n## Abstract\nIn this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intractable for exact algorithms because of the large number of dependency relationships in their structure. Our method effectively maps such a dense problem to a sparser one which is in some sense \"closest\". Exact methods can be run on the sparser problem to derive bounds on the original answer, which can be quite sharp. We present empirical results demonstrating that our method works well on the tasks of belief inference and finding the probability of the most probable explanation in belief networks, and finding the cost of the solution that violates the smallest number of constraints in constraint satisfaction problems. On one large CPCS network, for example, we were able to calculate upper and lower bounds on the conditional probability of a variable, given evidence, that were almost identical in the average case.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1212.2476v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"335187\"], \"outgoing_citations\": [\"349235\"]}","task_split":"paper_retrieval"}
{"document_id":"353246","document_content":"# An Empirical Study of w-Cutset Sampling for Bayesian Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nThe paper studies empirically the time-space trade-off between sampling and inference in a sl cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. Consequently, while the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. The w-cutset sampling selects a sampling set such that the induced-width of the network when the sampling set is observed is bounded by w, thus requiring inference whose complexity is exponential in w. In this paper, we investigate performance of w-cutset sampling over a range of w values and measure the accuracy of w-cutset sampling as a function of w. Our experiments demonstrate that the cutset sampling idea is quite powerful showing that an optimal balance between inference and sampling benefits substantially from restricting the cutset size, even at the cost of more complex inference.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1212.2449v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"335187\", \"357948\", \"357311\", \"356868\", \"356553\", \"356575\"], \"outgoing_citations\": [\"326076\", \"348240\", \"348592\", \"349861\"]}","task_split":"paper_retrieval"}
{"document_id":"353350","document_content":"# Gender identity and lexical variation in social media\n## Categories\n- Computation and Language\n## Abstract\nWe present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female\/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1111\/josl.12080\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"19940\", \"28399\", \"516\", \"32587\", \"49962\", \"56060\", \"56479\", \"59194\", \"71827\", \"127554\", \"132077\", \"90943\", \"119022\", \"177884\", \"184142\", \"169369\", \"180341\", \"181274\", \"186245\", \"199498\", \"200484\", \"201322\", \"209959\", \"208724\", \"209754\", \"207393\", \"232792\", \"234860\", \"246778\", \"261421\", \"264026\", \"266575\", \"265307\", \"274586\", \"274596\", \"275680\", \"280040\", \"279053\", \"280033\", \"296714\", \"301070\", \"304542\", \"314353\", \"307406\", \"301151\", \"304623\", \"311628\", \"324825\", \"329173\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"354075","document_content":"# Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition\n## Categories\n- Artificial Intelligence\n- 97R40\n- I.2\n## Abstract\nThis paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1209.6195v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"97R40\", \"I.2\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"97R40\", \"I.2\"], \"incoming_citations\": [], \"outgoing_citations\": [\"354076\", \"362078\"]}","task_split":"paper_retrieval"}
{"document_id":"354338","document_content":"# Application of Fuzzy Mathematics to Speech-to-Text Conversion by Elimination of Paralinguistic Content\n## Categories\n- Artificial Intelligence\n## Abstract\nFor the past few decades, man has been trying to create an intelligent computer which can talk and respond like he can. The task of creating a system that can talk like a human being is the primary objective of Automatic Speech Recognition. Various Speech Recognition techniques have been developed in theory and have been applied in practice. This paper discusses the problems that have been encountered in developing Speech Recognition, the techniques that have been applied to automate the task, and a representation of the core problems of present day Speech Recognition by using Fuzzy Mathematics.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1209.4535v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"330232\", \"354339\", \"354357\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"356102","document_content":"# FST Based Morphological Analyzer for Hindi Language\n## Categories\n- Computation and Language\n- Information Retrieval\n## Abstract\nHindi being a highly inflectional language, FST (Finite State Transducer) based approach is most efficient for developing a morphological analyzer for this language. The work presented in this paper uses the SFST (Stuttgart Finite State Transducer) tool for generating the FST. A lexicon of root words is created. Rules are then added for generating inflectional and derivational words from these root words. The Morph Analyzer developed was used in a Part Of Speech (POS) Tagger based on Stanford POS Tagger. The system was first trained using a manually tagged corpus and MAXENT (Maximum Entropy) approach of Stanford POS tagger was then used for tagging input sentences. The morphological analyzer gives approximately 97% correct results. POS tagger gives an accuracy of approximately 87% for the sentences that have the words known to the trained model file, and 80% accuracy for the sentences that have the words unknown to the trained model file.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1207.5409v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"2107\", \"139341\", \"209217\", \"330799\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"356668","document_content":"# Improvement of ISOM by using filter\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n## Abstract\nImage compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depend upon this the image compression is classified into two categories : lossy and lossless image compression. There are many lossy digital image compression techniques exists. Among this Incremental Self Organizing Map is a familiar one. The good pictures quality can be retrieved if image denoising technique is used for compression and also provides better compression ratio. Image denoising is an important pre-processing step for many image analysis and computer vision system. It refers to the task of recovering a good estimate of the true image from a degraded observation without altering and changing useful structure in the image such as discontinuities and edges. Many approaches have been proposed to remove the noise effectively while preserving the original image details and features as much as possible. This paper proposes a technique for image compression using Incremental Self Organizing Map (ISOM) with Discret Wavelet Transform (DWT) by applying filtering techniques which play a crucial role in enhancing the quality of a reconstructed image. The experimental result shows that the proposed technique obtained better compression ratio value.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1207.2268v1\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.MM\", \"cs.CV\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Multimedia\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"356884","document_content":"# Hybrid Bayesian Networks with Linear Deterministic Variables\n## Categories\n- Artificial Intelligence\n## Abstract\nWhen a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations required for performing inference with conditionally deterministic variables in hybrid Bayesian networks are developed. These methods allow inference in networks with deterministic variables where continuous variables may be non-Gaussian, and their density functions can be approximated by mixtures of truncated exponentials. There are no constraints on the placement of continuous and discrete nodes in the network.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1207.1369v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"357256\"], \"outgoing_citations\": [\"356566\"]}","task_split":"paper_retrieval"}
{"document_id":"357149","document_content":"# Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs\n## Categories\n- Artificial Intelligence\n- Computational Engineering, Finance, and Science\n- Machine Learning\n## Abstract\nWe consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to directed paths in a causal model. This type of knowledge naturally arises from experimental data, among others. Specifically, we consider the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their Markov equivalence classes: Partially Directed Acyclic Graphs and Partially Oriented Ancestral Graphs. We introduce sound and complete procedures which are able to incorporate causal prior knowledge in such models. In simulated experiments, we show that often considering even a few causal facts leads to a significant number of new inferences. In a case study, we also show how to use real experimental data to infer causal knowledge and incorporate it into a real biological causal network. The code is available at mensxmachina.org.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6390v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CE\", \"cs.LG\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computational Engineering, Finance, and Science\", \"Machine Learning\"], \"incoming_citations\": [\"21521\", \"193208\", \"30106\", \"280626\", \"289822\", \"339836\"], \"outgoing_citations\": [\"348588\"]}","task_split":"paper_retrieval"}
{"document_id":"357211","document_content":"# Smoothness and Structure Learning by Proxy\n## Categories\n- Machine Learning\n- Optimization and Control\n## Abstract\nAs data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and generate a score for each that takes all of the observed data into account. For instance, Bayesian networks, the model chosen in this paper, have a super-exponentially large search space for a fixed number of variables. One possible method to alleviate this problem is to use a proxy, such as a Gaussian Process regressor, in place of the true scoring function, training it on a selection of sampled networks. We prove here that the use of such a proxy is well-founded, as we can bound the smoothness of a commonly-used scoring function for Bayesian network structure learning. We show here that, compared to an identical search strategy using the network?s exact scores, our proxy-based search is able to get equivalent or better scores on a number of data sets in a fraction of the time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6452v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"math.OC\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Optimization and Control\", \"Machine Learning\"], \"incoming_citations\": [\"189083\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"357240","document_content":"# Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning\n## Categories\n- Computation and Language\n- Information Retrieval\n- Machine Learning\n## Abstract\nIn many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. This method is built on parallel corpora produced by machine translation. It jointly minimizes the training error of each classifier in each language while penalizing the distance between the subspace representations of parallel documents. Our empirical study on a large set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6481v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"43595\", \"78934\", \"120961\", \"157047\", \"151790\", \"185664\", \"251385\", \"251751\", \"254577\", \"267233\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"357275","document_content":"# Sequential Document Representations and Simplicial Curves\n## Categories\n- Information Retrieval\n- Machine Learning\n## Abstract\nThe popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present a continuous and differentiable sequential document representation that goes beyond the bag of words assumption, and yet is efficient and effective. This representation employs smooth curves in the multinomial simplex to account for sequential information. We discuss the representation and its geometric properties and demonstrate its applicability for the task of text classification.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6858v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\"], \"incoming_citations\": [\"357583\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"357281","document_content":"# Structured Priors for Structure Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n## Abstract\nTraditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in. Here we capture this form of prior knowledge in a hierarchical Bayesian framework, and exploit it to enable structure learning and type discovery from small datasets. Specifically, we present a nonparametric generative model for directed acyclic graphs as a prior for Bayes net structure learning. Our model assumes that variables come in one or more classes and that the prior probability of an edge existing between two variables is a function only of their classes. We derive an MCMC algorithm for simultaneous inference of the number of classes, the class assignments of variables, and the Bayes net structure over variables. For several realistic, sparse datasets, we show that the bias towards systematicity of connections provided by our model yields more accurate learned networks than a traditional, uniform prior approach, and that the classes found by our model are appropriate.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6852v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Machine Learning\"], \"incoming_citations\": [\"33938\", \"28154\", \"145169\", \"158877\", \"176146\", \"215715\", \"292244\", \"331740\", \"351109\", \"353292\", \"357584\"], \"outgoing_citations\": [\"353190\", \"356824\"]}","task_split":"paper_retrieval"}
{"document_id":"357284","document_content":"# General-Purpose MCMC Inference over Relational Structures\n## Categories\n- Artificial Intelligence\n## Abstract\nTasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system requires coding MCMC state representations and acceptance probability calculations that are specific to a particular application. An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries. We also show how to use a context-specific Bayes net to identify the factors in the acceptance probability that need to be computed for a given proposed move. Experimental results on a citation matching task show that our general-purpose MCMC engine compares favorably with an application-specific system.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.6849v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"110164\", \"289314\", \"360708\"], \"outgoing_citations\": [\"350519\", \"356907\"]}","task_split":"paper_retrieval"}
{"document_id":"357415","document_content":"# Representation Learning: A Review and New Perspectives\n## Categories\n- Machine Learning\n## Abstract\nThe success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.5538v3\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"46898\", \"1734\", \"1802\", \"2288\", \"9497\", \"12645\", \"21103\", \"31054\", \"32774\", \"38327\", \"34478\", \"41514\", \"44400\", \"55919\", \"60450\", \"64060\", \"64244\", \"65457\", \"66565\", \"68390\", \"73056\", \"76208\", \"83187\", \"85361\", \"103854\", \"123263\", \"154035\", \"184612\", \"191885\", \"227041\", \"185065\"], \"outgoing_citations\": [\"350253\", \"352250\", \"356259\", \"357080\", \"357151\", \"357193\", \"357591\", \"357658\", \"358901\", \"360567\", \"361817\", \"362725\"]}","task_split":"paper_retrieval"}
{"document_id":"357561","document_content":"# Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models\n## Categories\n- Methodology\n- Artificial Intelligence\n## Abstract\nWhen observational data is available from practical studies and a directed cyclic graph for how various variables affect each other is known based on substantive understanding of the process, we consider a problem in which a control plan of a treatment variable is conducted in order to bring a response variable close to a target value with variation reduction. We formulate an optimal control plan concerning a certain treatment variable through path coefficients in the framework of linear nonrecursive structural equation models. Based on the formulation, we clarify the properties of causal effects when conducting a control plan. The results enable us to evaluate the effect of a control plan on the variance from observational data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.5272v1\", \"primary_category\": \"stat.ME\", \"categories\": [\"cs.AI\", \"stat.ME\"], \"primary_category_human_readable\": \"Methodology\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Methodology\"], \"incoming_citations\": [], \"outgoing_citations\": [\"348578\", \"348925\", \"348926\", \"356562\", \"356877\"]}","task_split":"paper_retrieval"}
{"document_id":"357613","document_content":"# Joint Reconstruction of Multi-view Compressed Images\n## Categories\n- Multimedia\n- Computer Vision and Pattern Recognition\n## Abstract\nThe distributed representation of correlated multi-view images is an important problem that arise in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed correlated images are jointly decoded in order to improve the reconstruction quality of all the compressed images. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among different cameras. A central decoder first estimates the underlying correlation model from the independently compressed images which will be used for the joint signal recovery. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images that comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be consistent with their compressed versions. We show by experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our proposed algorithm compares advantageously to state-of-the-art distributed coding schemes based on disparity learning and on the DISCOVER.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/TIP.2013.2240006\", \"primary_category\": \"cs.MM\", \"categories\": [\"cs.MM\", \"cs.CV\"], \"primary_category_human_readable\": \"Multimedia\", \"categories_human_readable\": [\"Multimedia\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"357707","document_content":"# Modeling Latent Variable Uncertainty for Loss-based Learning\n## Categories\n- Machine Learning\n- Artificial Intelligence\n- Computer Vision and Pattern Recognition\n## Abstract\nWe consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.4636v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"cs.AI\", \"cs.CV\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Artificial Intelligence\", \"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [\"111154\", \"208797\", \"223539\", \"324957\", \"332853\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"357880","document_content":"# Inference for Multiplicative Models\n## Categories\n- Artificial Intelligence\n## Abstract\nThe paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture multiple forms of contextual independence between variables, including decision graphs and noisy-OR functions. An inference algorithm for multiplicative models is provided and its correctness is proved. The complexity analysis of the inference algorithm uses a more refined parameter than the tree-width of the underlying graph, and shows the computational cost does not exceed that of the variable elimination algorithm in graphical models. The paper ends with examples where using the new models and algorithm is computationally beneficial.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.3296v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"342534\", \"343831\"], \"outgoing_citations\": [\"157691\", \"346888\", \"348963\"]}","task_split":"paper_retrieval"}
{"document_id":"357901","document_content":"# Explanation Trees for Causal Bayesian Networks\n## Categories\n- Artificial Intelligence\n## Abstract\nBayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.3276v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"102965\", \"184390\", \"275120\", \"339819\", \"335124\", \"358897\"], \"outgoing_citations\": [\"349243\", \"351398\", \"357314\"]}","task_split":"paper_retrieval"}
{"document_id":"357922","document_content":"# Latent Topic Models for Hypertext\n## Categories\n- Information Retrieval\n- Machine Learning\n- Computation and Language\n- Machine Learning\n## Abstract\nLatent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext [6, 9]. These approaches typically model links in an analogous fashion to how they model words - the document-link co-occurrence matrix is modeled in the same way that the document-word co-occurrence matrix is modeled in standard topic models. In this paper we present a probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far fewer free parameters and obtain better link prediction results.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.3254v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.IR\", \"cs.LG\", \"cs.CL\", \"stat.ML\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Information Retrieval\", \"Machine Learning\", \"Computation and Language\", \"Machine Learning\"], \"incoming_citations\": [\"270348\", \"300613\", \"339278\", \"346647\", \"360263\", \"358899\"], \"outgoing_citations\": [\"351402\", \"356577\"]}","task_split":"paper_retrieval"}
{"document_id":"357948","document_content":"# AND\/OR Importance Sampling\n## Categories\n- Artificial Intelligence\n## Abstract\nThe paper introduces AND\/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND\/OR importance sampling caches samples in the AND\/OR space and then extracts a new sample mean from the stored samples. We prove that AND\/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND\/OR importance sampling is far more accurate than importance sampling in many cases.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.3232v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [\"2380\", \"339840\", \"360726\"], \"outgoing_citations\": [\"348592\", \"353246\", \"356868\"]}","task_split":"paper_retrieval"}
{"document_id":"358266","document_content":"# UNL Based Bangla Natural Text Conversion - Predicate Preserving Parser Approach\n## Categories\n- Computation and Language\n## Abstract\nUniversal Networking Language (UNL) is a declarative formal language that is used to represent semantic data extracted from natural language texts. This paper presents a novel approach to converting Bangla natural language text into UNL using a method known as Predicate Preserving Parser (PPP) technique. PPP performs morphological, syntactic and semantic, and lexical analysis of text synchronously. This analysis produces a semantic-net like structure represented using UNL. We demonstrate how Bangla texts are analyzed following the PPP technique to produce UNL documents which can then be translated into any other suitable natural language facilitating the opportunity to develop a universal language translation method via UNL.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.0381v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"358314","document_content":"# Language Acquisition in Computers\n## Categories\n- Computation and Language\n- I.2.6; I.2.7\n## Abstract\nThis project explores the nature of language acquisition in computers, guided by techniques similar to those used in children. While existing natural language processing methods are limited in scope and understanding, our system aims to gain an understanding of language from first principles and hence minimal initial input. The first portion of our system was implemented in Java and is focused on understanding the morphology of language using bigrams. We use frequency distributions and differences between them to define and distinguish languages. English and French texts were analyzed to determine a difference threshold of 55 before the texts are considered to be in different languages, and this threshold was verified using Spanish texts. The second portion of our system focuses on gaining an understanding of the syntax of a language using a recursive method. The program uses one of two possible methods to analyze given sentences based on either sentence patterns or surrounding words. Both methods have been implemented in C++. The program is able to understand the structure of simple sentences and learn new words. In addition, we have provided some suggestions regarding future work and potential extensions of the existing program.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1206.0042v1\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\", \"I.2.6; I.2.7\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\", \"I.2.6; I.2.7\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"358507","document_content":"# FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model\n## Categories\n- Computation and Language\n## Abstract\nLexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction. However the computational complexity of accurately identifying the most likely substitutes for a word has made large scale experiments difficult. In this paper I introduce a new search algorithm, FASTSUBS, that is guaranteed to find the K most likely lexical substitutes for a given word in a sentence based on an n-gram language model. The computation is sub-linear in both K and the vocabulary size V. An implementation of the algorithm and a dataset with the top 100 substitutes of each token in the WSJ section of the Penn Treebank are available at http:\/\/goo.gl\/jzKH0.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1109\/LSP.2012.2215587\", \"primary_category\": \"cs.CL\", \"categories\": [\"cs.CL\"], \"primary_category_human_readable\": \"Computation and Language\", \"categories_human_readable\": [\"Computation and Language\"], \"incoming_citations\": [\"300574\", \"326598\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"358882","document_content":"# Discrimination of English to other Indian languages (Kannada and Hindi) for OCR system\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIndia is a multilingual multi-script country. In every state of India there are two languages one is state local language and the other is English. For example in Andhra Pradesh, a state in India, the document may contain text words in English and Telugu script. For Optical Character Recognition (OCR) of such a bilingual document, it is necessary to identify the script before feeding the text words to the OCRs of individual scripts. In this paper, we are introducing a simple and efficient technique of script identification for Kannada, English and Hindi text words of a printed document. The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. The feature extraction is done based on the horizontal projection profile of each text words. We analysed 700 different words of Kannada, English and Hindi in order to extract the discrimination features and for the development of knowledge base. We use the horizontal projection profile of each text word and based on the horizontal projection profile we extract the appropriate features. The proposed system is tested on 100 different document images containing more than 1000 text words of each script and a classification rate of 98.25%, 99.25% and 98.87% is achieved for Kannada, English and Hindi respectively.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1205.2164v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"358941","document_content":"# Monolingual Probabilistic Programming Using Generalized Coroutines\n## Categories\n- Programming Languages\n- Artificial Intelligence\n## Abstract\nProbabilistic programming languages and modeling toolkits are two modular ways to build and reuse stochastic models and inference procedures. Combining strengths of both, we express models and inference as generalized coroutines in the same general-purpose language. We use existing facilities of the language, such as rich libraries, optimizing compilers, and types, to develop concise, declarative, and realistic models with competitive performance on exact and approximate inference. In particular, a wide range of models can be expressed using memoization. Because deterministic parts of models run at full speed, custom inference procedures are trivial to incorporate, and inference procedures can reason about themselves without interpretive overhead. Within this framework, we introduce a new, general algorithm for importance sampling with look-ahead.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1205.2636v1\", \"primary_category\": \"cs.PL\", \"categories\": [\"cs.AI\", \"cs.PL\"], \"primary_category_human_readable\": \"Programming Languages\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Programming Languages\"], \"incoming_citations\": [\"322323\", \"320464\", \"332931\", \"341761\"], \"outgoing_citations\": [\"346873\", \"346895\", \"356567\", \"357921\"]}","task_split":"paper_retrieval"}
{"document_id":"359118","document_content":"# Robot Navigation using Reinforcement Learning and Slow Feature Analysis\n## Categories\n- Artificial Intelligence\n- Neural and Evolutionary Computing\n## Abstract\nThe application of reinforcement learning algorithms onto real life problems always bears the challenge of filtering the environmental state out of raw sensor readings. While most approaches use heuristics, biology suggests that there must exist an unsupervised method to construct such filters automatically. Besides the extraction of environmental states, the filters have to represent them in a fashion that support modern reinforcement algorithms. Many popular algorithms use a linear architecture, so one should aim at filters that have good approximation properties in combination with linear functions. This thesis wants to propose the unsupervised method slow feature analysis (SFA) for this task. Presented with a random sequence of sensor readings, SFA learns a set of filters. With growing model complexity and training examples, the filters converge against trigonometric polynomial functions. These are known to possess excellent approximation capabilities and should therfore support the reinforcement algorithms well. We evaluate this claim on a robot. The task is to learn a navigational control in a simple environment using the least square policy iteration (LSPI) algorithm. The only accessible sensor is a head mounted video camera, but without meaningful filtering, video images are not suited as LSPI input. We will show that filters learned by SFA, based on a random walk video of the robot, allow the learned control to navigate successfully in ca. 80% of the test trials.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1205.0986v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.NE\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"357686\"], \"outgoing_citations\": [\"346732\"]}","task_split":"paper_retrieval"}
{"document_id":"359490","document_content":"# EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect\n## Categories\n- Artificial Intelligence\n- Databases\n- Machine Learning\n## Abstract\nObjectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that \"learn\" over time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1016\/j.hlpt.2012.03.001\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.DB\", \"stat.ML\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Databases\", \"Machine Learning\"], \"incoming_citations\": [\"215167\", \"269208\"], \"outgoing_citations\": [\"358316\"]}","task_split":"paper_retrieval"}
{"document_id":"359842","document_content":"# Modeling Relational Data via Latent Factor Blockmodel\n## Categories\n- Data Structures and Algorithms\n- Machine Learning\n- Machine Learning\n- H.2.8; J.4\n- 15A83\n## Abstract\nIn this paper we address the problem of modeling relational data, which appear in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with latent characteristics of objects. To combine the benefits of the previous work, we propose a novel model that can simultaneously incorporate the effect of latent features and covariates if any, as well as the effect of latent structure that may exist in the data. To achieve this, we model the relation graph as a function of both latent feature factors and latent cluster memberships of objects to collectively discover globally predictive intrinsic properties of objects and capture latent block structure in the network to improve prediction performance. We also develop an optimization transfer algorithm based on the generalized EM-style strategy to learn the latent factors. We prove the efficacy of our proposed model through the link prediction task and cluster analysis task, and extensive experiments on the synthetic data and several real world datasets suggest that our proposed LFBM model outperforms the other state of the art approaches in the evaluated tasks.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1204.2581v1\", \"primary_category\": \"cs.DS\", \"categories\": [\"stat.ML\", \"cs.LG\", \"H.2.8; J.4\", \"cs.DS\", \"15A83\"], \"primary_category_human_readable\": \"Data Structures and Algorithms\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\", \"H.2.8; J.4\", \"Data Structures and Algorithms\", \"15A83\"], \"incoming_citations\": [], \"outgoing_citations\": [\"361840\"]}","task_split":"paper_retrieval"}
{"document_id":"360671","document_content":"# A Sensitive Attribute based Clustering Method for kanonymization\n## Categories\n- Cryptography and Security\n## Abstract\nIn medical organizations large amount of personal data are collected and analyzed by the data miner or researcher, for further perusal. However, the data collected may contain sensitive information such as specific disease of a patient and should be kept confidential. Hence, the analysis of such data must ensure due checks that ensure protection against threats to the individual privacy. In this context, greater emphasis has now been given to the privacy preservation algorithms in data mining research. One of the approaches is anonymization approach that is able to protect private information; however, valuable information can be lost. Therefore, the main challenge is how to minimize the information loss during an anonymization process. The proposed method is grouping similar data together based on sensitive attribute and then anonymizes them. Our experimental results show the proposed method offers better outcomes with respect to information loss and execution time.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1203.3622v1\", \"primary_category\": \"cs.CR\", \"categories\": [\"cs.CR\"], \"primary_category_human_readable\": \"Cryptography and Security\", \"categories_human_readable\": [\"Cryptography and Security\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"360685","document_content":"# Automated Text Summarization Base on Lexicales Chain and graph Using of WordNet and Wikipedia Knowledge Base\n## Categories\n- Information Retrieval\n- Computation and Language\n## Abstract\nThe technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. The lexical cohesion structure of the text can be exploited to determine the importance of a sentence\/phrase. Lexical chains are useful tools to analyze the lexical cohesion structure in a text .In this paper we consider the effect of the use of lexical cohesion features in Summarization, And presenting a algorithm base on the knowledge base. Ours algorithm at first find the correct sense of any word, Then constructs the lexical chains, remove Lexical chains that less score than other, detects topics roughly from lexical chains, segments the text with respect to the topics and selects the most important sentences. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1203.3586v1\", \"primary_category\": \"cs.IR\", \"categories\": [\"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Information Retrieval\", \"categories_human_readable\": [\"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [\"169011\", \"209919\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"360728","document_content":"# Intracluster Moves for Constrained Discrete-Space MCMC\n## Categories\n- Computation\n- Artificial Intelligence\n## Abstract\nThis paper addresses the problem of sampling from binary distributions with constraints. In particular, it proposes an MCMC method to draw samples from a distribution of the set of all states at a specified distance from some reference state. For example, when the reference state is the vector of zeros, the algorithm can draw samples from a binary distribution with a constraint on the number of active variables, say the number of 1's. We motivate the need for this algorithm with examples from statistical physics and probabilistic inference. Unlike previous algorithms proposed to sample from binary distributions with these constraints, the new algorithm allows for large moves in state space and tends to propose them such that they are energetically favourable. The algorithm is demonstrated on three Boltzmann machines of varying difficulty: A ferromagnetic Ising model (with positive potentials), a restricted Boltzmann machine with learned Gabor-like filters as potentials, and a challenging three-dimensional spin-glass (with positive and negative potentials).","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1203.3484v1\", \"primary_category\": \"stat.CO\", \"categories\": [\"cs.AI\", \"stat.CO\"], \"primary_category_human_readable\": \"Computation\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation\"], \"incoming_citations\": [], \"outgoing_citations\": [\"356553\", \"356860\"]}","task_split":"paper_retrieval"}
{"document_id":"360923","document_content":"# Modeling multistage decision processes with Reflexive Game Theory\n## Categories\n- Multiagent Systems\n- Artificial Intelligence\n## Abstract\nThis paper introduces application of Reflexive Game Theory to the matter of multistage decision making processes. The idea behind is that each decision making session has certain parameters like \"when the session is taking place\", \"who are the group members to make decision\", \"how group members influence on each other\", etc. This study illustrates the consecutive or sequential decision making process, which consist of two stages. During the stage 1 decisions about the parameters of the ultimate decision making are made. Then stage 2 is implementation of Ultimate decision making itself. Since during stage 1 there can be multiple decision sessions. In such a case it takes more than two sessions to make ultimate (final) decision. Therefore the overall process of ultimate decision making becomes multistage decision making process consisting of consecutive decision making sessions.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1203.2315v1\", \"primary_category\": \"cs.MA\", \"categories\": [\"cs.MA\", \"cs.AI\"], \"primary_category_human_readable\": \"Multiagent Systems\", \"categories_human_readable\": [\"Multiagent Systems\", \"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"361135","document_content":"# Autocalibration with the Minimum Number of Cameras with Known Pixel Shape\n## Categories\n- Computer Vision and Pattern Recognition\n## Abstract\nIn 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1007\/s10851-014-0492-5\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\"], \"incoming_citations\": [], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"361624","document_content":"# $Q$- and $A$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes\n## Categories\n- Methodology\n- Artificial Intelligence\n## Abstract\nIn clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his\/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.1214\/13-STS450\", \"primary_category\": \"stat.ME\", \"categories\": [\"cs.AI\", \"stat.ME\"], \"primary_category_human_readable\": \"Methodology\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Methodology\"], \"incoming_citations\": [\"55683\", \"170695\", \"22306\", \"31177\", \"60775\", \"65803\", \"90341\", \"151275\", \"81790\", \"121753\", \"126685\", \"129645\", \"132750\", \"167853\", \"204248\", \"169958\", \"216242\", \"356455\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"361797","document_content":"# Active Semi-Supervised Learning using Submodular Functions\n## Categories\n- Machine Learning\n## Abstract\nWe consider active, semi-supervised learning in an offline transductive setting. We show that a previously proposed error bound for active learning on undirected weighted graphs can be generalized by replacing graph cut with an arbitrary symmetric submodular function. Arbitrary non-symmetric submodular functions can be used via symmetrization. Different choices of submodular functions give different versions of the error bound that are appropriate for different kinds of problems. Moreover, the bound is deterministic and holds for adversarially chosen labels. We show exactly minimizing this error bound is NP-complete. However, we also introduce for any submodular function an associated active semi-supervised learning method that approximately minimizes the corresponding error bound. We show that the error bound is tight in the sense that there is no other bound of the same form which is better. Our theoretical results are supported by experiments on real data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1202.3726v1\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\", \"stat.ML\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\", \"Machine Learning\"], \"incoming_citations\": [\"186030\", \"72371\", \"115712\", \"157287\", \"94926\", \"170726\", \"175153\", \"185779\", \"209909\", \"208784\", \"230190\", \"271131\", \"274071\", \"275033\", \"292338\", \"323904\", \"329554\"], \"outgoing_citations\": [\"349957\"]}","task_split":"paper_retrieval"}
{"document_id":"361839","document_content":"# The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information\n## Categories\n- Computer Science and Game Theory\n- Artificial Intelligence\n## Abstract\nTraditional economic models typically treat private information, or signals, as generated from some underlying state. Recent work has explicated alternative models, where signals correspond to interpretations of available information. We show that the difference between these formulations can be sharply cast in terms of causal dependence structure, and employ graphical models to illustrate the distinguishing characteristics. The graphical representation supports inferences about signal patterns in the interpreted framework, and suggests how results based on the generated model can be extended to more general situations. Specific insights about bidding games in classical auction mechanisms derive from qualitative graphical models.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1202.3768v1\", \"primary_category\": \"cs.GT\", \"categories\": [\"cs.GT\", \"cs.AI\"], \"primary_category_human_readable\": \"Computer Science and Game Theory\", \"categories_human_readable\": [\"Computer Science and Game Theory\", \"Artificial Intelligence\"], \"incoming_citations\": [\"353359\"], \"outgoing_citations\": [\"335332\", \"349530\", \"349835\"]}","task_split":"paper_retrieval"}
{"document_id":"361844","document_content":"# Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test\n## Categories\n- Artificial Intelligence\n## Abstract\nApproximate Bayesian inference is NP-hard. Dagum and Luby defined the Local Variance Bound (LVB) to measure the approximation hardness of Bayesian inference on Bayesian networks, assuming the networks model strictly positive joint probability distributions, i.e. zero probabilities are not permitted. This paper introduces the k-test to measure the approximation hardness of inference on Bayesian networks with deterministic causalities in the probability distribution, i.e. when zero conditional probabilities are permitted. Approximation by stochastic sampling is a widely-used inference method that is known to suffer from inefficiencies due to sample rejection. The k-test predicts when rejection rates of stochastic sampling a Bayesian network will be low, modest, high, or when sampling is intractable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1202.3773v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\"], \"incoming_citations\": [], \"outgoing_citations\": [\"346873\", \"348240\", \"353179\", \"356583\", \"356868\", \"357881\"]}","task_split":"paper_retrieval"}
{"document_id":"361882","document_content":"# Concept Relation Discovery and Innovation Enabling Technology (CORDIET)\n## Categories\n- Artificial Intelligence\n- Information Retrieval\n- Machine Learning\n- 68T05\n- H.2.8; H.3.1; I.2.6\n## Abstract\nConcept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document's timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1202.2895v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.IR\", \"stat.ML\", \"68T05\", \"H.2.8; H.3.1; I.2.6\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Information Retrieval\", \"Machine Learning\", \"68T05\", \"H.2.8; H.3.1; I.2.6\"], \"incoming_citations\": [\"272005\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"361959","document_content":"# Efficient Web-based Facial Recognition System Employing 2DHOG\n## Categories\n- Computer Vision and Pattern Recognition\n- Networking and Internet Architecture\n## Abstract\nIn this paper, a system for facial recognition to identify missing and found people in Hajj and Umrah is described as a web portal. Explicitly, we present a novel algorithm for recognition and classifications of facial images based on applying 2DPCA to a 2D representation of the Histogram of oriented gradients (2D-HOG) which maintains the spatial relation between pixels of the input images. This algorithm allows a compact representation of the images which reduces the computational complexity and the storage requirments, while maintaining the highest reported recognition accuracy. This promotes this method for usage with very large datasets. Large dataset was collected for people in Hajj. Experimental results employing ORL, UMIST, JAFFE, and HAJJ datasets confirm these excellent properties.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1202.2449v1\", \"primary_category\": \"cs.CV\", \"categories\": [\"cs.CV\", \"cs.NI\"], \"primary_category_human_readable\": \"Computer Vision and Pattern Recognition\", \"categories_human_readable\": [\"Computer Vision and Pattern Recognition\", \"Networking and Internet Architecture\"], \"incoming_citations\": [\"348360\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}
{"document_id":"362883","document_content":"# Distance-Based Bias in Model-Directed Optimization of Additively Decomposable Problems\n## Categories\n- Neural and Evolutionary Computing\n- Artificial Intelligence\n- I.2.6; I.2.8; G.1.6\n## Abstract\nFor many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1201.2241v1\", \"primary_category\": \"cs.NE\", \"categories\": [\"cs.AI\", \"I.2.6; I.2.8; G.1.6\", \"cs.NE\"], \"primary_category_human_readable\": \"Neural and Evolutionary Computing\", \"categories_human_readable\": [\"Artificial Intelligence\", \"I.2.6; I.2.8; G.1.6\", \"Neural and Evolutionary Computing\"], \"incoming_citations\": [\"360432\"], \"outgoing_citations\": [\"348948\", \"349241\"]}","task_split":"paper_retrieval"}
{"document_id":"362902","document_content":"# Pbm: A new dataset for blog mining\n## Categories\n- Artificial Intelligence\n- Computation and Language\n- Information Retrieval\n## Abstract\nText mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.","parent_id":null,"metadata":"{\"url\": \"http:\/\/arxiv.org\/abs\/1201.2073v1\", \"primary_category\": \"cs.AI\", \"categories\": [\"cs.AI\", \"cs.CL\", \"cs.IR\"], \"primary_category_human_readable\": \"Artificial Intelligence\", \"categories_human_readable\": [\"Artificial Intelligence\", \"Computation and Language\", \"Information Retrieval\"], \"incoming_citations\": [], \"outgoing_citations\": [\"362900\"]}","task_split":"paper_retrieval"}
{"document_id":"362955","document_content":"# Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management\n## Categories\n- Machine Learning\n## Abstract\nLeveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.","parent_id":null,"metadata":"{\"url\": \"http:\/\/dx.doi.org\/10.4156\/AISS.vol3.issue9.31\", \"primary_category\": \"cs.LG\", \"categories\": [\"cs.LG\"], \"primary_category_human_readable\": \"Machine Learning\", \"categories_human_readable\": [\"Machine Learning\"], \"incoming_citations\": [\"160670\"], \"outgoing_citations\": []}","task_split":"paper_retrieval"}